The AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering: Proceeding of RCTEMME2021, Hanoi, Vietnam (Lecture Notes in Mechanical Engineering) 9811919674, 9789811919671
146 9 239MB
English Pages [1402]
Contents
Investigation of Protrusion Height and Wall Thickness Distribution of Tube Hydroforming Y-Shaped Joint
1 Introduction
2 Materials and Methods
3 Results and Discussion
3.1 Effect of Internal Liquid Pressure
3.2 Influence of Axial Feed
3.3 Effect of Friction
4 Conclusion
References
Evaluation of Factors Influencing the Freezing Time of the Pangasius Fillets
1 Introduction
2 Materials and Methods
2.1 Material Information
2.2 The Product’s Thermal Properties
2.3 The Establishment of a Mathematical Model for Freezing Processing
2.4 Computational Results
2.5 Evaluation of the Reliability of the Problem Solution
2.6 Evaluation of Factors Affecting Freezing Time
3 Conclusion
3.1 The Effect of Air Velocity on Freezing Time
3.2 The Effect of Air Temperature on the Freezing Time
3.3 The Effect of Product Thickness on Freezing Time
References
Method to Improve the Power Coefficient of Vertical Axis Wind Turbines
1 Introduction
2 Methodology
2.1 Axial Momentum Theory
2.2 Double Multiple Streamtube Theory
2.3 Theoretical Calculation Using DMST Model
3 MPPT Algorithms to Extract Maximum Power from Wind Turbines
4 Conclusion
References
On the Dynamic Response of a Functionally Graded Funnel Shell with Graphene Nanoplatelet Reinforcement
1 Introduction
2 Computation Model
2.1 Geometry
2.2 Graphene Reinforcement
3 Computing Technique
3.1 Material Formulation
3.2 Governing Equations and Solution
4 Results and Discussions
4.1 Verification
4.2 Dynamic Behaviors
5 Conclusion
References
Study on the Process and Formation of Fiber Structure During Forming of an Automobile Joint Part in Closed-Die Forging
1 Introduction
2 Materials and Methods
2.1 Materials and Subject
2.2 Forging Process
3 Results
4 Conclusions
References
A Study on the Effect of Compression Ratio and Bowl-In-Piston Geometry on Knock Limit in Port Injection Natural Gas Converted Engine
1 Introduction
2 Theoretical Framework
3 Engine Simulation, Calibration and Control Model
3.1 Experimental Setup
3.2 Simulating Theoretical Framework
3.3 Model Calibration
3.4 Controlling the Model
4 Results and Discussion
4.1 Effects of Compression Ratio on Required Octan Number
5 Conclusion
References
Effect of Different Heat Recovery Tube Structure on the Exhaust Heat Utilizing Ability in Internal Combustion Engine
1 Introduction
2 Numerical Model
2.1 Computational Theory in Ansys Fluent
2.2 Boundary Condition
2.3 Meshing
3 Results and Discussion
3.1 Distribution of Temperature and Velocity in the Exhaust Heat Recovery Tank
3.2 Effect of Engine Load on Exhaust Heat Recovery
3.3 Effect of Tank Structure on Heat Recovery of Exhaust Gas
4 Conclusion
References
A-star Algorithm for Robot Path Planning Based on Digital Twin
1 Introduction
2 A Brief Overview of Digital Twin
3 A-star Algorithm
4 Experiment
4.1 The Physical Space
4.2 The Digital Space
5 Results and Discussion
5.1 Digital Twin Application for the Robot’s Path-Planning in the Large-Sized Obstacle-Free Phase
5.2 Application of A-star Algorithm to Find the Robotic Path in the Phase with a Large-Sized Obstacle
6 Conclusion
References
Development of a CFD Tool Based on SnappyHexMesh/OpenFOAM for the Axial Fan Performance
1 Introduction
2 BKIAF – the CFD Techniques for Ducted Axial Fans Simulation
2.1 Geometry of Ducted Fan
2.2 Automatic Meshing with BKIAF
2.3 Turbulence Model
2.4 SimpleFoam Solver
2.5 Boundary Conditions
3 Results of the Case Study
3.1 Grid Independent Evaluation
3.2 Simulation Results of the Original Huynh Thao Composite Fan
3.3 Simulation Results of the Enhanced Huynh Thao Composite Fan
4 Conclusion
References
Effect of Clamping Distances on Residual Stress in Butt Welded Joint of Stainless Steel Plates
1 Introduction
2 Material and Speciment Dimensions
3 Welding Simulation
4 Simulation Results and Discussion
4.1 Longitudinal Stress on the Weld Seam
4.2 Transverse Stress on the Weld Seam
4.3 Longitudinal Stress on the Line Perpendicular to the Weld Seam
4.4 Transverse Stress on the Line Perpendicular to the Weld Seam
5 Conslusions
References
Experimental Design and Manufacture a Pair of the Internal Non-circular Gears with an Improved Cycloid Profile
1 Introduction
2 Design Parameters of the Rack Cutter with an Improved Cycloid Profile
2.1 Mathematical Model of the Tooth Profile of the Rack Cutter
2.2 Design Parameters of the Rack Cutter
3 Mathematical Model of the Centrodes of Internal Gear Driver
4 Design of the Tooth Profile of the Internal Gear Drive
4.1 Shaping the Tooth Profile of the Eccentric Gear by Rack Cutter with an Improved Cycloid Profile
4.2 Shaping the Tooth Profile of the Internal NCG by Shaper Cutter
5 Design and Experimental Manufacturing of an Internal NCGs Pair
5.1 Experimental Design
5.2 Experimental Machining of NCG by 3 Axis CNC Milling Machine
6 Conclusions
References
Development of a Software for Laser-Based Micromachining of Piezoceramic Microactuators
1 Introduction
2 Machine Description
3 Implementation
3.1 Developing Workflow
3.2 Information Extraction Fom DXF
3.3 Object Visualization and G-code Generation
4 Results
5 Conclusion and Outlook
References
Vibration Analysis of Thick Rotating Laminated Composite Conical Shells by the Dynamic Stiffness Matrix Method
1 Introduction
2 Theory of Rotating Composite Conical Shell
2.1 Laminate Constitutive Relations
2.2 Force Resultant-Displacement Relations
2.3 Equations of Motions
3 Dynamic Stiffness Matrix of Rotating Thick Cross-Ply Laminated Composite Conical Shells
4 Numerical Results and Discussion
4.1 Comparative Studies
4.2 Parameter Effect Studies
5 Conclusions
References
Research on the Characteristics of Tooth Shape and Size of the Oval Gear Drive with an Involute Profile
1 Introduction
2 Design the Tooth Profile of the Oval Gear Drive by Rack Cutter
2.1 Design Centrodes of the Oval Gear Drive
2.2 Shaping the Tooth Profile of the Oval Gear Drive by Rack Cutter
3 Kinematic Analysis of the Meshing Process of the Oval Gear Drive
3.1 Mathematical Modelling the Relative Sliding Velocity Between Two Mating Profiles at the Meshing Point
3.2 The Sliding Curve of Tooth Profile
3.3 Dimensional Parameters of Tooth Geometry of the OG with an Involute Profile
4 Investigate Tooth Shape Parameters and Kinematic Analysis of the Meshing Process
4.1 Design of Oval Gear Drive with an Involute Profile
4.2 Investigate Parameters of Tooth Size and Kinematic Analysis of the Meshing Process
4.3 Kinematic Analysis of the Meshing Process
5 Conclusions
References
Vibroacoustic Behavior of Double-Composite Plate Filled with Porous Materials
1 Introduction
2 Theoretical Study
2.1 Plate Geometry and Assumption
2.2 Flexural Motion of the Plate
2.3 Boundary Conditions of the Plate-Porous Coupling
2.4 Determination of Sound Transmission Loss
3 Modelling the Poroelastic Material
4 Validation
5 Results and Discussion
5.1 Effect of Composite Materials Density
5.2 Effect of Porous Core Layer Density
5.3 Effect of Porous Layer Young’s Modulus
5.4 Effect of Porous Layer Poisson’s Ratio
6 Conclusions
References
Research of Calculation and Testing Assessment of Impact Resistance of Automobile Alloy Wheel Rims Used in Vietnam
1 Introduction
2 Basic for Calculation and Testing Assessment of the Impact Resistance Ablity of Automobile Alloy Wheel Rims
3 Calculation of Car Alloy Wheel Structure During the Impact by Ansys Software
3.1 Building a Model to Calculate the Durability of the Car Wheel Rim by ANSYS Software
3.2 Loading Plan
3.3 Problem Resolution
4 Testing Assessment of Impact Resistance of Alloy Wheel Rim
5 Conclusion
References
One-Dimensional and Entropy Generation Analyses of a Solar Chimney
1 Introduction
2 Model Formulation and Validation
3 Results and Discussion
4 Conclusions
References
Oscillation Measurement of the Magnetic Compass Needle Employing Deep Learning Technique
1 Introduction
2 Methodology
2.1 Needle Tracking
2.2 Oscillation Measurement
3 Experimental Results
4 Conclusion
References
Development of Grading System Based on Machine Learning for Dragon Fruit
1 Introduction
2 Natural Features of Dragon Fruits
3 Structure of Grading System
4 Using Machine Learning for Grading Dragon Fruit
4.1 Stage 1 - Using KNN to Extract the Dragon Fruit
4.2 Stage 2 - Using CNN to Extract Feature of the Dragon Fruit-Image Processing
4.3 Stage 3 - Sorting the Dragon Fruit
5 Results and Discussions
6 Conclusions
References
Disturbance Observer-Based Speed Control of Interior Permanent Magnet Synchronous Motors for Electric Vehicles
1 Introduction
2 Mathematical Model
3 Robust Backstepping Controller Design
3.1 Speed Controller Design
3.2 Current Controller Design
3.3 Disturbance Observer Design
4 Stability Analysis
5 PIL Simulation and Results
5.1 PIL Simulation
5.2 Simulation Results
6 Conclusion
References
Optimizing the Parameter of the LQR Controller for Active Suspension System
1 Introduction
2 Materials and Methods
2.1 Quarter Vehicle Dynamics Model
2.2 Optimization Process
3 Results and Discussion
3.1 Parameter of the Vehicle
3.2 Simulation Results
4 Conclusion
References
Integral Action Finite Set Model Predictive Current Control for Brushless DC Motor
1 Introduction
2 Mathematical Model of Brushless DC Motor in D-Q Rotating Reference Frame
3 Integral Action Finite Set Model Predictive Control
3.1 Controller Design
3.2 Algorithm Strategy
4 Simulation
5 Conclusion
References
A Study on Selecting Cutting Regime to Attain Suitable Roughness and Dimensional Precision in Both When Drilling Tempered Steel 20XHM
1 Introduction
2 Methodology
2.1 Taguchi Method and ANOVA [6]
2.2 Grey Relation Analysis [7, 8]
3 Experimental Setup and Design
3.1 Experimental Conditions
3.2 Experimental Design
3.3 Investigating the Impact of Cutting Regime Parameters on the Roughness
3.4 Investigating the Impact of Cutting Regime Parameters on the Roundness
3.5 Multi-object Optimization for Suitable Roughness and Roundness in Both
3.6 A Case Study
4 Conclusions
References
An Overview on the Viability of Hydrous Bioethanol as Gasoline Fuel Blend in the Philippines
1 Introduction
2 Demand on Bioethanol Blend
2.1 Global Bioethanol Demand
2.2 Projected Consumption of the Philippines
2.3 Feedstocks for Bioethanol
3 Method of Bioethanol Production
3.1 Process of Anhydrous Ethanol Production
3.2 Cost of Bioethanol Production
3.3 Process of Hydrous Ethanol Production
3.4 Cost of Hydrous Bioethanol Production
4 Hydrous Bioethanol as Gasoline Fuel Blend
4.1 Performance of Some Spark Ignition Engines using Hydrous Gasoline Fuel Blend
4.2 Utilization of Hydrous Ethanol as Straight Fuel or as Fuel Blend
4.3 Challenges of Hydrous Ethanol as Gasoline Blend
5 Discussion
5.1 Reduction of Bioethanol Production Cost
5.2 Hydrous Bioethanol Blend Properties
5.3 Long Term Trial
5.4 High Altitude Test Runs
6 Conclusion
References
Numerical Optimization of the Process Conditions to Improve the Warpage Inside the Product and Reduce the Cooling Time in the Injection Molding Product
1 Introduction
2 Mathematical Model
3 Results and Discussion
4 Conclusions
References
Geometrically Nonlinear Behaviour of Functionally Graded Beam and Frame Structures Under Mechanical Loading
1 Introduction
2 Functionally Graded Materials
3 Finite Element Formulation
4 Equilibrium Equations
5 Numerical Investigation
5.1 Cantilever Beam Under a Tip Moment
5.2 Cantilever Beam Subjected to an Eccentric Axial Load
5.3 Asymmetric Frame
6 Conclusions
References
Effect of Geometric Parameters of Heat Sink on Thermal Dissipation for Active Antenna Unit
1 Introduction
2 Methodology
2.1 Model Parameters
2.2 Computational Fluid Dynamics Analysis
3 Results and Discussions
3.1 Simulation and Experimental Results
3.2 Investigating on Model 32T32R and 64T64R
4 Conclusions
References
An Overview of Pedestrian Detection Based on LiDAR for Advanced Driving Assistance System
1 Introduction
2 LiDAR Data Processing Method for Classification
2.1 Grid-Based Approach
2.2 Point-Based Approach
2.3 A Two-Stage 3D Detection Framework
3 Experiments
3.1 Evaluation Algorithm
3.2 Datasets
4 Results and Discussion
5 Conclusion
References
Stability and Viscosity of Mono and Hybrid Nanolubricant
1 Introduction
2 Materials and Methods
2.1 Material
2.2 Nanolubricant Preparation
2.3 Evaluation of Dispersion Stability
2.4 Thermal Conductivity
2.5 Viscosity Measurement
3 Result and Discussion
3.1 Stability of Nanolubricant
3.2 Thermal Conductivity
3.3 Viscosity
4 Conclusion
References
Detection Fault Symptoms of Rolling Bearing Based on Enhancing Collected Transient Vibration Signals
1 Introduction
2 Theoretical Background
2.1 Tunable Q-Factor Wavelet Transform
2.2 Hilbert Envelope Spectrum
3 The Proposed Method
3.1 Flowchart of Method
3.2 The Method Validation by Simulation
4 Application on Experiment Data
4.1 The Dataset Description
4.2 Analysis and Evaluation of Method Based on Experiment Test
5 Conclusion
References
Human-Robot Interaction System Using Vietnamese
1 Introduction
2 Interaction System
2.1 Structure of Interaction System
2.2 Text Classification Progress
2.3 Search Text Processing
2.4 Conversational Text Processing
3 Experiment
3.1 Experimental Setup
3.2 Results
4 Conclusion
References
Effect of the Elliptical Shape on the Performance of the Modified Savonius Wind Turbine
1 Introduction
2 Modified Blade: Effect of Elliptical Profile
3 Numerical Simulation
3.1 Numerical Method
3.2 Computational Domain and Simulation Conditions
4 Results and Discussion
4.1 Effect of R*
4.2 Effect of Angle α
5 Conclusion
References
Using Artificial Neural Network to Grade Internal Quality of Coconuts Based on Density
1 Introduction
2 Determination of Density
3 Quality of Coconut Related Density
4 Artificial Neural Network for Grading the Quality of Coconut
5 Experiments and Discussions
6 Conclusions
References
The Modeling of Ion-Selective Membrane
1 Introduction
2 Numerical Model and Method
3 Numerical Methods
4 Results and Discussions
4.1 Current-Voltage Response
4.2 Effect of the Different Approaches for Modeling Charged Membrane on the Vertical Distribution of Ions
4.3 Development of Extended Space Charge Layer
5 Conclusion
References
Apply Image Processing Techniques into the Process of Reading and Calibrating Levelling Staffs
1 Introduction
2 Methods and Materials
2.1 Calibration System Design
2.2 Build Software to Detect Graduation Lines Using Camera
3 Results and Discussion
4 Conclusion
References
A Method for Cooling Bearings of Motorized Spindles
1 Introduction
2 Structure of the Cooling Spacer
3 Cooling Effects on Bearing Arrangements
4 Conclusion
References
Analysis Hydrodynamic Performance of the Autonomous Underwater Vehicle for a Different Hull Shape
1 Introduction
2 Hull Design for the AUV
3 Hydrodynamic Performances of the AUV
4 Hull Resistance Acting on the AUV
5 Conclusions
References
The Effect of Biodiesel-Ethanol-Diesel Blends on Performance and Emissions of a Diesel Engine
1 Introduction
2 Exprimental
2.1 Experimental Apparatus
2.2 Test Fuels
2.3 Test Modes
3 Results and Discussion
3.1 Engine Performance
3.2 Engine Emissions
3.3 The Number of Particles in the Exhaust Gas
4 Conclusions
References
Inverse Kinematics Analysis of 7 DOF Collaborative Robot by Using Coordinate and Velocity Projection Methods
1 Introduction
2 Kinematic Analysis
2.1 Direct Kinematics
2.2 Inverse Kinematics
3 Numerical Simulation
4 Conclusion
References
Investigating the Effect of Pulsed Fiber Laser Parameters on the Roughness of Heat-Resistant Parts in Cleaning Processes
1 Introduction
2 Materials and Methods
2.1 Pulsed Fiber Laser Cleaning System
2.2 Samples and Measurement Devices
3 Results and Discussions
4 Conclusions
References
Fiber Laser Cleaning to Remove Paint on the Surface of Mechanical Parts
1 Introduction
2 Materials and Methods
2.1 Pulsed Fiber Laser Cleaning System
2.2 Paint Samples and Cleaning Grades
2.3 Results and Discussion
3 Conlusion
References
A Durability Test of Motorcycle Engine Fueled with Ethanol-Gasoline Blends
1 Introduction
2 Material and Method
2.1 Experiment Procedure
2.2 Testing Equipment and Fuels
2.3 Experimental Apparatus
3 References
3.1 The Effects of Fuels on the Surface of the Main Components
3.2 The Effects of Fuel Blend on the Dimension of Main Components
3.3 Evaluating Engine’s Performance After 200 h of Operating
4 Conclusions
References
Mapping and Path Planning for the Differential Drive Wheeled Mobile Robot in Unknown Indoor Environments Using the Rapidly Exploring Random Tree Method
1 Introduction
2 Kinematic Modelling of the Differential-Drive Mobile Robot
3 Exploration Strategies Algorithm for Mobile Robot
4 Results and Discussion
4.1 Mapping Solution
4.2 Path Planning for Mobile Robot
5 Conclusion
References
Research on Resistance of Some Typical Fishing Vessels in Vietnam
1 Introduction
2 Numerical Modeling
2.1 Geometric Model
2.2 Computational Domain
2.3 Boundary Condition
3 Results
3.1 Simulation Results
3.2 Validation Study
3.3 Result Analysis
4 Conclusion
References
A Study on Design Two Pitch Propeller to Apply for the Small Fishing Vessel
1 Introduction
2 Theoretical Foundation
2.1 Propeller’s Hydrodynamic Features
2.2 Governing Equations
3 Design the Two-Pitch Propeller
3.1 Features of the Trawling boat’s Operating Stages
3.2 Identify Number of Blade and Optimal Diameter of the Two - Pitch Propeller
3.3 Design the Blade’s Geometry
4 Ivestigated Hydrodynamic Performances of the Two-Pitch Propeller
4.1 Geometry and Mesh
4.2 Boundary Conditions
4.3 Results and Discussion
5 Conclusion
References
Analysis Hatchback Vehicle Structure in Car-to-Car Frontal Impact Using Finite Element Model
1 Introduction
2 Crashwothiness Model Building
2.1 Motion Relationship of Two Cars in Collision
2.2 Equations
2.3 Validation of FE Model
2.4 Frontal Crash Simulation of Full Vehicle Model
3 Conclusion
References
A Study on Vibration and Noise of Manual Transmission by Using Simulation
1 Introduction
2 Comsol Multiphysics Sofware
2.1 History
2.2 Classification of Modules
3 Manual Transmission Simulations and Results
3.1 Manual Transmission Structure Analysis
3.2 Manual Transmission Vibration Analysis
3.3 Manual Transmission Noise Analysis
4 Conclusion
References
The Pollutant Reduction Potential of Old Generation Diesel Engine Retrofitting After-Treatment System
1 Introduction
2 Material and Method
2.1 Test Engine Specification
2.2 Testing Apparatus
2.3 Designed Parameters of the After-Treatment System
3 Results and Discussions
3.1 The Effect of After-Treatment System on Engine Performance Characteristics
3.2 The Effects of NH3Flow Rate on NOxReduction Efficiency
3.3 The Effects of After-Treatment System on Exhaust Pollutants
4 Conclusions
References
Head Injury Criterion of Vietnamese Pedestrian Struck by a Sedan
1 Introduction
2 Literature Review
3 Numerical Simulation
3.1 Simulation Setup
3.2 Results
4 Discussion
5 Conclusions
References
NURBS Curve Trajectory Tracking Control for Differential-Drive Mobile Robot by a Linear State Feedback Dynamic Controller
1 Introduction
2 Mathematical Models of Dynamics
2.1 Kinematic Model
2.2 The Dynamic Model of the Robot
3 Design of Linear State Feedback Dynamic Controller
4 Simulation Results
4.1 System Setup
4.2 Results and Discussion
5 Conclusions
References
A Thoroughly Approach: Pulley Kinematic, Actuator Dynamic and Stiffness on Cable Suspended Parallel Robots
1 Introduction
2 System Kinematic and Dynamic Model
3 Stiffness Analysis
4 Controller Solution
5 Experimental Results
5.1 Prototype
5.2 Experimental Results
6 Conclusion
References
An Approach to Biomass Selection Based on Thermal Properties for Co-firing Technology
1 Introduction
2 Experimental
2.1 Material Preparation
2.2 Equipment and Experiment Setup
3 Results and Discussion
3.1 Evaluate the Technological Composition of Some Biomass Types
3.2 Evaluate the Calorific Heating Value Regarding the Selection of Co-firing Ratio
3.3 Evaluate Combustion Characteristics of Single Fuel and Fuel Mixtures
3.4 Thermogravimetric Kinetic Analysis
4 Conclusion
References
Electric Load Disaggregation Using Non-intrusive Load Monitoring Algorithm for Home Energy Management: Case-Study for an Apartment in Hanoi
1 Introduction
2 Methodology
2.1 Non-intrusive Load Monitoring Process
2.2 Machine Learning Algorithms
2.3 Open-Source Machine Learning Tool
2.4 Case-Studies
3 Results and Discussion
3.1 Case-Study 1
3.2 Case-Study 2
3.3 Case-Study 3
3.4 Case-Study 4
3.5 Case-Study 5
4 Conclusion
References
Experimental Study of Infrared – Assisted Heat Pump Drying of Lime Slices
1 Introduction
2 Experimental Equipment and Procedures
3 Results and Discussions
4 Conclusions
References
An Efficient Pantograph Dynamic Mechanism for Massage Robot Arm Acting Therapy on Human Back
1 Introduction
2 Massage Robot Arm Mechanism Analysis
2.1 DynamicsGeometry Analysis
2.2 Inverse Dynamics
3 Analysis on Mechanism Model and Stimulate Results
4 Conclusions
References
Elastic Mechanical Properties of Transition Metal Dichalcogenides Monolayer Using Atomic Finite Element Method
1 Introduction
2 Numerical Procedure
2.1 Model and Interatomic Potential
2.2 Atomic Finite Element Method Using VFF Potential
3 Results and Discussion
4 Conclusion
References
Research on the Friction Effect on Product Quality in Sheet Hydroforming
1 Introduction
2 Methodology and Simulation
2.1 Methodology
2.2 Simulation Results
3 Discussions
4 Experimental Verification
5 Conclusions
References
Electromechanical Actuators Based on Monolayer Borophene withβ12andχ3Structures
1 Introduction
2 Computational Methodology
3 Results and Discussion
4 Conclusions
References
Study Optimization of Process Parameters in Overmolding of Multi Plastic Materials
1 Introduction
2 Experiment Methodology
3 Results and Discussions
3.1 Effects of Injection Pressure on the Formation of the TBHS
3.2 Effects of Injection Pressure on the Formation of the TBHC
4 Conclusion
References
Numerical Study for Flow Behavior and Drag of Axisymmetric Boattail Models at Different Mach Number
1 Introduction
2 Numerical Setup and Validations
2.1 Numerical Model
2.2 Numerical Algorithm
2.3 Mesh Generation
3 Results and Discussion
3.1 Validations
3.2 Pressure Distribution on Boattail Surface
3.3 Drag of the Model
3.4 Skin-Friction Topology
3.5 Flow Around the Models
4 Conclusions
References
Adaptive Observation Controller for an Uncertain Hysteresis System with Input Constraint
1 Introduction
2 Problem Statement
3 Nonlinear Adaptive Control Design for Uncertain Hysteretic Systems
3.1 Control Design
3.2 Stability Analysis
4 Simulation
5 Conclusions
References
Numerical Simulation of Hot Water Flashing Flow in a Converging - Diverging Nozzle
1 Introduction
2 Numerical Method
3 Results and Discussion
4 Conclusion
References
A Case Study on Humanoid Robot Using Robotics Software in E-learning
1 Introduction
2 NAO Robot Modeling
3 A Brief Overview of the Software Interface
3.1 Webots
3.2 Chorgraphe - A Graphical Tool for Humanoid Robot Programming
3.3 Communication Between the Choregraphe Application and Webots Platform
4 Case Study
5 Conclusion
References
LQR Control Design in Vibration Control of a Benchmark Building Structure Subjected to Seismic Load
1 Introduction
2 Controlled Model
3 Controller Design
4 Numerical Simulation Results
5 Conclusions
References
Detecting Crack on a Beam Subjected to Impact Load
1 Introduction
2 Research Methods
3 Results and Discussion
4 Conclusion
References
Thermal Analysis by Finite Element Model for Powder Screw Extruder for 3D Printing Method
1 Introduction
2 Models and Simulation Setting
2.1 Model Design for Analysis
2.2 Material Extrusion Process
2.3 Simulation Setup
3 Results and Discussion
3.1 The State of PCL Powder in the Barrel
3.2 The Kickoff Temperature of the Sensor to Start the Printing Process
3.3 Sensor’s Temperature Effect on the Melting Point
3.4 The Effect of O-ring in the Extruder’s Design on the Melting Point
4 Conclusion
References
The Effect of Printing Parameters on the Characteristics of PCL Scaffold in Tissue Engineering Application
1 Introduction
2 Experimental Equipment and Model
3 Result and Discussion
3.1 Effect of Printing Temperature
3.2 The Effect of Extrusion Ratio
3.3 The Effect of Printing Table’s Velocity and Acceleration
4 Conclusion
References
Investigation and Optimization of Surface Roughness and Material Removal Rate in Face Finishing Milling of Ti-6Al-4V Under MQL Condition
1 Introduction
2 Research Methodology
2.1 Optimization Issue
2.2 Optimization Framework
3 Experimental Procedures
3.1 Design of Experiment
3.2 Cutting Tools and Their Parameters
4 Results and Discussions
4.1 ANOVA Analysis
4.2 Optimization Results
5 Conclusion
References
Evaluating the Influence of Cutting Mode and Workpiece Parameters on Surface Roughness When External Cylindrical Grinding 65Mn Steel
1 Introduction
2 Experimental Setup
2.1 Grinding Machine
2.2 The Surface Roughness Tester
2.3 The Workpiece and Grinding Wheel
3 Taguchi Experimental Design
3.1 Taguchi Experimental Design with Material Hardness and Workpiece Diameter
3.2 Taguchi Experimental Design with Cutting Mode Parameters
4 Result and Discussion
4.1 Evaluating the Influence of Cutting Mode and Workpiece Parameters on Surface Roughness
4.2 Mathematical Model
5 Conclusions
References
Numerical Simulation of Thermal Stress and Creep Fatigue Crack Growth Analysis for Thick-Walled Tubes of Header
1 Introduction
2 Background
3 Finite Element Stress Analysis
3.1 Finite Element Model and Boundary Conditions
3.2 Finite Element Analysis Results
4 Creep Fatigue Crack Growth Assessment
4.1 Crack Growth Laws
4.2 Creep Crack Growth Assessment
4.3 Fatigue Crack Growth Assessment
4.4 Results and Discussions of Remaining Life Assessment
5 Conclusions
References
A Method of Determination of the Remaining Geometrical Accuracy of Lathe Machine Based on the Wear of Slideway
1 Introduction
2 Slideway Wear Measurement Method
2.1 Specifications and Characteristics of Slideway
2.2 Measuring Method Wear of Lathe Machine Slideway
3 Result and Discussion
3.1 The Result of 1K62 Lathe Slideway Wear
3.2 Accuracy Determination of the Geometry of the 1K62 Machine Due to Slideway Wear
4 Conclusion
References
Development of Vibration Absorber System Using Tunable Stiffness Material
1 Introduction
2 Magnetorheological Elastomer (MRE)
2.1 Experimental Tests and Validation
2.2 Model of MRE-Based Isolator
3 A Vibration System Using MRE-Based Absorber
4 Conclusions
References
A Computational Method of Air-Electric Equivalent in Air Spindle
1 Introduction
2 Fundamentals of Air-Electrical Calculation Method
2.1 Model of Air-Electric Conversion
2.2 Construction of Calculating Formulas for Model of Lower Air Bearings
2.3 Investigation of Thrust Force in Air Bearings
3 Conclusion
References
CFD Simulation of Temperature Distribution of Atrium Space in a Residential Building
1 Introduction
2 Model Description
2.1 Model Parameters
2.2 Solver Settings
3 Results and Discussion
3.1 Results
3.2 Discussion
4 Conclusion
References
CFD Simulation of Swirl-Stabilized Pulverized Coal Flames in a Cylindrical Combustion Chamber
1 Introduction
2 Model Descriptions
3 Results and Discussion
3.1 Temperature Distribution
3.2 Gas-Phase Species Concentration
3.3 Burnout Rate of Coal
4 Conclusion
References
The Process of Custom Designing Replacement Cranial Bone Patches in Human Body
1 Introduction
2 Fabrication Process of the Cranial Patch
2.1 Methods and Boundary Conditions
2.2 Material Properties
2.3 Simulation Results
3 Result and Discusion
4 Conclusion
References
Research and Design of Compact Equipment Using PhaseShifting Deflectometry Apply in Optical Surface Measurement
1 Introduction
2 Basic Principle
2.1 Governing Equation
2.2 Data Capture and Processing
3 Experiment Setup
4 Result and Discussion
5 Conclusions
References
Numerical Study of Ultraviolet Germicidal Effect Against SARS-CoV-2Virus
1 Introduction
2 Theory Background
2.1 UV Irradiance
2.2 Numerical Approach
2.3 Disinfection Rate
3 Results and Discussion
3.1 UV Disinfection Performance
3.2 Exposure Duration
4 Conclusions
References
Thermal Efficiency and Exhaust Emission of an SI Engine Using Hydrogen Enriched Gas from Exhaust Gas Fuel Reforming Based on Ni-Cu/Al2O3Catalysts
1 Introduction
2 Experiments and Method
2.1 Engine
2.2 Catalyst Selection
2.3 Test Fuels
2.4 Test Bench Specification
2.5 Test Procedures
3 Results and Discussion
3.1 Structural and Morphological Characterizations
3.2 Catalytic Activity
3.3 Engine Test
4 Conclusion
References
Photocatalytic Activity of Tungsten-Loaded Titanium Dioxide Photocatalysts Against Dyes and Bacteria in Water System
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Preparation of Tungsten-Loaded Titanium Dioxide Photocatalysts
2.3 Dyes Decolourization Studies
2.4 Antibacterial Activities
3 Results
4 Discussions
5 Conclusions
References
Free Vibration Analysis on Stepped Composite Cylindrical Shells Reinforced by Inner/outer Ring Stiffeners
1 Introduction
2 Theoretcal Fomulations
2.1 Description of the Model
2.2 Kinematic Relations
2.3 Lamina Constitutive Relations
2.4 Stress, Forces and Moment Resultants
2.5 Force Resultant
3 Continuous Element for Vibration Analysis of Composite Cylindrical Shells and Ring Stiffeners
3.1 State Vectors
3.2 Dynamic Stiffness Matrix K
4 Dynamic Stiffness Matrix for Ring Composite Inner/Outer Ring-Stiffened Cylindrical Shells
4.1 Dynamic Stiffness Matrix Assembly
4.2 Determination of Natural Frequencies
5 Numerical Results and Discussion
5.1 Modal Analysis
5.2 Harmonic Responses of Composite Cylindrical Shells Reinforced by Inner/Outer Ring Stiffeners
5.3 The effect of the Layer configuration on the Frequency of Cylindrical Shell
6 Conclusion and Developments
Appendix
References
Subcooled Flow Boiling and Its Application in Designing LNG Vaporizers
1 Introduction
2 Model Description
2.1 Setting up Subcooled Boiling Model
2.2 Conservation Equation
3 Validation and Discussion
3.1 Validation with Umekawa’s Result
3.2 Application of Flow Boiling Model in Designing LNG Vaporizer
4 Conclusion
References
Patent Map for Forecasting Technology Trends and Policymaking Case Study: IoT in Agriculture
1 Introduction
2 Patent Map and Application
3 Patent Mapping Process
4 IoT in Agriculture
5 Patent Map Analysis for IoT in Agriculture
5.1 Overall Development Trend
5.2 Countries and Assignees
5.3 Technology Forecasting
6 Conclusions
References
Modelling for Sliced Avocado Drying in Modified Air
1 Introduction
2 Model Description
2.1 Mass and Energy Conservation Equation
2.2 Constitutive Relationships
3 Results and Discussion
3.1 Model Validation
3.2 Spatio-temporal Temperature and Moisture Content Distributions
4 Conclusion
References
Experimental Study on Sliced Avocados Drying in Modified Air
1 Introduction
2 Experiment Methodology and Data Evaluation
2.1 Experiment Description
2.2 Data Analyzing
3 Results and Discussion
3.1 Drying Kinetic
3.2 Product Quality
4 Empirical Modeling for Drying Kinetics
5 Conclusion
References
Determining the Residual Stress of K Welded Pipe Joint by Hole Drilling Method
1 Introduction
2 Materials and Methods
2.1 Material Parameters
2.2 Welding Parameters
2.3 Experimental Procedure
2.4 Hole Drilling Device and Drilling Method
2.5 Strain Gauge Installation
2.6 Calculation of Residual Stresses
3 Results and Discussion
4 Conclusions
References
The Effect of Microstructure and Nano Additive Lubrication on the Specific Grinding Energy and Surface Roughness in Ti-6Al-4V Grinding
1 Introduction
2 Experimental Setup
3 Results and Discussion
3.1 Influence of Grinding and Lubricating Condition on the Surface Roughness
3.2 Influence of Grinding and Lubricating Condition on the Specific Grinding Energy
4 Conclusions
References
Experimental Investigation of Performance of Cellulose Cooling Pad
1 Introduction
2 Experimental Model
2.1 Apparatus
2.2 Data Reduction
3 Results and Discussions
4 Numerical Model
5 Conclusion
References
Design and Construction for Computational Models of Ultrasonic Transducers
1 Introduction
2 Objectives and Methods
3 Modelling with ANSYS Mechanical
3.1 Input of Materials
3.2 Preparing the Geometric Models
3.3 Configuration for Harmonic Response
3.4 Configuration for Harmonic Acoustics
4 Results and Discussion
4.1 Results from Harmonic Response
4.2 Results from Harmonic Acoustics
4.3 Evaluation of Computational Models
4.4 Discussing the Influence of Beam Spread Angle
4.5 Discussing the Influence of Operating Frequencies
5 Conclusion
References
A Study on Forced-Air Thermal Dissipation in Lithium-Ion Batteries Using Numerical Method
1 Introduction
2 Battery Package Modeling
2.1 Battery Model Construction
2.2 Mathematical Model
2.3 Numerical Solver and Mesh Resolution
2.4 Boundary Conditions
3 Results and Discussion
3.1 Examination Thermal State of Model 2.56C Rate Discharge
3.2 Examination Thermal State of Model 3.65C Rate Discharge
3.3 Examination Thermal State of Model 7.3C Rate Discharge
4 Conclusion
References
A Study on Anti-pedal Error System in Car Based on Hydraulic Approach
1 Introduction
2 Fundamentals of Work
2.1 Proposed Model
2.2 Working Data Collected
2.3 Model Parameters
3 Established Model and Control Diagram
4 Results and Discussion
5 Conclusion
References
High-Speed Focus Detection System Using Diffractive Beam Sampler and Position-Sensitive Detector
1 Introduction
2 Working Principle of the Focus Determination System
3 Simulated Setup and Results
3.1 Simulated Setup
3.2 Results
4 Discussion
5 Conclusion
References
An Experimental Study on Position Control of Pneumatic Cylinder Using Programmable Logic Controller and Pneumatic Proportional Valves
1 Introduction
2 Experimental System
3 Controller Design
4 Experimental Results and Discussion
5 Conclusion
References
Control Strategies for Beer Recovery Process from Surplus Yeast
1 Introduction
2 Design of Control Strategies for Beer Recovery Process from Surplus Yeast
2.1 Problem
2.2 Control Design for Centrifugal Separation Process
2.3 Design of a Control and Monitoring System
3 Results
4 Conclusions
References
Robust Control of Quadcopter in Case of Releasing Liquid and Encountering Uncertainties
1 Introduction
2 Modelling of Quadcopter Dynamics
2.1 Translation Model of Quadcopter
2.2 Rotational Motion of Quadcopter
2.3 Actuator Dynamic
2.4 Dynamic Equation of Quadcopter System
2.5 Moment of Inertia Evaluation
2.6 The Dynamic Model of Wind Disturbance
2.7 Jet Discharge Propulsion
3 Control Design
3.1 Control Design Strategy
3.2 Backstepping Control Method
3.3 Position Controller
3.4 Rotational Controller
3.5 Yaw and Altitude Control
3.6 Closed Loop Control System
4 Simulation and Result
4.1 First Case: The Quadcopter Drops the Liquid Vertically
4.2 Second Case: The Quadcopter Sprays the Liquid Horizontally
4.3 Comments on Results
4.4 Conclusions and Future Work
References
Structural Durability Analysis of Offshore Wind Turbine Tower with Monopile Foundation According to ICE 61400 Standards
1 Introduction
2 FEA Modelling
2.1 Geometry
2.2 Materials Properties
2.3 Loads on Wind Turbine
3 Results and Discussion
3.1 Validation of Structure Model
3.2 Influence of Wind Speed in Structural Strength
3.3 Validation of Turbine Tower Strength According to ICE 64100-3 Standard
4 Conclusion
References
Developed Programmable Logic Controllers with PI-Iterative Learning Control Algorithm a Case Study for BioGas-Based Generators
1 Introduction
2 Mathematical Model of Biogas Generator
3 Controller Design
3.1 PI Controller Design
3.2 PI-ILC Controller Design
4 Simulation and Practice Test
5 Conclusion
References
Sub-nanometer Displacement Measurement Using Heterodyne Interferometer and Down-Beat Frequency Technique
1 Introduction
2 Methodology
2.1 Heterodyne Interferometer
2.2 Down-Beat Technique
3 Experiments and Results
4 Conclusions
References
Economic Analysis of a Grid-Connected Rooftop PV System for a Factory in Phnom Penh
1 Introduction
2 Methodology
2.1 Cost Analysis Concept by HOMER Pro
3 Case Study: A Factory in Phnom Penh
3.1 Studied Site
3.2 Load Profile
3.3 Solar Radiation
3.4 Electricity Tariff at the Selected Site
3.5 Simulation Model
3.6 Components and Economic Parameters
4 Results and Discussion
5 Conclusion
References
Effect of Turning Vanes on Heat Exchange Characteristics of Cooling Channel in Turbine Blade
1 Introduction
2 Numerical Analysis
2.1 Description of Geometry
2.2 Numerical Method
2.3 Parameter definition
3 Result and Discussion
4 Conclusion
References
Designation and Simulation of Dehumidification Integrated Air Conditioning
1 Introduction
2 Methodology
2.1 Operating Principle
2.2 Specifications of the Heat Exchangers
2.3 Overall Heat Transfer Coefficient
2.4 Simulation Model of DHIAC
3 Results and Discussion
4 Conclusions
References
Nummerical Analysis on Lift and Drag of a Finite-Thickness Circular Arc Hydrofoil in Different Camber
1 Introduction
2 Numerical Simulation Method
2.1 Model Configuration and Meshing
2.2 Numerical Models and Governing Equations
2.3 Boundary Conditions
3 Results and Dicussion
3.1 Validation of Simulation Method
3.2 The Flow Characteristics of a Finite-Thickness Circular-Arc Hydrofoils CAβxδy
3.3 Flow Characteristics
4 Conclusion
References
Experimental Two-Phase Flow Boiling Heat Transfer Coefficient and Pressure Drop of Refrigerant in a Horizontal Micro-fin Tube
1 Introduction
2 Experimental Model
2.1 Apparatus
2.2 Data Reduction
3 Results and Discussions
4 Conclusion
References
Surrounding Environment Detection of an Intelligent Wheelchair Using Improved Convolutional Neural Networks
1 Introduction
2 System Configuration
3 Methodology
4 Experiment Results and Discussion
5 Conclusion and Future Work
References
Smoothed Particle Hydrodynamics Simulation of a Wave Making System
1 Introduction
2 Background Theory
2.1 SPH Methodology
2.2 Governing Equations of the Fluid Flow
3 Computational Techniques
3.1 Search of Neighboring Particles
3.2 Boundary Conditions
4 Simulation of a Wave Making System
4.1 Simulation Domain
4.2 Simulation Results
5 Summary
References
Evaluation of Tensile and Fatigue Strength of ANSI 304 Steel Pipe Welds
1 Introduction
2 Experimental Setup
2.1 Experimental Modeling
2.2 Equipment and Test Workpieces
3 Taguchi Experimental Design to Evaluate the Tensile Strength of Welds
4 Result and Discussion
5 Conclusion
References
A Study on Influence of MQL Parameters on Cutting Heat Generated During Machining Based on Numerical Simulation
1 Introduction
2 Influence of MQL Parameters on Machining Quality
3 Numerical Simulation of the Influence of MQL Parameters on Heat During Machining
4 Conclusion
References
A Numerical Simulation and Design of an Inspection System to Evaluate the Quality of Home Appliance Products
1 Introduction
2 Design of Inspection System
3 Results and Discussion
4 Conclusion
References
Parameter Auto-tuning for Improving Scale Factor and Washout Effect of Classical Motion Cueing Algorithm with Cylindrical Coordinates
1 Introduction
2 Classical Motion Cueing Algorithm with Cylindrical Coordinate
3 The Auto-tuning Method with the Improment of Scale Factor and the Washout Effect
4 Auto-tuning Parameters of the ClCy Algorithm
5 Conclusion
References
Effect of the Pressure Ratio on the Heat Transfer Phenomena of the Evaporator in CO2Air Conditioning System
1 Introduction
2 Experiment Setup
3 Results and Discussion
4 Conclusion
References
Aerodynamic Performance of a Multi-stage Axial Compressor with Tip Clearance Coupled with Hub Fillet
1 Introduction
2 Numerical Analysis
2.1 Design Description
2.2 Numerical Method
3 Result and Discussion
3.1 Grid Independence Test
3.2 Effect of Fillet Radius
3.3 Effect of Tip Clearance
4 Conclusion
References
Theoretical and Experimental Study of the Effective Operation Mode of Absorption Refrigeration Chiller for Ice Production
1 Introduction
2 Absorption Chiller Cycle
2.1 Thermodynamic Parameters
2.2 Mathematical Model of the Absorption Chiller
3 Experimental Analysis
3.1 Experimental Model
3.2 Experimental Results
4 Results and Discussion
4.1 Optimal Generation Temperature
4.2 Evaluation of Error
5 Conclusion
References
Design of Agriculture Robot for Tomato Plants in Green House
1 Introduction
2 Working Environment and Robot Design
2.1 Working Environment and Robot Design
3 Robot Navigation and Monitor Task
3.1 Path Planning
3.2 Temperature and Humidity Monitor
4 Ripe Tomato Detection Using Deep Learning Technique
4.1 Yolov4 Structure
4.2 Materials and Application of Yolov4 for Ripe Tomato Detection
5 Conclusion and Future Works
References
Modeling and Controller Design of a Tilt Tri-Rotor UAV
1 Introduction
2 Modeling of Tilt Tri-Rotor UAV
2.1 Description of the Tilt Tri-Rotor UAV
2.2 Nonlinear Equations of Motion
3 Control Design
3.1 Position Controller
3.2 Attitude Controller
4 Simulation and Discussion
4.1 Comparison Between the Simulation and Experimental Results
5 Conclusions
References
A Numerical Method and OpenFOAM Solver for Microfludic Problems with Geometrical Periodicity
1 Introduction
2 Numerical Methods
2.1 Physics Model
2.2 Immersed Boundary Method
2.3 The Periodic Boundary Multi-pair Algorithm
3 Application of the Iterative Boundary Solver for the Microfluidics Problem
4 Conclusion
References
Correction to: Theoretical and Experimental Study of the Effective Operation Mode of Absorption Refrigeration Chiller for Ice Production
Correction to: Chapter “Theoretical and Experimental Study of the Effective Operation Mode of Absorption Refrigeration Chiller for Ice Production” in: A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, https://doi.org/10.1007/978-981-19-1968-8_112
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Lecture Notes in Mechanical Engineering
Anh-Tuan Le Van-Sang Pham Minh-Quy Le Hoang-Luong Pham Editors
The AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering Proceeding of RCTEMME2021, Hanoi, Vietnam
Lecture Notes in Mechanical Engineering Editorial Board Member Francisco Cavas-Martínez , Departamento de Estructuras, Construcción y Expresión Gráfica Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Series Editor Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Editorial Board Member Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany Series Editor Francesco Gherardini , Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy Editorial Board Member Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia Series Editor Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine Editorial Board Members Young W. Kwon, Department of Manufacturing Engineering and Aerospace Engineering, Graduate School of Engineering and Applied Science, Monterey, CA, USA Justyna Trojanowska, Poznan University of Technology, Poznan, Poland
Lecture Notes in Mechanical Engineering (LNME) publishes the latest developments in Mechanical Engineering—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNME. Volumes published in LNME embrace all aspects, subfields and new challenges of mechanical engineering. Topics in the series include: • • • • • • • • • • • • • • • • •
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Anh-Tuan Le Van-Sang Pham Minh-Quy Le Hoang-Luong Pham •
•
•
Editors
The AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering Proceeding of RCTEMME2021, Hanoi, Vietnam
123
Editors Anh-Tuan Le Hanoi University of Science and Technology Hanoi, Vietnam
Van-Sang Pham Hanoi University of Science and Technology Hanoi, Vietnam
Minh-Quy Le Hanoi University of Science and Technology Hanoi, Vietnam
Hoang-Luong Pham Hanoi University of Science and Technology Hanoi, Vietnam
ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-19-1967-1 ISBN 978-981-19-1968-8 (eBook) https://doi.org/10.1007/978-981-19-1968-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022, corrected publication 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Contents
Investigation of Protrusion Height and Wall Thickness Distribution of Tube Hydroforming Y-Shaped Joint . . . . . . . . . . . . . . . . . . . . . . . . . Vu Duc Quang, Dinh Van Duy, Nguyen Dac Trung, Tran Anh Quan, and Le Trung Kien
1
Evaluation of Factors Influencing the Freezing Time of the Pangasius Fillets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huu Hoang Do and T. N. Huong Hoang
12
Method to Improve the Power Coefficient of Vertical Axis Wind Turbines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bao The Nguyen, Minh Van Ngo, and Huy Gia Ngo
29
On the Dynamic Response of a Functionally Graded Funnel Shell with Graphene Nanoplatelet Reinforcement . . . . . . . . . . . . . . . . . . . . . . . . . . Minh-Quan Nguyen, Gia-Ninh Dinh, and Van-Bao Hoang
40
Study on the Process and Formation of Fiber Structure During Forming of an Automobile Joint Part in Closed-Die Forging . . . . . . . . . Quang-Thang Nguyen, Viet-Tien Luu, and Trung-Kien Le
49
A Study on the Effect of Compression Ratio and Bowl-In-Piston Geometry on Knock Limit in Port Injection Natural Gas Converted Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sy Vong Le, Ho Huu Chan, and Tran Dang Quoc Effect of Different Heat Recovery Tube Structure on the Exhaust Heat Utilizing Ability in Internal Combustion Engine . . . . . . . . . . . . . . . . . . Khong Vu Quang, Le Manh Toi, Le Dang Duy, Vu Minh Dien, Nguyen Duy Tien, and Nguyen The Truc A-star Algorithm for Robot Path Planning Based on Digital Twin . . . . Doan Thanh Xuan, Le Giang Nam, Dang Thai Viet, and Vu Toan Thang
56
74
83
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vi
Contents
Development of a CFD Tool Based on SnappyHexMesh/OpenFOAM for the Axial Fan Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Van Long Le, Lu Tien Truong, and Khanh Hieu Ngo
91
Effect of Clamping Distances on Residual Stress in Butt Welded Joint of Stainless Steel Plates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 Nguyen Tien Duong Experimental Design and Manufacture a Pair of the Internal Noncircular Gears with an Improved Cycloid Profile . . . . . . . . . . . . . . . . . . 118 Nguyen Hong Thai, Phung Van Thom, and Nguyen Thanh Trung Development of a Software for Laser-Based Micromachining of Piezoceramic Microactuators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Ha Xuan Nguyen and Hung Anh Nguyen Vibration Analysis of Thick Rotating Laminated Composite Conical Shells by the Dynamic Stiffness Matrix Method . . . . . . . . . . . . . . . . . . . 146 Manh Cuong Nguyen and Nam Le Thi Bich Research on the Characteristics of Tooth Shape and Size of the Oval Gear Drive with an Involute Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Nguyen Hong Thai and Phung Van Thom Vibroacoustic Behavior of Double-Composite Plate Filled with Porous Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Tran Ich Thinh and Pham Ngoc Thanh Research of Calculation and Testing Assessment of Impact Resistance of Automobile Alloy Wheel Rims Used in Vietnam . . . . . . . . . . . . . . . . 200 Thanh Cong Nguyen One-Dimensional and Entropy Generation Analyses of a Solar Chimney . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Cao Trung Hau, Doan Thi Hong Hai, Nguyen Van Hap, and Nguyen Minh Phu Oscillation Measurement of the Magnetic Compass Needle Employing Deep Learning Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Thanh-Hung Nguyen and Minh-Chien Nguyen Development of Grading System Based on Machine Learning for Dragon Fruit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 Nguyen Minh Trieu and Nguyen Truong Thinh Disturbance Observer-Based Speed Control of Interior Permanent Magnet Synchronous Motors for Electric Vehicles . . . . . . . . . . . . . . . . . 244 Duc Thinh Le, Van Trong Dang, Bao Hung Nguyen Dinh, Hoang Phuong Vu, Viet Phuong Pham, and Tung Lam Nguyen
Contents
vii
Optimizing the Parameter of the LQR Controller for Active Suspension System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 Thi Thu Huong Tran, Tuan Anh Nguyen, Thang Binh Hoang, Duc Ngoc Nguyen, and Ngoc Duyen Dang Integral Action Finite Set Model Predictive Current Control for Brushless DC Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Lam Cuong Quoc Thai, Van Tu Duong, Huy Hung Nguyen, and Tan Tien Nguyen A Study on Selecting Cutting Regime to Attain Suitable Roughness and Dimensional Precision in Both When Drilling Tempered Steel 20XHM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 Ngoc Tuyen Bui and Thanh Ha Phan An Overview on the Viability of Hydrous Bioethanol as Gasoline Fuel Blend in the Philippines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Nathaniel Ericson R. Mateo, Roque A. Ulep, Marilou P. Lucas, Shirley C. Agrupis, Janssen Sagadraca, and Christopher Baga Numerical Optimization of the Process Conditions to Improve the Warpage Inside the Product and Reduce the Cooling Time in the Injection Molding Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Tran-Phu Nguyen and Tuan-Anh Bui Geometrically Nonlinear Behaviour of Functionally Graded Beam and Frame Structures Under Mechanical Loading . . . . . . . . . . . . . . . . . 326 Thi Thu Hoai Bui, Thi Thu Huong Tran, and Dinh Kien Nguyen Effect of Geometric Parameters of Heat Sink on Thermal Dissipation for Active Antenna Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Van-Tinh Nguyen and Chi-Cong Nguyen An Overview of Pedestrian Detection Based on LiDAR for Advanced Driving Assistance System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Cao Vu Kieu, Ngoc Ninh Pham, Anh Tuan Le, Anh Son Le, and Xuan Nang Ho Stability and Viscosity of Mono and Hybrid Nanolubricant . . . . . . . . . . 364 M. A. Saufi, Y. A. A. Ibrahim Ahmad, and Hussin Mamat Detection Fault Symptoms of Rolling Bearing Based on Enhancing Collected Transient Vibration Signals . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Nguyen Trong Du, Nguyen Phong Dien, and Nguyen Huu Cuong Human-Robot Interaction System Using Vietnamese . . . . . . . . . . . . . . . 385 Nguyen Khac Toan, Le Duc Thuan, Le Bao Long, and Nguyen Truong Thinh
viii
Contents
Effect of the Elliptical Shape on the Performance of the Modified Savonius Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Minh Banh Duc and Anh Dinh Le Using Artificial Neural Network to Grade Internal Quality of Coconuts Based on Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 Nguyen Tran Trung Hieu, Nguyen Minh Trieu, and Nguyen Truong Thinh The Modeling of Ion-Selective Membrane . . . . . . . . . . . . . . . . . . . . . . . 424 Dung T. Nguyen, Khai H. Nguyen, and Van-Sang Pham Apply Image Processing Techniques into the Process of Reading and Calibrating Levelling Staffs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 Vu Khanh Phan, Vu Tien Dung, Ta Van Doanh, and Nguyen Thi Kim Cuc A Method for Cooling Bearings of Motorized Spindles . . . . . . . . . . . . . 447 Xuan Quang Ngo, Hoang Long Phan, Van Tu Duong, Huy Hung Nguyen, and Tan Tien Nguyen Analysis Hydrodynamic Performance of the Autonomous Underwater Vehicle for a Different Hull Shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458 Ngo Van He, Vu Ngoc Tuan, and Ngo Van Hien The Effect of Biodiesel-Ethanol-Diesel Blends on Performance and Emissions of a Diesel Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Nhinh Nguyen Van, Tuan Pham Minh, and Tuyen Pham Huu Inverse Kinematics Analysis of 7 DOF Collaborative Robot by Using Coordinate and Velocity Projection Methods . . . . . . . . . . . . . . . . . . . . . 479 Phuong Thao Thai and Quang Hoang Nguyen Investigating the Effect of Pulsed Fiber Laser Parameters on the Roughness of Heat-Resistant Parts in Cleaning Processes . . . . . . 487 Toan Thang Vu, Thanh Dong Nguyen, Thanh Tung Vu, and Hong Hai Hoang Fiber Laser Cleaning to Remove Paint on the Surface of Mechanical Parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496 Toan Thang Vu, Thanh Dong Nguyen, Thanh Tung Vu, Cong Tuan Truong, and Xuan Hieu Dong A Durability Test of Motorcycle Engine Fueled with Ethanol-Gasoline Blends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506 Nguyen Duc Khanh, Nguyen The Truc, Nguyen Duy Tien, Nguyen The Luong, and Pham Huu Tuyen
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Mapping and Path Planning for the Differential Drive Wheeled Mobile Robot in Unknown Indoor Environments Using the Rapidly Exploring Random Tree Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516 Ngoc-Tien Tran, Tien-Dung Ngo, Dinh-Khoi Nguyen, Phung Xuan Son, and Nguyen Hong Thai Research on Resistance of Some Typical Fishing Vessels in Vietnam . . . 528 Hoang Cong Liem and Nguyen Duc Manh A Study on Design Two Pitch Propeller to Apply for the Small Fishing Vessel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 542 Luong Ngoc Loi, Nguyen Chi Cong, Nguyen Manh Nen, and Ngo Van He Analysis Hatchback Vehicle Structure in Car-to-Car Frontal Impact Using Finite Element Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 558 Nguyen Phu Thuong Luu, Nguyen Thanh Don, and Ly Hung Anh A Study on Vibration and Noise of Manual Transmission by Using Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572 Nguyen Phu Thuong Luu and Ly Hung Anh The Pollutant Reduction Potential of Old Generation Diesel Engine Retrofitting After-Treatment System . . . . . . . . . . . . . . . . . . . . . . . . . . . 586 Tuan Pham Minh, Dung Nguyen Manh, Vinh Tran Quang, Nguyen The Luong, Truc Nguyen The, Nguyen Duy Tien, and Khanh Nguyen Duc Head Injury Criterion of Vietnamese Pedestrian Struck by a Sedan . . . 597 Hung Anh Ly and Phu Thuong Luu Nguyen NURBS Curve Trajectory Tracking Control for Differential-Drive Mobile Robot by a Linear State Feedback Dynamic Controller . . . . . . . 610 Nguyen Hong Thai, Hoang Thien, and Trinh Thi Khanh Ly A Thoroughly Approach: Pulley Kinematic, Actuator Dynamic and Stiffness on Cable Suspended Parallel Robots . . . . . . . . . . . . . . . . . . . . 624 Le Duc Duy and Nguyen Truong Thinh An Approach to Biomass Selection Based on Thermal Properties for Co-firing Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Tien Quang Nguyen Electric Load Disaggregation Using Non-intrusive Load Monitoring Algorithm for Home Energy Management: Case-Study for an Apartment in Hanoi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651 Hoang-Anh Dang and Van-Dung Dao Experimental Study of Infrared – Assisted Heat Pump Drying of Lime Slices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 668 Nguyen Minh Ha and Ha Anh Tung
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An Efficient Pantograph Dynamic Mechanism for Massage Robot Arm Acting Therapy on Human Back . . . . . . . . . . . . . . . . . . . . . . . . . . 678 Nguyen Dao Xuan Hai and Nguyen Truong Thinh Elastic Mechanical Properties of Transition Metal Dichalcogenides Monolayer Using Atomic Finite Element Method . . . . . . . . . . . . . . . . . . 687 Danh-Truong Nguyen Research on the Friction Effect on Product Quality in Sheet Hydroforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701 Thu Nguyen Thi, Trung Nguyen Dac, Trung-Kien Le, and Nam Dao Ngoc Minh Electromechanical Actuators Based on Monolayer Borophene with b12 and v3 Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 710 Nguyen Duy Van, Vuong Van Thanh, Nguyen Tuan Hung, and Do Van Truong Study Optimization of Process Parameters in Overmolding of Multi Plastic Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 Truong Duc Phuc and Nguyen Anh Dung Numerical Study for Flow Behavior and Drag of Axisymmetric Boattail Models at Different Mach Number . . . . . . . . . . . . . . . . . . . . . . 729 The Hung Tran, Cong Truong Dao, Dinh Anh Le, and Trang Minh Nguyen Adaptive Observation Controller for an Uncertain Hysteresis System with Input Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742 Phuc Than Huynh Numerical Simulation of Hot Water Flashing Flow in a Converging - Diverging Nozzle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753 Anh Dinh Le, Quan Hoang Nguyen, Long Ich Ngo, Anh Viet Truong, Okajima Junnosuke, and Iga Yuka A Case Study on Humanoid Robot Using Robotics Software in E-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 Duc-An Pham, Huy-Anh Bui, Xuan-Thuan Nguyen, Thi-Thoa Mac, and Hong-Hai Hoang LQR Control Design in Vibration Control of a Benchmark Building Structure Subjected to Seismic Load . . . . . . . . . . . . . . . . . . . . . . . . . . . 771 Thi-Thoa Mac and Hai-Le Bui Detecting Crack on a Beam Subjected to Impact Load . . . . . . . . . . . . . 781 Fergyanto E. Gunawan, Tran Huu Nhan, Sutikno, and Insannul Kamil
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Thermal Analysis by Finite Element Model for Powder Screw Extruder for 3D Printing Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 790 Quang Duy Do, Hung Quang Tran, Thien Bat Le, Lan Xuan Phung, and Trung Kien Nguyen The Effect of Printing Parameters on the Characteristics of PCL Scaffold in Tissue Engineering Application . . . . . . . . . . . . . . . . . . . . . . 802 Tuan Quang Ta, Trung Kien Nguyen, Son Hoanh Truong, and Lan Xuan Phung Investigation and Optimization of Surface Roughness and Material Removal Rate in Face Finishing Milling of Ti-6Al-4V Under MQL Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 Van-Canh Nguyen, Dung Hoang Tien, Van-Hung Pham, and Thuy-Duong Nguyen Evaluating the Influence of Cutting Mode and Workpiece Parameters on Surface Roughness When External Cylindrical Grinding 65Mn Steel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826 Tuan-Linh Nguyen Numerical Simulation of Thermal Stress and Creep Fatigue Crack Growth Analysis for Thick-Walled Tubes of Header . . . . . . . . . . . . . . . 837 Hong Bo Dinh and Kee Bong Yoon A Method of Determination of the Remaining Geometrical Accuracy of Lathe Machine Based on the Wear of Slideway . . . . . . . . . . . . . . . . . 851 Hung Pham Van, Canh Nguyen Van, and Duong Nguyen Thuy Development of Vibration Absorber System Using Tunable Stiffness Material . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 860 Duc Long Nguyen A Computational Method of Air-Electric Equivalent in Air Spindle . . . 868 Truong Minh Duc, Ta Thi Thuy Huong, Nguyen Thanh Trung, and Vu Toan Thang CFD Simulation of Temperature Distribution of Atrium Space in a Residential Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 878 Kieu Hiep Le, Tien Cong Do, Van Thuan Nguyen, Tien Anh Nguyen, and Viet Dung Nguyen CFD Simulation of Swirl-Stabilized Pulverized Coal Flames in a Cylindrical Combustion Chamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 887 Tien Anh Nguyen, Kieu Hiep Le, Gia My Tran, and Tran Tho Dang The Process of Custom Designing Replacement Cranial Bone Patches in Human Body . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 897 Thi Kim Cuc Nguyen, Hoang Hong Hai, Cao Xuan Binh, and Vu Tien Dung
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Research and Design of Compact Equipment Using Phase_Shifting Deflectometry Apply in Optical Surface Measurement . . . . . . . . . . . . . . 905 Nguyen Thi Kim Cuc, Vu Danh Tien, Cao Xuan Binh, and Vu Tien Dung Numerical Study of Ultraviolet Germicidal Effect Against SARS-CoV-2 Virus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913 Nguyen Duy Minh Phan, Ngo Quoc Huy Tran, Le Anh Doan, Quang Truong Vo, Duy Chung Tran, Thi Thanh Vi Nguyen, Van Sanh Huynh, Tran Anh Ngoc Ho, Duc Long Nguyen, and Cuong Mai Bui Thermal Efficiency and Exhaust Emission of an SI Engine Using Hydrogen Enriched Gas from Exhaust Gas Fuel Reforming Based on Ni-Cu/Al2O3 Catalysts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 925 Nguyen The Luong, Tran Van Hoang, Pham Minh Tuan, and Le Anh Tuan Photocatalytic Activity of Tungsten-Loaded Titanium Dioxide Photocatalysts Against Dyes and Bacteria in Water System . . . . . . . . . . 938 Saepurahman, Muhammad Eka Prastya, Mohd Azmuddin Abdullah, Keiichi N. Ishihara, and Tjandrawati Mozef Free Vibration Analysis on Stepped Composite Cylindrical Shells Reinforced by Inner/outer Ring Stiffeners . . . . . . . . . . . . . . . . . . . . . . . 953 Nguyen Quoc Hung, Nguyen Manh Cuong, and Ta Van Cuong Subcooled Flow Boiling and Its Application in Designing LNG Vaporizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 969 Tuan Le and Kieu Hiep Le Patent Map for Forecasting Technology Trends and Policymaking Case Study: IoT in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 980 Pha N. Pham and Trong-Hieu Nguyen Modelling for Sliced Avocado Drying in Modified Air . . . . . . . . . . . . . . 991 Thi Thu Hang Tran Experimental Study on Sliced Avocados Drying in Modified Air . . . . . . 1001 Van Thuan Nguyen, Thi Thu Hang Tran, Viet Dung Nguyen, and Hong Nam Nguyen Determining the Residual Stress of K Welded Pipe Joint by Hole Drilling Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012 Nguyen Hong Thanh and Nguyen Tien Duong The Effect of Microstructure and Nano Additive Lubrication on the Specific Grinding Energy and Surface Roughness in Ti-6Al-4V Grinding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1023 Hung Phi-Trong, Trung Nguyen-Kien, Chung Luong-Hai, and Son Truong-Hoanh
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Experimental Investigation of Performance of Cellulose Cooling Pad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1033 Nguyen Viet Dung, Nguyen Tien Hung, Nguyen Ba Chien, and Nguyen Dinh Vinh Design and Construction for Computational Models of Ultrasonic Transducers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1042 Anh-Duy Truong, Van-Son Dinh, Van-Sang Pham, and Manh-Tuan Ha A Study on Forced-Air Thermal Dissipation in Lithium-Ion Batteries Using Numerical Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064 Khai H. Nguyen, Tung X. Vu, Le Thi Thai, and Van-Sang Pham A Study on Anti-pedal Error System in Car Based on Hydraulic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1080 Sy-Le Ho, Xuan-Giao Nguyen, Van-Hop Pham, Tien-Bang Nguyen, and Van-Thuan Truong High-Speed Focus Detection System Using Diffractive Beam Sampler and Position-Sensitive Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 Xuan Dat Tran, Xuan Binh Cao, and Le Phuong Hoang An Experimental Study on Position Control of Pneumatic Cylinder Using Programmable Logic Controller and Pneumatic Proportional Valves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1098 Duc Thinh Pham, Dinh Son Tran, and Xuan Bo Tran Control Strategies for Beer Recovery Process from Surplus Yeast . . . . . 1106 Lan Anh Dinh Thi, Viet Dung Nguyen, Van Nhat To, Thu Ha Nguyen, and Thanh Ha Tran Robust Control of Quadcopter in Case of Releasing Liquid and Encountering Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 Nguyen Son Hoang and Manh-Tuan Ha Structural Durability Analysis of Offshore Wind Turbine Tower with Monopile Foundation According to ICE 61400 Standards . . . . . . . . . . . 1150 Vu Dinh Quy, Le Thi Tuyet Nhung, and Nguyen Viet Hoang Developed Programmable Logic Controllers with PI-Iterative Learning Control Algorithm a Case Study for BioGas-Based Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159 Anh Hoang, Duc Tung Trinh, Thanh Trung Cao, and Hung Dung Pham Sub-nanometer Displacement Measurement Using Heterodyne Interferometer and Down-Beat Frequency Technique . . . . . . . . . . . . . . 1170 Nguyen Thanh Dong, Nguyen The Tai, Do Viet Hoang, Nguyen Thi Phuong Mai, Vu Thanh Tung, and Vu Toan Thang
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Economic Analysis of a Grid-Connected Rooftop PV System for a Factory in Phnom Penh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1177 Vannak Vai, Samphors Eng, Chhith Chhlonh, and Hideaki Ohgaki Effect of Turning Vanes on Heat Exchange Characteristics of Cooling Channel in Turbine Blade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1187 Tien-Dung Nguyen, Hai-Quang Do, Cong-Hung Hoang, Minh-Hieu Nguyen, Mai-Anh Thi Bui, Cong-Truong Dinh, and Hong-Quan Luu Designation and Simulation of Dehumidification Integrated Air Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1200 Ngo Minh Đuc and Ta Van Chuong Nummerical Analysis on Lift and Drag of a Finite-Thickness Circular Arc Hydrofoil in Different Camber . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1215 Thanh Van Nguyen, Anh Dinh Le, and Anh Viet Truong Experimental Two-Phase Flow Boiling Heat Transfer Coefficient and Pressure Drop of Refrigerant in a Horizontal Micro-fin Tube . . . . . . . . 1228 Viet Dung Nguyen, Quoc Dung Trinh, Tuan Anh Vu, Ba Chien Nguyen, and Jong-Taek Oh Surrounding Environment Detection of an Intelligent Wheelchair Using Improved Convolutional Neural Networks . . . . . . . . . . . . . . . . . . 1238 Hai-Le Bui, Tuan Truong Cong, Pham Anh Quan, and Thi Thoa Mac Smoothed Particle Hydrodynamics Simulation of a Wave Making System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1246 Huy Nguyen Tran and Thinh Xuan Ho Evaluation of Tensile and Fatigue Strength of ANSI 304 Steel Pipe Welds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1255 Xuan Chung Nguyen and Tuan-Linh Nguyen A Study on Influence of MQL Parameters on Cutting Heat Generated During Machining Based on Numerical Simulation . . . . . . . . . . . . . . . . 1268 Van-Hung Pham, Thi-Thu-Ha Nguyen, Van-Huy Nguyen, Trung-Kien Quach, Trong-Nghia Nghiem, and Tuan-Anh Bui A Numerical Simulation and Design of an Inspection System to Evaluate the Quality of Home Appliance Products . . . . . . . . . . . . . . . . 1279 Trong-Thanh Nguyen, Hoang-Vuong Pham, Xuan-Nam Tran, Phu-Minh Pham, and Tuan-Anh Bui Parameter Auto-tuning for Improving Scale Factor and Washout Effect of Classical Motion Cueing Algorithm with Cylindrical Coordinates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1292 Duc An Pham and Quang Nguyen Huu
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Effect of the Pressure Ratio on the Heat Transfer Phenomena of the Evaporator in CO2 Air Conditioning System . . . . . . . . . . . . . . . . . . . . . 1299 Thanhtrung Dang and Tronghieu Nguyen Aerodynamic Performance of a Multi-stage Axial Compressor with Tip Clearance Coupled with Hub Fillet . . . . . . . . . . . . . . . . . . . . . . . . . 1306 Hoang-Quan Chu, Quang-Hai Nguyen, Quang-Huy Nguyen, Quoc-Viet Nguyen, Van-Hoang Nguyen, Kim-Dung Thi Hoang, Xuan-Truong Le, Cong-Truong Dinh, and Thanh-Tung Tran Theoretical and Experimental Study of the Effective Operation Mode of Absorption Refrigeration Chiller for Ice Production . . . . . . . . . . . . . 1324 Nghia-Hieu Nguyen, Hiep-Chi Le, and Quoc-An Hoang Design of Agriculture Robot for Tomato Plants in Green House . . . . . . 1347 Thi Thoa Mac, Van Thu Hoang, Hai-Le Bui, Tai Nguyen Sy, and Hong Hai Hoang Modeling and Controller Design of a Tilt Tri-Rotor UAV . . . . . . . . . . . 1356 Vu Dinh Quy, Le Thi Tuyet Nhung, Nguyen Quang Hung, and Nguyen Ngoc Quynh A Numerical Method and OpenFOAM Solver for Microfludic Problems with Geometrical Periodicity . . . . . . . . . . . . . . . . . . . . . . . . . 1369 Manh-Hung Nguyen, Thi-Thai Le, and Van-Sang Pham Correction to: Theoretical and Experimental Study of the Effective Operation Mode of Absorption Refrigeration Chiller for Ice Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nghia-Hieu Nguyen, Hiep-Chi Le, and Quoc-An Hoang
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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1383
Investigation of Protrusion Height and Wall Thickness Distribution of Tube Hydroforming Y-Shaped Joint Vu Duc Quang1 , Dinh Van Duy2(B) , Nguyen Dac Trung2 , Tran Anh Quan3 , and Le Trung Kien2 1 University of Economics - Technology for Industries, Hanoi, Viet Nam 2 Hanoi University of Science and Technology, Hanoi, Viet Nam
[email protected] 3 IMI Holding, Hanoi, Viet Nam
Abstract. Tube hydroforming (THF) is an important technology for the production of parts with complex shapes from tube blanks. In some cases, for some parts with complex shapes, only THF can fabricate them. The parts are manufactured using THF technology and are used in many fields especially in the automotive, aerospace industry. In manufacturing, the tube wall’s protrusion height and material thickness distribution significantly affect the product quality. They depend on many input parameters, such as internal liquid pressure, axial feed, friction. Therefore, based on numerical simulation, this study investigates the influence of key process parameters on effective protrusion height and wall thickness distribution of tube hydroforming Y-shaped joint without counter pressure. Tubular blank material has been used SUS304 stainless steel. The results show the appropriate process parameters to achieve the effective protrusion height and material thickness distribution at each position of the part. They may be applied in product design and a more effective process design, calculation, and input parameters, avoiding costly physical experimentation. Keywords: Tube hydroforming · Y-shaped joint · Protrusion height · Wall thickness
1 Introduction THF is the advanced metal-forming process for hollow parts, including T-shape, Yshape, and X-shape joint connectors. Liquids with pressures of hundreds of Mpa are generated by the pressure intensifier to form the tube profile according to the die cavity. In THF technology, the workpiece can be initially straight or preformed before using high internal pressure to deform the workpiece according to a shape die. Refer to document [1, 2] for more parts on the THF process. In recent decades, the THF process has received great attention from various industries. And so it has become one of the most commonly used processes in many industries, such as the automotive and aerospace industry or sanitary and piping manufacture. The © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1–11, 2022. https://doi.org/10.1007/978-981-19-1968-8_1
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increased interest is because of the THF process, manufacturers can produce intricately shaped parts with continuous materials, improved mechanical properties after deformation, reduced number of operations compared to other fabrication methods. (typical examples are shown in Fig. 1) [3–5]. Components manufactured using other forming techniques may experience spring back. However, this effect is smaller in THF-treated components [3].
Fig. 1. Conventional design versus hydroformed exhaust Y-joint connector pipe [5].
Tube products shaped by THF technology are characterized by thinning in the expansion regions because these regions deform freely under the effect of tensile stress (Fig. 2) [6], and due to the influence of friction force, the distribution of material thickness at each position on the product is different. On the issue of this degree of material deformation, there have been prominent studies mentioned such as Rafal Stadnik & Jan Kazanecki [3], Cheng, Teng, Guo, Yuan [7], Muammer Koc¸, Ted Allen, Suwat Jiratheranat, Taylan Altan [4], etc. However, the distribution of material thickness on formed products according to the total and effective protrusion height has not been studied in detail. However, this is an issue that manufacturers are often concerned with products formed by THF technology.
Die Expansion zone Leftward Guiding zone
Rightward fillet transition zone
Fig. 2. The hydroforming process of Y-shaped joint [6].
Investigation of Protrusion Height and Wall Thickness Distribution
3
The Finite Element Method (FEM) is widely used in the field of metal forming. Using this approach can save testing costs and shorten the time to market [8]. The purpose of this study is to establish the graphs of the comparison of total and effective protrusion height, material thickness distribution according to input parameters including internal fluid pressure, axial feed, the coefficient of friction after part expansion for a standard geometry, namely Y-shaped joint shown in Fig. 1 by using finite element analysis (FEA). The object of the study is a Y-shaped part, the workpiece material is SUS304. This is a common part of products used in heating, water and gas systems. It is also interesting because it is perhaps the most specific part involving an axial asymmetry expansion zone, characterising many well-founded design geometries.
2 Materials and Methods The FEA simulation process is set up with input parameters close to the actual production. The mechanical properties of material and parameters as shown in Tables 1 and 2. Figure 3 shows the modelling of THF a Y-shaped part. At the beginning of the THF process simulation study, three important parameters, including internal pressure pi , axial stroke si and friction coefficient µ (lubrication condition), were determined. The internal pressure to initiate yield deformation (Pi )y and the maximum internal fluid pressure during the forming process was determined as a good initial prediction for hydroforming of the more complex parts where an axial feed is applied. See [1, 10] for more parts about the primary internal pressure. The liquid pressure inside the die is used for simulation with a maximum value of up to 130 Mpa. The velocity boundary condition is safer to apply than the displacement boundary condition to prevent erroneous inertial stresses in the simulation. As a rule of thumb, which is borrowed from sheet metal forming simulation, the speeds of tooling components such as those of axial punches should be kept below 10–15 mm/s to minimise such erroneous stresses [11]. n the metal forming process, two laws of friction are commonly used: Coulomb’s law of friction and the law of shearing friction. While Coulomb’s law is more suitable for sliding contact with minor deformation, in contrast, the law of shear friction is ideal for sliding objects subjected to bulk plastic deformation. The shear friction model is used to describe the friction between the workpiece and the tools, and the friction coefficient for the lubricated cold forming process is selected according to the entire film lubrication µ < 0.03 and the mixed layer lubrication 0.03 < µ < 0.1 [2, 12]. Simulations were conducted with critical process parameters, and the geometry of the die and tube were modelled to be consistent with the experimental. Only the workpiece’s deformation behaviour was studied and meshed with 3D tetrahedral elements in the present analysis. The models were built in five parts: flexible tube blank, rigid lower die half, rigid upper die half, rigid left punch and rigid right punch (Fig. 3). The FE simulations were accurate and convergent; the tubular blank was set as plastic deformation type and meshed by 45,000–50,000 tetrahedral elements with an absolute mesh pattern. To investigate the distribution of total protrusion height, effective protrusion height, and material thickness of the product, a tubular blank with a specifically selected dimension has an initial length L0 and thickness t0 of 160 mm and 1.2 mm respectively. The study has determined the degree of thickness deformation at seven measured positions
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Left punch
Right punch
Workpiece
Fig. 3. Workpiece and tools used in FEM simulation.
numbered from 1 to 7 on the Y-shaped longitudinal section, as shown in Table 2. The degree of material thickness deformation is determined by Eq. (1). γ =
t0 − ti ∗ 100(%) t0
(1)
where ti – the wall thickness at each measuring position (mm). Table 1. Room temperature mechanical properties of tubular blank [9] Alloy
Density (kg/m3)
Yield strength (MPa)
Ultimate Tensile Elongation (%) Strength (MPa)
Hardness (HB)
SUS304
7930
230
480
183 max
40
3 Results and Discussion 3.1 Effect of Internal Liquid Pressure The numerical simulations were conducted with internal pressure pi = constant ranging from 40 MPa to 130 MPa. The total and effective protrusion height development, material thinning at the top protrusion with different internal pressures were studied for the part expansion. The simulation results are shown in Fig. 4. Figure 4 shows that the total height of protrusion and material thinning at the top of protrusion increase as the internal liquid pressure pi increases. By a series of numerical simulations, a suitable loading curve was applied for all simulations, as shown in Fig. 5. The Y-shapes hydroformed by this pressure path will give the optimal shape. If the liquid pressure is increased in the bowl, it will lead to more thinning at the top of the lug. Therefore, In the next part of this paper, only pi pressures, as Fig. 5 were considered. As the research literature shows, when using higher internal fluid pressure will cut down the flow of material into the expansion zone.
Investigation of Protrusion Height and Wall Thickness Distribution Table 2. Tubular blank and Y-shaped tube parameters
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- p i max = 130 MPa
- p i max = 100 MPa
- p i max = 70 MPa - p i max = 40 MPa
Fig. 4. Effect of internal fluid pressure on the distribution of total protrusion height and material thinning (Total stroke 66 mm, friction coefficient µ = 0.03, vleft = 1 mm/s, vright = 2 mm/s).
Fig. 5. Pressure curve applied in the numerical simulations.
3.2 Influence of Axial Feed Numerical simulations were conducted with different values of axial displacement (the total stroke = 60, 66, 78, 90 mm), axial punch displacement velocity (vright = 1.5vleft , vright = 2vleft , vright = 3vleft with vleft = 1 mm/s) and coefficient of friction. The results show that the appropriate axial punch displacement velocity condition is selected as vright = 2vleft = 2 mm/s to obtain the total protrusion height, effective protrusion height (Fig. 6) and material thickness distribution of part are guaranteed as design (Fig. 7). Figure 6 shows that as total stroke increases, both total and effective protrusion height increase. The cutting height of four full strokes is quite similar, averaging about 9.05 mm. It is worth noting that the total stroke rate to effective height is quite similar, with an average value of about 1.90. Figure 7 shows a comparison of the thickness variations at seven measured points (corresponding to total protrusion height) on the Y-shaped longitudinal section predicted with FEM for various full strokes. The above graph has shown that the measuring point at position 4 (the top of protrusion) was the most thinned, followed by measured points 3 and 5, respectively. With a total stroke of 90 mm, minimum wall thickness at the top of protrusion is 0.77 mm (the thinning rate is 35.8%), measuring points 3 and 5 have wall thicknesses of 0.96 mm and
Investigation of Protrusion Height and Wall Thickness Distribution
7
Fig. 6. Effect of axial feed on total and effective protrusion height (vright = 2vleft = 2 mm/s, µ = 0.05).
Fig. 7. Effect of axial feed on the degree of material thickness deformation with total protrusion height (vright = 2vleft = 2 mm/s, µ = 0.05).
0.99 mm (the thinning rates are 20% and 17.5% respectively). The feed areas of two tube end (the measured points 1 and 7) and both sides of the fillet transition zone (the measured points 2 and 6) experience varying degrees of thickness. Also, with a total stroke of 90 mm, the right fillet transition zone (the measured point 6) is the thickest, which is 2.6 mm, the greatest thickening rate is 117.0%. At the same time, the thickness of the leftward at the fillet transition zone (the measured point 2) is 2.2 mm, whose thickening rate is 83%. The feeding areas of the left (the measured point 1) and right (the measured point 7) tube ends are thickened, which are 1.89 mm and 2.26 mm, corresponding to the thickening rate of which are 58.0% and 88.0%. Figure 8 shows a comparison of the thickness variations at six measured points (corresponding to the effective protrusion height) on the Y-shaped longitudinal section predicted with FEM for various total strokes. The thinning at measured points 3 and 5 is checked to ensure that the shaped effective protrusion area was not in the practically acceptable failure range with various total strokes. The numerical simulation results predicted a sound Y-shaped part with a thinning rate at the measured point 3, which are 22.5%, 18.3%, 21.7% and 20.0% for the total stroke of 60 mm, 66 mm, 78 mm and
8
V. D. Quang et al.
Fig. 8. Effect of axial feed on the degree of material thickness deformation with effective protrusion height (vright = 2vleft = 2 mm/s, µ = 0.05).
90 mm, respectively, meanwhile, at the measured point 5, which are 15%, 19.2%, 10.8% and 17.5 respectively for the total strokes as above. Thus, the top of protrusions is thinned most intensely (the measured point 4). And the thinning rates are 27.5%, 28.3%, 30.8% and 35.8%, respectively, for a total stroke of 60 mm, 66 mm, 78 mm and 90 mm. These thinning rates are greater than 25.0%, but the Y-shaped part still meets the design requirements for the effective protrusion height.
Fig. 9. Effect of friction on total and effective protrusion height (vright = 2vleft = 2 mm/s, µ = 0.05, total stroke 60 mm).
3.3 Effect of Friction Numerical simulations were conducted with the different coefficient values of friction as 0.03, 0.05, and 0.08 to study their effects on the change of protrusion height and thickness distribution of part, as shown in Fig. 9. The results show a significant impact of friction on the variation of protrusion height and part thickness. It was observed that with smaller values of friction, increasing the chances of total and effective protrusion height increased (the height rate increased from 8.2% to 14.4%) (Fig. 9) as well as
Investigation of Protrusion Height and Wall Thickness Distribution
9
reducing the chance of thinning at the top of the protrusion (at the measured point 4, the thinning rate decreased by 39.2% to 30.0%) (Fig. 10a).
Fig. 10. The degree of thickness deformation with different friction coefficient (vright = 2vleft = 2 mm/s, µ = 0.05, total stroke 60 mm).
Figure 10a shows the thickness distribution of 7 points measured with different friction coefficients and a total stroke of 60 mm. It can be seen from Fig. 10a that the thickness distribution of the measured point fillet transition zones 2 and 6 are more variable as the coefficient of friction increases. The material wall thickness at the measured point 2 ranges from 1.73 mm to 1.83 mm, at measured point 6 values with confusion (1.83 mm with the coefficient of friction 0.3, 2.05 mm with the coefficient of friction 0.05 and 1.65 mm with the coefficient of friction 0.08). The wall thickness of the measured spot 4 is the thinnest and undergoes larger changes. The thinning rate is more significant as the coefficient of friction increases. Thinning at the top of the protrusion (the measured point 4) is at least when the coefficient of friction is 0.03 for a wall thickness of 0.84 mm, the thinning rate is 30.0%. With coefficients of friction of 0.05 and 0.08, the wall thicknesses are 0.81 mm (the thinning rate 32.5%) and 0.73 mm (the thinning rate 39.2%), respectively. Figure 10b shows a comparison of the thickness variations at six measured points (corresponding to the effective protrusion height) on the Y-shaped longitudinal section
10
V. D. Quang et al.
predicted with FEM for the different coefficients of friction with the same total stroke of 60 mm. The thinning at the measured points 3 and 5 is also checked (same as the effect of axial feed) to ensure that the shaped effective protrusion area was not in the practically acceptable failure range with the different coefficients of friction. The thinning rate of measured points 3 and 5 have little changes with an increase of friction, their mean values being 21.4% and 13.3%, respectively. Therefore, a trade-off is also required here to select suitable lubrication conditions so that the final wall thickness distribution can be more uniform throughout the highly stressed areas of the part.
4 Conclusion The discussed Y-shape tube models for the analysis of asymmetrical expansions help capture essential material properties of specimens in a tube form. The numerical simulation research indicates that the total, effective protrusion height and material thickness distribution depend on the main process parameters. Coordination of internal fluid pressure, axial feed, and lubrication are crucial to achieving the designed protrusion height without defects and proper material thickness distribution. The location of thinning is mainly at the top of the protrusion and protrusion wall, while the thickening was in both the guiding and transition zones. The top branch is susceptible to excessive tension because it has no support from the counter punch. Investigating the effective protrusion height and wall thickness distribution of tube hydroforming Y-shaped joint without counter pressure has been considered. The forming laws of the tube blank can be predicted; Thereby shortening the testing time, quickly bringing the product to the market, which is also an urgent requirement today.
References 1. Pham, V.N.: Hydroforming Technology, 1st edn. Bach Khoa Publishing House, Hanoi (2007) 2. Koç, M.: Hydroforming for Advanced Manufacturing, 1st edn. Woodhead Publishing Limited, Cambridge (2008) 3. Rafal Stadnik & Jan Kazanecki (2009). Investigation of hydroforming of the Y-shape branch. Metallurgy and Foundry Engineering, 35(1), 13, 2009–06–30, https://doi.org/10.7494/mafe. 2009.35.1.13 4. Koç, M., Allen, T., Jiratheranat, S., Altan, T.: The use of FEA and design of experiments to establish design guidelines for simple hydroformed parts. Int. J. Mach. Tools Manuf. 40, 2249–2266 (2000) 5. Singh, H.: Fundamentals of Hydroforming, 1st edn. The Society of Manufacturing Engineers (2003) 6. Dochmann, F., Hartl, C.: Hydrofomring-applications of coherent FE-simulations to the development of products and processes. J. Mat. Proc. Techn. 150, 18–24 (2004) 7. Cheng, D.M., Teng, B.G., Guo, B., Yuan, S.J.: Thickness distribution of a hydroformed Y-shape tube. Mater. Sci. Eng. A 499, 36–39 (2009) 8. Bell, C.., Corney, J., Zuelli, N., Savings, D.: A state of the art review of hydroforming technology. Int. J. Mat. Forming 13(3), 789–828 (2020) 9. Gale, W.F., Totemeier, T.C.: Smithells Metals Reference Book-Butterworth-Heinemann, 8th edn. Elsevier and The Materials Information Society, Oxford (2004)
Investigation of Protrusion Height and Wall Thickness Distribution
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10. Bogoyavlensky, K.N., Vagin, V.A., Kobyshev, A.N., et al.: Hydro-plastic Processing of Metals. Mashinostroenie, Moscow; Teknika, Sophia, U.S.S.R (1988) 11. Suwat Jirathearanat, M.S.: Advanced methods for finite element simulation for part and process design in tube hydroforming, Ph.D. Dissertation, Department of Mechanical Engineering The Ohio State University (2004) 12. Schey, J.A.: Tribology in metalworking: friction, lubrication, and wear. J. Appl. Metalworking 3, 173 (1984). https://doi.org/10.1007/BF02833697
Evaluation of Factors Influencing the Freezing Time of the Pangasius Fillets Huu Hoang Do1(B) and T. N. Huong Hoang2 1 Faculty of Mechanical Technology, Ho Chi Minh City University of Food Industry,
140 Le Trong Tan Street, Tan Phu District, Ho Chi Minh City, Vietnam [email protected] 2 Thermal and Refrigeration Technology Department, Ho Chi Minh University of Technology, 268 Ly Thuong Kiet Street, Ho Chi Minh City, Vietnam
Abstract. The article presents the simulation results to determine the freezing time of the Pangasius fillet, which has been experimentally validated. The difference in freezing time between the experiment and the simulation results is 4.3%. The temperature difference between simulation and experimental results of the product does not exceed 1 K in the precooling stage, 1.5 K in the phase change stage, and 4 K in the subcooling stage. Based on the simulation results to determine freezing time, evaluate influencing factors such as velocity, air temperature, and product size on freezing time. As a result, the relationship model between freezing time and factors provides the framework for selecting a suitable freezing mode of the frozen pangasius fillet process. Keywords: Freezing time · Air velocity · Air temperature · Product thickness · Regression equation · Finite difference and finite process
1 Introduction Pangasius is a catfish originating from the Mekong River that has been bred and reared by fishermen in southern Vietnam. Fish contains unsaturated fats such as omega-3 and omega-6 fats, as well as minerals such as iron, phosphorus, calcium, zinc, and so on, all of which are beneficial to human health. As a result, many countries around the world prefer and import Vietnamese pangasius, which has aided in the development of our country’s pangasius industry. Vietnamese pangasius produce 99% of total pangasius production [1]. However, this rapid development has resulted in issues such as unstable product quality, preservative and antibiotic residues, environmental pollution, and price problems. As well as market competition, etc. This has a significant impact on our country’s pangasius export capacity. The quality of aquatic products is determined by raw materials and processing technology, which includes the cooling and freezing process.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 12–28, 2022. https://doi.org/10.1007/978-981-19-1968-8_2
Evaluation of Factors Influencing the Freezing Time
13
In general, the faster the freezing speed, the shorter the freezing time, the higher the quality of the product, and the longer the storage time. However, this causes the freezing temperature to be very low, increasing the capacity of the refrigeration system, decreasing energy efficiency, and increasing the product cost [2, 3]. Due to these reasons, evaluation of the factors influencing the freezing time of pangasius fillets is critical for assessing product quality, freezing technology, and energy consumption during the cooling and freezing process.
2 Materials and Methods The freezing process is extremely complicated with several phenomena occurring at the same time, including heat transfer, mass transfer, phase change, mechanical property change, and so on. Especially at the freezing point, the thermal and physical properties of the fish (specific heat c, thermal conductivity λ, density ρ, and thermal diffusivity) change abruptly, making the nonlinear differential equation describing the process extremely difficult to solve. In this paper, we utilize the finite element method to solve the heat conduction problem with phase change in order to determine the freezing time in various modes [4–9]. Based on the determined freezing time, carry out a detailed assessment of the factors influencing the freezing time and construct a regression equation to establish the relationship between the freezing time and the factors. 2.1 Material Information Pangasius samples are box-shaped and have specific dimensions, as shown in Table 1 [2]. Table 1. Pangasius fillet geometrical parameters No.
Mass (g)
Thickness δ (mm)
Width W (mm)
Length L (mm)
1
85.5 ÷ 124.5
10–14
60–80
150–200
2
124.5 ÷ 199.5
14–16
80–90
200–250
3
199.5 ÷ 256.5
16–18
90–110
250–270
4
256.5–313.5
18–21
110–120
270–290
5
≥320
≥24
120–130
280–300
Fonte: Data collected from factories [2].
Pangasius is basically frozen on IQF with the following working parameters (Table 2):
14
H. H. Do and T. N. H. Huong Table 2. The working parameter range of IQF
No.
Parameter
Working range
1
Air temperature te (°C)
(–45 ÷ –35)
2
Air velocity ω (m/s)
(5 ÷ 15)
Fonte: Data collection from factories [2].
2.2 The Product’s Thermal Properties The composition of Vietnamese Pangasius sample (%) identified at the Service Center for experimental analysis in Ho Chi Minh City could be as follows: 15.4% Fat; 67.3% water; 16.3% Protein; 0% Carbohydrates; 0% Sugar; 1.02% Ash. The specific heat, thermal conductivity and density of the Vietnamese Pangasius (between –40 °C and +40 °C) were considered dependent on temperature and composition. According to Choi, Y. et al. (1986) [10], the density of the product ρ(t) was calculated according to Eq. (1): ρ(t) = n
1
xw i i = 1 ρi
(1)
In 1981, Levy proposed the following model expression (Eq. 2) for the thermal conductivity of a two – component system [11]. λ=
λ2 [(2 + ) + 2( − 1)F1 ] (2 + ) − ( − 1)F1
(2)
where is the thermal conductivity ratio ( = λ1 / λ2 ), λ1 is the thermal conductivity of component 1, and λ2 is the thermal conductivity of component 2. The parameter F 1 introduced by Levy is given as follows in Eq. (3): ⎧ 0.5 ⎫ 2 ⎨ 2 ⎬ 2 8R1 − 1 + 2R1 − − 1 + 2R1 − F1 = 0.5 (3) ⎩ σ ⎭ σ σ where: σ=
( − 1)2 ( + 1)2 +
2
(4)
and R1 is the volume fraction of component 1, or
−1 1 ρ1 R1 = 1 + −1 x1 ρ2
(5)
Here, x1 is the mass fraction of components 1, ρ1 is the density of component 1, and ρ2 is the density of component 2.
Evaluation of Factors Influencing the Freezing Time
15
The specific heat of the Vietnamese Pangasius was estimated using the following Eq. (6) Schwartzberg (1976) [12]: Rt20 (6) cp = cpu + (bXs − Xwo )cp + EXs 18(t0 − t)2 − 0.8cp where t is the temperature of the food product (o C); to is the freezing point of water (o C); R is the universal gas constant; cp is the difference between specific heats of water and ice (J·kg−1 ·K−1 ); cpu is the specific heat of food above its initial freezing point; b is the amount of unfrozen water per unit weight of solids; Xs is the mass fraction of solids per unit weight of food; Xwo the mass fraction of water per unit weight of food above the initial freezing point; E is the molecular weight ratio of water (Mw ) and food solids (Ms ): E=
18 Mw = Ms MS
(7)
According to J. Willix et al. (2006) [13], the convection coefficient, α, can be approximated by either of the following Eq. (8): (8) α = 25(ω)0.6 , W · m−2 · K−1 In which, ω is the air velocity (m·s−1 ). Calculation results of the thermophysical properties of Pangasius are shown in Fig. 1 (a, b, c). 2.3 The Establishment of a Mathematical Model for Freezing Processing The process of freezing the Pangasius filets is done on the IQF conveyor belt. The belt thickness is δ = 1 (mm), the thermal conductivity of the conveyor material is λ = 21.5 (Wm−1 ·K−1 ), and the thermal resistance of the conveyor is ignored. Because the above fish samples have a much larger length than width and thickness, heat transfer along the product length is very small and can be neglected, so temperature changes are assumed to occur only in two width directions (x) as well as thickness (y): T = f(x, y, τ) (Fig. 2 a, b). The mathematical model for freezing processing was presented according to Eq. (9a, b, c, d, e): Differential Eq. (9a): 2 ∂T ∂ T ∂ 2T (9a) + 2 + qV = ρ(T)CP (T) λ(T) 2 ∂x ∂y ∂τ Initial condition Eq. (9b): τ = 0 → T = T(x, y, 0) = const
(9b)
16
H. H. Do and T. N. H. Huong
(a)
(b)
(c) Fig. 1. a. Specific heat. b. Density. c. Thermal conductivity
Evaluation of Factors Influencing the Freezing Time
17
(a) qα
air
air
sample
qα
(b) Fig. 2. a. Products on IQF. b. The schematic of a sample heat exchange
Boundary conditions Eq. (9c,d): α ∂T =± ± (T − Tf )x=±δx ∂x x=±δx λ(T) ∂T α ± =± (T − Tf )y=±δy ∂y y=±δy λ(T) Symmetry condition Eq. (9e): ∂T ∂T = =0 ∂x x=0 ∂y y=0
(9c) (9d)
(9e)
By using the finite element method, the factors will be chosen as a two-dimensional first-order rectangle with four nodes, and the grid will be divided in the same way. According to the Galerkin method [14], the general form of the characteristic matrix equation for the factors with no radiation is described in Eq. (10): ∂T (10) + [K]{T} = {f} [C] ∂τ In which. [C] = ρ.cP [N]T [N]dV is heat capacity matrix; V [K] = [B]T [D][B]dV + h[N]T [N]dS is heat conduction matrix; V
S
18
H. H. Do and T. N. H. Huong
{f} =
hTK [N]T dS is heat load vector.
S
[N] is interpolation fuction; [B] is derivation of [N]; [D] is matrix of heat conductivity coefficients; V is element volume, S is outer surface existing radiation and convection; T is temperature vector of nodes; {∂T/∂τ} is variation of temperature by time; TK is environment temperature. Equation (10) must be discreted by time following the finite difference method (FDM) or finite element method (FEM) to solve the temperature inside the sample. When FDM is used here, the solution is as follows: [[C] + θ.τ.[K]]{T}p+1 = [[C] − (1 − θ).τ.[K]]{T}p + θ.τ{f}p+1 + (1 − θ).τ{f}p (11) Whereas θ is parameter with 0 ≤ θ ≤ 1. Therefore, the corresponent solutions are: Tp+1 = [[C] − [K]τ]−1 ∗ [C]Tp + fP+1 τ , θ = 1 (12a) Tp+1 = [C]−1 ∗ ([C] − [K]τ) TP + fP τ ; θ = 0
(12b)
If θ = 1, the Eq. (12a) is totally implicit. τ can be selected arbitrarily while the solutions still converge, but the solution will be less accurate with a large τ. If θ = 0, the Eq. (12b) is totally explicit, and τ should be small enough to converge the solutions. 2.4 Computational Results Using Ansys sofware: The problem is solved using Ansys software as follows, with θ = 1, selected time step τ = 5 s, and automatical iteration, Eq. (12a) is solved with load applied according to time step. Figure 3 shows the iterated computation process to get absolutely convergent solutions.
Fig. 3. The iterated computation process to get absolutely convergent solutions
The panorama of temperature varying by time and position in the sample is displayed in Fig. 4, and phase change process in the product sample is shown in Fig. 5.
Evaluation of Factors Influencing the Freezing Time
19
Fig. 4. Temperature history
Fig. 5. The phase change in the product
2.5 Evaluation of the Reliability of the Problem Solution To control the reliability of the solution, a survey using an experimental model developed at Industrial University of Ho Chi Minh City has been conducted on five experiments. Each experiment was carried out on three samples with the same size and mass (Fig. 6 a, b) in the same freezing condition te = (−38 ± 0.5) o C and air velocity ω = (10 ± 0.5) m·s−1 . The result was shown in Fig. 7. The product’s temperature mentioned below is the temperature at the center of the product (sample) and freezing time is necessary time for the product to be frozen from its original consistent temperature to a certain center temperature depending on freezing requirements.
20
H. H. Do and T. N. H. Huong
Fig. 6. a. Samples before freezing. b. Samples after freezing
The result in Fig. 7 (a, b) shows that the average freezing time from 12 °C to − 18 °C of five replicates of experiment is τ = 625 s. Theoretical computation results show necessary freezing time to reach the temperature above is τ = 599 s, with a relative deviation of 4.3% between the two results. During the freezing process, the deviation between theoretically and experimentally computed temperature is not equal. In the precooling stage, the temperature difference does not exceed 1.0 K; in the phase change stage, the temperature difference does not exceed 1.5 K; in the subcooling stage, the temperature difference does not exceed 4 K. However, considering the complexity of the process, especially the sudden variation of thermophysical properties at the freezing point, this difference is totally acceptable and this theoretical model can be applied with high reliability to study the freezing process of many kinds of food, firstly Pangasius in fillet form. As a result, this method is completely acceptable for resolving the problem of unstable heat conduction in a variety of freezing modes. Table 3 shows the results of the freezing time.
Evaluation of Factors Influencing the Freezing Time
21
(a)
(b) Fig. 7. a. Experiment result to determine freezing time for pangasius fillet. b. Experimentally verifying simulation results to determine the freezing time of pangasius fillet
Table 3. The results of the freezing time of pangasius fillet Dimension (Thickness × Width) Temperature te (o C) Air velocity ω (m/s) (mm) 5 7.5 10 12.5 12 × 70
15 × 85
15
−35
1212
985
851
761
695
−37.5
1119
907
783
699
638
−40
1039
842
725
647
589
−42.5
969
784
675
601
547
−45
909
734
631
561
510
−35
1561 1272 1102
987
902
−37.5
1440 1171 1013
905
827
(continued)
22
H. H. Do and T. N. H. Huong Table 3. (continued)
Dimension (Thickness × Width) Temperature te (o C) Air velocity ω (m/s) (mm) 5 7.5 10 12.5
18 × 100
21 × 115
24 × 120
15
−40
1335 1084
936
836
763
−42.5
1245 1009
869
775
706
−45
1165
811
722
658
−35
1925 1573 1366 1226 1123
−37.5
1773 1447 1253 1122 1028
−40
1643 1336 1159 1035
946
−42.5
1530 1242 1072
959
876
−45
1430 1158
892
815
−35
2302 1886 1641 1477 1357
−37.5
2118 1731 1504 1352 1241
−40
1960 1598 1386 1245 1143
−42.5
1823 1483 1285 1153 1058
−45
1703 1382 1197 1073
−35
2682 2204 1924 1737 1601
−37.5
2465 2021 1761 1589 1464
−40
2279 1864 1622 1463 1347
−42.5
2117 1728 1504 1354 1247
−45
1975 1610 1399 1260 1160
942
999
984
2.6 Evaluation of Factors Affecting Freezing Time The following parameters have a direct impact on the freezing time as well as the electrical capacity of the freezing system during the process of freezing pangasius fillet: freezing air temperature, freezing air velocity, and product thickness. 2.6.1 The Effect of Air Velocity on Freezing Time By performed a regression equation between air velocity and freezing time in the range of ω from 5 (m/s) to 15 (m/s) at different temperature regimes from –45 °C to –35 °C to evaluate in detail the influence of air velocity in the freezing environment on the freezing time. The regression results are presented in Table 4 and Fig. 8. It has been found that increasing the air velocity speed significantly reduces the setting time. Table 5 shows the reduction of freezing time corresponding to the increase per m/s (1 m/s). Increasing the cold air velocity will significantly reduce the freezing time at all speeds in the range of ω from 5 to 15 m/s. However, at the same temperature with different speed modes, the freezing time reduction is not uniform, as shown below:
Evaluation of Factors Influencing the Freezing Time
23
Table 4. The regression equation showing the relationship of air velocity to freezing time t (o C)
Regression equations
– 35 °C
τ = 2269 – 169.949.ω + 5.29143.ω2
– 37.5 °C
τ = 2100 – 158.423.ω + 4.93714.ω2
– 40 °C
τ = 1951.6 – 148.023.ω + 4.61714.ω2
– 42.5 °C
τ = 1825.6 – 139.337.ω + 4.34286.ω2
– 45 °C
τ = 1713.2 – 131.646.ω + 4.11429.ω2
Fig. 8. Relationship between freezing time and air velocity Table 5. Freezing time reduction (s) corresponding to ∇ω = 1 m/s in different modes Temperature (o C)
Air velocity, ω (m/s) 5
7.5
10
12.5
15
– 35
– 117
– 91
– 64
– 38
– 11
– 37.5
– 109
– 84
– 60
– 35
– 10
– 40
– 102
– 79
– 56
– 33
– 10
– 42.5
– 96
– 74
– 52
– 31
– 9
– 45
– 91
– 70
– 49
– 29
– 8
With the velocity of air ranging from 5 to 15 (m/s), increasing the cold air velocity significantly reduced the freezing time. However, under the same freezing temperature conditions, the freezing time reduction was not uniform across speed modes. Specifically, when the freezing velocity increased by 1 m/s, the freezing time decreased by around 7.5% in the velocity range of (5–7.5) m/s, the freezing time decreased by around 5.5%,
24
H. H. Do and T. N. H. Huong
in the velocity range of (7–10) m/s, the freezing time decreased by around 3.9% in the velocity range of (10–12.5) m/s, and the freezing time decreased by around 2% in the velocity range of (12.5–15) m/s. Thus, when the freezing mode is set to low speed, the freezing time decreases faster than when set to high speed. 2.6.2 The Effect of Air Temperature on the Freezing Time Likewise, for the evaluation of the effect of air velocity on the freezing time of pangasius fillet, we used the freezing time determination results in Table 3 to construct a regression equation establishing the relationship between the freezing time and the freezing temperature. Table 6 and Fig. 9 show the results of varying the freezing time with temperature. Table 6. Relationship between freezing time and freezing ambient temperature ω (m/s)
Regression equations
5
τ = 4688.26 + 128.166.te + 1.10857.te 2
7.5
τ = 3862.23 + 106.023.te + 0.914286.te 2
10
τ = 3414.09 + 94.8686.te + 0.822857.te 2
12.5
τ = 3098.43 + 86.7429.te + 0.754286.te 2
15
τ = 2852.31 + 80.1314.te + 0.697143.te 2
Fig. 9. Relationship between freezing time and air temperature
According to the relationship shown in Table 6, lowering the temperature will reduce the finishing time. Table 7 shows the decreasing time for each temperature (∇te = 1 °C).
Evaluation of Factors Influencing the Freezing Time
25
Table 7. Freezing time reduction (s) corresponding to ∇te = 1 °C in different modes ω (m/s)
– 45
– 42.5
– 40
– 37.5
– 35
5
28
34
39
45
51
7.5
24
28
33
37
42
10
21
25
29
33
37
12.5
19
23
26
30
34
15
17
21
24
28
31
The following are the author’s observations based on the calculation results: Reducing the cold air temperature minimized the freezing time for all freezing air temperatures in the range from –45 to –35 °C, but the degree of reduction is not uniform at other temperatures. In detail: With all freezing temperatures ranging from –45 °C to –35 °C, lowering the cold air temperature reduced freezing time, but the degree of reduction was not uniform at other temperatures under the same freezing velocity conditions. Specifically, when the freezing temperature was reduced by te = 1 °C, the time reduction was on average 3.2% and 3% in the temperature ranges of te from –37.5 °C to –35 °C and from –40 °C to –37.5 °C, respectively. In the freezing temperature range of less than –40 °C, the time reduction will continue, such as 2.85% and 2.5% reduction in the temperature range from –42.5 °C to –40 °C and from –45 °C to –42.5 °C. 2.6.3 Effect of Product Thickness on Freezing Time The freezing time of pangasius fillets was determined in various ways for different sizes. Similarly to the aforementioned, the author goes on to construct the regression equation between freezing time and product thickness in various modes, the results of which are shown in Table 8 and Fig. 10. Table 8. Relationship between freezing time and product size No.
Regression equation
(a)
τ = –90.0571 + 101.271.δ + 0.595238.δ2
(b)
τ = –71.6857 + 75.7905.δ + 0.47619.δ2
(c)
τ = –49.6571 + 59.3714.δ + 0.428571.δ2
(d)
τ = –6.57143 + 44.5143.δ + 0.507937.δ2
(e)
τ = 14.9429 + 34.7714.δ + 0.539683.δ2
26
H. H. Do and T. N. H. Huong
Fig. 10. Relationship between freezing time and product size in various freezing modes
We can deduce from the regression equations that the reduction of freezing time is dependent on the increase in speed, as shown in Table 9. Table 9. Freezing time increment, (s) corresponding to ∇δ = 1 mm in different modes No.
Product size (mm) 12 × 70
15 × 85
18 × 100
21 × 115
24 × 125
(a)
116
119
123
126
130
(b)
87
90
93
96
99
(c)
70
72
75
77
80
(d)
57
60
63
66
69
(e)
48
51
54
57
61
The data in Fig. 10 and Table 9 show that as product size increases at high temperatures, the freezing time at low velocity also increases significantly. Using the same temperature and speed regimes, as the product size increases, the incubation time increases almost linearly (Fig. 10). As product thickness increases at high temperatures and low velocity, freezing time increases faster than at low temperatures and high velocity. Large products should not be frozen in mode (ω = 5 m/s, te = –45 °C). The freezing time is very long, especially for the 24 × 125 mm sample, which has a freezing time (τ) of 1975 s.
Evaluation of Factors Influencing the Freezing Time
27
3 Conclusion The problem of determining the freezing time of the pangasius fillets with the phase change of heat transfer has been solved effectively by the finite element method, although it is difficult to solve by other methods. The theoretical computational results by this method are most suitable to the experimental results. This indicates that the selected study method has high reliability and the results are accepted. Factors affecting freezing time, including velocity, freezing ambient air temperature, and product thickness, are assessed. The results of the evaluation are presented as follows: 3.1 The Effect of Air Velocity on Freezing Time Increasing the cold air speed will significantly reduce the freezing time at all speeds in the range from 5 m/s to 15 m/s. However, the freezing time reduction is not uniform in the same temperature regime with different speed modes; when increasing freezing time, the decrease in freezing time at low speed is greater than at high speed; at ω = 5 m/s when increasing ω = 1 m/s, the freezing time reduction is 7.5% (τ = 103 s), and at ω = 15 m/s when increasing ω = 1 m/s the freezing time reduction is 2% (τ = 10 s). 3.2 The Effect of Air Temperature on the Freezing Time With all freezing air temperatures in the range te from –45 °C to –35 °C, reducing the cold air temperature lessens freezing time, but the degree of reduction varies depending on the temperature. At high temperatures, freezing time decreases more than at low temperatures; at te = –35 °C when reducing ∇te = 1 °C, freezing time decreases by 3.2% (τ = 42 s), while at te = –45 °C when reducing ∇te = 1 °C, freezing time decreases by 2.5% (τ = 22 s). 3.3 The Effect of Product Thickness on Freezing Time The freezing time increased linearly as pangasius fillet size increased, with an average increase of about 1 mm in pangasius fillet thickness. The freezing time for pangasius fillets thicker than 24 mm is very long, making them unsuitable for the IQF conveyor belt. The results of the evaluation of factors influencing the freezing time of the Pangasius fillets shown above can be used as a data base for other studies on freezing, such as optimization in processing and the preservation of the nation’s important export product.
References 1. https://www.foodnk.com/gia-tri-dinh-duong-cua-ca-tra-ca-basa-ca-da-tron-viet-nam.html ´ thuy hai san (2015–2020) (Cu,u long, Nam viê.t, V˜ınh 2. Sô´ liê.u thu tâ.p tu`, các nhà máy chê´ biên Hoàn, Afiex, Công ty Cataco, Công ty Cafatex, Afiex….) ij
ij
ij
ij
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3. http://agro.gov.vn/vn/tid22345_ham-luong-nuoc-trong-ca-tra-xuat-khau-la-chi-tieu-ve-chatluong.html 4. Scheerlink, N., Verboven, P., Fikin, K.A., De Baerdemaeker, J., Nicolai, B.M.: Finite element computation of unsteady phase change heat transfer during freezing or thawing of food using a combined enthalpy and Kirchhoff transform method. Trans. ASAE 44(2), 429–438 (2001) 5. Santos, M.V., Lespinarda, A.R., Mascheronia, R.H., Califanoa, A., Zaritzkya, N.: Prediction of freezing times in vegetables using the finite element method and a combined enthalpy and Kirchhoff formulation. Mecánica Computacional XXIX, 5849–5862 (2010) 6. Hoang, D.K., Lovatt, S.J., Olatunji, J.R., Carson, J.K.: Experimental measurement and numerical modelling of cooling rates of bulk-packed chicken drumsticks during forced-air freezing. Int. J. Refrig. 114, 165–174 (2020) 7. Hoang, D.K., Lovatt, S.J., Olatunji, J.R., Carson, J.K.: Validated numerical model of heat transfer in the forced air freezing of bulk packed whole chickens. Int. J. Refrig. 118, 93–103 (2020) ´ dông thi.t heo nu,a con ba`˘ ng ansys. Ta.p chí n˘ang lu,o.,ng 8. Sim, V.V., Hoàng, ÐH.: Mô phong câp nhiê.t 156, 10–16 (2021) 9. Bassani, A., Garrido, G.D., Giuberti, G., Dordoni, R., Spigno, G.: Comprehensive mathematical model for freezing time prediction of finite object. Chem. Eng. Trans. 87, 211–216 (2021) 10. Choi, Y., Okos, M.R.: Effects of temperature and composition on the thermal properties of foods. In: Food Engineering and Process Applications. Elsevier Applied Science Publishers, London, vol. 1, pp. 93–101 (1986) 11. Levy, F.L.: A modified Maxwell-Eucken equation for calculating the thermal conductivity of two-component solutions or mixtures. Int. J. Refrig. 4, 223–225 (1981) 12. Schwartzberg, H.G.: Effective heat capacities for the freezing and thawing of food. J. Food Sci. 41(1), 152–156 (1976) 13. Willix, J., Harris, M.B., Carson, J.K.: Local surface heat transfer coefficients on a model beef side. J. Food Eng. 74, 561–567 (2006) 14. Lewis, R.W.: Fundamentals of the finite element method for heat and fluid flow, pp. 154–170 (2004) ij
ij
Method to Improve the Power Coefficient of Vertical Axis Wind Turbines Bao The Nguyen1(B) , Minh Van Ngo1,2 , and Huy Gia Ngo1 1 University of Technology – Vietnam National University, Ho Chi Minh City, Vietnam
[email protected] 2 Institute of Sustainable Energy Development, Ho Chi Minh City, Vietnam
Abstract. Vertical axis wind turbines (VAWTs) have recently been gotten the attention for off-grid power generation due to their advantages over the popular horizontal axis wind turbines (HAWTs) such as omni-directionality, simplicity in design, operating at a lower tip speed ratio (TSR) leading to lower noise emission, etc. These advantages of VAWTs make them more suitable for the urban environment. The paper first applies the Double-Multiple Stream tube (DMST) Model to calculate the power coefficient of a VAWT using GOE 222 blades, then determines the optimum tip speed ratio to achieve the maximum power coefficient CP . After that, a method to improve the power coefficient CP is proposed. This method is based on an MPPT algorithm of adjusting the output voltage of the controller to achieve the maximum power output of VAWTs. Keywords: Vertical axis wind turbines (VAWT) · Double-Multiple Stream tube (DMST) model · Tip speed ratio (TSR) · MPPT algorithm · Power coefficient CP
1 Introduction Energy is an essential requirement for the life of all living things and the functioning of all economies on the earth. In the 20th Century, people have easily accessed and exploited abundant fossil fuel resources, which has contributed to the outstanding development of mankind in this century. But this has also arisen its consequences, namely the destruction of the global environment and the depletion of fossil fuels. Therefore, since the beginning of the 21st century, people have paid more attention to renewable energy sources, in which solar energy and wind energy are the most important because these are inexhaustible and environmentally friendly energy sources. In terms of wind energy, Vietnam is one of the countries with the largest wind power potential in Asia due to its mountainous terrain and its 3000 km long coastline. Recently, a total of 106 wind power plants with a total capacity of 5755.5 MW have submitted documents and applications for registration of energizing, grid synchronization, testing, and commercial operation acceptance (COD). By the end of 31 October 2021, there are a total of 84 wind power plants with a total capacity of 3980.27 MW in the national power system have been accepted for COD. Moreover, the research, manufacture and installation of wind turbines for power generation are mainly HAWTs. Although VAWTs © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 29–39, 2022. https://doi.org/10.1007/978-981-19-1968-8_3
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have many advantages such as omni-directionally, simplicity in design, operating at a lower tip speed ratio (TSR) leading to lower noise emission, there have not been any academic studies on VAWT in Vietnam yet [1]. Therefore, this paper will present preliminary theory in the calculation and design of VAWT as a foundation for future studies in Vietnam. Another problem is that despite being renewable energy sources, solar and wind energy have the disadvantages of being unstable, constantly changing and unpredictable. Therefore, there have been many researches trying to track the maximum output power of wind turbines, either by controlling the pitch angles of the wind turbine blades or using MPPT (Maximum power point tracking) algorithms to maximize the power outputs of wind turbines. However, for small-scale wind turbines, the pitch angle control method becomes unrealistic because of their mechanical structures. Therefore, the maximum power point tracking techniques become suitable methods for small-size turbines [2]. There are several MPPT algorithms for wind turbines, including tip speed ratio (TSR) MPPT algorithm, optimal torque (OT) MPPT algorithm, power signal feedback (PSF) MPPT algorithm, hill climb search (HCS) MPPT algorithm, incremental conductance (INC) MPPT algorithm, optimal-relation-based (ORB) MPPT algorithm, etc. [2–6]. Each of the above methods has its advantages and disadvantages [3, 4]. This paper proposes a new algorithm that is to track the maximum power point of the turbine’s output by changing the output voltage of the MPPT controller.
2 Methodology 2.1 Axial Momentum Theory Axial Momentum Theory [1, 8] gives the relationship of wind speed with the force acting on the blade. Express the law of conservation of mass of the air stream before and after the blade (Fig. 1).
Fig. 1. Law of conservation of airflow
V∞ , A∞ , P∞ is velocity, swept area and airflow pressure in front of the blade Vd , Ad , Pd is velocity, swept area and airflow pressure at the blade Vw , Aw , Pw is velocity, swept area and airflow pressure behind the blade Applying Bernoulli’s equation to the flow at infinity and in front of the rotor, we get the following formula [1, 8]: Vd =
1 (V∞ + Vw ) 2
(1)
Method to Improve the Power Coefficient of Vertical Axis Wind Turbines
31
Let a be the axial induction factor: a=
V∞ − Vd V∞
(2)
From (1) and (2): Vd = V∞ (1 − a)
(3)
Vw = V∞ (1 − 2a)
(4)
CT = 4a(1 − a)
(5)
Thrust coefficient is:
2.2 Double Multiple Streamtube Theory In Fig. 2, the wind direction convention is from left to right. Then, the left half-circle is upstream, the right half-circle is downstream. Azimuth is the angle formed by the line from the center to the chord with the horizontal line dividing the circle into two equal parts. The relative velocity of the flow with the blade element is determined by the following equation [7, 9]: W = [TSR + (1 − a)sinθ ]2 + [(1 − a)cosθ ]2 (6) V∞
Fig. 2. The geometry of a VAWT
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The tip speed ratio is determined by the relationship between the angular velocity and the wind speed, which is calculated as the following equation: TSR =
R V∞
Since the chord is tangent to the radius of the circle, the angle of attack is [7]: (1 − a)sinθ −1 α = tan (1 − a)cosθ + TSR
(7)
(8)
The normal and tangential coefficients are: Cn = CL cosα + CD sinα
(9)
Ct = CL sinα − CD cosα
(10)
where CL is lift coefficient and CD is drag coefficient. The thrust coefficient and instantaneous torque are calculated as follows [7, 9]: CT = Cn sinθ − Ct cosθ Ti =
1 ρHCRW 2 Ct 2
(11) (12)
where C is chord length and H is blade length. Combined upstream and downstream, N is the number of stream tubes. The average torque is given by [7, 9]: Ta =
B N Ti i=1 2N
(13)
where B is the number of blades. Then the torque coefficient for the entire circumference of the turbine is: CQ =
Ta 1 2 2 ρV∞ DHR
=
Wi 2 BC N Ct i=1 V∞ ND
(14)
where D is the diameter of the VAWT. The power coefficient of the turbine is: CP = CQ TSR
(15)
Method to Improve the Power Coefficient of Vertical Axis Wind Turbines
33
Figure 3 presents the flowchart to calculate induction factor a.
Fig. 3. Flowchart to solve the induction factor
2.3 Theoretical Calculation Using DMST Model Because of several advantages of the airfoil profile GOE 222 [10, 11], it is used in this study. The airfoil has a chord length of 0.16 m, a thickness of 32 mm and is made by anodized aluminum. The dimension of the wind turbine chosen for calculation in this paper is 1.5 m in diameter and 1 m in blade length (Fig. 4).
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Fig. 4. GOE 222 airfoil
For profile GOE 222, at a low Reynolds number, the blade is unstable leading to difficulty in the calculation. For simplicity, Re = 200000 was chosen for this study. Using ‘XFOIL Direct Analysis’ from QBlade [12, 13] software, the curves for Cl , Cd and Cl /Cd are achieved. Unfortunately, the functionality of the software only allows angles of attack from –10° to 20°, whereas the DMST model can require a lot of data outside that range. Therefore, an extrapolation method must be used to solve this problem. In extrapolating the lift and drag coefficients of an airfoil, The Montgomerie method is also one of the popular methods. It is formulated based on the assumption that there exists some potential-flow-like behavior in a real airfoil around 0° angle of attack. At higher angles of attack, the airfoil performance behaves like a basic thin plate [14]. Using ‘Polar Extrapolation to 360’ from QBlade [12, 13] software, the Cl and Cd are calculated. 0.3
Normal force coefficient
0.2 0.1 0 0
60
120
180
240
300
360
-0.1 -0.2 -0.3 -0.4
Azimuth TS R=2
TSR=2.5
TSR=3
Fig. 5. Local normal force coefficient
For symmetric airfoil profiles, under ideal conditions, the blade generates lift and drag when the airflows equally on the upper surface and lower surface, which can be an advantage in choosing the profile for VAWT. As for the asymmetric airfoil profile like GOE 222 in this paper, the most important difference is optimized for a positive angle of attack. It generates less drag for the same amount of lift and can generate more lift before stalling. A cambered airfoil will be less efficient and stall earlier when inverted. And a symmetrical airfoil generates less drag when there’s no lift. Figure 5 and Fig. 6 show the relationship between the azimuth angles of the turbine’s blades and the normal and tangential force coefficients respectively. As shown in these figures, there is almost no torque at half-stream. This leads to the prediction that the power coefficient of VAWT will be low.
Method to Improve the Power Coefficient of Vertical Axis Wind Turbines
35
Tangenial force corfficient
0.03 0.025 0.02 0.015 0.01 0.005 0 0
60
120
-0.005
180
240
300
360
Azimuth TS R=2
TSR=2.5
TSR=3
Fig. 6. Local tangential force coefficient
0.30
Power coefficient
0.25 0.20 0.15 0.10 0.05 0.00 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
Tip speed ratio
Fig. 7. Wind turbine power coefficient
Figure 7 shows the dependence of the turbine’s power coefficient CP on the tip speed ratio TSR. This figure confirms the prediction of low CP of VAWTs, as mentioned above. As shown in the figure, a maximum power coefficient of 0.15 achieves at TSR of 3, which is much lower than Betz’s limit power coefficient of 0.59. Considering the TSR corresponding to the maximum power coefficient as the optimal TSR, the turbine will be controlled based on this value, which will be presented in the next section.
3 MPPT Algorithms to Extract Maximum Power from Wind Turbines The above Eq. (5), Eq. (6), Eq. (13) and Eq. (14) show that for each VAWT of a certain size and configuration, the power coefficient of the turbine CP will be the largest corresponding to a certain TSR value. As shown in Fig. 7, the TSR in this study is 3 to achieve the maximum power coefficient CP . Therefore, to optimize the power coefficient of the turbine, it is necessary to optimize the power according to TSR. The method used is the MPPT algorithm [2–6]. As mentioned, each turbine with a different size and configuration will have a different optimal TSR value. Therefore, it is necessary to determine the TSR at the value with the maximum power coefficient CP before inputting the data into the MPPT control system.
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The MPPT algorithm proposed in this study allows setting the maximum input and output voltage. The output voltage is set to charge depending on the voltage of the battery (12 V, 24 V or 48 V). When the output voltage changes, the charging power changes, leading to the input power changes. The input power depends on the TSR, so changing the output power will affect the TSR, current and inputs the optimal TSR value. In addition, the proposed algorithm uses signals from two sensors, a sensor getting data of wind speed, measured by anemometer installed on the tower, and the other measuring the angular speed of the turbine shaft. These two sensors will provide the measured signal to the information processing part, then the system will calculate the TSR and adjust the output voltage accordingly. If the calculated TSR is equal to the input optimal TSR, the input and output power will be compared. If they are equal, the operation is normal. If they are not equal, the output voltage will be increased or decreased accordingly. If the calculated TSR is less than the optimal TSR value, the output voltage will decrease. Conversely, if the calculated TSR is larger than the input optimal TSR value, the output voltage will increase. The voltage value increases or decreases each time can be adjusted from 0.1 to 0.5 V, depending on the discretion of the regulator to achieve the desired fine or coarse adjustment. Figure 8 presents the schematic diagram of the MPPT algorithm proposed in this study. START
Iin max, Uin max, Iout max, Uout max, R, TSR
Iin, Uin, Iout, Uout, Vin, ω
Iin-Iin max >0 Uin-Uin max>0 Iout-Iout max>0 Uout-Uout max>0
YES Dumpload
NO
TSRin=(ω*R)/Vin
YES
NO
TSRin-TSR=0
YES
Pin-Pout=0
YES
Uout +0.5
NO
Pin-Pout>0
YES
TSRin-TSR>0
NO
NO
Uout-0.5
Uout +0.5
END
Fig. 8. Flowchart of MPPT algorithm
Uout-0.5
Method to Improve the Power Coefficient of Vertical Axis Wind Turbines
37
The advantage of this method is that the maximum power can be optimized according to the efficiency of the wind turbine blades and the degree of response by changing the voltage is also faster than the method of adjusting the turbine rotation speed, which has been used in [4, 5] and [6]. Because in practice, if the wind speed changes continuously, it will be difficult to determine the maximum power point. This is a common disadvantage of wind turbines with variable rotational speed because they are continuously variable, so there is almost no fixed maximum power point [3]. The proposed algorithm in this study will solve this problem. The algorithm can fully meet the monitoring of TSR with highly fluctuating wind speeds if a high-speed processor is used. Because the current algorithm is simple and uncomplicated, sampling and data processing will not take much time. For example, in battery charging mode, the controller always allows charging the battery even when the optimal TSR point cannot be tracked. 120
y = 2E-06x3 - 0.0001x2 + 0.0189x - 0.3873 R² = 0.9998
100
Power (W)
80
60
40
20
0 0
50
100
150
200
250
300
350
400
450
Rotational speed (rpm) V=0.01
V=1
V=2
V=4
V=5
V=6
V=3 V=7
V=8
V=9
Desired Curve
Poly . (Desired Curve)
Fig. 9. Maximum power point curve at different wind speeds and wind turbine rotational speeds
The simulated typical power curves of the studied wind turbine under various rotor speeds are shown in Fig. 9. For each curve, the wind turbine starts at the no-load condition with the highest output voltage. As the out current increases, the output power will increase, but the output voltage will drop. Gradually, the output voltage will drop significantly, leading to a steep output power drop. As also shown in Fig. 9, when the wind speed increases, the output power of the wind turbine also increases. For each simulated wind speed, there is a maximum power point (MPP). The solid blue line in Fig. 9 represents the MPPT curve of the wind turbine under different wind speeds. The dotted line shows the third-order polynomial fitting
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curve with very high accuracy (R2 = 0.9998). This is agreeable with the theory stating that under a constant power coefficient Cp , the MPPT curve is a cubic function of wind speeds [1, 2]. Table 1. Simulation results of the studied wind turbine with and without MPPT controller Without MPPT
With MPPT
Battery
Wind speed (m/s)
Output voltage (V)
Output current (A)
Output power (W)
Output voltage (V)
Output current (A)
Output power (W)
12 V 70 Ah
4
12.2
0.423
5.16
12.2
0.725
8.84
5
12.5
0.994
12.43
12.6
1.378
17.36
6
12.7
1.8
22.86
12.9
2.327
30.02
7
12.9
2.808
36.22
13.2
3.618
47.76
8
13.5
4.463
60.25
13.8
5.138
70.9
9
13.9
6.065
84.3
14.2
7.097
100.79
Table 1 and Fig. 10 show the simulation results of the studied wind turbine with and without MPPT. As shown in Table 1, when the wind speed increases, the output power of the wind turbine increases. Furthermore, the output power of the wind turbine using the MPPT controller is higher than that of the wind turbine without the MPPT controller. The average power increase after installing the MPPT controller is 29%. Figure 10 represents the graphical form to clearly show the effect of the MPPT controller on various wind speed variations. 120
Power (W)
100 80 60 40 20 0 0
1
2
3
4
5
6
7
8
9
10
Wind speed (m/s) Without MPPT
With MPPT
Fig. 10. Wind turbine output power
4 Conclusion This paper presented a preliminary theory of calculation and design of VAWT, an important part of the theory that has not been found in research and academic documents in
Method to Improve the Power Coefficient of Vertical Axis Wind Turbines
39
Vietnam. This is the background knowledge that will serve as the basis for future VAWT studies and development. The paper used DMST theory to calculate and design the blades of VAWT as well as calculate the optimal TSR value for the case of VAWT with the size and configuration selected in this study. The paper also proposed an MPPT algorithm to obtain the maximum power of the wind turbine, from which the power coefficient of the wind turbine reached the maximum value. This algorithm uses the simple solution of changing the output voltage to adjust the output and input power of the MPPT unit equally, as well as the TSR value to reach the optimal value for each wind turbine with configuration and defined size. Due to the COVID-19 pandemic, a VAWT and MPPT unit have not been built and tested to compare and verify the theoretical results presented in this paper.
References 1. Nguyen, T.B.: Renewable Energy and Sustainable Development Textbook. Vietnam National University Publishing House, HCM City (2021) 2. Kuo-Yuan, L., Yaow-Ming, C., Yung-Ruei, C.: MPPT battery charger for stand-alone wind power system. IEEE Trans. Power Electron. 26(6), 1631–1638 (2011) 3. Dipesh, K., Kalyan, C.: A review of conventional and advanced MPPT algorithms for wind energy systems. Renew. Sustain. Energy Rev. 55, 957–970 (2016) 4. Jogendra, S.T., Mohand, O.: MPPT Control Methods in Wind Energy Conversion Systems. Fundamental and Advanced Topics in Wind Power (2019) 5. Mali, S.S., Kushare, B.E.: MPPT algorithms: extracting maximum power from wind turbines. IJIREEICE 1(5), 199–202 (2013) 6. Alawekar, P.P., Bombale, U.L., Shinde, N.N.: MPPT based charge controller for off-grid small wind machine using PWM technique. IJCA 81(9), 22–25 (2013) 7. Lalit, R., Kellis, K., Roohany, M., David, W.: Double-multiple streamtube analysis of a flexible vertical axis wind turbine. Fluids 6, 118 (2021) 8. Habtamu, B., Yingxue, Y.: Double multiple stream tube model and numerical analysis of vertical axis wind turbine. Energy Power Eng. 2011(3), 262–270 (2011) 9. Ion, P.: Double-multiple streamtube model for studying vertical-axis wind turbines. J. Propulsion 4(4), 370–377 (2012) 10. Nguyen, T.B., Ngo, V.M., Ngo, G.H.: Studying and manufacturing GOE 222 blade for smallscale horizontal axis wind turbines. Vietnam Mech. Eng. J. 12, 367–374 (2021) 11. Nir, M., Avraham, S.: Fluidic flow control applied for improved performance of Darrieus wind turbines. Wind Energy 2016(19), 1585–1602 (2016) 12. Mustafa, A., Oday, A., Mahir, H.M.: Analysis of wind turbine using QBlade software. ICSET 2019, 518 (2019) 13. Marten, D.: QBlade Guidelines v0.6. TU Berlin (2012) 14. Faisal, M., Syerly, K., Husni, S., Surya, H.: Airfoil lift and drag extrapolation with Viterna and Montgomerie methods. ICAE, pp. 811–816 (2017)
On the Dynamic Response of a Functionally Graded Funnel Shell with Graphene Nanoplatelet Reinforcement Minh-Quan Nguyen(B) , Gia-Ninh Dinh, and Van-Bao Hoang School of Mechanical Engineering, Hanoi University of Science and Technology, Ho Chi Minh, Vietnam [email protected]
Abstract. Different from conventional shells such as cylindrical or conical shells, a new type of shell with a complicated shape was introduced in this paper. In addition to functionally graded material in the core, the shell is reinforced with graphene nanoplatelet layers. Classical thin shell theory and Von Karman-Donnell geometrical nonlinearity assumptions were employed to establish governing equations. Subsequently, dynamic responses of the shell can be obtained by using Galerkin’s method. Amplitudes of nonlinear vibrations of the shell under a periodic excitation were investigated with several changing geometrical or material parameters and surrounding conditions. Before that, the computation was confirmed by comparing with a model established in Finite Element Analysis. Influences of the design parameters and external factors were evaluated with FG-X distribution of graphene. This study is believed to make an involvement in enhancing structural design for various industries. Keywords: Functionally graded material · Galerkin’s method · Graphene nanoplatelets
1 Introduction Graphene is known as an allotrope of carbon and this material has appealed much interest in a huge number of studies due to its superior mechanical properties. These advantages have initiated research on various kind of this material. Recently, a novel structure of graphene material, graphene nanoplatelet, has been shown to be an effective reinforcement for various types of composite material, including functionally graded material (FGM). Thanks to its potential applications in device parts for various industries such as rocket shell in defense engineering, ship hulls in transportation engineering, … mechanical properties of FGM reinforced by graphene nanoplatelets have been investigated for several kinds of shells. With the modified Halpin–Tsai micromechanics approach and the rule of mixtures [1], D. Shahgholian et al. evaluated mechanical properties including modulus of elasticity, density and Poisson ratio of a porous FGM cylindrical shell reinforced by graphene © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 40–48, 2022. https://doi.org/10.1007/978-981-19-1968-8_4
On the Dynamic Response of a Functionally Graded Funnel Shell
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nanoplatelets. Based on that, effect of graphene distribution as well as geometrical parameters on the buckling performance of this shell was analyzed with the Rayleigh–Ritz method. Under axial compression and lateral pressure, post-buckling of functionally graded graphene nanoplatelet-reinforced composite (FG-GPLRC) circular cylindrical shell was studied by S. Blooriyan et al. [2]. Considering nonlinear dynamics of a doublycurved panel under a harmonic load [3], M.S.H.Al-Furjan et al. employed perturbation approach and generalized differential quadrature method to develop an accurate solution. The results indicated a huge influence of dimension parameters on chaotic motion of the panel. Based on the variational differential quadrature method and Fourier series [4], R. Ansari et al. acquired a solution to investigating post-buckling of FG-GPLRC conical shell. Several material parameters including weight fraction and some dispersion patterns were analyzed. Moreover, X. Huang et al. presented their interest in fracture behavior [5] of an FG-GPLRC strip. The linear multi-layered fracture model was employed for a parametric study. Meanwhile, a parametric study was also conducted by C. Tao et al., but for the nonlinear load-deflection response of a sandwich cylindrical panel [6]. It was noted that this panel had a complicated reinforcement with two metal face layers and a graphene platelet reinforced functionally graded porous core. Imposing an external pressure [7], Z. Li et al. evaluated the stability of a confined FG-GPLRC cylinder. The sustainability of the cylinder was analyzed and compared among different kinds of material distributions. Furthermore, effects of material properties on the buckling pressure and bending moment were also investigated. For a spherical shell [8] with the consolidation of graphene, D. Liu et al. employed three-dimensional elasticity theory to analyze the free vibration and static bending. This topic was focused before by C.H. Lee et al. using iso-geometric analysis method [9] and a comparison between cylindrical and spherical panels was made. In addition to geometrical and material parameters, temperature difference was included in the study of A. Wang et al. on exploring the transient response of doubly curved shallow shell [10]. It can be seen from the previous works that in general, the FG-GPLRC shells and panels can be considered as uncomplicated shapes and it is possible to investigate the shells’ behavior by directly applying governing equations of shells or plates. The problem of complicated shell has not been mentioned in the literature while this should be paid attention for further application. Consequently, in this paper, a new shell model was proposed where the radius was considered as a cubic function, i.e., the funnel shell. After governing equations were derived, Galerkin’s method was employed to obtain the solution for the vibration of the shell. The computation results were compared with a FEM model. It is noted that the dynamic behavior of the funnel shell was investigated with an external periodic load. Moreover, a full parametric study was performed to evaluate influences of geometrical, material parameters as well as environmental conditions on the dynamic response of the shell.
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2 Computation Model 2.1 Geometry As mentioned above, a funnel shell with the radius as a cubic function R(x) = Ro + βx2 + γ x3 was considered. Some more dimensions were al so defined as the length L, the thickness h and as shown in Fig. 1.
Fig. 1. Model dimensions
2.2 Graphene Reinforcement
Fig. 2. FG-X distribution
As can be seen in Fig. 2, the shell structure is composed of N L layers that have the same thickness. It is noticed that in addition to matrix material, the shell is reinforced by graphene nanoplatelets with one type of FG-X distribution. FG-X indicates an outer concentration of graphene (higher graphene weight fraction in outermost layers). The geometrical parameters and graphene distribution would be investigated in a parametric study for dynamic response of the shell. Before that, material parameters should be formulated for solving governing equations.
3 Computing Technique 3.1 Material Formulation The effective Young’s modulus of the funnel shell can be calculated applying the HalpinTsai micromechanics model: (k) (k) 5 1 + ξL ηL VG 3 1 + ξL ηL VG (1) E= Em + EG 8 1 − ηL V (k) 8 1 − ηL V (k) G
G
On the Dynamic Response of a Functionally Graded Funnel Shell
43
in which ηL =
EG Em EG Em
−1
+ 2 atGG
, ηT =
EG Em EG Em
−1
+ 2 btGG
(2)
where E m and E G are correspondingly the Young’s moduli of matrix and GPLs, while aG , bG and t G denote the average length, the width and the thickness of the graphene nanoplatelets. From the rule of mixture, effective mass density, Poisson’s ratio, and thermal expansion can be computed: (k) (k) ρ (k) = VG ρ G + 1 − VG ρ m (k) (k) ν (k) = VG ν G + 1 − VG ν m (3) (k) (k) α (k) = VG α G + 1 − VG α m in which ρ m , ν m , α m and ρ G , ν G , α G denote the mass density, Poisson’s ratio, thermal expansion of the matrix and GPLs, respectively; k = 1, 2, …, N L . In the k th layer, the GPLs volume fraction, V G (k) can be computed for FG-X distribution by: (k)
VG =
∗ |2k − N − 1| 2VGPL L NL
(4)
where: ∗ VGPL =
WGPL G WGPL + ρρ m (1 − WGPL )
(5)
in which W GPL represents the GPLs weight fraction. 3.2 Governing Equations and Solution By employing the classical shell theory and Von Karman-Donnell geometrical nonlinearity assumptions, the strain-displacement relations of the shells are given below: ⎛ ⎛ ⎞ ⎛ 0⎞ ⎞ εx κx εx ⎟ ⎜ ⎝ εy ⎠ = ⎝ εy0 ⎠ − Z ⎝ κy ⎠ (6) 0 γxy γxy κxy where: ⎞ ⎛ ∂u 1 ∂w 2 ⎞ εx0 ∂x + 2 ∂x 2 ⎟ ⎜ 0⎟ ⎜ w 1 ∂w ∂v ⎟ ⎝ εy ⎠ = ⎜ ⎝ ∂y − R + 2 ∂y ⎠ 0 ∂u γxy + ∂v + ∂w ∂w ⎛
∂y
∂x
∂x ∂y
(7)
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⎛ 2 ⎞ ∂ w 2 κx ⎜ ∂∂x2 w ⎝ κy ⎠ = ⎜ 2 ⎝ ∂y 2 κxy 2∂ w ⎛
⎞ ⎟ ⎟ ⎠
(8)
∂x∂y
After that, relations of stress and strain can be formulated by Hooke’s law. Subsequently, all force resultants N x , N y , N xy and moment resultants M x , M y , M xy can be obtained. Consequently, the nonlinear motion equations of the funnel shell with damping force is expressed below: ∂Nxy ∂Nx ∂2u ∂x + ∂y = ρ ∂t 2 ∂Nxy ∂Ny ∂2v ∂x + ∂y = ρ ∂t 2 2 ∂2M ∂2M ∂ 2 Mx + 2 ∂xyxy + ∂y2y + Nx ∂∂xw2 ∂x2 N ∂2w +2Nxy ∂x∂y + Ry + q − K1 w 2 2 2 +K2 ∂∂xw2 + ∂∂yw2 = ρ ∂∂tw2 + 2ρε ∂w ∂t
(9)
where ε is damping coefficient, K 1 and K 2 denote the elastic foundations. The boundary conditions were chosen as “simply supported” at x = 0, L as follows: w = 0, v = 0, Mx = 0
(10)
The solutions of the system of Eq. (9) meeting the conditions (10) were selected as: mπ n N u= M m=1 n=1 Umn cos L x sin R y M (11) v = m=1 N Vmn sin mπ x cos n y Lmπ Rn M n=1 N w = m=1 n=1 Wmn sin L x sin R y where U mn , V mn , W mn designate the time-depending amplitudes of vibration, m and n denote the half-wave number in x and y directions, respectively. Galerkin’s method was employed to derive an equation system from (9) and (11) and then MATLAB® software was used for the solution. From that, dynamic behavior of the FG-GPLRC funnel shell can be investigated.
4 Results and Discussions 4.1 Verification In the literature, dynamic behavior of the FG-GPLRC shell with such a complex shape has not been implemented. Therefore, computed results were compared with a model from Finite Element Method for verification. In this study, the radius function in the FEM model was taken as R(x) = Ro + 0.25x2 + 0.5x3 while aG , bG and t G are the average length, the width and the thickness of the GPLs were chosen as: aG = 2.5 µm, bG = t G = 1.5 µm. Some temperature-dependent material properties of the matrix and GPLs are introduced in Table 1. Moreover, the graphene distribution was selected to be FG-X and the weight fraction W GPL = 1%. The dimensions of the shell were picked: h = 0.002 m; Ro = 100 h; L = 2Ro . From that, the non-dimensional natural frequencies were computed and compared in Table 2. As can be seen, there is a good agreement between FEM and the computation.
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Table 1. Material properties Young’s modulus (MPa)
Poisson’s ratio
Mass density (g/cm3 )
Thermal expansion (1/K)
Matrix
4854.6–6.1816T
0.34
1.2
60 × 10–6
GPLs
1087.8–0.261T
0.186
1.06
(13.92–0.0299T ) × 10–6
Table 2. Comparison of non-dimensional natural frequencies Mode (1,5) Present 526.55
Mode (1,4)
Mode (1,3)
FEM Present FEM 535.98 592.02 599.64
Present 723.76
Mode (1,2)
FEM Present 748.99 981.27
Mode (2,2)
Mode (2,3)
FEM 1026.3
Present 1322.4
FEM Present FEM 1129.9 1538.5 1521.4
4.2 Dynamic Behaviors After the verification was made, the dynamic behavior of the shell would be analyzed. Before that, the natural frequencies at modes (m, n) should be compared for choosing the characteristic mode. Values of these natural frequencies were demonstrated in Table 3 with some selected parameters: h = 0.001 m; Ro = 100 h; L = 2Ro; N L = 20; T = 300 K; W GPL = 1%; K 1 = 2.5 × 108 ; K 2 = 5 × 105 . The radius function was taken as R(x) = Ro + 3x2 + 4x3 . As can be seen, the smallest natural frequency is at the mode (1, 3); thus, this mode would be used for all later computations. The nonlinear dynamic behavior of the shell would be demonstrated including analyses of various parameter effects in a parametric study. It should be remarked that the study was conducted with a periodic excitation q = 60sin(50t). Table 3. Natural frequencies (×104 Hz) at modes (m, n) m
N 1
3
5
7
9
1
2.9249
2.4769
2.7045
3.2207
3.8123
2
3.5527
3.2345
3.3297
3.6919
4.1581
Influences of the shell dimension on the dynamic response are illustrated in Fig. 3. It is clear from the figure that the vibration amplitudes increase with bigger shell sizes. The maximum vibrating amount is only about 0.5 × 10–7 m with Ro = 100 h but rises nearly
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four times up to closely 2 × 10–7 m when Ro doubles to 200 h. This vibration tendency can be argued that the shell stiffness is assumed to decrease with larger dimensions. Therefore, the vibration amplitudes are considered to increase accordingly. The role of graphene nanoplatelets in reinforcing the funnel shell can be clarified in effect of the weight fraction W GPL as shown in Fig. 4. With more graphene in the layers, the funnel shell is supposed to be stiffer, which leads to smaller vibration under the periodic excitation. The amplitude goes down from around 5 × 10–8 m to more than 3 × 10–8 m when the weight fraction grows from 1% to 4%. It can be implied from the graph that graphene has demonstrated its superior potential in improving mechanical properties of the shell.
Fig. 3. Nonlinear behavior of the shell with changing Ro
Fig. 4. Nonlinear behavior of the shell under effect of graphene distribution
On the Dynamic Response of a Functionally Graded Funnel Shell
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Moreover, it is noteworthy that environment conditions have big influences on the shell’s dynamic behavior as shown In Figs. 5and6. In Fig. 5, it can be witnessed that the shell’s vibration has significant changes with different elastic foundations. In this study, Winkler and Pasternak elastic foundations were employed for the investigation. Without these elastic foundations, the amplitude reaches about 2.5 × 10–7 m while the appearance of the Pasternak foundation helps reduce the amplitude to only around 1 × 10–7 m and the decrease continues to 0.5 × 10–7 m with simultaneous appearances of the two foundations. Energy absorption into the foundations can be reasoned for this phenomenon.
Fig. 5. Nonlinear behavior of the shell with different elastic foundations
Fig. 6. Nonlinear behavior of the shell under temperature effect
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Meanwhile, thermal expansion should be considered as one of the most influential parameters on the shell’s behavior. It can be seen from Fig. 6 that differences among temperature values are only 5 K but the vibration largeness has a swift jump to approximately 3.5 × 10–5 m. The shell is presumed to be much “softer” due to thermal deformation.
5 Conclusion Dynamic behaviors of FG-GPLRC funnel shell with a newly proposed model was analyzed in this paper. Particularly, the radius of this shell was considered as a cubic function throughout the length. Material parameters were calculated using the Halpin-Tsai micromechanics model. From that, Galerkin’s method was employed to solve the governing equations derive from the classical shell theory and Von Karman-Donnell geometrical nonlinearity assumptions. The computation was verified by comparing the obtained natural frequencies with those of a FEM model at different modes. Finally, a parametric study was conducted to investigate the influences of geometrical, material and surrounding parameters on the dynamic response of the funnel shell. The results indicate significant impacts of these parameters, especially surrounding temperature. This study demonstrates a huge meaning in structure design of funnel shells.
References 1. Shahgholian, D., et al.: Buckling analyses of functionally graded graphene-reinforced porous cylindrical shell using the Rayleigh-Ritz method. Acta. Mech. 231, 1887–1902 (2020) 2. Blooriyan, S., Ansari, R., Darvizeh, A., Gholami, R., Rouhi, H.: Postbuckling analysis of functionally graded graphene platelet-reinforced polymer composite cylindrical shells using an analytical solution approach. Appl. Math. Mech. 40(7), 1001–1016 (2019). https://doi.org/ 10.1007/s10483-019-2498-8 3. Al-Furjan, M.S.H., Habibi, M., won Jung, D., Chen, G., Safarpour, M., Safarpour, H.: Chaotic responses and nonlinear dynamics of the graphene nanoplatelets reinforced doubly-curved panel. Eur. J. Mech. A Solids 85, 104091 (2021) 4. Ansari, R., Torabi, J., Hasrati, E.: Postbuckling analysis of axially-loaded functionally graded GPL-reinforced composite conical shells. Thin-Walled Struct. 148, 106594 (2020) 5. Huang, X., Gao, K., Yang, J.: Fracture analysis of functionally graded multilayer graphene nanoplatelets-reinforced composite strips. Eur. J. Mech. A Solids 83, 104038 (2020) 6. Tao, C., Dai, T.: Isogeometric analysis for postbuckling of sandwich cylindrical shell panels with graphene platelet reinforced functionally graded porous core. Compos. Struct. 260, 113258 (2021) 7. Li, Z., Zheng, J.: Structural failure performance of the encased functionally graded porous cylinder consolidated by graphene platelet under uniform radial loading. Thin-Walled Struct. 146, 106454 (2020) 8. Liu, D., Zhou, Y., Zhu, J.: On the free vibration and bending analysis of functionally graded nanocomposite spherical shells reinforced with graphene nanoplatelets: Three-dimensional elasticity solutions. Eng. Struct. 226, 111376 (2021) 9. Van Do, V.N., Lee, C.H.: Static bending and free vibration analysis of multilayered composite cylindrical and spherical panels reinforced with graphene platelets by using isogeometric analysis method. Eng. Struct. 215, 110682 (2020) 10. Wang, A., Chen, H., Zhang, W.: Nonlinear transient response of doubly curved shallow shells reinforced with graphene nanoplatelets subjected to blast loads considering thermal effects. Compos. Struct. 225, 111063 (2019)
Study on the Process and Formation of Fiber Structure During Forming of an Automobile Joint Part in Closed-Die Forging Quang-Thang Nguyen1 , Viet-Tien Luu2 , and Trung-Kien Le2(B) 1 Hanoi Industrial Textile Garment University, Hanoi, Vietnam 2 School of Mechanical Engineering, Hanoi University of Science and Technology,
Hanoi, Vietnam {tien.luuviet,kien.letrung}@hust.edu.vn
Abstract. The forging in closed-die without flash allows fabricating the complex shape of bulk parts with a good completion surface. In particular, the mechanical properties are much greater than that of the original material due to the formed fibrous organization and improved structural metal. The objective of this work was to investigate the effect of hot forging in closed-die without flash on fiber structural and microstructural study of Companion Flange part. Optical microscope images showed that the fiber of the part was more homogeneous in a forged steel DIN 17210 compared to the initial material. Additionally, some limitations and objective suggestions for further modeling of microstructural evolution during hot forging are proposed. Keywords: Closed-die forging · Metal forming · Flash · Under-filling · Fiber flow
1 Introduction The metal forming process, such as forging, is one of the manufacturing processes where metal is pressed or forced under tremendous pressure into high-strength parts. Metal forming technology has several advantages in industrial production, particularly for transmission parts [1]. For example, a near net shape forming technology was studied in the forging industry to realize low-cost and environment-friendly manufacturing. The well-known leading technologies for manufacturing bulk parts are closed-die forging with flash [2–4] and closed-die forging without flash [5]. Recently, forming in closeddie has many the risks of causing die breakage due to extensive deformation resistance. This formation has a significant change in the properties of the deformed metal and the shape. However, the grain size structure is refined and improved. Many researchers have studied the microstructural material of post deformation with different materials such as Mg-A-Zn alloys [6–8], Cu [9]. However, there are still quite limited on steel by virtual of extensive technological force, high capital investment and costly dies, challenging to conduct in laboratories. The studies mainly used numerical simulation methods [10, 11] to control technological parameters and form processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 49–55, 2022. https://doi.org/10.1007/978-981-19-1968-8_5
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The closed-die forging without flash can improve material utilization and increase the deform uniformity [12]. But some problems should be resolved in the closed-die forging without flash: high billet accuracy, constant tool temperature and forging temperature, low die life, etc. Thus, the stamping technology used closed-die is currently implemented very little. In forging, two main reasons cause short tool life: damage occurs during the die steel strength cannot withstand the load during the forging process, and die wear when the material is removed from the die surface by pressure and sliding of the deforming material [13]. In the present study, the process and formation of grain flow during closed-die forging for the transmission part of the car are reported. Specifically, the new closed-die forging without the flash process is proposed by using the finite element method for simulation of technology steps analysis and determining critical load. Based on simulation results, it can be used to conduct experimental verification of results.
2 Materials and Methods 2.1 Materials and Subject The material for forging was case hardening steel DIN 17210 (16MnCr5). The chemical composition of this steel is shown in Table 1. Table 1. Physical parameters of selected materials Chemical composition [percent by weight] C
Mn
Si
Cr
min
0.14
1.1
0.17
0.8
max
0.19
1.4
0.37
1.1
P
S
0.035
0.035
The research subject is companion flange in the automotive driving system. Its weight is 1.23 kg. 3D modeling and 2D drawing of Companion Flange are shown in Fig. 1.
Fig. 1. 3D modeling and 2D drawing of Companion Flange
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2.2 Forging Process New technological process and manufacturing steps of parts are defined by selecting workpiece with a diameter of φ50 mm, after upsetting and preforming, then finish forging in closed-die for filling metal in the Flange. After piercing the hole, the part is inspected to ensure zero defects. Companion Flange can be formed by one-heating forging on the hot die-forging press. The closed-die forging process without a flash of Companion Flange includes six steps (in Fig. 2).
Fig. 2. Forging process of Companion Flange
By numerical simulation combined with experiment, the technological process and the exact size of the input workpiece have been established. As expected results by running simulation software, the volume of the geometry of the workpiece (Vwp ) and that of the final forging (Vf ) is equal. If no burry is observed, then under-filling is checked for the process. If no under-filling is found, then stops the process; otherwise, the process is repeated till no under-filling is achieved. The optimal length and radius of the workpiece can be obtained from the numerical simulation. Substituting the workpiece volume with diameter d = 50 mm, we get length values (L1). The numerical simulation performs three types of workpiece configuration with different heights from 79.7 mm, 80.9 mm, and 81.6 mm (respectively initial weight 1220 g, 1240 g, and 1250 g). The numerical simulation results determine the exact size of the workpiece with the technological parameters used for the experiment, as listed in Table 2. The results are shown in Table 3. Table 2. Parameters, characteristics in simulation and experiment. Parameters, characteristics
Values
Work-piece temperature (°C)
1,130 ± 50
Friction (f) Between parts and die
0.25
Between the parts and punch
0.15
Initial height of workpiece
79.7 mm 80.9 mm 81.6 mm
According to the experiment result, the obtained product with an initial height of 80.9 mm is qualified for shape and dimension requirements (Figs. 3 and 4).
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Q.-T. Nguyen et al. Table 3. Filling results by simulation and experiment. Case
Initial height (mm)
Flash
I
79.7
Under-filling
II
80.9
Filling
III
81.6
Burry
Fig. 3. The view of companion flange formation for simulation with an initial height of 80.9 mm
Billet
Final part
Fig. 4. The view of Companion Flange formation for experimental with an initial height of 80.9 mm
The experimental result shows that the product is qualified for the requirement about shape and dimension. After numerical simulation and experimental analysis, closeddie forging without flash is considered for manufacturing companion flange products. The value of the force required for the closed-die forging stage is calculated using the corresponding force-time diagram and compared with its experimental counterpart.
3 Results To investigate the formation of fiber structure and microstructure of the workpiece after hot forging in closed-die without flash was carried out at marked position as shown in Figs. 5 and 6. The microstructure at each position of the specimens was observed using optical microscopy. The results are shown in Figs. 7, 8 and 9.
Study on the Process and Formation of Fiber Structure
53
Fig. 5. Cross-sectional images of sampling positions
Fig. 6. Grain flow structure after closed-die forging process
a, 100x
b, 500x
Fig. 7. Microstructure and metal flow of sample M1 in a direction perpendicular to the axis
To observe the metal flow filling the cavity of the die during forming process, the sample was cut along the parting line of the workpiece, then grind and separately cut at M1, M2, and M3 positions (as marked in Fig. 5). The microstructure and metal flow of sample M1 were observed perpendicular to the axis, while samples M2 and M3 were observed along the axial direction. These are the positions that characterize the metal flow directions when forming. The microstructural observation shows that the fibers
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a, 100x
b, 500x
Fig. 8. Microstructure and metal flow of sample M2 in a direction along to the axis
a, 100x
b, 500x
Fig. 9. Microstructure and metal flow of sample M3 in a direction along to the axis
flow along the body part. The results of the microstructural analysis in all three samples (M1, M2, and M3) reveal that the bright phase is ferritic while the dark phase is perlite. The metal flow of sample M1 is horizontally parallel to the direction perpendicular to the axis, while samples M2 and M3 metal flow are parallel. Note that the grain size of selected samples was similar and uniform, and the grain order is arranged in the flow direction. The formation of this fiber structure may improve the mechanical properties of the selected material and refine the original coarse grain structure. Thus, the part will not experience stresses at locations where the horizontal section changes under loading. After the second-step forming position, the directions of the metal fibers are distributed along the longitudinal axis of the part, without the occurrence of folding defects.
4 Conclusions In the present study, the technological process and formation of fiber structure during closed-die forging without flash for Companion Flage parts was investigated. As shown in the microstructure analysis results, the metal flow of the selected part was homogeneous in a forged steel DIN 17210 after hot forging without the flash process. The optimization and selection of reasonable shaping steps to avoid defects were accomplished based on the numerical simulation results.
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Author Contributions. T.-K.L. designed the study and performed simulations. Q.-T.N; V.-T.L. performed the experiments. All authors discussed the results and wrote the manuscript. T.-K.L. led the research project. All authors have read and agreed to the published version of the manuscript.
References 1. Doege, E., Bohnsack, R.: Closed die technologies for hot forging. J. Mater. Process. Technol. 98(2), 165–170 (2000) 2. Behrens, B.-A., Nickel, R., Müller, S.: Flashless precision forging of a two-cylindercrankshaft. Prod. Eng. Res. Devel. 3(4), 381 (2009) 3. Qi, Z., Wang, X., Chen, W.: A new forming method of straight bevel gear using a specific die with a flash. Int. J. Adv. Manuf. Technol. 100(9–12), 3167–3183 (2018). https://doi.org/10. 1007/s00170-018-2862-4 4. Langner, J., Stonis, M., Behrens, B.-A.: Investigation of a moveable flash gap in hot forging. J. Mater. Process. Technol. 231, 199–208 (2016) 5. Douglas, R., Kuhlmann, D.: Guidelines for precision hot forging with applications. J. Mater. Process. Technol. 98(2), 182–188 (2000) 6. Guo, W., et al.: Microstructure and mechanical properties of AZ31 magnesium alloy processed by cyclic closed-die forging. J. Alloy. Compd. 558, 164–171 (2013) 7. Toscano, D., Shaha, S.K., Behravesh, B., Jahed, H., Williams, B.: Effect of forging on microstructure, texture, and uniaxial properties of cast AZ31B alloy. J. Mater. Eng. Perform. 26(7), 3090–3103 (2017). https://doi.org/10.1007/s11665-017-2743-2 8. Yang, T.S., Hwang, N.C., Chang, S.Y.: The prediction of maximum forging load and effective stress for different material of bevel gear forging. J. Mech. Sci. Technol. 21(10), 1566 (2007) 9. Luo, J., et al.: Diminishing of work hardening in electroformed polycrystalline copper with nano-sized and uf-sized twins. Mater. Sci. Eng. A 441(1), 282–290 (2006) 10. Chen, X., Jung, D.: Gear hot forging process robust design based on finite element method. J. Mech. Sci. Technol. 22(9), 1772–1778 (2008) 11. Liu, Y., Wu, Y., Wang, J., Liu, S.: Defect analysis and design optimization on the hot forging of automotive balance shaft based on 3D and 2D simulations. Int. J. Adv. Manuf. Technol. 94(5–8), 2739–2749 (2017). https://doi.org/10.1007/s00170-017-1080-9 12. Altinbalik, T., Akata, H.E., Can, Y.: An approach for calculation of press loads in closed – die upsetting of gear blanks of gear pumps. Mater. Des. 28(2), 730–734 (2007) 13. Lange, K., et al.: Tool life and tool quality in bulk metal forming. CIRP Ann. 41(2), 667–675 (1992)
A Study on the Effect of Compression Ratio and Bowl-In-Piston Geometry on Knock Limit in Port Injection Natural Gas Converted Engine Sy Vong Le1 , Ho Huu Chan2 , and Tran Dang Quoc1(B) 1 School of Transportation Engineering, Hanoi University of Science and Technology, No. 1,
Dai Co Viet Street, Hai Ba Trung, Hanoi, Vietnam [email protected] 2 Faculty of Automotive Technology, Vinh Long University of Technology Education, 73 Nguyen Hue Street, Ward 2, Vinh Long City, Vinh Long Province, Vietnam
Abstract. Natural gas is known as a potential alternative fuel for internal combustion engine due to several advantages such as high octane-number (ON = 130), environment friendly property, safety usage or high low heating value. To enhance reserve capacity on transportation vehicles, the natural gas was compressed to 250 bars into the special cylinder and called CNG fuel. Although high octane-number, the CNG spark ignition engines are without difficulty reached to the knocking combustion when the compression ratio of spark ignition engine was high, too. The main factor of this complication has demonstrated in the previous researches which were the low heating value of natural gas. Therefore, the goal of this study not only is to find out the compression ratio limit for port injection natural gas converted engine by means of simulating research but also enhanced the mass fraction burned. The name’s software using in this study that is AVL Boost, the calculation results were focused on solving the process to get the required octane number as well as the compression ratio limit of real engine. The obtained results showed that, for engine operation in the range of n = 1000–2200 rpm without knocking risk, the converted engine should be fixed the compression ratio at ε = 10. In case of higher compression ratio, the engine should be extended lambda and/or the range of engine speed. Especially, the burning rate of natural gas was increased by varying the bowl-in-piston geometry, and the cause of this increase was due to the enhancement of squish velocity. Keyworks: Knock limit · Natural gas · Compression ratio · Knock · Required octane number
1 Introduction Nowadays, the strict emission standards and the fueled crisis are the main challenges for the transportation sector. Using alternative fuels is a promising solution for overcoming these issues. It is well known that natural gas is a potential substitute as an alternative to gasoline and diesel. The chemical composition of natural gas includes the largest © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 56–73, 2022. https://doi.org/10.1007/978-981-19-1968-8_6
A Study on the Effect of Compression Ratio and Bowl-In-Piston Geometry
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component is methane (CH4 ) accounting for 85–96%, with a minor quantity of ethane (C2 H6 ), propane (C3 H8 ), butane (C4 H10 ) and other gases [1]. Natural gas provides 23,7% of global energy, and there are over 18 million natural gas vehicles distributed through more than 86 countries all over the world [2]. The CNG is recognized as having suitable characteristics such as higher low heating value, higher research octane number and cleaner combustion as compared with gasoline and diesel and plentiful reserves all over the world. Since being gaseous in ambient condition, it allows the mixture to burn more easily and cleaner than liquid fuel [3]. The emissions in combustion process products such as CO, particulate matter (PM), and NOx will be lower as a result of cleaner combustion and the combustions do not produce CH4 emissions, as CH4 is the main component [4]. A disadvantage of natural gas fuel is lower burning rate as compared with gasoline and diesel [5]. This is a challenge when converting from a diesel engine to a natural gas engine that has high power, torque, and thermal efficiency. In addition, natural gas’s research octane number is 130, which is suitable for the engine running at higher compression ratio. The natural gas engine with larger compression ratio provide better combustion and achieving greater power output and better economic benefit. However, it is also accompanied with the possibility of knocking once the compression ratio reaches a certain limitation because larger compression ratios produce higher in-cylinder pressure and temperature, which promote spontaneous combustion before flame front arrives. The engine must increase the burning rate or heat release rate without knocking occurrence at a high compression ratio to overcome the above challenges by operating at suitable conditions. Knock is the name given to the noise which is transmitted through the engine structure when spontaneous ignition of a significant portion of the end-gas, the fuel, air, residual gas, mixture ahead of the propagating flame-occurs. When this abnormal combustion process takes place, there is usually an extremely rapid release of chemical energy in the end-gas, causing very high local pressures and the propagation of pressure waves of substantial amplitude across the combustion chamber [6]. According to all knocking studies, the sources of an unwanted flame point are the hot spots on combustion chamber such as the exhaust valve or the spark plug. These hot spots always contact with the high temperate of burned gases and are capable of igniting fuel [7–9]. Many published researches showed that the gas flows into the engine cylinder are very complex and the turbulent kinetic energy at the end of compression stroke must be controlled by means of creating the coherent gas flow [10–12]. The research groups of Abdul Gafoor C.P and Z. Barbouchi have affirmed that the turbulent kinetic energy (TKE) is strongly influenced by the combustion chamber geometry [13, 14]. Meanwhile, the research of A. Mirmohammadia and F. Ommib was denoted TKE is rapidly increased at the end of the intake stroke, but then it decreases rapidly when the piston has traveled one-third of the compression stroke [15]. When the piston keeps moving closer to the top dead center, the turbulent kinetic energy will increase, and with a suitable piston shape, it is possible to increase the turbulent kinetic energy at the end of the compression stroke [16]. Controlling the squish motion and squish velocity will increase the turbulent kinetic energy at the main combustion process. Due to the increase in the value and orientation of squish velocity, the mass fraction burned and the heat released are both higher [17]. For diesel engines, to control the squish velocity, it is necessary to consider the influences
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of the combustion chamber volume on the piston’s top through two key parameters: the diameter of the bowl (Db ) and the depth of the bowl (Hb ) as shown in Fig. 1. Up to now, specific datas or complete studies on the influence of these two parameters on the working quality of natural gas engines are very difficult to access. According to the above analyses, performing this study is extremely necessary and suitable with current research conditions.
2 Theoretical Framework A theoretical squish velocity can be calculated from the instantaneous displacement of gas across the inner edge of the squish region (across the dash lines in the drawings in Fig. 1). The original diesel engine’s cylinder head and piston top are both flat. Ignoring the effects of gas dynamics (non-uniform pressure), frictions, leakage past the piston rings, and heat transfer, the squish velocity’s expression is:
Fig. 1. Schematic of the bowl-in-piston geometry and squish area
vsq Db = Sp 4z
B Db
2
−1
VB Ac z + VB
(1)
where: – – – – –
VB : The volume of the piston bowl. Ac : The cross-sectional area of the cylinder. Sp : The instantaneous piston speed. z: The distance between the piston crown top and the cylinder head. l: The connecting road length, a: the crank radius, s: the distance between the crank axis and the piston pin axis, c: the clearance height. – Db : The diameter of the bowl. – Hb : The depth of the bowl.
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The mass fraction of fuel burned is the ratio calculated from the sum of the heat release at that crank angle divided by the total heat release between intake valve closing (IVC) and exhaust valve opening (EVO). It can be expressed as a function of the crank angle: θ δQgen dθ θsoc dθ (2) MFB(θ) = mf,total × ηcomb × QLHV where: MFB: the mass fraction of fuel burned; θ: crank angle; Qgen : Total heat release of the intake mixture mass; mf,total : Total mass of the intake mixture; ηcomb : Combustion efficiency; QLHV : Lower heating value. The heat release rate (HRR) is the rate of heat released from the combustion process inside the engine cylinder. It is possible to analyse the characteristics of the combustion processes inside the engine cylinder and diagnose the composition of the exhaust gases basing on the HRR value. The heat release rate is determined using the first law of thermal dynamics with a non-dimensional dynamic model and one-range mixture in the cylinder. From the pressure parameters in the cylinder measured by 100 cycles, HRR can be calculated according to the following general equation: dQch =
γ 1 Vdp + pdV + dQht γ−1 γ−1 Vcr 1 + cv T + RT 1 + dp γ−1 RTw
(3)
To prevent the auto-ignition phenomenon in spark-ignition engines, it is necessary to determine the knocking limit by combining the maximum pressure value and the required octane number (ON). According to Heywood, knocking occurs when the integration in below equation reaches unity: ti dt =1 (4) 0 τ where τ is the induction time at the instantaneous temperature and pressure for the mixture, t is the elapsed time from the start of the end-gas compression process (t = 0), and ti is the time of auto-ignition: 3800 ON 3.402 −1.7 (5) p exp τ = 17.68 100 T where τ is in milliseconds, p is absolute pressure in atmosphere, and T is in kelvin. ON is the appropriate octane number of the fuel. In AVL Boost software, the required octane number is considered as the following formula: 1 ON = 100. A
t85%MBF tSOC
p pRef
n
.exp −
B TUBZ
1a dt
(6)
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3 Engine Simulation, Calibration and Control Model 3.1 Experimental Setup The experimental setup is an important step to collect the parameters on the test bench, which will be used to calibrate the model. Research equipments and engines were arranged as shown in Figs. 2 and 3, including the following equipment: Ricardo singlecylinder research engine redesigned from a horizontal single-cylinder diesel engine with the parameters presented in Table 1; the CNG fuel supply system (Mass Flow Controller: MFC) and a port CNG injector; a Dynamometer was used to measure the engine’s torque. In addition, there were the intake/exhaust system, the cooler system, the engine control unit, the data collector and others measuring systems.
Fig. 2. Scheme of the experimental equipments setup
Fig. 3. Pictorial view of experimental setup
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Table 1. Research engine parameters Parameter
Symbol
Value
Unit
Cylinder bore
D
103
mm
Stroke
S
115
mm
Displacement
Vtp
1.03
Liter
Compression ratio
ε
10
–
3.2 Simulating Theoretical Framework The Fractal Combustion Model was selected as the research model for the mixed charge flow from the AVL Boost software’s library. This was the suitable model for CI engines [18], the theoretical framework is summarized below: – – – –
Ignition timing was considered as the start of the combustion of simulation. The flame front formation was the parameter to calibrate the ignition delay (Cign ). The flame propagation speed was the parameter to calibrate the ignition delay (rf,ref ). The burned mass of fuel in a time unit was calculated as the formula: D −2 L1 ρsoc m 3 dmb × AL × SL = ρu dt lk ρuz
(7)
where m is the calibration parameter of turbulence model; ρsoc is the unburn density at the start of combustion; ρuz is the unburn density. – The small amount of burned mass at start of wall combustion determined in-wall combustion process was mmb tr , where the transition time ttr has been determined when a small amount of mass was burned. – The laminar burning speed SL = clfs SL,RG=0 (1 − mfRG )d has been determined at the start of wall combustion (d), allowed to adjust more SL depending on residual gas mass coefficiency. Figure 4 presented the elements of the QTC 2015 engine simulated by AVL Boost software, each element of the simulation engine had the same parameters as the experimental engine. The elements, which were used to set up input and output boundary conditions of the intake and exhaust system, were SB1 and SB2. On intake elements 1, 2, 3, 4, CL1 was used as an air cleaner, the I1 element simulated the position and characteristics of a port injector. The elements MP1, MP2, MP3, MP4 simulated the sensors’ position and measured the pressure, temperature values and flow parameters before they entered the cylinder C1. MP5, MP6, MP7 were the positions of the sensors that measured the pressure, temperature values and flow parameters before they went through the exhaust elements: 6, 7, 8. R1, R2 and R3 simulated the loss coefficients of the flow in intake and exhaust ports, while PL1 simulated the plenum.
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Fig. 4. The simulation engine the QTC2015
3.3 Model Calibration Figure 5 presented the results such as torque (Me ) and power (Ne ) of the experimental and simulation engine, with the solid lines were the results of the real engine on the test bench. The dash lines represented the simulation model’s results after recalibrating the model. However, the parameters of the QTC2015 experimental engine such as cylinder bore, piston’s parameters, stroke, lengths and diameters of intake and exhaust ports were used to input for the model. The experimental conditions of the test engine: wideopen throttle (WOT) so this element wasn’t used in the model, spark angle was adjusted before top dead center (IT: BTDC) and compression ratio E = 10. Considering the whole experimental range (n = 1000 ÷ 2000 rpm), the maximum and minimum errors between the simulation results and experimental results were about 5% and 2%. However, at the speed n = 1800 rpm, the errors of both torque and power were approximately 2% and this speed was fixed to study the influences of the structuring parameters on combustion duration. 3.4 Controlling the Model In order to consider the knock limits with high compression ratio, the simulation study was proceeded as following: the intake port’s pressure was a constant Pf = 1, the throttle was opened widely (Throttle: WOT) to reduce losses in the intake port. The center of the bowl volume in the piston top and the center of the spark plug coincided with the axis of the engine cylinder. The following structuring parameters were modified: the depth of the bowl volume in the piston top was changed with Hb = 0 (Piston shape: Flat), Hb = 10 mm and Hb = 17 mm, while the diameter of the bowl volume in the piston top was varied from Db = 0 (Piston shape: Flat), Db = 60 mm and Db = 66 mm. Combining the changes above, there were the types of the piston top detailed as the following table: Table 2 The modified operation conditions included: the engine speed was varied from n = 1000 rpm to n = 2200 rpm with the step n = 200. The compression ratio was varied
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from E = 10 until the simulation results showed the ON value was greater than or equal to 130 then stop. The equivalence ratio was varied from λ = 0.8 to λ = 1.2 with the step λ = 0.1. During the simulation, the ignition timing was adjusted to achieve maximum torque.
Fig. 5. Validation results of the model
Table 2. Structuring parameters of the piston shapes Type Flat
Bowl Depth, Hb (mm)
Bowl Diameter, Db (mm)
0
0
Heron 1
10
60
Heron 2
17
60
Heron 3
17
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4 Results and Discussion 4.1 Effects of Compression Ratio on Required Octan Number Figure 6 and Fig. 7 below show that increasing compression ratio also increase the power and decrease the brake-specific fuel consumption. That is due to increased in cylinder pressure and temperature at higher compression ratio, resulting in improving combustion. The results obtained under these conditions: the pressure of CNG port injection and the equivalence ratio conditions were maintained at Pf = 1 bar and λ = 1; the combustion chamber geometry was original as the diesel engine (Piston shape: Flat); the ignition timing was adjusted to achieve maximum brake torque (IT = MBT) and the throttle opened widely (Throttle: WOT) to reduce losses in the intake port caused by the throttle itself.
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Fig. 6. Effect of compression ratio on engine’s power (Ne)
Fig. 7. Effect of compression ratio on brake specific fuel consumption (BSFC)
Figure 8 shows the ON results obtained with different engine speeds, because the ON of natural gas is 130, only the results with ON < 130 were obtained. From the results, it could be seen that the required octane number decreased with an increase in the engine speed for each compression ratio. Conversely, as the compression ratio grew, the ON value increased at each engine speed. This pattern showed that when converting a diesel engine to a natural gas engine, the compression ratio should be reduced to avoid the abnormal combustion phenomenon at low engine speeds. Working at high speeds is the best way to successfully decrease the ON value for natural gas engines. Because the times fresh mixtures enter the engine cylinder at the increases as the engine speed increases, this fresh mixture has a significantly lower temperature than the in-cylinder temperature, and it will absorb and decrease the risk of auto-ignition sources. However, in order to analyse the effects of the compression ratio on required octane number more clearly, the next study will consider the influence of intaked fuel mass on ON at constant engine speed using the equivalence ratio (lambda: λ).
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Fig. 8. Effects of operation conditions on required octane number (ON)
Fig. 9. Effects of lambda on the required octane number (ON)
Figure 9 presented the effects of lambda on the required octane number (ON). In this section, there is only one difference from the previous: the engine speed was fixed at n = 2200 rpm and lambda was varied from λ = 0.8 to λ = 1.2 with step λ = 0.1. The obtained results shows that for each compression ratio, the changes in ON as lambda grows are the same. ON values tend to increase and reach the maximum value at λ = 1 as lambda increases between λ = 0.8 and λ = 1, but when lambda is higher than λ = 1, ON values instantly decrease. The cause of the decreases in ON is mainly due to the drop in combustion chamber temperature. The drop in cylinder temperature happened as the lack of oxygen needed for the combustion reaction (when λ < 1) or the lack of fuel when engine worked with λ > 1.
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The obtained ON when varying engine speed with the compression ratio E = 15 was presented in Fig. 10. Although the engine operated with three different lambda (λ = 0.8, λ = 1.1 and λ = 1.2), the ON values all decrease as the speed increases. However, working in high-speed range with lambda greater than λ = 1 (λ = 1.1 and λ = 1.2) not only decreases ON values but also improves the engine’s thermal efficiency. Lowering ON by increasing lambda greater than λ = 1 is more effective than increasing engine speed. The changes of mass fraction burned versus crank angle with both three lambda values will demonstrate this lowering.
Fig. 10. Effects of engine speed on required octane number (ON)
The engine speed was fixed at n = 1000 rpm with both three lambda values (λ = 0.8, λ = 1 and λ = 1.2) in order to eliminate the influences of engine speed on combustion processes. Mass fraction burned is a function of crank angle, with different fuel supplied for each cycle, has the same tendencies (Fig. 11). At least 53° of crankshaft angle (from CA = 345° to CA = 398°) is the required duration to burn the amount of fuel entered the engine cylinder with λ = 1. When λ = 1.2, that duration is 93° of crankshaft angle (from CA = 345° to CA = 438°). The longest duration is from CA = 345° to CA = 468° (as 123° of crankshaft angle) with the lack of oxygen in mixture λ = 0.8, compared to the other two cases. These results show that the combustion duration heavily depends on the oxygen ratio of the mixture. From this, it could mean that the ON value is influenced by the λ values. For further clarification, it was necessary to consider the heat release possibility for these three lambda cases (Fig. 12). The mass fraction burned (MFB) of every single engine cycle was normalized with a scale from 0 to 1, which is used to describe the chemical energy of fuel releasing as a function of crankshaft angle. Figure 14 showed the varies of mass fraction burned versus crank angle with four different piston top shapes. The combustion process of the SI engine can be divided into three basic stages defined as: the flame-development stage, the main combustion stage and the after burning stage. The first stage, which is the development of the flame film, is initiated from the appearance of a spark at the spark plug, then forms a fire point and starts to develop the flame front, the mass fraction burned MFB = 0–10% [18]. This stage corresponded to the degrees of crankshaft angle
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Fig. 11. Mass fraction burned as a function of crank angle
Fig. 12. Heat release rate as a function of crank angle
CA = 345–355 (deg). The main combustion stage, which has the largest released heat stage, corresponded to the degrees of crankshaft angle CA = 355–370 (deg) and the MFB reaches 90% in-cylinder mass. In this stage, if the turbulent kinetic energy of the gas flow inside the engine cylinder were controlled, the released heat would be highest. In the after burning stage (or flame termination stage), there is only about 10% of fuel mass continue to burn. In this stage, as the piston traveled down to the bottom dead center, the combustion chamber’s volume increases and the gas flow dynamic decreases. As shown in the figure, it’s well known that in the flame development stage and the after burning stage, the effects of piston top shapes on MFB were not much because of small squish velocity. The effects of piston top shapes presented clearly in the main stage, during CA = 355–370 (deg). Because the turbulent kinetic energy (TKE) of the flat piston top is lower as compared with three other piston top shapes, the main combustion duration is longer (Fig. 13).
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Figure 15 presented the obtained results with the same operating condition with four different piston top shapes, the change in heat release rate versus crank angle curves were quite different. The effect of the combustion chamber geometry was shown clearly at the main combustion stage, the fuel mass was concentrated to provide to the combustion reaction, so the heat release rates of three piston (Heron 1, Heron 2 and Heron 3) are higher than the flat piston top. The increase in the conversion rate from chemical energy to thermal energy will increase the combustion chamber’s temperature, so the required octane number will increase. In the case of the same working conditions, such as compression ratio E = 10, engine speed was fixed at n = 1000 rpm, injection pressure Pf = 1 bar, fuel quantity assigned to a cycle was a constant. The required octane number results for all four piston top shapes tended to increase the same when the ignition timing was advanced (ignition angle tends to move away from the top dead center). The result in Fig. 16 showed that for each ignition timing (IT), the required octane number (ON) of the flat piston top is always lower than three other piston top shapes with advanced ignition timing. This result demonstrated that the piston top shape influences on ON value significantly. Because the gas motion in the main combustion stage is improved by controlling the squish velocity at the end of the compression stroke, the fuel mass burned and the heat release rate per time unit increases quickly. So with the same fuel mass supplied to a cycle and more complex combustion chamber geometry, the required ON of three piston top shapes (Heron 1, Heron 2 and Heron 3) are still higher and the risk of abnormal combustion occurring at the same ignition timing is higher. In order to better understand how the geometry parameters influence the fuel heat release rate inside the engine cylinder, the next section will consider the influences of the diameter of the bowl (Db ) and depth of the bowl (Hb ) on the required octane number (ON).
Fig. 13. Turbulent kinetic energy (TKE) as a function of crank angle (CA)
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Fig. 14. Mass fraction burned versus crank angle
Fig. 15. Heat release rate as a function of crank angle
Fig. 16. Effect of ignition timing (IT) on required octane number (ON)
In order to eliminate the influence of the diameter of the bowl-in-piston but still take advantage of the squish velocity at the end of the compression ratio, in this study the
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Fig. 17. Effect of the depth of the bowl-in piston (Hb) on the required octane number (ON) and ignition timing (IT)
Fig. 18. Effects of the diameters of the bowl (Db) on the required octane number (ON) and ignition timing (IT)
Fig. 19. Effects of the diameters of the bowl (Db) on the engine’s performances
Db value was fixed, and the depth of the bowl (Hb ) was varied to consider (Hb = 0, 10 and 17), the ignition timing was adjusted to achieve the maximum torque. As shown in Fig. 17, the ON value is increased and ignition timing is retarded when increasing the depth of the bowl (Hb ). From that result, it can be considered that the depth of the bowl (Hb ) has a huge influence on the required octane number and ignition timing, the increase
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of the depth improves the burning rate of the natural gas fuel. Therefore, the ignition timing is retared to achieve the maximum torque, but the risk of abnormal combustion is high because of the increase of ON as shown in the figure. For the case of fixing the depth of the bowl (Hb ) and varying diameter of the bowl (Db ) also resulted in a similar tendency. Increasing Db will increase the fuel’s ability to burn and released heat at the end of the compression stroke, so the required octane number increases and ignition timing can be adjusted as presented in Fig. 16. When comparing the results in Figs. 17 and 18, it can be observed that Db has greater influences on the ON value and ignition timing (IT) than the depth of the bowl (Hb ).
Fig. 20. Effect of the depth of the bowl-in piston (Hb) on the engine’s performances
The effect of bowl-in-piston geometry on engine’s performances is presented in below figures (Figs. 19 and 20). It could be seen that the bowl diameter and the bowl depth have influences on engine power and brake-specific fuel consumption. As having biggest TKE, the Heron 3 provides lower power and more BSFC. The Heron 1 shows a little bit lower in power, but has lowest BSFC. The Flat shape has higher power but higher BSFC. The results shows that higher turbulent kinetic energy of the gas motion promotes the heat transfer from the burning mixture to the combustion chamber wall. So the engine losses more power.
5 Conclusion In this study, the effects of engine speed, lambda and structuring parameters on the CNG engine’s performances and the required octane number were presented. The major conclusions are as follows: – Higher compression ratio provides better performances: higher power and lower brake-specific fuel consumption. – The highest compression ratio that the engine can operate without knock is E = 15. In the case of high compression ratio, the operating condition of the natural gas engine must be high speed and lambda must be greater than λ = 1. Operating in high-speed range and λ > 1 will significantly reduce the ON value.
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– In order for the engine to work safely in the speed range of the original diesel engine (n = 1000 rpm to n = 2200 rpm), it is necessary to decrease the compression ratio to E = 10. – With the same compression ratio, the diameter of the bowl (Db ) and the depth of the bowl (Hb ) have huge effects on the mass fraction burned and the heat release rate. Controlling the squish velocity inside the engine cylinder will not only increase the mass fraction burned of the natural gas fuel, but also extends the lean limit of the converted engine. Therefore the engine’s performances are improved. – The Heron 1 piston top type and the compression ratio E = 10 are the most suitable structuring parameters for the converted engine to operate safely and achieve good performances without occuring abnormal combustion phenomenon. This converted engine can operate at the lambda greater than λ = 1 condition and the limit speed can be higher than n = 2200 rpm.
References 1. Kakaee, A.-H., Rahnama, P., Paykani, A.: Influence of fuel composition on combustion and emissions characteristics of natural gas/diesel RCCI engine. J. Nat. Gas Sci. Eng. 25, 58–65 (2015) 2. ImranKhan, M., Yasmin, T., Shakoor, A.: Technical overview of compressed natural gas (CNG) as a transportation fuel. Renew. Sustain. Energy Rev. 51, 785–797 (2015) 3. ImranKhan, M., Yasmeen, T., Ijaz Khan, M., Farooq, M., Wakeel, M.: Research progress in the development of natural gas as fuel for road vehicles: A bibliographic review (1991–2016). Renew. Sustain. Energy Rev. 66, 702–741 (2016) 4. Chauhan, B.S., Cho, H.-M.: A study on experiment of CNG as a clean fuel for automobiles in Korea. J. Korean Soc. Atmospheric Environ. 26, 469–474 (2010). https://doi.org/10.5572/ KOSAE.2010.26.5.469 5. Jeevan Dass, G., Lakshminarayanan, P.A.: Conversion of diesel engines for CNG fuel operation. In: Lakshminarayanan, P.A., Agarwal, A.K. (eds.) Design and Development of Heavy Duty Diesel Engines. EES, pp. 341–392. Springer, Singapore (2020). https://doi.org/10.1007/ 978-981-15-0970-4_9 6. Heywood, J.B.: Internal Combustion Engine Fundamentals. McGraw-Hill Book Company (1988), ISBN 0-07-100499-8 7. Zhao, X., Zhu, Z., Zheng, Z., Yue, Z., Wang, H., Yao, M.: Effects of flame propagation speed on knocking and knock-limited combustion in a downsized spark ignition engine. Fuel 293, 120407 (2021) 8. Wang, Z., Liu, H., Reitz, R.D.: Knocking Combustion in Spark-ignition Engines. (2017) 9. Lin, C., Zhang, R., Wei, H., Pan, J.: Effect of flame speed on knocking characteristics for SI engine under critical knocking conditions. Fuel 282, 118846 (2020). https://doi.org/10.1016/ j.fuel.2020.118846 10. Sevik, J.: “Influence of Charge Motion and Compression Ratio on the Performance of a Combustion Concept Employing In-Cylinder Gasoline and Natural Gas” Blending. J. Eng. Gas Turb. Power 140, 121501–1 (2018) 11. Perini, F., et al.: Piston geometry effects in a light-duty, swirl-supported diesel engine: flow structure characterization (2017) 12. Kaplan, M.: Influence of swirl, tumble and squish flows on combustion characteristics and emissions in internal combustion engine review. Int. J. Autom. Eng. Technol. IJAET 8(2), 83–102 (2019)
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13. Abdul Gafoor, C.P., Gupta, R.: Numerical investigation of piston bowl geometry and swirl ratio on emission from diesel engines. Energy Conver. Manage. 101, 541–551 (2015) 14. Barbouchi, Z., Bessrour, J.: Turbulence study in the internal combustion engine. J. Eng. Technol. Res. 1(9), 194–202 (2009) 15. Mirmohammadia, A., Ommi, F.: Internal combustion engines in cylinder flow simulation improvement using nonlinear k-ε turbulence models. J. Comput. Appl. Res. Mech. Eng. 5(1), 61–69, Autumn-Winter 2015–16 16. Lee, K.H., Lee, C.S.: Effects of tumble and swirl flows on turbulence scale near top dead centre in a four-valve spark ignition engine. J. Autom. Eng. 217, 607–615 (2003) 17. Pradeep, B., Ravi, K., Porpatham, E., Krishnaiah, R., Ekambaram, P., Jayapaul, P.: Investigations on the effect of piston squish area on performance and emission characteristics of LPG fuelled lean burn SI engine. SAE Technical Paper 2016-28-0123 (2016) 18. Chin, Y.-W., Matthews, R.D., Nichols, S.P., Kiehne, T.M.: Use of fractal geometry to model turbulent combustion in SI engines. Combust. Sci. Technol. 86(1–6), 1–30 (1992) 19. Abdullah, S., Shamsudeen, A.: Turbulence and heat transfer analysis of intake and compression stroke in automotive 4-stroke direct injection engine. Algerian J. Appl. Fluid Mech. 1, 37–50 (2007) 20. Urushihara, T., Murayama, T., Takagi, Y., Lee, K.H.: Turbulence and cycle-by-cycle variation of mean velocity generated by swirl and tumble flow and their effects on combustion”, SAE Paper 950813 (1995) 21. Olalekan, W., Aziz, R., Muhamad, W., Mansor, W.: Mass fraction burned of the directinjection hydrogen enriched compressed natural gas engine employing exhaust gas recirculation. J. Adv. Res. Dynam. Control Syst. 11, 82–94 (2019)
Effect of Different Heat Recovery Tube Structure on the Exhaust Heat Utilizing Ability in Internal Combustion Engine Khong Vu Quang1 , Le Manh Toi1 , Le Dang Duy1(B) , Vu Minh Dien2 , Nguyen Duy Tien1 , and Nguyen The Truc1 1 School of Transportation Engineering, Hanoi University of Science and Technology, Hanoi,
Vietnam [email protected] 2 Center for Automotive Technology and Driver Training, Hanoi University of Industry, Hanoi, Vietnam
Abstract. In recent years, environmental pollution and depletion of fossil fuel resources are hot issues, attracting scientists’ attention to research. Among the methods to solve these problems, utilizing exhaust gas energy of internal combustion engines (ICE) is a promising approach in improving thermal efficiency, reducing fuel consumption and exhaust gas of ICE. This paper will present the results of the study on the waste heat utilizing ability of the exhaust heat recovery tank (EHR) with two different structures by simulation method using Ansys fluent software. The study shows that the waste heat utilizing efficiency depends mainly on: heat exchange area, movement of fluid flow and working mode of the ICE. With a reasonable structure of the EHR, the heat recovery of the exhaust gas can be achieved up to 40%. Keywords: Internal combustion engine · Waste heat · Exhaust gas recovery tank
Nomenclature ICE EHR GDI HCCI VVTi ORC TEG TWC CFD TEx TEx ηRe ρ
Internal Combustion Engines Exhaust gas Heat Recovery Gasoline Direct Injection Homogeneous Charge Compression Ignition Variable Valve Timing intelligence Organic Rankine Cycle ThermoElectric Generator Three-Way Catalytic converter Computational Fluid Dynamics Exhaust Gas Temperature (K) Reduction of Exhaust Gas Temperature Heat Recovery Efficiency (%) Density (kg/m3)
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 74–82, 2022. https://doi.org/10.1007/978-981-19-1968-8_7
Effect of Different Heat Recovery Tube Structure on the Exhaust
p u k Cp μ
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Pressure (Pa) Velocity Component (m/s) Thermal Conductivity (W/m.K) Specific Heat in Constant Pressure (J/kg.K) Viscosity (Pa.s)
1 Introduction In today’s society, energy is an important part of our lives and one of the decisive factors for the sustainable economic development of countries around the globe. The increase of population and the improvement of people’s living standards has increased energy consumption worldwide while energy resources are increasingly depleted. One of the solutions to effectively overcome this problem is to effectively use renewable energy sources. In addition, another way to approach the situation is to improve conventional energy systems to increase the maximum efficiency of the system obtained from an energy source. Currently, ICE is the main driving force in the fields of transportation, agriculture, forestry and fishery. It accounts for 60–70% of the total fossil fuel consumption worldwide [1]. However, the average efficiency of ICE can only reach 30–40% [2], some studies show that the efficiency of ICE can reach up to 48% [3]. In recent years, rising fuel prices and global warming are major issues affecting the automotive and ICE industry. There have been studies to increase performance as well as reduce fuel consumption in the ICE. Advanced technologies have been used in the engine such as direct fuel injection (GDI) [4], Homogeneous Charge Compression Ignition (HCCI) [5], turbocharger [6], high injection pressure, advanced injection timing [7], VVT-i (Variable Valve Timing intelligence) [8]. The ability to improve fuel efficiency of the engine can be evaluated through experiments or numerical methods based on mathematical simulation. The experimental process requires a lot of time and cost while numerical simulation methods can reduce the expenditure of a research. Despite great efforts of developers and scientist to apply advanced technologies into the ICE, there is still about 50% of fuel energy lost to exhaust gas and cooling water in the form of waste heat (of which cooling water accounts for 15–30% and exhaust gas accounts for 25–35%). Exhaust gas in ICE contains large amount of energy and has a high temperature, therefore exhaust heat recovery is one of the simple and effective way to improve fuel efficiency and heat efficiency of the system. Some technologies are studied that utilizing exhaust gas energy such as: Organic Rankine Cycle (ORC), thermoelectric generator (TEG), etc. Liu Tong et al. analyzed and simulated the recovery performance of a waste heat recovery system based on the Organic Rankine Cycle under different engine conditions and the results showed that the rated power of the motor is improved by up to 50% [9]. Haoqi Yang and his colleagues studied the optimization of the combination of TEG and the three-way catalytic converter (TWC), the results showed that the engine power increased by 37% and with a reasonable structure, the performance of TEGs can be improved by more than 16% compared to a single set of TEGs [10]. Hoang Anh Tuan [11] has studied the use of exhaust heat to heat up biofuels
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used on marine engines. In this study, the test object was the D243 engine (4-stroke, 4cylinder diesel, unified combustion chamber, cylinder capacity of 4.75 L, rated capacity of 58.8 kW). Experimental results show that the exhaust heat used is 5.62 kW when the engine is working at 100% load and 2000 rpm. From the above reasons, the content of this article will study, calculate, and optimize the design structure of the exhaust heat recovery tank (EHR) to evaluate the ability to utilize waste heat of the exhaust gas according to the working modes of the ICE using CFD simulation. In which the boundary conditions of the model are determined through experiments on the D243 engine at the research center for engines, fuels and emissions, Hanoi University of Science and Technology.
2 Numerical Model Figure 1 shows the structure of the exhaust heat recovery (EHR) tank. In which the exhaust gas from the ICE goes inside the core of the tube and it exchange heat with 9 inner blades, these blades are evenly arranged in the tube along its body. Along the outside length of the pipe body, guide vanes are arranged to increase the heat exchange capacity between sea water, the pipe wall and exhaust gas. In this paper, the research team conducts a simulation with two cases in order to select the appropriate structure. Case 1 (EHR 1) with 9 triangular wings has a height of 55 mm and an acute angle of 8° and case 2 (EHR 2) with a plate-type structure which has 9 channels, each channel has a width r = 3 mm and is arranged along the tube body.
Fig. 1. EHR structure with 2 different cases.
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2.1 Computational Theory in Ansys Fluent In order to solve the problem using the Ansys Fluent software, governing equations for the flow and conjugate transfer of heat were customized according to the conditions of the simulation setup. The governing equations for mass, momentum, energy, turbulent kinetic energy and turbulent energy dissipation are expressed as follow [12]. • Continuity equation ∂u (ρui ) = 0 ∂xi
(1)
∂uj ∂ ∂p ∂ (ρui uj ) = (μ )− ∂xi ∂xi ∂xi ∂xj
(2)
∂ k ∂T ∂ (ρui T ) = ( ) ∂xi ∂xi Cp ∂xi
(3)
• Momentum equation
• Energy equation
In this research, standard k–ε turbulence model is adopted because it can provide improved predictions of near-wall flows. In addition, the standard k-ε model is a semiempirical model synthesized from experimental phenomenon and has been widely used in heat exchange simulation because of its economy and accuracy factors. For this reason, the standard k-ε model was chosen to simulate heat transfer and flow in the EHR. • Turbulent kinetic energy: μt ∂ grad (k) − ρε + 2μt Sij .Sij (ρk) + div(ρkui ) = div ∂t σk
(4)
• Turbulent energy dissipation: ∂ ε ε2 μt grad (ε) − C2ε ρ + C1ε 2μt Sij .Sij (ρε) + div(ρεui ) = div ∂t σε k k
(5)
The equations contain five adjustable constants: Cμ, σk , σε , C1ε and C2ε . The standard k–ε model employs values for the constants that are arrived at by comprehensive data fitting for a wide range of turbulent flows: C = 0.09, σk = 1.00, σε = 1.30, C1ε = 1.44 and C2ε = 1.92
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2.2 Boundary Condition Velocity and temperature were applied to the inlet of the exhaust heat recovery tank as boundary conditions of the model, with values used according to prior studies [13, 14]. For the outlet, a pressure boundary condition was selected, where the measured pressure of the exhaust and seawater outlet was set to 0 Pa. The input velocity profile is assumed to be uniform. Based on the studied model size, the hydraulic diameter and turbulence at the inlet and outlet of the exhaust gas are 0.05 m and 5%. The hydraulic diameter and turbulence of seawater inlet and outlet are 0.02 m and 5%. The non-moving wall boundary condition is applied to the outer enclosure and the heat flux here is 0 (assuming the enclosure is completely insulated). A simple algorithm for the velocity-pressure coupling is adopted, second-order upwind method for energy, momentum, turbulent kinetic energy, and turbulent dissipation rate equation is prescribed. Under relaxation factors 0.4 for pressure, 0.6 for momentum, 0.75 for turbulent kinetic energy, 0.75 for turbulent dissipation rate, 0.75 for energy equation and 1 for turbulent viscosity are considered. The meshing model of the EHR is presented in Fig. 2.
Fig. 2. Meshing model of EHR.
2.3 Meshing In CFD analysis, the accuracy of the results and the computation time are two important parameters that are determined by the quality of the mesh, so grid independence is checked. In this problem 6 different mesh sizes (756636; 1251891; 1926205; 2515164; 4474689; 7606640) were tested to find the effect on the calculated Nusselt number at a distance of 500 mm from the exhaust stream inlet. There were no significant change in Nusselt number when the grid size is from 2515164 onwards, as shown in Fig. 3. Based on the above analysis, the grid size 2515164 was selected for the simulation models. The simulation is considered to be convergent when the remainder of the energy and mass equations is less than 10–4 and the other remainders are less than 10–6 .
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Fig. 3. Comparison of Nusselt number for different grid sizes.
3 Results and Discussion 3.1 Distribution of Temperature and Velocity in the Exhaust Heat Recovery Tank Figure 4 shows the distribution of exhaust gas and seawater temperature in EHR in 2 cases. The results show that, in both cases, the exhaust gas temperature and sea water
Fig. 4. Temperature distribution of exhaust gas and seawater in EHR with 2 cases when the engine is working at 100% load and 2200 rpm.
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temperature are relatively similar, the exhaust gas temperature tends to decrease gradually along the EHR length; Exhaust gas temperature between blades or between channels is always higher than other locations. Meanwhile, seawater temperature tends to increase gradually from inlet to outlet along the body of the EHR, details as shown in the cross-sections in Fig. 4. The distribution of flue gas and seawater flow velocities in the EHR for the two cases is shown in Fig. 5. However, it can be seen that the nature of the turbulent motion of the liquid depends on factors such as velocity, flow, number of blades, blade type. Regarding case 2 (EHR 2), with the advantage of channel structure, the velocity of sea water can be increased and can enhance the heat transfer capacity between pipe wall and sea water [15].
Fig. 5. Velocity distribution of exhaust gas and seawater in EHR with 2 cases when the engine is working at 100% load and 2200 rpm.
Fig. 6. The temperature reduction and heat recovery efficiency of the exhaust gas in 2 cases when the power generator is working at a speed of 2200 rpm.
3.2 Effect of Engine Load on Exhaust Heat Recovery The reduction in exhaust gas temperature TEx and exhaust heat recovery efficiency ηRe (recovered exhaust heat of EHR divided to the exhaust heat of ICE discharged into
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the environment) in the two cases are shown in Fig. 6. The results show that, in both cases, TEx and ηRe increase when the load of ICE increases. However, in the case of EHR 2, TEx and ηRe are higher than in the case of EHR 1. This may be because in the case of EHR 2 it is a plate-type structure, so seawater moves in the form of a thin film, which is the main factor leading to an improved heat transfer coefficient, thereby increasing the heat transfer of the exhaust gas to seawater compared to the case of EHR1. Specifically, the case of EHR 1 has TEx = 193.4 °C and ηRe = 33.81%; EHR 2 has TEx = 219.9 °C and ηRe = 38.38% when the ICE works at 100% load and speed of 2200 rpm.
Fig. 7. Temperature drops and heat recovery efficiency of EHR 2 as r varies. Table 1. Reduction of exhaust gas temperature TEx and efficiency ηRe TEx
ηRe
r = 2 mm
r = 3 mm
r = 4 mm
r = 2 mm
r = 3 mm
r = 4 mm
20
114.2
118.2
115.4
33.3
34.6
33.8
40
141.6
146.6
143.6
34.8
36.1
35.0
60
166.9
172.1
169.8
35.9
37.1
36.5
80
191.0
196.0
192.4
36.8
37.8
37.2
100
214.8
219.9
216.1
37.4
38.4
37.7
Load (%)
3.3 Effect of Tank Structure on Heat Recovery of Exhaust Gas Figure 7 and Table 1 show the reduction in exhaust gas temperature TEx and exhaust heat recovery efficiency ηRe in the case of EHR 2 plate-type structure when r varies from 2 to 4 mm. The results show that, when r increases, TEx and ηRe both increase; however, r = 3 mm will give the best results compared to the other 2 cases. Therefore, the research team will choose r = 3 for the next studies to evaluate the heat utilization of the exhaust gas in the ICE.
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4 Conclusion The article has succeeded in building a model of exhaust gas heat recovery tank as well as evaluating the influence of EHR structure on exhaust heat recovery. The simulation results show that with the EHR2 plate-type structure, the exhaust heat recovery is better. In addition, when we change the width of the channel (r), it will affect the heat recovery efficiency of the exhaust gas and in the case of r = 3 mm then ηRe reaches the maximum value of 38.38% when the engine is working at 2200 rpm and 100% load. The results of this study will be an important factor to calculate, design and build a system to utilize exhaust gas energy to generate useful work in the ICE.
References 1. Shu, G., Liu, L., Tian, H., Wei, H., Yu, G.: Parametric and working fluid analysis of a DualLoop Organic Rankine cycle (DORC) used in engine waste heat recovery. Appl. Energy. 113, 1188–1198 (2014) 2. Pha.m, M.T.: Theory of the Internal Combustion Engine. Publisher Science and technology, Viet Nam (2008) 3. Internal Combustion Engine Fundamentals: 2nd Edition, John B. Heywood. ISBN: 978-126-011611-3 4. Park, C., Kim, S., Kim, H., Moriyoshi, Y.: Stratified lean combustion characteristics of a spray-guided combustion system in a gasoline direct injection engine. Energy 41, 401–407 (2012) 5. Sudheesh, K., Mallikarjuna, J.M.: Diethyl ether as an ignition improver for biogas homogeneous charge compression ignition (HCCI) operation - an experimental investigation. Energy 35(9), 3614–3622 (2010) 6. Zhang, B., Sarathy, S.M.: Lifecycle optimized ethanol-gasoline blends for turbocharged engines. Appl. Energy 181, 38–53 (2016) 7. Mavropoulos, G.C.: Possibilities to Achieve Future Emission Limits for HD DI Diesel Engines Using Internal Measures, SAE Tech. Pap. (2005) 8. Fontana, G., Galloni, E.: Variable valve timing for fuel economy improvement in a small spark-ignition engine. Appl. Energy 86, 96–105 (2009) 9. Tong, L., et al.: Dynamic simulation of an ICE-ORC combined system under various working conditions. IFAC - PapersOnLine 51(31), 29–34 (2018) 10. Yang, H., et al.: Optimization of thermoelectric generator (TEG) integrated with three-way catalytic converter (TWC) for harvesting engine’s exhaust waste heat. Appl. Therm. Eng. 144, 628–638 (2018) 11. Hoang, A.T.: Research on improving some properties of pure vegetable oils used as fuel for diesel engines, Thesis of Doctor of Engineering, Ha Noi University of Science and Technology (2015) 12. Hatami, M., Ganji, D., Gorji-Bandpy, M.: Experimental and numerical analysis of the optimized finned-tube heat exchanger for OM314 diesel exhaust exergy recovery. Energy Convers. Manage. 97, 26–41 (2015) 13. Quang, K.V., et al.: Study of effect of heat exchange tube structure on waste heat recovery capacity of internal combustion engine in the fresh water distillation system, J. Sci. Technol., 145 (2020) 14. Quang, K.V., et al.: Developing a waste heat recovery tube used in the seawater distillation system. Appl. Therm. Eng. 195, 117229 (2021) 15. Abeykoon, C., et al.: Compact heat exchangers – design and optimization with CFD. Int. J. Heat Mass Transf. 146, 118766 (2020)
A-star Algorithm for Robot Path Planning Based on Digital Twin Doan Thanh Xuan(B) , Le Giang Nam, Dang Thai Viet, and Vu Toan Thang Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Robotic applications have become more and more popular with the development of the automation industry. The research on robotics is now constantly increasing due to a great demand in the market, a broad range of applications and potential technical development. In particular, the robot path planning has numerously been studied and applied using many algorithm generations. Path Planning optimization will help reduce the time and cost of robot operating procedures. Recently, there has been an emerging technology named Digital Twins that are virtual replicas of physical devices used for running simulations prior to the building and deployment of actual devices. This study presents a combination of the well-known A-star algorithm and digital twin technology for robot path planning. This combination has not been reported previously in the literature. In our study, the advantages of A-star algorithm and digital twin technology are applied to suitable robotic movement stages to propose an effective plan for the whole operating procedure. Keywords: Path planning · Robot · Digital twin · A-star algorithm · Simulation
1 Introduction The measurement and observation of various phenomena are made possible through Industrial Internet and Internet of Things (IoT). System modeling capabilities such as Digital Twins (DT) are then developed by using data available from such phenomena. The emergence of the concept of DT has aimed at helping software developers and users on digital platforms access the physical things and related data sources [1]. The following terms (i.e. digital counterpart, virtual twin, virtual object, product agent, and avatar) could be used for similar or partially overlapping concepts [2]. It is debated that DT is composed of three parts: “physical product, digital product and the linkage between the physical and digital products” [3]. Being often applied in many fields of computer science, A-star is a graph-traversal and path-search algorithm thanks to its completeness, optimality, and optimal efficiency [4]. In this study, the combination of digital twins created by using Tecnomatix software and A-star algorithm implementation in Python was proposed for robot path-planning in a bulb assembly system. This combination takes advantage of both the digital twin technology and A-star algorithm for finding the way in the different stages of the robotic movement i.e. without large-size obstacles and with large-size obstacles. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 83–90, 2022. https://doi.org/10.1007/978-981-19-1968-8_8
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2 A Brief Overview of Digital Twin The comprehension of changing a physical object or system (e.g. a product, a machine or a process) existed in a real form into a virtual version, is done in the first phase. Various approaches can be applied for creating the virtual model e.g. developing CAD models on the consideration that they can characterize the physical object to support executing simulation and data analysis. The comprehension of establishing a communication between the physical part and the Digital Twins and vice versa, is done in the second phase. It allows a collection of the real-time data for feeding the virtual model. With reference to this, the usual usage and cluster of several industrial communication protocols are performed in agreement with the industrial networks categories, Fieldbus networks (e.g., PROFIBUS and Modbus), Ethernet-based networks and Wireless networks (e.g., wifi, Bluetooth and Zigbee) [5]. Digital twins (DT) can be applied to different industries and in different stages of product or system development (planning, implementation, operation, retirement). Depending on the scope of use, DT can be applied to some extent to optimize work performance. Specifically, in this study DTs were used to plan a path for the robot in a small light bulb assembly system.
3 A-star Algorithm One of the search approaches is A-star. It generates a solution after obtaining an input and calculating several possible tracks. A-star is defined as a computer science algorithm considerably employed in path planning and graph traversal (i.e. a reliable traversable path plotted between positions). Based on a best-first search, A-star can create a least-cost path from a given start sector to the desired one. As crossing the map, A-star tracks the lowest known heuristic cost while maintaining an arranged priority queue of consecutive track sectors along the path. It is highly accurate due to taking into account the sectors already calculated. It is considered to be a best-first search algorithm because it is able to find the least-cost track to the sector. The following equation describes the basic principle of the method: f(x) = g(x) + h(x). Where g(x) is the total distance from the initial sector to the current sector “X”; h(x) is used to calculate the distance from the current position “X” to the desired location. By using this method, the robot can find another path once it comes to a dead end as well as bypass paths leading to a dead end when creating two lists CLOSED and OPEN. These lists are the basic characteristics for setting up the A-star algorithm; “closed list” is for writing and saving sectors tested, whereas “open list” is for recording adjacent sectors to those already calculated, calculating the distances moved from the “initial sector” with distances to the “target sector”, and also saving the parent sector of each sector (used at the last step of the algorithm for planning the track from the target to the Start location); the optimal path is thus found. The flow chart of the A-star algorithm is displayed below [6] (Fig. 1):
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Fig. 1. Flow chart of A-star algorithm
4 Experiment 4.1 The Physical Space The proposed method was tested in a scenario in a high-skill preparation of a lamp assembly system. The considered setup (Fig. 2) was created by a Universal Robots’ UR3 mounted on a stand and placed on a table. UR-3 has 6 degrees of freedom, a payload capacity of 3 kg and a reach of 500 mm. After receiving a sub-assembly from the previous station, the station performs mounting of additional parts and transfering the sub-assembly to the next station.
Fig. 2. A real image of the entire assembly system
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The UR3 robot picks up a socket and puts it in the punch hole, then picks up the bulb and puts it in the punch hole. Light bulbs are brought from the conveyor belt, sockets are placed in the pallet. After the stamping cylinder is lowered for creation of a stamping force to stick the socket to the bulb, the UR3 robot picks up the finished product and puts it on the conveyor belt to bring it to the warehouse. Figure 2 is a real image of the entire assembly system. The sequence of steps is shown in Fig. 3. Task 1:
Task 2:
Task 4:
Task 5:
Task 6:
Task 7
Task 3:
Task 1
Pick the socket
Task 2
Place the socket
Task 3
Pick the bulb
Task 4
Place the bulb
Task 5
Connect bulb and socket
Task 6
Pick the finished product Place the finished product
Task 7
Robot
Cylinder
Robot
Fig. 3. A sequence of tasks in the bulb assembly system
4.2 The Digital Space The simulation model (digital space in the DT framework) is a dynamic environment constructed by importing 3-dimensional computer aided design (CAD) objects into Tecnomatix Process Simulate software. Tao [7] suggested that “the virtual model is composed of four layers i.e. geometry (creation of 3D CAD objects), physics (placement of CAD objects in the scene), behavior (kinematics of robot), and rule (assembly process sequence)”. The 3D kinetic CAD objects of the robot are available at online-library maintained by Siemens Support Center. The creation of 3D objects is applied to all other components and parts at the workstation, CAD data can be imported into Tecnomatix environment in JT (Jupiter Tessellation) format. When the system works, 7 tasks are completed; the Robot performs 6 tasks (tasks 1–4 and 6–7) and the stamping cylinder performs 1 task (task 5). The tasks are depicted in Fig. 3, with the robot missions (and trajectories) defined by their start/end points.
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The robot’s task execution time is determined by the total time to perform all the tasks. We can see that the robot’s tasks can be divided into two stages: phase 1, a path without large obstacles, and phase 2, a path with a large obstacle. The stage is for tasks 1 to 4, and the stage 2 is for tasks 6 and 7, the obstacle is the stamping cylinder. The next section will present implementation methods to reduce assembly cycle time.
5 Results and Discussion 5.1 Digital Twin Application for the Robot’s Path-Planning in the Large-Sized Obstacle-Free Phase In the initial phase without large obstacles, the ability of the Tecnomatix software to detect collision is to act as a digital model in the digital twin used to find the point with a minimum travel distance without any collision occurred. Thus, it helps to optimally reduce a travel distance in the movement trajectory of the Real Robot in the digital twin. Conflict detection image is displayed in Fig. 4
Fig. 4. Conflict detection in Tecnomatix
The coordinate parameters for robotic movement before applying the path-planning method is shown in Table 1(a) as the distance from the pick point is raised to a value of 100 mm (currently applied in the system). The coordinate parameters for robotic movement when applying collision detection in Tecnomatix software shown in Table 1(b). With a robotic speed of 350 mm/s, the stage 1’s time was reduced from 10.67 s to 9.62 s corresponding to a time reduction of 9.8% in a single bulb assembly.
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Table 1. Coordinate parameters and durations for robotic movement in the large-sized obstaclefree phase
5.2 Application of A-star Algorithm to Find the Robotic Path in the Phase with a Large-Sized Obstacle In the plane, the robot moves from the position of picking up the finished product and placing it on the conveyor for storage, we have an image of an obstacle and the start and stop points of the robot and Programming software using Python language generates the path results after applying the A-star algorithm as follows (Figs. 5 and 6):
Fig. 5. Graphical display of the robotic path in Python programming language
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Obstacle
A (98; -353; -3) B (150; -257; 106) C (150; -257; 230)
D (159; -240; 230) E (241; -87; 58) F (308; 38; 58)
Fig. 6. Robotic path found by A-star algorithm implemented in Python with coordinates of moving points
Robotic movement times before and after applying algorithm A-star correspond to Table 2(a) and (b), respectively. Table 2. Robotic movement times applied A-star algorithm X
Y
Z
98
-353
-3
98 308 308
Duration 5.39 1.14
-353 327 38 327 38 58 Total time: 5.39 s
1.29 1.62 1.34
(a) X
Y
Z
Duration 4.94
98 150
-353 -257
-3 106
1.14 0.79
150 159 241 308
-257 -240 -87 38 Total time: 4.94 s
230 230 58 58
0.7 0.28 1.05 0.98
(b)
Robotic movement time has been reduced from 5.39 to 4.94 s (equivalent to 8.3%).
6 Conclusion The purpose of this study is to provide a method of planning the path of a robot with a combination of digital twin technology using Tecnomatix software and A-star algorithm programmed in Python language. In previous studies, these two path-planning techniques
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were applied separately. At a constant speed of 350 mm/s, the combination of these two techniques will solve the problem i.e. applying the path-planning algorithm alone throughout robotic operation will unnecessarily complicate the large sized obstacle-free phase and applying the better path-planning technique in Tecnomatix software will be difficult to apply to the phase with large-sized obstacles. The study is based on the idea of combining the advantages of digital twin technology and an AI path-planning algorithm as the A-star algorithm. With this hybrid application, the path-planning could be divided into different stages to apply different suitable methods. It makes, the path-planning problem become simpler but still effective. According to our preliminary data, however, the A-star technique gave a robotic winding part with not totally optimized parameters. Improving A-star algorithms to provide a more optimal path trajectory for the Robot is therefore worth further investigation in the future. Acknowledgments. This study was conducted at the Technical Center of Smart Digital factory, School of Mechanical Engineering, Hanoi University of Science and Technology.
References 1. Laaki, H., Miche, Y., Tammi, K.: Prototyping a digital twin for real time remote control over mobile networks: application of remote surgery. IEEE Access. 7, 20325–20336 (2019) 2. Autiosalo, J.: Platform for industrial Internet and digital twin focused education, research, and innovation: Ilmatar the overhead crane. In: Proceedings IEEE 4th World Forum Internet Things (WF-IoT), pp. 241–244 (2018) 3. Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and U.S. air force vehicles. In: Proceedings 53rd AIAA/ASME/ASCE/AHS/ASC Structures Structural Dynamics Matererials Conference, Honolulu, HI, USA (2012). https://doi.org/10.2514/6.2012-1818 4. Russell, S., Norvig, P.: Artificial Intelligence a Modern Approach, 4th edn. Pearson, Boston (2018) ISBN 978–0134610993. OCLC 1021874142 5. Lu, Y., Liu, C., Wang, K.I.-K., Huang, H., Xu, X.: Digital twin driven smart manufacturing: connotation, reference model, applications and research issues. Robot. Comput. Integr. Manuf. 61, 101837 (2020) 6. Larsen, L., Kim, J., Kupke, M., et al.: Automatic path planning of industrial robots comparing sampling-based and computational intelligence methods. Procedia Manuf. 11, 241–248 (2017) 7. Tao, F., Zhang, M.: Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. IEEE Access 5, 20418–20427 (2017)
Development of a CFD Tool Based on SnappyHexMesh/OpenFOAM for the Axial Fan Performance Van Long Le1 , Lu Tien Truong1 , and Khanh Hieu Ngo1,2(B) 1 Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh, Vietnam
[email protected] 2 VNU-HCM Key Laboratory of Internal Combustion Engine, Ho Chi Minh, Vietnam
Abstract. Nowadays, axial fans are widely used in industry to solve the problem of ventilation and cooling. This type of fan converts electrical energy into kinetic energy of the airflow to disperse heat radiation. However, at present, the manufacturing process of these devices in Vietnam is still simple and has not achieved high performance. In this paper, the authors concentrate to build an automatic meshing tool, named BKIAF, for the performance simulation of the axial fans in normal air conditions. This automatic meshing tool is based on snappyHexMesh and blockMesh which are tools of the open source software OpenFOAM. Additionally, the Multiple Rotating Frame (MRF) method is applied in combination with the SIMPLE algorithm and Reynolds Averaged Navier-Stoke (RANS) turbulence method. On that basis, the use of BKIAF to improve the performance of a composite axial fan size 48 in. of Huynh Thao Fans Ltd., indicated that the fan’s efficiency could be increased to at least 20% by modifying the shape of fan inlet. In comparison with other CFD tools, the BKIAF tool, based on snappyHexMesh/OpenFOAM, would offer significant advantages to a design and analysis process for the axial ventilating system. Keywords: blockMesh · Reynolds Averaged Navier-Stokes · snappyHexMesh
1 Introduction Vietnam has a territory located entirely in the tropics, so in daily life as well as in industrial production activities, temperature is one of the priority issues that need to be solved to ensure a good quality of work and quality of life for people. Excessively high temperatures affect human health, making equipment and machinery reduce operating efficiency and tool life. Currently, the most popular and widely applied solution in our country is to use axial fans to generate cooling airflow and air exchange. Fan applications include local ventilation, cooling towers for air and wind machines, humidifiers in textile mills, ventilation, and exhaust in the mining industry, and dynamic cooling electromechanical and generator. However, the design and manufacture of fans in Vietnam currently still have many limitations, leading to the low efficiency of the fan, as well as the loss of electrical energy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 91–103, 2022. https://doi.org/10.1007/978-981-19-1968-8_9
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Nowadays, there are many research papers/projects on axial fans in many different aspects, to optimize the performance of this type as well as reduce energy costs. In the study [1], D.C. Panigrahi and D.P. Mishra conducted a study on the blade design of the general type of ventilation under the mines, by simulating 6 types of blade profiles commonly used in air conditioning turbulent flow condition with the number Re = 3 × 106 with the variation of the angle of attack to find the angle of attack at which the fan has the highest aerodynamic efficiency. Panigrahi used Ansys Fluent tool and Gambit software to create simulation mesh. In his master’s thesis at Stellenbosch University [2], O.P.H. Augustyn used Ansys Fluent tool with multi-reference region method, built numerical simulation cases to calculate and predict performance characteristics by the mass flow rate of several a different type of fans. The obtained results show the high reliability of the CFD model. Similarly, H. Kumawat [3] used Ansys CFX to study the behavior of the fluid passing through the axial fan, thereby surveying the model with different number of blades, and giving the numerical design propeller suitable for the environment. However, the application of the multi-reference region method has not been found in this publication. In the study [4], D. Dwivedi et al. conducted an experiment of the axial fan model with the propeller slanting forward and backward, in order to compare aerodynamic parameters such as static pressure, flow, coefficient of fluid movement between the two models to find the most effective model, and at the same time compare with the experimental model to check the actual errors. In two previous research projects of these authors [5, 6], Khanh Hieu Ngo and his partners in VNU-HCM Key Laboratory of Internal Combustion Engine have evaluated the simulation with LBV150 ROV thruster and axial flow pump model, respectively. The authors concentrate to build the CFD numerical model which use the Multiple Rotating Frame (MRF) method combined with the SIMPLE algorithm in OpenFOAM - the open source software. This has proved the suitability of the selection algorithm and the MRF method that it is completely suitable for the rotating de-vices with 1-phase condition by reliable estimates between simulation and experiment. In the International Conference of Fluid Machinery and Automation System 2018 [7], Ngo Khanh Hieu and his colleagues has continued to introduce the BKASM which is an automatic meshing software for propeller simulation in open-water condition. At the National Technical Mechanics Conference in 2019 [8], these authors from the Key Laboratory of Internal Combustion Engines at Ho Chi Minh University of Technology pointed out the evaluation of the characteristic for axial - flow household fan using numerical simulation combined with experimental method. Also in this paper, the authors proved the accuracy of the axial fan simulation using the snappyHexMesh and blockMesh tools of OpenFOAM software to generate the suitable mesh to apply with k–w SST turbulence model, MRF method and the SIMPLE algorithm.
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In this paper, the authors concentrate to introduce the BKIAF tool which is developed based on snappyHexMesh and blockMesh by the authors to provide a solution for industrial ducted fan simulation. The Computational Fluid Dynamics (CFD) technique is used to simulate the performance of an industrial ducted axial fans of Huynh Thao Fan Co., Ltd., with the aim to enhance this fan performance as a study case in order to verify the accuracy of this software. Firstly, a CFD model of the original ducted axial fan is validated by its experimental data. Then, the improved design of this ducted axial fan is processed by the CFD model. Finally, the performance testing of the im-proved ducted axial fan is taken aiming to evaluate reliability of CFD technique to enhanced industrial axial fans performance. The performance testing of the Huynh Thao ducted axial fan is set up in the VNU-HCM Key Lab. for Internal Combustion Engine in the context of a research, funded by VNU-HCM under grant number C2020-20-43.
2 BKIAF – the CFD Techniques for Ducted Axial Fans Simulation In the BKIAF, the authors combine all the six steps to generate the mesh and simulate the axial ducted fan. This tool has adjusted about 70 parameters to fit the axial ducted fan meshing and provided the standard solver in OpenFOAM software. BKIAF is suitable for this problem in static condition, that means in a laboratory environment and an inlet velocity is equal to zero approximately. Users only import the geometry file; this tool will automatically perform the following steps: • • • •
Set up the compute domain. Determine the fineness of each domain and sub-partition fan surface. Customize the boundary layer. Some dynamic parameters of the fluid have been established to suit the static condition. However, users can recalibrate to better suit specific conditions. • The boundary conditions for this problem have also determined. • The turbulence model and OpenFOAM solver are also recommended in this tool. 2.1 Geometry of Ducted Fan To achieve reliable results, the digital geometry must be as close to the real model as possible. For this purpose, a composite fan of size 48 in. of Huynh Thao Fan Co., Ltd. is used as a case study. Figure 1 shows the 3D model of Huynh Thao composite fan of size 48 in. (the dimensions of this fan are listed in Table 1). In this article, the authors use this ducted fan, which is widely used in the factories in Vietnam, to verify the results obtained from BKIAF tool.
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Fig. 1. 3D model of the duct (top image), the fan (left center image), the Huynh Thao composite fan of size 48 in. (right center image) and the rounded inlet fan (bottom image - enhanced performance geometry) Table 1. Dimensions of the Huynh Thao composite fan size 48 in. Details
Dimensions (mm)
Input length
1472
Input width
1431
Output diameter
1500
Diameter of position close to impeller outlet
1440
Fan diameter
1210
2.2 Automatic Meshing with BKIAF For the accuracy of simulation results, the computational domain must be large enough, However, it is necessary to partition the volume mesh to minimize the number of mesh cells. This paper as well as the BKIAF tool proposes a computational domain with dimensions presented in Table 2, Table 3, and Fig. 2.
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Fig. 2. Domain size for CFD simulation of ducted axial fans performance
Table 2. Size of blocks in the computational domain (where D is the fan diameter) Size of blocks
Cell size [mm]
9D diameter, 12D length (Block 1)
(150 150 150)
4D diameter, 5D length (Block 2)
(37.5 37.5 37.5)
2D diameter, 3D length (Block 3)
(18.75 18.75 18.75)
1.02D diameter, 0.33D length (Block (4)
(18.75 18.75 18.75)
The difficulty in our case is the gap between the duct and the fan is about 1.15 cm, which is smaller than the diameter of the fan. Therefore, the adjustment to create boundary layers and the number of boundary layers greatly affects the results of the problem. This paper selects two boundary layers the fan and the duct. The average boundary layer thickness of the fan is 0.3 mm. Similarly, the average boundary layer thickness corresponds to a duct is also 0.3 mm. In addition to having four boundary layers on fan tip surface and duct surface at the tip of fan, the minimum clearance of fan and duct is always guaranteed to have at least 4 grid cells (4 layers) (Fig. 3). Although the number of boundary layer are only two for both fan and duct, it is suitable for the clearance between them which is 1.15 cm in thickness. For the mesh elements close to the final boundary layers, they are from 1.5 to 2 times the thickness of the outermost boundary layer depending on the location. Therefore, the streamline near the wall boundary is relatively properly calculated in this case.
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Fig. 3. Mesh distribution on the fan surface (top image), duct surface (left image) and the tip clearance between the duct and fan (right image)
Table 3. Dimensions of the surface of the calculated domain, fan, and duct (where D is the fan diameter) Surfaces
Surface element size [D]
Surface of the outermost layer of the computational domain (Block Min: (0.125, 0.125) 1) Max: (0.125, 0.125) Rotation domain surface (Block 4)
Min: (0.0078, 0.0078) Max: (0.0625, 0.0625)
Fan surface
Min: (0.002, 0.002) Max: (0.0078, 0.0078)
Duct surface
Min: (0.002, 0.002) Max: (0.0078, 0.0078)
This paper applies the blockMesh meshing tool to create background mesh elements. Subsequently, snappyHexMesh specifies the surfaces and volume areas to be finely meshed, and manages the construction of the boundary layer on the geometric surfaces. The mesh quality of our simulation problem is listed in Table 4. Note that, the k–ω SST is used in combination with wall function which is pre-developed with OpenFOAM software. Therefore, y+ is suggested in this case to be greater than 30.
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Table 4. Mesh quality using snappyHexMesh and blockMesh tools Criteria
Value
Number of grid elements
17 788 914 cells
Max-skewness
3.9862
Max AR
21.4978
Max non-orthogonality
66.5164
Average non-orthogonality
7.0022
Number of impeller and duct boundary layers
2
Fan first boundary layer size
0.3 mm
Fan last boundary layer size
0.3 mm
Maximum boundary layer development
1.2
Average y+ (calculated after running simulation)
32.16
2.3 Turbulence Model Three basic approaches can be utilized to calculate a turbulent flow: Direct Numerical Simulation (DNS), Large Eddy Simulation (LES) and Reynolds Averaged Navier-Stokes Simulation (RANS). Compared to other turbulent models, the RANS modeling method is among the most well–developed and popularly used due to its suitability providing acceptable accuracy and consuming less computing resources [9]. Hence, RANS is the obvious choice to model the turbulent flow surrounding the object in this research. There are some different types of turbulence model in the RANS methodology, some of them are well – known to industrial applications such as the one–equation Spalart–Allmaras or the two–equation k–ε and k–ω and their variations, and the other less well–known models. Based on the features of each model, the authors decide to examine the k–ω SST, which also is the most suitable for turbomachinery applications. The first variable is turbulence kinetic energy k. It determines the turbulent energy of the fluid flow. This variable is shown by the following equation [10]: ∂ui ρk ∂k ∂ ∂ ∂ μ + σk ρkuj = τij − β ∗ ρωk + (ρk) + ∂t ∂x j ∂x j ∂x j ω ∂xj The second variable is specific turbulence dissipation ω. This is the rate at which turbulence kinetic energy is converted into thermal internal energy per unit volume and time. It is shown by the following equation [10]: σd ∂k ∂ω ∂ ρk ∂ω ∂ ω ∂ui ∂ μ + σω +ρ (ρω) + (ρuj ω) = α τij − βρω2 + ∂t ∂xj k ∂xj ∂xj ω ∂xj ω ∂xj ∂xj α=
5 9 1 1 3 ,β = , β∗ = , σk = , σω = 9 40 100 2 2
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2.4 SimpleFoam Solver When discretizing the governing equations for incompressible flow over a control volume, the equations will be computed without respect to the pressure in the node. The equation is solved but the pressure is highly oscillating. This problem is called the checker-board pressure field. Because there is no equation for the pressure, the mass conservative equation can be modified to give an indirect equation for the pressure. Discretizing the continuity equation creates the pressure correction equation and it contains the old velocity term and one correction term plus a term for the continuity error. Different algorithms exist to solve the modified equations such as the Semi-Implicit Method for Pressure-Linked Equation (SIMPLE), the Pressure Implicit with Split Operator (PISO), the SIMPLE Consistent (SIMPLEC) and PIMPLE (merged PISOSIMPLE) algorithm [11]. Solve the momentum equation for the velocity field. This velocity field does not satisfy the continuity equation. MU = −∇p where M is a matrix of coefficients that are calculated by discretizing the velocity terms in the incompressible Navier–Stokes equation. These coefficients are all known. Solve the Poisson equation for the pressure field. ∇ • A−1 ∇p = ∇ • A−1 H We also use the pressure field to correct the velocity field so that it satisfies the continuity equation. U = A−1 H − A−1 ∇p The velocity field now does not satisfy the momentum equations. They repeat the cycle. The turbulence scalar (k, ε, ω) and species transport equations are solved within the loop, after the volume flux corrector: Mk k = Sk and Mω ω = Sω . 2.5 Boundary Conditions Boundary conditions for the quantities to be calculated in the fan simulation problem have been standardized by the research team to be similar to the actual conditions listed in the table below. Furthermore, with the application of Multiple Reference Frame (MRF) model, the specified angular velocity of rotation domain is uniform throughout the simulation (Table 5).
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Table 5. The boundary conditions for the axial ducted fan simulation [8]. (f = fixedValue, i = inletOutlet, s = slip, z = zeroGradient, c = calculated, k = kqRWall-Function, o = omegaWallFunction Domain
U
p
Inlet
f
z
Outlet
i
f
outerCylinder
s
s
Fan and ducted
f
z
k
ω
c
f
f
c
i
i
c
s
s
c
k
o
Nut
3 Results of the Case Study In this paper, the authors use the 10 cores CPU and 64 GB RAM computer. Each case takes 20 h to run simulation approximately. To evaluate the performance improvement, the numerical simulation CFD results for both original geometry and improved geometry are presented in this. The results will be verified unequivocally with the experiment before providing a reliable solution to the manufacturer. The problem is to simulate the axial fan operating in the laboratory atmosphere with the following characteristic parameters: • • • • •
Air density at laboratory temperature (30 °C degrees): 1.16 kg/m3 . Kinematic viscosity of air at laboratory temperature: 1.516 m/s2 . The inlet velocity of the air stream is 0 m/s. Rotation speed is 600 rpm. Turbulence intensity 5%. The characteristic parameters of fan are calculated by the following formulas:
• • • •
Required power: Preq = 2 × π × n × τ. Useful power: Puseful = (pi × Qi ). Fan efficiency: ηblade = Puseful /Preq. Electric motor efficiency: ηelectric_mechanic = Preq /Pelectric.
where τ is a torque which is determined by numerical simulation, n is the number of revolutions in 1 s. pi = ρ × Vi 2 /2 is kinetic pressure of the fluid flow and Qi is the volume rate at ith annular surface position (see Fig. 4 and Fig. 5).
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3.1 Grid Independent Evaluation Table 7 compare the differences between the proposed mesh (Table 3) and the finer mesh (Table 6). Based on that, the authors evaluate the dependency of mesh and demonstrate that it is appropriate to use the mesh configuration as shown in the Table 3. This mesh quality ensures accuracy of the results and saves computational resources. Table 6. Dimensions of the surface of the finer calculated domain, fan, and duct (where D is the fan diameter) Surfaces
Surface element size [D]
Surface of the outermost layer of the computational domain (Block Min: (0.0625, 0.0625) 1) Max: (0.0625, 0.0625) Rotation domain surface (Block 4)
Min: (0.005, 0.005) Max: (0.0312, 0.0312)
Fan surface
Min: (0.002, 0.002) Max: (0.005, 0.005)
Duct surface
Min: (0.002, 0.002) Max: (0.005, 0.005)
The size of duct and fan surface (finer mesh) are similar to the grid size introduced in the Table 3 in order to ensure the average y+ is equal to 30 approximately. While the outer computational domains have the grid element size reduced by nearly half the size. Table 7. The comparison between proposed mesh and finer mesh of the original design Parameter
Proposed mesh
Finer mesh
Error (%)
Mass flow (m3 /h)
36900
37251
0.95
Torque (M/m)
10.09
10.17
0.79
Required Power (W)
633.65
641.25
0.79
Useful power (W)
415.49
427.46
2.88
Average speed at the inlet position (m/s)
6.30
6.36
0.95
Efficiency (%)
65.57
66.66
1.09
Overall, the quality of finer mesh is still guaranteed to satisfy the mesh quality evaluation criteria of proposed mesh as in Table 4. The average y+ and the number of boundary layers of duct/fan are 27.7 and 2, respectively. The number of cells is 47 521 332. However, the difference in the results of two meshes are lower than 3%. Therefore, in the next section of this paper, the proposed mesh as shown on the Table 3 is selected for our ducted axial fan simulation. It consumes less computational resources but still ensures the accuracy of the results.
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3.2 Simulation Results of the Original Huynh Thao Composite Fan Table 8 describes the simulation results and experimental results of the original Huynh Thao composite fan of size 48 in. Figure 4 shows the velocity distribution on the inlet surface of the experiment and numerical simulation (CFD), where the difference in flow rate is about 3%, useful power is 9%, and fan efficiency is 7.9%.
Fig. 4. Velocity distribution at inlet surface of the original Huynh Thao composite fan
Table 8. The comparison between simulation and experiment of the original design. Parameter
CFD result
Exp result
Error (%)
Mass flow (m3 /h)
36900
38052
3.06
Torque (M/m)
10.09
10.21
1.21
Required Power (W)
633.65
641.25
1.21
Useful power (W)
415.49
455.53
9.03
Average speed at the inlet position (m/s)
6.30
3.49
3.06
Efficiency (%)
65.57
71.04
7.70
3.3 Simulation Results of the Enhanced Huynh Thao Composite Fan Table 9 compares the simulation results of original ducted fan design and the simulation results of the improved ducted fan design (rounded inlet). In general, with this new design, mass flow is improved 11.90%. Besides, the fan efficiency is also increased from 65.57% to 86.18% (see Fig. 5). As we can see in the Fig. 6, at the corners of the original design inlet, it occurs vortex phenomena. These vortices convert kinetic energy into pressure causing energy loss. These problems at the rounded inlet are better solved as well as the streamline at the rounded duct inlet is straighter. Therefore, by changing the shape of a duct from square to round, the fan efficiency could be considerably enhanced with the same power consumption.
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Fig. 5. Velocity distribution at inlet surface of the enhanced Huynh Thao composite fan.
Fig. 6. Pressure distribution in the original design (left) and the improved design (right)
Table 9. The comparison between simulation of original design and the rounded inlet design Parameter
CFD rounded inlet CFD original Difference (%)
Mass flow (m3 /h)
41292
36900
11.90
Torque (M/m)
10.09
10.09
0.00
Required Power (W)
633.65
633.65
0.00
Useful power (W)
546.07
415.49
31.42
Average speed at the inlet position(m/s) 6.75
6.30
11.90
Efficiency (%)
65.57
31.42
86.18
4 Conclusion In this research, the BKIAF tool is used to generate the mesh and simulate the performance of an industrial axial fan of Huynh Thao Fan Co., Ltd., with the aim to enhance its performance in constraints of cost-effectiveness and in-house technology. This tool acts as a powerful support tool for manufacturers and businesses to predict fan characteristics before setting up a test model. In industry, this tool supports the analysis of aerodynamic characteristics as well as virtualize the distribution the velocity in space. Because of this, the manufacturer not only evaluates the features of the fan, but also helps to consider the installation of fans in factories effectively. In this article, the authors use the ducted fan
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developed by Huynh Thao Co., Ltd. as a study case to prove the reliability of BKIAF tool as well as improving the manufacturer’s design to improve performance. The enhanced design of the Huynh Thao axial fan rounded inlet could increase the fan efficiency up to at least 20% with the power consumption almost unchanged. In comparison with other CFD tools, the BKIAF tool, based on snappyHexMesh/OpenFOAM, would offer significant advantages to a design and analysis process for the axial ventilating system. Acknowledgments. We sincerely thank the Engineering Mechanics Lab at HCMUT for the great support on of the facility and tools to our research. This research is funded by VNU-HCM under grant number C2020-20-43. We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for this study.
References 1. Panigrahi, D.C., Mishra, D.P.: CFD simulations for the selection of an appropriate blade profile for improving energy efficiency in axial flow mine ventilation fans. J. Sustain. Min. 13(1), 15–21 (2014) 2. Augustyn, O.P.H.: Experimental and Numerical Analysis of Axial Flow Fans. M.Sc Thesis, University of Stellenbosch (2013) 3. Kumawat, H.: Modeling and Simulation of Axial Fan Using CFD. Int. J. Aerospace Mech. Eng. 8(11), (2014) 4. Dwivedi, D., Dandotiya, D.S.: CFD analysis of axial flow fans with skewed blades. Int. J. Emerging Technol. Adv. Eng. 3(10), 741–752 (2013) 5. Hieu, N.K., Thien, P.Q., Nghia, N.H.: Numerical analysis of lbv150 ROV thruster performance under open water test condition. In: Duy, V.H., Dao, T.T., Zelinka, I., Kim, S.B., Phuong, T.T. (eds.) AETA 2017 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application, pp. 1037–1046. Springer International Publishing, Cham (2018). https://doi. org/10.1007/978-3-319-69814-4_99 6. Hieu, N.K., Long, L.V., Huy, L.D.A.: Numerical simulation of an industrial centrifugal fan 5.5 kW with OpenFOAM. Sci. Technol. Develop. J. Eng. Technol. VNU-HCM Press 3(4), 508–522 (2020) 7. Long, L.V., Luan, M.N., Hieu, N.K.: Propeller simulation in open-water condition with snappyHexMesh/OpenFOAM mesh generator. In: International Conference of Fluid Machinery and Automation Systems, pp. 51–56 (2018) 8. Luan, M.N., Long, L.V., Hieu, N.K.: Evaluate the characteristic of axial fan by numer-ical simulation combined with the experiment method. In: National Technical Mechanics Conference (2020) 9. ANSYS: Introduction to ANSYS FLUENT, Lecture 6: Turbulence Modeling (2010) 10. Wilcox, D.C.: Formulation of the k–ω turbulence model revisited. AIAA J. 46(11), 2823–2838 (2008) 11. Klasson, O.K.: A Validation, Comparison and Automation of Different Computational Tools for Propeller Open Water Predictions, Göteborg, Sweden (2011)
Effect of Clamping Distances on Residual Stress in Butt Welded Joint of Stainless Steel Plates Nguyen Tien Duong(B) Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Stainless steel has a high coefficient of thermal expansion. So, it is easy to create large deformation after welding, especially for welding of thin plates. With many welded joints due to being constrained by surrounding parts, they cannot deform freely, so there is a high residual stress in the welded structure after welding. In this paper, the butt welded joint of AISI 316L stainless steel plates was studied. This study focuses on determining the residual stress of a butt welded joint of two stainless steel plates with a thickness of 5 mm, which is performed by automatic metal inert gas (MIG) welding. This paper uses the finite element method based on SYSWELD software to simulate, calculate and determine the residual stress of the butt welded joints that are clamped at the outer edge of two plates in the joint. The study goes in depth to compare and evaluate the influence of clamping distance on residual stress components in butt welded joints. The obtained results have shown that the clamping distance greatly affects the residual stress, especially the transverse stress component. When the clamping distance is too short, there is a sudden change in residual stress. Therefore, it is necessary to maintain a minimum clamping distance to ensure that the residual stress is not too high. Keywords: Butt joint · Clamping distance · Finite element · Residual stress · Stainless steel
1 Introduction Stainless steel is used in many fields because it has high cold resistance, heat resistance and corrosion resistance. Stainless steel is a poor conductor of heat but has a high coefficient of thermal expansion. Therefore, it is easy to create large deformations in welding of stainless steel, especially for welding of thin plates. If clamping is used to reduce deformation after welding, a high residual stress will be generated. Therefore, the study of residual stress after welding stainless steel is of special importance. There are many welding processes that commonly used to weld stainless steel. However, to ensure uniform quality and high productivity, the automatic MIG welding process is the most suitable. MIG welding is an arc welding process that uses a continuous solid wire electrode heated and fed into the weld pool from a welding gun. The arc and weld pool are protected by inert gas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 104–117, 2022. https://doi.org/10.1007/978-981-19-1968-8_10
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Stainless steel butt welded joints are used in many welded structures such as pressure vessels, tanks, containers,… These structures have small thickness, so they should be considered as the welding problem of thin plates. In practice, the welded joints are constrained by different surrounding parts, the welded joints are not deformed freely. They are considered to be clamped during welding. Therefore, the residual stress after welding is very high. The residual stress can be calculated by the analytical method [1]. The analytical method has the advantage of fast computation. However, the analytical method gives coarse approximation because it uses many assumptions, so sometimes obtained results are far from the reality. Especially with large structures, complex structures, these structures subject to many constrains of surrounding parts, the analytical method is difficult to apply or it gives results with large errors. Residual stress can be determined experimentally through residual stress measuring devices, for example: The hole-drilling method [2], X-ray diffraction method [3],… However, in order to measure residual stress, modern measuring equipment is required, which requires large costs. Today, with the strong development of computer science, simulation and numerical calculation tools have appeared. They meet the needs of structural design calculations in general and welded structures in particular. There are many software based on finite element method that have been developed for stress and strain analysis, such as: ANSYS, MARC, NASTRAN, ABAQUS,… [3]. In which, SYSWELD software is specialized software for thermo-mechanical analysis in welding [4]. This paper uses finite element method based on SYSWELD software to simulate, calculate and determine residual stress of butt welded joints. The study goes in depth to compare and evaluate the influence of clamping distance on residual stress components of AISI 316L stainless steel plates. The butt welded joint of two plates has the same thickness of 5 mm. This joint is made by automatic MIG welding process. The V-bevel welded joint has a welding gap. The weld is welded in one pass.
2 Material and Speciment Dimensions The base material of AISI 316L stainless steel is used in this study. The deposition metal is the same base material. The composition of this material is shown in Table 1 [5]. Table 1. Chemical compositions of AISI 316L stainless steel (wt. %) C
Mn
Si
Cr
Ni
Mo
Cu
0.03
2.0
0.5
18.0
12.5
2.7
0.3
The melting point of AISI 316L stainless steel is 1450 °C; At the room temperature, the Yield strength of this material is 207 MPa and the ultimate tensile strength of 316L is 538 MPa [6].
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In reality, the material properties depend on temperature. SYSWELD software includes an extensive database about the physical and mechanical properties of 316L material at different temperatures (Figs. 1, 2, 3, 4 and 5).
Fig. 1. Density of 316L at different temperatures.
Fig. 2. Young’s modulus of 316L depends on temperature.
Each butt welded joint includes the two plates that have the same dimensions: 5 mm thickness, 500 mm length and the plate width depends on different clamping distances that are given in Table 2. The form of weld is the single V groove with the groove angle α = 60°, the root face f = 1.5 mm, the root gap g = 1 mm. The single pass weld is perfomed. The excess weld thickness is e = 1 mm and the penetration bead thickness is p = 0.5 mm (Fig. 6).
Effect of Clamping Distances on Residual Stress in Butt Welded
Fig. 3. Influence of temperature on thermal conductivity of 316L.
Fig. 4. Relationship between yield strength and temperature of 316L.
Fig. 5. Change of specific heat of 316L.
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Case
1
2
3
4
5
6
7
8
Plate width (mm)
100
150
200
300
350
400
500
400
Clamping distance (mm)
200
300
400
600
700
800
1000
400
Residual stress components on the weld line at the lower face of plate (in MPa) Maximum longitudinal stress
274.9
276.9
326.9
Transversal stress at −49.4 −42.4 52.1 the center Transversal stress at 76.2 the weld end
66.8
319.9
320.7
317
316.1
325.9
34.3
30.4
27
26.2
49.2
−103.6 −104.1 −102.4 −106.5 −117.6 −105
Residual stress components on the line perpendicular to the weld line at the lower face of plate (in MPa) Maximum longitudinal stress
374.2
372.7
Minimum longitudinal stress
45.5
Longitudinal stress at the edge Maximum transversal stress
397.8
454.6
462.4
418.4
374.4
421.3
−23.1 −72.5
−78
−82.9
−82.8
−81.8
−61.4
59.3
48.6
35.6
21.6
17.5
13.2
12.9
16.8
345.8
293.5
240.8
186.4
163.4
145
119.5
246.8
Fig. 6. Weld preparation and sizes.
3 Welding Simulation The welding analysis is a coupled thermal–mechanical problem. At first, a heat transfer analysis is realized to have temperature-time history at each node. Then, in the mechanical analysis, the transient temperature field is used as the initial heat input to obtain the residual stresses. The different heat source models are used for different welding processes. In this paper, Goldak’s double ellipsoid heat source model [7] is used for (MIG) welding process.
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The parameters for the double ellipsoidal model are obtained by comparing simulation with experiment. The parameters of heat source model in this welded joint are 10 mm length, 6 mm width, 5 mm penetration (Fig. 7). The energy per unit length of weld is 653.3 J/mm. The welding speed is 4.5 (mm/s). The heat efficiency of welding arc is 0.85.
Fig. 7. Heat source model for MIG welding.
The welding torch moves in a straight line along the welding direction. The initial temperature of specimens is equal to the ambient temperature (25 °C). The 3D elements in brick-type elements are selected (Fig. 8). The uneven meshing technique is used in order to reduce the time calculation by decreasing the number of nodes and elements. The fine mesh is used for the vicinity of the welding seam. The coarser mesh is used for regions away from the weld line. The smallest element size in the HAZ is 0.7 mm. The finite elements have the same mesh in both thermal and mechanical analyses. The clamping is used at two side faces (Fig. 6): the displacement in 3 directions OX, OY and OZ of all nodes on two side faces at both end edges are fixed. The outer surface of the model has the convection heat exchange and the heat radiation from the model to the surrounding environment. In both thermal and mechanical analyses, the element birth and death technique is used to simulate weld filler deposition. By using this method, the weld elements are first deactivated in the thermal analysis and their conductivity is set to zero. When the heat input is applied, the weld elements are reactivated and their conductivity is set to the original value. Similarly, in the mechanical analysis, all the weld elements are firstly deactivated and their stiffness is set to zero. When the elements are reactivated, their stiffness is reset to the original value. In SYSWELD software, the element birth and death technique is used to simulate the deposition metal in welding.
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Fig. 8. Finite element mesh in brick-type.
4 Simulation Results and Discussion The 8 cases with different clamping distances are considered (Table 2). The residual stress results in the 8 cases with different clamping distances are shown in Table 2. In which, the longitudinal stress is the stress component which is parallel to the weld seam (σ x ); the transverse stress is the stress component which is perpendicular to the weld seam (σ y ). Due to the thin thickness of the plate, the stress on the upper face and lower face of the plate is not much different. For example, in the case 4, the maximum longitudinal residual stress at the weld line on the upper face of the plate is 322.5 MPa, while it is 319.9 MPa on the lower face of the plate (Fig. 9). So, only residual stress distribution on the lower face of the plate (the bottom of the weld) is considered in the next parts. The two cases with the same clamping distance (400 mm) but different plate widths are studied: – Each plate has 200 mm of width (the distance between two edge is 400 mm) is clamped at the 2 edges of the plate (Case 3 in Table 2). – Each plate has 400 mm of width (the distance between two edge is 400 mm) is clamped at the center of each plate (Case 8 in Table 2). For example, the maximum longitudinal stress on the line perpendicular to the weld line at the lower face of plate in case 3 is 397.8 MPA; In case 8, it is 421.3 MPa. So the longitudinal stress in case 8 is 5.9% higher than that in case 3. Another example, the maximum transverse stress on the line perpendicular to the weld line at the lower face of plate in case 3 is 240.8 MPa; In case 8, it is 246.8 MPa. So the stress in case 8 is only 2.5% higher than that in case 3. Thus, when the plate width increases and the clamping distance is constant, the longitudinal and transverse stress increases, but
Effect of Clamping Distances on Residual Stress in Butt Welded
111
290
330
Upper face
90
130
170
Stress (MPa) 210
250
Lower face
0
80
160
240 Distance (mm)
320
400
480
Fig. 9. Longitudinal stress distribution along the weld seam in case 4.
this increasing is not significant: the changing of longitudinal stress is less than 6%, the changing of transverse stress is about 2.5%. So in the next parts, the cases of different clamping distances corresponding to different plate widths are considered. In the cases 1 to 7, clamping is done at the outer 2 edges of each plate (the clamping distance will be 2 times the width of each plate plus the root gap). Since the welding gap is very small (only 1 mm) in comparing to the width of the two plates (more than 200 mm), so we can ignore the value of the root gap in the clamping distance. 4.1 Longitudinal Stress on the Weld Seam At the two ends of the weld seam, the longitudinal residual stress has a large variation (Fig. 10, 11). In the middle of the weld line, the longitudinal residual stress changes a small amount. Below, we consider the longitudinal residual stress in the middle of the weld line, where the stress stabilized. With a clamping distance of 300 mm, the maximum longitudinal stress on the weld line is 276.9 MPa (Fig. 10). With a clamping distance from 400 mm, the maximum longitudinal stress on the weld line is more than 316 MPa. It is 326.9 MPa in case 3 with 400 mm clamping distance (Fig. 11). We find that when the clamping distance is too close (less than 300 mm), the longitudinal stress on the weld line is much smaller than that when the clamping distance is from 400 mm or more. It means that when the clamping distance is high enough (≥400 mm), the longitudinal stress on the weld line is almost unchanged. The longitudinal stress value is about 320 MPa (Table 2).
N. Duong
120
150
180
Stress (MPa) 210 240
270
300
330
112
0
80
160
240 Distance (mm)
320
400
480
90
130
170
Stress (MPa) 210
250
290
330
Fig. 10. Longitudinal stress distribution along the weld seam in case 2.
0
80
160
240 Distance (mm)
320
400
480
Fig. 11. Longitudinal stress distribution along the weld seam in case 3.
4.2 Transverse Stress on the Weld Seam When the clamping distance is small ( 0. Once the shell starts to rotate, the standing wave motion will be transformed to backward or forward waves, depending on the direction of rotation. It is generally noticed that the values of the backward waves are larger than those of forward waves.
4 Numerical Results and Discussion In this section, the free vibration of rotating cross-ply laminated composite conical shells is under consideration. The numerical results obtained by the Continuous element method will be compared with other studies to confirm the correctness of the current formulation. Then various studies have been conducted to envisage the effects of cone angles, cone thickness, number of composite layers as well as boundary conditions on the natural vibration of composite rotating conical shells.
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4.1 Comparative Studies The Continuous Element (CE) model for the rotating composite cone shell will be validated by comparing with other studies. First, the anti-symmetric cross-ply composite shells are considered with two types of laminates ([0/90] and [0/90]10 ) and simply supported- simply supported (S-S) boundary condition. The layer material properties are taken to be E 1 = 15E 2 , υ 12 = 0.25, υ 13 = υ 23 = 0.3, G12 /E 2 = 0.5, G13 = G23 = E 2 /(2(1 + υ 23 )). The geometry parameters are given as α = 300 , L/R2 = 0.5 and h/R 2 =
0.01 ÷ 0.1. The non-dimension frequency parameters ω* is defined as ω∗ = ωR2 ρh A . Tables 1 and 2 show the obtained frequencies using thin shell theories and FSDT. It is shown that the present solutions are in good agreement with other results in the case of thin shells. The difference between the present results and those of [27, 28] using thin shell theories increases as the shell becomes thick and the results of FSDT are always lower than those of CST. CE results give close agreement with the FSDT solutions by Tong [25] and Wu [26] using the approximate differential quadrature method (DQM) in both cases of thin and thick shells. Table 1. Comparison of non-dimensional frequency parameters for [0/900 ] laminated composite conical shells (α = 300 , L/R2 = 0.5) h/R
Shu [27]
Kouchakzadeh and Shakouri [28]
Tong [25]
Wu and Lee [26]
Present
0.01
0.1799
0.1770
0.1768
0.1759
0.1729
0.02
0.2153
0.2119
0.2091
0.2093
0.2094
0.03
0.2397
0.2359
0.2304
0.2320
0.2372
0.04
0.2620
0.2577
0.2495
0.2520
0.2627
0.05
0.2841
0.2793
0.2681
0.2710
0.2856
0.06
0.3061
0.3008
0.2862
0.2892
0.3054
0.07
0.3277
0.3220
0.3033
0.3061
0.3225
0.08
0.3484
0.3424
0.3193
0.3217
0.3375
0.09
0.368
0.3618
0.3338
0.3358
0.3494
0.10
0.3863
0.3800
0.3469
0.3484
0.3595
Due to the lack of numerical results of thick rotating composite conical shells, the results of isotropic rotating conical shells will be used to confirm the correctness of our model. Table 2 illustrates the comparison of present results with those from by Irie et al. [28] for the clamped-clamped conical shell using the numerical integration method at the circumferential modes from 1 to 9. It is remark from this table that CE results are very close to solution issued from various approaches. Therefore, CE model is considered to be exact for studying standing wave vibration of composite conical shells. As the conical shell rotates, it is necessary to investigate the variation of rotational speed for both forward and backward frequencies. Here, the frequency parameters
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M. C. Nguyen and N. L. T. Bich
Table 2. Comparison of non-dimensional frequency parameters for [0/900 ]10 laminated composite conical shells (α = 300 , L/R2 = 0.5) h/R
Shu [27]
Kouchakzadeh and Shakouri [28]
Tong [25]
Wu and Lee [26]
Present
0.01
0.1976
0.1976
0.1959
0.1958
0.1954
0.02
0.2351
0.2354
0.2318
0.2317
0.2318
0.03
0.2667
0.2700
0.2608
0.2607
0.2635
0.04
0.2987
0.2990
0.2884
0.2882
0.2933
0.05
0.3303
0.3305
0.3137
0.3134
0.3202
0.06
0.3602
0.3603
0.3358
0.3356
0.3432
0.07
0.3873
0.3873
0.3547
0.3544
0.3628
0.08
0.4113
0.4113
0.3704
0.3701
0.3787
0.09
0.4322
0.4321
0.3835
0.3832
0.3919
0.10
0.4502
0.4500
0.3943
0.3940
0.4022
Table 3. The non-dimensional backward and forward frequency parameters of a S-S isotropic rotating conical shell (ν = 0.3, α = 300 h/R1 = 0.01, L/R1 = 6). Mode
ωb * ωf * (m,n) (rad/s) Lam and Hua Han and Chu Present Lam and Hua Han and Chu Present [29, 30] [31] [29, 30] [31] (1,1)
0 0.1
0.8154
0.8116
0.8015
0.6735
0.6692
0.6689
(1,2)
0
0.4456
0.4456
0.4456
0.4456
0.4456
0.4456
0.1
0.5399
0.5320
0.5311
0.4177
0.4095
0.4092
0
0.2785
0.2785
0.2785
0.2785
0.2785
0.2785
0.1
0.4410
0.4308
0.4304
0.3467
0.3362
0.3360
0
0.1888
0.1888
0.1888
0.1888
0.1888
0.1888
0.1
0.4633
0.4538
0.4531
0.3893
0.3797
0.3793
(1,3) (1,4)
0.7414
0.7414
0.7414
0.7414
0.7414
0.7414
achieved from the CE model will be compared with those of [29–31] using the analytical procedure for a rotating conical shell with the simply supported-simply supported (S-S) boundary condition made of isotropic material: E 1 = E 2 = E, υ 12 = υ 13 = υ 23 = υ, E G12 = G13 = G23 = 2(1+ϑ) . The non-dimensional frequency parameters are computed asωf∗ = ωf ρR2 1 − υ 2 /E, ωb∗ = ωb ρR2 1 − υ 2 /E, and the results are listed in Table 3.
Vibration Analysis of Thick Rotating Laminated Composite Conical Shells
157
As shown in Table 3 for a rotating isotropic conical shell with S-S boundary condition, the results of the CE model are also in good agreement with that of Lam and Hua [29, 30] and Han and Chu [31] using the analytic procedure. From this tables, it is evident that the CE model for thick rotating conical shells is reliable and can be used to examine the vibration of thick rotating laminate composite conical shells. 4.2 Parameter Effect Studies 4.2.1 Effect of the Semi-cone Angle Next, examining the first six vibration modes of rotating laminated composite conical shells with Clamped-Clamped and Supported-Supported boundary conditions, and with the four different semi-vertex angles: α = 150 , 300 , 450 , and 600 and cover five different rotating speeds (N round per minute) ranged as follows: −3000, −1500 (backward frequencies), 0 (standing frequency), 1500, 3000 (forward frequencies). The composite material parameters are: E 2 = 8.96 GPa, E 1 = 15E 2 , υ 12 = 0.25, G13 = G23 = E 2 /2(1 + 0.3). The calculated results by the CE model are expressed in Tables from 4 to 7. The variation of frequencies concerning rotating speed is shown in Figs. 2, 3, 4, 5, 6, 7, 8 and 9. 1800
Frequency (Hz)
1600 1400 1200 1000
1
2
3
800
4
5
6
600 400 200 -3000
N (rpm) -1500
0
1500
3000
Fig. 2. Variation of the first six vibration modes with rotating speed for a C-C rotating composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 150 )
The Tables from 4, 5, 6 and 7 and Figures from 2, 3, 4, 5, 6, 7, 8 and 9 show the variations of calculated frequencies by the CE model for the rotating composite conical shell concerning the rotating speed with different rotation angles. It is observed that the natural frequencies of conical shells increase when rotating speeds increase with different semi-vertex angles. Similar to the rotating cylinder shells, forward wave frequencies are greater than standing frequencies, the standing frequencies are greater than backward ones because of the Coriolis forces. This effect in the C-C rotating conical shell is more obvious than those in the S-S shells. In addition, when the cone angles are larger the natural frequencies are higher.
158
M. C. Nguyen and N. L. T. Bich 2450 Frequency (Hz) 2400 2350 2300 2250 2200 2150 -3000
1
2
4
5
-1500
0
3 6 1500
N (rpm) 3000
Fig. 3. Variation of the first six vibration modes with rotating speed for a S-S rotating composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 300 )
2600 2550
Frequency (Hz)
2500 2450
1
2
3
4
5
6
2400 2350 2300 2250 2200 2150 -3000
N (rpm) -1500
0
1500
3000
Fig. 4. Variation of the first six vibration modes with rotating speed for a C-C rotating composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 300 ) 1800
Frequency (Hz)
1600 1400 1200
1
2
3
4
5
6
1000 800 600 -3000
N(rpm) -1500
0
1500
3000
Fig. 5. Variation of the first six vibration modes with rotating speed for a S-S rotating composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 150 )
Vibration Analysis of Thick Rotating Laminated Composite Conical Shells 2600
159
Frequency (Hz)
2550 2500 2450
1
2400
2
3
2350 2300 2250 2200 2150 -3000
N(rpm) -1500
0
1500
3000
Fig. 6. Variation of the first six vibration modes with rotating speed for a S-S rotating composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 450 ) 1800
Frequency (Hz)
1700 1600 1500 1400 1300
1
2
3
4
5
6
1200 1100 1000 -3000
N(rpm) -1500
0
1500
3000
Fig. 7. Variation of the first six vibration modes with rotating speed for a S-S rotating composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 600 )
2600 2550
Frequency (Hz)
2500
1
2
3
2450
4
5
6
0
1500
2400 2350 2300 2250 2200 2150 -3000
N (rpm) -1500
3000
Fig. 8. Variation of the first six vibration modes with rotating speed for a C-C rotating composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 600 )
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M. C. Nguyen and N. L. T. Bich 1900
Frequency (Hz)
1800
1 4
1700
2 5
3 6
1600 1500 1400 1300
N (rpm)
1200 -3000
-1500
0
1500
3000
Fig. 9. Variation of the first six vibration modes with rotating speed for a C-C rotating composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 450 ) Table 4. The first six vibration modes for a (0o /90o /90o /00 ) rotating laminated conical shell with S-S and C-C boundary conditions (h = 0.0254m, R2 = 20h, L = 10h, α = 150 ) Boundary condition
N (r/m)
Mode 1
Mode 2
Mode 3
Mode 4
Mode 5
Mode 6
CC
−3000
2208
2227
2232
2295
2312
2399
−1500
2218
2240
2241
2302
2328
2405
0
2229
2249
2252
2309
2345
2411
1500
2239
2258
2265
2317
2361
2418
3000
2250
2267
2279
2325
2378
2425
−3000
373
678
1205
1541
1571
1585
−1500
373
678
1205
1551
1579
1597
0
373
678
1206
1561
1587
1609
1500
373
678
1206
1566
1592
1615
3000
374
678
1206
1582
1605
1633
SS
Table 5. The first six vibration modes for a (0o /90o /90o /00 ) rotating laminated conical shell with S-S and C-C boundary conditions (h = 0.0254m, R2 = 20h, L = 10h, α = 300 ) Boundary condition
N (r/m)
Mode 1
Mode 2
Mode 3
Mode 4
Mode 5
Mode 6
CC
−3000
2223
2224
2277
2294
2382
2525
−1500
2232
2234
2283
2308
2388
2543
0
2240
2244
2290
2322
2394
2553
1500
2249
2255
2297
2336
2400
2558
3000
2258
2266
2305
2350
2406
2563 (continued)
Vibration Analysis of Thick Rotating Laminated Composite Conical Shells
161
Table 5. (continued) Boundary condition
N (r/m)
Mode 1
Mode 2
Mode 3
Mode 4
Mode 5
Mode 6
SS
−3000
765
−1500
765
947
1408
1557
1570
1631
948
1409
1565
1580
1638
0
765
948
1409
1573
1590
1645
1500
765
949
1410
1582
1600
1652
3000
766
949
1410
1591
1611
1660
Table 6. The first six vibration modes for a (0o /90o /90o /00 ) rotating laminated conical shell with S-S and C-C boundary conditions (h = 0.0254m, R2 = 20h, L = 10h, α = 450 ) Boundary condition
N (rpm)
Mode 1
Mode 2
Mode 3
Mode 4
Mode 5
Mode 6
CC
−3000
2218
2250
2260
2348
2456
2514
−1500
2225
2256
2270
2352
2469
2518
0
2232
2262
2279
2357
2482
2522
1500
2240
2267
2289
2362
2495
2526
SS
3000
2247
2273
2299
2367
2508
2531
−3000
1109
1218
1543
1557
1584
1726
−1500
1109
1219
1550
1557
1590
1731
0
1109
1219
1558
1558
1596
1736
1500
1109
1220
1559
1566
1602
1741
3000
1109
1220
1559
1573
1608
1746
Table 7. The first six vibration modes for a (0o /90o /90o /00 ) rotating laminated conical shell with SS-SS and C-C boundary conditions (h = 0.0254m, R2 = 20h, L = 10h, α = 600 ) Boundary condition
N (rpm)
Mode 1
Mode 2
Mode 3
Mode 4 Mode 5 Mode 6
CC
−3000
2223
2224
2276
2294
2382
2525
−1500
2232
2234
2283
2308
2388
2543
0
2240
2244
2290
2322
2393
2553
1500
2249
2255
2297
2336
2399
2558
3000
2258
2266
2305
2350
2406
2563 (continued)
162
M. C. Nguyen and N. L. T. Bich Table 7. (continued)
Boundary condition
N (rpm)
Mode 1
Mode 2
Mode 3
Mode 4 Mode 5 Mode 6
SS
−3000
1300
1362
1504
1512
1615
1823
−1500
1300
1362
1506
1516
1618
1825
0
1300
1362
1507
1520
1622
1828
1500
1300
1362
1509
1524
1625
1831
3000
1300
1362
1511
1528
1628
1833
4.2.2 Effect of the Rotating Angular Velocity Next, the effect of rotating angular velocity on the free vibration of the laminated composite conical shell is illustrated in Fig. 6. It is easy to see that the forward natural frequencies increase but are not linear as the rotational velocity increases, while the backward frequencies decrease at the same speed. The frequencies of the rotating composite conical shell increase and decrease non-linearly due to the influence of the semi-cone angle on both the Coriolis acceleration and rotational moment of inertia. 4.2.3 Effect of the Thickness to Radius Ratio h/R In this section, the influence of the thickness to radius ratio h/R on the vibration of the C-C rotating composite conical shell at two rotating speeds N = 1500 rpm and N = 3000 rpm is in an investigation. All forward and backward wave frequencies concerning the first circumferential mode are presented in Fig. 7. It is seen that the frequencies of the studied shell increase when the h/R ratio increases because a thicker shell will have greater stiffness. Moreover, the curves are almost parallel to each other, thus it can be concluded that the influence of the m-circumferential vibration patterns on the forward, backward, and standing frequencies are independent of each other. Finally, as known, the forward frequency has a higher value than the standing frequency, and the standing frequency in his turn is higher than the backward frequency. When the rotational speed N increases, the forward and backward frequency values increase also (Figs. 10 and 11). 4.2.4 Effect of the Number of Layers Finally, Fig. 12 is shown the influence of the number of layers on the first frequency of the rotating C-C laminated composite conical shell at two rotating speeds 1500 rpm and 3000 rpm. The laminated layer configurations of the examined conical shell are 2, 4, 6, 8, 10, and 12 layers with cross-fly layers arrangement: [0/90], [0/90/0/90], [0/90/0/90/0/90], [0/90/0/90/0/90/0/90], [0/90/0/90/0/90/0/90/0/90], [0/90/0/90/0/90/0/90/0/90/0/90]. The graph shows that free frequencies change significantly with the increasing number of layers (2, 4, 6). When the number of layers increases, the frequency increases almost insignificantly, which is appropriate with previous studies. Adding, as N increases, the forward and backward vibration frequencies of the structure also increase non-linearly.
Vibration Analysis of Thick Rotating Laminated Composite Conical Shells
163
Fig. 10. Effect of the rotating angular velocity on the rotating C-C laminated composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, [0o /90o /90o /0o ], α = 30o )
Frequency(Hz)
2500 2300 2100
Forward N=1500 1900
Backward N=1500
1700
Forward N=3000 Backward N=3000
1500
h/R
1300 0.02
0.04
0.06
0.08
0.1
Fig. 11. Effect of the h/R ratio on the vibration of the rotating C-C laminated composite conical shell (R2 = 0.508 m, L = 0.254 m, [0o /90o /90o /0o ], α = 30o , m = 1)
2600
Frequency(Hz)
2550 2500 2450
Forward N=1500
2400
Backward N=1500 Forward N=3000
2350
Backward N=3000
2300
Number ofl ayers
2250 2
4
6
8
10
12
Fig. 12. Effect of the number of layers on the vibration of the rotating C-C laminated composite conical shell (h = 0.0254 m, R2 = 20 h, L = 10 h, α = 300 , m = 1)
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M. C. Nguyen and N. L. T. Bich
5 Conclusions This research succeeded in presenting a new continuous element for the rotating thick cross-ply laminate conical shells. Numerical results obtained by the present formulation are in close agreement with those in the literature that confirm the precision of the presented formulations. The influences of different shell parameters: rotation angular velocity, semi-cone angle α, the thickness to radius ratio h/R, layer configuration, and boundary conditions on backward, forward, and standing frequency are investigated, and obtained conclusions are as follows: • When raising the rotating velocity of conical shells, the forward wave frequencies are always larger than the backward ones due to the Coriolis force and the forward frequencies increase and the backward frequencies decrease nonlinearly at the same speed. • As the cone angle increases, the natural frequency of the conical shell increases. • When the ratio of thickness to radius h/R increases, the frequency of the shell increases, the rate of increase in mode 3 is faster than that of modes 1 and 2 (circumference mode). • The effect of the vibration patterns in the circumference on the vibration frequency of the structure is independent of each other. • With the same thickness, the frequency of the rotating conical shell increases rapidly when the number of layers is increased from 2 to 4 and 6. However, when the number of layers continues to increase (n = 8, 10, 12 layers), the frequency will increase almost insignificant. The developed model can be extended to efficiently solve the problem of the composite rotating ring, rotating combined cylindrical-conical shells as well as rotation shells containing fluid.
References 1. Li, H., Lam, K.Y., Ng, T.Y.: Rotating Shell Dynamics, 1st end. Elsevier Science, San Diego (2005) 2. Zhang, X.M.: Parametric analysis of frequency of rotating laminated composite cylindrical shells with the wave propagation approach. Comput. Methods Appl. Mech. Eng. 191, 2029– 2043 (2002) 3. Zhang, X.M.: Vibration analysis of cross-ply laminated composite cylindrical shells using the wave propagation approach. Appl. Acoust. 62, 1221–1228 (2001) 4. Kadivar, M.H., Samani, K.: Free vibration of rotating thick composite cylindrical shells using layerwise laminated theory. Mech. Res. Commun. 27, 679–684 (2000) 5. Ramezani, S., Ahmadian, M.T.: Free vibration analysis of rotating laminated cylindrical shells under different boundary conditions using a combination of the layerwise theory and wave propagation approach. Trans. B Mech. Eng. 16(2), 168–176 (2009) 6. Lam, K.Y., Wu, Q.: Vibrations of thick rotating laminated composite cylindrical shells. J. Sound Vibr. 225(3), 483–501 (1999) 7. Zamani: Free vibration of rotating graphene-reinforced laminated composite conical shells. Comp. Part C Open Acess 5, 100153 (2021)
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8. Casimir, J.B., Nguyen, M.C., Tawfiq, I.: Thick shells of revolution: derivation of the dynamic stiffness matrix of continuous elements and application to a tested cylinder. Comput Struct 85, 1845–1857 (2007) 9. Banerjee, J.R., Williams, F.W.: Coupled bending–torsional dynamic stiffness matrix of an axially loaded Timoshenko beam element. Int. J. Solids Struct. 31(6), 749–762 (1994) 10. Boscolo, M., Banerjee, R.J.: Dynamic stiffness formulation for composite Mindlin plates for exact modal analysis of structures. Part I Theory Comput. Struct 96–97, 61–73 (2012) 11. Nguyen, M.C.: Element continus de plaques et coques avec prise en compte du cisaillement transverse. Application a l’interaction fluid-structure, Thesis of PhD, University Paris 6 (2003) 12. Thinh, T.I., Nguyen, M.C., Ninh, D.G.: Dynamic stiffness formulation for vibration analysis of thick composite plates resting on non-homogenous foundations. Comp. Struct. 108, 684–695 (2014) 13. Thinh, T.I., Nguyen, M.C.: Dynamic stiffness matrix of continuous element for vibration of thick cross-ply laminated composite cylindrical shells. Compos. Struct. 98, 93–102 (2013) 14. Nam, L.T.B., Cuong, N.M., Thinh, T.I.: Continuous element formulation for thick composite annular plates and rings resting on elastic foundation. ICEMA3 (2014). ISBN: 978-604-913367-1 15. Bich Nam, L.T., Cuong, N.M., Thinh, T.I., Hien, T.T.: Dynamic analysis of stepped composite cylindrical -conical shells surrouded by Pastenak elastic foundations based on the CEM. In: The 14th National Conference on Mechanics of solid, HCM (2018) 16. Cuong, N.M., Thinh, T.I., Nam, L.T.B., Minh, D.P.T., Vinh, L.Q.: Dynamic analysis of complex composite tubes by continuous element method. J. Sci. Technol. 119, 48–53 (2017) 17. Nam, L.T.B., Cuong, N.M., Thinh, T.I.: Dynamic analysis of stepped composite cylindrical shells surrounded by Pasternak elastic foundations based on the continuous element method. Vietnam. J. Mech. 40(2), 105–119 (2018) 18. Harbaoui, I., Casimir, J.B., Khadimallah, M.A., Chafra, M.: A new prestressed dynamic stiffness element for vibration analysis of thick circular cylindrical shells. Int. J. Mech. Sci. 140, 37–50 (2018) 19. Khlifi, K., Casimir, J.B., Akrout, A., Haddar, M.: Dynamic stiffness method: New Levy’s series for orthotropic plate elements with natural boundary conditions. Eng. Struct. 245, 112936 (2021) 20. Gholamia, M., Alibazib, A., Moradifardb, R., Deylaghianb, S.: Out-of-plane free vibration analysis of three-layer sandwich beams using dynamic stiffness matrix. Alex. Eng. J. 60(6), 4981–4993 (2021) 21. Banerjee, J.R.: Frequency dependent mass and stiffness matrices of bar and beam elements and their equivalency with the dynamic stiffness matrix. Eur. J. Mech. A Sol. 83 (2020) 22. Nam, L.T.B.: Nguyen Manh Cuong “A new continuous element for vibration analysis of thick rotating cross-ply cylindrical shells using FSDT.” J. Sci. Technol. 14, 75–76 (2015) 23. Reddy, J.N.: Mechanics of Laminated Composite Plates and Shells, Theory and Analysis, 2nd edition, CRC Press (2003) 24. Irie, T., Yamada, G., Tanaka, K.: Natural frequencies of truncated conical shells. J. Sound Vib. 92, 447–453 (1984) 25. Tong, L.: Free vibration of laminated conical shells including transverse shear deformation. Int. J. Solids Struct. 31, 443–456 (1994) 26. Wu, C.-P., Lee, C.-Y.: Differential quadrature solution for the free vibration analysis of laminated conical shells with variable stiffness. Int. J. Mech. Sci. 43, 1853–1869 (2001) 27. Shu, C.: Free vibration analysis of composite laminated conical shells by generalized differential quadrature. J. Sound Vib. 194, 587–604 (1996) 28. Kouchakzadeh, M.A., Shakouri, M.: Free vibration analysis of joined cross-ply laminated conical shells. Int. J. Mech. Sci. 78, 118–125 (2014)
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29. Lam, K.Y., Hua, L.: Vibration analysis of a rotating truncated circular conical shell. Int. J. Solids Struct. 34(17), 2183–2197 (1997) 30. Lam, K.Y., Hua, L.: Influence of boundary conditions on the frequency characteristics of a rotating truncated circular conical shell. J. Sound Vib. 223(2), 171–195 (1999) 31. Han, Q., Chu, F.: Parametric resonance of truncated conical shells rotating at periodically varying angular speed. J. Sound Vib. 333, 2866–2884 (2014) 32. Lam, K.Y., Loy, C.T.: Analysis of rotating laminated cylindrical shells by different thin shell theories. J. Sound Vib. 186, 23–35 (1995)
Research on the Characteristics of Tooth Shape and Size of the Oval Gear Drive with an Involute Profile Nguyen Hong Thai(B) and Phung Van Thom School of Mechanical, Hanoi University of Science and Technology (HUST), Hanoi, Vietnam [email protected]
Abstract. A circle’s involute curve is widely used to design tooth profiles of noncircular gear drives in general and oval gear drives in particular. Nevertheless, the tooth shape and size, so as the kinematics of the meshing process, have not been thoroughly studied. Meanwhile, these factors affect the working ability and load capacity of the gear drive. To solve the above problems in this paper, the authors used an oval gear drive as the representative object for non-circular gear drives to survey and research the above characteristics without generality is lost. The research results show that the teeth distributed in the centrode position with a minimal radius often have tooth pointing and the weakest load capacity. In addition, for the non-circular gear with an involute tooth profile, it is necessary to distribute many teeth on the gear to avoid undercutting phenomena. Naturally, that results in small teeth and reduced load capacity of the gear drive. Keywords: Oval gear · Rack cutter · Involute profile · Undercutting
1 Introduction Oval gears (OG) are a type of non-circular gears (NCGs). Among the NCGs, the OGs are most commonly used. Therefore, the OG has been improved from the original elliptical gear into different versions to suit each specific application area. It must mention research on the application of the OGs by Erika et al. [1] in the design of external blood pumps for cardiac surgery; Prikhodko [2] used elliptical planetary gears train to provide the rotationally reciprocating motion of the impeller of the stirred tank; Guarnieri et al. [3] used compound oval gear train in agricultural machines; Zhou et al. [4] applied OG train in transplanting mechanism of the rice planting machine; Zhao et al. [5] improved the OGs in the planetary NCG train in Guo’s rice transplanter to develop the transplanting mechanism for other leafy vegetables; Thai et al. [6–9] used oval gears drive to driving for Roots-type volumetric hydraulic machine, etc. However, the approach of the above studies is the study of the application of the OGs to replace the traditional motion conversion mechanisms to meet the functional characteristics of the machine. In particular, surveys show that the machine’s advantages are compact size and increased working quality when replacing traditional motion converters by the OG with an involute or arc profile. The studies on kinematics, geometrical © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 167–184, 2022. https://doi.org/10.1007/978-981-19-1968-8_14
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shape and meshing characteristics of tooth profile have not been interesting. Meanwhile, these factors partly influence the gear drive’s working quality and service life, such as tooth pointing, tooth root weakness, number of teeth distributed on the gear, kinematic wear due to relative sliding velocity, etc. In this research, the authors evaluated the influence of geometric shape tooth on relative sliding velocity, meshing flank length, the thickness of tooth top and root. To solve the above problem, the content of the paper includes (1) Design of the OG pair with an involute profile by rack cutter, (2) Mathematical modelling slip velocity and coefficient of slip between two mating profiles at the meshing point and (3) Investigation of the meshing flank length, the thickness of tooth top and root of conjugation profiles participating in the meshing process.
2 Design the Tooth Profile of the Oval Gear Drive by Rack Cutter 2.1 Design Centrodes of the Oval Gear Drive Consider a pair of mating oval centrodes Σ 1 and Σ 2 as shown in Fig. 1. Wherein, Σ 1 is the given oval centrode and is determining by the polar equation [10]: ρ1 (ϕ1 ) =
2a1 b1 (a1 + b1 ) − (a1 − b1 ) cos(2ϕ1 )
(1)
where: a1 , b1 are the semi-major and semi-minor axes of the oval centrode and the ϕ1 ∈ [0 ÷ 2π ] polar angles, respectively.
Fig. 1. The geometric relationship between mating centrodes Σ 1 and Σ 2
According to [11, 12] and the symbols defined in Fig. 2, the oval centrode Σ 2 conjugation to centrode Σ 1 is determined by: b1 ρ2 (ϕ2 (ϕ1 )) = a12 − (a1 +b1 )−(a2a11−b 1 ) cos(2ϕ1 ) ϕ1 (2) 2a1 b1 ϕ2 (ϕ1 ) = 0 a12 ((a1 +b1 )−(a1 −b1 ) cos(2ϕ1 ))−2a1 b1 d ϕ1
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where: ρ2 (ϕ2 (ϕ1 )), ϕ2 (ϕ1 ) are the pole radius and pole angle of centrode Σ 2 . On the other hand, according to [13], the two OGs the same. Thus the shaft distance a12 of the gear drive is determined:
2π
0
2a1 b1 d ϕ1 = 2π a12 ((a1 + b1 ) − (a1 − b1 ) cos(2ϕ1 )) − 2a1 b1
(3)
Solve Eq. 3 by the integral Dwight [14], the shaft distance a12 given by: a12 (a1 , b1 ) = a1 + b1
(4)
2.2 Shaping the Tooth Profile of the Oval Gear Drive by Rack Cutter a) Design parameters of the rack cutter with isosceles trapezoidal profiles according to centrode of oval gear According to [10, 15, 16], the rack cutter with isosceles trapezoidal profiles shapes the involute profile of the OG’s as described in Fig. 2 a. The design parameters of the rack cutter are determined as follows: (1) Tooth pitch on datum line Δ of rack cutter pc =
Where:
C
1
=
2π 0
ρ12 (ϕ1 ) +
C1
(5)
z1
d ρ1 (ϕ1 ) ϕ1
2
d ϕ1 is the centrode circumference
Σ 1 , with ρ 1 (ϕ 1 ) is defining according to Eq. 1 and z1 is the number of teeth of the OG. (2) The modulus mc of the rack cutter mc =
pc π
(6)
(3) The pressure angle α c of the rack cutter is defined αc = (tt , ) (4) The whole depth of the rack cutter ⎧ ⎨ ha = ka mc h = kf mc ⎩ f h = ha + hf = (ka + kf )mc
(7)
(8)
Here, ha , hf are the addendum and dedendum of the rack cutter, respectively, with k a and k f as the addendum and dedendum coefficients.
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Fig. 2. Rack cutter with (a) Design parameters and (b) Modelling the working regions of the tooth profile
(5) Tooth thickness sc and width of space wc on the datum line Δ sc = wc =
mc π pc = 2 2
(9)
(6) Fillet radius co of root and tip co = 0.25mc
(10)
b) Mathematical modelling the isosceles trapezoidal profile of the rack cutter With the definitions shown in Fig. 2b, the mathematical model of the tooth profile of the rack cutter on each region is mathematical modelling as follows: i)
Counterpart rack tooth profile ➀ (AB) and ➅ (IJ):
xK(i)R (−1)j π 2mc + (−1)j+1 l (i) (i) rKR = = −hf + r sin αc − r yK(i)R
(11)
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ii) Fillet radius of rack tooth profile ➁ (BD) và ➄ (GI):
(i) xKR (−1)j π 4mc + (−1)j hf tan αc + (i) = rKR = (i) −hf + r sin αc − yKR +(−1)j r cos αc + (−1)j+1 r sin θ −r cos θ iii) Basic rack tooth profile ➂ (DE) and ➃ (FG):
(i) xKR (i) = rKR = (i) yKR (−1)j π 2mc + (−1)j+1 h tan αc + (−1)j l (i) sin αc = ha − l (i) cos αc
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(12)
(13)
Where: j = 1, 2 corresponds to the rack cutter’s right and left tooth profile; (i) are the parameters determining the position of K R points in the region ➀, ➅ and ➂, ➃ on the rack tooth profile; θ is the position angle of K R points in the region ➁, ➄ on the rack tooth profile. c) Shaping the tooth profile of the oval gear by the rack cutter To shape the involute profile Γ C of the OG. In the fixed coordinate system ϑf {Of xf yf }, let ϑ1 {O1 x1 y1 } is the coordinate system of the OG, while ϑc {Oc xc yc } is the coordinate system of the rack cutter, as described in Fig. 3.
Fig. 3. Illustrating the shape of the involute profile of the oval gear by the rack cutter
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Transformation coordinates of any point K R on Γ R of the rack cutter to the coordinate system of the OG, the involute profile equation Γ G is given by: rK = 1 Mf Mf f MR c rKR wherein:
(14)
⎡
⎡ ⎡ ⎤ ⎤ ⎤ 1 0 s2 (ϕ1 ) 10 0 cos ψ1 sin ψ1 0 fM = ⎣ 0 1 −a1 ⎦; Mf = ⎣ 0 1 s3 (ϕ1 ) ⎦; 1 Mf = ⎣ − sin ψ1 cos ψ1 0 ⎦; R 00 1 00 1 0 0 1 Mealwhile c rKR is determining from formulas (11, 12, 13) corresponding to the rack profile regions. μ; Here, s2 (φ1 ) = s1 (φ 1 ) + ρ1 (φ1 ) cos 2 ϕ s1 (φ1 ) = e = 0 (ρ1 (φ1 ))2 + d ρd1φ(φ1 1 ) d φ1 ; s3 (φ1 ) = a1 − s3 (φ1 ) = a1 − ρ1 (φ1 ) sin μ; ρ1 (φ1 ) π −1 ψ1 = φ1 + μ − 2 ; μ = tan d ρ1 (φ1 )/d φ1 is the angle at the instantaneous center I (pitch point); ρ1 (φ1 ), ϕ 1 are the polar radius and polar angle of the centrode Σ 1 . After transforming Eq. 8, coordinates of point K on the tooth profile Γ G is rewritten as:
(i) (i) (i) xKR cos ψ1 + yKR sin ψ1 − xK (i) rK = = (i) (i) (i) yK −xKR sin ψ1 + yKR cos ψ1 − −a1 sin ψ1 + s2 (φ1 ) cos ψ1 (15) −a1 cos ψ1 − s2 (φ1 ) sin ψ1 + s3 (φ1 ) On the other hand, from Fig. 3, the design parameters of the addendum oval limit Σ AE of the OG are given by: aAE = a1 + hf : (16) AE bAE = b1 + hf And the parameters of the dedendum oval limit BE of the OG: BE
:
aBE = a1 − ha bBE = b1 − ha
(17)
d) Number of teeth on the oval gear According to [17, 18], the undercutting occurs of the oval gear, then the rack tooth profile equation Γ R must satisfy: ⎧ dxKR cv ⎪ − trx ⎪ 1 = dl ⎪ =0 ⎪ ∂f (φ ∂f (φ ) ) d φ 1 1 1 ⎨ ∂
∂φ1 dt (18) dyKR ⎪ cv ⎪ − try ⎪ dl ⎪ ⎩ 2 = ∂f (φ1 ) ∂f (φ1 ) d φ1 = 0 ∂
∂φ dt 1
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where: c vtrx , c vtry are the relative sliding velocity components between the two conjugation profiles Γ R and Γ C at the point K R . After solving Eq. 18, the condition to undercutting are obtained: ρ(φ) sin2 αc = (1 − x)mc
(19)
with ρ(ϕ) is the radius of the centrode Σ 1 , x is the coefficient of rack cutter taken as 0 [10]. Therefore, the condition to avoid undercutting is given by: mc ≤ ρmin sin2 αc
(20)
In the Eq. 20 ρmin is the minimal radius of the centrode Σ 1 , and is given by [6]: ρmin =
a1 b1 2a1 − b1
(21)
From inequality (20), it can see that the modulus mc of the rack cutter is dependent on the centrode Σ 1 and the pressure angle α c . Derived from the condition to avoid undercutting (20) and combination with Eq. (5, 6), we have: C1 zπ
≤ ρmin sin2 αc
(22)
From inequality (22), we have the minimal number of teeth of the OG: zmin ≥
C1 πρmin sin2 αc
(23)
Condition (23) shows that if α c is small, the minimal number of teeth will increase, the tooth size will be smaller, and if αc is large, the number of teeth will decrease, and the tooth size will increase.
3 Kinematic Analysis of the Meshing Process of the Oval Gear Drive 3.1 Mathematical Modelling the Relative Sliding Velocity Between Two Mating Profiles at the Meshing Point Consider a pair of the conjugation profiles Γ 1 and Γ 2 rolling and sliding on each other at the meshing point K (Γ 1 of the OG1, Γ 2 of the OG2). Thus, a relative sliding velocity generates between the two mating profiles, as described in Fig. 4. At the meshing point K then K 1 ≡ K 2 ≡ K, where K 1 ∈ Γ 1 and K 2 ∈ Γ 2 , then the velocity in the fixed coordinate system ϑf {Of xf yf } is given by: VK1 = ρ1 (φ1 )ω1 (24) VK2 = ρ2 (φ2 (φ1 ))ω2 = ρ2 (φ2 (φ1 ))ω1 (i12 (φ1 ))−1
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Fig. 4. Kinematic relationship at the meshing point of the conjugation profiles Γ 1 and Γ 2
wherein: ρ1 (φ1 ), ρ2 (φ2 (φ1 )) are the meshing radius at the point K, respectively. While i12 (ϕ 1 ) is gear ratio function and defined as: ω1 = ω2 a12 ((a1 + b1 ) − (a1 − b1 ) cos(2φ1 )) − 2a1 b1 = 2a1 b1
i12 (φ1 ) =
(25)
Let VKt 1 , VKt 2 are the projections vector of VK1 , VK2 on the tangent direction tt’, respectively, then we have: t VK1 = VK1 cos β1 (φ1 ) (26) VKt 2 = VK2 cos β2 (φ2 (φ1 )) → → here β1 (φ1 ) = VK1 , tt , β2 (φ2 (φ1 )) = VK2 , tt .
Thus, the relative sliding velocity at the meshing point K is given by: tr VK12 = VKt 1 (φ1 ) − VKt 2 (φ2 (φ1 )) VKtr21 = VKt 2 (φ2 (φ1 )) − VKt 1 (φ1 )
(27)
3.2 The Sliding Curve of Tooth Profile According to [19, 20] to evaluate the influence of kinematic wear of tooth profile, we use profile slip coefficient μ, which is defined as follows: ⎧ VKt 2 (φ2 (φ1 )) V tr (φ1 ) ⎪ ⎪ ⎪ μ1 (φ1 ) = Kt12 = 1 − ⎪ ⎨ VK1 (φ1 ) VKt 1 (φ1 ) (28) ⎪ VKt 1 (φ1 ) VKtr21 (φ2 (φ1 )) ⎪ ⎪ ⎪ ⎩ μ2 (φ2 (φ1 )) = V t (φ (φ )) = 1 − V t (φ (φ )) K2 2 1 K2 2 1 wherein: μ1 (ϕ 1 ), μ2 (ϕ 2 (ϕ 1 )) are the profile slip coefficients of Γ 1 relative to Γ 2 and Γ 2 relative to Γ 1 , respectively.
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Fig. 5. Meshing process of conjugation tooth profiles pair
3.3 Dimensional Parameters of Tooth Geometry of the OG with an Involute Profile a) Meshing flank length on the involute profile When the oval gear drive operates, driver gear 1 transmits the torque to the driven gear 2 through the meshing process of the conjugation profile pair as described in Fig. 5. In the general case, consider the meshing process of a pair mating involute profiles Γ 1 and Γ 2 . Where Γ 1 is the profile of tooth number ➀ of the OG1, meanwhile Γ 2 is 18 of the OG2. Thus, in the meshing process, point K will the profile of tooth number
move: (1) from the mesh-in point to the pointed tip of the profile Γ 1 and (2) from the pointed tip to the mesh-in point of the profile Γ 2 . With the clockwise rotation of the (Fig. 5a), whileK r will be OG1 as shown in Fig. 5, point K v will be the mesh-in point the mesh-out point. With the above definition, L1 = e and L2 = e are 1 Kv1Kr1 2 Kv2Kr2 the length of the meshing flank of the profile Γ 1 and Γ 2 , respectively.
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b) The thickness of tooth top and root The teeth distributed at different positions on the centrode of the oval gear will have different tooth shapes and sizes. Therefore, to investigate these parameters, we define sa , sf as the thickness of tooth top and root, respectively, as described in Fig. 6.
Fig. 6. Illustration thickness of tooth top and root
Two parameters influence pointed teeth and tooth load capacity on the NCGs.
4 Investigate Tooth Shape Parameters and Kinematic Analysis of the Meshing Process 4.1 Design of Oval Gear Drive with an Involute Profile Investigate tooth shape parameters and the meshing process kinematic analysis of the oval gear drive. In this content the design parameters of the centrodes Σ 1 and Σ 2 : a1 = a2 = 50 mm, b1 = b2 = 30 mm are determined according to gears driving of the Roots blower [7, 9]. On that basis, the design parameters of the oval gear drive are performing in the following steps: Step 1. Determining the design parameters of the rack cutter Substitute design data of the centrodes Σ 1 and Σ 2 into the Eq. 21, we have ρmin = 21.43 mm and C1 = 258.84 mm. The standard pressure angle αc = 20◦ substitute condition (23), the minimal number of teeth zmin = 32.87. In addition, for the teeth of the oval gear to be symmetrical on the two semi-axes, the number of teeth is selected z = 36. (i) Modulus mc of the rack cutter Substitute z = 36 into Eq. (5) and (6) we have mc = 2.29 mm.
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(ii) Determine the remaining parameters of the rack cutter Choosing the tooth addendum and dedendum coefficient are k a = 1and k f = 1.25, substitute of the expressions in Sect. 2.2a, we have the rack cutter data designing data shown in Table 1. Table 1. Design data of rack cutter Parameter
Notation
Module
Rack cutter
mc
2.29
Pressure angle (o )
αc
20.00
Tooth pitch (mm)
pc
7.19
Tooth thickness (mm)
sc
3.59
Width of space (mm)
wc
3.59
Tooth addendum (mm)
ha
2.29
Tooth dedendum (mm)
hf
2.86
Whole depth (mm)
h
5.15
Step 2. Design parameters for oval gear drives The design parameters data of centrodes Σ 1 , Σ 2 and rack cutter in Step 1. substitute into the expressions presented in Sect. 2.2c. The oval gear drive design data are shown in Table 2, while Fig. 7 is a design result programmed on Matlab. Figure 7b is zooming teeth from tooth number ➀ to tooth number ➈ of the oval gear 1. Observe Fig. 7b is easy to see: (i) For involute profile, the shape and size of teeth distributed at different positions on the oval gear are not the same; (ii) The involute profile tends to increase the radius from tooth number ➀ to tooth number ➈. In particular, at tooth number ➈ curvature profiles degenerates into a straight line the same as the rack cutter and (iii) The most significant difference is shown on the root side, especially tooth number ➀ and tooth number ➈ (see a dashed circle in Fig. 7b). Observing the position of the teeth in Fig. 7, we have a foundation that: the teeth in position ➀ (semi-major axis of Σ 1 ) will load capacity than the tooth number ➈ (semiminor axis of Σ 1 ). Thus, calculating the durability according to the load capacity of the oval gear drive shall be calculated for the tooth in position ➀. 4.2 Investigate Parameters of Tooth Size and Kinematic Analysis of the Meshing Process To evaluate the working ability of each tooth on the OG more accurately in this content determines (1) The meshing flank length of each tooth; (2) The thickness of the tooth top and root. With the assumption that there is no shaft distance error and backlash.
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Parameter
Notation
OG1
OG2
Elliptical centrode (mm)
a1 b1
50.00 30.00
50.00 30.00
Addendum ellipse (mm)
aAE bAE
52.29 32.29
52.29 32.29
Base ellipse (mm)
aBE bBE
47.14 27.14
47.14 27.14
Module
m
2.29
2.29
Number of teeth
z
36.00
36.00
Shaft distance (mm)
a12
80
Tooth pitch (mm)
p
7.19
7.19
Tooth thickness (mm)
s
3.59
3.59
Width of space (mm)
w
3.59
3.59
Whole depth (mm)
h
5.15
5.15
Tooth addendum (mm)
ha
2.29
2.29
Tooth dedendum (mm)
hf
2.86
2.86
a) Meshing flank length of teeth distributed on oval gear With the concepts and definitions presented in Sect. 3a and the symbols of the corresponding pairs of conjugation teeth engaged in the meshing process in Fig. 7a, the meshing flank length of each pair mating profiles participating in meshing calculate. Figure 8 is a bar chart depicting the meshing flank length of each mating profile pair of meshing processes corresponding to the numbered conjugation tooth pairs described in Fig. 7a. The corresponding values and percentages on the bar graph described the meshing flank length and rate compared to the tooth profile length, respectively. From Fig. 8, it is easy to see that the meshing flank length of the teeth engaged pair in meshing is different. The more significant the meshing flank length difference, the faster of mating profile pair will wear out. Therefore, there is a slippage between the two profiles during the meshing process. The shorter the meshing flank length of the tooth profile in a couple of the mating profiles, the faster it will wear out. That shows that when designing the oval gear drive, it is necessary to take this parameter into account and optimize to reduce wear and increase the service life of the gear drive. b) The thickness of tooth top and root of teeth distributed on oval gear From the definitions in Sect. 3.3b, the thickness of tooth top sa influences the pointed teeth and interference, while the thickness of tooth root sf influence the tooth’s loadcarrying capacity. With the oval gear drive design data in Sect. 4.1, Fig. 9 is a graph
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Fig. 7. The design of the oval gear drive
Fig. 8. Meshing flank length of the mating profile pair
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showing the thickness of tooth top and root corresponding to the numbered teeth in Fig. 7b and the radius of the centrode at the radial center position of the tooth. From Fig. 9, it can see that the thickness of the tooth top and root increases from tooth number ➀ to tooth number ➈, which is the radius of the centrode, respectively. Thereby, it can identify the teeth in the centrode position with minimal radius often, and the teeth are pointed teeth and weak roots. Therefore, when designing, it is necessary to pay attention to this position.
Fig. 9. The thickness of the tooth top and root of the teeth are numbering from ➀ to ➈
4.3 Kinematic Analysis of the Meshing Process From Fig. 8, it can see that the difference in meshing flank length of the two mating profiles in the pair of teeth ➈- ➉ is the largest. To avoid losing generality in this content, we will investigate profile Γ 1 of tooth number ➈ on OG1 and profile Γ 2 of tooth number ➉ on OG2, while other profiles pair are exploring similarly. From the mathematical model in Sect. 3.1, design data in Table 2 and the OG1 driving with angular velocity ω1 = 10 rad/s, we have: (1) Fig. 10 is the velocity of the point K 1 , K 2 corresponds to Γ 1 , Γ 2 of tooth pair ➈-➉; (2) Fig. 11 relative sliding velocity of point K 1 relative to point K 2 and point K 2 relative to K 1 ; (3) Fig. 12 is the sliding curve of profile Γ 1 relative to Γ 2 and Γ 2 relative to Γ 1 . From Figs. 10, 11 and 13, it is founding that the root profile of tooth number ➈ will wear more than the top profile, whereas conversely, it will wear the top profile of tooth number ➉ more than the root profile. In the case of point K coincides with pitch point I, the relative sliding velocity between the two shapes is zero. The two profiles just roll over each other without slipping. Moreover, for a more general estimate, we call μ = |μ12 | − |μ21 | the difference values of the slip coefficient. Figure 13 depicts the slip coefficient curves of the conjugate 18 to ➉ of the OG2, respectively. tooth pairs from ➀ to ➈ of the OG1 and from
Figure 13, it is showing that the kinematic wear of the mating profile pair is different. In which the root side of the tooth wears more than the top of the tooth.
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Fig. 10. The velocity of points K 1 , K 2 corresponds to Γ 1 , Γ 2 of the tooth pair ➈ - ➉
Fig. 11. The relative sliding velocity of point K 1 relative to point K 2 and point K 2 versus K 1
Fig. 12. Sliding curves of profile Γ 1 relative to Γ 2 and of Γ 2 versus Γ 1
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Fig. 13. Sliding curves μ of the involute profile pair participating in a meshing process
5 Conclusions From the results of research, discussion and detailed evaluation above. In this study, there are the following new findings of the involute profile in the NCG design that other studies have not paid attention to: (1) A formula has been establishing to determine the minimal number of teeth when distributing teeth on the OG with an involute profile according to the pressure angle of the rack cutter and the OG’s centrode. It’s an issue that previous studies have not paid attention to, meanwhile empirically distributed or adjusted design parameters conditions manually. (2) The number of teeth is mainly due to avoiding undercutting, leading to small teeth, weak load capacity. In particular, the teeth are distributed at a minimal position radius of the centrode, while some other curves [15, 21] overcome this drawback. (3) In shapes and sizes of teeth distributed on NCGs are often uneven. The teeth at the maximum radius position of the centrode, the tooth profile degenerates into a straight line. When teeth are distributed at the minimal radius position of the centrode are often pointed, and the thickness of the tooth root is small, leading to weak roots. (4) The relative sliding velocities between the two mating profiles during the meshing process are different, leading to unequal wear between the teeth. These research results have implications for designing the CVT transmission of automobiles or steering mechanisms of autonomous robots by oval gear. Also, the problems of tooth load capacity, stress, deformation, manufacturing and assembly errors leading to transmission function errors and enhancement of the gear drive’s load capacity will be considered part of our future research goals.
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Acknowledgments. This work was supported by Project of Ministry of Education and Training, Vietnam. Under grant Number: B2019 - BKA – 09.
References 1. Ottaviano, E., et al.: An experimental comparative study on non-circular gears and cam transmissions for a blood pumping system. Inte. Design Eng. Tech. Conf. Comp. Info. Eng. Conf. 42568, 371–380 (2006) 2. Prikhodko, A.A.: Force analysis of the two-satellite planetary mechanism with elliptical gears. Mecha. Mecha. Eng. 25(1), 39–46 (2021) 3. Guarnieri, A., Maglioni, C., Molari, G.: Dynamic analysis of reciprocating single-blade cutter bars. American Soc. Agri. Biolo. Eng. 50(3), 755–764 (2007) 4. Zhou, M., Yang, Y., Wei, M., Yin, D.: Method for generating non-circular gear with addendum modification and its application in transplanting mechanism. Int J Agric & Biol Eng 13(6), 68–75 (2020) 5. Zhao, X., Liao, H., Ma, X., Dai, L., Yu, G., Chen, J.: Design and experiment of double planet carrier planetary gear flower transplanting mechanism. Int J Agric Biol Eng 14(2), 55–61 (2021) 6. Thai, N.H., Long, N.D.: A new design of the Lobe pump is based on the meshing principle of elliptical gear pairs. Sci. Technol. Develo. J.-Eng. Technol. 4(2), 861–871 (2021) 7. Tien, T.N., Thai, N.: H, A novel design of the Roots blower, Vietnam. J. Sci. Technol. 57(2), 249–260 (2019) 8. Tien, T.N., Thai, N.H., Long, N.D.: Effects of head gaps and rotor gap on flow rate and hydraulic leakage of a novel non-contact rotor blower. Vietnam J. Sci. Technol. 57(6A), 125–140 (2019) 9. Tinh, T.D., Tien, T.N., Thai, N.H.: Building a mathematical equation for describing volume changes in suction and pumping chambers of an improved type of the roots blower. J. Sci. Technol. Tech. Univ. 41, 022–027 (2020) 10. Thai, N.H.: Shaping the tooth profile of elliptical gear with the involute ellipse curve. Sci. Technol. Develo. J.-Eng. Technol. 4(3), 1048–1056 (2021) 11. Thai, N.H., Trung, N.T.: Pitch line synthesis of noncircular planetary gears. J. Sci. Technol. Tech. Univ. 140, 05–010 (2020) 12. Thai, N.H., Trung, N.T., Nghia, L.X., Duong, N.T.: Synthesis of the external non-circular gear-train with cycloid profile. J. Sci. Technol. Tech. Univ. 145, 033–039 (2020) 13. Viet, N.H., Thai, N.H.: Geometric design and kinematics analysis of non - circular planetary gear train with cycloid profile. Eng. Technol. Sustain. Develo. 31(3), 105–112 (2021) 14. Litvin, F.L., Alfonso, F.-A., Ignacio, G.-P., Kenichi, H.: Noncircular gears Design and Generation. Cambridge University Press, New York (2009) 15. Thai, N.H., Ly, T.T.K., Trung, N.T.: Research design and experimental manufacturing of compound non-circular gear train with an improved cycloid profile of the ellipse. In: International Conference on Engineering Research and Applications (ICERA 2021) 16. Thai, N.H., Thom, P.V., Lam, D.B.: Effects of pressure angle on uneven wear of a tooth profile of an elliptical gear generated by ellipse involute. Sci. Technol. Develo. J. 24(3), 2031–2043 (2021) 17. Thai, N.H., Trung, N.T., Viet, N.H.: Research and manufacture of external non-circular gearpair with improved cycloid profile of the ellipse. Sci. Technol. Develo. J.-Eng. Technol. 4(2), 835–845 (2021) 18. Faydor, L.L., Alfonso, F.: Gear Geometry and Applied Theory. Cambridge University Press, New Yord (2004)
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19. Thai, N.H., Giang, T.C.: Influence of geometrical dimensions on the profile slippage in the hypogerotor pump. Vietnam J. Sci. Technol. 56(4), 482–491 (2018) 20. Thai, N.H., Tien, T.N.: Influence of the designing parameters on the profile slippage and flow of the Roots blower. Sci. Technol. Develo. J.-Eng. Technol. 1(1), 13–19 (2018) 21. Thai, N.H., Trung, N.T., Nghia, L.X., Duong, N.T.: Profile sliding phenomenon in the external non-circular gear-train with cycloidal profile. Eng. Technol. Sustain. Develo. 2(31), 053–057 (2021)
Vibroacoustic Behavior of Double-Composite Plate Filled with Porous Materials Tran Ich Thinh1(B) and Pham Ngoc Thanh2 1 Hanoi University of Science and Technology, Hai Ba Trung District, Hanoi, Vietnam
[email protected] 2 Viet Tri University of Industry, Viet Tri City, Phu Tho Province, Vietnam
Abstract. In this study, based on a modal superposition method and Biot’s theory, an analytical model on vibroacoustic behavior of clamped and simply supported orthotropic rectangular double-composite plates filled with porous materials has been derived. Theoretical predictions of sound transmission loss (STL) across finite double-composite plates lined with poroelastic materials agree well with the existing results in most frequency ranges of interest. Basing on the numerical results obtained, the influence of different parameters on STL of double-composite plates lined with porous materials is quantitatively evaluated and discussed in detail. Keywords: Double-composite plate · Composite sandwich plate · Poroelastic materials · Sound transmission loss
1 Introduction The double-composite plates lined with porous materials or composite sandwich plate with a foam core is widely used in many industries, including aircraft, building, motor vehicle and ships because of the advantages like high strength, light weight, low cost and good sound insulation. The sound transmission loss (STL) of sandwich structures has been the subject of many studies. As far as sound insulation improvement is concerned, porous materials among various sandwich cores have been widely applied in double-wall structures due to their excellent sound absorption capability. By employing Biot’s theory [1] on wave propagation in a poroelastic medium, Bolton et al. [2, 3] modelled analytically the problem of sound transmission through infinite double-wall sandwich panels lined with porous materials and validated the theoretical model against their experimental results. Panneton and Atalla [4] studied the sound transmission loss through multilayer structures made from a combination of elastic, air, and poroelastic materials. The presented approach is based on a three-dimensional finite element model. It used classical elastic and fluid elements to model the elastic and fluid media and used a two-field displacement formulation derived from the Biot theory [1]. Sgard et al. [5] predicted the sound transmission loss through a metal sandwich panel with a porous (fiberglass and foam) core, subjected to hinge-like bonding in a low-frequency diffuse field. The study was based on the finite © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 185–199, 2022. https://doi.org/10.1007/978-981-19-1968-8_15
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element model (FEM) for different layers combined with the boundary element method (BEM) to calculate the sound propagation loss. The theoretical and experimental results have been compared and discussed. Lee and Kondo [6] have studied theoretically and experimentally about sound transmission loss through a sandwich panel structures with aluminum skins, cores is elastic polymer materials subjected to hinged bonding excited in a sound field to examine the effect of the damping layer caused by the elastic core of the plate during sound propagation. To calculate the sound propagation loss, the above study used the Rayleigh integral combined with the hinge boundary condition. The experimental results are in good agreement with the results calculated according to the current finite plate theory but show a significant difference from the infinite plate theory. Lee et al. [7] simplified Bolton’s formulation by retaining only the energetically strongest wave among those propagating in the poroelastic material because the contribution of the shear wave to the transmission loss was found to be always negligible. In recent years, D’Alessandro et al. [8] reviewed the most significant works in the literature about the acoustic response of sandwich panels. The focus is on presenting an exhaustive list of dedicated and validated models, which are able to predict the sound transmission through sandwich panels according to their specific configuration. Lu and Xin [9] adopted the equivalent fluid model to study sound transmission through an infinite double-wall panel with and without rib-stiffened core, respectively, filled with porous sound absorptive materials. Enhanced sound insulation properties of the double-wall sandwich panels have been observed in all these previous works.
Fig. 1. Schematic of a clamped double-laminated composite plate lined with poroelastic materials: (a) global view, (b) side view.
In present study, an analytical model on vibroacoustic behavior of clamped and simply supported orthotropic rectangular double-composite plates filled with foam core has been derived. Biot’s theory is employed to describe wave propagation in poroelastic media. The two composite faceplates are modeled as classical thin plates. By using the modal superposition theory, a double series solution for the sound transmission loss of the structure is obtained with the help of the Galerkin method. Theoretical predictions of sound transmission loss across finite composite sandwich plates with porous material
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agrees well with the existing results in the literature in most frequency ranges of interest. Basing on the numerical results obtained, the influence of the main parameters related to the composite material of faceplates and porous material of core layer on the STL of double-composite plate filled with foam core is quantitatively evaluated and discussed in detail.
2 Theoretical Study 2.1 Plate Geometry and Assumption A double-laminated composite plate lined with poroelastic materials consists of two rectangular, orthotropic, homogeneous and sufficiently thin plates commonly made of laminated composite, as illustrated in Fig. 1. The two elastic plates are fully clamped along their edges to an infinite rigid acoustic baffle. This double-composite plate is filled with porous materials. The bottom plate is excited by √a plane harmonic sound wave with ω being the angular frequency and the symbol j = −1. Without loss of generality, an incident sound wave of unit amplitude is assumed, and its incident direction is determined by the elevation angle ϕ and the azimuth angle θ (see Fig. 1). The vibrations of the upper plate are transmitted through the structure via the poroelastic medium to the bottom plate which induces pressure variations and thus a transmitted sound wave. The incidence field and transmission field separated by the infinite rigid baffle are assumed to be semi-infinite with identical air properties, i.e. air density ρ 0 and speed of sound in ambient air c0 . The panel dimensions are chosen as follows: the length and width of the plate are a, b; the bottom and upper plates thickness are h1 , h2 , and H is the core thickness of the composite sandwich (porous materials) in between the two plates. 2.2 Flexural Motion of the Plate The vibro-acoustic response of an orthotropic symmetric double-composite plate with poroelastic materials (Fig. 1) induced by sound excitation for the bottom and upper plates, respectively [4, 9]: ∂ 4 w1 (x, y; t) ∂ 4 w1 (x, y; t) + 2(D + 2D ) 12 66 ∂x4 ∂x2 ∂y2 4 2 ∂ w1 (x, y; t) ∂ w1 f +D22 + m∗1 2 = jωρ0 1 + σzs + σz 4 ∂y ∂t
(1)
4 ∗ ∂ 4 w2 (x, y; t) ∗ ∂ w2 (x, y; t) + 2 D + 2D 12 66 ∂x4 ∂x2 ∂y2 4 2 ∂ w2 f ∗ ∂ w2 (x, y; t) +D22 + m∗2 2 = −jωρ0 3 − σzs − σz 4 ∂y ∂t
(2)
D11
∗ D11
where: w1 , w2 are the transverse displacements of the upper and bottom plates, respectively. m∗i = ρp hi (i = 1, 2) is the mass per unit area of the bottom and upper plates. ρ p
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is the density of the upper or bottom plates; ρ 0 is the density of air. F1 and F3 are the f acoustic velocity potential in the incident sound field and the transmission field. σzs ; σz are the normal stresses in the z-direction in the exterior fluid field and solid field. Dij and Dij∗ are the flexural rigidities (see any textbook of Mechanics of composite materials): For the bottom plate: 1 k 3 Qij zk+1 − zk3 3
(3)
1 ∗k ∗3 Qij zk+1 − zk∗3 3
(4)
n
Dij =
k=1
For the upper plate: Dij∗ =
n
k=1
2.3 Boundary Conditions of the Plate-Porous Coupling Boundary conditions representing the continuity of the normal velocity and displacement are applied at the surfaces of the porous layer and the facing plate, dependent on the coupling method. Since the plates are assumed to be homogeneous, orthotropic and sufficiently thin compared with the lateral dimensions, the in-plane deformations (and shear stresses) are commonly neglected according to the classical Kirchhoff-Love plate theory. For the coupling method as shown in Fig. 2, three boundary conditions must be satisfied at the bonded interface of the porous layer and facing plate, i.e., one normal velocity condition and two normal displacement conditions: v∗z = jωw; usz = w; ufz = w
(5)
where: w is the transverse (normal) displacement of the elastic plate that contains the convention eiωt , usz and ufz are the solid and fluid displacement vectors and v∗z is the normal acoustic particle velocity in the exterior fluid field defined in Eq. (18). Moreover, the boundary conditions of the bonded coupling in the Eq. (5) must be applied at the interface of the plate and the porous lining:
Fig. 2. Different configurations of the double- composite plate with foam core.
at z = h1 ; −
∂1 = jωw1 , usz = w1 , ufz = w1 ∂z
(6)
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at z = h1 + H; ∂3 − = jωw2 , usz = w2 , ufz = w2 ∂z
(7)
Under the excitation of harmonic sound waves, the transverse deflection of the upper and bottom plates can be expressed in a form of modal decomposition: w1 (x, y, t) = w2 (x, y, t) =
∞ m,n=1 ∞
ϕmn (x, y)α1,mn ejωt ; (8) ϕmn (x, y)α2,mn e
jωt
m,n=1
with ϕ mn is the modal function for the clamped boundary can be written as: 2nπ y 2mπ x 1 − cos ϕmn (x, y) = 1 − cos a b and the simply supported boundary takes the form: mπ x nπ y ϕmn (x, y) = sin sin a b
(9)
(10)
and α 1,mn ; α 2,mn are the modal coefficients of the plate displacement and will be determined by applying the clamped and simply supported boundary conditions. The fully clamped boundary of the bottom and upper plates, the transverse displacement and the rotation moment must be equal to zero along the plate edges, i.e. the following clamped boundary conditions should be satisfied: x = 0, a, ∀0 < y < b, ∂w2 ∂w1 = =0 w1 = w2 = 0, ∂x ∂x
(11)
y = 0, b, ∀0 < x < a, ∂w2 ∂w1 w1 = w2 = 0, = =0 ∂y ∂y
(12)
and the simply supported boundary conditions should be satisfied: x = 0, a, ∀0 < y < b, w1 = w2 = 0,
∂ 2 w1 ∂ 2 w2 = =0 ∂x2 ∂x2
(13)
y = 0, b, ∀0 < x < a, w1 = w2 = 0,
∂ 2 w1 ∂ 2 w2 = =0 2 ∂y ∂y2
(14)
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2.4 Determination of Sound Transmission Loss The acoustic velocity potential in the incident sound field (field 1 in Fig. 1) consists of an incident wave and a reflective wave with the amplitudes I and β, respectively, and it can be expressed in a harmonic form as [9]: 1 = Ie−j(kx x+ky y+kz z−ωt ) + βe−j(kx x+ky y−kz z−ωt )
(15)
The transmission field (field 3 in Fig. 1) with the amplitude γ in this field and its velocity potential can be written as: 3 = γ e−j(kx x+ky y+kz z−ωt )
(16)
The poroelastic medium is excited by an incident plane harmonic sound wave. For a given incidence direction (ϕ, θ ), the components of the acoustic wavenumber are defined as: kx = k0 sin φ cos θ ; ky = k0 sin φ cos θ ;
(17)
kz = k0 cos φ
where k x , k y , k z are wave number in the x, y, and z directions and k0 = ω/c0 is the acoustic wave number in air and c0 is the acoustic speed in the air. The amplitudes of the reflected and transmitted waves, β(x,y) and γ (x,y) are dependent on the local positions on the upper and bottom plates, respectively. These acoustic velocity potentials are related to the acoustic pressure p and the normal acoustic particle velocity v∗z in the fluid field and solid field [4]: p = ρ0
∂ ∂ = jωρ0 ; v∗z = − ∂t ∂t
(18)
The sound power of the incident or transmitted wave per unit area (i.e. acoustic intensity) is defined as p = Re(pv∗ )/2, and the acoustic particle velocity is related to the sound pressure through p = p/(ρ0 c0 ) for harmonic waves, ρ 0 is the density air and c0 is the acoustic speed in the air. The sound power can be expressed as [9] =
1 Re 2
b a 0
0
pvz∗ dA =
1 2ρ0 c0
b a 0
p2 dA
(19)
0
Considering the relation (18) and the velocity potential definitions (15) and (16), the sound power of incident and transmitted are determined by. 1 =
ω2 ρ0 2c0
0
b a 0
21 dxdy; 3 =
ω2 ρ0 2c0
0
b a 0
23 dxdy
(20)
The power transmission coefficient for a single incident wave with fixed direction angles is defined as τ (φ, θ ) =
3 1
(21)
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For diffuse incident sound, the averaged transmission coefficient can be derived by integration as [3, 9]:
2π φlim τdiff =
0
τ (φ, θ ) sin φ cos θ d φd θ 0
2π φlim sin φ cos θ d φd θ 0 0
(22)
where ϕlim is the limiting angle defining the diffuseness of the incident field. The sound transmission loss is defined as: STL(φ, θ ) = 10 log[1/τ (φ, θ )]
(23)
3 Modelling the Poroelastic Material According to Biot’s theory [1], an elastic porous material is assumed statistically isotropic and has the solid phase (elastic frame) as well as the fluid phase (air contained in pores). The model of Bolton [3] allows three waves in the porous material, two longitudinal waves, and one shear wave. Meanwhile, Lee et al. [7] shown that the shear wave is always negligible compared to the longitudinal waves. Hence, there are four wave components within the porous material, i.e. positive and negative propagating components of the two most energetically waves along the z-axis, which are characterised by the complex amplitudes D1 –D4 . Thus, the displacements of the solid and fluid phase are obtained as [10]: ⎛ ⎞ k1z −jk1z z k1z jk1z z D e − D e 1 2 ⎜ ⎟ k12 k12 ⎜ ⎟ (24) uzs = je−j(kx x+ky y−ωt ) ⎜ ⎟ k2z −jk2z z k2z jk2z z ⎠ ⎝ +D3 2 e − D4 2 e k2 k2 ⎛ ⎞ k1z −jk1z z k1z jk1z z c D e − c D e 1 2 2 ⎜ 1 1 k2 ⎟ k1 ⎜ ⎟ f 1 uz = je−j(kx x+ky y−ωt ) ⎜ (25) ⎟ k2z −jk2z z k2z jk2z z ⎠ ⎝ +c2 D3 2 e − c2 D4 2 e k2 k2 The expressions of the normal stresses in the z-direction are derived as ⎡ ⎤ ε1s D1 e−jk1z z + D2 ejk1z z ⎥ ⎢ ⎦ σzs = e−j(kx x+ky y−ωt ) ⎣ s −jk2z z jk2z z +ε2 D3 e + D4 e ⎡ ⎤ f ε1 D1 e−jk1z z + D2 ejk1z z ⎥ ⎢ f ⎦ σz = e−j(kx x+ky y−ωt ) ⎣ f −jk2z z jk2z z +ε2 D3 e + D4 e
(26)
(27)
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f
where the parameters ε1s , ε2s , ε1 , ε2 are in the form: 2 k1z
ε1s = 2N f ε1
k12
+ A + c1 Q, ε2s = 2N
= Q + c1 S,
f ε2
2 k2z
k22
+ A + c2 Q
(28)
= Q + c2 S
and c1 = b1 − b2 k12 , c2 = b1 − b2 k22 ; ∗ ∗ ∗ ∗ b1 = ρ11 S − ρ12 Q / ρ22 Q − ρ12 S ; ∗ ∗ b2 = PS − Q2 / ω2 ρ22 Q − ρ12 S
(29)
Es νs Es ;N= ; A= 2(1 + νs ) (1 + νs )(1 − 2νs ) Q = (1 − β)Ef ; S = βEf where the wavenumbers, k 1 2 , k 2 2 , and the complex equivalent densities, ρ ij (i, j = 1, 2) are detailed in [3]. E s is the static Young’s modulus of solid phase; ν s is the Poision’s ratio and E f denotes the bulk modulus of the fluid in the pores; β is the porosity of the porous material. Combining Eqs. (24)–(27), yields a set of four algebraic equations for four unknown (1.e. D1 – D4 ) which can be rearranged into a matrix equation: MD = R
(30)
where M is a 4 × 4 transfer matrix: ⎡ ⎤ kI −kI kII −kII ⎢ ⎥ c1 kI −c1 kI c2 kII −c2 kII ⎥ M=⎢ ⎣ e−jk1z (H +h1 ) kI −ejk1z (H +h1 ) kI e−jk2z (H +h1 ) kII −ejk2z (H +h1 ) kII ⎦ (31) c1 e−jk1z (H +h1 ) kI −c1 ejk1z (H +h1 ) kI c2 e−jk2z (H +h1 ) kII −c2 ejk2z (H +h1 ) kII with the auxiliary coefficients kI = jk1z /k12 and kII = jk2z /k22
(32)
T the 4 × 1 unknown vector D = D1 D2 D3 D4 , and the forcing vector T R = ej(kx x+ky y) 1 1 2 2 where the parameters G1 , G2 are in the form: 1 = φmn (x, y)α1,mn ; 2 = φmn (x, y)α2,mn m,n
(33)
(34)
m,n
The unknown vector D can be solved simultaneously from the matrix Eq. (30) as D = V.R, where V = M −1 is the inverse of the transfer matrix M. Substituting the solutions
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of D1 –D4 into Eqs. (26) and (27), the expressions of the normal stresses in the solid and f fluid phases, σzs , σz can be obtained as: −jk z 1z [(V 11 + V12 )1 + (V13 + V14 )2 ] s,f s,f jωt e σz = ε1 e +ejk1z z [(V21 + V22 )1 + (V23 + V24 )2 ] −jk z (35) 2z [(V 31 + V32 )1 + (V33 + V34 )2 ] s,f jωt e +ε2 e +ejk2z z [(V41 + V42 )1 + (V43 + V44 )2 ] where V ij (i, j = 1, 2, 3, 4) are the elements of the inverse matrix V. The entire problem will therefore be solved once α 1,mn and α 2,mn are determined. Applying the Galerkin method to the plate motion Eqs. (1) and (2) leads to: ⎛ ⎞ ∂ 4 w1 (x, y; t) ∂ 4 w1 (x, y; t) + 2(D12 + 2D66 ) b a ⎜ D11 ⎟ ∂x4 ∂x2 ∂y2 ⎜ ⎟ ⎜ ⎟.ϕmn (x, y)dxdy = 0 4 2 ⎝ ∂ w1 (x, y; t) 0 0 f⎠ ∗ ∂ w1 s +D22 + m − jωρ − σ − σ 0 1 z z 1 ∂y4 ∂t 2 (36) ⎛ ⎞ 4 4 ∗ ∗ ∂ w1 (x, y; t) ∗ ∂ w1 (x, y; t) + 2 D22 + 2D66 b a ⎜ D11 ⎟ 4 ∂x ∂x2 ∂y2 ⎜ ⎟ ⎜ ⎟.ϕmn (x, y)dxdy = 0 4 w (x, y; t) 2w ⎠ ∂ ∂ 0 0 ⎝ 1 1 f ∗ ∗ s +D22 + m2 2 − jωρ0 2 − σz − σz ∂y4 ∂t (37) To solve the entire problem numerically, a truncation is needed: 1 ≤ m, n ≤ N max , where the choice of N max is a trade-off between accuracy and computation cost. Substif tuting Eqs. (9), (10), (32), (33) and the σzs ; σz expressions into Eqs. (1) and (2) yields a N max x N max system. Once this matrix equation is solved, the unknowns α i,mn ( i = 1, 2) are determined. Consequently, all the variables dependent on α 1,mn and α 2,mn including f β, γ , σzs and σz will be completely resolved.
4 Validation For the first validation study, the sound transmission loss across a finite clamped doublealuminium plate with poroelastic material is calculated and compared with result of Bolton et al. in [3]. The properties of aluminium plates and poroelastic material as follows: For aluminium plate: length x width of plate, a x b = 1.2 m × 1.2 m; upper plate thickness, h1 = 1.27 mm; bottom plate thickness, h2 = 0.762 mm; porous layer thikness, H = 27 mm; Young’s moduluas, E p = 70 GPa; Bulk density, ρ p = 2700 kg/m3 ; Poisson’s ratio, ν p = 0.33. For poroelastic material: Bulk density of the solid phase, ρ s = 30 kg/m3 ; In vacuo, bulk Young’s modulus, E s = 8.105 Pa; Bulk Poisson’s ratio, ν s = 0.40; flow resistivity, ψ = 5.103 MKS Rayls m−1 ; Porosity, β = 0.9; geometrical structure factor, ϑ = 0.78; density, ρ 0 = 1.21 kgm−3 , speed of sound, c0 = 343 ms−1 , ratio of specific heats, χ = 1.4; Prandtl number, Pr = 0.71.
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Fig. 3. Comparison between the predicted STL curve and result of Bolton et al. [3].
It is obvious from Fig. 3 that there is an acceptable agreement between present analytical and theoretical results shown in [3]. For the second validation study, the STL across a finite simply supported doublecomposite plate with poroelastic material is calculated and compared with result of Lee et al. [7]. The properties of aluminium plates and poroelastic material as follows: For aluminium plate: Length x width of plate, a x b = 0.303 m × 0.203 m; upper plate thickness, h1 = 0.5 mm; bottom plate thickness, h2 = 0.5 mm; porous layer thikness, H = 1 mm; Young’s modulus, E p = 73.2 GPa; Bulk density, ρ p = 2720 kg/m3 ; Poisson’s ratio, ν p = 0.33. For poroelastic material: Bulk density of the solid phase, ρ s = 1600 kg/m3 ; In vacuo bulk Young’s modulus, E s = 4.12 GPa; Poisson’s ratio, ν s = 0.40; flow resistivity, ψ = 25.103 MKS Rayls m−1 ; Porosity, β = 0.9; geometrical structure factor, ϑ = 0.78; density, ρ 0 = 1.21 kgm−3 , speed of sound, c0 = 343 ms−1 , ratio of specific heats, χ = 1.4; Prandtl number, Pr = 0.71. Figure 4 shows a comparison between the present predicted STL curve and the experimental curve of Lee and Kondo in [7]. A good agreement between two results is observed.
Fig. 4. Comparison between the predicted STL curve and results of Lee and Kondo [7].
5 Results and Discussion 5.1 Effect of Composite Materials Density The influence of composite materials density on STL through a finite clamped doublecomposite plate filled with porous material is studied in this section by selecting four
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types of materials: Boron/Epoxy, Glass/Epoxy, Graphite/Epoxy and Kevlar/Epoxy. The double-plate consists of two identical orthotropic laminated composite faceplates. Laminate configuration of the bottom and the upper plate is [0/90/0/90]s . Geometric dimensions of the skin: length x width, a x b = 1.2 m × 1.2 m; bottom plate thickness, h1 = 1.27 mm; upper plate thickness, h2 = 0.762 mm and core thickness, H = 27 mm. The mechanical properties of Glass/Epoxy skin are: ρ = 1946 kg/m3 ; E 1 = 40.851 GPa; E 2 = 10.097 GPa; G12 = 3.788 GPa; ν 12 = 0.27. For Boron/Epoxy skin: ρ = 2000 kg/m3 ; E 1 = 204 GPa; E 2 = 18.5 GPa; G12 = 5.590 GPa; ν 12 = 0.23. For Graphite/Epoxy skin: ρ = 1600 kg/m3 ; E 1 = 181 GPa; E 2 = 10.3 GPa; G12 = 7.170 GPa; ν 12 = 0.28 and for Kevlar/Epoxy skin: ρ = 1460 kg/m3 ; E 1 = 76 GPa; E 2 = 5.5 GPa; G12 = 2.300 GPa; ν 12 = 0.34. The porous material has the following mechanical properties: Young’s modulus, E p = 73.2 GPa; Bulk density, ρ p = 2720 kg/m3 ; Poisson’s ratio, ν p = 0.33. For porous material: Bulk density of the solid phase, ρ s = 1600 kg/m3 ; In vacuo bulk Young’s modulus, E s = 4.12 GPa; Poisson’s ratio, ν s = 0.40; flow resistivity, ψ = 25.103 MKS Rayls m−1 ; Porosity, β = 0.9; geometrical structure factor, ϑ = 0.78; density, ρ 0 = 1.21 kgm−3 , speed of sound, c0 = 343 ms−1 , ratio of specific heats, χ = 1.4; Prandtl number, Pr = 0.71. Speed of sound in air, c = 343 m/s; the density of the air, ρ 0 = 1.21 kg/m3 and the initial amplitude, I 0 = 1 m2 /s.
Fig. 5. Effect of composite materials density on STL of a clamped double-composite plate lined with porous materials.
Fig. 6. Influence of composite materials density on STL of a simply supported double-composite plate lined with porous materials.
It is readily seen from Figs. 5 and 6 that, as a result of the density of the materials, the STL curves are ascending. Hence, the Boron/Epoxy and Glass/epoxy composite
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materials demonstrate a better transmission loss due to the densities that are greater than the other two materials. Next, the influence of the main parameters related to the porous materials on the STL through clamped and simply supported orthotropic composite sandwich plates with foam core excited by an incident sound wave with an incident angle ϕ = 30° and an azimuth θ = 45o is considered. Two skins made of the same Glass/Epoxy layer composite material have a configuration of [90/0/0/90]s . The mechanical properties of the skins and porous core layer have been clearly introduced as in previous subsection. 5.2 Effect of Porous Core Layer Density From Figs. 7 and 8, it can be seen that, in the low frequency region, the STL value increases as the density of the porous material increases or decreases.
Fig. 7. Effect of porous material density on STL through a finite clamped composite sandwich plate.
Fig. 8. Effect of porous material density on STL through a finite simply supported composite sandwich plate.
In particular, the STL value increases for a simply supported composite sandwich plate in the frequency region, f < 1600 Hz while, for a clamped composite sandwich plate, STL increases in the frequency region, f < 2000 Hz. In the high frequency region, the STL value decreases slightly as the density of the core layer increases, specifically, for a simply supported composite sandwich plate at the frequency region, f > 1600 Hz while that of the clamped composite sandwich plate at the frequency region, f > 2000 Hz.
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5.3 Effect of Porous Layer Young’s Modulus Figures 9 and 10 show that, in the low frequency region, the STL value increases as the Young’s modulus of the porous material increases or decreases. Specifically, the STL value increases for a simply supported composite sandwich plate in the frequency region, f < 1250 Hz, while for a clamped composite sandwich plate, the STL value increases in the frequency region, f < 1600 Hz. In the high frequency region, the STL value decreases as the density of the core increases. Specifically, for a simply supported composite sandwich plate, at the frequency region, f > 1250 Hz, and for a clamped composite sandwich plate, in the frequency region, f > 1600 Hz.
Fig. 9. Effect of Young’s modulus of porous core layer on STL through a finite clamped composite sandwich plate.
Fig. 10. Effect of Young’s modulus of porous core layer on STL through a finite simply supported composite sandwich plate.
5.4 Effect of Porous Layer Poisson’s Ratio Figures 11 and 12 show that, in the low frequency region, the STL value increases as the Poisson coefficient of the porous material increases or decreases. Specifically, the STL value increases for a simply supported composite sandwich plate, at the frequency range, f < 2000 Hz, while for a clamped composite sandwich plate, the STL increases in the frequency region, f < 1600 Hz. In the high frequency region, the STL value decreases as the Poisson’s ratio of the core increases. Specifically, the simply supported composite sandwich plate at the frequency range, f > 2000 Hz while the clamped composite sandwich plate at the frequency range, f > 1600 Hz.
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Fig. 11. Effect of Poisson’s ratio of porous core layer on STL through a finite clamped composite sandwich plate.
Fig. 12. Effect of Poisson’s ratio of porous core layer on STL through a finite simply supported composite sandwich plate.
6 Conclusions An analytical approach has been developed to investigate the vibroacoustic behavior of double-composite plates lined with porous materials. The influence of several key structure parameters on the sound insulation capacity of clamped and simply supported composite sandwich plate is then explored, including the density, Poisson’s ratio and the Young’s modulus of porous materials. Based on the results of this study, the following is concluded: • The theoretical predictions on STL are in good agreement with existing results. • The surface density of skin composite materials influences considerably on STL of finite double-composite plate lined with porous materials. • With both boundary conditions considered, in the low frequency region, increasing the density of the porous layer will significantly improve the sound insulation of the sandwich composite plate. However, in high frequencies, it reduces the sound transmission loss through the structure. The modulus and Poisson’s ratio have a slight influence on the sound insulation of the structure. • The sound insulation of the sandwich composite plate with a clamped boundary condition is better than that of the sandwich plate with a simply supported boundary condition.
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References 1. Biot, M.A.. Theory of propagation of elastic waves in a liquid-saturated porous solid I. Lowfrequency range. II. Higher frequency range. J Acoust Soc Am (1956); 28(2):168–191 2. Bolton, J.S., Green, E.R.: Normal incidence sound transmission through double-panel systems lined with relatively stiff, partially reticulated polyurethane foam. Appl Acoust 39, 23–51 (1993) 3. Bolton, J.S., Shiau, N.M., Kang, Y.J.: Sound transmission through multi-panel structures lined with elastic porous materials. J. Sound Vib. 191(3), 317–347 (1996) 4. Panneton, R., Atalla, N.: Numerical prediction of sound transmission through finite multilayer systems with poroelastic materials. J Acoust Soc Am 100(1), 346–354 (1996) 5. Sgard, F.C., Atalla, N., Nicolas, J.: A numerical model for the low frequency diffuse field sound transmission loss of double-wall sound barriers with elastic porous linings. J Acoust Soc Am 108(6), 2865–2872 (2000) 6. Lee, C., Kondo, K.: Noise transmission loss of sandwich plates with viscoelastic core. American Ins Aero Asreon, 2137–2147 (1999). AIAA-99–1458 7. Lee, J.S., Kim, E.I., Kim, Y.Y., Kim, J.S., Kang, Y.J.: Optimal poroelastic layer sequencing for sound transmission loss maximization by topology optimization method. J Acoust Soc Am 122(4), 2097–2106 (2007) 8. D’Alessandro, V., Petrone, G., Franco, F., De Rosa, S.: A review of the vibroacoustics of sandwich panels: models and experiments. J Sandw Struct Mater 15(5), 541–582 (2013) 9. Lu, T.J., Xin, F.X.: Vibro-acoustics of Lightweight Sandwich. Science Press Beijing and Springer-Verlag, Berlin Heidenberg (2014) 10. Thinh, T.I., Thanh, P.N.: Theoretical and experimental study of sound transmission loss across finite clamped composite sandwich plates.In: Proceedings of the ICERA 2020 indexed by SCOPUS, LNNS 178, pp. 820–831
Research of Calculation and Testing Assessment of Impact Resistance of Automobile Alloy Wheel Rims Used in Vietnam Thanh Cong Nguyen(B) Faculty of Mechanical Engineering, University of Transport and Communications, Lang Thuong Ward, Dong Da District, Hanoi, Vietnam [email protected]
Abstract. Automobile alloy wheel rims are the parts that are frequently subject to mechanical forces so they are easily deformed, scatched and damaged. Evaluation of the durability of automobile alloy wheel rims are of particular interest for the purpose of ensuring safety for cars during the participation in traffic. The article presents the contents of calculation and testing assessment of the impact resistance ability of automobile alloy wheel rims used in Vietnam. Based on National technical regulation QCVN78:2014/BGTVT of Vietnam on prescribing the durability testing process of automobile alloy wheels to conduct testing and build a computational model in Ansys software, it includes three (3) components: (1) an object with mass of 500 kg, (2) presumably non-deformed rack and (3) alloy rims for 235/50 ZR18 101W tires. Calculation results determine that the maximum stress on the rims concentrated at the contract position between the object block and the car wheel rims in the impact process was 163.84 (Mpa), smaller than the allowable stress of the material and the testing result showed no radial crack appreared based on the National technical regulation QCVN78:2014/BGTVT on quality ensured wheels. Keywords: Alloy wheel rims · Durability calculation · Impact resistance · QCVN78:2014/BGTVT · Ansys
1 Introduction Nowadays, cars have been working as the favorite and popular means of transport in the world in general and in Vietnam in particular. For that reason, safety, comfort, friendliness and aesthetics always requires this mean of transport to constantly develop. On cars, the alloy wheel rims play an extremely important role because they are the parts that directly receive all impact forces when the cars move. However, because of the fact that they are the parts that frequently bear mechanical forces, the car alloy wheel rims are very susceptible to deformation, scratches and damages. Therefore, there have been problems about the design, testing and exploitation of the alloy wheel rims used on cars in the context that the transport infrastructure condition in Vietnam remain much limited. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 200–210, 2022. https://doi.org/10.1007/978-981-19-1968-8_16
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Fig. 1. Alloy rim structure. 1-Spokes; 2-Bolt holder; 3-Mounting flanges; 4-Outer rim; 5-flange; 6-Centering hole.
The alloy wheel rims are the rotating load bearing parts located between the axle shafts and tires, normally the rim include the components namely spokes, bolt holders, outer rim, flange, centering hole and valve hole as shown in Fig. 1. To study about the traffic safety relating to the durability of the alloy wheel rim, it is necessary to take into account the specific working conditions, based on the assessment of the factors affecting the safe movement of cars and from there, recommend solutions to improve the safe movement for the purpose of limiting and/or minimizing the possibilities of accidents. The main research directions on the durability of the alloy wheel rims in the world focus on a number of the following issues: – Calculating the working strength, fatigue strength and maximize the structure of the alloy wheel rim: the demand for the change of the shape as well as the structure of the wheel rim often requires the manufacturers to digitize the alloy wheel rim model, check the quality assessment in the short time but still ensure high accuracy and test in virtual space environment to help save costs and increase competitiveness. Normally, calculations in static mode are performed to determine the stress, the deformation of the wheel for the purpose of controlling the durability as well as the impact of dimension specifications on the durability as in studies of Siva Prasad et al. [1]; Rajarethinam P. et al. [2]. Studying the contact pressure on the wheel rims, determining the fatigue strength of the wheel in the working conditions under cyclic loads in the research works of authors Sourav das et al. [3]; Ch. P. V. Ravi Kumar et al. [4]; Hongyu Wang et al. [5]. – Testing and assessment according to standards: In countries with developed industries such as the US, Japan and many European countries, the car alloy wheels are the objects that are compelled to be inspected and tested before prior to installation on vehicles and sale on the market. A number of standards to control car light alloy wheels in the world include ECE R124, ISO 7141:2005, AS 1638:1991 - NZS 5419:1991 [6–8]. In Vietnam, besides the control of technical safety of cars, the wheel rim is a component that needs to be controlled in terms of the quality, especially the durability. Up to now, the National Technical Regulation QCVN78:2014/BGTVT [9] has been issued for the purpose of stipulating the technical requirements and checking the quality of technical safety for aluminum alloy rims and new magnesium alloy wheel rims (generally referred as light alloy wheels). To work as the testing basis, an author group
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named Dang Viet Ha and the colleagues [10] presented the testing method and introduced the impact testing device of the car light alloy wheel rims in accordance with QCVN78:2014/BGTVT. In addition, the author group Dang Viet Ha and the colleagues [11] also published the research work on the flexural fatigue strength testing method of the rims used for cars manufactured and assembled in Vietnam. However, in reality, Vietnam’s research works related to car alloy wheel rims still remain limited. Therefore, the study of the impact resistance ability evaluation and calculation method of the wheel rims helps the manufacturers, the manufacture and assembly quality manager to be able to control the quality of the rims and optimize the costs.
2 Basic for Calculation and Testing Assessment of the Impact Resistance Ablity of Automobile Alloy Wheel Rims According to QCVN78:2014/BGTVT, the essence of the impact strength testing is to evaluate the impact resistance ability of the wheel rim when the wheels move impacting with the roadside or obstacles. There are various ways to generate the impact energy but the simplest way is to convert potential energy into impact kinetic energy by lifting loads to a certain height and dropping them freely. The impact testing model is described in Fig. 2. The spring loaded system is guided to freely fall to impact on the tire portion of the wheel which is tilted 13° from the horizontal plane. The device to test load impacting with the impact load is cast of steel, vertically operated with the impact surface of at least 125 mm in width and 375 mm in length and has been de-angled by chamfering or rounding the angle. The mass of the impact load M whose unit is kg with the tolerance ± 2%, is calculated as shown in Eq. 1 [9]: M = 0.6xW + 180
(1)
where: W is the maximum load bearing capacity of the wheel, specified by the manufacturers for the car or the wheel, calculated in kg.
Fig. 2. The alloy wheel impact testing device
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The testing process is performed as follows: – Mounting the testing wheel on the testing device with the impact load on the rim flange. The wheel is mounted with its axle at an angle of 13° ± 1° to the vertical direction. – The tire is fitted to the test wheel rim as specified by the manufacturers. The tire inflation pressure is determined by the manufacturers or if no technical document is available, the pressure shall be 200 kPa. – Impact locations: one location in the adjoining area between spokes and rim and another location in the middle of two spokes, close to the valve hole. New wheel rim shall be used for each trial. – Make sure that the impact load passes through the tire, and rests on the rim flange 25 mm ± 1 mm. Pull the impact load up to the height of 230 mm ± 2 mm above the highest part of the rim flange and let it fall freely, then the wheel rim shall be inspected to detect damage(s). The evaluation standards are based on Evaluation Item 4.4 in the QCVN 78:2014/BGTVT, in which the wheel must withstand a single impact at a specified force without damages. The wheel shall be considered defective if, after the test, one of the following signs is found: – – – –
Crack appears through the center of the rim. Spokes are separated from the wheel rim. Reduced pressure inside the tire is equal to the outer air pressure within 01 min. If the wheel is deformed or cracked in the part of the rim contacting with the surface of the impact load, it shall not be considered a failure.
3 Calculation of Car Alloy Wheel Structure During the Impact by Ansys Software 3.1 Building a Model to Calculate the Durability of the Car Wheel Rim by ANSYS Software The model to calculate and evaluate the durability of the car wheel rim is built based on QCVN78:2014/BGTVT, which is to build the model with three (3) components: alloy wheel rim, mount rack and impact load block. The alloy wheel rim is mounted with the mount rack through the contact condition between the surfaces. Emulate the impact process of the load block and the alloy wheel rim at the height of 230 mm.
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Fig. 3. Tire and rim
Based on the types of tires manufactured and used to assemble on cars in Vietnam, select the car wheel rim for the tire with the symbol of 235/50 ZR18 101W to build geometric model as shown Fig. 3, finite element model and durability. The car wheel rim has the shape as shown Fig. 4 and the dimension specifications as shown in Table 1. Table 1. Basic specifications of the wheel rim Specification
Symbol Value Unit
Rim diameter
D
457
mm
Rim width
B
191
mm
Rim thickness
t
Lateral deviation of wheel rim b
7
mm
45
mm
Build the finite element model for the car wheel rim with the type of element SOLID185, the contact surfaces between the object block and the rim, the car rim with the base having the element type CONTA174, TARGE170 and the alloy material having the properties are shown in Table 2, through dividing mesh of geometric model, the car wheel rim structured finite element model is obtained as shown in Fig. 5.
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Fig. 4. Geometric model of car rim
Fig. 5. Finite element model of car rim
Table 2. Material properties of car alloy wheel. Material properties
Symbol
Value
Unit
Modulus of elasticity
E
71,7
GPa
Poisson coefficient
ν
0.33
Tensile yield strength
σc
505
MPa
Ultimate tensile strength
σk
570
MPa
3.2 Loading Plan When applying load to the model based on the Technical Regulation QCVN 34:2017/BGTVT on checking car alloy wheel, it shall be conducted as follows: – The impact object block has the calculated mass according to Eq. 1 of 500 kg with the maximum load bearing capacity of the wheel. When the object block impacts in its
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movement the alloy wheel rim, which is converted from the height of 230 mm down to the height of 1 mm, the velocity of the impacting object block at 1 mm from the wheel rim is calculated according to Eq. 2: v = 2.g.h = 2,2321(m/s) (2) where: h = 0.229 (m) is the height of the original object block compared to the position at 1mm from the impact surface of the car wheel rim, g = 9.81 m/s2 is the acceleration due to gravity. – The pressure exerted by air inside the tire on the car wheel rim is divided to 02 component: The first component acting on the outer rim surface (denoted as No. 4 on Fig. 1) is the static pressure according to standard with the value P0 = 2 × 105 Pa. The second component acting on the wheel rim flange (denoted as No. 5 on Fig. 1) has the value calculated according to Eq. 3 [12]: Tf =
P Fp o = a2 − rf2 = 14771(Pa) 4π.rf 4rf
(3)
where: Tf is the force exerted by the compressed air inside the tire on one car rim flange. Po is the standard static pressure of the tire according to the Regulation, a = 0.346 (m) is the outer diameter of the wheel, rf = 0.457/2 = 0.2285 (m) is the nominal radius of the alloy rim. The rack is fixed on the plan and the impacting load is attributed to the velocity, air pressure inside tire acting on the rim is shown in Fig. 6.
Fig. 6. Model of durability analysis of impacting car wheel rim in Ansys software
3.3 Problem Resolution After solving the mathematical problem on Ansys software, the obtained result shown on Fig. 7 is the nodal displacement distribution field, Fig. 8 is the nodal stress distribution field according to Von Mises, Fig. 9 is the stress value diagram at the contact position between the mass and the wheel rim and Fig. 10 is the deformation distribution field according to Von Mises.
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From Fig. 7, we can see that the largest displacement at elements on the alloy wheel rim is 0.125 mm. Figure 8 shows the place of the largest deformation and the largest stress concentration at the contact position between the wheel rim and the impacting object block. Figure 9 shows the maximum generated stress value is 163.84 (MPa) < 505 (MPa) (limited stress of rim material) the rim is strong enough. Equivalent deformation distribution according to Von Mises on Fig. 10 shows the maximum value at the contact area between the object block and the wheel rim during the impact and has the value of 0.003764 (m). Thus, based on the evaluation criteria under Evaluation Item 4.4 in the Technical Regulation QCVN 78:2014/BGTVT, the calculated rim meets the requirements.
Fig. 7. Displacement distribution in the vertical direction
Fig. 8. Equivalent stress distribution according to Von Mises
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Fig. 9. Equivalent stress diagram according to Von Mises at maximum stress location
Fig. 10. Equivalent deformation distribution according to Von Mises
4 Testing Assessment of Impact Resistance of Alloy Wheel Rim The testing process to evaluate the impact resistance ability of the alloy wheel rim is performed in accordance with the Technical Regulation QCVN78:2014/BGTVT and is detailed in Ref. [10]. The car wheel rim is installed on the testing device as shown in Fig. 11. The testing result is shown in Fig. 12.
Fig. 11. Installation of rim on the testing device
Checking the wheel rim after the testing showed that there was no crack through the center of the wheel, the spokes were not separated from the rim. The testing rim satisfied the requirements in accordance with the technical regulation QCVN78:2014/BGTVT.
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Fig. 12. Testing results
5 Conclusion The car alloy wheel rims are the parts that are often subjected to the mechanical force so they are easily deformed, scratched and damaged. Evaluation of the durability of car alloy wheel rim is of particular interest to ensure safety for cars during traffic participation. The study is based on QCVN78:2014/BGTVT of Vietnam prescribing the durability testing process of the car alloy wheel rim to conduct testing and build a computational model in Ansys on the basis of finite element method platform. The calculation results for a specific alloy wheel rim mounted for the tire with the symbol of 235/50 ZR18 101W determine the maximum stress on the wheel rim concentrated at the contact position between the object block and the car wheel rim during the impact is 163.84 (MPa) which is smaller than the allowable stress of materials and the testing results show no radial crack appears based on QCVN78:2014/BGTVT the wheel rim meets the quality. Thus, through this study, it shows the result may be used as a basis for studying the wheel rim change parameters like new material, thickness, dimension and type of spokes. Therefore, more researches shall provide better solution to industrial issues related to manufacturing, conditions to use the car wheel rim installed on cars used in Vietnam.
References 1. Siva Prasad, T., Krishnaiah, T., Iliyas, J.M., Jayapal Reddy, M.: A review on modeling and analysis of car wheel rim using CATIA & ANSYS. Int. J. Innov. Sci. Mod. Eng. (IJISME) 2(6) (2014). ISSN: 2319-6386 2. Rajarethinam, P., Periasamy, K.: Modification of design and analysis of motor cycle wheel spokes. Int. J. Mod. Eng. Res. (IJMER), 123–127 (2014) 3. Das, S.: Design and weight optimization of aluminium alloy wheel. Int. J. Sci. Res. Publ. 4(6), 1–12 (2014) 4. Ravi Kumar, C.P.V., Satya Meher, R.: Topology optimization of aluminium alloy wheel. Int. J. Mod. Eng. Res. (IJMER) 3(3), 1548–1553 (2013) 5. Wang, H.: Geometric parameters optimal design of variable cross-section rim. In: Proceedings of the FISITA 2012 World Automotive Congress. Lecture Notes in Electrical Engineering, vol. 196. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-33738-3_5 6. ECE R124: Uniform provisions concerning the approval of wheels for passenger cars and their trailers 7. ISO 7141: Road vehicles - light alloy wheels - impact tests 8. AS 1638:1991 - NZS 5419:1991: Motor vehicles - light alloy road wheels
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9. QCVN78:2014/BGTVT: National technical regulation on light alloy wheels for automobiles (2014) 10. Ha, D.V., Niem, V.T., Vu, D.Q.: Impact test of automotive light alloy wheel. Transp. J. (2015) 11. Ha, D.V., Niem, V.T., Vu, D.Q.: Research of testing assessment of the fatigue strength of automobile light alloy wheel rims according to QCVN 78:2014/BGTVT. Transp. J. (2018) 12. Stearns, J., Srivatsan, T.S., Gao, X., Lam, P.C.: Understanding the influence of pressure and radial loads on stress and displacement response of a rotating body: the automobile wheel. Int. J. Rotating Mach. 2006, 1–8 (2006). Article ID 60193
One-Dimensional and Entropy Generation Analyses of a Solar Chimney Cao Trung Hau1 , Doan Thi Hong Hai1 , Nguyen Van Hap2,3 , and Nguyen Minh Phu1(B) 1 Faculty of Heat and Refrigeration Engineering, Industrial University of Ho Chi Minh City,
Ho Chi Minh City, Vietnam [email protected] 2 Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam 3 Vietnam National University, Ho Chi Minh City, Vietnam
Abstract. Solar chimney is an essential natural ventilation device for high density urban areas like Ho Chi Minh City. A local temperature and entropy generation analysis for the solar chimney is needed to evaluate the laws of thermodynamics. In this paper, a simplified one-dimensional mathematical model is presented to predict the temperature distribution, induced airflow and entropy generation of a solar chimney. The mathematical formulation consisting of algebraic and ordinary differential equations is solved simultaneously by manipulating a numerical integration. Calculation results are tested against many published data. The comparison shows that the air flow in this study is slight underestimation. Key parameters include collector length, air gap and solar irradiance. When the length increased from 1 to 2.5 m, the flow increased from 0.055 to 0.1 kg/s. In contrast, entropy generation increases from 1.5 to 3.7 W/K. The induced airflow significantly increases with the air gap. But the air gap has little effect on entropy generation. The chimney length needs to be optimized in terms of multiple-objective optimization of maximum airflow and minimum exergy destruction. Keywords: Local temperature · Natural convection · Discharge coefficient · Numerical integration
1 Introduction Natural ventilation is a thermal comfort solution for hot and humid countries like Vietnam. It can be applied in a wide range of spaces by placing suitable windows to catch the wind or creating movement of air due to temperature difference. With abundant solar radiation source, a solar chimney for natural ventilation is the most feasible device for limited spaces in urban area of Vietnam. There are two common types of solar chimney for ventilation, namely vertical chimney and inclined chimney. In addition, solar chimney is also integrated with photovoltaic panels to achieve many goals such as ventilation, power generation and panel cooling [1]. Nguyen and Wells [2] numerically surveyed a © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 211–219, 2022. https://doi.org/10.1007/978-981-19-1968-8_17
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horizontal solar chimney. The influence of width and height of inlet and outlet has been focused in the study. They have shown that airflow is maximal at a certain outlet width. Furthermore, as the inlet width increases, the airflow increases. Nguyen and Wells [3] numerically simulated to examine the effect of horizontal wall position at the inlet or outlet of a solar chimney. They recommended proper spacing of the wall so as not to reduce the air flow. Nguyen [4] developed a simple algorithm using the lattice Boltzmann method to prevent reverse current at the outlet of a vertical solar chimney. Recently, Phu et al. [5] investigated the effect of the size and position of a chimney cap on buoyancy induced airflow. The results show that the cap reduces about 20% airflow and the effect of cap position is more severe than that of cap width. From the most recent literature review [6] revealed that there has been no study that has developed a one-dimensional (1D) analytical model to predict the temperature distribution of the absorber plate, glass and air flow. In addition, entropy generation behavior with key parameters of a solar chimney was also not found. The present study, therefore, aims to form a mathematical model to predict the local temperatures and entropy production of a solar chimney.
2 Model Formulation and Validation Figure 1 outlines the schematic diagram of a solar chimney. It consists of an absorber plate and a glass cover in vertical orientation. The gap in the middle is natural convection air going up. Solar radiation passes through the glass to the absorber surface. The absorber surface receives heat and increases its temperature leading to reduce the density of the air. Light air ascends and the air in the environment below the chimney enters for substitute according to conservation of mass. The plate transfers radiative heat to the glass, causing the air near the glass to also heat and move up. In this study, the important parameters of chimney including length (L), air gap (D), and solar radiation (I) were changed to investigate their influence on temperature distribution of the absorber plate, glass, and air along the flow direction, entropy generation, and the buoyancy induced airflow. The width of chimney is fixed W = 1 m. The mathematical model is established from the steady heat transfer equations as follows. The glass receiving radiative heat from solar and the absorber plate is balanced with the amount of heat exchanged by convection with the air in the chimney and the heat loss due to convection and radiation to the surrounding environment [7, 8]: I αg + hw Ta − Tg + hra Tsky − Tg + hr,g,p Tp − Tg + hf ,g Tf ,m − Tg = 0 (1) where Tsky is the sky temperature, Tsky = 0.0552Ta1.5 [9], other quantities will be defined in Table 1 and Eqs. (5)–(13). The air moving in the chimney receives convective heat from the glass and the absorber plate: Whf ,g Tg − Tf ,m + Whf ,p Tp − Tf ,m To − Ta = (2) L mc ˙ p
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Tf(y) Tp(y)
Tg(y)
Fig. 1. The solar chimney with notations.
Considering a spatial difference (dy) of solar chimney, the air temperature gradient is calculated as follows [10]: Whf ,g Tg,i − Tf ,i + Whf ,p Tp,i − Tf ,i dTf ,i = (3) dy mc ˙ p The absorber plate receives solar radiation and transfers heat by convection to the air and transmits radiant heat to the glass cover: I τg αp + hf ,p Tf ,m − Tp + hr,g,p Tg − Tp = 0 (4) Convective heat transfer coefficient due to wind is evaluated as [9]: hw = 2.8 + 3Vwind
(5)
The natural convective heat transfer coefficient (hf ) of the air in the chimney is calculated through the Nusselt number as follows: [7, 9]: For laminar regime (Ra < 109 ): Nusseltlam = 0.68 + 0.67
Ra1/4 1 + (0.492/Pr)9/16
4/9
(6)
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For turbulent regime (Ra > 109 ): Nusselttur = 0.825 +
0.387Ra1/6 1 + (0.492/Pr)9/16
2 8/27
(7)
where Pr and Ra are respectively Prandtl number and Rayleigh number. The dimensionless numbers are defined as: Pr = μcp /k
(8)
Ra = Gr · Pr
(9)
in which Gr is Grashof number, Gr = gβTL3 μρ 2 . Radiative heat transfer coefficients can be found by the following correlations: Radiative heat transfer coefficient from the glass to the sky: 2 Tg + Tsky (10) hra = σεg Tg2 + Tsky 2
Radiative heat transfer coefficient of glass and absorber plate: hr,g,p = σ Tg2 + Tp2
Tg + Tp 1/εg + 1/εp − 1
Air mass flow rate of a solar chimney can be estimated as [7, 9]: Tf ,m − Tr Ao m ˙ = Cd ρf ,o √ 2gL Tr 1 + Ar
(11)
(12)
where Cd is discharge coefficient, Cd = 0.61 [9], Tr is room temperature. The average temperature of the air in the chimney is calculated by: Tf ,m = (1 − ω)Ta + ωTo
(13)
where ω is mean temperature weighting factor, ω = 0.75 [7]. Entropy generation of a control volume when neglecting air pressure loss is expressed as [10]: Sgen = (1/Ta − 1/Ts )Qs + (ln(To /Ta ) − To /Ta + 1)mc ˙ p
(14)
where Ts is sun temperature, Ts = 5777K, and Qs is solar radiation absorbed by the absorber plate, Qs = I τg αp LW . Table 1 presents the fixed parameters and the range of key parameters used in this study. The thermophysical parameters of the air (density ρ, specific heat cp , conductivity k, viscosity μ) are calculated according to the average temperature of the air Tf,m . The temperature gradient equation is solved by numerical integration in EES software. The details of the solution strategy were described in our previous studies [10, 11]. It should
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be noted that the solution is independence of spatial discretization (y). Large y leads to unsmooth results, and small y results in long computation time. Therefore, the increment in y-coordinate as one-tenth was chosen in this study. Figure 2 shows a comparison of air flow by solar irradiance while chimney geometry is fixed. It can be seen that the current 1D model calculation results closely follow the theoretical, experimental and simulation results of Kong et al. [12]. The present calculation result is underestimation. However, the current mathematical model is simple and cost-effective to predict the axial temperature distribution and entropy generation of a solar chimney. Table 1. The parameters inputted to the mathematical model. Parameter
Value
Ref.
Ambient temperature
Ta = 300 K (= Tr )
–
Absorptivity of glass cover
αg = 0.06
[7]
Absorptivity of absorber plate
αp = 0.95
[7]
Emissivity of glass cover
εg = 0.86
[12]
Emissivity of absorber plate
εp = 0.95
[12]
Transmissivity of glass cover
τg = 0.84
[7]
Chimney length
L = 1–2.5 m
–
Air gap
D = 0.1–0.3 m
–
Chimney width
W=1m
–
Solar radiation
I = 200–800 W/m2
–
Increment in y-coordinate
y = L/10
–
Wind velocity
Vwind = 1 m/s
–
Outlet flow rate (m3/s)
0.016 0.014 0.012 0.01
Theo. (Kong et al., 2020) Exp. (Kong et al., 2020)
0.008
Num. (Kong et al., 2020)
0.006
Present study
0.004 0.002 0
D=0.04 [m]
200
W=1 [m]
400
L=0.521 [m]
600
800
Solar radiation heat flux absorbed by absorber plate (W/m2)
Fig. 2. Validation with the theoretical, experimental, and numerical results [12]
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3 Results and Discussion Figure 3 presents the influence of solar radiation from 200 to 600 W/m2 on temperature distribution, air flow and entropy generation with fixed solar chimney parameters including D = 0.1 m and L = 2 m. As shown in Figs. 3a–c, the absorber plate, glass and air temperatures increase along the tube height as the air receives heat in the direction of flow. The rate of increase in air temperature is greater than that of increase in glass temperature because the air directly receives heat from the absorber plate. While the glass loses heat to the surroundings due to convection and radiation. As the irradiance increases, these temperatures increase, as expected. It can be observed that at 200 W/m2 (Fig. 3a), the air temperature is higher than the glass temperature. But at 600 W/m2 (Fig. 3c), the temperature cross occurs at y = 0.5 m. Thus, when the solar radiation increases, radiant heat transfer dominates compared to natural convection heat transfer. The absorber plate emits thermal radiation to the glass, causing the glass temperature to be higher than the air temperature in the range of 0 < y < 0.5 m. In addition, increasing radiation intensity increases the temperature difference between the absorber plate and the air. Figure 3d shows that both airflow and entropy generation increase with radiation intensity. Entropy generation increases linearly with radiation intensity. This is because entropy generation is strongly dependent on radiation intensity as shown in Eq. 14. Entropy generation increases from 1 W/K to 3 W/K as the irradiance increases from 200 to 600 W/m2 . Thus, increased solar irradiance increases exergy destruction, increases irreversibility and decreases exergy efficiency.
Fig. 3. Effect of solar radiation (I).
The impact of the air gap from 0.1 to 0.3 m is observed in Fig. 4. As D increases, the absorber plate temperature increases and the slope of the temperature profile decreases.
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Increasing D increases the cross-sectional area of the air flow in the chimney thereby decreasing the air velocity. This causes the temperature difference of air and absorber plate increases with D. At the largest D, the air temperature is lower than the glass temperature. Figure 4d shows that the air flow is strongly affected by the air gap because the air is easily convective in the large gap. Whereas entropy generation increases slightly with the gap due to convection term in exergy balance.
Fig. 4. Effect of air gap (D).
Fig. 5. Effect of the chimney length (L) on air mass flow rate and entropy generation.
Figure 5 shows the effect of chimney length on induced airflow and entropy generation. As the length increases, the flow rate increases due to an increase in the area of the
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absorber plate. The flow rate has a little increase with the length because the longer the chimney, the more it impedes natural convection. The length increased from 1 to 2.5 m; the flow increased from 0.055 to 0.1 kg/s. In contrast, entropy generation increases from 1.5 to 3.7 W/K because exergy destruction is proportional to the heat absorbed by the plate. From the above analysis results, it is necessary to increase the air gap to yield high air flow. While the chimney length needs to be optimal to achieve a reasonable flow rate and entropy generation.
4 Conclusions A 1D mathematical model has been established to predict temperature distribution in the solar chimney and entropy generation. The model has been validated against published results. Calculation results show that the air gap should be increased to achieve high buoyancy airflow with unchanged entropy generation. The chimney length should be optimized to ensure the suction airflow and exergy destruction. When solar radiation is low, the glass temperature is lower than the air temperature. When the air gap is maximum, the air temperature is less than the glass temperature. Temperature cross occurs between the glass and air temperatures for the remaining cases. The air temperature slope along the solar chimney is greater than the glass temperature slope due to the heat loss of the glass to the environment. The temperature profiles of the absorber plate and the air are almost parallel.
References 1. Ahmed, O.K., Hussein, A.S.: New design of solar chimney (case study). Case Stud. Therm. Eng. 11, 105–112 (2018) 2. Nguyen, Y., Wells, J.: A numerical study on induced flowrate and thermal efficiency of a solar chimney with horizontal absorber surface for ventilation of buildings. J. Build. Eng. 28, 101050 (2020) 3. Nguyen, Y.Q., Wells, J.C.: Effects of wall proximity on the airflow in a vertical solar chimney for natural ventilation of dwellings. J. Build. Phys. 44(3), 225–250 (2020) 4. Nguyen, Y.Q.: Studying convective flow in a vertical solar chimney at low rayleigh number by lattice Boltzmann method: a simple method to suppress the reverse flow at outlet. In: Nguyen-Xuan, H., Phung-Van, P., Rabczuk, T. (eds.) ACOME 2017. LNME, pp. 807–820. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7149-2_57 5. Nguyen Minh, P., Nguyen Hoang, K., Nguyen Van, H.: Impact of the V cap on induced turbulent air flow in a solar chimney: a computational study. J. Therm. Eng. 9, 1–12 (2022) 6. Li, W., et al.: Energy assessment methods for solar chimney in buildings: a review. J. Renew. Sustain. Energy 13(4), 042701 (2021) 7. Ong, K.: A mathematical model of a solar chimney. Renew. Energy 28(7), 1047–1060 (2003) 8. Mathur, J., et al.: Experimental investigations on solar chimney for room ventilation. Sol. Energy 80(8), 927–935 (2006) 9. Bassiouny, R., Koura, N.S.: An analytical and numerical study of solar chimney use for room natural ventilation. Energy Build. 40(5), 865–873 (2008) 10. Phu, N.M., Tu, N.T., Hap, N.V.: Thermohydraulic performance and entropy generation of a triple-pass solar air heater with three inlets. Energies 14(19), 6399 (2021)
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11. Phu, N.M., Tu, N.T.: One-dimensional modeling of triple-pass concentric tube heat exchanger in the parabolic trough solar air collector (2021) 12. Kong, J., Niu, J., Lei, C.: A CFD based approach for determining the optimum inclination angle of a roof-top solar chimney for building ventilation. Sol. Energy 198, 555–569 (2020)
Oscillation Measurement of the Magnetic Compass Needle Employing Deep Learning Technique Thanh-Hung Nguyen(B) and Minh-Chien Nguyen School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. This paper presents a novel approach to measure the oscillation of the magnetic compass needle. The proposed approach uses a deep convolutional neural network to detect both pivot and the end of the needle. Based on the detected positions of these parts, the angle between the current and initial positions of the needle is estimated. The oscillation of the magnetic needle can be determined through the relationship of the angle to time. The experimental results indicate that the deep learning based method outperforms the traditional object tracking methods in term of accuracy for the application of measuring the oscillation of the magnetic needle. Keywords: Oscillation measurement · Magnetic compass · Compass needle · Object tracking
1 Introduction Measuring the number of oscillations of a magnetic needle is one of the checks to ensure the quality of the compass. In this test, the compass needle is stimulated to oscillate by a magnet sample. After that, a quality checker counts the number of oscillations of the needle. If this number satisfies a predefined criterion, the compass is classified as the good product. This test method is only suitable for small production. To automate the oscillation testing process, several approaches has been proposed. Monsoriu et al. [1] applied the template matching based method to detect the gliders on each captured frame. The positions of these gliders versus time are used to analyze the damped coupled oscillators. Kamil et al. [2] used an object segmentation technique to determine pendulum ball motion. The angle of pendulum computed from the location of segmented ball is utilized to measure the oscillation of simple pendulum. Höpfner et al. [3] proposed the approaches for measuring mechanical oscillations using smartphone sensors. Their methods are only suitable for the mechanical structure. Kavithaa et al. [4] applied the Lukas-Kanade’s optical flow and blob library to track the pendulum ball. The time, amplitude and velocity of the simple pendulum are then calculated after tracking. Li et al. [5] used the light sensor in a smartphone to measure the period of the oscillation of a simple pendulum. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 220–229, 2022. https://doi.org/10.1007/978-981-19-1968-8_18
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In general, the above methods can work best for the simple pendulums and small initial oscillations. However, for products with complex patterns and moving at a fast speed, it is difficult to accurately estimate the position of the target object. In this paper, an effective method is proposed to solve these difficulties in oscillation measurement. The deep learning based method is developed to measure the oscillation of the compass needle. The paper is organized as follows. Section 2 presents the proposed method for measuring the number of oscillations of the magnetic needle. The experimental results are shown in Sect. 3. Finally, the conclusions are summarized in Sect. 4.
2 Methodology To measure the oscillation of the needle, the YOLO-v4 [6] is applied to track both pivot and the end of the needle. After that, the positions of these parts are used to estimate the angle between the current and initial positions of the needle. The relationship of the angle to time presents the oscillation of the magnetic needle. To remove the noise in the estimated data, the Gaussian filter is employed. The number of oscillations can be determined in real-time by automatic counting the number of peaks in the filtered data. 2.1 Needle Tracking To track the oscillation of the needle, the YOLO-v4 is applied for each captured image in real-time. The position of the needle is determined through the position of the detected pivot and the end of the needle. The YOLO-v4 is trained with about 3700 images of the magnetic compass needle. These images are annotated to generate the dataset for training step. The dataset of labeled images is divided into training and test sets with the ratio of 0.80:0.20. The performance of the training step is evaluated through the Intersection over Union (IoU), the loss, and the mean Average Precision (mAP) parameters. The IoU is a function that evaluates the accuracy of a class, and it is calculated as the intersection of the predicted bounding boxes and the ground truth. If it is greater than a certain threshold, the prediction is considered good. The mAP compares the ground-truth bounding box to the detected box and returns a score. The score is higher, the model is more accurate. The loss is used to evaluate the deviation between the prediction result and the truth. It is lower, the training is better. In the final iteration, the average IoU is 91.59%, the average loss is 0.04, and the [email protected] is 100%. The detection result on a test image is shown in Fig. 1. 2.2 Oscillation Measurement The locations of the detected pivot and the end of the needle are represented by the centers of two bounding boxes. Let V 0 (x 0 , y0 ) is the vector from the pivot to the initial position of the end of the needle and V e (x e , ye ) is the vector from the pivot to the current position of the end of the needle as shown in Fig. 2. The angle α between the current and initial positions of the needle can be determined as follow: ⎞ ⎛ x x + y y ye xe 0 e 0 e ⎠ ⎝ (1) − ∗ sign α = arccos x0 y0 x2 + y2 x2 + y2 0
0
e
e
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Fig. 1. Detection of the pivot and the end of the needle using YOLO-v4.
Angle α is estimated for the captured images in real-time. The relationship between angle α and time is presented in the Fig. 3. Due to the errors in the detected positions of the pivot and the end of the needle, the measured angle contains noise and needs to be filtered. The Gaussian filter was used to perform this task. The result of the filtered angle is shown in Fig. 3. The number of oscillations of the needle N is estimated from the numbers of local maxima and minima in real-time angle data. Let N max is the number of the local maxima and N min is the number of the local minima. The number N can be determined as follow: N=
Nmax + Nmin 2
(2)
The local maximum α max (t) and the local minimum α min (t) at time t satisfies the following conditions. αmax (t) > α(t − 1) && αmax (t) > α(t + 1)
(3)
αmin (t) < α(t − 1) && αmin (t) < α(t + 1)
(4)
δ = α max (i)−α min (i) < δth
(5)
where α max (i) and α min (i) are two successive peaks, and δ th is a defined threshold. Figure 4 shows a result of the oscillation measurement on real-time angle data.
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Fig. 2. Estimated angle of the needle for oscillation measurement.
Fig. 3. The measured and the filtered angles to the time.
Fig. 4. Real-time oscillation measurement of the compass needle.
3 Experimental Results Figure 5 shows the experiment setup to measure the oscillation of the magnetic compass needle. A magnet sample is used to set the initial position of the needle which makes angle 90° with the equilibrium position. The magnet sample is then released. The needle oscillates around the equilibrium position and stops a few seconds later. Three experiments are performed with different lighting conditions and difference cameras. The sample images used in the study are shown in Fig. 6. The number of oscillations that is measured by the developed algorithm is compared with the number counted by human. In all 15 tests, the results from the proposed method and human are the same as shown in Table 1. Figure 7 illustrates the process of measuring magnetic needle oscillations in real-time.
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Fig. 5. Experiment setup for oscillation measurement.
Fig. 6. The sample images used in the case study 1 (a), case study 2 (b), and case study 3 (c).
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Fig. 7. Real-time measuring magnetic needle oscillations in case study 1 (a), case study 2 (b), and case study 3 (c).
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Fig. 8. Oscillation measurement using CSRT in case study 2: (a) tracking result and (b) measured angle vs time.
Fig. 9. Oscillation measurement using KCF in case study 2: (a) tracking result and (b) measured angle vs time.
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Fig. 10. Oscillation measurement using MEDIANFLOW in case study 2: (a) tracking result and (b) measured angle vs time.
Fig. 11. Oscillation measurement using MIL in case study 2: (a) tracking result and (b) measured angle vs time.
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To verify the performance of the proposed object tracking algorithm for oscillation measurement, the tests are performed with traditional tracking methods, such as CSRT [7], KCF [8], MEDIANFLOW [9], and MIL [10]. These methods only achieve good results when using high speed cameras and smaller initial angle of the needle. The oscillation measurement results of the CSRT, KCF, MEDIANFLOW, and MIL in case study 2 are shown in Figs. 8, 9, 10, and 11, respectively. In the experiment, the CSRT based method is only achieved good results in case study 1. The KCF, MIL, and MEDIANFLOW based methods are all failed to track the end of the needle. The graph showing the relationship between measured angle and time does not represent the oscillation of the needle. In comparison, the proposed deep learning based method performed very well in these experiments as shown in Table 1. Table 1. The number of oscillations measured by the proposed method in comparison with manual measurement performed by human and the other methods Method
Case study 1 1
2
3
Human
9
8
9
CSRT
9
8
9
Case study 2 4
Case study 3
5
1
2
3
4
5
1
2
3
4
5
9
9
9
11
9
9
9
10
9
10
10
10
9
9
8
1
1
8
1
9
1
1
10
9
KCF
0
2
2
3
1
0
1
0
1
0
0
0
0
0
0
MEDIANFLOW
1
1
3
2
2
2
1
1
1
1
1
2
1
2
2
MIL
2
4
7
10
9
2
0
1
2
0
2
1
1
2
2
Ours
9
8
9
9
9
9
11
9
9
9
10
9
10
10
10
4 Conclusion In this study, a new method for measuring the oscillation of the magnetic compass needle has been developed employing a deep learning technique. The experiment results indicate the effectiveness of the proposed approach. The number of the oscillations of the needle is determined accurately at the human-level in real-time. In addition, the developed method outperforms the traditional object tracking methods in terms of accuracy in measuring needle oscillations. The proposed algorithm is very suitable for the automation of magnetic needle oscillation measurement.
References 1. Monsoriu, J.A., Giménez, M.H., Riera, J., Vidaurre, A.: Measuring coupled oscillations using an automated video analysis technique based on image recognition. Eur. J. Phys. 26, 1149– 1155 (2005) 2. Kamil, M.Y., Al-Zuky, A.A.D., Al-Tawil, R.S.: Study of experimental simple pendulum approximation based on image processing algorithms. Appl. Phys. Res. 3, 29 (2011)
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3. Höpfner, H., Morgenthal, G., Schirmer, M., Naujoks, M., Halang, C.: On measuring mechanical oscillations using smartphone sensors: possibilities and limitation. ACM SIGMOBILE Mob. Comput. Commun. Rev. 17, 29–41 (2013) 4. Kavithaa, R., Babu, R.U., Deepak, C.R.: Simple pendulum analysis — a vision based approach. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (2013) 5. Li, D., Liu, L., Zhou, S.: Exploration of large pendulum oscillations and damping using a smartphone. Phys. Teach. 58, 634–636 (2020) 6. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv:2004.10934 (2020) ˇ 7. Lukežiˇc, A., Vojíˇr, T., Cehovin, L., Matas, J., Kristan, M.: Discriminative correlation filter tracker with channel and spatial reliability. Int. J. Comput. Vision 126, 671–688 (2018) 8. Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 702–715. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_50 9. Kalal, Z., Mikolajczyk, K., Matas, J.: Forward-backward error: automatic detection of tracking failures. In: The 20th International Conference on Pattern Recognition (ICPR), pp. 2756–2759 (2010) 10. Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 983–990 (2009)
Development of Grading System Based on Machine Learning for Dragon Fruit Nguyen Minh Trieu and Nguyen Truong Thinh(B) Department of Mechatronics, Ho Chi Minh City University of Technology and Education, Thu Duc, Ho Chi Minh City, Vietnam [email protected]
Abstract. The dragon fruit is well-known for providing a variety of nutrients and is widely available in international markets. This paper presents an automatic evaluation and sorting system that uses a combination of the KNN model and a convolutional neural network (CNN) to extract features of dragon fruit such as length, width, and defects. Then this process was to analyze and classify the group of the dragon fruits including G1, G2, G3. The dragon fruit was classified according to different standards according to requirements. The data was collected from 2610 dragon fruits from farms, dragon fruit packing facilities. An automatic dragon fruit classifying system has high accuracy of 92.85% and productivity approximately 5 times higher than that of manual dragon fruit classification. Keywords: Machine learning · CNN · ANN · KNN · Dragon fruit · Classification · Sorting · Grading · Automatic system
1 Introduction Dragon fruit (Hylocereus spp.) with many other names pitaya, pitahaya, or thanh long (Vietnamese) is a tropical fruit grown in many countries around the world such as Vietnam, Thailand, Indonesia, Cambodia, … In Vietnam, dragon fruit is widely planted in 63/65 provinces/cities of Vietnam [1], according to the Vietnam Import and Export Report 2020 of the Ministry of Industry and Trade for dragon fruit exports 1,363.8 thousand tons. In Vietnam, the dragon fruit is consumed commercially as well as exported to a lot in markets around the world. The fruit is very popular because of benefit for human health [2] such as vitamins (B1, B2, B3, C, moisture, protein, carbohydrates, … [3]. It also has been shown that in addition to the nutrients from the flesh of the dragon fruit, the seeds of the dragon fruit contain omega-3 and omega-6 that help removes heavy metals from the body, increase eyesight. Dragon fruit skin also contains pectin - lowers cholesterol, improves blood sugar, betalains - makes dyes, food colors. Currently, in Vietnam, there are 3 varieties of dragon fruit, including red skin white flesh, red skin red flesh, and yellow skin white flesh, but the two types of dragon fruit that account for a large percentage of the growing area are red skin white flesh and red skin red flesh, for the yellow skin type is a new variety that has only been tested in a few localities. The area of dragon fruit growing is increasing, so the export of dragon fruit to other countries also © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 230–243, 2022. https://doi.org/10.1007/978-981-19-1968-8_19
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increases. However, to be able to export the agricultural products, specifically dragon fruit in this study, the quality of agricultural products will determine the price and market for that type of agricultural product. Each country has own import standard, China is the largest dragon fruit import market (80%) according to the report of Vietnam’s Ministry of Industry and Trade in 2019, followed by Singapore, Indonesia, … in addition to other markets such as Japan, the US, Australia, … To be able to package and export, it is necessary to grade and sorting the dragon fruit according to the standards of the countries. Based on the results of surveys, at the factories of purchasing, packaging, and exporting dragon fruits in provinces as Long An and Tien Giang (Southern of Vietnam) at the present time, the evaluation and sorting of dragon fruit by humans, takes a long time and the accuracy is not high, but the cost is very high. To be able to import dragon fruit in fastidious but potential countries such as the US, Japan, EU, China… requires the evaluation and classification of dragon fruit to be strictly implemented to achieve high accuracy with the classification, currently, it costs a lot of money to do this. With the development of science and technology, many studies on automatic classification of agricultural products have been proposed and applied in practice such as fruit classification system, tomato, mango, lemon classification [4–7]. The proposal builds an automatic dragon fruit classification system is extremely necessary. There are some studies on dragon fruit such as the quality classification system of white flesh dragon fruit [8] using the backpropagation method combined with image processing to classify dragon fruit, the study was carried out an experimental model with a white background. It is easy to segment dragon fruit and achieve an accuracy of 86.67%, identify ripeness of dragon fruit [9], detect diseases on the stem of dragon fruit [10]. In general, the studies on the current automatic dragon fruit classification system have very few studies on this issue, due to the different climatic and environmental characteristics of each country, the fruits account for a high proportion of the fruit species different imports and export. In Vietnam, according to the actual survey at the classification and packaging workshops of dragon fruit for export, the demand for an automatic dragon fruit sorting system is very large, but there has not been any in-depth research on the issue of automatic classification of dragon fruit. Due to its different appearance from other fruits, dragon fruit has red skin with green scaly spikes and tail. In fact, according to the dragon fruit export standards, the outer shape of the dragon fruit must be balanced, not too short, or too long, so it is necessary to extract the real length and width of the dragon fruit for consideration shape standards. Defects on dragon fruit such as cracks, bee stings, white spots, … need to identify and calculate the area of defective areas to classify dragon fruit. In order to accurately identify the dragon fruit from the image processing chamber (including dragon fruit and background color), the defect standards are decisive to the group of dragon fruit, the sum of the defective area on the fruit can only be from 0 to 4 cm2 , avoiding the confusion between the background color and the color of the fruit proposes to use the KNN model to segment the dragon fruit and remove the background color. Using CNN to identify external features of the dragon fruit, then calculates the classification of dragon fruit according to the parameters installed on the system. This study aims to propose an automatic dragon fruit grading system that meets the needs of the current dragon fruit sorting and packing facilities in terms of productivity and accuracy. In summary, this study has some main contributions like as
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using the KNN model for detecting the color threshold to remove the background color; using the CNN model for extracting the features of fruits; propose an automatic dragon fruit classification system suitable for businesses in Vietnam with an accuracy of up to 92.85% after testing. The content of this paper including 5 sections: introduction of study and overview of dragon fruits described in Sects. 1 and 2, Sect. 3 and 4 are the structure of sorting system and calculation to predict the group of dragon fruit, results and discussion are presented in Sect. 5.
Tail Red flesh
Scaly spike Defect
Seed
(a)
(b)
Fig. 1. Structure of the red dragon fruit, (a) the outside structure of the dragon fruit, (b) the inside structure of the dragon fruit.
2 Natural Features of Dragon Fruits Dragon fruit is a tropical fruit with high nutrients content and medicinal value, belongs to the cactus family, so the fruit has different external features compared to other fruits. In this study, only two types of dragon fruits as red skin with red flesh (Hylocereus costaricensis), red skin with white flesh (Hylocereus undatus) are considered, because these are being grown and exportsd to different contries [11]. The external structure of the dragon fruit consists of red or pink skin, green scaly spikes, and a tail. The inside includes red or white flesh, black seeds, which contain many healthy nutrients such as vitamins B1, B2, especially high vitamin C content 3 times higher than carrots shown in Fig. 1. Each country has own import standards. In terms of weight standard, there are obvious differences in each region, each country, the US market prefers the weight of dragon fruit to be smaller than other markets as Asia, only from 300 g–350 g for a fruit, the Chinese market larger than 300 g for a fruit, Thailand market from 200–600 g for a fruit. Besides, grading of dragon fruit quality depends on defects on the fruit such as insect bites, cracks, white spots, … is evaluated according to the percentage or the area of defects on the fruit from 0–4 cm2 for the Chinese market, 0–10% per total exterior areas for the Thailand market, … Besides, to ensure the quality of dragon fruit during cleaning and ship, the number of broken scaly spikes also affect the grading results, the deep cracks cause the dragon fruit to be excluded from the direct system. The current
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classification of dragon fruit is only used by human power, causing errors to be inevitable, the cost of manual classification accounts for a very high amount in the export packaging process of dragon fruit. Dragon fruit will be classified into 3 main group G1, G2, G3 with G1 being the best. Currently, the dragon fruit market is growing, and increasingly diversifying products made from dragon fruit such as wine, bread, drying, … because the rich in nutrients makes dragon fruit attractive more used, favored in markets around the world. Harvest Dragon Fruit from Farms
Sorting (G1, G2, G3) Cleaning & Drying
Cold Storage (5 ± 2 ºC)
Packing
Fig. 2. The process of packing and storing dragon fruit.
Short tail Broken scaly spike
Snail bite
Insect bites
Waterlogged
Fig. 3. The defects on dragon fruit.
However, the export of fresh dragon fruits still accounts for a high proportion of Vietnam’s economy. The nutrients from the dragon fruit help come largely from the flesh and seeds of the dragon fruit. Dragon fruit can give fruit from the 2nd year after planting and lasts for 12 years, dragon fruit flowers from May-August every year, harvested fruit 50 days later [12], to increase the yield of dragon fruit, farmers turn on the lights at night to stimulate the flowering of dragon fruit to increase crop yield. The process of packing dragon fruit for export at dragon fruit exporting factories showed in Fig. 2. Dragon fruit is harvested from the farms then brought to the workshop for sorting and packaging, stored in cold storage, and transported to the dragon fruit importing countries. The best
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temperature of the cold storage for dragon fruit is 5 ± 2 °C [13] for up to 90 days. Most of the stages from harvesting to sorting dragon fruit are done by human power, only the cleaning stage is done by automatic cleaning machines. Dragon fruit is an easy plant to grow because it is resistant to high temperature and light, but on the trunk and fruit of dragon fruit there are many different diseases such as external diseases of dragon fruit caused by insect bites, cracks in the heavy rainy, snails also cause damage on the surface of dragon fruit, broken scaly spike, all that affects the classification process of dragon fruit. Defects due to external factors of dragon fruit, the scaly spikes of dragon fruit not long enough (>1.5 cm) are also considered defects for some import markets as shown in Fig. 3.
3 Structure of Grading System Along with the development of technology, many studies on automatic classification of agricultural products using digital image processing technology and machine learning such as apple, strawberry, mango, … [14–16]. In this study, an automatic sorting system is proposed based on the import standard of countries. The system is designed based on surveys and actual needs from dragon fruit exporting factories in Vietnam. This system consists of two main parts. The first part is the image processing chamber to collect the images of the dragon fruit and transmit them to the central processor to extract the features on the fruit, namely length, width, and defects of the dragon fruit. Part 2 is a conveyor with separate trays for sorting dragon fruit, combined with a load cell sensor to calculate the weight of the fruit. The image processing chamber is designed with a roller conveyor, the roller is designed to use a soft-material that does not cause damage to the fruit surface during the sorting process, the distance between 2 rollers is designed and fit the dragon fruit, collect the image of the whole dragon fruit and do real-time processing extracting the characteristics of the dragon fruit. The image processing chamber is arranged with the RBG camera with light arranged with the led bulb to minimize the black shadow, and the glare of the dragon fruit, causing errors in the classification process. The required image must be sharpened, without black shadows to avoid confusion of the system with the defects of the dragon fruit. The dragon fruit is rotated by rollers to collect images of all surfaces on the fruit and the processing center identify and extract the features of the dragon fruit for the process of calculating and sorting dragon fruit. The conveyor part with trays arranged on the conveyor is also the actuator to classify dragon fruit according to the group calculated from the system. The loadcell sensors are used to weigh the actual weight of the dragon fruit, then using reducing the noise filter proposed in the study [17] helps the system to classify accurately according to the weight standard. The automatic dragon fruit sorting system is designed based on the property of the type of dragon fruit, and the actual requirements from the workshop for sorting and packaging, productivity and accuracy are two important factors in this system. The dragon fruit is put into the image processing chamber by the workers, the rollers rotate the fruits evenly so that the camera captures the image of the entire dragon fruit, processing to extract the features such as length, width, defects, number of broken scaly spikes. Then the dragon fruit is moved through the tray to weigh and sorting based on the signal from the central processor, the process of the system is shown in Fig. 4.
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Processing Center (sorting)
ACTUATOR CAMERA Actuator 3
Actuator 2
Actuator 1
G3
G2
G1
Loadcell
INPUT
Conveyor Tray
Fig. 4. The processing diagram of automatic dragon fruit sorting system.
4 Using Machine Learning for Grading Dragon Fruit Object recognition is the technology of identifying objects from images or videos using computer vision or machine learning. Identifying the dragon fruit is the first basic element to be able to extract the features of the dragon fruit. The image obtained from the image processing chamber includes both the background and the dragon fruit, in order to segment the dragon fruit proposed using the KNN model to remove the background of the dragon fruit. With the difference in the appearance of the dragon fruit making automatic classification difficult, the scaly spikes and tails make the size of the dragon fruit significantly deviate if only image processing, as usual, is used to determine the size and extract length and width like other fruits. This study proposes using CNN to identify scaly spikes, tails, and the body of dragon fruit. Color space survey shows that some color spaces of dragon fruit tails and scaly spikes are close to the color space of some types of fruit defects such as white spots, green spots on the fruit, insect bite, etc., avoiding confusion it, proposed removing the scaly spikes and tails of the dragon fruit in the process of extracting the characteristics of the fruit, the process of evaluating the quality of the dragon fruit is done through 3 stages as shown in Fig. 5. Stage 1
Stage 2
Reduce image noise
CNN extract features (scaly spikes, tail, body)
KNN model to color threshold recognize
Remove the color values of the scaly spike
Remove the background
Binary image to find contours
Stage 3
External features
Weight
Group of the dragon fruit
Fig. 5. Stages of the automatic sorting dragon fruit system
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4.1 Stage 1 - Using KNN to Extract the Dragon Fruit Using K - Nearest Neighbor (KNN) for color threshold detection to decide which color value is for dragon fruit and background color. Color values were collected from dragon fruit images obtained from the image processing chamber of the system. Each image pixel contains 3 values (R (red), B (blue), G (green)), all the obtained color values are put into the data set, the color values believed to be a good representation of the data space were selected for labeling. The accuracy of the system is evaluated through the values of sensitivity, precision, and F1-Score.The obtained color values X0 = {(R0 , G0 , B0 ), …, (Rn , Gn , Bn )} are the obtained set of colors including the background color value and the dragon fruit color with the RBG color space corresponding to the labeled set Y 0 = {Y 1 , Y 2 , …, Y n } with the Y having two values: “0” label of the background color; “1” label of the fruit’s color. The KNN algorithm [18] is one of the simplest algorithms for classification. For an unlabelled value is predicted X j (Rj , Bj , Gj ) based on the votes of the nearest objects where k is the number of objects considered. The distance of the object X j from the ith object i of the set X 0 is expressed through the formula (1). d = (RX 0 − Rj )2 + (BX 0 − Bj )2 + (GX 0 − Gj )2 (1) i
i
i
Training data for the KNN model is collected from images of dragon fruit from the image processing chamber of the system from 387 dragon fruits selected from local agricultural experts. 4.2 Stage 2 - Using CNN to Extract Feature of the Dragon Fruit-Image Processing Due to the nature of the climate, soil, and environment in different countries, fruits are also different. Nowadays, along with the development of science and technology, there are more open data sources, so that data of artificial intelligence (AI) models are more and more abundant. However, for the agricultural sector, especially the typical fruits of countries, open data sources are still limited. In this study, data were collected from 2610 dragon fruits at the farms, dragon fruit export packing facilities in the south of Vietnam, the data of dragon fruit is a minor contribution of this study. In some cases of model training, there are overtraining cases so this study applies some measures to enhance data [19] such as image rotation, zoom, flipping, … Because dragon fruit has a very different appearance from other fruits so methods for extracting features namely determining length, width, and defects are not as simple as that of other fruits. The outer scaly spikes and tails can significantly increase the size of the dragon fruit, so this study proposes a convolutional neural network (CNN) to identify the body of the dragon fruit with the scaly spikes and the tail removed because it can increase the error of the system. The length and width of the dragon fruit are graded according to the standard of the shape, and it is used to determine the volume of the fruit to predict the sweetness of dragon fruit by the density of the fruit, this part will be presented in the following article. Besides, the color space of dragon fruit scaly spikes is very close to the color space of some defects on the fruit, the scaly spikes and tail of the dragon fruit are identified and deleted to avoid affecting the extraction of defects on fruit. Convolutional neural network
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(CNN) is one of the most popular deep learning models today. Many applications show the effectiveness of CNN in classification problems, the structure of CNN includes input, convolutions layers, activation functions, pooling layers, softmax, output. In this study, a CNN is proposed to identify the body and scaly spikes of dragon fruit. The input is an RBG image (214 × 214 × 3) with (L × W × C) with L: Length, W: Width, C: Chanel then convolution with kernels (L k × W k × C). In the deep convolutional neural network, the weights of the following layer are equal to the sum of the contributions of the previous layer, add a bias then pass through the activation function. The contribution of each convolution layer can be represented by the Eq. (2). Ti = σ (W.x + bias)
(2)
where: σ is activation function, W is the weight, x is output of previous layer. The pooling layer has the main task to reduce the complexity for the next layer, reduce the number of neurons, reduce overfitting. Max-pooling is one of the popular pooling methods, which returns the maximum value in each scan region defined Pmax = Max(x) with x as the pooling region. Average-pooling computes and returns the average of the region. To extract features of the dragon fruit, we propose using a CNN with 8 convolutional layers, 2 fully connected layers, 4 layers max-pooling layers, and a softmax layer that capitalizes on the softmax function, also known as multinomial logistic regression. The input layer is an RGB image, and the model uses the 3 × 3 and 1 × 1 kernels to decrease the number of parameters and speed up the system’s calculation. The length and width of the dragon fruit are measured at this stage, and the shape of the dragon fruit must be balanced in order to export. When the dragon fruit is rotated by the rotators, the extracted image gets 2 width values, one is considered the height of the fruit with ratio of h/w ≈ 1(height/ width). At this stage, having extracted features such as length (L), width (W), height (H) in units of pixels, the next task is to determine the ratio between pixel sizes compared with the actual size, which depends mainly on the camera correction factor proposed in the study [20]. The processed image is cropped to the size that fits the dragon fruit, in order to speed up the system’s processing. In contour finding, the contour is found using the contour finding algorithm [21]. After the survey, the defects on the fruit are easy to see when converting the image to binary image with threshold (T), the defect area (S de ) is calculated by formula (3) with Si de the defect area i. i Sde = Sde (3)
4.3 Stage 3 - Sorting the Dragon Fruit Depending on different regions and countries, there are different import standards about weight, shape, and defects on dragon fruit. The classification is evaluated and classified in order of priority on the shapes, and the unbalanced shapes of the dragon fruit are eliminated immediately. Evaluation of the quality of dragon fruit by weight, external defects of the fruit, classification form is shown in Fig. 6, the standards of the national regions are shown in general in Table 1. Dragon fruit is evaluated through scoring for
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N. Trieu and N. Thinh Table 1. Standard import the dragon fruit of some country
Country
Weight (g)
Defect
China
200–>600
0–10%, number of broken scaly spikes ≤ 3 spikes/fruit
Thailand
300–600
0–5 cm2
America
300–350
No mechanical damage or dark spot
Europe
350–400
No mechanical damage or dark spot
each different standard, evaluated in order of weight, defects, the number of broken dragon fruit scaly spikes, cracks on the fruit is also immediately disqualified because it affects the quality of the fruit during cleaning and distribution storage. Calculating to predict the type of dragon fruit defined through the Eq. (4).
Weight
Shape
Defect
Processing center (TW ,TS ,TDe )
G1
G2
G3
Fig. 6. The calculating process of sorting dragon fruit
G = kw + kde + p
(4)
where k w ∈ {1, 2, 3} is coefficient weight with group 1 (G1) = 1, and increase accordingly for each group, k de ∈ {0, 1, 2} is coefficient defect with group 1 (G1) = 0, and increase accordingly for each group, p is a penalty point for different standards with G1 = 0. The coefficients of the threshold standard set for the standards may vary to suit different countries.
5 Results and Discussions The sorting and evaluation quality dragon fruit system automatically is proposed based on the needs of dragon fruit exporting companies. In Vietnam, farmers used scientific
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methods to cultivation, they use lights for dragon fruit trees at night to promote flowering for dragon fruit, dragon fruit can be harvested after 50 days of flowering. Dragon fruit is an export agricultural product for a high proportion of Vietnam’s export turnover, and the production and planting area increase each year. The data used in this study was collected over 12 months to obtain the variety of dragon fruit color according to different seasons of dragon fruit, the image was collected from 2610 fruits selected by the expert agricultural exports. Due to the shape characteristics of dragon fruit, the study proposes to use the KNN model to segment dragon fruit and CNN to extract the characteristics of the fruit, the image processing methods are applied such as thresholding, finding contour, binary image, … to calculate the areas of defects on the fruit. In the automatic classifying system, dragon fruits are divided into three groups G1, G2, G3 with G1 is the best group, with parameters are set up following the export standard. The image of dragon fruit is collected from a camera in the image processing chamber and the image is reduced noise by filters, the KNN model and CNN are combined to extract the features of fruit such as length, width, height, defects on the fruit, then based on standards to calculate the classification of dragon fruit. The results of the system evaluation are satisfied with the accuracy of the actual testing process of 92.85%. The system is calculated and designed in accordance with the requirements from the dragon fruit packaging and exporting facility, so in addition to the standard of accuracy, the processing speed is also an important criterion. The automatic dragon fruit sorting system has been tested and calculated to handle 2–4 tons/h, with 3 lands separate processing. The color values of each pixel are selected and labeled correctly, a data set of 3000 color values (R, B, G) collected from the image from the image processing chamber of the system is used to train the KNN. model, the accuracy of the model when tested is shown in Table 2, with an accuracy of 92.5%, which can meet the requirements of the classification system. This study proposes CNN model to identify three features of the dragon fruit named scaly spikes, tails, body. The input is an RBG image with size 214 × 214, kernels of size 1 × 1, 3 × 3, do not use large kernels to reduce weights to increase computational speed for the system. The accuracy of models in deep learning is greatly affected by datatraining, collecting databases from 2610 dragon fruits, with data expansion methods, data collection about dragon fruit is one of the contributions of this study. The accuracy of the proposed CNN is 97.4%. The automatic sorting system is designed and developed to meet actual requirements from dragon fruit packing and exporting factories, in order to increase the accuracy of the sorting process and increase the productivity of the sorting process, making it possible for dragon fruit to be exported to fastidious markets such as the US, Japan, Europe, etc. The actual processing is described in Fig. 7, dragon fruit is put into the image processing chamber, using KNN model to remove background color, identify dragon fruit, CNN is used to extract features such as the scaly spikes, body, tail. Image processing methods to extract the features of the length, width, and defects of the dragon fruit, send the signal to the processing center, combined with the signal of the loadcell sensor to evaluate and classify the quality of the dragon fruit according to three groups. To evaluate the accuracy of the proposed dragon fruit classification system, this study uses the local dragon fruit import standard for testing, the standards are shown in the Table 3.
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91.4
93.9
92.6
92.5
Table 3. The standard of local grading Group 1
Group 2
Group 3
Weight (g)
461–600
381–460
300–380
Defect area (cm2 )
0. When the derivative of V1 is negative definite, the error of the speed controller will increasingly approach zero.
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3.2 Current Controller Design The reference q-axis current is the virtual control variable from Eq. (13), and the reference d-axis current x3∗ is normally set to zero to maintain constant flux. The current error on the d-q axis is determined as follows: e2 = x2∗ − x2 e3 = x3∗ − x3
(14)
The time derivative of e2 , e3 are given as: ⎛ ⎞ np φx2 − Bx1 ∗ + d1 + Jm x¨ 1 ⎟ 1 ⎜B Jm e˙ 2 = ⎝ ⎠ np φ +k1 Jm x˙ 1∗ − np φx2 + Bx1 − Jm d1 Rs x2 + np x1 Ld x3 + np x1 φ − uq − d2 Lq Rs x3 − np x1 Lq x2 1 e˙ 3 = − ud − d3 Ld Ld +
(15)
Define Lyapunov candidate function:
Then,
1 1 V2 = V1 + e22 + e32 2 2
(16)
Rs x3 − np x1 Lq x2 1 2 ˙ − ud − d3 V2 = −k1 e1 + e3 Ld Ld ⎛ ⎞⎞
⎛ np φx2 − Bx1 ∗ B + d1 + Jm x¨ 1 ⎟⎟ ⎜ 1 ⎜ Jm ⎜ n φ⎝ ⎠⎟ ⎜ p ⎟ +k1 Jm x˙ 1∗ − np φx2 + Bx1 − Jm d1 ⎟ +e2 ⎜ ⎜ ⎟ ⎝ Rs x2 + np x1 Ld x3 + np x1 φ − uq ⎠ − d2 + Lq
(17)
The current errors can be stabilized if the control laws are designed as follows: ⎛ ⎞ np φiq − Bω ∗ + d1 + Jm ω¨ Lq ⎜ B ⎟ Jm uq = ⎝ ⎠ np φ ∗ +k1 Jm ω˙ − np φiq + Bω − Jm d1 +Rs iq + np ωLd id + np ωφ − Lq d2 + k2 e2
Rs id − np ωLq iq ud = Ld − d3 + k3 e3 Ld
(18)
where k2 , k3 > 0. Substituting Eq. (18) into the derivative of Lyapunov function V˙ 2 , we get: V˙ 2 = −k1 e12 − k2 e22 − k3 e32 ≤ 0
(19)
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Remark 1. In order to achieve this convergence as Eq. (19), then the disturbances d1 , d2 , d3 must be explicitly known or zero. However, in practice, the disturbances of IPMSM are always existed and are unknown. Thus, the derivative of Lyapunov function V˙ 2 results in: V˙ 2 = −k1 e12 − k2 e22 − k3 e32 + e1 d1 + e2 d2 + e3 d3
(20)
Let,
ς = e1 d1 + e2 d2 + e3 d3 χ = min{k1 , k2 , k3 }
(21)
Then, Eq. (20) is rewritten as: V˙ ≤ −2χ V + ς
(22)
From the Eq. (22) can realize that the deviation of the control system is strongly influenced by the disturbances d1 , d2 , d3 . Thus, to improve the control system, a highgain disturbance observer is proposed that provides disturbance information such as uncertainty parameters and load torque to the controller.
3.3 Disturbance Observer Design Define the estimated disturbances as dˆ 1 , dˆ 2 , dˆ 3 the estimated errors are defined as follows: d˜ 1 = d1 − dˆ 1 , d˜ 2 = d2 − dˆ 2 , d˜ 3 = d3 − dˆ 3
(23)
With ε1 , ε2 , ε3 are the observation coefficients, dynamic equations of the estimated disturbances are designed as follows: ⎞ ⎛ np Ld − Lq x3 + φ Bx1 − x2 ⎟ 1 ⎜ x˙ 1 + ˙ Jm Jm dˆ 1 = ⎝ ⎠ ε1 −dˆ 1 ⎛ −Rs x2 − np x1 φ − np x1 Ld x3 ⎞ x˙ 1 − ⎟ Lq 1⎜ ˙ ⎟ (24) dˆ 2 = ⎜ ⎝ ⎠ 1 ε2 − uq − dˆ 2 Lq ⎛ ⎞ −Rs x3 + np x1 Lq x2 1 − ud 1 ⎜ x˙ 3 − ˙ Ld Ld ⎟ dˆ 3 = ⎝ ⎠ ε3 −dˆ 3 Define the auxiliary state variables: xi ξi = dˆ i − , i = [1, 3] εi
(25)
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The dynamic equations of the auxiliary state variables are: np Ld − Lq x3 + φ 1 Bx1 + x2 − ξ˙1 = − ε1 Jm Jm
1 x1 − ξ1 + ε1 ε1
−Rs x2 − np x1 φ − np x1 Ld x3 1 1 ˙ξ2 = − uq + ε2 Lq Lq
x2 1 − ξ2 + ε2 ε2
−Rs x3 + np x1 Lq x2 1 1 ˙ξ3 = − ud + ε3 Ld Ld
1 x3 ξ3 + − ε3 ε3
(26)
Theorem 1. For the nonlinear system of the interior permanent magnet synchronous motor in Eqs. (1)–(7), the unknown disturbances are bounded and shown in Eqs. (6), (9) can be observed by using the high-gain disturbance observers as Eq. (26) combined the auxiliary state variables in Eq. (25). Proof of Theorem 1. The following disturbance estimation error dynamics is obtained as [13]: x˙ i ξ˙i = d˙˜ i − εi
(27)
From Eqs. (26) and (27), estimation error dynamics of the observers is obtained as:
1 ˜ (28) di d˙˜ i = d˙ i − εi Consequently, ˜ di ≤ e−(1/εi )t d˜ i (0) + εi ρi (t)
(29)
Then, the upper bound of d˜ i (∞) becomes smaller by εi gets smaller. Remark 2. In Eq. (26), observers with auxiliary state variables do not need to use derivatives of ω, id , iq as in Eq. (24). Then the measurement noise amplification by using the high gain 1/εi will be reduced, thus observers will be more feasible when applied in practice.
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In order to improve the efficiency of the backstepping controller, the observed values dˆ 1 , dˆ2 , dˆ 3 from the Eq. (26) are substituted into Eqs. (13) and (18) as follows: 1 Bx1 − Jm dˆ 1 + Jm x˙ 1∗ + k1 Jm e1 np φ ⎛ n φi − Bω ⎞ p q ˆ 1 + Jm ω¨ ∗ B + d Lq ⎜ ⎟ Jm uq = ⎝ ⎠ np φ ∗ +k1 Jm ω˙ − np φiq + Bω − Jm dˆ 1 x2∗ =
(30)
+Rs iq + np ωLd id + np ωφ − Lq dˆ 2 + k2 e2
Rs id − np ωLq iq ud = Ld − dˆ 3 + k3 e3 Ld Compared with the conventional backstepping control law in Eqs. (13) and (18), the novel control law Eq. (30) is more convenient to be adjusted with the observed values dˆ 1 , dˆ 2 , dˆ 3 from Eqs. (25) and (26) that are continually updated for the control system.
4 Stability Analysis Theorem 2. Consider the interior permanent magnet synchronous motor form as Eq. (5) under the bounded disturbance as Assumption 1. By using the control law in Eq. (30) with the positive constants. k1 , k2 , k3 and the observer gains 1/ε1 , 1/ε2 , 1/ε3 of the high-gain disturbance observer Eqs. (25) and (26) guarantee the Input-to-State Stability [17] of the closedcontrol system. Proof of Theorem 2. Define the Lyapunov candidate function as follows: V =
1 2 1 ˜2 1 2 1 ˜2 1 2 1 ˜2 e + d + e2 + d2 + e3 + d3 2 1 2 1 2 2 2 2
(31)
Taking derivative of Eq. (31) with respect to time yields: V˙ = e1 e˙ 1 + d˜ 1 d˙˜ 1 + e2 e˙ 2 + d˜ 2 d˙˜ 2 + e3 e˙ 3 + d˜ 3 d˙˜ 3
(32)
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Substituting the disturbance estimation error dynamics Eq. (28) into Eq. (32) results in:
np φx2 1 ˜ Bx1 ∗ ˜ ˙ ˙ + − d1 + d1 d1 − d1 V = e1 x˙ 1 − Jm Jm ε1 ⎛ ⎞
⎛ ⎞ np φx2 − Bx1 ∗ B + J + d x ¨ 1 1 m ⎜ ⎟⎟ 1 ⎜ Jm ⎜ n φ⎝ ⎠⎟ ⎜ p ⎟ ∗ +k1 Jm x˙ 1 − np φx2 + Bx1 − Jm d1 ⎟ +e2 ⎜ ⎜ ⎟ ⎝ Rs x2 + np x1 Ld x3 + np x1 φ − uq ⎠ + − d2 Lq
Rs x3 − np x1 Lq x2 1 +e3 − ud − d3 Ld Ld
1 1 ˜ +d˜ 2 d˙ 2 − d˜ 2 + d˜ 3 d˙ 3 − d3 ε2 ε3
(33)
With the control laws in Eq. (30), the time derivative of Lyapunov candidate function V is obtained as follows:
1 ˜ V˙ = e1 −k1 e1 − d˜ 1 + d˜ 1 d˙ 1 − d1 ε1
1 ˜ +e2 −k2 e2 − d˜ 2 + d˜ 2 d˙ 2 − d2 ε2
(34) 1 ˜ +e3 −k3 e3 − d˜ 3 + d˜ 3 d˙ 3 − d3 ε3
3 1 −ki ei2 − e1 d˜ i − d˜ i + d˜ i d˙ i = εi i=1
Using the inequality |a||b| ≥ ab, Eq. (34) is obtained as follows:
3 1 ˙ 2 ei αi ˜ 2 ˜ ˙ + di di + τi di − ki V ≤− αi 2ki 2τi i=1 3 3 βi2 2 1 ˙ 2 1 2 di + − di 1 − 2 ki ei + 4ki 4τi αi i=1
where τi =
1 εi
(35)
i=1
αi2 +βi2 ; αi > 0; βi = 0. Exist that: − 4ki ⎧ 3 ⎪ 1 2 ⎪ ⎪ ⎪ γ = δ ⎪ ⎨ 4τi i i=1 ⎪ 2 ⎪ β 1 ⎪ i ⎪ κ = min 1 − ki , ⎪ ⎩ 4ki αi2
(36)
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Then, Eq. (35) can be rewritten as: V˙ ≤ −2κV + γ
(37)
Consequently, V (t) ≤ V (0)e−2κt +
γ 1 − e−2κt 2κ
(38)
Remark 3. The Eq. (38) shows that the tracking errors e1 , e2 , e3 , and estimation errors d˜ 1 , d˜ 2 , d˜ 3 exponentially converge to an arbitrarily small ball γ /2κ that can be shrunk by γ via the high observer gains 1/ε1 , 1/ε2 , 1/ε3 [18]. This adjustment is different from when using only the conventional backstepping as in Remark 1, which is greatly influenced by the disturbances.
5 PIL Simulation and Results 5.1 PIL Simulation This paper uses PIL test technique [19], i.e., Texas Instrument’s TI C2000 F28377S microcontroller to validate the controller, while the plant will be built on Matlab/Simulink environment. The specifics of the 0.72 kW 3-pole IPMSM are given as [20] rated speed is 3000 r/min; rated torque is 2.3 Nm; Rs = 4.8 ; Ld = 19.5 mH; Lq = 27.5 mH; φ = 0.15 Wb; Jm = 0.001 kgm2 . The general PIL simulation diagram of the control system for IPMSM is shown in Fig. 1. Choose constants as: k1 = 5000, k2 = 9000, k3 = 3000, observation coefficients: ε1 = ε2 = ε3 = 0.0001. Furthermore, actual parameter values are set with an error of about 10% compared to nominal values.
Fig. 1. Overview simulation diagram
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5.2 Simulation Results To demonstrate the efficiency of the proposed backstepping algorithm and the high-gain observer, the speed value and load torque are set to the controller as in Fig. 2 (a), (b). Figure 3 shows the rotor speed response using the controllers as PID, the conventional backstepping control, and the high-gain disturbance observer-based backstepping control. When the load torque changes suddenly at 2 s, the rotor speed response using the PID controller is significantly overshot, and the conventional backstepping is the appearance of steady state error, as shown in Figs. 3 and 4(a). The steady state error of the traditional backstepping is caused by the disturbances as given in Remark 1. In order to deal with these problems, the control system using the high-gain disturbance observerbased backstepping control is proposed and obtains fast transient responses and strong robustness. The deviation of the proposed controller is minimal, and the control quality is clearly improved compared to the PID controller and the conventional backstepping, as in Figs. 3 and 4.
Fig. 2. Reference load torque.
Fig. 3. Rotor speed response of IPMSM.
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Figures 5, 6 and 7 show the estimation performance of the disturbances d1 , d2 , and d3 , respectively. The disturbance included uncertainty parameters, and the load torque of IPMSM is accurately estimated using the high-gain disturbance observer. The estimation errors d˜ 1 , d˜ 2 and d˜ 3 are minimal, as shown in Figs. 5(b), 6(b), and 7(b), from which the controller has the information of the disturbance to improve the accuracy of the closed-control system.
Fig. 4. (a) Speed error without disturbance observer. (b) Speed error using disturbance observer.
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Fig. 5. Disturbance estimation d1
Fig. 6. Disturbance estimation d2
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Fig. 7. Disturbance estimation d3
6 Conclusion This paper presents the robust speed control for IPMSM using a backstepping controller combined with a high-gain disturbance observer. Disturbances include uncertainty parameters and external load torque. In order to improve the accuracy of the controller, the nonlinear observer is applied to calculate the disturbance components in the system. The controller combines the advantages of a backstepping controller and the high-gain observer. The effectiveness and feasibility of the proposed control and observer are demonstrated by using the PIL test technique. Acknowledgement. This research is funded by the Hanoi University of Science and Technology (HUST) under project number T2021-PC-001. Van Trong Dang was funded by Vingroup JSC and supported by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Institute of Big Data, code VINIF.2021.ThS.26.
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Optimizing the Parameter of the LQR Controller for Active Suspension System Thi Thu Huong Tran1 , Tuan Anh Nguyen2(B) , Thang Binh Hoang3 , Duc Ngoc Nguyen2 , and Ngoc Duyen Dang2 1 Faculty of Vehicle and Energy Engineering, Phenikaa University, Nguyen Van Trac, Ha Dong,
Hanoi, Vietnam 2 Automotive Engineering Department, Thuyloi University, 175 Tay Son, Dong Da,
Hanoi, Vietnam [email protected] 3 Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam
Abstract. The Automobiles are vehicles widely used to transport passengers and goods around the world. When traveling on the road, many factors affect the oscillation of the automobile. In particular, the stimuli from the road surface are the direct causes of its instability. The automobile’s suspension system has the role of regulating and extinguishing these oscillations quickly. To improve the efficiency, stability, and comfort of the vehicle, the active suspension system is proposed. This study establishes the quarter dynamics model to describe the vehicle’s oscillation. Besides, the LQR control method for the active suspension system equipped on the vehicle also is introduced. In this paper, the closed multi-loop algorithm is used to optimize the controller’s parameters. This is one of the novel and original methods, and it gives high efficiency and stability under many conditions. The results of the article showed that when the vehicle used the active suspension system controlled by the LQR controller, the vehicle’s oscillation parameters were significantly reduced. For cyclic oscillations, the average values of displacement and acceleration of the sprung mass are only about 25.25% and 32.47% respectively compared to the case of the vehicle using the passive suspension system. For random oscillators, this falls between 43.37% and 73.23% respectively. In the future, more complex control models can be further researched and developed. Keywords: Vehicle dynamics · Active suspension system · LQR controller · Closed multi-loop
Nomenclature zs : zu : r: FC : FK : FKT : FS :
Displacement of the sprung mass, m Displacement of the unsprung mass, m Roughness on the road, m Force of the damper, N Force of the spring, N Force of the tire, N Force of the actuator, N
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 260–270, 2022. https://doi.org/10.1007/978-981-19-1968-8_21
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ms : mu : C: K: KT :
261
Sprung mass, kg Unsprung mass, kg Damper coefficient, Ns/m Spring coefficient, N/m Tire coefficient, N/m
1 Introduction Today, automobiles are popular vehicles widely used for many different purposes. Automobiles play an important role in the field of passenger and cargo transport and take on several other special tasks. In addition to the strong development of the 4th Science and Technology Revolution, the automotive industry is also gradually changing positively. The growth of the automotive manufacturing industry has led to increased requirements and technical standards for vehicles. Many factors affect a vehicle’s oscillation. Roughness on the road is the main cause of these fluctuations. Although the road quality has improved significantly, however, many road conditions still do not meet the allowable standards. When traveling in these situations, the vehicle will strongly oscillate. To limit unwanted oscillations, suspension systems are used on all vehicles today. The suspension is the part linked between the wheel (unsprung mass) and the vehicle body (sprung mass) [1]. It helps to balance and extinguish the oscillations transmitted from the road surface to the vehicle body quickly. Thus, the smoothness and stability of the vehicle can be guaranteed. On small passenger vehicles (Sedans, Hatchbacks, SUVs, etc.), the suspension system consists of three parts: the spring (or leaf spring), the shock absorber, and the lever arm. Each part performs its separate functions. In some special conditions, the passive suspension system is not able to meet the requirements set forth in terms of oscillation and comfort. Therefore, the use of electronically controlled suspension systems is proposed, such as an active suspension system, semi-active suspension system, air suspension system, etc. [2, 3]. These suspension systems can automatically adapt to the constantly changing parameters that characterize the vehicle’s oscillations. In particular, the active suspension system can have many outstanding effects on maintaining the stability and smoothness of the vehicle when traveling on the road [4]. To evaluate the vehicle’s oscillation, parameters such as displacement of the sprung mass, acceleration of the sprung mass, etc. are considered. For each of these parameters, the maximum value, the average value, and the frequency of the oscillation are the important factors. Usually, the average value is used more. The mean of the oscillations (for continuous oscillations) is calculated by the Root Mean Square (RMS): k 1 2 2 2 z1 + z2 + ... + zk = zj2 (1) z= k k j=1
There’s been a lot of research on the subject of active suspension system control, which has been done all along. In [5], Nguyen proposed the use of the double integrated
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suspension system to improve the vehicle’s oscillation. He used the PID controller to control the operation of two hydraulic actuators. In [6], Mauryan and Bhangal also introduced the idea of using linear controllers for the quarter model. The parameters of the control matrix are chosen at random. Therefore, the performance of the controller is not really good. Similarly, Nagarkar et al. also use the LQR controller for the active suspension model. According to [7], six types of pavement excitation were used in their paper. If the LQR controller is integrated with a Gaussian filter, it becomes the LQG controller, which was proposed by Pang et al. [8]. According to Bello et al., when the active suspension system was used to replace the conventional passive suspension system, the acceleration and displacement of the sprung mass were greatly reduced [9]. This is again demonstrated in Soliman’s study [10]. Besides linear control methods, many nonlinear and intelligent control methods have been used for the active suspension system. According to Bai and Guo, the Sliding Mode Control method (SMC) can effectively control the actuator operation through the designed sliding surface [11]. Similarly, Deshpande et al. also use this method for a quarter model. However, the disturbance observer has been integrated [12]. This method has again been optimized through the study of Bayar and Khaneghah [13]. According to Nguyen, the performance of the Sliding Mode Control method is very high, and it helps to ensure the stability and smoothness of the vehicle when traveling on the road [14]. The parameters of the Sliding Mode Controller can be selected experimentally, or optimally calculated by the swarm algorithm [15]. In addition, intelligent control methods are also commonly used to control the operation of the active suspension system [16]. In [17], Palanisamy and Karuppan proposed using the Fuzzy controller with simple algorithms. For many different motion conditions, the Adaptive Fuzzy controller is a suitable choice [18]. In [19], Fuzzy algorithms are proposed with more complexity. Therefore, its performance is also higher than that of other common controllers. According to [20], Lin et al. combined the Sliding Mode Control method and Fuzzy algorithms for a quarter model. This option has also been introduced by Lin et al. and used in their study [21]. It gives very high performance in many simulation cases. Besides, many Robust control methods and Adaptive control have also been proposed [22–25]. In general, these control methods all bring about distinct effects, and they have both advantages and disadvantages. In this study, the LQR control method is proposed to be used. Previous studies usually only give controller parameters without showing how to define them. This paper introduces the optimal algorithm to determine the parameters of the control matrix. The simulation was performed based on the established dynamics model. This process is taken place in Matlab-Simulink environment. Specific content is presented below.
2 Materials and Methods 2.1 Quarter Vehicle Dynamics Model The automobile is a complex system. To simplify the simulation of the vehicle’s oscillation, a quarter dynamics model is proposed (Fig. 1). The equations describing the oscillation of the vehicle is given as follows: ms z¨s = FC + FK + FS
(2)
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Fig. 1. Quarter vehicle dynamics model.
mu z¨u = FKT − FC − FK − FS
(3)
FK = K(zu − zs )
(4)
FC = C(˙zu − z˙s )
(5)
FKT = KT (r − zu )
(6)
where: Force of the spring:
Force of the damper:
Force of the tire:
2.2 Optimization Process This paper uses the LQR controller to control the operation of the active suspension system. Controller parameters play an important role in ensuring the stable performance of the controller. Assume that: x1 = zs ; x2 = zu ; x3 = x˙ 1 ; x4 = x˙ 2 The equations established above can be rewritten in matrix form as follows: x˙ (t) = Ax(t) + Bu(t) + Wr(t) y(t) = C(x) + Du(t)
(7)
(8)
where: A is the state matrix, B is the control signal matrix, W is the excitation signal matrix from the pavement, C is the output matrix, and D is the navigation matrix. ⎡ ⎤ 0 0 1 0 ⎢ 0 0 0 1 ⎥ ⎢ ⎥ A=⎢ K K C C ⎥ − − ⎣ ms ms ms ms ⎦ K+KT K C C − mu mu mu − mu
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B=
00
1 ms
− m1u
T
⎤ 0 0 0 j1 × 10i1 ⎥ ⎢ 0 0 0 j2 × 10i2 ⎥ C=⎢ i ⎦ ⎣ 3 0 0 0 j3 × 10 i 0 0 0 j4 × 10 4 D = 10α ⎡
W =
00 0
KT mu
T
The control signal u(t) is determined based on the state feedback controller R. u(t) = −Rx(t)
(9)
For the system to be stable, the value of the cost function Q must be minimal. This means: 1 Q= 2
∞ xT (t)Cx(t) + uT (t)Du(t) dt 0
1 = 2
∞
xT (t) C + RT DR x(t)dt −→ min
(10)
R
0
The parameter of controller R depends on the matrix of control signal B and the navigation matrix D. R = D−1 BT P
(11)
where: P is the solution of the Riccati Algebraic Equation (ARE) PBD−1 BT P − PA − AT P = C
(12)
To be able to optimize the R controller, the relevant matrices need to be calculated and selected appropriately. Matrices A and B contain fixed parameters, and matrix D is just a simple constant. Therefore, most of the performance of the controller will depend on the parameters of the C matrix. The algorithm diagram for choosing the optimal parameters of the C matrix is shown in Fig. 2. The parameter optimization algorithm of the LQR controller uses eight closed loops and one condition. Based on the condition of the minimum average value, the optimization algorithm can find suitable parameters for the controller.
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Fig. 2. Algorithm diagram.
3 Results and Discussion 3.1 Parameter of the Vehicle The specifications of the vehicle that are used in the simulation are given as in Table 1. In this study, the vehicle’s oscillation is shown through two survey situations. In the first situation, the excitation from the road surface is a cyclic function. In the second situation, this excitation is random (Fig. 3). In each situation, the vehicle’s oscillations (including displacement and acceleration of the sprung mass) will be compared with two cases: a vehicle with the passive suspension system and a vehicle with the active suspension system controlled by the LQR controller. The excitation from the road surface has a cyclic form: r(t) = A sin 2π ft
(13)
Table 1. Specifications of the vehicle. Symbol
Description
ms
Sprung mass
Value 400 60
Unit kg
mu
Unsprung mass
C
Damper coefficient
2500
Ns/m
kg
K
Spring coefficient
30000
N/m
KT
Tire coefficient
180000
N/m
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The excitation from the road surface has a random form: r˙ (t) = −2π fr(t) + 2π Gvω(t)
(14)
Fig. 3. Roughness on the road.
3.2 Simulation Results Simulation results are given in two survey situations. Sine Wave In the first situation, the results obtained will be cyclically variable based on the stimulus from the road. Figure 4 shows the change of displacement of the sprung mass with time. According to (1), its average value when calculated according to RMS reaches 20.56 (mm) and 81.42 (mm) respectively for the case of using the active suspension and the passive suspension system. This difference is huge.
Fig. 4. Displacement of the sprung mass.
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For a comprehensive assessment of the vehicle’s oscillations, the acceleration of the sprung mass is used. The change in this value is demonstrated based on Fig. 5. If the vehicle uses only a conventional passive suspension system, the acceleration of the sprung mass is large. Its maximum value is greater than 2.5 (m/s2 ). In addition, the mean, which is calculated according to the RMS, is 0.77 (m/s2 ). In contrast, the LQR controller helps to stabilize the vehicle’s oscillations. When the vehicle uses the active suspension, which is controlled by the LQR controller, the average value of acceleration is only about 0.25 (m/s2 ). It oscillates continuously at a very small interval, and it does not affect the smoothness of the vehicle.
Fig. 5. Acceleration of the sprung mass.
Random In the other situation, if the excitation from the road is random, the obtained values will fluctuate continuously. The displacement of the sprung mass can be greater than 145 (mm) if the vehicle is equipped with a conventional mechanical suspension system (Fig. 6). Besides, the average value of oscillation is 54.65 (mm). However, the LQR
Fig. 6. Displacement of the sprung mass.
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controller can reduce the oscillation value a lot. Its average value is only about 23.70 (mm). When the vehicle uses the active suspension system controlled by the LQR controller, the vehicle body’s acceleration is effectively controlled. This is demonstrated by the graph in Fig. 7. The average value of the acceleration is only 2.27 (m/s2 ). Its maximum value does not exceed 7 (m/s2 ). Meanwhile, if the vehicle does not have the LQR controller, the maximum value of acceleration can be up to 9 (m/s2 ). At the same time, the average value of acceleration calculated according to RMS also reached 3.10 (m/s2 ). The results of the simulation process are shown as Table 2.
Fig. 7. Acceleration of the sprung mass.
Table 2. Results of the simulation. Passive Displacement
LQR Acceleration
Displacement
Acceleration
Average value Sine wave
81.42
0.77
20.56
0.25
Random
54.65
3.10
23.70
2.27
Sine wave
127.15
2.52
39.82
1.38
Random
145.83
9.84
57.7
7.02
Maximum value
4 Conclusion The suspension system helps to regulate and extinguish the oscillations of the vehicle when traveling on the road. However, the conventional passive suspension system is not
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able to fully meet the requirements set for smoothness and comfort. Therefore, the active suspension system is suggested to be used instead of the passive suspension system. The performance of the active suspension system depends on its controller. In this study, an LQR controller is introduced. The parameters of the control matrix play an important role in maintaining the efficiency and stability of the controller. Therefore, it is necessary to choose the control parameters reasonably and accurately. This paper introduces the closed multi-loop optimization method which is used to accurately calculate the parameters of the LQR controller. With the presented loop algorithm, these parameters are calculated based on the condition of minimizing the vehicle body’s oscillations. The results of the paper have shown that if the vehicle uses the active suspension system with the parameters of the controller, which has been optimized, the vehicle’s smoothness and stability will be better guaranteed. This will be the basis for continuing to develop other more complex studies in the future. Besides, the experimental process needs to be conducted to be able to comprehensively evaluate the results of the study.
References 1. Yin, J., et al.: Investigation of equivalent unsprung mass and nonlinear features of electromagnetic actuated active suspension. Shock Vibr. 2015, 8 (2015). Article ID 624712 2. Basargan, H., et al.: Adaptive semi-active suspension and cruise control through LPV technique. Appl. Sci. 11, 16 (2021). Article ID 290 3. Abid, H.J., Chen, J., Nassar, A.A.: Equivalent air spring suspension model for quarter-passive model of passenger vehicles. Int. Sch. Res. Not. 2015, 6 (2015). Article ID 974020 4. Anh, N.T.: Control an active suspension system by using PID and LQR controller. Int. J. Mech. Prod. Eng. Res. Dev. 10, 7003–7012 (2020) 5. Nguyen, T.A.: Improving the comfort of the vehicle based on using the active suspension system controlled by the double-integrated controller. Shock Vibr. 2021, 11 (2021). Article ID 1426003 6. Maurya, V.K., Bhangal, N.S.: Optimal control of vehicle active suspension system. J. Autom. Control Eng. 6, 22–26 (2018) 7. Nagarkar, M.P., et al.: Active control of quarter-car suspension system using linear quadratic regulator, international journal of automotive and mechanical. Engineering 3, 364–372 (2011) 8. Pang, H., et al.: Design LQG controller for active suspension without considering road input signals. Shock Vibr. 2017, 13 (2017). Article ID 6573567 9. Bello, M.M., Shafie, A.A., Khan, R.: Active vehicle suspension control using full statefeedback controller. Adv. Mater. Res. 1115, 440–445 (2015) 10. Soliman, A.M.A.: Adaptive LQR control strategy for active suspension system. In: SAE Technical Paper, p. 9 (2011). Article ID 2011-01-0430 11. Bai, R., Guo, D.: Sliding-mode control of the active suspension system with the dynamics of a hydraulic actuator. Complexity 2018, 6 (2018). Article ID 5907208 12. Deshpande, V.S., Bhaskara, M., Phadke, S.B.: Sliding mode control of active suspension system using a disturbance observer. In: Proceedings of the 12th IEEE Workshop on Variable Structure System, pp. 70–75 (2012) 13. Bayar, K., Khaneghah, F.S.: Optimal sliding mode control method for active suspension control. IFAC Papers-Online 53, 14285–14291 (2020) 14. Nguyen, T.A.: Study on the sliding mode control method for the active suspension system. Int. J. Appl. Sci. Eng. 18, 10 (2021). Article ID 2021069
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15. Wei, S., Su, X.: Sliding mode control design for active suspension systems using quantum particle swarm optimisation. Int. J. Veh. Des. 81, 93–114 (2019) 16. Fu, Z.J., et al.: Online adaptive optimal control of vehicle active suspension systems using single-network approximate dynamic programming. Math. Probl. Eng. 2017, 9 (2017). Article ID 4575926 17. Palanisamy, S., Karuppan, S.: Fuzzy control of active suspension system. J. Vibroeng. 18, 3197–3204 (2016) 18. Soleymani, M., Montazeri-Gh, M., Amiryan, R.: Adaptive fuzzy controller for vehicle active suspension system based on traffic conditions. Scientia Iranica 19, 443–453 (2012) 19. Na, J., et al.: Adaptive finite-time fuzzy control of nonlinear active suspension systems with input delay. IEEE Trans. Cybern. 50, 2639–2650 (2020) 20. Lin, B., Su, X., Li, X.: Fuzzy sliding mode control for active suspension system with proportional differential sliding mode observer. Asian J. Control 21, 1–13 (2019) 21. Lin, J., et al.: Enhanced fuzzy sliding mode controller for active suspension systems. Mechatronics 19, 1178–1190 (2009) 22. Kaleemullah, M., Faris, W.F., Ghazaly, N.M.: Analysis of active suspension control policies for vehicle using robust controllers. Int. J. Adv. Sci. Technol. 28, 836–855 (2019) 23. Singh, N., Chhabra, H., Bhangal, K.: Robust control of vehicle active suspension system. Int. J. Control Autom. 9, 149–160 (2016) 24. Rizvi, S.M.H., et al.: H∞ control of 8 degrees of freedom vehicle active suspension system. J. King Saud Univ. Eng. Sci. 30, 161–169 (2018) 25. Huang, Y., et al.: Adaptive control of nonlinear uncertain active suspension systems with prescribed performance. ISA Trans. 54, 145–155 (2015)
Integral Action Finite Set Model Predictive Current Control for Brushless DC Motor Lam Cuong Quoc Thai3 , Van Tu Duong1,2,3 , Huy Hung Nguyen4 , and Tan Tien Nguyen1,2,3(B) 1 Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT),
268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam [email protected] 2 Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam 3 National Key Laboratory of Digital Control and System Engineering (DCSELAB), HCMUT, 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam 4 Faculty of Electronics and Telecommunication, Sai Gon University, 273 An Duong Vuong, District 5, Ho Chi Minh City, Vietnam
Abstract. This paper presents an integral action finite set model predictive control (I-FCS-MPC) to apply BLDC motor control. One of the advantages of this method is it allows the current timeslot to be optimized about energy (current or voltage), which will be expressed obviously by the controller design theory with one step future prediction; furthermore, an implementation strategy is given step by step. Evaluations with the other method about current and speed are considered based on the simulation results by the Simulink model. Through that assessment, the efficiency and feasibility of I-FCS-MPC are verified, and a combination technique solution also is proposed to elucidate the existing disadvantage of this method. Keywords: BLDC motor · Model predictive control · Finite set control · Integral action · Current control
1 Introduction In recent years, BLDC motors have been widely used in various applications such as automobiles, manufacturing assembly lines, home appliances, etc., owing to their torque characteristic, high efficiency, high-speed ranges, etc. However, the development of inverters for controlling BLDC motors is still a challenge. There are many methods to control the BLDC motor, such as scalar control (V/f) [1], direct torque control (DTC) [2, 3], field-oriented control (FOC) [4–6], and each method has different advantages and disadvantages. Model predictive control (MPC) also has been applied as a novel method of variable-frequency drive (VFD) and has been proven to be more effective than the FOC and DTC methods [7]. Deadbeat model predictive control (DB-MPC) [8] and finite control set model predictive control (FCS-MPC) [9–11] are two attractive solutions to researchers in the industry and academy. FCS-MPC stands out with several benefits [12] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 271–285, 2022. https://doi.org/10.1007/978-981-19-1968-8_22
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that make it appropriate for the optimal control of power converter based on energy (current or voltage). The FCS-MPC algorithm has several following advantages: first is straightforward to implement irrespective of any control application, and secondly the resultant dynamic performance of FSC-MPC is superior because the modulation stage is eliminated. The main challenge for developing a closed-loop controller of the traditional FCSMPC is to eliminate the steady-state velocity errors caused by the external disturbance, uncertain parameters of the BLDC motor, and the temperature-variant parameters, e.g., armature resistance/inductance. Therefore, integrated action has been included in the FCS-MPC to improve steady-state error with the variation of system parameters. Many studies have also been implemented on FCS-MPC by several researchers, such as a novel procedure for estimating prediction of phase voltage, phase current, speed for predictive controllers with integral action is proposed by P. C. N. Verheijen [13] and et al.; instead of using an identified state-space model to build the prediction matrices, which is directly computed from the perceived signals (phase current, phase voltage, speed of rotor). This proposal has dealt well with noise and does not require complicated computations for the on-line implementation. The simulation results have illustrated the effectiveness of our methodology in terms of tracking performance, robustness to noisy measurements, and disturbance rejection. Through analysis and experiment, Ricardo P. Aguilara and et al. [14] have shown drawback of FCS-MPC as a non-zero steady-state error, even when models and parameter values are exactly known. They proposed two different strategies to deal with this issue: first is intermediate sampling MPC, which is a combination of FSC-MPC and sinusoidal PWM schemes; secondly an extra term in the cost function is integrated as the integral error between the reference and the predicted behavior of the controlled variable. Each strategy reduces the average tracking error, which is verified by the experiment. To reduce torque ripple, A. G. Castro and et al. [10] have proposed a model-based predictive direct power control with a duty cycle (MPDPC) for BLDC motor. Prioritizing the reduction of active power error, the duty cycle is calculated based on a deadbeat concept where the duration of the active voltage vector is determined to produce zero active power error at the end of the control period. The different frequency simulation results of MPDPC have demonstrated that not only improve steady-state performance, provide lower torque ripple and less harmonic current but also maintain the fast dynamic response. In this paper, we concentrate on improving zero steady-state by including current error as integral action into FCS-MPC to control BLDC motor through a two-level inverter. To execute I-FCS-MPC control method, the mathematical model of the BLDC motor is required to convert to the D-Q reference frame through the Park transformation, presented in Sect. 2. In spite of uncomplicated practice, the amount of computation is large because it is exponential to the level of a converter, especially for the higher-level converters. In Sect. 3, the controller design procedure for one-step future prediction is expressed, and simultaneously algorithm strategy is also given specifically. The effectiveness and robustness of I-FCS-MPC method are investigated via the simulation results compared with the classical commutation method (CCM) by MATLAB/Simulink model in Sect. 4. Finally, the several main points of this paper are concluded in Sect. 5.
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2 Mathematical Model of Brushless DC Motor in D-Q Rotating Reference Frame The mathematical model of the BLDC motor, which has three-phase is developed under the following assumptions: • The BLDC motor does not operate in an overload mode. • The resistance/inductance of each winding phase of the BLDC motor is equivalent, respectively. • Hysteresis, eddy current losses, and load torque are eliminated. • All semiconductor switches are ideal.
Fig. 1. Full-bridge driving circuit for Y-connected motor
In Fig. 1, the two-level inverter is powered by DC voltage source (Us ), the BLDC motor which is represented by inductances (L, M ), resistances (R), and phases back EMF (ea, b, c ), is configured in a start pattern. The two-level inverter control the BLDC motor via phase voltage (Va, b, c ) which is generated by six switches commutation circuit (S1, 2, 3, 4, 5, 6 ). The supplied voltage of a winding phase of the BLDC motor is determined based on Kirchhoff’s circuit law: ⎡ ⎤ ⎡ ⎤⎡ ⎤ Va R00 ia ⎣ Vb ⎦ = ⎣ 0 R 0 ⎦⎣ ib ⎦+ Vc ic 00R (1) ⎡ ⎤⎡ ⎤ ⎡ ⎤ ea L−M 0 0 ia d⎣ 0 L−M 0 ⎦⎣ ib ⎦ + ⎣ eb ⎦ dt ic ec 0 0 L−M where R is armature resistance (); L is armature self-inductance (H); M is mutual inductance (H); ia, b, c is the input current of winding phase (A); ea, b, c is the back-EMF of phase a, b, c.
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The back EMF voltage is proportional to the rotor speed and is calculated by the following equation: ea, b, c =
ke ωm F θea, b, c 2
(2)
where ωm is the angular speed of rotor rad.s−1 . θea, b, c is the electrical angle of each phase (rad) (θea = θe ; θeb = θe − 2π/3; θec = θe + 2π/3). θe = pθm /2 is the electrical angle of rotor (rad). ke is back EMF constant. F θea, b, c is the back EMF references function of rotor position, which is calculated by: F θea, b, c = ⎧ 1, 0 ≤ θea, b, c < 2π ⎪ 3 ⎪ ⎪ 6 2π ⎨ 1 − π θea, b, c − 3 , 2π (3) 3 ≤ θea, b, c < π 5π −1, π ≤ θ < ea, b, c ⎪ 3
⎪ ⎪ ⎩ −1 + 6 θ 5π 5π , − ≤ θ < 2π ea, b, c ea, b, c π 3 3 According to Euler – Lagrange’s law, the relation between the angular speed of the rotor and the electromagnetic torque is presented as the following equation: J
d ωm + Kf ωm = Te − Tl dt
(4)
where J is the lumped inertia of the rotor and coupled shaft (kg.m2 ); Kf is the friction coefficient of the rotor (Nms.rad−1 ); Tl is load torque of rotor (Nm); Te is the electromagnetic torque depends on the back EMF of each phase that is calculated by Eq. (5) with the assumption that the torque coefficient and the back-emf coefficient are the same for each phase. Te = (ea ia + eb ib + ec ic )/ωm
(5)
Phase current or phase voltage will be converted to D-Q reference frame based on Park transform, which is dependent on the electrical angle θe , and is defined by Eq. (6): ⎡ ⎤ ⎡ cos(θe ) cos(θe − 2π/3) d ⎣ q ⎦ = 2 ⎣ −sin(θe ) −sin(θe − 2π/3) √ √ 3 1/2 1/2 0 ⎤⎡ ⎤ a cos(θe + 2π/3) −sin(θe + 2π/3) ⎦⎣ b ⎦ √ 1/2 c
(6)
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By using Park transformation, Eq. (1) and Eq. (5) are transformed from three-phase reference frame to D-Q rotating reference frame as Eq. (7) [15]: ⎧ diq (t) ⎪ = (Vq (t) − Riq (t) − ωe (t)Ld id (t) ⎪ ⎪ ⎨ dt −ωe (t)kt )/L (7) did (t) ⎪ = (V − Rid (t) + ωe (t)Liq (t))/L (t) d ⎪ dt ⎪ ⎩ Te = iq (t)kt
3 Integral Action Finite Set Model Predictive Control 3.1 Controller Design Observing the block diagram in Fig. 2, the DC voltage source is supplied for the variable source inverter (VSI), which has six switches commutation circuit. Then phase voltages are controlled via six switches commutation circuit of VSI to execute the operation of the BLDC motor. Phase current, phase voltage (Va, b, c ; ia, b, c ) and electrical angle rotor (θe ) are need to be measured and fed to I-FCS-MPC to compute Park transformation (Vd , q (tk ); id , q (tk )). Besides, the rotor speed also needs to be measured and fed to the PI speed controller. I-FCS-MPC block has D, Q current input, and output is six switches commutation circuit. Assuming that a sampling interval t is utilized as a time interval between two samplings id (tk+1 ) and id (tk ), let define the first derivative with respect to time of D, Q current as follows: id (tk+1 ) − id (tk ) did (t) ≈ dt t diq (t) iq (tk+1 ) − iq (tk ) ≈ dt t
(8)
Substituting Eq. (8) into Eq. (7), the following discretized equation is obtained: ⎧ id (tk+1 ) = id (tk ) + t ⎪ L (Vd (tk ) − Rid (tk )+ ⎪ ⎨ ωe (tk )Liq (tk )) ⎪ i (t ) = iq (tk ) + t ⎪ L (Vq (tk ) − Riq (tk )− ⎩ q k+1 ωe (tk )Lid (tk ) − ωe (tk )kt )
(9)
By using the same manner defined in Eq. (9), the discretized differential equation at sampling time (t − t) (id (tk ), iq (tk )) is rewritten from Eq. (9) as follows: ⎧ ⎪ ⎪ ⎨
id (tk ) = id (tk−1 ) + t L (Vd (tk−1 ) −Rid (tk−1 ) + ωe (tk−1 )Liq (tk−1 )) ⎪ i (t ) = iq (tk−1 ) + t ⎪ L (Vq (tk−1 ) − Riq (tk−1 ) ⎩ q k −ωe (tk−1 )Lid (tk−1 ) − ωe (tk−1 )kt )
(10)
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Fig. 2. Block diagram of speed controller using integral action finite set model predictive control
Subtracting Eq. (10) from Eq. (9), we obtain the following equation: id (tk+1 ) − id (tk ) iq (tk+1 ) − iq (tk ) id (tk ) − id (tk−1 ) = (I + tAm (tk )) iq (tk ) − iq (tk−1 ) v (t ) − vd (tk−1 ) +tBm d k vq (tk ) − vq (tk−1 ) 0 − (ωe (tk )−ωe (tk−1 ))φmg t Lq
i (t ) +(Am (tk ) − Am (tk−1 ))t d k−1 iq (tk−1 )
(11)
When sampling interval t is sufficiently small, elements (ωe (tk ) − ωe (tk−1 )), (Am (tk ) − Am (tk−1 )) which relate to t are approximately zero and are ignored. The discretized model described the error of current between two-time intervals is obtained
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as follows: id (tk ) id (tk+1 ) = (I + tAm (tk )) iq (tk+1 ) iq (tk ) Vd (tk ) +tBm Vq (tk )
(12)
where
R/L ωe (tk ) ωe (tk ) −R/L 1/L 0 Bm 0 1/L
Am (tk )
id (tk+1 ) id (tk+1 ) − id (tk ) denotes the D current error between the predictive value and the instantaneous value. iq (tk+1 ) iq (tk+1 ) − iq (tk ) denotes the D current error between the predictive value and the instantaneous value. To integrate the integral action into the FSC-MPC controller, id (tk+1 ), iq (tk+1 ) is considered to be the steady-state of ed (tk ),eq (tk ). By subtracting that steady-state ed (tk ), eq (tk ) from the discretized model Eq. (12) [16], the model including integral action is defined as follows: id (tk ) − ed (tk ) id (tk+1 ) − ed (tk ) = (I + tAm (tk )) iq (tk+1 ) − eq (tk ) iq (tk ) − eq (tk ) (13) Vd (tk ) +tBm Vq (tk ) where ed (tk ) kd id −ref (tk ) − id (tk ) is the error between the instantaneous D current and the D desired current. eq (tk ) kq iq−ref (tk ) − iq (tk ) is the error between the instantaneous Q current and the Q desired current. kq , kd are positive constant. The objective is to regulate ed (tk ), eq (tk ) to be as close as possible to id (tk+1 ), iq (tk+1 ), it leads to the control variable reaching the desired value. All the variables in Eq. (13), only the error of D-Q voltage variable (Vd (tk ), Vq (tk )) can be controlled. So after substituting Eq. (13) into the current cost function, which is chosen as Eq. (14), the first-order derivative of the current cost function with respect to (Vd (tk ), Vq (tk )) is implemented to find out the min value, and the results is expressed in Eq. (15).
id (tk+1 ) − ed (tk ) Ji = iq (tk+1 ) − eq (tk ) id (tk+1 ) − ed (tk ) iq (tk+1 ) − eq (tk )
T (14)
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L/t 0 Vd (tk ) = (I + tAm (tk )) 0 L/t Vq (tk ) kd id −ref (tk ) − id (tk ) id (tk ) − iq (tk ) kq iq−ref (tk ) − iq (tk )
(15)
3.2 Algorithm Strategy The voltage of each phase is controlled by the states (open or close) of two semiconductor switches, in detail phase a (S1 , S2 ); phase b (S3 , S4 ); phase c (S5 , S6 ). At the same time, one of the two gates of each half-bridge side is closed while the other one is opened. For instance, the state of phase A with (S1 closed, S2 opened) is high and (S1 opened and S2 closed) is low. It is similar for the states of phase B and phase C, and there is a total of 8 steps gates for VSI shown as the following matrix. ⎡ ⎤ 0 1 1 0 0 0 1 1 Sabc = ⎣ 0 0 1 1 1 0 0 1 ⎦ (16) 0 0 0 0 1 1 1 1 For each state of VSI, the voltage phase to neutral of the three-phase is different. In order to calculate the voltage cost function as Eq. (17), the three voltages of each state have to be transformed into D-Q reference frame by using the Eq. (18). For BLDC motors, it requires strictness in the commutation to produce a synchronization between the rotor rotation speed and the rotational speed of the magnetic field. Thus, it is necessary to use a sensor to define the electrical angle and the rotor speed on which D-Q transformation depends.
2 op 2 op Jv = Vd (tk ) − Vds (tk ) + Vq (tk ) − Vqs (tk ) Vds, q
2Us cos(θe ) = 3 −sin(θe )
√ 3 −1 2 cos(θe ) +√ 2 sin(θe ) 3 1 2 sin(θe ) + 2 cos(θe )
√ 3 −1 cos(θ sin(θ ) − ) e e 2 √2 Sa, b, c 3 1 sin(θ ) − e 2 2 cos(θe )
(17)
(18)
The I-FSC-MPC controller computes the next state of six switches commutation circuit of VSI at time instant tk according to the following procedure: 1) Measure phase current ia, b, c , phase voltage Va, b, c and rotor speed at time instant tk , then transforming it to a D-Q reference frame based on Eq. (6). op op 2) Optimal voltage Vd , q (tk ) are calculated as Eq. (15) from parameters Vd , q (tk−1 ), idq (tk ), idq (tk−1 ), ωe (tk ), idq−ref (tk ). 3) Use Eq. (17) to calculate cost function from optimal voltage calculated before and a state voltage corresponding to eight states of semiconductor as defined in matrix S a,b,c by using Eq. (18).
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4) Find the minimized value of eight cost functions and the corresponding state with that value. 5) Implement six gates signal for VSI from found out state and wait for next sampling instant to return the first step.
4 Simulation In order to illustrate the effectiveness of I-FCS-MPC for BLDC motor, a model based on MATLAB/Simulink software is built as Fig. 3. The speed and current simulation results of I-FSC-MPC are compared to the classical commutation. For simulation purposes, this study is taken from Maxon EC 45 flat, the brushless motor datasheet, and the parameters are shown in Table 1. In the no-load operation of I-FSC-MPC, the objective of control is to maintain the D reference current (the non-torque producing component) at zero id-ref = 0 (A) and to control the Q reference current so that the PI speed controller with iq-ref output is designed with coefficients Kp = 0,0005, Ki = 0,0005. Table 1. Parameters of Maxon brushless DC motor Nominal voltage
12 (V)
No-load speed
4370 (rpm)
Resistance
1.2 ()
Inductance
0.56 (mH)
Torque constant
25.5 (mNm/A)
Speed constant
374 (rpm/V)
Number of pole pair
8
Rotor inertia 92.5 (gcm2 )
Fig. 3. Simulink model of I-FSC-MPC
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Figure 4 expresses the current and speed of three-phase BLDC motor simulation results of I-FSC-MPC. Comparing the simulation results with the classical commutation method (CCM) in Fig. 5, the no-load speed response of I-FSC-MPC, which attains about 2250 (RPM), is twice as small as the speed response of CCM, about 4450 (RPM). The starting current amplitude of I-FSC-MPC reaches a certain value of 2 (A) in the transient state and is obtained a certain of 0.5 (A) in the steady-state, whereas CCM has a larger starting current amplitude, but the steady-state current is smaller than I-FSC-MPC, respectively it is about 4.5 (A) and 0.05 (A).
Fig. 4. Current and speed simulation results with I-FSC-MPC
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Fig. 5. Current and speed simulation results with classical commutation method (CCM)
The maximum speed of I-FCS-MPC cannot reach the rated speed of the motor, but the starting current is small, and the torque generated is larger when compared to CCM. For applications requiring higher speeds, the field weakening control (FWC) technique can be applied. FWC [17–19] is a method enables higher motor speed by reducing the back EMF generated by motor and it also relies on FOC. As the simulation results are shown in Fig. 6 with id-ref = −2 (A), the maximum speed of the motor can reach more than 4000 (RPM), and the higher the speed, the higher the frequency of current, and the variation of current amplitude reach certain small values.
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Fig. 6. Current and speed simulation results with integral action finite set model predictive control associated with field weakening control
Figure 7 expresses the fast Fourier transform analysis results. When applying FWC, the fundamental frequency is about 250 (Hz), and the fundamental frequency has a scattered distribution from 0 (Hz) to 1000 (Hz) without applying FWC. Moreover, the total harmonic distortion (THD) is improved significantly by THD = 146% compared to THD = 1272%.
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Fig. 7. Fast Fourier transform (FFT) analysis results with FWC and without FWC
In order to illustrate the robustness of I-FCS-MPC, a simulation, for validating the speed change with respect to the changing load torque and external disturbance, is implemented. The results are shown in Fig. 8, at t = 2 (s), the load torque change from 0 to 10−2 (Nm) and white noise is integrated during simulation.
Fig. 8. The speed response with changing load torque and external disturbance
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5 Conclusion Theory and steps to implement the I-FCS-MPC algorithm have been expressed in this paper. The speed comparisons with CCM have shown that the speed motor of I-FCSMPC can not reach the high value. The simulation analysis of I-FSC-MPC combined with the FWC shows that the maximum speed has been improved when compared to I-FSC-MPC, and the THD has also been reduced significantly through the FFT analysis results of the phase current which is generated by VSI. Hence, I-FCS-MPC, which is one of the methods without pulse modulation, can be applied for the BLDC motor control. In this paper, we have controlled the speed of the BLDC motor through the feedback phase current. The feedback torque for cost function is a novel aspect that we have not mentioned yet can be considered for future development. Additionally, a robustness control theory regarding saturated input applied for the integral action of FSC- MPC is a potential research field in further works. Acknowledgments. This research is supported by DCSELAB and funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant numbers C2020-20b-01 and TX2022-20b01. We acknowledge the support of time and facilities from Ho Chi Minh University of Technology (HCMUT), VNU-HCM, for this study.
References 1. Bejenar, C., Irimia, N.D., Luchian, M., Lazar, F.I.: Dynamic behavior analysis of a three-phase BLDC motor under scalar control strategy for automotive actuation systems. In: 2020 15th International Conference on Development and Application Systems, DAS 2020 - Proceedings, pp. 7–15 (2020) 2. Wu, L., Ling, L.Y., Chen, S.: Direct torque control of brushless DC motor. Adv. Mater. Res. 591–593, 1651–1654 (2012) 3. Masmoudi, M., El Badsi, B., Masmoudi, A.: DTC of B4-inverter-fed BLDC motor drives with reduced torque ripple during sector-to-sector commutations. IEEE Trans. Power Electron. 29, 4855–4865 (2014) 4. John, J.P., Kumar, S.S., Jaya, B.: Space vector modulation based field oriented control scheme for brushless DC motors. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology, ICETECT 2011, pp. 346–351 (2011) 5. Gujjar, M.N., Kumar, P.: Comparative analysis of field oriented control of BLDC motor using SPWM and SVPWM techniques. In: RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2018-January, pp. 924–929 (2017) 6. Lazor, M., Stulrajter, M.: Modified field oriented control for smooth torque operation of a BLDC motor. In: 10th International Conference on ELEKTRO 2014 - Proceedings, pp. 180– 185 (2014) 7. Wang, F., Zhang, Z., Mei, X., Rodríguez, J., Kennel, R.: Advanced control strategies of induction machine: Field oriented control, direct torque control and model predictive control. Energies 11, 1–13 (2018) 8. Salem, F., Awadallah, M.A., Bayoumi, E.H.E.: Model predictive control for deadbeat performance of induction motor drives. WSEAS Trans. Circ. Syst. 14, 304–312 (2015)
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9. Salehifar, M., Moreno-Eguilaz, M., Putrus, G., Barras, P.: Simplified fault tolerant finite control set model predictive control of a five-phase inverter supplying BLDC motor in electric vehicle drive. Electr. Power Syst. Res. 132, 56–66 (2016) 10. De Castro, A.G., Pereira, W.C.A., Almeida, T.E.P., De Oliveira, C.M.R., Monteiro, J.R.B.A., De Oliveira, A.A.: Improved finite control-set model-based direct power control of BLDC motor with reduced torque ripple. In: 2016 12th IEEE International Conference on Industry Applications, INDUSCON 2016 (2017) 11. De Castro, A.G., et al.: Finite control-set predictive power control of BLDC drive for torque ripple reduction. IEEE Lat. Am. Trans. 16, 1128–1135 (2018) 12. Yaramasu, V., Wu, B.: Model predictive control of wind energy conversion systems. In: IEEE Press Series on Power Engineering, vol. 55. IEEE Press-Wiley (2017) 13. Verheijen, P.C.N., da Silva, G.R.G., Lazar, M.: Data-driven predictive control with estimated prediction matrices and integral action (2021) 14. Aguilera, R.P., Lezana, P., Quevedo, D.E.: Finite-control-set model predictive control with improved steady-state performance. IEEE Trans. Ind. Informatics 9, 658–667 (2013) 15. Fouad, G.: AC Electric Motors Control AC Electric Motors Control Advanced Design Techniques (2013) 16. Liuping Wang, K.N., Chai, S., Yoo, D., Gan, L.: PID and Predictive Control of Electrical Drives and Power Converter Using MATLAB/Simulink. Wiley-IEEE Press, New York (2015) 17. Lenke, R.U., De Doncker, R.W.: Field weakening control of interior permanent magnet machine using improved current interpolation technique (2006) 18. Sue, S., Pan, C.: Voltage-constraint-tracking-based field-weakening control of IPM synchronous motor drives. IEEE Trans. Ind. Electron. 55(1), 340–347 (2008) 19. Selection, P., Harnefors, L., Pietiläinen, K., Member, S., Gertmar, L.: Torque-maximizing field-weakening control. IEEE Trans. Ind. Electron. 48(1), 161–168 (2001)
A Study on Selecting Cutting Regime to Attain Suitable Roughness and Dimensional Precision in Both When Drilling Tempered Steel 20XHM Ngoc Tuyen Bui1(B) and Thanh Ha Phan2 1 Hanoi University of Science and Technology, Hanoi, Vietnam
[email protected] 2 The 13 Chemical Mechanical One Member Limited Liability MTV Company, Tuyenquang,
Vietnam
Abstract. The paper presents an experimental study about the effect of some machining parameters (cutting speed, feed rate, cutting fluid) on the surface roughness and the machined hole roundness when machining tempered steel 20XHM on the vertical machining center with carbide drills. Taguchi technique in designing the experiment and analyzing results has been applied to define the contribution of machining parameters on the process performance indicators. The results show that, the contributions of cutting speed, feed rate, cutting fluid and errors to the surface roughness (Ra) are 27.6%, 52.5%, 17.4%, 2.5% in turn; meanwhile, the contribution of the machining parameters on the roundness of the machined hole are respectively 46%, 32%, 19% and 3%. Through the combination of the Taguchi method and the Gray Relation Analysis (GRA), the optimal machining parameters are also determined to achieve the surface roughness and the dimensional accuracy in both at the same time. An application study case is introduced, too. The optimal parameters have been effectively used in machining holes for mounting cutting particles on the bodies of mining drills. Keywords: Surface roughness (Ra) · Roundness · Taguchi method · Grey Relation Analysis (GRA)
1 Introduction Surface roughness and dimensional accuracy of parts are two important criteria for ensuring assembly characteristics of machine parts. When fabricating mining drills, the machined holes on the drill body made of 20XHM steel to install the carbide cutting particles need to meet the two above criteria to ensure intermixing, interference fit properties as well as working life of the drills. Therefore, choosing the right drilling equipment, the drill as well as the appropriate technological parameters (cutting speed, feed rate, type of coolant) when machining holes is necessary. Some specialized publications and textbooks have presented the general theoretical basis of the influence of cutting parameters on surface roughness as well as accuracy [1]. Besides, articles, doctoral theses have © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 286–300, 2022. https://doi.org/10.1007/978-981-19-1968-8_23
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also published research results on the effects of cutting parameters on surface roughness [2–4] or of cutting regime parameters to machining accuracy [5] through empirical regression formulas established for specific machining cases. These studies are usually about turning or milling and usually only consider the effect of cutting parameters on surface roughness, or on dimensional accuracy, separately. This paper presents an experimental study of drilling precision holes on 20XHM heat-treated material with a solid carbide drill on a CNC machining center. The research results show the influence of each technology parameter (cutting speed, feed rate, type of coolant) and interference factors (vibration, heterogeneous materials, …) on surface roughness and machining dimension accuracy. From there, analysis and selection of optimal cutting parameters are performed to simultaneously achieve the smallest surface roughness (Ra) and the smallest deviation in machined hole diameter (roundness). Taguchi experimental planning method, Analysis of variance (ANOVA) and Gray Relation Analysis (GRA) with the support of Minitab software tools have been applied in the process of experimental design, analysis and evaluation.
2 Methodology 2.1 Taguchi Method and ANOVA [6] Taguchi method was introduced since the late 1940s. The method is broadly accepted as a DOE (Design of Experiment) which has proven to produce high quality products at subsequently low cost. Two important tools used in the method are Orthogonal Arrays (OAs) and Signal to Noise (S/N) ratios. The orthogonal array is an experimental matrix, such that the fewest number of trials but the greatest amount of information obtained. An orthogonal array describes the inputs, the levels of those factors, the number of experiments and the configuration of those experiments. It is selected from the set of Taguchi OAs according to the number of experimental variables, levels of each variable, accuracy requirements, experimental conditions… S/N (η, dB) is defined as the ratio of the wanted signal towards random noise and, in general, it represents quality characteristics for the observed data. Maximization of S/N is desired for parameters of the design. In the design, there are often many interacting design parameters. Therefore, it is essential to explore the effect of a combination of the parameters. It will, however, not be realistic if there are many numbers of parameters. The signal to noise ratio (S/N) is a measure of the magnitude of a data set relative to the standard deviation. If the S/N is large, the magnitude of the signal is large relative to the noise as measured with the standard deviation. (1) S N = −10log10 (MSD) where MSD = mean squared deviation from the target value of the quality characteristic. Consistent with its application in engineering and science, the value of S/N is intended to be the large, hence the value of MSD should be small. Thus, the mean squared deviation (MSD) is defined differently for each of the quality characteristics considered, smaller, nominal or larger.
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For smaller is better: MSD =
y12 + y22 + y32 + . . . + yn2 n
(2)
1 1 1 + 2 + ... + 2 )/n 2 yn y1 y2
(3)
For larger is better: MSD = (
where y1 , y2 … yn results of experiments, observations or quality characteristics such as length, weight, surface finish… n: Number of repetitions (yi ) n Let T = (yi − y0 ) the sum of all deviations from the target value, we have the i=1
sum of squares of deviations: ST =
n
T2 2 yi − = n
n
n
yi2 −
i=1
2 yi
i=1
n
i=1
(4)
– The sum of squared deviations factor A is calculated by the following formula: SA =
L n 1 2 yiAk − nAk k=1
i=1
n
2 yi
i=1
n
(5)
Herein: yiAk – The ith result of factor A at level k. nAk – Number of repetitions of factor A at level k. If there are 3 factors A, B, C and does not take into account the interactions of the factors we can define the error sum of square: Se = ST − SA − SB − SC – Contribution level of factors and error: PA = SA ST ; PB = SB ST ; PC = SC ST ; Pe = Se ST
(6)
(7)
2.2 Grey Relation Analysis [7, 8] Grey System theory was introduced to science world in 1982. Grey Relational Analysis (GRA) is a part of Grey System theory and used for decision making in multi attribute cases. The GRA process includes the following steps: Step 1: Preprocessing data Data values of target function are normalized to scalar values in the range from 0 to 1. Denote:
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y0(0) (k): Original reference sequence of the target functions values (0) yi (k): Comparable sequence of the target functions values yi∗ (k): Normalized sequence of the target functions values With i = 1, 2, 3, …., m are experimental indicators; k = 1, 2, 3,…., n are the target functions. – If the data have “the larger-the better” characteristic: yi∗ =
max(yij ) − yij max(yij ) − min(yij )
(8)
– If the data have “the Smaller-the better” characteristic: yi∗ =
yij − min(yij ) max(yij ) − min(yij )
(9)
Step 2: Calculating of Grey Relational Coefficient and Grey Relational Grade Let the deviation sequence between reference sequence yi∗ (k) and comparable (0) sequence y0 (k) as (10) 0i (k) : 0i (k) = y0j − yij , Grey Relational Coefficient is calculated based on normalized sequences: (min + ξ max ) ; (0i (k) + ξ max ) 0 < γ [y0∗ (k), yi∗ (k)] < 1 γ [y0∗ (k), yi∗ (k)] =
(11)
The biggest deviation and the smallest deviation are calculated as: max = max 0i (k), i = 1, 2, ..., m, k = 1, 2, .., n
(12)
min = min 0i (k), i = 1, 2, ..., m, k = 1, 2, .., n
(13)
ξ is distinguishing coefficient in [0, 1] and its value is usually 0.5 in literature. Grey Relational grade is weighted sum of Grey Relational coefficients and it can be shown as: Γ (y0∗ , yi∗ ) =
n
wk γ (y0∗ (k), yi∗ (k))
(14)
k=1
n
k=1
wk = 1
(15)
The performances of the runs are ranked from the biggest to the smallest Grey Relational grade.
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3 Experimental Setup and Design 3.1 Experimental Conditions The experiment is conducted at 13. Chemical Mechanical One Member Limited Liability MTV Company, with conditions as follow: Workpiece: Steel 20XHM (20CrNiMo-GB/T 3077–2015) has the chemical composition as Table 1. The workpiece has been heat treated to the hardness of 40 ÷ 42 HRC. It has the dimensions and location of the drill holes as described in Fig. 1. Cutting Tool: solid carbide drill Ø14 with TiAlN/TiN coating of Hartner (Germany) has symbol 89413. Machine Tool The experiment is performed on the vertical machining center with the symbol Fv-800 (Taiwan). Some its specifications are presented in Table 2. Cutting Fluids Experimental process is conducted with 3 types of cutting fluids: The first cutting fluid is VBC Cut NCO-20V. This is an oil-based cutting fluid, that made by Buhmwoo. The cutting fluid is more effective as a lubricant than a coolant. For brevity, we call the fluid oil. The second cutting fluid is an emulsifier, which provide
Fig. 1. Workpiece and the location of the drill holes.
Table 1. Main chemical composition of 20XHM Chemical composition, (%) C
Si
Mn
Cr
Mo
Ni
0,17 ÷ 0,23
0,17 ÷ 0,37
0,60 ÷ 0,95
0,40 ÷ 0,70
0,20 ÷ 0,30
0,35 ÷ 0,75
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less lubrication but better cooling and chip clearing abilities than oil-based cutting fluid. This is VBC COOL ALBIO-118V, that made by Buhmwoo, too. The third cutting fluid is water, a cheap fluid that does not pollute the environment. Table 2. Several main specifications of the vertical machining center FV-800 X-axes travel
800 mm
Y-axes travel
500 mm
Z-axes travel
505 mm
Spindle speed
8000 rpm
Spindle Motor
11 kw
Cutting Feed rate
1–10000 mm/min
Maximum tool weight
7 kgf
Maximum tool length
250 mm
Maximum tool diameter
φ80 mm
Table dimension
425 × 950 mm
Controller
FANUC 640-M
Measurement Equipment The surface roughness and hole diameter deviations are checked with the roughness measuring device POCKETSURF (USA) and the electronic micrometer Mitutoyo (Japan) with an accuracy of 0.001 mm. 3.2 Experimental Design The target in this study is to obtain the best surface quality and the highest accuracy. Therefore, the output quantity is the smallest surface roughness and the smallest machining dimension deviation. Taguchi orthogonal array OA9 (33 ) includes 9 tests is selected for the experiment. Three technological parameters (cutting speed V, feed rate S and type of cutting fluid dd) are performed at 3 levels. These values are selected according to the cutting tool manufacturer’s recommendations and are presented in the Table 3. Each experimental sample is measured for the roughness (Ra) and the hole diameter difference (roundness) 3 times. The mean values of the three measurements are recorded in Table 4 for each sample. Experimental design and experimental data processing are performed with the support of Minitab [9].
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Levels of factors 1
2
3
Cutting speed
V
m/min
20
50
80
Feed rate
S
mm/min 0.1 0.35
Cutting fluid type
dd
–
0.6
Oil Emulsifier Water
Table 4. Orthogonal array L9 of the experimental runs and results N0
Cutting speed V
Feed rate S
Cutting fluid dd
Roughness Ra (μm)
Roundness Ro(μm)
Value (m/min)
Level
Value (mm/rev)
Level
Type
Level
1
20
1
0.1
1
Oil
2
20
1
0.35
2
Emulsifier
3
20
1
0.6
3
4
50
2
0.1
1
5
50
2
0.35
6
50
2
7
80
3
8
80
3
0.35
9
80
3
0.6
S/NRa
S/NRo
1
0.372
1.000
8.589
2
0.468
3.000
6.595
−9.542
Water
3
0.716
4.000
2.902
−12.041
EmulsifWer
2
0.232
2.000
12.690
−6.021
2
Water
3
0.480
4.000
6.375
−12.041
0.6
3
Oil
1
0.668
3.000
3.505
−9.542
0.1
1
Water
3
0.569
5.000
4.898
−13.979
2
Oil
1
0.765
5.000
2.327
−13.979
3
Emulsifier
2
0.680
6.000
3.350
−15.563
0.0000
3.3 Investigating the Impact of Cutting Regime Parameters on the Roughness The main effects of V, S, dd on the surface roughness (Ra) are shown in Fig. 2. The average S/N values of influence levels of V, S, dd on Ra are presented in Table 5. When S/N is bigger, it means that Ra is smaller. Using Eqs. (4)–(7) we have determined the influence of cutting parameters on the surface roughness as shown in Table 6. The feed rate has the greatest impact (52.5%), followed by the cutting speed (27.6%), and finally the cutting fluid (17.4%). The effect of noise factors on surface roughness is 2.5%. The analysis results also show that the predicted smallest value of Ra corresponding to the parameters of the cutting regime at (V2, S1, dd2). Thus, the machining hole will have the lowest roughness when selecting the cutting speed V = 50 m/min, the feed rate S = 0.1 mm/rev, the cutting fluid is emulsifier.
A Study on Selecting Cutting Regime to Attain Suitable Roughness
Fig. 2. Effect of the machining parameters (V, S, dd) on Ra
Table 5. Average S/N values of influence levels of V, S, dd on the roughness S/N Level
V
S
Dd
1
6.029
8.726*
4.807
2
7.523*
5.099
7.545*
3
3.525
3.252
4.725
Table 6. Contribution of factors to the roughness N0
Parameter
Percentage contributions, %
1
Cutting speed (V)
27.6
2
Feed rate (S)
52.5
3
Cutting fluid (dd)
17.4
4
Error
2,5
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3.4 Investigating the Impact of Cutting Regime Parameters on the Roundness The main effects of V, S, dd on the roundness (Ro) are shown in Fig. 3. The average S/N values of the influence levels of V, S and dd on Ro are presented in Table 7. When S/N is bigger, it means that Ro is smaller. Using Eqs. (4)–(7) we have determined the influence of cutting parameters on the roundness as shown in Table 8. The cutting speed (V) has the greatest impact (46%), followed by the Feed rate (S) (32%), and finally the cutting fluid (dd) (19%). The effect of noise factors on the roundness is 3%. The analysis results also show that the predicted smallest value of the roundness corresponding to the parameters of the cutting regime at (V1, S1, dd1). This means that the machining hole will achieve the highest accuracy when selecting the cutting speed V = 20 m/min, the feed rate S = 0.1 mm/rev, the lubricating fluid is oil.
Fig. 3. The effect of machining parameters (V, S, dd) on the roundness
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Table 7. Average S/N values of influence levels of V, S, dd on the roundness S/N Level
V
S
dd
1
−7.195*
−6.667*
−7.841*
2
−9.201
−11.854
−10.375
3
−14.507
−12.382
−12.687
Table 8. Contribution of factors to the roundness N0
Parameter
Percentage contributions, %
1
Cutting speed (V)
46
2
Feed rate (S)
32
3
cutting fluid (dd)
19
4
Error
3
3.5 Multi-object Optimization for Suitable Roughness and Roundness in Both Firstly, with the measurement data Ra and Ro of nine samples, the values of columns 2 and 3 in Table 9 are determined by using formula (9) to normalize the data of two target functions Ra and Ro. The deviation sequences between reference sequences and comparable sequences are found by (10), and the results are shown in column 4 and column 5 of the table. Next, using formulas (12), (13), the values of the gray coefficient for each experiment (column 6, column 7 in the table) are determined. And finally, the Grey relational coefficients for each response are accumulated by using formulas (14), (15) to evaluate Grey relational grade, which is the overall representative of all the features of cutting process quality. After the gray relational grade are determined, the efficiency level of each test for both roughness and roundness are ranked (column 8). The rank shows that the most optimal set of machining parameters in the nine experimental runs is V2S1dd2 (the fourth run). But this is not the most optimal set of the machining parameters in the experimental domain. By using Taguchi method and ANOVA with the option of “larger is better” we can find this set. The ratios S/N of the grades are calculated according to (1), (3) and presented in column 9 of Table 9. When S/N is bigger, that means higher grade. So, the roughness and the roundness are better. The main effects of V, S, dd on the grade are shown in Fig. 4. The average S/N values of influence levels of V, S, dd on the grade are presented in Table 10. The analysis results also show that the predicted highest value of the grade corresponding to the parameters of the cutting regime at (V1, S1, dd2). It means that, when drilling tempered alloy steel 20XHM steel on the CNC machining center by the solid carbide drill, the optimal parameters for the roughness and the roundness in both are v = 20 m/min, s = 0.1 mm/rev, dd = Emulsifier.
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Runs
Deviation sequence
Grey relational coefficient
Roughness Ra
Normalization Roundness Ro
Roughness Ra
Roundness Ro
Roughness Ra
Roundness Ro
Grade
S/N
1
0.737
1
0.263
0
0.655
2
0.557
0.6
0.443
0.4
0.530
1
0.8275
−1.64464
0.555
0.5425
3
0.092
0.4
0.008
0.6
−5.31201
0.984
0.454
0.719
4
1
0.8
0
−2.86542
0.2
1
0.714
0.857
−1.34038
5
0.535
0.4
0.465
0.6
0.518
0.454
0.486
−6.26727
6
0.182
7
0.368
0.6
0.818
0.4
0.379
0.555
0.467
−6.61366
0.2
0.632
0.8
0.442
0.385
0.4135
8
−7.67049
0
0.2
1
0.8
0.333
0.385
0.359
−8.89811
9
0.085
0
0.015
1
0.971
0.333
0.652
−3.71505
Fig. 4. The effect of the machining parameters (v, s, t) on the grade
The sample drilling process on the FV-800 is shown in Fig. 5. The observation of chip morphologies also shows that the cutting fluid is also an important factor affecting
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Fig. 5. Drilling the samples on the FV-800
Fig. 6. Chip morphologies
the hole machining process. With the same cutting speed V = 20 m/min and the feed rate S = 0.35 mm/rev when machining with oil, discontinuous chips are formed (Fig. 6a) and when machining with emulsifier, continuous chips are formed (Fig. 6b).
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V
S
Dd
1
−3.274*
−3.552*
−5.719
2
−4.740
−6.826
−3.456*
3
−6.761
−4.398
−5.601
3.6 A Case Study
Fig. 7. The miling drill bit (a) and its rotator (b)
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Spherical rotary drill bits (Fig. 7) are widely used in mining, drilling earth, rock with high hardness. They work in hard conditions as large impact impulses, large abrasion under high pressure and high temperature. Each drill bit consists of 3 rotators. To withstand the abrasion during working, rotators are usually made of alloy steels as 20CrNiMo and heat-treated reach hardness 40–42 HRC. On the rotator body are drilled holes φ14, φ13, φ10 for mounting the carbide cutting particles. In order to ensure the fitting properties of the redundancy of the cut particles on the rotator body, the holes need to be precisely machined to ensure the minimum roughness and roundness at the same time. So, they are usually drilled before the heat treatment and grinding after the heat treatment to ensure the dimensional accuracy and the surface roughness. The manufacturing process of rotators is therefore often quite complicated and takes a lot of time. With the application of hard drilling technology, the manufacturing process can be shortened. The mining drill bit rotator body after shaping by turning is heat treated and then drilled precisely holes φ14, … on the CNC machining center by the solid carbide drill bit. Using this proposed process, with the optimal parameters presented above, the authors have carried out experimentally fabricating the rotator body. After drilling, the shaft body of the drill bit is checked for the roughness and the roundness of the holes and fitted with cutting particles. With the proposed process, the mining drill bit manufacturing time is shortened. The assembly properties of the cutting particles on the drill bit are guaranteed and the drill bit life is increased.
4 Conclusions In this study, the Grey based Taguchi method is applied for the multiple performance characteristics of hard drilling the tempered alloy steel 20CrNiMo on the CNC machining center by solid carbide drills. By Taguchi method and analysis of Variance (ANOVA) the authors have defined the influence of each cutting parameter such as cutting speed, feed rate and cutting fluid on the roughness and the roundness of drilling holes. Two sets of the optimal parameters for each output criteria have been found, too. A Grey relational analysis of the surface roughness and the dimensional precision obtained from the experiment data allows multi optimization for minimum surface roughness and high dimensional precision, simultaneously. It is necessary to select the cutting speed V = 20 m/min, the feed rate S = 0.1 mm/rev and the cutting fluid of emulsifier to achieve the minimum roughness and roundness at the same time. Furthermore, the hard drilling with the parameters of the multi optimization has been effectively applied to fabricate the rotator of mining drill bit.
References 1. Stephenson, D.A., Agapiou, J.S.: Metal Cutting Theory and Practice. CRC Taylor and Francis Group, New York (2006) 2. Singh, D., Rao, P.V.: A surface roughness prediction model for hard turning process. Int. J. Adv. Manuf. Technol. 32, 1115–1124 (2007) 3. Sahin, Y., Motorcu, A.R.: Surface roughness model in machining hardened steel with cubic boron nitride cutting tool. Int. J. Refract Metal Hard Mater. 26, 84–90 (2008)
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4. Abdullah, A.A., Naeem, U.J., Xiong, C.H.: Estimation and optimization cutting conditions of surface roughness in hard turning using Taguchi approach and artificial neural network. Adv. Mater. Res. 662, 463–464 (2012) 5. Tuyen, B.N., Cong, N.C.: An experimental study on selecting cutting regime to attain suitable roughness and dimensional accuracy in both when CNC turning SUS304 stainless steel by carbide insert which has been made in Vietnam. J. Sci. Technol. 110, 107–112 (2016) 6. Taguchi, G.: A primer on the Taguchi method - Joyce Cary, TS156.R69 (1990) 7. Julong, D.: Introduction to grey system theory.J. Grey Syst. 1(1), 1–24 (1989) 8. Lin, J.L., Tarng, Y.S.: Optimization of the multi-response process by the Taguchi method with grey relational analysis. J. Grey Syst. 4, 355–370 (1998) 9. Du, N.V., Binh, N.D.: Experimental Planning in Engineering. Scientific and Technical Publishing House, Hanoi (2011)
An Overview on the Viability of Hydrous Bioethanol as Gasoline Fuel Blend in the Philippines Nathaniel Ericson R. Mateo1,2(B) , Roque A. Ulep2,3 , Marilou P. Lucas2,4 , Shirley C. Agrupis2,5 , Janssen Sagadraca2,6 , and Christopher Baga2,6 1 Mechanical Engineering, Mapua University, Manila, Philippines
[email protected]
2 Mariano Marcos State University, Batac, Philippines
[email protected]
3 Chemistry Department, College of Arts and Sciences, Iloilo City, Philippines 4 Department of Economics, College of Business Economics and Accountancy, Batac,
Philippines 5 Biology Department, College of Arts and Sciences, Batac, Philippines 6 National Bioenergy Research and Innovation Center, Paoay, Philippines
Abstract. Bioethanol is an environment friendly ethanol made from sugar or starchy crops and palms which undergoes fermentation and distillation processes. In the Biofuels Act of 2006, anhydrous bioethanol is blended in gasoline for better emission of vehicles. This paper aims to provide the viability of hydrous bioethanol as an alternative fuel blend in the Philippines that might: contribute in the reduction of bioethanol production cost; involve the country side in the bioethanol production; and use hydrous fuel blend as effective as the anhydrous blend. Using the Facilities of Far East Alcohol Corporation in Pampanga, Php1.18 per liter of bioethanol can be saved if the 95% hydrous bioethanol will no longer undergo dehydration process. About Php1.4 to Php6.29 billion will be saved due to the reduced production cost of the 330 million gallons of bioethanol demand for the country. Hydrous bioethanol can be produced in the village level of Pamplona. Hydrous bioethanol fuel blend using MMSUhBE20 formulation were tested on vehicles for extensive period, application, and altitudes had been proven as effective as the anhydrous blend E10. The presence of corrosion on the fuel tank of the motorcycle test vehicle suggest that modifications must be considered on the materials of the fuel systems of new vehicles. The sedan car did not exhibit any sign of corrosion in the tank and other component of the fuel system which is made up of stainless and non-corrosive materials. Keywords: Gasoline fuel blend · MMSUhBE20 · Hydrous bioethanol · Fuel blend · Hydrous blend
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 301–314, 2022. https://doi.org/10.1007/978-981-19-1968-8_24
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1 Introduction Bioethanol is an environment friendly ethanol made from crops or plants similar with sugar cane, corn, palm as 1st generation or 2nd generation fermentation process. Basically, bioethanol and ethanol mean the same. In the Philippine Biofuels Act of 2006, 10% (E10) of pure ethanol is blended in gasoline fuels for environment friendly combustion from motor vehicles. The mandate also sought to involve its citizens in the production of ethanol for the, as additional source of income in the countryside. However, the feedstock for the bioethanol production in the country is not enough based from the ethanol demand for gasoline blend of 330 million gallons this 2020–2021, taking note that 57% of the demand were imported from the United States in 2015 alone [1]. Gatdula et al. (2021) also projected 876 million liters as bioethanol requirement of the country. Interventions such as the production of hydrous ethanol either in the village level or in the industrial distillation level, and as well as the utilization of hydrous bioethanol as fuel blend may help alleviate the bioethanol requirement of the country. Bioethanol production may start from the farm lands if the feedstock is sugarcane, sweet sorghum, corn, and others. However, in the production process which includes harvesting, crushing, fermentation, and anhydrous distillation, only multi-million facilities can produce absolute ethanol and no way farmers can participate in this endeavor as farmers can only produce 20–60% ethanol for their local wines called “basi” using traditional distillation technique. The Mariano Marcos State University thru its National Bioenergy Research and Innovation Center (MMSU-NBERIC) developed an apparatus that can produce 95% hydrous ethanol grade as pure fuel or as gasoline fuel blend in its research activities. Reduction of the steps or processes in bioethanol production such as in the purification process from 95% to 99.7% bioethanol leads to reduction of the anhydrous bioethanol production costs. This lessen the overall costs of the petroleum products. Also, hydrous bioethanol production may allow the involvement of the nipa farmers as apparatus in the village level production are available for the farmers to utilize. Nearly all engine experimental studies published showed a substantial improvement in engine combustion characteristics and engine performance using hydrous ethanol as fuel. According to El-Faroug et al. (2016) emissions of carbon monoxide, and oxides of nitrogen emissions were substantially decreased. It is also important to note that unburned hydrocarbon and carbon dioxide emissions were also reduced for the use of hydrous ethanol [2]. Al-Harbi et al. (2022) used E10 and E20 with hydrous ethanol at different moisture content of 5%, 10%, 30%, and 40% in their study with evident positive effect operating on lean conditions [3]. From Rufino et al. (2020) in the comparison of the combustion characteristics of the hydrous ethanol with gasoline (E95h), and gasohol blend (E27) major differences observed were reduced sensitivity to engine knock, and a shorter duration of combustion. In addition to shorter combustion duration, Wiebe exponent of ethanol presented lower value [4]. Hydrous ethanol as gasoline fuel blend appeared to be a promising alternative fuel for SI engines according to the study conducted by Deng et al. (2018) when tested
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for exhaust noise, performance and emission characteristics. Hydrous ethanol with 5% moisture was used and compared to the performance of hydrous E10, E20 and E0 (pure gasoline fuel) in a 4-cylinder, In-line gasoline engine [5]. In the study conducted by Costa et al. (2009), a four stroke, four cylinder gasoline engine was tested using hydrous ethanol at 6.8% water (or 6.8% hydrous), and gasolineethanol blend of 78% part gasoline and 22% part hydrous ethanol [6]. It appeared in the result that both pure hydrous ethanol, and gasoline fuel blend as fuel of the test engine produced about the same power output. The performance of the gasoline-ethanol blend appeared to be better with lower specific fuel consumption compared to the hydrous ethanol fuel. The findings from another study conducted by Shirazi et al. (2018) mentioned that the only significant difference between different blends is within the viscosity and phase separation properties. The viscosity of hydrous blend increases, and the phase separation temperature increases as the water content of the bioethanol increases [7]. These studies correspond to the positive effect of hydrous bioethanol in the engine’s performance that supports the viability of hydrous bioethanol as gasoline fuel blend in the Philippines. This paper aims to provide some perspective on the viability of using hydrous ethanol as gasoline fuel blend that might contribute in the reduction of production cost of the bioethanol requirement of the country without significant harm in the engine’s performance, and may involve the country side in the production of bioethanol.
2 Demand on Bioethanol Blend 2.1 Global Bioethanol Demand Brazil and Paraguay started Bioethanol blending in 2001, and by 2016 the ethanol demand increases from 5 billion gallons to 27 billion gallons with the addition of 26 countries [1] including the Philippines. As the bioethanol production of 110 billion liters in 2018, this is continuously increasing as it was projected to 140 billion liters in production by 2022 [8] with United States, Brazil, European Union, China and Canada as the global powerhouses in bioethanol production respectively [8]. These bioethanol productions are used for gasoline blend. 2.2 Projected Consumption of the Philippines The biofuels Act of 2006 that mandated the use of bioethanol blend to petroleum products by 10% in the country bolstered the demand of bioethanol, E10 by 2011 to E20 by 2020, and E85 by 2030 as suggested in the Philippine Energy Plan [9]. The ethanol blend in the Philippines is still at 10% blend or E10 due to the scarcity of ethanol supply, and E20 as forecasted to be the mandated blend for 2020 is still on hold. The Ethanol importation from 257 million liters in 2020 was projected to decrease to 225 million liters due to the pandemic that lessen some fuel consumption due to travel restrictions [9]. Even though with this projection, importation of ethanol is still needed either as a raw material ethanol for the required blend or as raw material to be processed as ethanol using molasses.
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2.3 Feedstocks for Bioethanol The main raw materials used in ethanol production may come from sugar or starchy plants such as sugar cane and corn through fermentation followed by distillation [10]. In the country, sugar cane and molasses (mostly imported) are its main bioethanol feedstock. Other feedstock in the bioethanol production may come from cassava [11, 12], nipa fruiticans [13], micro algae as promising raw material [14], and other lignocellulosic material but with some problems in the pre-treatment such as in fermentation process as it differs in terms in its fermentation protocols [15]. Nevertheless, there are abundant feedstock in the country as mentioned in terms of crops and palms such as nipa as potential feedstock [16]. These raw materials offer different requirements in the production such as farm management, collection, and bioethanol production.
3 Method of Bioethanol Production Bioethanol production may start from the farm lands if the feedstock are sugarcane, sweet sorghum, corn, and others. However, in the distillation process which includes harvesting, crushing, fermentation, and distillation, only multi-million distillation facilities can produce absolute ethanol. Farmers cannot participate in this endeavor as they can only produce 20–60% alcohol purity usually for their local wines called “basi”, using traditional distillation techniques. MMSU-NBERIC developed an apparatus that can produce 95% ethanol grade (5%hydrous). The said apparatus was used to produce 95% ethanol grade as pure fuel or as gasoline fuel blend in its research activities, and in the village level. 3.1 Process of Anhydrous Ethanol Production In the bioethanol production using sugar cane [17], the process starts as the cane stalks are pressed for juice extraction. The cane juice is collected for the fermentation process by adding yeast. The fermentation process exhaust carbon dioxide through agitation. The leftover bagasse during the juice extraction process is used as fuel to generate electricity and steam for the distillery plant. Approximately 5–12% alcohol (v/v) of fermented juice called beer is pumped to the rectification column where the ethanol is recovered. In this process, the purity of ethanol can be up to 95% ethanol grade where further water separation process is required. Commonly, dehydration of the residual water is carried out using molecular sieves resulting in the final product of 99.6% fuel-grade called anhydrous ethanol [17]. 3.2 Cost of Bioethanol Production The cost of bioethanol is equal to the feedstock cost plus the cost of bioethanol production. The cost of feedstock as a result of the different farm management system from planting, harvesting, up to transporting the said crop in the processing facilities is one major factor affecting the price of bioethanol per liter. Additional process to saccharify
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the starch to glucose units before fermentation using rice straw costs Php59.39 per liter and cassava to Php64.00 per liter leads to higher bioethanol cost compared to sugarcane and sweet sorghum at Php33.34 per liter and Php36 per liter respectively [18]. A study conducted by Ahsan Farooq et al. (2020) is the distribution cost of ethanol production using molasses as feedstock as shown in Table 1 below [19]. Using an ethanol price index per liter of Php63.00, the production cost is Php48.74 per liter, Php6.33 per liter, and Php7.94 per liter on cost of feedstock, Operation and Maintenance, and Capital Investment Cost respectively. Table 1. Ethanol production cost distribution Cost type [19]
Percentage share (%) [19]
Estimated cost (Php/L)
Feedstock
77.34
48.74 [18]
Operation and maintenance
10.05
6.33
Capital investment
12.61
7.94
3.3 Process of Hydrous Ethanol Production In industrial level distillation facilities similar to Far East Alcohol Corporation (FEAC), the hydrous ethanol can be produced up to the rectification column. In this production, the purification process using molecular sieve is omitted hence reducing the cost of electricity, steam, and fuel during the bioethanol production. However, village scale hydrous ethanol production as shown in Fig. 1 can be an alternative in the bioethanol production [20] in the Philippines.
Fig. 1. Photo of the 850-L batch capacity reflux distillation facility in Brgy. Cabaggan, Pamplona, Cagayan
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In the said bioethanol production, reflux distillation technique is utilized to produce 95% fuel grade compared to the rectification column used in industrial scale distillation facilities. The said apparatus with Philippine Utility Model Registration Number 2017000,287 [21] for village scale production comes into two batch capacity design: 150-L and 850-L capacities, and was deployed to four different locations in Pamplona, Cagayan for the bioethanol research project of MMSU. All hydrous ethanol produced in the village level were collected and stored in a rigid stainless tank designed and prepared by MMSU before delivering to FEAC for its dehydration process with a recommended volume of 10,000 to 20,000 L to save on freight services considering that FEAC is located in Apalit, Pampanga in the Philippines, about 600 km away from the nipa bioethanol producing community of Cagayan. 3.4 Cost of Hydrous Bioethanol Production Far East Alcohol Corporation revealed the estimated cost of dehydrating ethanol from 92.5% grade to 99.7% grade ethanol of Php1.18 per liter. The cost cover for its electricity, and steam consumption. The trial dehydration run using hydrous ethanol of MMSU in the FEAC facility was conducted on August 22, 2019 which is recorded as the first distillation run from the community level up to the industrial level distillation facilities accredited by the Philippine government thru the Ethanol Producers Association of the Philippines.
4 Hydrous Bioethanol as Gasoline Fuel Blend One major contributor in a very good engine performance is caused mainly by an efficient combustion process as it affects better power output, longer mileage runs, and clean gas emissions. Studies using hydrous ethanol as pure fuel or as gasoline blend in a spark-ignition engine (SI) or gasoline engines exhibited good engine performances which supports the observations of the authors using hydrous gasoline ethanol blend formulation of MMSU (MMSUhBE20). 4.1 Performance of Some Spark Ignition Engines using Hydrous Gasoline Fuel Blend Long term testing was conducted using MMSUhBE20 to run selected SI engine that includes brand new sedan, and two brand new motorcycles. The test engines were utilized in a span of one year and travelled at least 10,000 km for both motorcycles. One of the motorcycles served as the control vehicle fueled with the commercially available E10 fuel while the other motorcycle was fueled with MMSUhBE20. The sedan used the MMSUhBE20 in the whole span of one year. The test vehicles used in the study fueled with MMSUhBE20 exhibited good engine performances corresponding to the result of the study conducted by Costa et al. (2009), no observed engine knocks as claimed by the study conducted by Rufino et al. (2020), smooth engine operation free from noise and vibration as established by Deng et al. (2018).
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The exhaust gas emission result as shown in Table 2 also proved the claims of Al-Harbi et al. (2022), Deng et al. (2018), and El-Faroug et al. (2016) to have better emission when hydrous bioethanol was used. When emission test result was obtained from the sedan test car at high rpm, it shows that gas emissions were reduced significantly compared to E10 fuel. Table 2. Exhaust gas emissions Fuel
CO
CO2
N2O
NO
MMSUhBE20
195.89
133.68
0.54
43.34
E10
256.67
183.03
7.85
69.81
% Diff
26.86%
31.16%
93.12%
46.78%
4.2 Utilization of Hydrous Ethanol as Straight Fuel or as Fuel Blend Utilization of hydrous fuels, either as pure or hydrous blend had been utilized by other countries already including the commercialization of flexi-fuel vehicles. In the Philippines, MMSU thru its research also tried its MMSUhBE20 hydrous formulation for one year in selected gasoline engines. Since 2003, vehicles in Brazil already have flexi fuels that can be fueled by E100 or E25. This E100 hydrous ethanol fuel had been sold at the pumps at 50–60% of the gasoline price. As of June 2015, flexi fuel light-duty vehicle sold 25.5 million units, and production of flexi fuel motorcycles totaled 4 million in March 2015 [22, 23]. While Brazil already started commercializing flexi fuel vehicles, the study conducted by Wang et al. (2015) in China on the other hand concluded that E10W or 10% hydrous blend fuel can be regarded as a potential alternative fuel for gasoline engine applications [23]. The study conducted by Regalado et al. (2018) in the Philippines in 2014, hydrous ethanol as pure fuel was utilized to drive different agricultural machineries using 90% to 95% hydrous ethanol. Modification such as using separate fuel tank for the hydrous ethanol, and by pass line going to the carburetor was made as adjustments for the engines of their agricultural machines [25]. The following studies as stated above only proved that challenges in the utilization of hydrous bioethanol blend can be addressed, but with some modifications in the system. 4.3 Challenges of Hydrous Ethanol as Gasoline Blend Stability of Hydrous Ethanol Fuel Blends. Ethanol absorbed moisture and phase separation is possible when ethanol absorbed more than the required in fuel blends at low temperature conditions. This was observed unintentionally in some samples of commercial gasoline E10 from gasoline stations during the formulation of MMSUhBE20. This coincide with the results of the study of Faroug et al. (2016) stating that the solubility of
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water and the possibility of phase separation in alcohol-gasoline blends is caused by any of the following: proportions of gasoline and alcohol; fuel temperature; composition of gasoline; polarity of the alcohol; and pressure of the fuel system [2]. Corrosion on Metallic Materials. Matˇejovský et al. (2017) used water purposely to contaminate gasoline-ethanol blends of E10, E40, E60, E85, and E100 shows traces of corrosion on mild steel, copper, and brass for E60 contaminated with water but high corrosion resistant in stainless steel was observed [26]. This was observed in the motorcycle fueled with MMSUhBE20 whose tank is made up of mild steel. However, the sedan did not show any sign of corrosion on its fuel system that is made up of stainless steel. Modification on Ignition System. Higher gasoline fuel blends using either hydrous or anhydrous ethanol requires some modification of the fuel-ignition system as reflected in the study conducted by Belonio et al. (2014) for pure 95% grade hydrous fuel. Also, valve timing on intake and exhaust must be optimized [27] to reduce negative combustion effect during engine warm up period was suggested by Passarini et al. (2020). However, using MMSUhBE20 as fuel in the test vehicles, no modifications were made in the fuel system of sedan car as it is equipped with a multi-port fuel ignition system. This is also true to the motorcycle fed with MMSUhBE20, except with some hard-starting experience in the morning which is also observed in the other test motorcycle fed with commercial gasoline E10.
5 Discussion 5.1 Reduction of Bioethanol Production Cost For the 330 million gallons of bioethanol needs as reflected in the projection of Beckman et al. (2017) and the ethanol price index in March 2021 of Php 63 per liter of Gatdula et al. (2021), about Php1.4 billion to Php6.29 billion will be saved from the 330-milliongallon demand of the blend requirement of the country when further dehydration process is omitted in the production process. This is based from the findings to have potential 8% savings from hydrous ethanol production cost reducing the use of cooling water, and energy input in the production [28], and the estimated 1.8% savings from FEAC is within the 10.05% [13] operational cost of Farooq et al. (2020). 5.2 Hydrous Bioethanol Blend Properties The MMSU hydrous formulation (MMSUhBE20) is a 20% hydrous bioethanol blend. Commercially available E10 gasoline was used to blend with MMSU’s 95% hydrous bioethanol grade produced from its reflux distillers. Table 3 presents the Research Octane Number Rating (RON) and Reid Vapor Pressure of the formulated hydrous gasoline fuel blend using ASTM D2699-12 procedures. Significantly, the hydrous ethanol blend improves the RON rating just as effective as the anhydrous blend E10 at 95 RON. Our results imply that hydrous blend using MMSUhBE20 is safe to use as it does not cause premature auto ignition that leads to
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Table 3. Properties of hydrous gasoline fuel blend Fuel blend
RON
RVP (10.1 psi max)
MMSUhBE20
98.3 to 98.5
8.71
engine knock. In addition, the 8.7 psi RVP of the hydrous blend is lower compared to the maximum RVP of 10.1 psi [29] denoting that the blend is highly volatile. Improved RON rating reduces pre-ignition related to engine knock supporting the result of the study of Rufino et al. (2020). The performance of the test vehicles Kia Rio and Honda 155 TMX Motorcycle as shown in Table 4 was compared when fueled with E10 and before shifted to MMSUhBE20. Two test motorcycles were used which is fueled separately with E10 and MMSUhBE20 respectively. Table 4. Performance of gasoline fuel blends on test vehicles Test vehicle
Fuel used
Kia Rio 155cc Honda TMX
Wheel power, kW
% Difference
E10
46.7
1.4
MMSUhBE20
47.76
E10
-
MMSUhBE20
-
Mileage, Km/liter
% Difference
16.64
−3.36
15.61 –
35.215
5.38
37.7
The output power on the wheels is slightly greater by 1.4% if hydrous fuel blend was used as fuel. The fuel economy however decreases by 3.36% due to the said power increase. The motorcycle fed with MMSUhBE20 exhibited a better mileage run of 5.38%. The sedan was subjected into a chassis dynamometer using Japanese Standard 10– 15 mode cycle test. The Japanese standard test simulates different acceleration runs while running stationary on top of the rollers of the dynamometer. The output power, fuel economy, and gas emissions were obtained simultaneously. The motorcycles were subjected in an actual fuel economy run at the same time. 5.3 Long Term Trial In the Philippines thru MMSU-NBERIC, selected SI engines were used to run on 20% hydrous ethanol blend with 95% ethanol purity in gasoline fuel formulation or the MMSUhBE20. This is supposed to be parallel to the E20 requirement of the country by 2020. The Kia Rio sedan car as shown in Fig. 2 with specifications as shown in Table 5 was subjected to one year long used as a normal service vehicle with 32,539 accumulated distance travelled.
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Fig. 2. Photo of the Kia Rio sedan test. Vehicle fed with MMSUhBE20
Table 5. Kia Sedan specification Kia Rio test vehicle specification Kia Model: Kia Rio 2012 Kia Series: Rio LX MT Engine Number: GALACPO36730 Chassis Number: KNADMA411aC6078335 1.6 L, 4-cylinder gasoline engine MPI Fuel System
The test car experienced no engine or system maintenance corrections related to the MMSUhBE20. The cylinder head and fuel system of the test vehicle sedan was physically examined at Kia Service Center and found very clean without trace of burnt piston due to proper combustion, and no sign of corrosion was present in the fuel system due to the material used in the fuel tank and other components of the fuel system which is made up of stainless as shown in Fig. 3. The motorcycle test vehicle fed with MMSUhBE20 fuel was used for one year with 10,000 km distance travelled as a service vehicle, and subjected to an artificial highway run as shown in Fig. 4 to accumulate the target mileage. Unlike test motorcycle fed with E10, the TMX motorcycle fed with hydrous fuel blend experienced minor hard starting procedures in the morning and servicing of the carburetor. Traces of corrosion was also observed in the fuel tank.
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Fig. 3. Components of Kia Rio (a) fuel tank (b) filter/floater mechanism (c) piston and cylinder
Fig. 4. Photo of the Honda 155 TMX test vehicle running on an artificial highway
5.4 High Altitude Test Runs Phase separation can be affected by low temperature as mentioned by previous studies conducted. This is true in countries with winter conditions where vehicles are installed with fuel preheaters to address the problem. Philippines, however is a tropical country where freezing ambient temperatures are hardly experienced, as such the so-called fuel blend stability may not be an issue for hydrous blend. Table 6 as shown below implies that MMSUhBE20 to MMSUhBE60 formulations are safe from phase separation and cloud point formation using a refrigerated temperature condition. The test vehicles were subjected to high altitude runs such as in Solsona-Apayao road as shown in Fig. 5 to experience running at cold temperatures. These vehicles underwent cold start-up procedures to observe any effect of the cold weather. The lowest ambient temperature recorded during the test is 12 °C. This implies that there is no tendency of reaching the 4 °C phase separation experienced by MMSUhBE80 and MMSUhBE90 in the refrigerated condition. Normal start-up and running condition were observed during the test runs.
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Hydrous Bioethanol fuel blend formulation
Visible phase separation 12 °C
8 °C
4 °C
MMSUhBE20
None
None
None
MMSUhBE40
None
None
None
MMSUhBE60
None
None
None
MMSUhBE80
None
None
Yes
MMSUhBE90
None
None
Yes
Fig. 5. High altitude test run (a) sedan, (b) motorcycles
6 Conclusion Hydrous bioethanol as fuel blend has the potential in the Philippines. Savings on the production cost can be realized if the hydrous bioethanol will no longer undergo into further dehydration process. The trial dehydration run in FEAC distillation facility without any minor retrofitting works implies the versatility and compatibility in handling hydrous bioethanol. However, FEAC suggested to study the installation of a centralized distillation with storage and blending facility near the community based hydrous bioethanol industries to address issues on storage, blending, and distribution to gasoline stations. With phase separation observed at 4 °C refrigerated temperature, it is important to note that seldom location in the Philippines have this ambient temperature and interventions can be made such as in Brazil for their flexi-fuel vehicles if ever this temperature will be reached. Vehicles fed with the MMSUhBE20 formulation perform well in high altitudes with low ambient temperatures. The hydrous bioethanol in gasoline fuel blend using MMSUhBE20 formulation on sedan and motorcycles had been proven to be as effective as the anhydrous gasoline fuel blend E10. However, the presence of corrosion on the fuel tank of the motorcycle test vehicle implies that necessary modifications are needed for some vehicles especially on the materials of the fuel tank and other components of the fuel system to adapt the hydrous blend. The sedan did not show any signs of corrosion due to the material used in its fuel lines that is made up of stainless steel and plastic.
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Acknowledgements. Special thanks to the following who sincerely supported this endeavor: the late President Romulo Kheyeng of FEAC, Ms. Queenie Rojo of EPAP, Engr. Rosemarie Gumera of Sugar Regulatory Administration, Engr. Ruby De Guzman of DOE-REMB, Engr. Jorge Maglasang, Kia Motors, Global City, the late Pamplona Mayor Aaron Sampaga, the municipality of Pamplona, MMSU-NBERIC staffs, UPD-VRTL, Dr. Fiorello Abenes, to our Pamplona community recipients, and to Dr. Blessie Basilia of Mapua University.
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16. Lim, K.M.R.C., et al.: Extraction of Ethanol from Nypa fruticans (Nipa) Palm fruit. Asian J. Phys. Chem. Sci. 8(4), 41–45 (2020) 17. Romanus, N.O.: Fuel ethanol production from sugarcane and corn: comparative analysis for a Colombian case related papers. Energy 33, 385–399 (2008) 18. Gatdula, K.M., Demafelis, R.B., Bataller, B.G.: Comparative analysis of bioethanol production from different potential biomass sources in the Philippines. In: Bioethanol Technology, February 2021 19. Farooq, A., Shabbir, G., Bangviwat, A.: Life cycle cost analysis of ethanol production from sugarcane molasses for gasoline substitution as transportation fuel in Pakistan. J. Sustain. Energy Environ. 11, 49–59 (2020) 20. Agrupis, S.C., Mateo, N., Birginias, M.C., Lucas, M.P., Madigal, J.P., Abenes, F.: MMSU hydrous bio-ethanol (MMSU95hBE) III: development of adaptable technologies for villagescale bio-ethanol production (2016). l_iii_scagrupis.pdf 21. Mateo, N.E.R., Agrupis, S.C., Ulep, R.A.: An apparatus for reflux distillation Hydrous Ethanol.pdf, 2–201700028 (2018) 22. “Ethanol fuel in Brazil - Wikipedia,” Wikipedia (2021). https://en.wikipedia.org/wiki/Eth anol_fuel_in_Brazil 23. Nogueira, L.A.H., Souza, G.M., Cortez, L.A.B., de Brito Cruz, C.H.: Biofuels for transport. In: Future Energy Improved Sustainable Clean Options Our Planet, pp. 173–197, January 2020. https://doi.org/10.1016/B978-0-08-102886-5.00009-8 24. Wang, X., Chen, Z., Ni, J., Liu, S., Zhou, H.: The effects of hydrous ethanol gasoline on combustion and emission characteristics of a port injection gasoline engine. Case Stud. Therm. Eng. 6, 147–154 (2015) 25. Regalado, M.J.C., Belonio, A.T., Villota, K.C., Rafael, M.L., Castillo, P.R.: Design, testing, and evaluation of a Hydrous Bioethanol Distiller for the production of fuel-grade alcohol from Nipa. Appl. Eng. Agric. 34(5), 759–765 (2018) 26. Matˇejovský, L., Macák, J., Pospíšil, M., Baroš, P., Staš, M., Krausová, A.: Study of corrosion of metallic materials in ethanol–gasoline blends: application of electrochemical methods. Energy Fuels 31(10), 10880–10889 (2017). https://doi.org/10.1021/acs.energyfuels.7b01682 27. Passarini, G.C., Fregoneze, M., Júnior, F.S.: Variable camshaft valve timing and its effects to Hydrous Ethanol (E100) combustion during engine warm up phase. In: SAE Technical Papers, January 2020 28. Saffy, H.A., Northrop, W., Kittelson, D., Boies, A.M.: Energy, carbon dioxide and water use implications of hydrous ethanol production. Energy Convers. Manag. 105, 900–907 (2015) 29. Philippine Inc.: “Philippine Mechanical Engineering Code 2008 ed. PSME Inc. Paredes Street, Sampaloc, Manila, Philippines, ISSN 2012-3426, p. 314 (2008)
Numerical Optimization of the Process Conditions to Improve the Warpage Inside the Product and Reduce the Cooling Time in the Injection Molding Product Tran-Phu Nguyen1(B) and Tuan-Anh Bui2 1 Faculty of Vehicle and Energy Engineering, Ho Chi Minh University of Technology and
Education (HCMUTE), Ho Chi Minh City, Vietnam [email protected] 2 School of Mechnical Engineering, Hanoi University of Science and Technology (HUST), Hanoi, Vietnam
Abstract. The effect of the parameters in the injection molding process conditions to the warpage inside the product and the maximum cooling time in the injection molding cycle is numerically considered in this research. A series of process conditions setting with different values of coolant temperature, packing time, and maximum packing pressure has been taken into account. The results indicate that the process conditions have a strong influence on the quality factors– the total displacement of product and the maximum cooling time. In the current model, the optimal process conditions are 50 °C of the coolant temperature, 14 s of the packing time, and 77% of the maximum packing pressure. With the optimal parameters, the quality of the product could be improved with low warpage and a short maximum cooling time. Keywords: Injection molding · Optimization · Process parameters · Warpage · Cooling time
1 Introduction Currently, injection molding is the most common method in the huge-demand plastic industry. The warpage inside the plastic product is one of the most critical defects that degrade the quality of the product. Besides, the important goal of the injection molding industry is to reduce the cooling time, which occupies more than 70% of the whole molding cycle [1]. Therefore, finding the optimal injection molding process conditions with low warpage inside the product and short cooling time is a significant challenge in the injection molding industry. There have been several types of research on the effect of the injection process conditions on the quality of the plastic product. Using the finite volume approach, Chang et al. [2] built a three-dimensional mold model to predict the behavior of the melt in the filling stage. In the papers of Hamdy et al. [3, 4], the injection molding model was © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 315–325, 2022. https://doi.org/10.1007/978-981-19-1968-8_25
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developed to be three-dimensional time-dependent. Different process parameters have been obtained: the inlet temperature, the injection temperature, and the filling time. The purpose of this work is to study the influence of these parameters on the cooling time. The results show that the filling time had a main effect on the plastification of the melt in the filling stage and the cooling time. In 2010, Park et al. [5] used a baffle cooling channel to archive the advanced mold design with the uniform cooling rate and the increase of the cooling efficiency. B. Ozcelik et al. [6–8] had several studies concerning the injection parameters in injection molding. In 2016, the effect of the melt temperature, the packing pressure, the packing time, the cooling time, the runner type, and the gate location are investigated to minimize the warpage of plastic parts. In 2010, the influence of the injection parameters on the mechanical characteristics, such as the elastic module, the tensile strength, and the tensile strain, was considered. The Taguchi method was obtained to find the main effect parameter. In 2011, the maximum tensile load of the specimens was considered by investigating the injection pressure and the melt temperature. In 2017, Dzukipli et al. [9] reported that the formation of weldline is strongly influenced by different process conditions – melt temperature, material, and mold design. Nian et al. [10] found a way to decrease the warpage inside the plastic part by controlling the local mold temperature setting in the cooling system in 2015. In the paper of Sánchez et al. [11], the effect of cooling parameters including the melt temperature, the cooling time, or the cooling conditions on the warpage was taken into account. In this research, the effect of different process parameters, including the coolant temperature, the packing time, and the maximum packing pressure to the warpage inside the product and the maximum cooling time in the cycle of injection molding is numerically investigated. This work aims to archive the optimal process conditions and improve the quality of the plastic part with low warpage inside the product and a short maximum cooling time.
2 Mathematical Model Figure 1 shows the schematic diagram of the injection molding model in this research. The model was assumed to be three-dimensional and time-dependent. The product is the 150 mmL × 50 mmW × 50 mmH × 3 mmT box. The material is ABS STYLAC VA29 plastic. The filling time is 1.92 s and the packing time is 9.15 s. There are a total of 5 cooling channels with 3 baffles with a diameter of 8 mm. The distance between each cooling channel is 40 mm. The flow in the cooling channel is turbulent since the Reynold number is 40,290. In the current model, the melt and the water are assumed to be incompressible fluids. The governing equations for the 3D, transient fluids are as follows [1]: ∂ρ + ∇ · ρu = 0, ∂t
(1)
∂ρ (ρu) + ∇ · (ρuu + τ ) = −∇p + ρg, ∂t
(2)
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Fig. 1. Schematic diagram of the injection molding model.
ρcp
∂T + u · ∇T ∂t
= ∇(k∇T ) + ηγ˙ 2
(3)
where ρ is density, t is time, u is velocity vector, T is temperature, p is pressure, τ is stress tensor, η is viscosity, k is thermal conductivity, Cp is specific heat and γ˙ is shear rate. In this model, the governing equations and boundary conditions are solved in the CAE software Moldex3D. The conservation of the control volume was applied to derive the governing equations. The authors use boundary layer mesh (BLM). BLM mesh includes 2 types of mesh: 5 layers of prism mesh on the surface and tetra mesh inside the product. BLM mesh could catch the shear heating effect caused by the melt with high accuracy. There are a total of 1,159,187 solid mesh elements in this model.
3 Results and Discussion The temperature and flow fields in the current injection molding model are simulated. Nine sets of process conditions including different values of coolant temperature, packing time, and maximum packing pressure are obtained, as shown in Table 1. The coolant temperatures are 50 °C, 60 °C, and 70 °C. The packing times are 9 s, 11.5 s, and 14 s. The packing pressures are 63%, 70%, and 77% compared with the maximum packing pressure. During the filling stage, Fig. 2 illustrates the melt front time which expresses the movement of the melt front boundary in different periods. The cavity is filled with 100% of plastic.
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Fig. 2. Melt front time during the filling process: 25%, 50%, 75%, and 100%.
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Table 1. Process parameter setting Control Factor Level Max Min 1 .Run2 2 .Run3 3 .Run4 4 .Run5 5 .Run6 6 .Run7 7 .Run8 8 .Run9 9 .Run10 Run11*
Default Coolant Temperature (°C)
Packing Time (sec)
3 70 50 50 50 50 60 60 60 70 70 70 50
3 14 9 9 11.5 14 9 11.5 14 9 11.5 14 14
Max. Packing Pressure Profile Value (%) 3 77 63 63 70 77 70 77 63 77 63 70 77
Quality Factor Target Goal WeighƟng 1 .Run2 2 .Run3 3 .Run4 4 .Run5 5 .Run6 6 .Run7 7 .Run8 8 .Run9 9 .Run10 Run11*
Warpage_Total Displacement (mm) Global Smaller 1 0.692056 0.603823 0.55078 1.09087 1.05732 0.991162 1.3543 1.32755 1.31398 0.55078
Cooling_Max. Cooling Time (sec) Global Smaller 1 46.6973 44.9352 42.8787 58.7806 56.3171 54.1039 85.9594 81.5248 77.6906 42.8787
The optimal process parameter was predicted based on the data from the previous nine sets of parameters. The quality factors are the total displacement of the product and the maximum cooling time. The detailed comparison is shown in Table 1. The optimal process conditions are predicted to be 50 °C of the coolant temperature, 14 s of the packing time, and 77% of the maximum packing pressure. The results indicate that there are significant drops in the total displacement of the product and the maximum cooling time in the optimal run, compared to that with the basic run. The pressure distribution of the cavity is shown in Fig. 3. The maximum pressure which is at the gate region significantly increases for the optimal run (12.65 MPa). The reason is the long packing time and the high maximum packing pressure in the optimal run. This could enhance the packing effect in the optimal run.
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Fig. 3. Pressure distribution of the cavity for the basic run (upper) and the optimal run (lower).
Figure 4 shows the temperature distribution of the part at the end of the cooling stage (EOC). The maximum temperature is at the corner of the part where heat accumulation is difficult to release. With the low coolant temperature in the optimal run, the temperature at EOC drops considerably. The maximum temperature decreases from 97.63 °C in the basic run to 86.02 °C in the optimal run. Moreover, the temperature distribution seems to be more uniform in the optimal run, especially at the corner regions.
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Fig. 4. Temperature distribution of the part at the end of cooling for the basic run (upper) and the optimal run (lower).
The low temperature of the part at EOC of the optimal run in Fig. 4 leads to the short maximum cooling time of the part, as illustrated in Fig. 5. Maximum cooling time significantly reduces from 58.52 s in the basic run to 42.88 s in the optimal run. It could play an important role in decreasing the injection molding cycle.
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Fig. 5. Maximum cooling time of the part for the basic run (upper) and the optimal run (lower).
During the cooling stage, the cooling efficiency of the channel is shown in Fig. 6. Among the 3 baffle cooling channels, 2 side channels have better efficiency than the middle channel. The reason is the heat concentration at the corner of the box makes the 2 side cooling channels work harder. In the optimal run, the cooling efficiency is in the range of 11.53%–19.18%, higher than that value in the basic run. With the higher cooling efficiency, the heat accumulation in the plastic part was released better. Therefore, the temperature and the maximum cooling time of the part are lower in the optimal run.
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Fig. 6. Cooling efficiency of the channel for the basic run (upper) and the optimal run (lower).
Figure 7 indicates the total displacement warpage distribution of the part with a scale factor of 10. It could be seen that the plastic product tends to shrink inside. Maximum total displacement warpage always occurs at the middle top of the part. It may be due to the thin-wall structure of the product. There is a significant drop of the maximum total displacement warpage, from 1.08 mm in the basic run to 0.55 mm in the optimal run. With the thin-wall plastic part, the low total displacement warpage plays a significant role in improving the quality of the product.
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Fig. 7. Total displacement warpage of the part with the scale factor of 10 for the basic run (upper) and the optimal run (lower).
4 Conclusions In this research, the influence of different process conditions on the warpage inside the plastic product and the maximum cooling time in the injection molding cycle is numerically considered. The process parameters include the coolant temperature, packing time, and maximum packing pressure. The results indicate that the process parameters have a significant effect on the quality factors. With the optimal process conditions (50 °C of the coolant temperature, 14 s of the packing time, and 77% of the maximum packing pressure), the maximum cooling time of the part reduces to 42.88 s from 58.52 s in the basic run, and the maximum total displacement warpage drops to 0.55 mm from 1.08 mm in the basic run. These results could help to improve the plastic part quality and shorten the injection molding cycle.
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Acknowledgments. This research is supported by Ho Chi Minh City University of Technology and Education (HCMUTE), Vietnam.
References 1. Wang, M.L., Chang, R.Y., Hsu, C.H.: Molding Simulation: Theory and Practice. Hanser Publications, Munich (2018) 2. Chang, R.Y., Yang, W.H.: Numerical simulation of mold filling in injection molding using a three-dimensional finite volume approach. Int. J. Numer. Meth. Fluids 37, 125–148 (2001) 3. Hassan, H., Regnier, N., Bot, C.L., Defaye, G.: 3D study of cooling system effect on the heat transfer during polymer injection molding. Int. J. Therm. Sci. 49, 161–169 (2010) 4. Hassan, H., Regnier, N., Pujos, C., Defaye, G.: 3D study on the effect of process parameters on the cooling of polymer by injection molding. J. Appl. Polym. Sci. 114, 2901–2914 (2009) 5. Park, H.S., Dang, X.P.: Optimization of conformal cooling channels with array of baffles for plastic injection mold. Int. J. Precis. Eng. Manuf. 11, 879–890 (2010) 6. Ozcelik, B., Erzurumlu, T.: Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm. J. Mater. Process. Technol. 171, 437–445 (2006) 7. Ozcelik, B., Ozbay, A., Demirbas, E.: Influence of injection parameters and mold materials on mechanical properties of ABS in plastic injection molding. Int. Commun. Heat Mass Transfer 37, 1359–1365 (2010) 8. Ozcelik, B.: Optimization of injection parameters for mechanical properties of specimens with weld line of polypropylene using Taguchi method. Int. Commun. Heat Mass Transfer 38, 1067–1072 (2011) 9. Dzulkipli, A.A., Azuddin, M.: Study of the effects of injection molding parameter on weld line formation. Procedia Eng. 184, 663–672 (2017) 10. Nian, S.C., Wu, C.Y., Huang, M.S.: Warpage control of thin-walled injection molding using local mold temperatures. Int. Commun. Heat Mass Transfer 61, 102–110 (2015) 11. Sánchez, R., Aisa, J., Martinez, A., Mercado, D.: On the relationship between cooling setup and warpage in injection molding. Measurement 45, 1051–1056 (2012)
Geometrically Nonlinear Behaviour of Functionally Graded Beam and Frame Structures Under Mechanical Loading Thi Thu Hoai Bui1,2(B) , Thi Thu Huong Tran1 , and Dinh Kien Nguyen2 1 Faculty of Vehicle and Energy Engineering, Phenikaa University, Yen Nghia, Ha Dong,
Hanoi 12116, Vietnam [email protected] 2 Graduate University of Science and Technology, VAST, 18 Hoang Quoc Viet, Hanoi, Vietnam
Abstract. Geometrically nonlinear behavior of functionally graded (FG) beam and frame structures under mechanical loading is studied in this paper using the finite element method. The structures are made of a mixture of ceramic and metal with material properties varying in the thickness direction by a power distribution law. Based on the first-order shear deformation theory, a nonlinear beam element is derived and used in conjunction with Newton-Raphson method to compute deflected curves of the beams. Numerical investigations are carried out in detail to highlight the influence of the material distribution on the nonlinear behavior of the beams. Keywords: FG beam and frame · Power-law distribution · Geometrically nonlinear analysis · Finite element formulation
1 Introduction Large displacement analysis of structures is an important topic in the field of structural and solid mechanics. This topic grows in importance due to the development of new materials which enable structures to undergo large deformation. Depending on the choice of reference configuration, the nonlinear beam elements can be classified into three types, the total Lagrangian formulation, the updated Lagrangian formulation and the co-rotational formulation. In the co-rotational formulation, which will be adopted in the present work, the kinematics are described in an element attached local co-ordinate system. The finite element formulation is firstly formulated in the local system and then transformed into a global system with the aid of transformation matrices. Functionally graded materials (FGMs) have received much attention from engineers and researchers since they were first initiated by Japanese scientists in Sendai in 1984 [1]. FGMs are formed by gradually varying the volume fraction of constituent materials, usually ceramics and metals, in a desired spatial direction. Many investigations on analysis of FGM structures subjected to different loadings are summarized in [2], only contributions that are most relevant to the present work are briefly discussed below. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 326–342, 2022. https://doi.org/10.1007/978-981-19-1968-8_26
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Taking the shift in position of the neutral axis into consideration, Kang and Li derived expressions for tip displacements of a nonlinear FGM cantilever beam under a tip load [3], and a tip moment [4]. Almeida et al. [5] studied the geometrically nonlinear behaviour of FGM beams by using a simple total Lagrangian finite element formulation. The large displacement response of tapered cantilever FGM beams were studied in [6–8] using the finite element method. Geometrically nonlinear analysis of FGM and FGM sandwich frames were carried in [9] using the co-rotational Euler-Bernoulli beam elements. In this paper, the large displacement behavior of FGM frame structure is studied using a finite element procedure. The structure is made from a mixture of ceramic and metal with material properties varying continuously in the thickness direction by a power distribution law. Based on a first-order shear deformation theory, a nonlinear beam element is formulated in the context of the co-rotational formulation. The solution of the governing differential equations of an FGM Timoshenko beam segment is employed to interpolate the displacements and rotation. The Newton-Raphson based incremental/iterative procedure in combination with the arc-length control method is used to compute the large displacement response of the structure. Numerical investigation is presented to show the accuracy and efficiency of the formulated element.
2 Functionally Graded Materials First, Fig. 1 shows an FGM beam composed of ceramic and metal in a Cartesian coordinate system (x1 , z1 ), where the x1 −axis is parallel to the longitudinal direction at the bottom surface, and z1 − axis is upward and normal to the bottom surface. The beam cross-section is assumed to be rectangular with width b and height h. The beam top surface corresponding to z1 = h consists only ceramic, and the bottom surface corresponding to z1 = 0 has only metal.
Fig. 1. Geometry and co-ordinates of an FGM beam
In between the volume fraction of ceramic, Vc , and metal, Vm , are varied continuously through the beam thickness by a power distribution law in term of volume fractions of the constituent materials as follows z n 1 , Vc + Vm = 1 (1) Vc = h where the subscripts ‘c’ and ‘m’ stand for ceramic and metal, respectively, and n is the non-negative power-law index, which dictates the material variation profile.
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A higher value of the index n, a large content of metal in the FGM. The effective material properties, P, are evaluated by a simple rule P(z1 ) = Pc Vc (z1 ) + Pm Vm (z1 )
(2)
where Pc and Pm are the material properties of ceramic and metal, respectively. Thus, the effective elastic properties such as Young’s modulus, E, and shear modulus, G, of the FGM beam are given by z n 1 + Em E(z1 ) = (Ec − Em ) h (3) z n 1 G(z1 ) = (Gc − Gm ) + Gm h Clearly, due to the variation of the effective Young’s modulus E in the thickness direction, the neutral axis of the FGM beam is no longer at the midplane [3]. By introducing a new Cartesian co-ordinate system (x, z) such as [3, 4] x = x1 , z = z1 − h0
(4)
where h0 is the distance from the neutral axis to the bottom surface of the beam, the position of the neutral axis can be determined by requiring the total axial force at crosssection to vanish [3, 5] A
b σx dA = ρ
h−h 0
zE(z)dz = 0
(5)
−h0
where A denotes the cross-sectional area; σx is the axial stress, and ρ is the curvature radius of the neutral axis. From Eqs. (3)–(5), one can obtain an explicit expression for h0 in the form h h0 =
E(z1 )z1 dz1
0
h
= E(z1 )dz1
h(n + 1)(2Ec + nEm ) 2(n + 2)(Ec + nEm )
(6)
0
3 Finite Element Formulation Co-rotational formulation is an efficient tool in dealing with geometrically nonlinear problems in which the structures often undergo large displacements. The central idea of the formulation is to introduce a local co-ordinate system that continuously moves and rotates with the element during its deformation process. By using such a local system, the geometrical nonlinearity induced by the large body motion is separated from the total deformation and incorporated in the transformation matrices. The element formulation is firstly derived in the local co-ordinate system and then transferred into the global system
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with the aid of the transformation matrices. Depending upon definition of the local coordinate system, different types of co-rotational beam elements can be obtained. The co-rotational formulation adopted in the present work is closely related to that described by Crisfield [10], and further developed in [11, 12]. For completeness, the main points of the formulation are summarized below. Figure 2 shows a planar two-node beam element, (i, j), and its kinematics in two coordinate systems, a local system (x, z), and a global one (x, z). The element is initially inclined to the x-axis an angle θ 0 . The global system (x,z) is fixed, while the local system (x, z) is continuously moved and rotated with the element during its deformation. The local system is chosen with its origin at node i and with the x−axis directed towards node j. By choosing such a local system, the axial displacement at node i and the transverse displacements at the two nodes i and j are always zero, ui = wi = wj = 0. Thus, the element vector of nodal displacements in the local system, d , contains only three components as T d = uj θ i θ j
(7)
with uj is the axial displacement at node j, and θ i , θ j are the rotations at nodes i and j, respectively. In Eq. (7) and hereafter, a superscript ‘T’ denotes the transpose of a vector or a matrix, and the bar suffix is used to indicate a variable or a quantity defined with respect to the local system. The global nodal displacements in general are nonzero, and the element vector of nodal displacements in the global system, d, contains six components as
Fig. 2. A 2D co-rotational beam element and it kinematics
T d = ui wi θi uj wj θj
(8)
where ui , wi , θi are respectively the global axial and transverse displacements and rotation at node i, and uj , wj , θj are the corresponding displacements and rotation at node j. The local nodal displacements in Eq. (7) can be computed as [10, 11] u = ln − l; θ i = θi − θr ; θ j = θj − θr
(9)
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In Eq. (9), l and l n are the initial and current lengths of the element
2
2 l= xj − xi + zj − zi xj + uj − xi − ui 2 + ln =
2 zj + wj − zi − wi
(10)
where (x i , zi ) and (x j , zj ) are the co-ordinates of nodes i and j, respectively; θ r is the rigid rotation of the element, which can be computed from the element co-ordinates and the vector of global nodal displacements [10]. The relation between the vector of local virtual displacements with that of global virtual displacements for the element can be obtained by differentiating the local nodal displacement and rotations defined in Eq. (9) with the aid of Eq. (10) as follows δu = −c −s 0 c s 0 δd = rT δd ⎧ ⎫ ⎪ 001000 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬ ⎨ 000001 ⎪ δθ i T δd = ⎪ ⎪ 1 z δθ j ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎭ ⎩ −l T n z = AT δd
(11)
where T r = −c −s 0 c s 0 T z = s −c 0 −s c 0
(12)
In Eqs. (11) and (12), the following notations have been used c = cos θ =
xj + uj − xi − ui ; ln
s = sin θ =
zj + wj − zi − wi ln
(13)
Equation (11) gives δd = Tδd where
T=
rT
(14)
AT ⎡ ⎤ −cln −sln 0 cln sln 0 1⎣ = −s c ln s −c 0 ⎦ ln −s c 0 s −c ln
(15)
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is the transformation matrix. The relation between the global and local vectors of internal forces for the element can be obtained by equating the internal virtual work for the element written in the two co-ordinate systems [5], which leads to fin = TT f in
(16)
where f in and fin denote the local and global vectors of internal forces for the element, respectively. The element tangent stiffness matrix for the element, kt , is obtained by differentiating the element internal force vector with respect to the nodal displacements as T
δfin = kt δd = TT δf in + f in δTe
(17)
where δTe is obtained by differentiating the columns of matrix TT δTe =
1 1 T 1 T zz 2 rz + zrT 2 rzT + zrT ln ln ln
T
δd
(18)
With the aid of Eqs. (15) and (18), Eq. (17) gives the expression for the element tanget stiffness matrix in the form f kt = TT kt T + u zz T + ln f θi + f θj T T rz + zr + ln2
(19)
where kt = ∂f in /∂d is the local tangent stiffness matrix; f u , f θi and f θ j are the local internal nodal forces corresponding to the local nodal displacements in Eq. (7). Equations (16) and (19) completely define the global internal nodal force vector and tangent stiffness matrix when the corresponding local vector and matrix are known. Next, this section derives the element internal nodal force vector, and tangent stiffness matrix in the local co-ordinate system, (x, z), where x− axis is chosen to be coincident with the neutral axis. Adopting the first-order shear deformation beam theory, the axial and transverse displacements are given by u(x, z) = u0 (x) − zθ (x), w(x, z) = w0 (x)
(20)
where z is the distance from the considered point to the neutral axis of the beam; u0 , w0 are the axial and transverse displacements of the point on the x− axis; θ(x) is the rotation of cross-section.
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A degenerated form of Green’s strains deduced from the displacement field (20) can be employed for the geometrically nonlinear analysis as ∂u0 (x) 1 ∂w0 (x) 2 + ε(x, z) = ∂x 2 ∂x ∂θ (x) −z ∂x ∂w0 (x) − θ (x) γ (x) = ∂x
(21)
where ε(x, z) and γ (x) are the axial and shear strains, respectively. Assuming linear elastic behaviour, the axial stress, σ (x, z), and the shear stress, τ (x, z), associated with the axial and shear strains in Eq. (21) are given by σ (x, z) = E(z)ε(x, z) τ (x, z) = ψG(z)γ (x)
(22)
where ψ is the shear correction factor; E(z) and G(z) are respectively the effective Young’s modulus and shear modulus defined by Eq. (3), with z = z1 − h0 , and h0 is given by Eq. (6). Interpolation functions are necessary to introduce the local displacements and rotation. Since the rotation θ and transverse displacement w0 are independent in the firstorder shear deformation beam theory, linear functions can be employed for both the displacements and the rotation [11, 13], provided a technique such as the reduced integral is necessary to use for avoiding the shear locking problem. Alternatively, the use of polynomials obtained from the field consistent approach can also enable the formulated element to be free from shear locking [14]. Here, the field consistent approach is adopted, and to this end, we write the axial and transverse displacements and rotation in the form u0 = Nu uj , w0 = NwT θ , θ = NθT θ
(23)
T where θ = θ i θ j ; Nu , Nw = {Nw1 Nw2 }T and Nθ = {Nθ1 Nθ2 }T are the matrices of interpolation functions which will be derived later. The beam element formulated from the interpolation functions (23), however still encounters the membrane locking problem. To overcome this problem, the membrane strain in Eq. (21) should be replaced by an average strain, εav , to ensure a constant membrane strain as εav
1 = l
l 0
! " ∂u0 1 ∂w0 2 + dx ∂x 2 ∂x
1 T = bu u + θ 2l
(24)
l bw bTw d xθ 0
where l is the element length, bu = ∂Nu /∂x, and bw = ∂Nw /∂x.
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Using the introduced average strain, the virtual internal work for the element can be written in the form l (σ δε + τ δγ )dAd x
∂U = 0 A
(25)
l (N δεav + M δκ + Qδγ )d x
= 0
where A is the cross-sectional area; κ = −∂θ/∂x is the beam curvature; N, M, Q are the stress resultants N = σ dA = A11 εav + A12 κ A
M =
zσ dA = A12 εav + A22 κ
(26)
A
Q=
τ dA = ψA33 γ A
In Eq. (26), A11 , A12 , A22 and A33 are respectively the stretching, stretchingbending coupling, bending and shear rigidities, and they are defined a (A11 , A12 , A22 ) = E(z) 1, z, z 2 dA A
A33 =
(27)
G(z)dA A
Substituting z = z1 − h0 into Eq. (27), one can rewrite Eq. (27) in the forms h (A11 , A12 , A22 ) = b
# $ E(z1 ) 1, (z1 − h0 ), (z1 − h0 )2 dz1
0
(28)
h A33 = b
G(z1 )dz1 0
where b, as mentioned above, is the beam width. It is worthy to note that because of Eq. (6), the stretching-bending coupling rigidity, A12 , in the above equation vanishes. The matrices of shape functions for the displacements and rotation in the forms Nu =
x l
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l Nw = 1+φ ⎧ 2 ⎫ 3 ⎪ ⎨ x − 2+ φ x + 1+ φ x ⎪ ⎬ l 2 l 2 l 3 2 ⎪ ⎪ x ⎩ ⎭ − 1 − φ2 xl − φ2 xl l 1 Nθ = ∗ 1+φ ⎧ ⎫ 2 ⎪ ⎨ 3 x − (4 + φ) x + (1 + φ) ⎪ ⎬ l l 2 ⎪ ⎪ ⎩ ⎭ 3 xl − (2 − φ) xl
(29)
where φ is the shear deformation parameter, defined as a ratio of the bending rigidity to 22 the shear rigidity, φ = l12A 2 ψA . 33 Using the shape function (29), one can express the average strain defined by Eq. (24) in an explicit form as 1 1 uj + [5φ(2 + φ)∗ l 120(1 + φ)2 $
2 2 θ i − θ j + 4 2θ i − θ i θ j + 2θ j
εav =
(30)
The virtual strains and virtual curvature can be written in the forms δεav = bu δuj + cwT δθ , δγ = bTw − NθT δθ ,
(31)
δκ = bTθ δθ where bu = ∂Nu /∂x = 1/l, and bw =
∂Nw ∂εav , cw = ∂x ∂θ
(32)
Substituting Eq. (31) into the expression of the virtual internal forces given by Eq. (25) one gets δU =
l % 0
# Nbu δuj + N cwT + M bTθ
$ & +Q bTw − NθT δθ d x
(33)
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From Eqs. (26) and (33), one can get the element nodal internal forces in the local co-ordinate system in the forms l fu =
Nbu d x 0
%
f θ = f θi f θj l =
'
&T
(34)
( N cw + M bθ + Q(bw − Nθ ) d x
0
The element tangent stiffness matrix in the local co-ordinate system can be written in sub-matrices as kuu kuθ kt = (35) T kuθ kθθ The sub-matrices in (35) are obtained by differentiation of the local internal forces with respect to the local nodal displacements as kuu
∂f = u = ∂u
kuθ =
kθθ
∂f u = ∂θ
∂f = θ = ∂θ
l b2u A11 d x 0
l bu cwT A11 d x 0
l #
(36)
cw cwT A11 + N B + bθ bTθ A22
0
$ +ψ(bw − Nθ ) bTw − NθT A33 d x where B is a symmetric matrix with the following form B=
1 ∂cwT
∗ = ∂θ 60 1 + φ 2
2 2 8 + 10φ + 5φ 2 − 2 + 10φ + 5φ2 8 + 10φ + 5φ − 2 + 10φ + 5φ
(37)
Equations (34), (36) together with Eqs. (16) and (19) completely define the element formulation.
4 Equilibrium Equations The nonlinear equilibrium equations for the structure can be written in the form g(p, φ) = qin (p) − λqef = 0
(38)
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where p and qin are the structural vectors of nodal displacements and nodal internal forces, respectively; qef is the fixed external loading vector, and the scalar λ is a load parameter. Vector g defined in Eq. (38) is known as the residual force vector. The system of Eq. (38) can be solved by an incremental-iterative procedure. The procedure results in a predictor-corrector algorithm, in which a new solution is firstly predicted from a previous converged solution and then successive corrections are added until some chosen convergence criterion is satisfied. In order to deal with the limit point, snap-through and snap-back situations, in which the structure tangent stiffness matrix ceases to be positive definite, the spherical arc-length constraint method developed by Crisfield [15, 16] is adopted herewith.
5 Numerical Investigation Numerical investigation is carried out in this section to demonstrate the accuracy of the formulated element and to highlight the influence of the material inhomogeneity on the large displacement behaviour of the FGM frame structure. 5.1 Cantilever Beam Under a Tip Moment A cantilever beam with length L = 5 m, width b = 0.15 m and height h = 0.1 m, subjected to a moment M at its free end is considered. The beam is assumed to be composed of Silicon Nitride (Si4 Ni4 ) and Aluminum (Al) (referred to as Si4 Ni4 /Al). The Young’s modulus and Poisson’s ratio of Si4 Ni4 are respectively 322.3 GPa and 0.24 [17], and that of Al are 70 GPa and 0.3 [18]. The tip axial and transverse displacements, u and w, obtained by Kang and Li [4] by using an analytical method based on Bernoulli beam theory are as follows ! " M A22 sin u= L −L M A22 ! " (39) M A22 1 − cos L w= M A22 where A22 is the effective bending rigidity defined by Eq. (28). For the given geometric and material data, Eq. (28) gives the effective bending rigidity A22 = 3.0757 × 106, 2.1138 × 107, 1.4741 × 106 and 1.3451 × 106 N/m2 for n = 0.3,1,5 and 10, respectively. In Table 1, the normalized tip axial and transverse displacements of the Si4 Ni4 /Al beam obtained by different number of elements are given for a tip moment M = 5E m I/L, where E m is Young’s modulus of Al. The corresponding tip displacements computed by Eq. (39) are also given in the table. As seen from the table, the results using element of the present work converge very fast, and both the axial and transverse tip displacements converge to the analytical solutions by using just six elements, regardless of the index n. Since the beam element of the present work is based on the exact interpolation functions, it is capable of giving exact displacements at the nodal points [19]. It should be noted that the effect of the shift in the neutral axis position has been taken into account in both the present work and Ref. [4], and the beam under consideration having a high aspect ratio, L/h = 50.
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Fig. 3. Tip displacements versus tip moment for cantilever Si4 Ni4 /Al beam under a tip moment. Table 1. Convergence of the present element in determination of tip displacements of cantilever Si4 Ni4 /Al beam under a tip moment M = 5E m I/L (nel : number of element). nel Response )u) ) ) L
w L
n
1
2
4
6
Equation (39)
0.3
0.3063
0.3048
0.3047
0.3047
0.3047
1
0.5805
0.5760
0.5758
0.5757
0.5757
5
0.9451
0.9419
0.9418
0.9417
0.9418
10
1.0310
1.0339
1.0340
1.0340
1.0340
0.3
0.5978
0.599
0.5991
0.5991
0.5991
1
0.7063
0.7139
0.7143
0.7143
0.7143
5
0.6306
0.6670
0.6687
0.6688
0.6688
10
0.5583
0.6106
0.6129
0.6130
0.6130
In Fig. 3 the normalized tip displacements versus the normalized tip moment for the cantilever Si4Ni4/Al beam is depicted for various values of the index n. The capability to withstand the load, as seen from the figure, is weaker for a beam associated with a higher index n, regardless of the applied load. This is due to the fact that, as seen from Eq. (3), the beam with a higher index n contains more Aluminum, and thus it is softer. The effect of the material inhomogeneity on the response of the beam can also be seen from the distribution through the beam thickness of the axial stress at the clamped section as illustrated in Fig. 4 for two values of the applied moment, M = 2E m I/L and M = 4Em I/L. The figure shows that maximum compressive and tensile axial stresses of the beam increase when raising the index n, regardless of the applied moment. Furthermore, the distribution of the axial stresses in an FGM beam is very different from that in an isotropic beam. For example, as clearly seen from Fig. 4, while the axial stress is linear
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and symmetric through the cross-section center for full ceramic (isotropic) beam, it is not linear and symmetric for the FGM beam. In addition, the maximum compressive and tensile axial stresses in the FGM beam do not always occur at the outmost surfaces as in case of the isotropic beam.
Fig. 4. Distribution through the thickness of axial stress at clamped section for cantilever Si4 Ni4 /Al beam under a tip moment.
5.2 Cantilever Beam Subjected to an Eccentric Axial Load A cantilever beam with the geometric data as mentioned in Subsect. 5.1 subjected to an eccentric axial load at the top surface of the free end section as shown in the lower left corner of Fig. 5 is analysed. The load-displacement curves of an isotropic beam computed by Wood and Zienkiewicz by using the paralinear elements [21] show snapback behaviour in the large displacement region, and thus the arc-length control method is necessary to employ in tracing the equilibrium paths. In addition to the beam studied above, a beam composed of Zirconia (ZrO2 ) and Aluminum (referred to as ZrO2 /Al), and another one made of Alumina (Al2 O3 ) and Aluminlum (referred to as Al2 O3 /Al) are also considered. The Young’s modulus and Poisson’s ratio of Zirconia are respectively 151 GPa and 0.3, and that of Alumina are 380 GPa and 0.3, respectively [18]. In Fig. 5, the tip displacements versus the applied load of the cantilever ZrO2 /Al beam subjected to the eccentric axial load are depicted for various values of the index n. Again, the effect of the index n on the large displacement response of the beam is clearly seen from the figure, where the ability to resist the applied load reduces for a beam associated with a higher index n. The effect of the constituent materials on the response of the beam can also be observed from its deformed configurations as depicted in Fig. 6 for an index n = 3. As seen from the figure, the beam composed from the lower Young’s modulus deforms more severely.
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Fig. 5. Tip displacements versus the applied load of cantilever ZrO2 /Al beam subjected to an eccentric axial load.
Fig. 6. Deformed configurations of cantilever FGM beam composed of different constituent materials subjected to an eccentric compressive load (n = 3).
5.3 Asymmetric Frame An asymmetric frame subjected to a downward load P as shown in lower part of Fig. 7 is investigated. The isotropic frame exhibits snap-through and snap-back behaviour [12, 20], and thus this example can be used as a good example to further test the formulated beam element and computer code. The geometric data for the frame are as follows: L = 120 cm, b = 3 cm, and h = 2 cm. The load-displacement curves of the frame composed of Zirconia and Aluminum are shown in Fig. 7 for various values of the index n. In the figure, the axial and vertical displacements were computed at the loaded point by using ten elements, five for each beam. The influence of the material distribution on the behaviour of the frame is clearly seen from the figure, where the limit load of the frame steadily reduces when increasing the index n. In Figs. 8 and 9, the axial stresses
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at the top and bottom points of the loaded section versus the applied load is shown for various values of the index n and different constituent materials, respectively. As seen from Fig. 8, while the axial stress at the points of an isotropic beam in is symmetric with respect to the mid-plane, the amplitude of the compressive stress is considerably higher than that of the tensile stress. The stress at the points, as seen from Fig. 9, is also remarkably affected by the constituent materials.
Fig. 7. Load-displacement curves for asymmetric frame composed of ZrO2 and Al under a point load
Fig. 8. Axial stress at top and bottom points of loaded section versus applied load for asymmetric ZrO2 /Al frame.
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Fig. 9. Axial stress at top and bottom points of loaded section versus applied load for asymmetric frame composed of different constituent materials (n = 3).
6 Conclusions The large displacement behaviour of FGM frame structure has been studied using a finite element procedure. The material properties of the structure are assumed to be graded in the thickness direction by a power distribution law. Based on the first-order shear deformation theory, a nonlinear beam element was formulated by using the solution of the governing differential equations of a beam segment to interpolate the displacement field. The element was derived in the context of the co-rotational formulation, taking into the shift of the neutral axis position into account, which has no stretching-bending coupling terms. An incremental/iterative procedure in combination with the arc-length control method was employed in solving the nonlinear equilibrium equations. Numerical examples demonstrated the capability of the formulated element in modelling the effects of shear deformation and material inhomogeneity. Results from the present set of examples have revealed that the FGM beam and frame structures associated with a larger value of the power-law index endure larger displacements than those with a smaller powerlaw index. It is necessary to note that the material is assumed to be linearly elastic in the present work. As demonstrated in the numerical examples, the stress increases when the structure undergoes large displacements, and it might exceed the yield stress at some points. More work is necessary to deal with the large displacement analysis of elastic-plastic FGM frame structure. Acknowledgments. PhD. Student Bui Thi Thu Hoai was funded by Vingroup Joint Stock Company and supported by the Domestic Ph.D. Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VINBIGDATA), code VINIF.2020.TS.15.
References 1. Koizumi, M.: FGM activities in Japan. Compos. Part B Eng. 28, 1–4 (1997)
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2. Birman, V., Byrd, L.W.: Modeling and analysis of functionally graded materials and structures. Appl. Mech. Rev. 60, 195–216 (2007) 3. Kang, Y.A., Li, X.F.: Bending of functionally graded cantilever beam with power-law nonlinearity subjected to an end force. Int. J. Non-linear Mech. 44, 696–703 (2009) 4. Kang, Y.A., Li, X.F.: Large deflection of a non-linear cantilever functionally graded beam. J. Reinf. Plast. Comp. 29, 1761–1774 (2010) 5. Almeida, C.A., Albino, J.C.R., Menezes, I.F.M., Paulino, G.H.: Geometric nonlinear analyses of functionally graded beams using a tailor Lagrangian formulation. Mech. Res. Commun. 38, 553–559 (2011) 6. Nguyen, D.K.: Large displacement response of tapered cantilever beams made of axially functionally graded material. Compos. Part B Eng. 55, 298–305 (2013) 7. Nguyen, D.K.: Large displacement behaviour of tapered cantilever Euler-Bernoulli beams made of functionally graded material. Appl. Math. Comput. 237, 340–355 (2014) 8. Nguyen, D.K., Gan, B.S.: Large deflections of tapered functionally graded beams subjected to end forces. Appl. Math. Model 38, 3054–3066 (2014) 9. Nguyen, D.K., Tran, T.T.: A corotational formulation for large displacement analysis of functionally graded sandwich beam and frame structures. Math. Prob. Eng. (2016). https:// doi.org/10.1155/2016/5698351 10. Crisfield, M.A.: Non-linear Finite Element Analysis of Solids and Structures, vol. 1, Essentials. Wiley, Chichester (1991) 11. Pacoste, C., Eriksson, A.: Beam elements in instability problem. Comput. Methods Appl. Mech. Eng. 144, 163–197 (1997) 12. Nguyen, D.K.: A Timoshenko beam element for large displacement analysis of planar beams and frames. Int. J. Struct. Stab. Dynam. 12, 1250048 (2012). https://doi.org/10.1142/S02194 55412500484 13. Nguyen, D.K.: Post-buckling behavior of beam on two-parameter elastic foundation. Int. J. Struct. Stability Dyn. 4, 21–43 (2004) 14. Luo, Y.H.: Explanation and elimination of shear locking and membrane locking with field consistence approach. Comput. Methods Appl. Mech. Eng. 162, 249–269 (1998) 15. Crisfield, M.A.: A fast incremental/iterative solution procedure that handles “snap-through.” Comput. Struct. 13, 55–62 (1981) 16. Crisfield, M.A.: An arc-length method including line searchs and accelerations. Int. J. Numer. Methods Eng. 19, 1269–1289 (1983) 17. Fallah, A., Aghdam, M.M.: Nonlinear free vibration and post-buckling analysis of functionally graded beams on nonlinear elastic foundation. Eur. J. Mech. A-Solid 30, 571–583 (2011) 18. Praveen, G.N., Reddy, J.N.: Nonlinear transient thermoelastic analysis of functionally graded ceramic-metal plates. Int. J. Solids Struct. 35, 4457–4476 (1998) 19. Cook, R.D., Malkus, D.S., Plesha, M.E.: Concepts and Applications of Finite Element Analysis, 3rd edn. Wiley, New York (1989) 20. Hsiao, K.M., Huo, F.Y.: Nonlinear finite element analysis of elastic frames. Comput. Struct. 26, 693–701 (1987) 21. Wood, R.D., Zienkiewicz, O.C.: Geometrically nonlinear finite element analysis of beams, frames, arches and axisymmetric shells. Comput. Struct. 7, 725–735 (1977)
Effect of Geometric Parameters of Heat Sink on Thermal Dissipation for Active Antenna Unit Van-Tinh Nguyen1(B) and Chi-Cong Nguyen2 1 School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi,
Vietnam [email protected] 2 Department of Mechanical Engineering, Viettel High Technology Industries Corporation, Hanoi, Vietnam
Abstract. Active Antenna Unit (AAU) is the main part of the 5G base station that has a great size with a high density of chipsets and be operated with significantly high temperature. This study addresses an effect of geometric parameters of heat sink on thermal dissipation for AAU. The heat sink is investigated in two designs of fin such as in-line and V-shaped arrangement fins heat sinks. This research is implemented in two stages for making comparisons of these types. Firstly, we validated the simulation results from the computational fluid dynamics (CFD) method with an in-lab experimental model measured by an infrared thermometer. In the next step, two larger prototypes of heat sink are proposed to consider the actual impacts of V-shaped heatsinks on thermal dissipation. The simulation and experimental results demonstrated that in general, the heat sink with V-shaped fin has the better performance on thermal dissipation in comparison to conventional models such as in-line arrangement fin heat sinks. It is worth noting that the V-shaped heat sink advantage is more noticeable in the bigger models. Keywords: 5G base · Active Antenna Unit · Heat sink · Design
1 Introduction Active Antenna Unit (AAU) is the main part of the 5G base station integrated important components, such as Power Amplifier (PA), Filter, and Antenna Array, where protocols are connected with users’ devices. In order for increasing the bandwidth, beamforming quality, capability of the Internet of Things (IoT), and applying multiple-input multipleoutput (MIMO) [1], AAUs need to perform with significantly high power and larger size. Meanwhile, the performance of current chipsets is just around 40–50% consuming power [2], therefore, the heat released from the electric components of AAU is extremely high. Alternatively, the chip density of devices like AAUs in 5G stations is very thick and is often arranged along the length of the antenna and concentrated in the PA area, the upperhalf part of AAU. This is combined with the large AAU height size, normally around 900–1000 mm as in the 64T64R (transmit and receive) AAU, leading to a disturbing consequence that the upper half of the AAU will become increasingly hot when the device © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 343–351, 2022. https://doi.org/10.1007/978-981-19-1968-8_27
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is operating in inclement weather and under solar radiation. Therefore, it is extremely important to optimize the heat sink design for AAU to help dissipate the heat and work at its highest efficiency. In general, there are many types of heat sinks that were designed to enhance the performance of electronic devices. C. J. Shih and G. C. Liu [3] have developed the Plate-Fin heat sinks for electronic cooling by using entropy generation strategy in calculating and designing this type of heat sink. This paper has demonstrated that the thermal performance can be notably improved; both the sink size and structural mass can apparently be reduced through the presented design method and process. Y. Huang, et al. [4] introduced a new heat sink design for a high-power Led lamp by combining plate fins with pin fins and oblique fins and made a comparison in heat dissipation performances of this design with three conventional fin heat sinks had been illustrated in this paper. The obtained result showed that the design models show lower junction temperatures by about 6 °C–12 °C than those of the three conventional models. In 2010, M.J. Sable, et al. [5] introduced a novel type of heat sinks with multiple V-fin arrays which enhance the natural convection heat transfer on a vertical heated plate. As a result of this paper, the V-type fin array design performs better than the rectangular vertical fin array. This type of heat sink has been investigated by R.B. Hagote and S.K. Dahake [6], in 2014, throughout the simulating and experimental work on three geometric orientations: vertical base with V-fin array, horizontal base with V-fin array inclined base with V-fin array. Its conclusion depicts that the vertical plate gives a greater average heat transfer coefficient compared to the two other types. In 2015, M. Solanki and M. Vedpathak [7] brought to the public a new model of Heat Sink with V-Shaped Fin Arrays. The researchers worked mainly on the geometry of the fin and concluded that the 35–45 mm V fin gives the maximum heat transfer coefficient. Regarding the heat sink de-sign for AAU in the 5G era, there are a few papers on this topic. B.M. Dinh and D.Q. Vuong [8] recently introduced an optimal calculation of plate-fin heat sinks for the Radio Remote Unit (RRU) of the 4G base station. The model of heat sinks has been optimized to improve the power loss and the heat transfer rate. However, RRU for the 4G base station has a small size and the consuming power is not too high in com-parison with AAU performance. There are three product lines of AAU including 8T8R AAU, 32T32R AAU, and 64T64R AAU with the increase in size and power consumption respectively. The larger the size, the higher the efficiency achieved when using V-shaped fins arrangement heat sink, which was demonstrated in detail in this paper. Figure 1 depicts the 8T8R AAU (a), 32T32R AAU (b), and 64T64R AAU (c) with the V-shaped fins arrangement heat sink integrated on the housing of the devices, which are 500 × 350 × 130 mm, 1000 × 350 × 130 mm, and 900 × 560 × 180 mm dimensions respectively and the average fin height is 70 mm.
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Fig. 1. Heat sink with V-shaped fin (a) 8T8R AAU; (b) 32T32R AAU; and (c) 64T64R AAU.
To this end, the rest of this paper is organized into eight sections. Section 2 presented the model and computational fluid dynamics analysis. Section 3 describes the results and discussions. Finally, Sect. 4 includes some brief conclusions.
2 Methodology 2.1 Model Parameters Following the trend of 5G technology, the products should be designed towards mass production, therefore, the research and development process has been hinged on many technological factors. Especially, the manufacturing technology readiness level for AAU is extremely important. As such, the AAU heat sink models have been built based on the technical parameters as defined in Table 1. However, not all these geometric parameters in Fig. 2 will have a significant effect on the performance of the model, to determine the most influential parameters, we apply CFD analysis for evaluation.
Fig. 2. Geometry parameters of V-shaped fins arrangement heat sink model. a) Back view of AAU; b) 3D view of AAU; c) Fin section.
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Parameters
Definition
Units
Fin length (L)
Average length of fins
mm
c − 2r L = D− 2cosα
Fin height (h)
Average height of fins
mm
Fin thickness (t)
Average thickness of a fin
mm
Orientation angle (α)
The angle formed by the fin orientation and the horizontal
Degree
Bottom spacing (c)
Space between 2 fin arrays
mm
Fins spacing (d)
Space between 2 fins
mm
Draft angle (β)
Technology tilt angle (is the angle used in die-casting technology, β (1.2°, 1.5°))
Degree
2.2 Computational Fluid Dynamics Analysis CFD is a prominent method that uses numerical analysis and data structures to analyse and solve problems that involve fluid flows. This method makes it exceedingly simple to input all the essential data to make a prediction for heat transfer. In other words, with a large and complex model, CFD will allow for a quick initial setup [9]. In this paper, the CFD was applied for complicated models of 8T8R, 32T32R, and 64T64R AAUs, throughout two phases including model meshing and fluent dynamics solving. In the very first step, the entire AAU model with a V-shaped heat sink was simplified to eliminate small components, that have insignificantly affected the analysis results. Initially, the cabinet size is designed to be bigger than the model size to assure both simulation accuracy and the ability to execute the computer simulation. The cabinet’s surfaces are all opened due to the actual product being mounted on the outdoor pole. The model is set to natural convection with a zero starting wind velocity and an ambient temperature of 40 °C. The Hexahedral-dominant meshing approach [10] was used in the next stage to make the model become countable nodes and elements. The meshing model was then read in FLUENT software to run the simulation. In FLUENT, the governing equations are solved using the Finite Volume Approach. From [7], we have the governing equations: Conversation of mass: ∂(ρu) ∂(ρv) ∂(ρw) + + =0 ∂x ∂y ∂z
(1)
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Conversation of momentum: ∂ ρu2 ∂(ρuv) ∂(ρuw) + + ∂x ∂y ∂z 2 2 ∂ u ∂ u ∂ 2 u ∂(ρvu) ∂ ρv2 ∂(ρuw) ∂p +μ + + + 2 + 2 =− ∂x ∂x2 ∂y ∂z ∂x ∂y ∂z 2 2 2 ∂ ρw2 ∂ v ∂ v ∂ v ∂p ∂(ρwu) ∂(ρwv) +μ + +w + 2 + 2 + g(ρ − ρ∞ ) =− ∂y ∂x2 ∂y ∂z ∂x ∂y ∂z 2 ∂p ∂ w ∂ 2w ∂ 2w =− +μ + 2 + 2 ∂z ∂x2 ∂y ∂z (2) Conversation of energy: ∂(ρwT ) μ ∂ 2T ∂(ρuT ) ∂(ρvT ) ∂ 2T ∂ 2T + +w = + + ∂x ∂y ∂z Pr ∂x2 ∂y2 ∂z 2
(3)
When the simulation finished, the temperature contour result plots were easily taken from the CFD software for making comparisons. By this way, the parameters that have the most considerable effect on the heat sink model would be obtained. It is important for the designing process in terms of improve the heat sink performance.
3 Results and Discussions 3.1 Simulation and Experimental Results In the first experiment, this study used two models of AAU for making comparisons, which included 8T8R with vertical fins heat sink and 8T8R with V-shaped fins arrangement heat sink. The parameters of the design as the follows: h = 70 mm, D = 350; c = 22 mm; r = 6.7 mm; β = 1.5°; α = 54°; d = 16.5 mm; t = 2 mm, materials: AL6061 with thermal conductivity: 200 w/mk. Technically, the layouts and wattages of chipsets for each AAU product line are the same. The simulation process is set up as the follows: Environment temperature: 40 °C; number of iterations: 100; convergence criteria: 0.001 - energy: 10–7 . The number of meshing element is 9230402 and 10041750 nodes. The simulation results are shown in Fig. 3, in general, the performance of the design with V-shaped fins is better than vertical fins. For instance, the maximum temperature on 8T8R AAU with V-shaped fins arrangement was declined slightly by 2.73% in comparison to the model with vertical fin (Fig. 4).
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Fig. 3. CFD thermal contour plots: a) 8T8R AAU vertical fins heat sink; b) 8T8R AAU V-shaped fins arrangement heat sink.
Infrared thermometer
8T8R AAU
Environment cabinet
Fig. 4. Experimental system.
Next stage is a validation of the simulation results, we applied the parameters of the simulation to fabricate 8T8R heat sink with V-shaped fins. The experimental system consists of an environment cabinet, 8T8R active antenna unit and an infrared thermometer as shown in Fig. 4. The environment cabinet is to generate working environment as reality with the temperature of 40 °C, an infrared thermometer named Fluke Ti27 is used to capture thermal image on the surface of the heat sink. The experimental result is shown in Fig. 5, as can be seen that the maximum temperature on surface of heat sink approximates 73 °C at the picked point in Fig. 5. The heat pattern of Fig. 5 is as similar as the simulation results in Fig. 3b. An error of 1.4% between the simulation and experimental result is acceptable for this research.
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Picked point.
Fig. 5. Experimental result.
To witness the significant improvement of the heat sink with V-shaped fins, in the next section, we will research on version 32T32R, and 64T64R which is bigger than 8T8R. 3.2 Investigating on Model 32T32R and 64T64R The simulation results are shown in Figs. 6 and 7. As can be seen in Fig. 6 that the maximum temperature of 32T32R AAU with V-shaped fins had a significant drop of 14.06% in comparison with vertical fins, meanwhile, that of 64T64R AAU in Fig. 7 had a drop of 7.5%. This simulation results confirm that the efficiency of the design with V-shaped fin would increase significantly when used with a larger AAU.
Fig. 6. CFD thermal contour plots: a) 32T32R AAU vertical fins heat sink; b) 32T32R AAU V-shaped fins arrangement.
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Fig. 7. CFD thermal contour plots: a) 64T64R AAU vertical fins heat sink; b) 64T64R AAU V-shaped fins arrangement.
4 Conclusions This paper studies on the effect of geometric parameters of heat sink on the thermal dissipation for Active Antenna Unit. It is implemented throughout CFD analysis in two stages: validation and investigation. In the first step, the simulation model is validated with the experimental model. Secondly, two more designs of heat sink are investigated to witness the significant effects of V-shaped fins on the thermal dissipation. The simulation results demonstrated that using V-shaped fins arrangement heat sink is better than vertical fins heat sink in terms of cooling performance, the larger the size of the AAU, the more pronounced the effect.
References 1. Tzanidis, I., Li, Y., Xu, G., Seol, J.-Y., Zhang, J.: 2D active antenna array design for FD-MIMO system and antenna virtualization techniques. Int. J. Antennas Propag. 2015, 1–9 (2015) 2. Webber, A.: Calculating useful lifetimes of embedded processors. Application report, Texas Instruments, pp. 1–6 (2014) 3. Shih, C., Liu, G.: Optimal design methodology of plate-fin heat sinks for electronic cooling using entropy generation strategy. IEEE Trans. Comp. Packag. Technol. 27, 551–559 (2004) 4. Huang, Y., Shen, S., Li, H., Gu, Y., et al.: Improved thermal design of fin heat sink for highpower LED lamp cooling. In: International Conference on Electronic Packaging Technology, pp. 1069–1074 (2016) 5. Sable, M., Jagtap, S., Patil, P., Baviskar, P., Barve, S.B.: Enhancement of natural convection heat transfer on vertical heated plate by multiple V-fin array. Int. J. Res. Rev. Appl. Sci. 5(2), 123–128 (2010) 6. Hagote, B., Dahake, K.: Study of natural convection heat transfer on horizontal, inclined and vertical heated plate by V fin array. Int. J. Sci. Eng. Res. 5(6), 1366 (2014) 7. Solanki, M., Vedpathak, M.: Modelling and analysis of heat sink with V shaped fin arrays. J. Mater. Sci. Mech. Eng. 3, 292–297 (2015)
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8. Minh, D., Quoc, V.: Optimal calculation of Plate Fin Heat Sinks for RRU LTE 4G. J. Measure. Control Autom. 1(1), 1–5 (2020) 9. https://blog.trimech.com/heat-transfer-and-thermal-analysis-when-to-use-fea-vs.-cfd. Accessed 16 Nov 2021 10. Sokolov, D., Ray, N., Untereiner, L., et al.: Hexahedral-dominant meshing. ACM Trans. Graph. J. 35, 1–23 (2015)
An Overview of Pedestrian Detection Based on LiDAR for Advanced Driving Assistance System Cao Vu Kieu1,2 , Ngoc Ninh Pham1,2 , Anh Tuan Le3 , Anh Son Le1,2(B) , and Xuan Nang Ho1,2 1 Phenikaa Research and Technology Institute, Phenikaa Group, Hanoi, Vietnam
[email protected]
2 Faculty of Vehicle and Energy Engineering, Phenikaa University, Hanoi, Vietnam 3 Hanoi University of Science and Technology, Hanoi, Vietnam
Abstract. Nowadays, cars have been becoming more and more essential as means of transport in our daily life. With the unstoppable development of science and technology, self-driving cars were developed as a smart transportation solution, which can solve a lot of problems such as car accidents, congestion, parking lots, and so on. Pedestrian detection is a part of object detection, plays an important foundation in advanced driving assistance systems, especially autonomous vehicles. Pedestrian detection using LiDAR signals is has attracted great attention from researchers because of its ability to provide accurate 3D information about the environment and operate in low-light conditions, which improves disadvantages when using a Camera. Pedestrian detection for autonomous vehicles not only needs to ensure real-time perception but also to ensure high accuracy because it is directly related to safety and eventually human life. Hence, this article will summarize and analyze the currently LiDAR-based detection methods namely grid-based and point-based approaches to apply the detection of pedestrians for autonomous vehicles. Then, we analyze and compare the results between the highly regarded algorithms using this approach and their applicability to autonomous vehicles, target finally is pedestrian. The results show us that there are advantages and disadvantages of the two grid-based and point-based approaches. The point-based approach stores information better, but requires higher computational resources than the grid-based method. And the combination of both methods yields an overall improvement in detection accuracy. Finally, we look at a few challenges that need to be overcome for greater detection accuracy. Keywords: Pedestrians detection · Autonomous vehicles · LiDAR
1 Introduction Object detection is a fundamental part of machine vision and deep learning (DL), and it creates the foundation for the in-depth development of many kinds of research, including object segmentation, object tracking, trajectory prediction. Pedestrian detection is a specific application of the object detection problem. With the development of autonomous © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 352–363, 2022. https://doi.org/10.1007/978-981-19-1968-8_28
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vehicles, and high applicability in various fields such as security monitoring, intelligent robots, it has become a topic of great interest in recent years. Pedestrian detection technology is becoming more and more advanced, the pedestrian database is also becoming larger and larger, which helps a lot in the research process. The stages of pedestrian detection are the same as other objects, but pedestrians can stand, sit, move and pedestrian shape is not limited; Clothing colors are diverse, it is much more difficult to detect pedestrians than other objects and there are many problems to be solved. It also requires more stringent requirements in terms of accuracy and real-time performance, especially for smart vehicles because pedestrians are vulnerable to direct and severe impacts. LiDAR and Camera are popular sensors for pedestrian detection tasks. Most previous pedestrian detection systems used a camera as a main - sensor. Current methods focus more on DL [1] for pedestrian detection due to the availability of highly parallelizable GP-GPU platforms and the availability of optimized DL architectures make them. The accuracy of pedestrian detection using the Camera has been greatly improved but is greatly affected by lighting conditions. Detection accuracy is significantly reduced in low-light conditions causing a serious safety hazard for smart vehicles. Independent tests conducted by NHTSA and the American Automobile Association (AAA) show that in all situations considered, the current ADAS intelligent driver assistance system uses pedestrian detection based on images that often fail to protect pedestrians in dark conditions [2]. LiDAR is becoming more popular due to its ability to produce highly accurate three-dimensional information, overcoming the lack of spatial information of images. LiDAR can always operate regardless of lighting conditions or any time of day, so LiDAR point cloud data is becoming increasingly important in 3D applications and point-cloud-based 3D detection devices. LiDAR uses lasers to project the object and uses a receiver to receive the returned lasers. Projection time, projection angle, reception time, and received reflectance coefficient will be recorded to create a 3D point cloud about the surrounding environment. Unlike images, Lidar data is a type of irregular, sparse, and unordered data type. This type of data is a challenge to extend 2D to 3D detection methods. The methods used for processing images cannot be directly applied to LiDAR data. Many methods use a combination of both LiDAR and imaging. The point clouds are transformed into 2D Bird view maps [3–5] or directly projected into the image and then using CNN [6] for feature extraction. However, combining LiDAR with the camera will need to be synchronized and calibrated continuously in time with each other. This will easily cause asynchronous errors between the sensors. In this paper, we are only interested in using LiDAR data for pedestrian detection. Most of the point cloud-based 3D detection methods can be divided into two main methods including point-based or grid-based approaches. The number of detection studies using LiDAR data has increased recently, but overall, the average accuracy of pedestrian detection is still relatively low compared to other objects. In this article, we will introduce and compare 3D detectors using these two methods, whose main goal is to detect pedestrians. The article consists of four sections. Section 2 will introduce two methods of pointbased and grid-based approaches to process LiDAR data. We will then compare and evaluate algorithms using these methods for this pedestrian detection task in Sect. 3, namely SECOND and PV-RCNN. And finally, Sect. 4 is the conclusion of the study.
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2 LiDAR Data Processing Method for Classification 2.1 Grid-Based Approach Convolutional neural networks (CNN) called ConvNets were first introduced in 1980 by Yann LeCun CNN [6] has many layers, including convolutional layer, non-linear layer, pooling layer, and fully connected layer. The convolutional and fully connected layers have parameters. The algorithm will learn and self-adjust these parameters to learn valuable features of the data. CNN is capable of processing large amounts of data and needs a lot of data and computational resources to work effectively. With the advancement of data science with increasingly large datasets and powerful computing devices, CNN became the most popular method in machine vision tasks. Today, many studies [7–13] or ResNet [14, 15] - a groundbreaking study that has continuously improved performance, has proven its reliability in both accuracy and processing time in many fields from image processing to natural language processing (Fig. 1).
Fig. 1. The overall architecture of VoxelNet [16]
LiDAR’s point cloud data gives accurate 3D information, but it is an irregular data type, with a high variable density of points so convolution cannot be used directly for this data type. This is a huge challenge without taking advantage of the potential and a large amount of research available to convolutional networks. Convolutional architectures require highly regular input data types such as images or 3D voxels to be able to share and optimize weights. To process this anomalous data, the grid-based method converts this data type to the regular data type 3D voxels [16–18]. The point cloud data will be a projector to 3D grid cells and then 3D CNN is applied to represent volumes for shape classifiers. The voxel-based method efficiently encodes the multi-scale feature representation and can create high-quality 3D proposals. D. Maturana introduces a method called VoxNet [19] that uses points mapped to an occupancy grid, then passes through two 3D convolutional layers to obtain the final representation. Wu et al. proposed 3D ShapeNet [20] to learn the distribution of points from different 3D shapes (3D shapes are represented by probability distributions of binary variables on a voxel grid). Although performance has been improved, these methods cannot large scale expansion because of the high computation and memory requirements when the input data is large. In 2017 Yin Zhou, Oncel Tuzel introduced VoxelNet [16], which takes all the raw point
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cloud as input, does the split of the cloud into voxels, apply random sampling to the set of points-wise in each voxel, and convert them into a new feature coding layer (VFE). The FCN network is applied to aggregate the information in the non-empty voxel into a feature vector for that voxel. Combining the voxels we get a dense 3D map to apply CNN and finally use RPN [13] for region recommendation and feature detection. Representing the non-empty voxels as a sparse tensor significantly reduced the memory as well as the in-process computation overhead in backpropagation processes. VoxelNet became the basis of much later modern research (Fig. 2).
Fig. 2. The overall architecture of PointNet [21]
2.2 Point-Based Approach Point-based [22, 23] methods directly extract distinguishing features from raw point clouds for 3D detection. The idea of this approach is to model each point independently with Multi-Layer Perceptron (MLP) and then aggregate a global feature using the asymmetric aggregation function. As a pioneer, PointNet [21] takes the point cloud directly as its input and uses the advantage of the network MLP. A point cloud is an unstructured data type, the author emphasizes the following factors: invariant to permutations, invariant to rigid transformations, capture interactions among points. The model uses the 3-dimensional coordinates of the point cloud to map to a higher dimension. Individual points are multiplied by an affine transformation matrix predicted by a mininetwork (predict T-Net) to hold invariance under geometric transformations. To respect Permutation Invariance the author has used symmetric functions – functions where n arguments are the same regardless of the order of arguments. The author maps n input points to a space of higher dimension by traversing a group of MLP networks followed by another joint alignment network. PointNet implements symmetric functionality with Max pooling. Since features are learning independently for each point in PointNet, locally structured information between points cannot be collected. Hence Qi et al. propose a hierarchical PointNet++ [24] network to capture the good learning structure from the neighborhood of each point. PointNet++ is a hierarchical network that applies Pointnet recursively on a nested portioning of the input point cloud. Like CNN learning features by a stack of convolutional layers multiple scales, PointNet++ locally extracts features from a neighborhood, processes to generate higher-level features. It proposes a novel set of new learning layers to adaptively combine features from multiple scales with different densities. This process is recursed when they have a special feature of the full of the
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point file. The hierarchy consists of a set number of levels of abstraction. Aggregation abstraction classes consist of three classes: Sampling layer, Group class, and PointNet layer. By stacking several set abstractions levels, PointNet++ [24] learns features from a local geometric structure and abstracts the local features layer by layer. Because PointNet based and PointNet++ set abstraction operation preserves accurate location information with flexible receptive fields, these two algorithms became the basis for the development of many algorithms later, constantly improving the feature of point abstraction such as network Mo-Net [25], PointWeb [26], SRN [27]. 2.3 A Two-Stage 3D Detection Framework The grid-based methods optimize the computational cost, but the inevitable loss of information because the original data has been changed when introduced into the model reduces the detection accuracy. As for the point-based methods, because they directly use point-to-point information in the cloud, they can preserve precise location information, so the accuracy of detection is high, but it also causes huge computational costs. We can see that integrating two types of grid-based and point-based feature learning frameworks can help optimize accuracy and time detection time. The frameworks that use this combination method are called two-stage methods (frameworks that use only one method are called single-stage methods). The important point is how to combine these two detection frameworks into one unified whole. Usually, in the first stage, the point clouds are voxelized, using the grid-based approach to generate the initial proposal, and in the second try, the proposal box is refined based on the point-based method. Chen et al. [28] first with voxelized and fed to a 3D backbone network to produce initial detection results. Then use the points in the suggestion box itself to refine the suggestion box. Performance is comparable to PointRCNN [29] while the speed is only 16.7 fps. At present, the two-stage network has become the widely used and developed detection framework today.
3 Experiments With the purpose to evaluate the ability of detection pedestrians, two highly rate frameworks included SECOND [30] and PV-RCNN [31] will apply. Grid-base and point-based stages were used in both algorithms to process LIDAR data with the data set from KITTI [32]. 3.1 Evaluation Algorithm In this section, we will evaluate pedestrian detection using two algorithms, SECOND [30] and PV-RCNN [31]. The two algorithms are highly regarded algorithms that use grid-based and point-based approaches in processing LiDAR data (Fig. 3).
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Fig. 3. The overall architecture of SECOND [30]
3.1.1 SECOND Although the point cloud contains about 100.000 data points when meshed about 90% of the voxels are empty. VoxelNet still shows high cost and computation time as it considers all voxels equal and scans all voxels, including empty ones. SECOND [30] introduced in 2018, is a grid-based single-stage method but has some convolutional network architecture improvements with the adoption of a sparse space convolutional network [33–35] to extract information. Unlike images, the data will be stored as a matrix or tensor. Sparse information signals are usually stored as a list or index. The algorithm collects all the operations of the convolutional receiver and saves them as a RuleBook as a calculation instruction. The output will only be calculated if the kernels of the input are non-empty voxels. Building a RuleBook is the key to sparse convolution. The sparse convolution is quite efficient since we won’t have to scan all the non-empty voxels. We only convolution when the elements are non-zero, thus greatly improving the computation time. Sparse convolution is now a very important part of LiDAR data processing. SECOND also proposes a new angular regression method to solve the ambiguity between angles 0 and π . In addition, it introduces a new data augmentation method for learning problems using only LiDAR greatly increasing convergence speed and performance (Fig. 4).
Fig. 4. The overall architecture of PV-RCNN [31]
3.1.2 PV-RCNN SECOND has improved the computation time but the accuracy is still low due to the loss of point information when using the grid-based method. PV-RCNN [31] is introduced
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as a 2-stage method that combines the advantages of both grid-based and point-based methods to improve accuracy. In stage 1, the input point cloud is voxelized and uses a 3D sparse convolutional network [34] [35] to learn intelligent features and generate high-quality proposals. In stage 2, the author does not directly use the points in the proposed box to refine the box but encodes the main point features based on the Voxel Set Abstraction Module (PointNet++). Additionally, they also recommend a keypoint-togrid ROI abstraction module to capture rich context information for box refinement. PVRCNN has received a high rating and ranking on the KITTI ranking for car 3D detection in all methods of combining Lidar + camera and lidar-only method for pedestrian detection. 3.2 Datasets The data set is a fundamental but important component of the pedestrian detection task. It is an important data source for researchers to test, compare, and evaluate algorithms. The quality of the dataset is judged on the number of samples as well as the labeled information. The richness of the data determines the robustness of the detectors. KITTI Dataset [32] and Waymo Open Dataset [36] are the two most popular datasets of 3D detection for autonomous vehicles. The KITTI dataset has 7,481 training samples and 7,518 test samples, where the training samples are generally divided into split 3,712 training samples and validation 3,769 samples. The Waymo dataset is the largest recently released dataset. The training sample is 158,361 samples, and the Validation set contains 40,077 samples. It annotates the object in the 360° field instead of the 90° like the KITTI set. In this paper, we only test on the KITTI dataset for the pedestrian detection task.
4 Results and Discussion The models are run on the same computer with specifications: CPU Intel® Core (TM) i7-10700 2.90 GHz and GPU NVIDIA GeForce GTX 1660 SUPER. During training and testing, the IoU = 0.5 threshold for pedestrians is applied. The model is trained with the train and validation datasets of the KITTI dataset. Currently, the most used rating for object detection is Average Precision (AP). Generally, the performance of the model is dynamically evaluated by drawing a P-R curve, where the horizontal coordinate is the recall rate, and the vertical coordinate is the accuracy rate. The average accuracy on the test set is calculated with 11 Recall positions. To compare algorithms, the system will usually evaluate the AP index at the “moderate” level of the data. Difficulties are defined as shown in Fig. 5. Processing speed is extremely important for pedestrian detection because it directly affects pedestrian safety. We need to pay attention to the Frames per second (FPS) index of these two algorithms.
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Fig. 5. Classification of the difficulty of the dataset KITTI [32]
The processing speed and accuracy of pedestrian detection when testing on the KITTI dataset are shown in Table 1 (Fig. 6): Table 1. Pedestrian detection performance comparison on the KITTI validation set Method
Sensor
FPS
The results are evaluated by the Average Precision with 11 recall positions Easy
Moderate
Hard
BEV
3D
BEV
3D
BEV
3D
SECOND
LiDAR
11.5
61.997
56.554
56.660
52.983
53.812
47.734
PV-RCNN
LiDAR
4.5
65.182
63.123
59.416
54.842
54.510
51.781
Fig. 6. Visualize images on BIRDS-VIEW and 3D-VIEW maps
According to the results of Table 1, the accuracy of both models are still low. SECOND is 52.98% on the 3D map and 56.66% on the BIRD-VIEW map, PV-RCNN is 54.84% on the 3D map and 59.42% on the BIRD-VIEW map. Compared with 2D pedestrian detection and other objects (cars can be up to 90%) in 3D object detection, for small targets such as pedestrians, the detection method using only LiDAR has many limitations and has not achieved high confidence. With a small target, the distribution of cloud
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points is very sparse, with poor information making it more difficult to detect than other objects. The lack of information of point cloud data with small targets is an inherent shortcoming that needs to be studied to be able to aggregate more information. Detection speed on SECOND is very fast (almost 3 times that of PV-RCNN). SECOND network processes LiDAR data based on a grid-based approach, reinforcement by a sparse spatial convolutional network that achieves very fast processing speeds that can completely respond to the real-time requirements in autonomous vehicles. Using only the grid-based approach shows the disadvantage of losing a lot of information, which inherently sparse pedestrian point data makes pedestrian point information even less so the low detection accuracy. PV-RCNN with a two-stage approach, in phase 2 refine the proposal box generated in phase 1. Thank to directly aggregating the point information in the original point cloud, there is not much loss of information, so the accuracy has increased significantly (at an easy level rise to 7%). This is also the advantage of this point-based approach, but it also causes a significant increase in processing time. The processing time of the 2-phase method – PV-RCNN is quite high compared to SECOND, but the performance is outstanding, and the time is still at an acceptable level. This makes the two-stage method trusted and received great attention in the research and development of modern algorithms.
Fig. 7. Occlusion pedestrian [37]
Pedestrian detection accuracy when using LiDAR is low because there are more false positives and false negatives compared to cars due to confusion. Pedestrians are taller than other objects, distributed vertically, so they are easily confused with other objects such as cyclists or roadside poles. The small pedestrian size also leads to a sparser point cloud, which greatly limits the processing power of the CNN. Pedestrians in the distance and near cause multi-scale problems. Deep CNNs have deep feature maps that are more suitable for large objects, so they are more difficult to detect when the object is far away. Reducing the down-sampling rate of the network, which is the simplest way to improve detection, can increase the detailed information on the object map. An extremely important problem that reduces pedestrian detection performance is crowding and occlusion between objects. Occlusion causes a loss of object information and invisibility of part of the area, potentially causing false detection or being missed by the detector (Fig. 7). This is shown very clearly when the accuracy of the two models is quite bad at the “Hard” level in the dataset. In comparison to normal objects, occlusion is more likely to occur in pedestrian detection because group movement behaviors are more likely to occur in pedestrians. The density of pedestrians and cyclists is usually higher
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than that of cars, easily obscured greatly affects the possibility of false detection, which is also a major obstacle limiting the application of detection pedestrians in autonomous driving missions. In addition, pedestrians are often easily disobedient to the rules, with high activism, causing unsafety on the road for drivers when they are unable to react in time at a close distance from the vehicle. Pedestrian detection algorithms are more concentrated in the area around the vehicle. This makes the dangerous areas more focused as well as reduces the cost and time of computation.
5 Conclusion In this paper, we summarize and discuss representative pedestrian detection algorithms from the perspective of traditional and deep learning algorithms. Through the description and comparison of the detection algorithm whose main goal is pedestrian detection, the efficiency, advantages, and disadvantages of each approach are analyzed. With the continuous improvement of the algorithm, the detection performance has increased, however, the problem of pedestrians being small-scale and frequently occlusion is still not well detected. There are still a lot of things to develop about pedestrian detection as well as challenges for completing. Acknowledgments. This work was partly supported by Phenikaa University. We thank Phenikaa University for facilitating our research.
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Stability and Viscosity of Mono and Hybrid Nanolubricant M. A. Saufi, Y. A. A. Ibrahim Ahmad, and Hussin Mamat(B) School of Aerospace Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia [email protected]
Abstract. Recently, many researchers are giving lots of attention towards the potential usage of nanolubricant because of their ability to enhance the thermophysical properties of heat transfer fluid and their application in various industries. The aim of this experiment is to evaluate the stability and thermophysical properties of functionalized multi-walled carbon nanotubes (F-MWCNT), graphene, silicon dioxide (SiO2 ), hybrid F-MWCNT/graphene and hybrid F-MWCNT/silica nanoparticle in polyester oil. The suspension is characterized by visual observation, thermal conductivity, and viscosity. The results showed that the highest enhancement of thermal conductivity at 0.1 vol% of mono nanolubricants and at ratio of 0.05:0.05 vol% for both hybrid nanolubricants. The thermal conductivity and viscosity increased with nanoparticle concentration. Keywords: Nanomaterials · Nanolubricant · Stability · Thermal conductivity · Viscosity
1 Introduction Incorporating nanofluids into the cooling system is one of the best ways to increase the performance of Automotive Air Conditioning (AAC) system by replacing traditional lubricants with nanolubricants [1]. The addition of nanoparticles to the base fluid will ultimately boost their transport properties and efficiency of the device, irrespective of the possibilities that should be purposely evaluated for the pressure effect. With substantial advantages for compressors against wear properties and high-pressure conditions, lubricity can also boost its tribological characteristics [2]. A solution for enhancing the cooling system’s efficacy may be the insertion of nanoparticles into the base fluid or working fluid, such as compressor oil [3]. In refrigeration systems, the use of nanolubricant can increase heat transfer. Still, at the same time, the pumping power is increased because the viscosity of the nanolubricant is greater than the standard oil viscosity [4]. A rise in viscosity is unavoidable when adding nanoparticles to the base fluid. The thermal conductivity coefficient or heat transfer should not be affected by the increase in nanofluid viscosity. Nanofluid usually uses water, ethylene glycol, and oil base fluid that can be considered water or oil-based fluid. The metal group, ceramic group, and carbon group are © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 364–372, 2022. https://doi.org/10.1007/978-981-19-1968-8_29
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nanoparticles that are commonly added. To make nanolubricants, nanoparticles may also be added to the base lubricant. Nanolubricant technology has evolved from time to time, together with the rapid development of technology. The first nanolubricant generation concentrates more on only one type of nanoparticles mixed into the base lubricant. Sharif et al. [5] examined the influence of Al2 O3 on the performance of PAG lubricant. The results of the thermophysical study indicate that thermal conductivity is increased to 4% at 1.0 vol%. However, the Al2 O3 /PAG viscosity has increased sharply to 700% at 0.4 vol%, which limits the potential of nanolubricants. This is because the high viscosity lubricant tends to gain greater pumping power despite having more better anti-friction properties [6]. Nanolubricant’s second generation is called a hybrid nanolubricant. In general, a mixture of two or more types of nanoparticles added to the base lubricant was compromised by hybrid nanolubricant. The usual hybrid nanolubricant improves stability, heat transfer, and tribology. However, depends on the ratio of nanoparticles inside the lubricant. Zawawi et al. [7] have studied Al2 O3 /SiO2 and PAG nanolubricant’s thermophysical properties. The thermophysical properties of the nanolubricant improved up to 2.4% and 9.71% respectively with improved thermal conductivity and viscosity at 0.1 vol%. The thermal conductivity improvements are better than Al2 O3 /PAG and SiO2 /PAG, with a small viscosity increase. A higher hybrid nanolubricant concentration can be used in AAC due to the minimal viscosity increase compared with the mono nanolubricant. The hybrid nanolubricant also has a higher value thermal conductivity, which is more stable with less sedimentation. In a further study, the Al2 O3 /SiO2 /PAG hybrid nanolubricant has a high friction coefficient (COF) with a better anti-wear property of 5% and 12% [8]. These helps to reduce mechanical friction within the compressor to lengthen the AAC compressor’s service life. Many researchers have extensively studied on oxide nanoparticles in nanolubricants in recent years. Little attention has been paid in evaluating the performance of carbonbased nanomaterials (F-MWCNT and graphene) in nabolubricants. In the present study, the stability, thermal conductivity, and viscosity of mono nanolurbicants (F-MWCNT, graphene, SiO2 ) and hybrid nanolubricant (F-MWCNT/graphene and F-MWCNT/SiO2 ) with polyester oil as the base fluid was carried out. To confirm the stability of nanolubricant, visual inspection was carried out for 14 days. The results are presented for volume concentration up to 0.1% and viscosity is measured at the temperature of 40 °C and 100 °C.
2 Materials and Methods 2.1 Material Graphene nanoplatelet with 11 to 15 nm thickness and 15 micron of average particles size was purchased from Skyping Nanomaterial. The F-MWCNT with outside diameter of 20–30 nm, inner diameter of 5–10 nm, length of 10–30 µm and 95–98% purity synthesized via catalytic vapour deposition (CVD) was supplied by USAINS, Universiti Sains Malaysia, Penang. SiO2 was purchased from Sigma-Aldrich with average particle size of 12 nm. The base fluid used in the experiment is SUNISO synthetic refrigeration
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polyester oil with additives referred as SUNISO SL68. It has kinetic viscosity at 40 °C of 70.1 cSt and density of 0.960 g/cm3 . 2.2 Nanolubricant Preparation Graphene, silica, F-MWCNT and hybrid nanolubricant were produced by two-step method, using the polyester oil as the base fluid. Mono nanolubricant was prepared at volume concentration of 0.025% to 0.1%. While for F-MWCNT/graphene and F-MWCNT/silica hybrid, volume concentration ratio of 0.075:0.025, 0.05:0.05 and 0.025:0.075 in polyester oil. 2.3 Evaluation of Dispersion Stability The stability of nanolubricants was evaluated using sedimentation photographs and metallographic microscopy. The nanolubricants were filled into clear vials of 30 ml after sonication to observe the nanolubricant’s stability over 14 days of visual inspection. The samples were observed daily until the nanoparticles sedimented at the base of the vial and obvious separation of nanoparticles and oil could be seen. 2.4 Thermal Conductivity The thermal conductivity of nanolubricant is measured using KD2 Pro Thermal properties analyser (made: Decagon, USA) with the sensor KS-1-0063. The thermal conductivities of the nanolubricant of different volume concentrations were recorded as parameters of result interpretation. This instrument uses the concept of transient line heat source to determine the thermal conductivity. This measurement was done at room temperature. 2.5 Viscosity Measurement Viscosity test was conducted to measure the viscosity of each of the different nanolubricant concentrations at temperature of 40 °C and 100 °C. It is the viscosity at the operating temperature of machine that is critical to understand. The cone and plate rheometer was used to measure the viscosity of nanolubricant. The measuring cone CP25-2 with diameter of 25mm (angle 2°) and a speed of 100 rpm was used in the measurement. The instrument was set to measure 29 points from shear rate of 0 to 100 s−1 .
3 Result and Discussion 3.1 Stability of Nanolubricant Visual inspection of F-MWCNT, graphene and SiO2 nanolubricant in polyester oil as the base fluid is shown in Fig. 1 and for hybrid nanolubricant it is shown in Fig. 2. On day 7, graphene nanolubricant with high concentration of nanoparticles have higher tendency to sediment. The graphene nanolubricant shows black deposition at the bottom
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Fig. 1. Samples a, b, c, d and e represents pure oil, 0.025, 0.05, 0.075, and 0.1 vol.%, repsectively of i) F-MWCNT, ii) graphene and iii) silica nanoparticles respectively for day 14.
Fig. 2. Samples a, b, c, d, e and f represents the pure oil, 0.1:0.0, 0.075:0.025, 0.05:0.05, 0.025:0.075, and 0.0:0.1 vol.% of hybrid nanoparticles, respectively. i) F-MWCNT/graphene and ii) F-MWCNT/SiO2 respectively for day 14.
of the bottle which relates to the settling of graphene and indicate that graphene is not stable beyond 2 weeks. By comparing these 3 nanolubricants, it can be seen that the settling of graphene nanoparticles is more obvious than the other. On the other hand, no obvious sedimentation was observed for F-MWCNT and SiO2 nanolubricant up to 14 days observation. On day 14, hybrid nanolubricant show obvious sedimentation when the volume concentration ratio of graphene is more than F-MWCNT (Fig. 2(i)(e) and (f)). Since F-MWCNT and graphene nanoparticle is an insoluble compound, the nanoparticles will sediment due to gravitational pull [9]. While for hybrid F-MWCNT/SiO2 nanolubricant, no sedimentation is observed until day 14 indicating that the nanolubricants are stable.
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3.2 Thermal Conductivity Thermal conductivity of mono and hybrid nanolubricant is shown in Figs. 3 and 4, respectively. The measurements were taken at room temperature and 1 h after sonication, letting the nanolubricant to cool down to get accurate readings. The thermal conductivity was measured using KD2 Pro and the measured value of polyester oil was 0.134 W/m.K. Addition of F-MWCNT, graphene and SiO2 increase the thermal conductivity of the oil. The highest enhancement was shown at 0.1 vol% for F-MWCNT, graphene and SiO2 which was 0.139 W/m.K. A value of 3.7% improvement in thermal conductivity of nanolubricant was observed at 0.1 vol% compared to pure oil. Improvement in a range of 1.5% to 3.0% was shown for the other samples. A previous study by Ettefaghi et al. [10] reported the use of 1 wt% MWCNTs in oil SAE20 has recorded an improvement of 15% in thermal conductivity. Singh et al. [11] examined thermal conductivity of CNT/EG nanofluid. The results showed that the thermal conductivity increased with higher mass concentration of CNT. At 0.4wt% nanotube loading, 72% improvement was achieved.
Fig. 3. The thermal conductivity of different concentration of a) F-MWCNT, b) graphene and c) silica nanoparticle at day 1 and day 7.
MWCNT and graphene nanoparticles are known to have high thermal conductivity, low specific gravity, high aspect ratio and high heat transfer potential [12]. Sanukrishna and Prakash [13] reported that the heat is assured to flow from the oil molecules to the graphene particles at solid-liquid interface as thermal conduction. For this reason, a nanofluid with long-term stability and proprietary thermal properties can be made with a combination of FMWCNT and graphene nanoparticles. The results in Fig. 4, show the highest increase in thermal conductivity of 0.143 W/m.K for ratio of 0.05:0.05 FMWCNT/graphene hybrid nanolubricant. An increase of 6.7% is found if compared to pure oil. For hybrid F-MWCNT/SiO2 , ratio of 0.075:0.025 shows the highest thermal conductivity enhancement of 3.7%. The improvement of the other samples shown to be
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between 3.7% to 5.2%. The findings concluded that the best ratio of hybrid nanolubricant is 0.05:0.05 hybrid F-MWCNT/graphene with the highest improvement in thermal conductivity.
Fig. 4. The thermal conductivity of each a) hybrid F-MWCNT/graphene and b) hybrid FMWCNT/silica concentration sample at day 1 and day 7.
3.3 Viscosity The viscosity test was conducted on the nanolubricant with volume concentration of 0.1%. First, the base oil and nanolubricant rheology was measured at shear rate 0 to 100 s−1 and at 40 °C and 100 °C. The measurements were taken 1 h after sonication process. Figure 5 represent the viscosity of nanolubricant with respect to shear rate at 40 °C and 100 °C. In Fig. 5(i), the viscosity of F-MWCNT nanolubricant is the highest among the other nanolubricant at low shear rate. The viscosity of nanolubricant with addition of F-MWCNT nanoparticles increased up to 144% compared to pure oil. The viscosity of graphene and SiO2 nanolubricant shows a similar behaviour with the pure oil where the viscosity drops with high shear rate. The F-MWCNT/graphene and F-MWCNT/SiO2 hybrid nanolubricant shows increment of 15% and 6.7%. At 100 °C, the viscosity of nanolubricant decreased. Figure 5 (ii) shows that FMWCNT has the highest increment in viscosity of 340%. At low shear rate, the graphene and SiO2 nanolubricant shows an increment of 1% and 6% in viscosity but drops at high shear rate. For F-MWCNT/graphene hybrid nanolubricant, it shows enhancement of 25% while for F-MWCNT/ silica it shows increment of 5%. The figures show that the viscosity of nanolubricant at 0.1 vol% display at varying temperatures. With the addition of nanoparticles, the viscosity of polyester oil increases.
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Fig. 5. Shear rate vs viscosity for 0.1 vol% (a) graphene, F-MWCNT and Hybrid FMWCNT/graphene and SiO2, F-MWCNT and Hybrid F-MWCNT/SiO2 nanolubricant (i) at 40 °C and (ii) at 100 °C.
This is due to the internal viscous of shear stress [14]. The figure also shows that, with rising temperatures, the nanolubricant viscosity dims rapidly. This is due to the impact of temperature on liquid viscosity, which weakens intermolecular forces and the forces between subatomic molecules as temperature rises [15].
Fig. 6. Viscosity for 0.1 vol% of nanolubricant at 40 °C and 100 °C.
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Figure 6 illustrates the pure oil and nanolubricant viscosity at varying temperatures. With addition of nanoparticles the viscosity of polyester oil increases. All the nanolubricant samples showed improvement in viscosity. In Fig. 6, shows that by addition of F-MWCNT nanoparticles in pure oil exhibits an enhancement of 40.65% and 97.19%. For graphene, the results show 1.5% and 1.0% increment at 40 °C and 100 °C. As for silica, the viscosity increases for 2.91% and 10.47%. The results of viscosity of hybrid F-MWCNT/graphene at 40 °C and 100 °C shows that the viscosity increased about 14.84% and 63.38%. While for hybrid F-MWCNT/silica, the viscosity increases 1.45% and 15.12%. It has been noticed that increase in the temperature reduces the viscosity of all nanolubricant samples.
4 Conclusion The effect of graphene, SiO2 , and functionalized multi-walled carbon nanotubes on the stability, thermal conductivity, and viscosity of polyester oil were shown in the results of this research. The following conclusions can be drawn based on the findings of nanolubricants with nanoparticles: 1. The higher the concentration of nanoparticles, the higher the tendency of nanolubricant to sediment. The thermal conductivity of nanolubricant improved as the concentration of volume increased. Regarding the viscosity of polyester oil, it increases with the addition of nanoparticles. 2. The most stable hybrid nanolubricant is when the nanolubricant ratio of FMWNCT is greater than graphene nanoparticles. In the meantime, all ratios of F-MWCNT/silica hybrid nanolubricant remain stable. The highest increase in thermal conductivity was achieved when the vol% ratio was 0.05:0.05 for FMWNCT/graphene hybrid nanolubricant while the vol% ratio was 0.075:0.025 for F-MWCNT/silica.
Acknowledgments. Acknowledgement to “Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme with Project Code: FRGS/1/2018/TK07/USM/03/1”.
References 1. Sharif, M.Z., Azmi, W.H., Mamat, R., Shaiful, A.I.M.: Mechanism for improvement in refrigeration system performance by using nanorefrigerants and nanolubricants – a review. Int. Commun. Heat Mass Transf. 92, 56–63 (2018) 2. Redhwan, A.A.M., Azmi, W.H., Sharif, M.Z., Hagos, F.Y.: Development of nanolubricant automotive air conditioning (AAC) test rig. MATEC Web Conf. 90, 01050 (2016) 3. Celen, A., Çebi, A., Aktas, M., Mahian, O., Dalkilic, S., Wongwises, A.: A review of nanorefrigerants: flow characteristics and applications. Int. J. Refrig. 44, 125–140 (2014) 4. Selim Dalkilic, A., et al.: Experimental study on the stability and viscosity for the blends of functionalized MWCNTs with refrigeration compressor oils. Curr. Nanosci. 13, 216–226 (2017)
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5. Sharif, M.Z., Azmi, W.H., Redhwan, A.A.M., Mamat, R.: Investigation of thermal conductivity and viscosity of Al2O3/PAG nanolubricant for application in automotive air conditioning system. Int. J. Refrig. 70, 93–102 (2016) 6. Yu, P.Y.: Influence of Lubricant Viscosity of POES on the Performance of Hermetic Reciprocating Compressors (2002) 7. Zawawi, N.N.M., Azmi, W.H., Redhwan, A.A.M., Sharif, M.Z., Sharma, K.V.: Thermophysical properties of Al2O3-SiO2/PAG composite nanolubricant for refrigeration system. Int. J. Refrig. 80, 1–10 (2017) 8. Zawawi, N.N.M., Azmi, W.H., Redhwan, A.A.M., Sharif, M.Z.: Coefficient of friction and wear rate effects of different composite nanolubricant concentrations on Aluminium, 2024 plate. IOP Conf. Ser. Mater. Sci. Eng. 257(1), 012065 (2017) 9. Ali, N., Teixeira, J.A., Addali, A.: A review on nanofluids: fabrication, stability, and thermophysical properties. J. Nanomater. 2018, 33 (2018) 10. Ettefaghia, E., Ahmadi, H., Rashidib, A.M., Mohtasebia, S.S., Nouralishahic, A.: Surveying and comparing thermal conductivity and physical properties of oil base nanofluids containing carbon and metal oxide nanotubes. Nanostructures 16, 405–413 (2013) 11. Singh, N., Chand, G., Kanagaraj, S.: Investigation of thermal conductivity and viscosity of carbon nanotubes-ethylene glycol nanofluids. Heat Transf. Eng. 33(9), 821–827 (2012) 12. Kumar, P., Wani, M.F.: Effect of temperature on the friction and wear properties of graphene nano-platelets as lubricant additive on Al-25Si Alloy|Request PDF, December 2018 13. Sanukrishna, S.S., Jose Prakash, M.: Experimental studies on thermal and rheological behaviour of TiO2-PAG nanolubricant for refrigeration system. Int. J. Refrig. 86, 356–372 (2018) 14. Abu-Nada, E.: Effects of variable viscosity and thermal conductivity of Al2O3-water nanofluid on heat transfer enhancement in natural convection. Int. J. Heat Fluid Flow 30(4), 679–690 (2009) 15. Kole, M., Dey, T.K.: Effect of aggregation on the viscosity of copper oxide-gear oil nanofluids. Int. J. Therm. Sci. 50(9), 1741–1747 (2011)
Detection Fault Symptoms of Rolling Bearing Based on Enhancing Collected Transient Vibration Signals Nguyen Trong Du(B) , Nguyen Phong Dien, and Nguyen Huu Cuong Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Rolling bearings are a part of the machine that must regularly work with incredible intensity in harsh environments. Therefore, rolling bearings are more accessible to damage than other components in devices. Fault detection of rolling bearings is a primary problem of prolonging the working life of the whole machine system. The local defect in the rolling bearings always gives rise to repetitive collected transient vibration signals. In fact, environmental noises or unstable operation conditions distort the transient signals. Therefore, identifying the rolling bearing faults still needs to be studied further to overcome this problem. In this paper, a signal analysis method based on Tunable Q-factor Wavelet Transform (TQWT) filter is used to analyze sparse signals. Firstly, the TQWT is applied to decompose the measured signal to a number of levels. After that, the Hilbert envelope spectrum analysis method is implemented to each component signals to look for the bearing fault symptoms. Both the simulated signal and the experiment signal are implemented to validate the efficacy of the proposed method. Keywords: Rolling bearing · Bearing diagnostics · TQWT · Envelope spectrum · Fault diagnostics
1 Introduction The rolling element bearing is one of the essential elements in the transmission system. It is a component between two parts that allow rotational or linear movement, reducing friction and enhancing performance to save energy. The rolling element bearing’s working efficiency will be reduced when damaged and generate abnormal transient signals during the operation conditions. The previous bearing rolling diagnosis fault methods were focused on analyzing the time or frequency domain to identify unique types of defects. In recent years, having methods have been innovated to detect faults of bearing, such as Fourier transform [1], Short-time Fourier transforms [2], wavelet transform [3], etc. Selesnick proposed Tunable Q-factor wavelet transform (TQWT) [4], which is a variant of the wavelet transform and is widely used. Previous research proved the efficacy of TQWT in rotating machinery fault detection [5]. However, intense environmental noises make the TQWT method significant deviations. This paper presents a new approach that combines TQWT and the Hilbert envelope spectrum [6] to detect the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 373–384, 2022. https://doi.org/10.1007/978-981-19-1968-8_30
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rolling bearing fault. Firstly, raw vibration signals are decomposed to several levels by TQWT. Then the decomposed signals are analyzed by Hilbert envelope spectrum. The rest of the paper has the following contents. Firstly, TQWT is implemented to reduce the measured signal’s noise and, after that, decompose it into small components. Then, the analytical method of The Hilbert energy spectrum was used to analyze each gained component signal and point out the bearing failure characteristic frequency.
2 Theoretical Background 2.1 Tunable Q-Factor Wavelet Transform Tunable Q-factor Wavelet Transform is used to denoise vibration signals based on the wavelet basis function. The Q-factor in TQWT transforms is used as a quantification method to detect transient signals. The TQWT transform based on filter bank, include low pass filter and high pass filter, with two parameters, respectively, α and β. Having two main parameters in TQWT is the Q-factor and the redundancy r. The Q-factor is defined as the ratio between the center frequency and its frequency band. This ratio represents the number of oscillations in the wavelet basis function. A lower Q-factor value means less oscillation and vice versa. Performing TQWT at infinity obtains a residual, which is influenced by the parameter r. Suppose the TQWT decomposes the original signal into J levels [4]. Note that Q-factor does not depend on the decomposed level j, just as the wavelet transform is a transform with a constant Q-factor. Furthermore, this parameter is affected only by the high pass scaling parameter β. The relations between parameters are: Q=
2 β log(βN /8) f0 = − 1, r = , J = BW β 1−α log(1/a)
(1)
The characteristic parameters of the TQWT are given in Table 1. Table 1. Component health by case. Components
Parameters
Q-factor
Q
Redundancy
r
Decomposition level
J
The low pass scaling parameter
α
The high pass scaling parameter
β
As usual, the measured vibration signal may contain ambient noise. So, the signal can be expressed as: x(t) = y(t) + n
(2)
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where x is the original signal, n is the noise, y is the bearing fault signal. The TQWT method decomposes the signal to base components and reconstructs them. So noise is strongly reduced. For perfect reconstruction, the low-pass filter and the high-pass filter should be constructed by ⎧ ⎪ |w| < (1 − β)π 1, ⎨ w+(β−1)π ), (1 − β)π ≤ |w| < απ θ ( H0 (w) = α+β−1 ⎪ ⎩ 0, απ ≤ |w| < π ⎧ (3) ⎪ |w| < (1 − β)π 0, ⎨ απ −w ), (1 − β)π ≤ |w| < απ H1 (w) = θ ( α+β−1 ⎪ ⎩ 1, απ ≤ |w| < π where H0 (w) and H1 (w) are low-pass and high-pass filter response functions, respectively, and θ (ω) is the 2π-periodic power complementary function. The process of decomposing and the way that wavelet coefficients w is found are in Fig. 1. Wavelet coefficients, namely the subband signals.
Fig. 1. Flow chart of decomposing signals x(t) with two levels to obtain wavelet coefficients.
The reconstruction’s accuracy is increased using Basis pursuit denoising (BPD), proposed in the TQWT algorithm [7]. BPD is essentially the optimal problem. We find the parameter w so that A reaches a minimum: A = ||x − TQWT −1 (w)||22 +
J +1
λi ||wi ||1
(4)
i=1
w = arg min ||x − TQWT −1 ||22 +
J +1
λi ||wi ||1
(5)
i=1
with λ are the parameters of TQWT. After finding the optimum w, we calculate the energy of the subband to choose the subband that has the most energy contribution. From the chosen subband, the denoised signal is estimated by taking the inverse TQWT transform of w. y = TQWT −1 (w)
(6)
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2.2 Hilbert Envelope Spectrum With signal x(t), the Hilbert transform is defined as: 1 h(t) = H {x(t)} = π
∞ −∞
x(τ ) 1 dτ = x(t) t−τ πt
(7)
The Hilbert transform signal h(t) combined with the original oscillator signal x(t) into an analytic signal has the following form: z(t) = x(t) + jh(t) The module of the new signal z(t) is its envelope: E(t) = |z(t)| = x2 (t) + h2 (t)
(8)
(9)
Hilbert envelope spectrum is obtained by applying the spectrum analysis to the module E(t) above. The bearing failure frequency is reliable in the envelope spectrum.
3 The Proposed Method 3.1 Flowchart of Method As shown in Fig. 2, we have two time-domain signals of 2 cases of normal bearings and inner race fault bearings. Looking at the normal vibration signal, it is not possible to determine the characteristic failure frequency. The main reason is due to non-stationary signal as below: x(t) = cos(at) + sin(bt)
(10)
Fig. 2. Time-domain signal of the normal bearing (a) and fault bearing (b)
Simultaneously, the signal is also affected by ambient noise, making it challenging to diagnose the damage. So we have to remove the noise from the original signal.
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Additionally, if applying the Hilbert transform for this signal will estimate an average instantaneous frequency of x(t) (instead of frequencies a or b). Thus, using Hilbert transform for the original signal will be made it more or less be skewed. To overcome this problem, use the previously mentioned TQWT algorithm to eliminate noise and analyze the signal after filtering the noise into small components. Therefore, instead of using the Hilbert transform for original signals, use the Hilbert transform for component signals. Decompose the original signal into component signals and choose the highest energy level component. The obtained signal is reconstructed and gets into Hilbert transform to obtain the Hilbert energy spectrum. This paper proposes a combination of the TQWT and Hilbert envelope analysis methods to detect rolling element bearing fault. The flowchart is shown in Fig. 3.
Fig. 3. Flow chart of proposed approach.
3.2 The Method Validation by Simulation Impact-triggered oscillations signals of bearings are simulated to demonstrate the efficacy of the proposed method. A periodic signal x(t) includes pure signal component and noise to represent characteristics of rolling bearing fault signal constructed as: x(t) = A e−ζ 2π fr (t−kT ) sin(2π fr (t − kT )) + n(t) (11) k
A = 5 is maximum amplitude, ζ = 0.02 the damping coefficient, fr = 600(Hz) is the carrier frequency, k = 50 is the number of impulse signal, T = f1f with ff = 10(Hz)
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is characteristic frequency. The power of the Gaussian noise n(t) can be set with the desired Signal-to-Noise Ratio (SNR), which is defined as: SNR = 10 log10 (
Psignal ) Pnoise
(12)
where Psignal is the power of the pure signal component and Pnoise is the power of noise. The pure signal component is shown in Fig. 4a, and the noisy signal is shown in Fig. 4b. After that, applying the TQWT transform to the noisy signal will get smaller signal components, called subband signals, as shown in Fig. 5.
Fig. 4. The fault simulation signal and the noisy signal.
Fig. 5. Subbands of a signal.
The decomposed signals are still strongly noise. Applying the BPD algorithm minimizes noise while signal characteristics become clearer, as in Fig. 6.
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To see the contribution of the subbands, we calculate the energy distribution of the subbands for the new signals. From Fig. 6, we see, the subband 4th has the highest energy level throughout the range. This subband will be used to reconstruct the signal, which is shown in Fig. 7.
Fig. 6. Denoised subbands of the signal and energy of each subbands.
Fig. 7. Reconstruct signal from the optimal signal.
By applying the Hilbert envelope transform to the reconstructed signal, Fig. 8 is got.
Fig. 8. The envelope spectrum of noisy signal and the reconstruction signal.
As in Fig. 8, the characteristic failure frequency ff = 10 Hz is reliable. Comparing with the envelope spectrum of the noisy signal, the envelope spectrum of the reconstruction signal is more outstanding.
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4 Application on Experiment Data 4.1 The Dataset Description An experimental model to test the ability to detect and assess local damage to the drive was built at Case Western Reserve University (USA) as part of a research project on oscilloscope diagnostics (“Case Western Reserve University Bearing Data Center Website”) [8]. The experimental model is a simple transmission system consisting of an electric motor that can change the rotation speed with a frequency converter, a drive shaft, a coupling, and a load-generating device (Fig. 9). The motor shaft is supported on an SKF 6205-2RS JEM type bearing. These bearings are subject to experimental research. The oscillometric system used on the experimental model consists of two accelerometers to measure system oscillations in the vertical direction and a reference pulse signal probe. The first accelerometer is mounted at the bearing housing. The returned results are the measured acceleration values with a sampling frequency of 12000 samples/s. The specific specifications of the bearings are shown in Table 2 [9]. With these specifications, fault frequencies are calculated by formulas in Table 3. Defect frequencies in different positions of the bearings with motor frequency frotary are shown in Table 4.
Fig. 9. (a) Actual test model for measuring the failure signal of bearings; (b) Detailed model of damaged bearings (inner race fault).
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Fig. 10. The measured original vibration signal.
Table 2. SKF 6205-2rs deep groove ball bearing parameter specification. Specifications
Value
Inner ring diameter D1 (mm)
25.0012
Outer ring diameter D2 (mm)
51.9989
Rolling diameter d (mm)
7.94
Pitch diameter D (mm)
39.0398
Contact angle β(degrees)
0.42
Number of rollers z
9
Table 3. Fault frequency calculation formulas. Position
Formula
Inner ring
z d 2 (1 + D cos β)frotary z d 2 (1 − D cos β)frotary 1 d 2 (1 − D cos β)frotary D [1 − ( d cos β)2 ]f rotary D d
Outer ring Cage train Rolling element
Table 4. Defect frequencies. Position
Value
Inner ring
5.4152 frotary
Outer ring
3.5848 frotary
Cage train
0.3982 frotary
Rolling element
4.7135 frotary
4.2 Analysis and Evaluation of Method Based on Experiment Test In this research, data on which the damage was intentionally created on the inner ring of the rolling bearing is used. The rotary frequency frotary = 29.53 Hz, the rotary
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frequency ffault = 159.93 Hz. Figure 10 shows the original vibration signal with background noise. TQWT is implemented with parameters (Q, r, J) calculated following the theory. Decomposing by TQWT, the obtained signals are shown in Fig. 11. The signal is still strongly disturbed. By applying the BPD algorithm, w is optimized while signal characteristics become prominent. Subband signals after optimization and their energy contributions are shown in Fig. 12. From Fig. 12, we see the 2nd subband has the highest energy level throughout the range. This subband will be used to reconstruct the signal, which is shown in Fig. 13. Hilbert envelope transform is applied to the noisy signal and the denoised signal, we obtained the final result, which is shown in Fig. 14. As in Fig. 14, not only the characteristic failure frequency is more reliable, but also rotary frequency appears.
Fig. 11. Subbands of the noisy signal.
Fig. 12. Denoised subbands of the signal and energy of each subbands.
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Fig. 13. Reconstruct signal from the optimal signal.
Fig. 14. Envelope spectrum of subbands signal.
5 Conclusion This study applies TQWT to decompose and reduce the noise of the original vibration signals. Using TQWT decomposes original signals into six energy levels. After that, the small component signal corresponding to the highest energy level is chosen to obtain a new signal without noise. Finally, the envelope spectrum method is implemented to denoise signals in order to detect the abnormal symptoms through the appearing fault characteristic frequency of rolling bearings. The results obtained from both simulation and experiment tests show that the proposed techniques can be used to detect and identify rolling element bearing faults in transmission systems. In addition, the signal processing results of this method show the efficiency compared to the traditional method by enhancing the fault frequency and rotating frequency of the shaft.
References 1. Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, New York (1999) 2. Heneghan, C., Khanna, S.M., Flock, A., Ulfendahl, M., Brundin, L., Teich, M.C.: Investigating the nonlinear dynamics of cellular motion in the inner ear using the short-time Fourier and continuous wavelet transforms. IEEE Trans. Signal Process. 42(12), 3335–3352 (1994) 3. Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process. 18, 199–221 (2004)
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4. Selesnick, I.W.: Wavelet transform with tunable Q-factor. IEEE Trans. Signal Process. 59(8), 3560–3575 (2011) 5. Cai, G., Chen, X., He, Z.: Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of the gearbox. Mech. Syst. Signal Process. 41(1–2), 34–53 (2003) 6. Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. A 454(1971), 903–995 (1998) 7. Papadakis, M., Selesnick, I.W., Van De Ville, D.G., Vivek, K.: Sparse signal representations using the tunable Q-factor wavelet transform. In: Wavelets and Sparsity XIV (2011). https:// doi.org/10.1117/12.894280 8. https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures 9. Randall, R.B., Antoni, J.: Rolling element bearing diagnostics-a tutorial. Mech. Syst. Signal Process. 25, 485–520 (2011)
Human-Robot Interaction System Using Vietnamese Nguyen Khac Toan, Le Duc Thuan, Le Bao Long, and Nguyen Truong Thinh(B) Department of Mechatronics, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam [email protected]
Abstract. Human-robot interaction is an important field that affects a robot’s friendliness and ability to communicate. This paper proposes a human-robot interaction system capable of communicating with humans in Vietnamese. The system is built and developed based on the human communication process. First, the identity and expression of the interlocutor are recognized by the system. This is to combine with speech of the interlocutor to form input data. They are categorized into search text and conversation text. The response of the search text is initialized from the results returned from the internet. Conversation text has responses generated from the trained model and data about the expressions and information of the interlocutor. These data are combined to form the final system response. Simultaneously, important data during the current conversation is also stored to use in the next conversation. The designed system is based on the interaction model of real people. That creates friendliness and empathy for the system. As the experimental results, the level of satisfaction in the process of interacting with the system reaches 78%. Keywords: Human-robot interaction · Interaction system · Interaction Vietnamese
1 Introduction Human-robot interaction is the research of human-robot connection known as HRI. Human-robot interaction is a multidisciplinary field with contributions from many fields including human-computer interaction, artificial intelligence, robotics, natural language understanding, social science, society and humanities. Research on human-robot interaction has been going on since the first robots were born. Nonetheless, until the 21st century, the field of robotics and artificial intelligence achieved numerous wonderful accomplishments. These accomplishments have rolled out critical improvements to the field of HRI. Currently, there are many projects underway in this field. These projects are distributed across many different industries, especially the service and entertainment industries. A few models can be referenced, for example, the amusement intelligent robot [1] created by the creators Gockley, the robot that helps with communicating with the old [2] made by Broekens, robot to help unknown dialect educating by Japanese engineer H. Ishiguro [3]. Existing intelligent robots can voice recognition and give fitting © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 385–398, 2022. https://doi.org/10.1007/978-981-19-1968-8_31
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responses with the interlocutor [4, 5]. These robots have yet to meet the requirements of friendliness and realism in human interaction. Some subsequent research has proposed an intelligent interaction system capable of displaying gestures in the communication process. A good example is the robot of the author group R. Meena [6]. These movements are pre-designed leading to limited robot movements. Many of the researches have analyzed and fixed the problem in different ways. One of them has been presented by the research of communication by Seiya Mitsuno’s group [7] and the research of communication gestures by Minjie Hua’s group [8]. Nonetheless, the above researches have not fully simulated the process of human-tohuman communication. There are no researches regarding the memory of robots during human interaction. In this paper, a realistic simulated human-robot interaction system is proposed. This includes building memory, designing the model to recognize the identity of the interlocutor. Retrieve previously stored information of the interlocutor. The system interacts through voice and has the capability of answering knowledge-related questions. At the same time, the system is capable of recognizing emotions, creating empathy for the communication process. This study is structured as follows: Sect. 1 focuses on HRI analysis and related studies, Sect. 2 shows the structure of the interacting system and analyzes each component separately, experimental process and obtained results were the main content of Sect. 3, Sect. 4 gives the conclusion.
2 Interaction System 2.1 Structure of Interaction System The human-robot interaction system in this research is built to improve and enhance the experience in the process of interacting with the robot. The proposed objectives include: First, the system must recognize the expressions of the interlocutor to enhance empathy during the interaction. Second, the system must to be able to remember information, which is an important human trait. Finally, knowledge is indispensable for humans. It is also a key element in the interactive system. The combination of all three goals allows the robot to create like-real conversations that improve and enhance the experience. The structure of the human-robot interaction system is shown in Fig. 1. The speech input of the interlocutor is processed through 3 tasks. The final result includes speech response and coded commands sent to the robot’s head to perform facial expressions. Task 1 includes two processing processes: emotion recognition in text after conversion and text classification. The structure of voice data is complex and able to noise. The data is converted from speech to text allowing for easier encoding. Next, these texts are used to recognize the expression to give an appropriate response expression to the robot. This is important processing to increase empathy between the interlocutor and the robot. Besides, the goal of the classification process is to simplify the machine learning model in the next step. Instead of directly text processing to give responses, the system must process a large amount of data. Therefore, text classification before model training is the solution used to minimize the computational burden on the system. Text was divided into 2 types: conversation text and search text. Where conversation texts are defined as normal dialogue texts, it is not necessary to use a lot of knowledge to generate responses. In contrast, search texts are response texts that need to provide a
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certain amount of knowledge to satisfy the interlocutor. Usually, the search text is the questions about a certain individual, object, or field.
Speech
Task 1 Expression recognition
Text
Classify
Conversation text
Văn bản tìm kiếm
Identity confirmation Internet Storage
Search response
Conversation response
Task 3
Task 2
Results
Fig. 1. Structure of the human-robot interaction system
When the input data is classified as search text, task 2 will be performed by the system. Which includes 2 processes: Internet search and search response initialization. The search text that requires a response contains a certain amount of knowledge. Using a static RNN model cannot meet the goal due to a large amount of knowledge. Besides, knowledge is subject to change over time, when using a static model, it is easy to lead to outdated knowledge. The solution for this problem is using the internet search method. A program built on top of the Google search engine was used to produce the required results. Once the search results are obtained, the information is passed to the search response initialization process. The system combines the search results and the initial search text to generate the input for the text response initialization. This process uses an improved RNN model to map the input to the appropriate output. Then combined with some optimization algorithms to give the search response. When the input data is classified as conversation text, task 3 will be performed. It includes 3 processes: Confirming the identity of the interlocutor, storing information, and generating conversation responses. Similar to the human communication process, the robot also needs to recognize the identity of the interlocutor. This allows the system to focus on processing information related to the interlocutor while eliminating unnecessary information for the communication process. Then, the system can retrieve and store the information of the interlocutor. This is one of the strengths of the robot interaction system proposed by us. The process allows the system to identify important information related to the interlocutor that needs to be stored. This can be thought of as creating long-term memory for the system. During operation, the system can communicate with many different people. Memory creates separate data stacks for each interlocutor. The system stores the information on the appropriate stack based on the results of the identity
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verification process. Then, the system proceeds with the conversation response initialization process. This process is similar to the search response initialization used in task 2. They both use an improved RNN model, however, the input data of the conversation response initialization block in task 3 is different. They are composed of information from stored memory, the original conversation text, and the results of the emotion recognition process. The output was also be optimized by the same algorithms in task 2 to give the conversation response. The resulting block is the final processing of the human-robot interaction system. The output from previous tasks was combined in this block. This includes textual and emotional feedback from the interlocutor. Responses in text form were converted back to speech to continue the communication process. 2.2 Text Classification Progress 2.2.1 Combined LSTM Model Combined LSTM is a supervised learning model built on top of Bi-directional long shortterm memory architecture (BiLSTM) as encoder and long short-term memory architecture (LSTM) as decoder. A recurrent neural network (RNN) is capable of handling problems with input and output in sequence [9]. This works on the ability to remember and synthesize information from previously processed elements of the sequence. BiLSTM uses two hidden layers called backward and forward. Each layer is a sequence of successive LSTM architectures (Fig. 2).
Fig. 2. BiLSTM encoder and LSTM encoder.
LSTM is an architectural form of RNN that appeared first in 1997. The important element of LSTM is the cell state, in Fig. 1 this state is represented by an arrow connecting Ct-1 and Ct [10]. Cell state operates like a data pipe. Every network node it passes through only performs a few linear transformations, so the transmission of information takes place more smoothly and with less loss. Inside the LSTM there are 3 different gates, each of which performs the necessary functions including: forget gate, input gate, and output gate. Information passing through the gates is screened, each gate has a structure consisting of a sigmoid function and a multiplication. The output value of the sigmoid function is always in the range [0,1], describing the amount of information passed. The number 0 represents that no information is allowed through and the number 1 allows all information to pass through it. The combination of LSTM and BiLSTM architectures
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will be the foundation to solve the problems of emotion recognition, text classification, and speech conversion, which were analyzed in the next sections. 2.2.2 Vietnamese Word Decomposition The human-robot interaction system uses speech as the main input signal for human communication. However, the speech format is complex, lacks visualization, and often has signal noise. The signal is converted from speech to text to solve this problem and facilitate data processing in the next steps [11]. Statistical results based on the Vietnamese Dictionary of the Institute of Linguistics show that Vietnamese words consist of one or more syllables. The number of words with 2 syllables makes up the majority (69.8%) and the least number of words with more than 4 syllables (3.6%). In Vietnamese, the word “cá nhân” - “person” contains 2 syllables, “cá” and “nhân”. When the two single words “cá” - “fish” and “nhân” - “human” stand separately, they have a different meaning. Therefore, Vietnamese is different from English, processing of words on computers requires encoding words into digital objects. To ensure the correct encoding of words in Vietnamese, we propose the word decomposition method in the data preprocessing. Multi-syllable words were encoded as a single word. Thus, compound words like “cá nhân” were encoded by 1 digital object instead of 2 objects.
Fig. 3. The block diagram of the word separation process
The word separation process requires a Vietnamese dictionary to identify multisyllable words. The VLSP dictionary was used to build the digital library containing about 35000 words with semantic and grammatical information. This dictionary contains all words of common words in modern Vietnamese and is encoded as XML. The number of words above 4 syllables accounts for only 3.6% and is rarely used in communication, so they are ignored. The Vietnamese word library includes words with 4 or fewer syllables. The process of using the longest word matching algorithm is like Fig. 3. The first step is to filter 4-syllables words. When looking at a word in a sentence, the system considers all cases of 3 syllables that are adjacent to it. Then checking whether the combination forms a 4-syllable compound word by comparing it with the digital library. The identified 4-syllable compound words do not participate in the next filter. After filtering all 4syllable compound words, the system continues to filter 3-syllable compound words according to the same rules. Continue until all the words in the sentence have been filtered.
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2.2.3 Training Model of Text Classification Based on automatic expression recognition and classification, the robot can give appropriate response to communication situations. Expressions affect the quality of dialogue as well as showing empathy with the interlocutor. Text classification helps the system to identify the needs of the interlocutor to determine appropriate processing methods in the next steps. The artificial intelligence model that performs emotion recognition and text classification is presented in this basic expressions including: happy, sad, surprised, scared, worried, normal, angry in the input text will be recognized. Two types of text, conversation text and search text, are also classified. Data for this training is collected from daily conversations and then labeled. Which combines 2 types in one label, the first is a classification label of 7 basic emotions, the second is a label that classifies 2 types of text. vui
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First, the input text is processed by word decomposition presented in Sect. 2.2.2. It continues to be encoded from text data to vector (word to vector) called embedding. Each word in the sentence is encoded into a vector to facilitate later computation. After embedding, each sentence becomes an array with each element being a vector representing a word. Sentences in vector form are concatenated into a 2-dimensional input array of size: m × n. Where m is the number of rows corresponding to the number of sentences in the input data. n is the number of elements (number of words) of each sentence. For shorter sentences were added vector 0 in empty positions. Similar, the labels are also embedding by this method. After processing the input data, the system uses a deep learning model for expression recognition and classification. This required the model to process input text data in vector form to make predictions. The combined LSTM model presented in Sect. 2.2.2 is used for this process as shown in Fig. 4. The data after the embedding was transmitted to the encoder block. Each element in this block is a BiLSTM architecture. The purpose of the encoder block is to synthesize the necessary information from the input and store it in a matrix called S. The system will continuously adjust the value of the weight matrices in the architecture to achieve the highest efficiency. This process is equivalent to synthesizing information to help the reader understand the input text. Next, the storage matrix S is passed to the Decoder block to make predictions. Each element in this block is an LSTM architecture. The input of the first element in the Decoder block is the matrix S and the null vector (vector zeros). The system also adjusts the numerical matrices of
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the LSTM architecture to achieve the output closest to the value of label. The results of the Decoder block are passed to the Sofmax block. Here, the results of each element were used to evaluate which words have the highest probability of appearing. The word with the highest probability is chosen to continue the prediction process. Finally, the system reverses the embedding process to restore the text formatting of the data. The combined LSTM model is used to perform two tasks of both emotion recognition and classification. 2.3 Search Text Processing 2.3.1 Transformers Model Transformer is a deep learning model applying self-attention mechanism [12]. It capable of recognizing the relationship between an element and the other elements in the input data. However, processing input data as other RNN models is not necessary for Transformer. In the case of text data, the attention mechanism helps the model to understand the meaning of the sentence even when processing the entire sentence simultaneously. It determines the position to each word and the relationship of each word to the rest. Transformer can process many calculations in parallel and reduce model training time, which is the biggest advantage of Transformer over most other RNN models. In addition, the Transformer model also processes input sentences in two directions. 2.3.2 Search the Internet For search text, the response requires a certain amount of information to satisfy the interlocutor. Collecting and storing knowledge to train a large model is one solution. However, this storage requires a large amount of memory. The amount of knowledge will increase rapidly over time. Some knowledge quickly becomes outdated or no longer relevant. Therefore, storing knowledge in static form is not a possible solution. Another solution is to use the internet search method. This method solves the problems of storage and ensures that the knowledge is not outdated. Among the search engines on the internet, google search is a typical tool and it allows users to reconfigure it to suit many different tasks. It is the search engine used in this research. First, the system performs the search process with the question as the search text defined in task 1. The query information is sent to the google search engine similar to the way we normally use it. The difference here is that this search engine has been reconfigured to minimize incorrect results. Specifically, the reconfigured tool only allows searching results from pre-specified web pages. The response from the search engine is in JSON format. Which includes many key-value pairs, the system filtered out the data related to the query question to transmit to the next processing block. 2.3.3 Training Model of Search Response The Combined LSTM model presented in Sect. 2.2.1 has a simpler structure than the Transformer, but the data must be processed sequentially. The combined LSTM model is only suitable for small tasks. For more complex tasks such as response initialization, the Transformer model presented in Sect. 2.3.1 is the right choice to significantly reduce
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data processing time and increase accuracy. The first step is processing the input data. There are 500 questions in many different fields. The internet search algorithm presented in Sect. 2.3.2 is implemented to collect results. Each original question and search results from the internet is concatenated into a text. Responses are also created manually from the above data to label the created text. The obtained results are input data and their labels to use for model training. The effectiveness of a model is dependent on the network configuration and the input data of the training process. However, the optimization algorithm is also one of the core factors determining the efficiency of a model. In this section, the Adam algorithm is used to optimize the Transformer model. The learning rate is not constant. The Adam algorithm treats it as a parameter that can change during training. The formula for the learning rate is shown in Eq. (1) [13]. Then, the theta value is updated according to Eq. (2) [14]: −0.05 ∗ min(step_num−0.5 , α = dmodel
step_num ∗ warmup_steps−1.5 ) mt θt = θt−1 − α √ vt + ε
(1) (2)
where: dmodel is the dimensionality of the input data. step_num is the number of training steps. warmup_step is the number of warmup steps. mt is internal state momentum. vt is internal state squared momentum. This model uses dmodel = 512, step_num = 10000 and warmup_step = 2000. Other parameters of the optimization algorithm are selected as follows: β1 = 0.9; β2 = 0.999; ε = 10 − 6; The results of model with a loss function of cross entropy are shown in Fig. 5. The cross entropy average value of the obtained model is 0.074, corresponding to an accuracy of 92.86%. 2.4 Conversational Text Processing 2.4.1 Identity Confirmation In the process of human-robot interaction, continuously processing facial recognition consumes computer resources and increases processing time. Therefore, a more optimal method is proposed to confirm and update the identity. This method uses a “recognition frame” to minimize the number of iterations of the face recognition algorithm as shown in Fig. 6. When a face appears in the recognition frame, the system performs face recognition. In this study, the Haar Cascade model was trained to detect faces [15]. In case the identity of interlocutor is recognized, the system labels one with a fixed identification code. Otherwise, if the identity cannot be determined, the system asks to
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Fig. 5. Cross entropy average of model
take a picture and get name of the interlocutor to initialize the database for the new person. This is equivalent to the process of making new friends in human communication. Once the identity is confirmed, the system will only perform face detection and not perform repeated identity verification. Except when the system detects the face of the interlocutor moving towards the recognition frame and then out of the recognition frame or suddenly disappear. This means that the interlocutor is leaving the field of view of system. The system will then perform facial recognition as soon as it detects a new face in the field of view. This process is repeated throughout the operation of the system.
Recognition frame
Face
Fig. 6. Identity confirmation and update model
2.4.2 Data Storage The memory is an indispensable component to improving the authenticity of the interaction between robots and humans. Retrieval and storing data into memory is one of the important works. Based on the similarity with the task of extracting object in the text, the Named Entity Recognition model (NER) used in this section. This is a model used to identify objects in sentences and documents such as proper name, time, organization, etc. The NER model is a combination of BiLSTM and CRF. In which the BiLSTM model was mentioned in Sect. 2.2.1. The CRF model is a conditional probability model for structured prediction problems [16]. CRF uses a scoring matrix that converts between tags to calculate the conditional probability for each tag. It allows the
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model to understand the relationship between the predicted tag and the previous tags. The combination of BiLSTM with CRF provides a significant improvement in the ability to extract information-carrying words. The model is shown in Fig. 7.
CRF
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´ ho. p BiLSTM và CRF Fig. 7. Mô hình kêt
2.4.3 Training Model of Conversional Response The conversation response model was implemented in this section based on the Trasnformer model, that was developed and completed in Sect. 2.2.1. The first step is to process the input data. 2000 dialogue sentences in many different fields have been collected. They combined with information retrieved from data storage and the results of emotion recognition in Sect. 2.2.3 to form an input data. The answers were also collected to make labels for the created input data. Finally, both of input data and labels were processed by embedding technique. This model has the parameters dmodel = 124, step_num = 20000 and warmup_step = 4000. Except for the parameters mentioned above, the conversation response model is almost the same in nature as the search response model. Both use the Adam optimal algorithm. The learning rate shown in equation Eq. (1) and the parameters mentioned in Sect. 2.2.1. The result of model evaluated using the cross entropy as loss function. The cross entropy average value of the model is 0.062, corresponding to an accuracy of 93.98%. The input data is processed through 3 main steps including: Text classification, search text processing and conversation text processing. The result is a response in text form. Finally, the system uses the text to speech model to convert the data back into speech [17].
3 Experiment The system is designed and built based on the purpose of interacting with humans. The quality of the interaction was determined by the perception and evaluation of the interlocutor. Therefore, it is necessary to experiment to test the interaction system. Hana robot was built based on the proposed human-robot interaction system to conduct experiments.
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3.1 Experimental Setup Experiments were conducted by 2 supervisors to ensure that no unexpected incidents occurred during the experiment. The number of volunteers participating is 50 people, including 3 engineers, 33 students, 8 employees, the rest come from a variety of various professions. The percentage of men is 74%, women 26%, people over 25 years old is 22%, people under 25 years old is 78%. Volunteers were asked to sit 1 m away in front of the robot. Each volunteer has three to five minutes to talk with the robot, and discussion issues are not limited. The volunteers were asked to say “Xin chào robot Hana” – “Hello Hana robot” to start a conversation. The volunteers were invited to fill out a survey once the conversation had finished. The survey consists of 4 items corresponding to 4 different questions about the interaction process. The questions are shown in Table 1. All questions above are rated at a 5-point Likert scale (range from 1 to 5, the higher the value, the better). Level 1 represents high negativity, level 2 is quite negative, level 3 is neutral or normal, level 4 is quite positive, and level 5 is highly positive. Table 1. Question in experiment Question 1 2 3 4
´ vê` robot không? Ba.n có biêt (Do you know about robots?) - ,o,c cung câp ´ bo,i robot Hana? Ba.n dánh giá sao vê` nhu˜,ng thông tin du . (How do you rate the information provided by the robot Hana?) ˜ k˜y thuâ.t không? ´ robot Hana có thu,o`,ng xuyên g˘a.p lôi Ba.n có thây (Do you see that Hana robot often encounters technical errors?) ij
Ba.n có hài lòng vo´,i cuô.c trò chuyê.n này không? (Are you satisfied with this conversation?)
3.2 Results Volunteers were asked to select one of five levels of the scale for the question “Do you know about robots?” Where values 1 and 2 show that they have no knowledge of robots, or have never experienced robots, values 4 and 5 show that candidates have knowledge of robots or have ever interacted with robots. Figure 8a shows that most of the volunteers have had experience with robots or have knowledge about robots. This rate is up to 72% (levels 4 and 5 of Likert scale) because most of the participants are students and engineers who have been exposed to robots. The percentage of people who have little knowledge about robots accounts for 8% (levels 1 and 2 of the Likert scale). Most of the volunteers who chose this item are workers and have little opportunity to interact with robots. The high percentage of participants with robot knowledge indicates the reliability of the experimental results. The question “How do you rate the information provided by the robot Hana?” shows how much the volunteers rate the information provided by the robot. 5 options corresponding to 5 levels of the Likert scale including level 1 and 2 are little or no usefulness,
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Fig. 8. Experience with robots and information evaluation
levels 4 and 5 represent very useful information. The results of the evaluation are shown in Fig. 8b. There are 14% (levels 1 and 2 of the Likert scale) of volunteers who think that the information has no or little usefulness, the volunteers think that the robot cannot answer questions related to technique or personal problems. The percentage of volunteers who think that the information Hana robot provides is very useful is up to 68% (levels 4 and 5 of the Likert scale). This shows that the Hana robot was able to answer most of the human questions.
Fig. 9. Satisfaction level and technical error
Figure 9a describes the satisfaction level of volunteers when communicating with the robot Hana. The question “Are you satisfied with this conversation?” has 5 Likert scales including level 1 and 2 expressing dissatisfaction, level 4 and 5 expressing that the volunteers are satisfied with the conversation. Results of the experiment showed that only 10% of the total volunteers felt dissatisfied when communicating with the robot (levels 1 and 2 of the Likert scale). The volunteers explained their choice because the way of talking and intonation of robot was not similar to that of a human. Some other volunteers said that sometimes the robot did not understand the meaning of the sentence and lacked humor. That makes them feel uncomfortable when communicating with Hana. In contrast, the percentage of volunteers who feel satisfied when talking with the robot Hana is up to 78% (levels 4 and 5 of the Likert scale). Volunteers said they felt the Hana robot was talking really kind and polite. For the question “Do you see that Hana robot often encounters technical errors?”, 5 levels of the Likert scale including level 1 and 2 show that the robot rarely encounters technical errors, level 4 and 5 show that the robot often makes technical errors. The results of the evaluation were shown in Fig. 9b. It shows that the percentage of volunteers who think that robots are prone to technical errors is 10% (levels 4 and 5 of the
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Likert scale). The volunteers found that the robot could not hear all the words they said. Sometimes it misses a few words. The cause of these problems was determined by their fast pronunciation or mispronunciation. It’s a problem with the local languages. The percentage of volunteers who found that the robot rarely had technical errors was up to 74% (levels 1 and 2 of the Likert scale). The volunteers said that the robot works stably when conversing in short and medium sentences. This result proves that the Hana robot will rarely encounter technical errors during daily communication. Based on the experiment results of each of the above questions, the satisfaction of the interlocutor and the possibility of not having technical errors of Hana are both highly rated, over 70%. However, the data used to train the model is still limited, leading to only 68% of the information evaluation experiment. In general, the Hana robot is considered to meet the requirements of the human-robot interaction system.
4 Conclusion The research proposed a method to build a human-robot interaction system based on simulating the interaction process between humans. The purpose is to enhance the friendliness and intelligence of the system. The input image is processed to confirm the identity of the interlocutor. Then, the system retrieves and stores important information of the interlocutor based on identity. This allows the system to have a human-like memory. The input speech is converted to text form and performs emotion recognition and classification. Depending on the text type, the system performs corresponding response initialization. Experimental results show that the system is evaluated to bring valuable information and satisfy the interlocutors. Besides, the error rate of the system is also low. Acknowledgments. We sincerely thank the OPEN laboratory and Scientific Research Program of Ho Chi Minh City University of Technology and Education, Vietnam for supporting us to complete this research.
References 1. Gockley, R., Bruce, A., et al.: Designing robots for long-term social interaction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2005) 2. Broekens, J., Heerink, M., Rosendal, H.: Assistive social robots in elderly care: a review. Gerontechnology 8, 94–103 (2009) 3. Kanda, T., Sato, R., Saiwaki, N., Ishiguro, H.: A two-month field trial in an elementary school for long-term human–robot interaction. IEEE Trans. Rob. 23, 962–971 (2007) 4. Hinton, G., Deng, L., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29, 82–97 (2012) 5. Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE (2013) 6. Meena, R., Jokinen, K., Wilcock, G.: Integration of gestures and speech in human-robot interaction. In: 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom). IEEE (2012)
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7. Mitsuno, S., Yoshikawa, Y., Ishiguro, H.: Robot-on-robot gossiping to improve sense of human-robot conversation. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE (2020) 8. Hua, M., Shi, F., et al.: Towards more realistic human-robot conversation: a Seq2Seq-based body gesture interaction system. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2019) 9. Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45, 2673–2681 (1997) 10. Graves, A.: Long short-term memory. Supervised Sequence Labelling with Recurrent Neural Networks, pp. 37–45. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-247 97-2_4 11. Yu, D., Deng, L.: Automatic Speech Recognition. Springer, London (2016). https://doi.org/ 10.1007/978-1-4471-5779-3 12. Vaswani, A., Shazeer, N., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. (2017) 13. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412. 6980 (2014) 14. Liu, L., Jiang, H., et al.: On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265 (2019) 15. Khan, M., Chakraborty, S., et al.: Face detection and recognition using OpenCV. In: 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE (2019) 16. Lample, G., Ballesteros, M., et al.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016) 17. Taylor, P.: Text-to-Speech Synthesis. Cambridge University Press, Cambridge (2009)
Effect of the Elliptical Shape on the Performance of the Modified Savonius Wind Turbine Minh Banh Duc1 and Anh Dinh Le2(B) 1 VNU-University of Engineering and Technology, Vietnam National University (Hanoi),
144 Xuanthuy, Caugiay, Hanoi 100000, Vietnam [email protected] 2 School of Aerospace Engineering, VNU-University of Engineering and Technology, Vietnam National University (Hanoi), 144 Xuanthuy, Caugiay, Hanoi 100000, Vietnam [email protected]
Abstract. In this study, the effect of the elliptical shape on the modified Savonius wind turbine, consisting of the original semicircular profile and an additional elliptical part, is investigated. The geometry configuration of this elliptical profile is determined by the non-dimension parameter R* and the angle α. The effect of the parameters on the rotor performance is numerically analyzed using the commercial software Ansys Fluent 2020R2. As a result, both R* and α plays an important role in the performance of the rotor. In that, the rotor shows the best efficiency at R* = 0.55 and α = 90°. For which the power coefficient improves up to 99.1% compared to Blackwell’s conventional rotor configuration at a tip speed ratio (TSR) 1.4 before decreasing at a higher TSRs. Keywords: Savonius turbine · Wind turbine · Blade optimization · Elliptical blade · Aerodynamic performance · Wind energy · Renewable energy · CFD
1 Introduction Savonius turbine is a vertical axis wind turbine (VAWT) invented by S. J. Savonius in 1922 [1]. With its unique features such as the simple assembly, low noise level, omnidirectional, self-starting ability at low wind speed, low cost, and the small space required [2, 3], the Savonius wind turbine become an interesting candidate for wind energy harvesting in urban environments. However, the disadvantage of Savonius turbines is the relatively low power efficiency compared to other wind turbines. A number of studies have been conducted to improve Savonius turbine performance, including the flow navigation using deflectors or curtains [4–7] and changing the shape of the turbine’s blades [8–23]. Typically, the deflectors or curtains are designed to gather the oncoming wind to the advancing blade and deflect the wind away from the returning blade. For example, in the study conducted by Altan et al. [4], a curtain arrangement was placed in front of the rotor to direct the wind to the advancing blade while preventing the wind’s negative effect on the returning blade, as shown in Fig. 1. However, the deflector makes the turbine system complex and direction of dependence [4–7]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 399–412, 2022. https://doi.org/10.1007/978-981-19-1968-8_32
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On the other hand, optimizing the blade configuration is an effective way to improve the efficiency of Savonius wind turbines. A number of techniques had been introduced in the previous studies such as the overlapping blade [8], the multi-thickness blade [9–12], the Bach type blade [13–15], twisted blade [16], the multi-curve blade [11, 17, 18], blade with vent [19, 20], and the multi-number blades [21, 23]. For which the performance of the rotor could be improved up to 41.1% compared to the conventional one [23].
Fig. 1. Design of the curtain arrangement placed in front of the Savonius wind rotor [4]
It was found that the flow is highly separated on the Savonius rotor blade at a high tip speed ratio (TSR) [12], resulting in the decrease of the rotor performance and inapplicable for the urban environments. Hence, reducing the separation at high TSR is important to increase the Savonius rotor’s efficiency at high TSR conditions. This study introduced a modification novel blade to improve the efficiency of the Savonius rotor. This modified blade profile makes by a semicircle shape of an original rotor and an elliptical profile in order to suppress the flow separation and increase the positive torque on the rotor. The impact of the elliptical profile on the rotor performance is numerically investigated in detail.
2 Modified Blade: Effect of Elliptical Profile Figure 2 illustrates the configuration of the present modified blade [25], designing based on Blackwell’s original rotor (Fig. 3) [22] with the configuration parameters shown in Table 1. As for the modified blade, the additional elliptical profile is added to the original semicircular blade and is characterized by the parameter R* and the angular α. In that, R* is defined by R2 /d. While α is the parameter that describes the length of the elliptical profile. It is clear that changing either R* or α will directly influence the flow mechanism around the rotor blade and therefore affect its performance. In this study, the impact of parameters R* and α are numerically investigated. In that, the R* and α are varied, as shown in Table 2. The blade configuration regarding different R* and α is illustrated in Figs. 4 and 5, respectively.
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Fig. 2. Geometry and design parameters of the modified rotor
Table 1. Detailed design parameters of the modified rotors (unit in m) d [m]
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Fig. 3. Geometry and design parameters of the original rotor
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Fig. 4. Rotor configuration with different R*
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Fig. 5. Rotor configuration with different angle α
3 Numerical Simulation 3.1 Numerical Method In this study, the air assumes as incompressible. Then, the flow around the Savonius rotor is analyzed by solving the 2D incompressible Reynolds Navier-Stocks equations (RANS) in commercial Computational Fluid Dynamics (CFD) software ANSYS Fluent, as follow [24]: ∂ui = 0, ∂xi
∂ui uj ∂ui ∂ui 1 ∂p ∂ ϑ + =− + − ui uj . ∂t ∂xj ρ ∂xi ∂xi ∂xj
(1) (2)
Here, p is the pressure. Besides, u is the mean velocity and u is the fluctuation velocity; the subscripts i and j denote the component directions and ϑ is the kinematic viscosity. In addition, ui uj is the Reynolds stress tensor. The realizable k-ε turbulence
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model with enhanced wall function is selected to account for the effect of the turbulence on the flow around the rotor [9]. The above governing equations are solved using the Finite Volume Method (FVM) [24]. The implicit pressure-based solver with the coupled algorithm is chosen for the simulation. The second-order upwind scheme is used for the discretization of the convection flux. The least-square cell-based algorithm is applied for the gradient’s spatial discretization. The second-order implicit scheme is used for the time discretization. In addition, the sliding mesh model is used to simulate the rotation of the rotor. 3.2 Computational Domain and Simulation Conditions The computational domain in this study is illustrated in Fig. 6 which is rectangular, with the Savonius wind turbine residing at the symmetric centerline along the y-direction. The inlet is located 6D from the centre of rotation of the rotor in the y-direction while the outlet is located 10D away. The symmetry boundaries locate at a distance of 7D along x-direction from the rotor centre O, to guarantee negligible effects of the boundaries around the rotor. The computational domain is divided into two separate zones, the stationary zone and the rotational zone, which are connected by a defined interface. This interface has a diameter equal to the rotor diameter, as 1.2D. The velocity inlet boundary condition is imposed at the inlet, with a uniform velocity U w = 7 m/s [22] is specified, while the pressure outlet boundary condition is used at the outlet. The symmetry condition is required at the two-side boundaries. Regarding the rotor blade, the no-slip condition is applied. The rotation speed ω, shown in Table 3, is specified to the rotating zone and rotate counterclockwise. In addition, a step of 20 is the rotation angle obtained for each time step. The rotor performance is compared for the different configurations through the distribution of torque, power coefficient, and pressure at wide TSRs. The TSR λ and the power (C p ) coefficients are calculated by [24]: λ= CP =
ωd , Uw
2P . ρDH U3w
(3) (4)
With P is the produced power. In addition, ρ is the density of air and U w is the free-stream wind velocity, which is specified at the inlet. H = 1 m is the height of the rotor. Unstructured mesh is used for simulation. The fine mesh is generated near the rotorstator interface with the minimum size of the mesh being 0.005 and close to the blade with the minimum size of the mesh being 0.001. This mesh used 15 layers of inflation with 1.2 growth rates and the first layer thickness is 10–5 m (Corresponds to grid resolution y+ < 1). Additionally, the coarse mesh is used for the static area to reduce memory usage and computation time while keeping the results reasonable, as shown in Fig. 7. The mesh sensitive, the time-step independence, variation and the turbulent model sensitive studies were performed. And the mesh of 166858 elements is used in this study.
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Fig. 6. Computational domain and boundary conditions
Fig. 7. Mesh detail around modified rotor
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1.4
1.6
ω [rad/s]
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11.2
14
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19.6
22.4
4 Results and Discussion 4.1 Effect of R* Figure 8 depicts the averaged power coefficient C p with R* of 025, 0.55 and 0.75 at different TRSs.
Fig. 8. Averaged C p with R* varied
For R* = 0.25, the C p rapidly decrease with a stiff slope as the TSR increases. A much lower C p is produced compared with the experimental results and simulation results of Blackwell’s original rotor [22]. A better C p profile is produced for R* = 0.55 and 0.75. The modified rotor produces the highest C p in the range of TSR from 0.6 to 1.2 with R* = 0.75 while better performance results with R* = 0.55 at a higher TSR. In that, the C p peak is at TSR = 1.0 and TSR = 1.4 for R* = 0.75 and 0.55, respectively. In comparison with to the original rotor, the modified blade profile with R* = 0.55 has C pmax = 0.2818, increasing by 99.13% and with R* = 0.75 has C pmax = 0.273, improving by 23.52% at the same TSR. Figure 9 shows the constructed polar charts for the torque T on the original rotor and the modified rotor with R* = 0.55, 0.75 in one rotation. The TSR of λ = 1.0 and λ = 1.4 are compared. It can be seen that the modified rotor produce higher torque than the original rotor at the rotation angle from 0° to 100° and from 180° to 280°. Otherwise, a lower torque than the original rotor is observed. The area with the higher torque is larger in the modified rotor at both TSRs than that in the conventional rotor, resulting in the better performance of the modified rotor, as shown in Fig. 7. More detail, at TSR = 1.0, the area with higher torque is larger at R* = 0.75 than that at R* = 0.55 in one rotation. On the other hand, by increasing the TSR, the area with
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higher torque becomes larger for R* = 0.55, resulting in higher C p at TSR of greater than 1.0.
Fig. 9. Torque distribution on rotor with R* varied at TSR: 1.0 and 1.4
4.2 Effect of Angle α Figure 10 shows the averaged C p of the rotor for cases α = 0°, α = 30°, α = 60°, and α = 90° at different TRSs. It should be noted that the R* = 0.55 is used in this subsection. Notably, the numerical simulation with α = 0° is very unstable at TSR > 0.6. The averaged data is thus plotted here only for TSR = 0.6. This can be explained by the fact that the distance between the two semicircular blades is too large, leading to strong flow fluctuation around the rotor’s blade. In addition, the rotor with α = 0° produces very low C p compared to the other cases at TSR = 0.6. Therefore, the case of α = 0° does not consider in the following sections. According to Fig. 10, with different α, the rotor results in a different peak of C p . For α = 30°, the C p gradually increases as λ increases to 1.0 before starting to decrease at higher TSRs. A better C p is produced as the α increases. A higher power coefficient is observed at the TSR ranging from 0.6 to 1.2 for α = 60° compared to α = 90°. On
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Fig. 10. Averaged C p with α varied
the contrary, at a higher TSR, the C p is rapidly reduced for α = 60°, resulting in much lower efficiency than that for α = 90°. Figures 11 and 12 depict the pressure distribution around the rotor with α = 30°, 60° and 90° at a rotation angle of 0° and 90° and TSRs of 1.2 and 1.4, respectively. The advancing blade and the returning blade are denoted as (1) and (2) on a white background as shown in this illustration figure. Regarding TSR = 1.2, at a rotation angle of 90°, the low-pressure pocket on the convex side of the advancing blade increases with the increase of angle α. The lowpressure pocket is largest with α = 90°. At the same time, the low-pressure pocket that is visualized at the smaller angle α is disappeared in the concave side of the returning blade. In addition, with the increase of angle α, the area with high pressure is increased. Therefore, a more positive pressure is added to the rotor at a rotation angle of 90°. At the rotation angle of 0°, a larger low-pressure pocket appears at the tip of the advancing blade with α = 90° comparison to the blade with a lower α angle, resulting in more positive driven torque on the rotor in this case. However, inside the concave side, the low-pressure region of α = 90° is much larger than the blade with α = 60° and 30°. Notably, the pressure distribution around the returning blade is nearly identical between the rotor with 3 different angles α. Hence, the lower torque is acted on the rotor with α = 90° at this position, as shown in Fig. 10. Similar behaviour can be seen in Fig. 12, showing the pressure contour around the rotor with α = 60° and 90° at a TSR of 1.4. In that, the low-pressure pocket is suppressed in the concave side of the returning blade with α = 90° while the larger low-pressure region is visualized on the convex side of the advancing blade with α = 90°. Thus, overall, the rotor with α = 90° has a better performance at high TSRs. More detail can be seen in Fig. 13, which shows the pressure distribution on a single blade of the rotor at a rotation angle of 0° and TSR = 1.4. Regarding blade 1 (advancing blade), a similar pressure tendency on the semicirculation profile is observed with both α = 60° and 90°. For which the minimum pressure occurs at the tip of the convex side. The lower value results for the blade with α = 90°, exhibiting higher positive driven torque on the blade. This pressure then rapidly increases along the convex side and decrease on the concave side. The negative pressure
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Fig. 11. Pressure contour around 3 rotors at TSR 1.2 and rotation angle of 0° (left) and 90° (right)
Fig. 12. Pressure contour around 3 rotors at TSR 1.4 at rotation angle of 0° (left) and 90° (right)
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difference between the convex side and the concave side is larger for the blade with α = 90° in the region x > −0, 4 m. The reason is that a longer elliptical profile creates a larger area with a low-pressure region, as shown in Fig. 11. The opposite pressure tendency appears in the elliptical part. The pressure on the concave side is higher than the pressure on the convex side when α = 90° while the reverse behaviour is visualized with α = 60°. Therefore, more positive torque is added to the blade with α = 90° while a new negative torque is acted on the blade with α = 60°. For blade 2 (returning blade), the pressure tendency is almost identical between the two cases. Regarding the semi-circular profile, the pressure at the concave side is higher than the convex side at the region 0 < x < 0, 1 and 0, 4 < x < 0, 5. This pressure discrepancy exhibits positive driven torque to the blade. At the same time, the pressure at the concave side is lower than that at the convex side in the region 0, 1 < x < 0, 4, inhibiting the blade rotation. The pressure peak corresponds to the stagnation point, as shown in Fig. 11. Regarding the elliptical profile, the pressure on the convex side is lower than that on the concave side. Thus, more positive driven pressure is added to the blade in both two cases. Since the length of the elliptical part is longer with α = 90°, a bigger driven force is produced. Hence, the better torque and performance is thus produced by the blade with α = 90°.
Fig. 13. Pressure distribution on the blade of 60° and 90° of rotors at TSR = 1.4
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The above results showed that the performance of the modified rotor is highly influenced by the blade configuration. Depending on the elliptical configuration, it can smooth the flow from blade to blade and significantly prevent flow separation, simultaneously, showing its significant impact on pressure distribution across the blade at high TSRs.
5 Conclusion This study presents the influence of the additional elliptical blade profile on the previously studied modified Savonius rotor profile. The aerodynamic performance on the modified blade was studied by 2D unstable numerical simulation using commercial CFD software ANSYS Fluent 2020R2. The analysis showed that the rotor performance is highly affected by the elliptical profile configuration. In that, the modified rotor produces different power coefficient at different TSR range regarding different R* and α. As the result, focusing on the application in urban areas where the rotor typically works at TSR higher than 1 and can be up to 1.6, this supposes that the modified rotor with R* = 0.55 and α = 90° will produce the best efficiency.
References 1. Savonius, S.J.: The S-rotor and its application. Mech. Eng. 53, 333–338 (1993) 2. Ishugah, T.F., Li, Y., Wang, R.Z., Kiplagat, J.K.: Advances in wind energy resource exploitation in urban environment: a review. Renew. Sustain. Energy Rev. 37, 613–626 (2014) 3. Akwa, J.V., Vielmo, H.A., Petry, A.P.: A review on the performance of Savonius wind turbines. Renew. Sustain. Energy Rev. 16(5), 3054–3064 (2012) 4. Altan, B.D., Atılgan, M.: An experimental and numerical study on the improvement of the performance of Savonius wind rotor. Energy Convers. Manage. 49(12), 3425–3432 (2008) 5. Mohamed, M.H., Janiga, G., Pap, E., Thevenin, D.: Optimal blade shape of a modified Savonius turbine using an obstacle shielding the returning blade. Energy Conserv. Manage. 52, 236–424 (2011) 6. El-Askary, W.A., Nasef, M.H., Abdel-Hamid, A.A., Gad, H.E.: Harvesting wind energy for improving the performance of Savonius rotor. J. Wind Eng. Indust. Appl. 139, 8–15 (2015) 7. Kang, C., Opare, W., Pan, C., Zou, Z.: Upstream flow control for Savonius rotor under various operation conditions. Energy 11(6), 1482 (2018) 8. Al-Faruk, A., Sharifian, A.: Blade overlap and blade angle on the aerodynamic coefficients in vertical axis swirling type Savonius wind turbine. In: 19th Australasian Fluid Mechanics Conference, Melbourne, Australia (2014) 9. Saeed, H.A.H., Elmekawy, A.M.N., Kassab, S.Z.: Numerical study of improving Savonius turbine power coefficient by various blade shapes. Alex. Eng. J. 58, 429–441 (2019) 10. Kollmann, T.: In: Kollmann, J.T., Kuckertz, A., Stöckmann, C. (eds.) Gabler KompaktLexikon Unternehmensgründung, pp. 223–224. Springer, Wiesbaden (2021). https://doi.org/ 10.1007/978-3-658-30901-5_10 11. Chan, C.M., Bai, H.L., He, D.Q.: Blade shape optimization of the Savonius wind turbine using a genetic algorithm. Appl. Energy 213, 148–157 (2018) 12. Tian, W., Mao, Z., Zhang, B., Li, Y.: Shape optimization of a Savonius wind rotor with different convex and concave sides. Renew. Energy 117, 287–299 (2018)
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13. Kamoji, M.A., Kedare, S.B., Prabhu, S.V.: Experimental investigations on modified Savonius rotor. Appl. Energy 86(7–8), 1064–1073 (2009) 14. Kacprzak, K., Liskiewicz, G., Sobczak, K.: Numerical investigation of conventional and modified Savonius wind turbines. Renew. Energy 60, 578–585 (2013) 15. Roy, S., Saha, U.K.: Review on the numerical investigations into the design and development of Savonius wind rotors. Renew. Sustain. Energy Rev. 24, 73–83 (2015) 16. Lee, J.-H., Lee, Y.-T., Lim, H.-C.: Effect of twist angle on the performance of Savonius wind turbine. Renew. Energy 89, 231–244 (2016) 17. Hassanzadeh, R., Mohammaddnejad, M., Mostafavi, S.: Comparision of various blade profile in a two-blade conventional Savonius wind turbine. ASME J. Energy Resour. Technol. 143(2), 021301 (2021) 18. Laws, P., Saini, J.S., Kumar, A., Mitra, S.: Improvement in Savonius wind turbines efficiency by modification of blade design-a numerical study. ASME J. Energy. Resour. Technol 142(6), 061303 (2020) 19. Abraham, J.P., Mowry, G.S., Plourde, B.P., Sparrow, E.M., Minkowycz, W.J.: Numerical simulation of fluid flow around a vertical-axis turbine. J. Renew. Sustain. Energy 3, 033109 (2011). https://doi.org/10.1063/1.3588037 20. Borzuei, D., Moosavian, S.F., Farajollahi, M.: On the performance enhancement of the threeblade Savonius wind turbine implementing opening valve. ASME J. Energy. Resour. Technol 143(5), 051301 (2021) 21. Mahmoud, N.H., El-Haroun, A.A., Wahba, E., Nasef, M.H.: An experimental study on improvement of Savoniusrotor performance. Alex. Eng. J. 51, 19–25 (2012) 22. Blackwell, B.F., Sheldahl, R.E., Feltz, L.V.: Wind tunnel performance data for two and threebucket Savonius rotors. In: SAND76-01321 UC-60, National Tech. Information Service. U. S. Dept. Commerce, Springfield (1977) 23. Yang, M.-H., Huang, G.-M., Yeh, R.-H.: Performance investigation of an innovative vertical axis turbine consisting of deflectable blades. Appl. Energy 179, 875–887 (2016) 24. Ansys Fluent 12.0 Theory Manual 25. Anh D.L., Minh B.D., Tam H.V.: A Modified Novel Blade Shape for Improving the Efficiency of Savonius Wind Turbine for Urban Application (under review)
Using Artificial Neural Network to Grade Internal Quality of Coconuts Based on Density Nguyen Tran Trung Hieu, Nguyen Minh Trieu, and Nguyen Truong Thinh(B) Department of Mechatronics, Ho Chi Minh City University of Technology and Education, Thu Duc City, Ho Chi Minh City, Vietnam [email protected]
Abstract. Evaluating and grading are an important stage after fruit harvesting because it affects the value of the product. An intelligent sorting and handling system is essential to ensure the yield and quality of the fruit. The application of automated and intelligent systems allows businesses to increase productivity, reduce labor and achieve high accuracy in processing stages. Currently, there is a lot of research related to the evaluation and classification of foods, especially fruits. Coconut is a fruit with delicious taste, many nutrients, and multi-use, so it is widely used all over the world. Therefore, a large amount of coconut needs to be exported to the international market to meet the demand. Each market has its own criteria for evaluating coconuts and the quality of the coconut is extremely important. This paper proposed an automatic and intelligent coconut grading model based on the artificial neural network (ANN). The system used image processing to extract the features of the coconut and feed those features to the input of the ANN. Then, the ANN system processed and classified into different types of coconuts. Through many experiments and adjustments, the classification system based on ANN achieved an accuracy of more than 98%, this system meets the classification requirements in the localities. Keywords: Fruit sorting · Grading · Classification · Coconut · ANN · AI
1 Introduction According to the Asia-Pacific Coconut Community (APCC) and the Food and Agriculture Organization of the United Nations (FAO), coconut trees have been planted in 90 tropical nations, covering 12.02 million hectares in 2010 and 12.200 million hectares in 2016, in which the coconut area of the Asia-Pacific countries is 11 million hectares and accounts for 85% of the total coconut area in the world. The top three coconut producers in the world - Indonesia, the Philippines, and India - account for more than 75% of the global coconut area [1]. Coconut has various nutritional components that are beneficial to human health, hence it is utilized to make a variety of high-nutrition goods such as coconut milk [2], coconut oil [3]. Coconut water is an ingredient of coconuts that is used a lot. Due to the good properties of the coconut, it is widely used as another beverage, a potential sports drink, and rehydration [4]. Coconut is one of the fruits with a high © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 413–423, 2022. https://doi.org/10.1007/978-981-19-1968-8_33
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proportion of exports in Vietnam. According to a survey in some localities, coconut production facilities and businesses in Vietnam currently still clean coconuts manually and evaluate coconut quality based on worker’s experience. The demand for coconut in the country and for export is large. This requires coconut production facilities to recruit a large number of experienced workers in cleaning and evaluating coconut quality. The use of many workers causes many limitations for production facilities. The high wages for workers lead to an increase in the price of coconuts. The cleaning and sorting of coconuts by human labor leads to low productivity, the classification of products based entirely on personal experience causes many difficulties and low accuracy. This requires a fully technology-based grading system that can improve productivity in manufacturing facilities. The use of technical technology to automatically clean and classify coconuts not only boosts productivity but also improves sorting accuracy and reduces subjective creation errors. Automated and intelligent systems play an increasingly important role in most fields, especially agriculture. The application of automatic and intelligent sorting systems after harvest is inevitable to ensure quality as well as productivity. Therefore, there have been many studies related to automatic fruit classification systems for not only coconuts but also many other fruits. A method of determining the ripeness of coconuts by noninvasive sensing method based on machine vision [5]. The system uses an ANN with 14 inputs and 3 outputs indicating the degree of coconut maturity. A study using fuzzy logic to assess the ripeness of coconuts [6]. The input of fuzzy logic uses color and sound to determine the color of the coconut and the relationship between shell hardness and flesh condition. From there, determine the ripeness of the coconut. Another model for classifying coconut maturity based on acoustic signals was also investigated [7]. This model is based on three machine learning techniques: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The input of each model is the transformed frequency spectrum samples of each sample signal to predict the fruit type. Coconuts are widely exported to international markets, so that coconuts can be evaluated and graded according to different standards to ensure the quality of the coconut. In this study, we propose the application of digital image processing and machine learning to classify coconuts, this solves the classification problems of workshops. Coconut images are collected in the image processing chamber, and they are extracted the features of the height and width of the fruit. The formulas are proposed to calculated volume and density and the error of the calculated process is very small ( 0.7
Type 2
0.57 < d < 0.7
Type 3
d < 0.57
4 Artificial Neural Network for Grading the Quality of Coconut Along with the development of science, more and more research on artificial intelligence (AI) has been applied, especially in the field of assessment and classification of agricultural products using artificial neural networks (ANN) such as classification of pomegranate [9], mango [10], lemon [11], apple [12] achieving high accuracy and applicability. Artificial Neural Networks (ANN) [13] is a computational system inspired by biological neural networks. ANNs use a network of functions to understand and transform a series of input data into desired outputs. ANN minimizes errors for nonlinear inputs and shows a relationship between input and output without complex mathematical equations. The ANN has three main components: an input layer, several hidden layers, and an output layer. The hidden layer is the layer between the other two layers, where the neutrons take a set of weighted input values and generate the output through an activation function. This study uses a set of input variables including weight, width, height, density and volume to assess and classify the quality of coconuts into 3 types: T1, T2, T3 with T1 is the best type. The structure of the ANN model in this study is shown in Fig. 5.
Fig. 5. The structure of an used model of ANN.
In the input and output layer, the number of nodes corresponds to the number of input and output variables. In this study, the input layer has 6 nodes representing extracted objects such as weight, length, height, volume, density, cultivar and a node in the output layer is the type of coconut. Compared with input and output layers, the design of the
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hidden layer is more complicated. The hidden layer has the function of receiving and processing information coming from the input layer. The number of hidden layers and the number of nodes differ among layers, these layers have different connections. The hidden layer has two main hyperparameters that control the architecture of the network: a number of layers (NL ) and a number of nodes in each hidden layer (Nn ), these two parameters are different for each layer. For an ANN system, the link weight is a very important component because it represents the strength of the input data. The learning process of ANN is essentially the process of adjusting the weights to get the desired result. Let W, b, and y be the weight matrix, bias vector, and output vector of each class. The output of the ith node is calculated in Eq. (8). yi = σ (Wij xi + b)
(8)
where σ is an activation function. The performance of the training process is determined by finding the difference between the ANN output and the correct output. The loss function is calculated (9) based on the mean square error (MSE). 1 E= (yj − yj )2 2p p
(9)
i=1
The weights are updated consecutively so that the loss function approaches the extreme value. The weights and biases are updated according to Eq. (10), corresponding to the learning rate. During each iteration, the learning rate controls how fast it moves to the minimum point. Through iterations, the downward slope converges at the minimum, it provides the best weight and bias. The coconut classification output prediction is determined by the Eq. (11). (n+1)
Wij
(n)
= Wij − α
T=
∂ (n) E(Wij ) ∂WijL
(Wk xk ) + bk
(10) (11)
5 Experiments and Discussions According to local surveys, coconut has the highest water content when the coconut is 5–7 months old, coconut contains many natural bioactive enzymes, minerals, B-complex vitamins, vitamin C,… High nutrients coconut is favored in the international market. The evaluation and classification of coconuts is a necessary task to be able to export coconuts to other markets in the world. According to local surveys, coconuts are classified into 3 types: T1, T2, T3. Classification of coconuts based on the factors of length, width, the volume of the coconut, the actual weight of the coconut obtained from the load cell sensor. This study considers the density factor to predict the water content inside the coconut, thereby providing a set of criteria to classify the coconut, which varies with
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each different type of coconut. This study used image processing to extract features of coconuts and used ANN to evaluate and classify coconuts. For the process of extracting the characteristics of the coconut fruit, after recording images from the camera, a number of methods are applied to extract features of coconut such as converting images to grayscale images, threshold, binary images, contours. The experimental results of the image processing stage are shown in Fig. 6. The actual size of the coconut is calculated by the size of the pixel multiplied by the size of the pixel. The experimental results are used to determine the coefficient of pixels in this system, the actual size is calculated as formula (12). However, image processing can be affected by noise. Noised image is the cause of errors in the extraction process. Therefore, removing noise is one of the important steps of image processing to get the best results. This paper uses sumof-variance conditioning [14] to filter out noise, through which unwanted details are removed while important details such as edges are retained. Lr = Lpixel · 2 · 638 (mm)
(12)
where L r is the calculated size (mm), L pixel is the size in pixels(pixel). After image processing, the features of coconut such as width and height are extracted. To evaluate the accuracy of the coconut dimensions calculated from the image processing, we compared those dimensions with the actual size of the same coconut. The evaluation was performed on 113 locally collected coconuts and the results are shown in Fig. 8, the error between the size extracted from the image and the actual one is very small (180 μW
Beam diameter
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Figure 5 describes the actual image of the graduation line captured on the camera. The distance from the graduation line to the centerline of the camera image was calculated by the number of pixels. Finally, the software collects the results displayed on the laser software in combination with the above-calculated distance to give the relative distance value at the position of the line being measured. The size of the image area is (100 × 100) mm. During the calibration process the camera captures an image of the graduation lines as the track moves, hence the camera is set to a short exposure time.
Fig. 5. Actual image of graduation line captured on camera. 1. Separated graduation line; 2. The number of pixels; 3. The centerline of camera image.
Environmental conditions of the system are stabilized at temperature: (20 ± 1) °C and humidity: (60 ± 20) %RH. Scope
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0.050 0.040 0.030 0.020 0.010 0.000 0 100 200 300 400 500 600 700 800 900 1000 1100 1200
Average error values (mm)
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Calibrated position (mm)
Fig. 6. Levelling staff average error values
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0.005 0.004 0.003 0.002 0.001 0 100 200 300 400 500 600 700 800 900 1000 1100 1200
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Fig. 7. Levelling staff error standard deviations
The software supporting the measurement process is written in Python language, operating on Windows 10 operating system. The core principle of the software is based on two algorithms Threshold and Find Contour of OpenCV library. To compare with the previous method - using a microscope to detect the graduation lines, the method of using the camera in combination with image processing software was performed on the same Levelling staff with a length of 1.2 m. The process was strictly executed according to the line scale calibration procedure at VMI. After mounting the staff and calibrating the camera, the process started with detecting the zero position at the first graduation line. Then the measurement was processed along the staff with a step of 100 mm until the whole length of 1200 mm had been measured. For each different method, the measurement process was carried out at least 5 times, with stable environmental conditions. The average results in Fig. 6 show that the error of the staff tends to be similar for both methods, with a difference of ≤0.012 mm. On the other hand, as shown in Fig. 7, the mean standard deviation of the camera method is much smaller than that of the old method, 0.015 mm versus 0.035 mm. The maximum standard deviation, when measured with a camera, is 0.024 mm. While the maximum standard deviation while using a microscope lens is 0.058 mm. From here we can see that the uncertainty of repeatability urep has been significantly improved while using the camera and image processing software to calibrate the Levelling staff. Besides, the measurement accuracy is improved when using high resolution camera as well. Specifically for cameras with a resolution of (3840 × 2160) pixels, with a field of view of (100 × 100) mm, the line detection method can be as accurate as the corresponding size of each pixel of 0.006 mm. Measurement accuracy is also likely to be further enhanced if in the future it is combined with a magnifying lens and a higher resolution camera. Compared with the previous method, this is an improvement that helps the uncertainty of standard resolution ures of the measurement to be significantly improved. The uncertainties for the calibration of the Levelling staff are shown in the following Table 2.
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Table 2. Uncertainties of the calibration method [8, 9] Sources of uncertainty
Symbol
ui
|ci |
Laser wavelength
us
1
Repeatability
urep
Standard resolution
ures
Line aiming error
ucam
Temperature of the Levelling staff
uT
Temperature expansion coefficient of the Levelling staff
uα
Us k srep √ n d √ 2 3 s√ cam 2 T √ 2 3 α √ 3
1 1 1 α×L T × L
In which: ui – uncertainty formula ci – sensitivity coefficient us – uncertainty of the wavelength of the laser head Us – uncertainty of the laser head provided by the manufacturer k – coverage factor urep – uncertainty of repeatability srep – standard deviation of measurement n – number of trials ures – uncertainty of standard resolution d – standard resolution ucam – uncertainty of line aiming error scam – difference between forward and backward measurement results uT – uncertainty of the temperature of the Levelling staff T – difference between temperature of the Levelling staff and standard temperature (20 °C) α – thermal expansion coefficient of the Levelling staff uα – uncertainty of the thermal expansion coefficient of the Levelling staff α – accuracy of the thermal expansion coefficient of the Levelling staff. The practice shows that the Levelling staffs are very diverse in size, more specifically in width and thickness. Therefore, mounting the Levelling staff with the systems using microscopes can be challenging due to the allowed distance from the surface of the staff to the microscope lens being short and limited.
4 Conclusion To ascertain the effectiveness of combining camera and image processing software in Levelling staff reading and calibrating, the results of the process were directly compared with the previous method in which using conventional microscope lenses on the same metrology standard system, with the same Levelling staff in similar stable environmental conditions. The similarity between the results of both methods proved that
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combining camera and image processing software is highly reliable. The difference in average standard deviation between the two methods has shown the ability to improve the uncertainty of the measurement by using camera and image processing software. In addition, the high resolution of the camera and its ability to furtherly enhance accuracy with magnifying lens make this method more accurate than the previous one. The integration of a high-precision camera and image-processing software into the line reading method enables calibration of various types of Levelling staff. Improved accuracy and automatic calculation greatly save effort and time in performing Levelling staff reading and calibrating.
References 1. Mitutoyo-Metrology Handbook. The Science of Measurement. Mitutoyo Asia Pacific Pte. Ltd., Singapore (2006) 2. Uncertainty Budget Line Standard Invar Standard Tape. China National Metrology Insitute (2018) ´ 3. J.G.P.C.K.R.J., Kuchmister, B.G.: A Functional-Precision Analysis of the Vertical Comparator, vol. 163, no. 107951. Elsevier (2020) 4. C.-T.W.M.-W.C., Chun-Sung Chen, W.-C. C.: Establishing an invar leveling calibration system. J. Chinese Inst. Eng. 35, 861–866 (2006) 5. Woschitz, H.B.F.: System calibration of digital levels – experimental results of systematic effects. In: INGEO2002, 2nd Conference of Engineering Surveying, pp. 165–172 (2002) 6. Agilent 5529A Dynamic Calibrator. Agilent Technologies (2001) 7. Chaper 5 Laser Heads. Agilent Technologies (2002) 8. Standard Levelling Staffs – Calibration Procedure. Vietnam Metrology Institute (2021) 9. 1. I. 9.-6.-1.-9. GUM-Guide to the Express of Uncertainty in Measurement. s.n., Switzerland
A Method for Cooling Bearings of Motorized Spindles Xuan Quang Ngo3 , Hoang Long Phan1,2,3 , Van Tu Duong1,2,3 , Huy Hung Nguyen3,4 , and Tan Tien Nguyen1,2,3(B) 1 Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT),
268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam [email protected] 2 Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam 3 National Key Laboratory of Digital Control and System Engineering (DCSELab), HCMUT, 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam 4 Faculty of Electronics and Telecommunication, Saigon University, Ho Chi Minh City, Vietnam
Abstract. Thermally-induced loads in motor built-in the spindle and bearings can cause several negative effects that lead to downgrading the efficiency of the spindles at high-speed working. For this reason, this paper carries out the cooling performance behavior research of spacers, taking into account different structures of the cooling spacer, and the different flow rates of cooling air, and different positions of the cooling spacer. A motorized spindle model lubricated by oil-air is simulated with the high-speed operating conditions which are commonly found in the machine tool main spindles. The simulation results illustrate the power of cooling spacers over lowering the heat of the bearings in the motorized spindles. Keywords: Motorized spindle · Cooling air · Cooling spacer · Back-to-back bearing arrangement · Tandem back-to-back arrangement · Drag force
1 Introduction The growing demand for producing small, tight-tolerance parts and high productivity in the industry requires increasingly high-speed machining [1]. High-speed machining is a metal cutting process that applies rapid speed and feed rates to enhance productivity and surface quality [2]. This demand for high-speed and high-efficiency machining recognizes a spindle which is one of the main components of high-speed machining, to decide the machining speed of the machine tool to perform high-speed and high power machining [3]. As a result, the bearings, one of the critical constituents of the high-speed spindle influences directly on the spindle’s speed and rigidity, become more and more strict about the ability to operate in the high-speed condition [4]. When the bearing operates at the high speed, the heat generation coming from the bearing increases [4]. There are some researches suggest the methods that help to decrease the harmful effects of heat on the bearings. For example, Arakami, H. takes the advantage of lower temperature © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 447–457, 2022. https://doi.org/10.1007/978-981-19-1968-8_36
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rise and higher speed ability of ceramic ball bearings relative to steel ball bearings for high-speed spindles [5]; Wu, C. H. and Kung, Y. T. proposes the procedure for designing the set of parameter of the oil-air lubrication of high-speed spindles’ bearings for the optimum lubrication condition by the Taguchi method [6]; Ngo, X. Q et al. applies the Lagrange multiplier-based approach for distributing tolerance of the motorized spindle judiciously, then from that result a suitable axial displacement of single row cylindrical roller bearings is designed as the support bearing at the rear of the motorized spindle for compensating the axial displacement of spindle shaft resulting from the heat generation in the spindle [7]. This paper solves the heat problem of the bearing by taking away these heat. The heat produced in the spindle bearings, which is capable of causing thermal distortion and cutting error, needs to be dealt with due to the proper operating condition [8]. The solution to cool the bearings should be considered based on the specific applications of the spindle. In [8], Judd, R. L. et al. equips a heat pipe in the spindle of a milling machine driven through a belt and pulley arrangement to remove the heat-induced of the spindle bearings. The results of the experimental investigation show that the bearings still worked well up to approximately 3000 rpm. The reference [9] discussed how Gebert, K. uses heat shields or, in some cases, one associated with additional cooling channels resulting in the passive protection of the bearings against thermal influences of the motor. Since the inner bearing rings are thermally indirectly coupled to the built-in motor by the spindle shaft in the high-speed spindles, such measures do not provide complete thermal protection. In recent years, the built-in motor integrated into the motorized spindle structure between front and rear bearings has emerged in manufacturing industries. In this structure, vibration and noise of the spindle are significantly reduced, and the motorized spindle can work at a higher rotation speed, i.e., more than 15000 rpm [10]. For these types of spindles, instead of using grease to lubricate the bearings, oil-air lubrication is more suitable because it not only lubricates but also cleans and cools the bearings [10]. By adjusting oil-air lubrication parameters, we have a further possibility for actively reducing the bearing temperature. An airflow carrying small oil droplets is injected into the contact point between balls and rings of the bearings by nozzles. The content of oil in the oil-air mixture is very low, and the heat taken away by the lubricating oil can be neglected. Thus, it can be assumed that the oil is used for lubrication, and the compressed air is used for heat transfer [10]. However, the reference [11] say that the quantity of air transporting the oil droplets to the oil-air nozzles must be within a certain value range. For instance, the airflow rate required for carrying the oil droplets properly in an internal diameter of 3 mm ranges from about 1000 to 1500 L/h, corresponding to oil viscosity classes ISO VG 32 to ISO VG 100 [11]. A higher viscosity or different adhesiveness oil requires more increased airflow, so the ability cooling of this method is limited [11]. In order to keep improving the proficiency in cooling the bearings, there are some researchers such as Baldwin, HJ in [12] and Onda, Y in [13] using the ring-shaped spacer with air cooling nozzles to cool themselves, and the temperature of the bearings, which is assembled adjacent to the spacer, is consequently lowered. In this paper, inspired by the above mentioned, we combine the cooling and lubricating flow into one spacer but still be guided by separate channels to put aside the downside of the cooling method by
A Method for Cooling Bearings of Motorized Spindles
Oil-Air Nozzle
Air Cooling Nozzle
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A Air gap
Cooling Air (Inlet)
Outer Spacer Inner Spacer
Cooling Air (Outlet) Bearing
Stator
Rotor A
Fig. 1. Structure of the bearing with air cooling spacer of the motorized spindle
oil-air lubrication and then apply them to the motorized spindles’ bearings. In Sect. 2, the optimal structure of the cooling spacer is studied. Then, Sect. 3 inspects the cooling efficiency of the spacer for the bearings of the motorized spindle. Finally, further potential works are discussed in Sect. 4.
2 Structure of the Cooling Spacer The structure of the motorized spindle uses the spacer to both cool and lubricate the bearing, as shown in Fig. 1. The cooling air is supplied by the inlet pipe to the air cooling nozzle in the outer spacer between the angular contact ball bearings in a back-to-back arrangement (DB arrangement) to cool the inner spacer. In addition, separate inlet pipes are included so as to transfer oil-air to oil-air nozzle for lubricating the bearings. The compressed room temperature air is firstly injected from air cooling nozzles in the outer spacer. Then, it goes into the air gap between the outer spacer and inner spacer, revolving in the rotational direction of the inner ring, as well as the inside of the bearing. At this time, the cooling air removes heat from the surface of the inner ring spacer and consequently pushes the heat out of the bearing. Finally, the airflow escapes to the other space of the motorized spindle. Increasing the air flow rate enhances the heat transfer the surface of the cylinder, which is the surface of the inner spacer in this case, and the cooling air flow [14]. Therefore, the position of the cooling nozzles, namely the Offset in Fig. 2, plays an essential role in the cooling efficiency (Table 1).
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Outer Spacer Inner Spacer
Air gap between outer and inner spacer Rotational Direction of Inner Spacer
Fig. 2. Structure of the air cooling spacer (A-A view)
Table 1. Test parameters for Set 1, Set 2 and Set 3 Bearing
φ110 × φ70 × 20 Oil-air lubricated angular contact ball bearing with ceramic balls
Flow rate of Inlet
50 m/L
Pressure of Outlet
1.013 bar
Speed rotation of inner spacer and bearings
17000 rpm
Number of cooling nozzles
3 nozzles
Spacer width
25 mm
Spindle direction
Vertical spindle
The simulation results in Fig. 3 show the different air velocities in the same air gap corresponding to three positions of the air cooling nozzle with an equal amount of cooling air supply. The area of the air gap between outer and inner spacer in A-A view, described in Fig. 2 and used to evaluate the Set 1, Set 2 and Set 3 shown in Fig. 3, is equal, resulting in the flow rate of cooling air increasing in proportion to the velocity of the cooling air in this area; thus, we can use the velocity quantity to evaluate the cooling quality instead of the flow rate when changing the position of cooling nozzles. Three simulated evaluations are performed as follows: For Set 1, no offset shows that the flow rate of cooling air is low at one time. Because the cooling air collides with the inner ring spacer’s surface in the direction perpendicular to each other, it escapes to other areas of the spindle quickly as shown in Fig. 4. For Set 2, offset 50% of the outer radius of the inner ring spacer, the more flow rate of cooling air keeps in the air gap than Set 1. For Set 3, offset 80% of the outer radius of the inner ring spacer. The flow rate of cooling air in this solution is significantly higher than the others. After the cooling air is injected into the air gap between the outer spacer and inner spacer, it tends to move on the inner ring surface in the direction of inner ring rotational direction due to the offset of the cooling nozzle from the center of the inner ring spacer, which leads to the more
A Method for Cooling Bearings of Motorized Spindles
a) Set 1: No offset
b) Set 2: Offset 50% of the outer radius of the inner ring spacer
c) Set 3: Offset 80% of the outer radius of the inner ring spacer Fig. 3. Cooling airflow in space between the inner and outer spacer view
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Inner Spacer
Outer Spacer
Air Gap
No Air
Fig. 4. Flow trajectories of the cooling air for Set 1 Air Noozle Inner Spacer
Outer Spacer
Air Gap
Housing Spindle
Cooling Air
Fig. 5. Flow trajectories of the cooling air for Set 3
flow rate of the cooling air on the surface as shown in Fig. 5. Therefore, the effect of removing more heat from the inner ring spacer surface during that time increases. Due to the better effective cooling for the inner spacer, the difference in temperature between the bearing inner ring and the outer ring is reduced indirectly, which is verified
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in Sect. 3. Also, the contact stress on the raceway surface decreases to allow for both higher speed and higher rigidity. Note: This paper uses the spacer with three nozzles to focus on describing how the cooling capacity is effected when offsetting the air nozzles instead of building the nozzle as usual, such as Set 1. Other simulations should be conducted to figure out the optimal number of the air cooling nozzles. The simulations are conducted by modifying the Offset from 0% to 90% with an increase of 10%. The results for 80% and 90% Offset are almost the same, and the results for 60% and 70% are higher insignificantly than 50% Offset.
3 Cooling Effects on Bearing Arrangements In this section, the above structure of the cooling air spacer, described in Sect. 2, is applied for the motorized spindles’ two popular bearing arrangements, namely back-toback bearing (DB) and tandem back-to-back (DTDB) arrangements, in order to evaluate the cooling efficiency of the cooling spacer for these bearing arrangements [15]. The cooling effect on the DB arrangement bearings with air cooling spacer is simulated in Fig. 6 with test condition as shown in Table 2, and the configuration of the tester is illustrated in Fig. 1. The simulation uses the temperature difference between inner and outer ring bearings, namely, point A and point B described in Fig. 6, which are the contact point between the ball and the bearing raceway surface of the outer ring bearing and inner ring bearing respectively, as the point from which the cooling strength of the spacer makes sense. Four simulated evaluations are performed as follows: Set 4: No cooling airflow rate Set 5: cooling airflow rate 50 NL/min Set 6: cooling airflow rate 75 NL/min Set 7: cooling airflow rate 100 NL/min The simulation results showed in Fig. 6, inner/outer ring temperature difference at 17000 rpm decreases by approximately 3 °C with cooling air of 50 NL/min, approximately 6 °C with cooling air of 75 NL/min, approximately 9 °C with cooling air of 100 NL/min compared with no cooling air conditions. The temperature difference is significantly reduced when increasing the amount of cooling air. This helps to reduce the contact stress on the bearing surface, allowing the motorized spindle to operate at a higher speed. However, the cooling capacity of this method is also limited to the quantity of the cooling air. If the heat generation coming from the motorized spindle reaches a certain high value, demanding more airflow to cool the temperature down, it should be assured that the drag force between the motion object and the surrounding air in the spindle, it is proportional to the velocity of flow, is interested in. The drag force is supposed to have effects on the performance of the ball bearings according to the investigation of Marchesse, Y. [16]. In that case, an alternative method should be considered to integrate with the air cooling spacer so that the cooling jacket helps lower the temperature of the built-in motor.
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Set 7 Set 6 Set 5 Set 4
Point A Point B
-1
1
3
5
7
9
11
13
15
Fig. 6. Temperature difference between the inner ring and outer ring of the bearing
Table 2. Test condition for DB and DTDB arrangement Bearing
φ110 × φ70 × 20 Oil-air lubricated angular contact ball bearing with ceramic balls
Power loss of stator
1553 W
Power loss of rotor
767 W
Bearing heat generation
262,5 W each bearing for DB arrangement 150 W each bearing for DTDB arrangement
Speed rotation
17000 rpm
Amount of lubricant
30 L/min
Spindle direction
Vertical spindle
Spacer width
25 mm
Following the evaluation test with DB arrangement, the cooling effects on the bearings under the DTDB arrangement with air cooling spacer are also conducted. The configuration of the tester is shown in Fig. 7. The results of three simulated evaluations are illustrated in Fig. 8 with the test condition as shown in Table 2. For Set 8, no cooling spacer, the bearings are still lubricated and cooled by the oil-air nozzle. It provides the larger inner/outer ring temperature differences are observed on the bearings closer to the motor, i.e., approximately 18 °C at bearing 3 and 19.6 °C at bearing 4. Therefore, the cooling spacer to cool down bearing 3 and bearing 4, which show larger inner/outer ring temperature differences and higher maximum contact stress on the raceway surface, are considered in Set 9 and Set 10. For Set 9, the cooling spacer is located in the center of the DTDB arrangement, the temperature difference of the bearings 3 and 4 is reduced, but the bearing 4 is still hotter than the other bearings significantly due to the impact of the built-in motor.
A Method for Cooling Bearings of Motorized Spindles DTDB Bearing arrangement with cooling spacer
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Built-in Motor Cooling Air (Inlet)
3 4
1 2
Cooling Air (Outlet)
Fig. 7. Test spindles for DTDB arrangement
Cooling Spacer M
Motor
Set 10
M
M
Set 9
M
Set 8
0
5 Bearing 4
10 Bearing 3
15 Bearing 2
20
25
Bearing 1
Fig. 8. The influence of the position of the cooling spacer on the bearing temperature
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For Set 10, the cooling spacer is moved in the middle of the DB arrangement which is closer to the built-in motor. In this case, we focus on cooling the bearings affected greatly by the built-in motor, which leads to the temperature difference of the bearing 3 and bearing 4 is significantly low, i.e., approximately 7.4 °C and 8.6 °C, respectively. From the above simulation, the results verify that the solution, concentrating the cooling air supply on the DB arrangement near the built-in motor as Set 10, can be intensively cooled down a large amount of heat in bearing 3 and bearing 4.
4 Conclusion The main contribution of this paper is to perform the change of the productivity in cooling the bearings of the motorized spindles when modifying the position of the cooling air nozzles in the cooling spacer, the flow rate of the cooling air, and the position of the cooling spacer in the bearing arrangement. Based on the simulation results, the cooling efficiency of the spacer having three nozzles with the same air supply provided promising results when the cooling nozzles are offset approximately 80% of the outer radius of the inner ring spacer. By applying the proposed solution, the temperature difference between the inner ring and outer ring of the bearing is significantly reduced, allowing to improve the speed of the spindles. And, in the case of using the DTDB bearing arrangement for the motorized spindle, the cooling spacer should be located between the DB arrangement near the built-in motor in order to optimize the cooling efficiency. However, because of the limit on the air gap area between the inner ring spacer and the outer ring spacer, the amount of cooling air supply is also limited, which leads to the restriction on the cooling power of this method. Consequently, the cooling air spacer should be referred to as the auxiliary method. It is able to be combined with other cooling methods such as the oil-air lubrication, the cooling jacket for the built-in motor, the cooling jacket for the bearings, etc., to ensure the high-speed working condition of the motorized spindles. Further potential works in this investigation should focus on the maximum speed of the motorized spindle can be achieved thermally when coordinating the different cooling methods and the maximum cooling air flow rate of the cooling spacer is able to be used without degrading the performance of the bearing. Acknowledgments. This research is supported by DCSELAB and funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number TX2022-20b-01. We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, for this study. This research is also funded by Department of Science and Technology under grant number 22/2019/HÐ-QPTKHCN.
References 1. Jain, A., Bajpai, V.: Introduction to high-speed machining (HSM). In: High-Speed Machining, No. January 2020, pp. 1–25 (2020)
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2. Schulz, H.: High-speed machining. In: Dashchenko, A.I. (ed.) Manufacturing Technologies for Machines of the Future, pp. 197–214. Springer, Heidelberg (2003). https://doi.org/10. 1007/978-3-642-55776-7_7 3. Chen, J.S., Chen, K.W.: Bearing load analysis and control of a motorized high speed spindle. Int. J. Mach. Tools Manuf. 45(12–13), 1487–1493 (2005) 4. De Lacalle, N.L., Mentxaka, A.L. (eds.): Machine Tools for High Performance Machining. Springer, London (2008). https://doi.org/10.1007/978-1-84800-380-4 5. Aramaki, H., Shoda, Y., Morishita, Y., Sawamoto, T.: The performance of ball bearings with silicon nitride ceramic balls in high speed spindles for machine tools. J. Tribol. 110(4), 693–698 (1988). https://doi.org/10.1115/1.3261715 6. Wu, C.H., Kung, Y.T.: A parametric study on oil/air lubrication of a high-speed spindle. Precis. Eng. 29(2), 162–167 (2005) 7. Ngo, X.Q., Duong, V.T., Bui, T.L., Nguyen, H.H., Nguyen, T.T.: An Optimal Method for Distributing Tolerance of Milling Spindle’s Components, ICIUS, p. 43 (2021) 8. Judd, R.L., Aftab, K., Elbestawi, M.A.: An investigation of the use of heat pipes for machine tool spindle bearing cooling. Int. J. Mach. Tools Manuf. 34(7), 1031–1043 (1994) 9. Denkena, B., Bergmann, B., Klemme, H.: Cooling of motor spindles—a review. Int. J. Adv. Manuf. Technol. 110(11–12), 3273–3294 (2021) 10. Wu, Y., Zhang, L.: Intelligent Motorized Spindle Technology. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3328-0 11. SKF Manufacturing Company. Product Series OLA, MV and 161 12. Baldwin, H.J.: Machine tool spindle cooling system, Patent no. US 3,221,606 A (1964) 13. Onda, Y., Mizutani, M., Mori, M.: Machine tool main spindle bearings with air cooling spacer. NTN Tech. Rev. 8038-41 (2002) 14. Seghir-Ouali, S., Saury, D., Harmand, S., Phillipart, O., Laloy, D.: Convective heat transfer inside a rotating cylinder with an axial air flow. Int. J. Therm. Sci. 45(12), 1166–1178 (2006) 15. SKF Manufacturing Company. Super precision bearings (2016) 16. Marchesse, Y., Changenet, C., Ville, F.: Numerical investigations on drag coefficient of balls in rolling element bearing. Tribol. Trans. 57(5), 778–785 (2014)
Analysis Hydrodynamic Performance of the Autonomous Underwater Vehicle for a Different Hull Shape Ngo Van He(B) , Vu Ngoc Tuan, and Ngo Van Hien Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Autonomous Underwater Vehicles (AUV) provide a useful tool to explore the detailed oceanography and other activity in ocean engineering. Hydrodynamic performance of an AUV and especially it’s hull resistance is an important factor in determining the power requirements and design hull shape for the AUV. This topic presents a procedure using a commercial Computational Fluid Dynamic (CFD) for determining hydrodynamic performance and hull resistance of the AUV with a different hull shape. The three different hull shapes are proposed to estimate hydrodynamic performance and hull resistance by using the CFD. From the results of analysis hydrodynamic performance of the AUV as well as pressure, velocity distribution around hull and resistance hull shape of the AUV, the detailed has been clearly. Keywords: AUV · Hydrodynamic · Hull · Resistance · CFD
1 Introduction Study on development of an Autonomous Underwater Vehicles (AUV) is popular topic. Today, the AUVs have been used in many fields such as explorer ocean engineering, oceanographic research, military applications and recently offshore engineering. To development of an AUV, understanding hydrodynamic performance of the AUV in the underwater environment is still important point for the researchers [1–5]. The results of hydrodynamic performances of the AUV are the basic data to design power, furniture propulsion, to determine the control structure and specify the physical component for the AUV [1, 3, 5–7]. The hydrodynamic performances of the AUV has been investigated by many different method and tools. Experimental model test is popular method to investigated the AUV hydrodynamic performance and resistance acting on hull of the AUV [7–12]. However, using a commercial Computational Fluid Dynamic (CFD) to investigate the hydrodynamic performances of the AUV has been still most popular and used by many researcher. Most research applied the CFD is based on the Reynolds averaged Navier-Stockes (RANS) equation because it can treat viscous effects much better than others models [9, 10, 12–14]. In many previous published papers, the CFD were used to estimate hull resistance and optimization hull shape of the AUV. Others one, the CFD © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 458–468, 2022. https://doi.org/10.1007/978-981-19-1968-8_37
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were used to analysis hydrodynamics performances of the AUV to investigate the power and physical components or to design control system for the AUV [1, 3–5, 7, 15]. In this study, we present a procedure using the CFD to investigate hydrodynamic performances of the AUV with a different hull shapes. The three different hull shapes have been used to analysis the hydrodynamic performances and hull resistance of the AUV in the different condition. From the results, the effects of hull shape on hydrodynamics performances and hull resistance acting on the AUV has been found.
2 Hull Design for the AUV In this study, three hull shapes are developed for the AUV, the all models has the same volume displacement but different shape. Figure 1 shows model of the AUV has developed. The detailed principal dimension of the AUV are listed in Table 1.
3 Hydrodynamic Performances of the AUV In this section, hydrodynamic performance and hull resistance acting on the AUV has been estimated by using a commercial CFD code. For CFD simulation, we must be done steep by steep as follows the manufacturing guider, user guider line for applying CFD published by the International Towing Tank conferences (ITTC), by many experiences and previous published paper [5–7, 12, 16–19]. For simulation, the computed domain was designed with 12 m length, 4 m breadth and 8.5 m height. Meshing the computed domain was generated in 1.2 million unstructured elements for the symmetrical domain. The turbulent viscous model k-ε for unsteady flow was used in computation. The inlet was setup at velocity inlet, and pressure out let was setup at the outlet of the computed domain [2, 5, 8–10, 12–15, 17–22]. In this study, the AUV has been investigated the hydrodynamics performances in the range of velocity from 0.5 m/s to 2.0 m/s and follows as the three moving direction of the Descartes coordinate system: forward in horizontal direction, diving and rising in vertical direction up and down. Figure 2 shows the domain and mesh of the computed domain.
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a) Model 1
b) Model 2
c) Model 3 Fig. 1. Model of the AUV used for computation. Table 1. Principal dimension of the AUV Name
M1
M2
M3
Unit
Length over, L
0.788
0.914
1.530
m
Breadth, B
0.393
0.300
0.158
m
Height, H
0.190
0.150
0.158
m
Volume displacement, V
0.025
0.025
0.025
m3
Wetted surface area, SW
0.605
0.614
0.729
m2
Coefficient of permeability, δ
0.720
0.790
0.780
–
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Figures 3, 4 and 5 show the CFD results of pressure and velocity distribution around hull of the AUV in the computed domain of the different cases.
Fig. 2. Computed domain and mesh.
The results as shown in the Figs. 3, 4, and 5 show velocity distribution around the AUV in the difference computed cases. In the figures, the blue color region shows low velocity and the red (yellow) color region shows high velocity around the AUV. The results show that, when the AUV moves it makes a clearly different separation region around the AUV by the different hull shapes. In the diving and rising conditions, the Model 1 makes a larger separation region after hull than those of the others models. The reason may become from the larger of the projected area of the Model 1 in vertical direction. The results as shown may suggested that the hydrodynamics forces acting on the AUV to be change different for the different models.
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a) Model 1
b) Model 2
c) Model 3
Fig. 3. Velocity distribution around the AUV in the moving forward conditon at velocity of 2 m/s.
Analysis Hydrodynamic Performance of the Autonomous Underwater Vehicle
a) Model 1
b) Model 2
c) Model 3
Fig. 4. Velocity distribution around the AUV in the diving conditon at velocity of 2 m/s.
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a) Model 1
b) Model 2
c) Model 3
Fig. 5. Velocity distribution around the AUV in the rising conditon at velocity of 2 m/s.
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4 Hull Resistance Acting on the AUV The resistance acting on hull of the AUV has been investigated by the CFD in the three moving conditions as moving forward in horizontal direction, diving and rising condition. Figures 6, 7 and 8 show results of resistance coefficient acting on hull of the AUV. The detailed hull resistance of the AUV with the different hull are listed in the Tables 2, 3 and 4.
Fig. 6. Resistance coefficient acting on the AUV in moving forward condition.
Fig. 7. Resistance coefficient acting on the AUV in diving condition.
Fig. 8. Resistance coefficient acting on the AUV in rising condition.
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The results as shown clearly that, resistance acting on the AUV with a cylinder hull as the Model 3 has smaller resistance than those of the other hull, Model 1 and Model 2. The difference of the hull resistance between the models is up to 45% as shown. In the moving forward in horizontal direction, Model 2 has the same hull resistance with those of the Model 3, the different between them about 13%. However, in other condition as diving and rising conditon, resistance acting on the Model 1 and Model 2 are most the same values and drastically different with those of the Model 3, up to 70% as shown. Table 2. Resistance acting on the AUV in moving forward condition. V, (m/s)
Resistance, (N) Model 1
Resistance coefficient
Model 2
Model 3
Model 1
Model 2
Model 3
0.50
0.926
0.658
0.669
0.01224
0.00857
0.00734
1.00
3.403
2.278
2.319
0.01125
0.00742
0.00636
1.50
7.354
4.806
4.979
0.01080
0.00696
0.00607
2.00
12.956
8.214
8.517
0.01070
0.00669
0.00584
Table 3. Resistance acting on the AUV in diving condition. V, (m/s)
Resistance, (N)
Resistance coefficient
Model 1
Model 2
Model 3
Model 1
Model 2
Model 3
0.50
43.452
40.100
12.105
0.5744
0.5225
0.1327
1.00
127.528
116.981
47.978
0.4214
0.3810
0.1315
1.50
255.976
242.143
109.075
0.3760
0.3506
0.1329
2.00
439.992
397.601
211.069
0.3635
0.3238
0.1447
Table 4. Resistance acting on the AUV in rising condition. V, (m/s) 0.50
Resistance, (N)
Resistance coefficient
Model 1
Model 2
Model 3
28.958
27.212
13.097
Model 1
Model 2
Model 3
0.3828
0.3546
0.1436
1.00
96.682
79.413
40.604
0.3195
0.2587
0.1113
1.50
189.155
183.985
86.722
0.2778
0.2664
0.1057
2.00
493.834
463.131
181.734
0.4080
0.3771
0.1246
5 Conclusions In this study, hydrodynamic performance of the AUV with the different hull shape has been investigated by the CFD. From the CFD results as shown the effects of hull shape
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on the hydrodynamic performance and hull resistance acting on the AUV in the different moving condition has been clearly. From the results we can understand the effect of hull shape on hydrodynamic of the AUV. The cylender hull form as the Model 3 has smaller resistance than those of the other model. In the moving forward condition, resistance acting on the Model 3 is less than other one up to 45%, in other moving condition as diving and rising condition the hull resistance acting on the Model 1 and Model 2 are higher than those of the Model 3 with the cylender hull shape up to 70%. The obtained results of the study may be useful to optimal design hull and need to furniture propulsion, to determine the control structure and specify the physical component for the AUV.
References 1. Allotta, B., et al.: Preliminary design and fast prototyping of an autonomous underwater vehicle propulsion system. Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ. 2293, 248–272 (2015) 2. Gao, T., et al.: Hull shape optimization for autonomous underwater vehicles using CFD. Eng. Appl. Comput. Fluid Mech. 101, 599–607 (2016) 3. Van Hien, N., Truong, V.-T., Bui, N.-T.: A model-driven realization of AUV controllers based on the MDA/MBSE approach. J. Adv. Transp. 2020, 1–14 (2020) 4. Isa, K., Arshad, M., Ishak, S.: A hybrid-driven underwater glider model, hydrodynamics estimation, and an analysis of the motion control. Ocean Eng. 81, 111–129 (2014) 5. Van Hien, N., Van He, N., Diem, P.G.: A model-driven implementation to realize controllers for autonomous underwater vehicles. Appl. Ocean Res. 78, 307–319 (2018) 6. Tan, K.M., Lu, T.-F., Anvar, A.: Drag coefficient estimation model to simulate dynamic control of autonomous underwater vehicle (AUV) motion. In: 20th International Congress on Modelling and Simulation (2013) 7. Wang, S.-X., et al.: Dynamic modeling and motion simulation for a winged hybrid-driven underwater glider. China Ocean Eng. 251, 97–112 (2011) 8. Da Silva Costa, G., et al.: Numerical analysis of stability and manoeuvrability of Autonomous Underwater Vehicles (AUV) with fishtail shape. Ocean Eng. 144, 320–326 (2017) 9. Leong, Z., et al.: Numerical investigation of the hydrodynamic interaction between two underwater bodies in relative motion. Appl. Ocean Res. 51, 14–24 (2015) 10. Leong, Z., et al.: RANS-based CFD prediction of the hydrodynamic coefficients of DARPA SUBOFF geometry in straight-line and rotating arm manoeuvres. Int. J. Marit. Eng. 157, A41–A52 (2015) 11. Saeidinezhad, A., Dehghan, A., Manshadi, M.D.: Experimental investigation of hydrodynamic characteristics of a submersible vehicle model with a non-axisymmetric nose in pitch maneuver. Ocean Eng. 100, 26–34 (2015) 12. Tian, W., Song, B., Ding, H.: Numerical research on the influence of surface waves on the hydrodynamic performance of an AUV. Ocean Eng. 183, 40–56 (2019) 13. Jinxin, Z., et al.: Hydrodynamic performance calculation and motion simulation of an AUV with appendages. In: Proceedings of 2011 International Conference on Electronic and Mechanical Engineering and Information Technology. IEEE (2011) 14. Rattanasiri, P., Wilson, P.A., Phillips, A.B.: Numerical investigation of a pair of self-propelled AUVs operating in tandem. Ocean Eng. 100, 126–137 (2015) 15. Alvarez, A., Bertram, V., Gualdesi, L.: Hull hydrodynamic optimization of autonomous underwater vehicles operating at snorkeling depth. Ocean Eng. 361, 105–112 (2009)
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16. ITTC. The Proceedings of the 25th International Towing Tank Conference, Fukuoka, Japan (2008) 17. Nguyen Dong, N.V.H., Hien, N.V., Quang, L.: Hydrodynamic Analysis to Improve the Controller of an Autonomous Underwater Vehicle RCMME, HUST, Hanoi, 9th–10th October 2014 (2014) 18. Van He, N., Ikeda, Y.: Optimization of bow shape for a non ballast water ship. J. Mar. Sci. Appl. 123, 251–260 (2013) 19. He, N.V., Ikeda, Y.: Added resistance acting on hull of a non ballast water ship. J. Mar. Sci. Appl. 13(1), 11–22 (2014) 20. Le, T.-K., et al.: Effects of a bulbous bow shape on added resistance acting on the hull of a ship in regular head wave. J. Marine Sci. Eng. 96, 559 (2021) 21. Nouri, N.M., Zeinali, M., Jahangardy, Y.: AUV hull shape design based on desired pressure distribution. J. Mar. Sci. Technol. 21(2), 203–215 (2015) 22. Sedini, A., et al.: Optimization and Analysis of the Hydrodynamic Coefficients for an Underwater Vehicle (UV)
The Effect of Biodiesel-Ethanol-Diesel Blends on Performance and Emissions of a Diesel Engine Nhinh Nguyen Van1 , Tuan Pham Minh2(B) , and Tuyen Pham Huu2 1 Hung Yen University of Technology and Education, Dan Tien, Khoai Chau, Hung Yen,
Vietnam 2 School of Transportation Engineering, Hanoi University of Science and Technology, Hanoi,
Vietnam [email protected]
Abstract. Research on using biodiesel and bioethanol as fuels for internal combustion engine is necessary to solve the shortage of fossil fuels and environmental pollution. This paper presents the performance and emissions of a 4-cylinder, naturally aspirated diesel engine fueled by diesel and blends of diesel, ethanol and biodiesel. The blends included DE10 (90% diesel-10% ethanol), DB5 (95% diesel5% biodiesel), DE10B5 (85% diesel-10% ethanol-5% biodiesel). The engine torque, specific fuel consumption and emissions were compared, the number of particles in the exhaust gas when the engine operated with diesel and fuel blends were also considered. The engine was tested at full load condition. Keywords: Biofuel · Emissions · Diesel engine · Diesel-ethanol-biodiesel
1 Introduction The rapid depletion of oil reserves and environmental pollutions created an incentive to study and evaluate alternative fuels. Biofuels, such as ethanol and biodiesel, have had a significant role in improving the sustainability of transport sector. They can be used to partly subsitute for fossil fuel, reduce toxic emissions. Biodiesel and ethanol are potential alternative fuel because they come from renewable bio-based resource and they have high oxygen content, there by possible to improve the exhaust gas emissions. Ethanol has been used normally as commercial fuel in term of blending with gasoline to use for gasoline engines [1, 2]. Beside that ethanol might also be blended with diesel to use as fuel for diesel engine. However, ethanol-diesel blend has not been commercially used due to the difference in chemical and physical properties between ethanol and diesel fuel. At present, some investigations of the potential application of diesel - ethanol (DE) and diesel - biodiesel (DB) and diesel - ethanol - biodiesel (DEB) fuel blends on diesel engine have been carried out. Huang et al., investigated the engine performance and exhaust emissions of diesel engine when using 10%, 20%, 25% and 30% ethanol blended diesel fuels [3]. In that study, the results showed that the brake thermal efficiencies © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 469–478, 2022. https://doi.org/10.1007/978-981-19-1968-8_38
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decreased with increasing amount of ethanol in the blended fuels. Rakopoulos et al., studied the effects of ethanol blends with diesel fuel, with 5% and 10% (by volume) on the performance and emissions of a turbocharged direct injection diesel engine [4]. The results showed that increasing the ethanol content in the fuel blend increased the brake specific fuel consumption and decreased the brake thermal efficiency. Besides ethanol, biodiesel which basically has very similar properties as fossil diesel is another kind of biofuel. Although biodiesel fuel can be used by itself for diesel engine, it is more commonly used as a blend component with conventional diesel. Biodiesel has lower heating value, higher oxygen content, higher cetane number, higher viscocity, lower compressibility, higher density as compared to fossil diesel that may effect on diesel engine performance and emissions [5, 6]. In general, biodiesel-diesel blends do not change much engine power, especially at low percentage of biodiesel [7], and brake specific fuel consumption can increase up to 14% or maybe higher with pure biodiesel used [6, 8, 9]. Combining ethanol and biodiesel as blend components with conventional diesel is also one more solution to promote the use of biofuels replacing mineral fuel. Nadir Yilmaz testing blends of 45% biodiesel - 10% ethanol - 45% diesel and 40% biodiesel - 20% ethanol - 40% diesel on a direct injected diesel engine pointed out that higher brake specific fuel consumption, higher CO and HC emissions, but lower NO emissions and no significant different in exhaust gas temperature when fueling the blends as compared to diesel [10]. All studies above provide the ability to use blends of ethanol, biodiesel and diesel as fuel for diesel engine. However, the effect of blends on engine performance and emissions depends on engine operating conditions as well as engine configuration and fuel properties. This paper investigates the performance and emission characteristics of an in-use diesel engine fueled by blends of 10% ethanol 90% diesel (DE10), 5% biodiesel - 95% diesel (DB5), 10% ethanol - 5% biodiesel 85% diesel (DE10B5) by volume.
2 Exprimental 2.1 Experimental Apparatus The test engine was a four-stroke, 4 cylinder, non-turbocharged diesel engine commonly used on 1.25 ton Hyundai truck in Vietnam. The engine specifications are shown in Table 1. The engine was coupled to an electrical dynamometer to provide brake load, and equipped with the instrumentation for its control (Fig. 1). The consumption of fuel and air was measured by Fuel Balance AVL 733S and Air Flow Meter Sensy Flow P. The cooling water temperature, oil temperature and pressure, cylinder pressure, intake and exhaust gas temperatures and lambda value were also measured or monitored by sensors. For emission analysis, an AVL Combustion Emission Bench (CEB II) and a Smoke Meter AVL 415 were installed and sampled the raw exhaust gas at the tail pipe. The CEB II comprises all analysers for HC, CO and NOx measurements. Moreove, the Research Center for Engines, Fuels and Emissions at Hanoi University of Science and Technology (HUST, Vietnam) has developed a dilution system for particle number measurement. Based on the requirement of Particle Measurement Program (PMP) [11], a such dilution system shall comprise a first dilution stage at which the
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Table 1. Engine specifications Description
Specification
Value
Unit
Engine type
D4BB
Injection sequence Total displacement
–
1-3-4-2
_
Vh
2.607
liter
Stroke
S
100
mm
Bore cylinder
D
91.1
mm
Early injection angle
ϕs
20
0
Con-Rod Length
Ltt
158
mm
Rate at speed
- /ndc Nedm
59/4000
kW/(rpm)
Rate torque at speed
MeMax /ndc
165/2000
Nm/(rpm)
Compression ratio
ε
22
_
Type of injection pump
Mechanical in-line
Fig. 1. Experimental setup
sample gas is heated to 150°C, an evaporation tube that heat sample gas to 300–400°C, and a second dilution stage which cool the sample gas down to about 30°C (Fig. 2). In this study experiments, an ejector was used for first dilution stage in which clean compressed air produced an under pressure at the nozzle that drew the sample gas. The second diluter was the mixer in which sample gas and clean air were mixed. Dilution factors of the first diluter and the second diluter were defined by measuring related flow rates. The overall dilution factor of the system was product of two dilution factors mentioned above. A Miniature Diffusion Size Classifier (DiSCmini) manufactured by Testo was used to determine the particle number. The DiSCmini can detect particle number concentration up to 106 #/cm3 with the size in the range of 20–700 nm with
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the sampling flow rate of 1l/min. Although this particle counting system had not been calibrated and validated by PMP method, but the results of comparative measurement could provide useful information of the change in particle number in exhaust gas when using the different fuels.
Fig. 2. Schematic of particle counting system developed at HUST
2.2 Test Fuels The test was conducted in order to assess engine performance and emission characteristics when using blends 90% diesel - 10% ethanol (DE10), 95% diesel -5% biodiesel (DB5) and 10% ethanol - 5% biodiesel - 85% diesel (DE10B5) by volume. Properties of the diesel fuel that has 0.05% sulfur available in Vietnam market according to TCVN 5689-2005 and properties of the ethanol are provided in Table 2. The ethanol and diesel fuels were mixed together without any additive by an agitator. Right after blending, the blends were fueled to engine for testing. Table 2. Properties of the test fuels Fuel properties
Diesel
Ethanol
Biodiesel
Density at 15°C (kg/m3 )
837
789
869
Kinematic viscosity at 40°C (mm2 /s)
3.14
–
4.1
Lower heating value (MJ/kg)
43
26.8
39.9
Flash point (°C)
60
152
–
Oxygen (% weight)
0
34.7
8.4
Cetane number
49
–
60
Octane number
–
113
–
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2.3 Test Modes In order to assess the effect of fuels on maximum engine torque, specific fuel consumption, emissions, lambda values and the number of particles in the exhaust gas. The test was carried out at full load condition at which the speed varied from 1000 rpm to 3500 rpm with an increment of 500 rpm were measured with diesel, DE10, DB5 and DE10B5 fuels in turn. The engine was not modified or adjusted throughout the test.
3 Results and Discussion 3.1 Engine Performance The variation of engine torque and specific fuel consumption at full load with all test fuels is plotted versus engine speed (Figs. 3 and 4). When using diesel-ethanol blends DE10, DB5, DE10B5 the engine torque decreased by 6.9%, 2.02% and 1.1%, fuel consumption increased by 6.9%, 1.96% and 1.1% on average over speed range, respectively, as compared to conventional diesel. This is attributed to the lower heating value of ethanol which is 26.8 MJ/kg as compared to 43 MJ/kg that of diesel and also the lower density of ethanol which may reduce the mass of fuel injected per cycle. However, this reduction can be negligible when 5% biodiesel was added, maybe due to the improvement of cetane number and lubricity of the blends. In case of DE10, DB5 and DE10B5 the lambda values increased by 10.69%, 6.40% and 9.87% due to high oxygen content in ethanol (Fig. 5).
Fig. 3. Comparison of the engine torque at full load
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Fig. 4. Comparison of brake specific fuel consumption at full load
Fig. 5. Lambda values
3.2 Engine Emissions Emissions including CO, HC, NOx and smoke were measured at each test mode (Fig. 6, 7, 8, and 9). The measured emissions are compared between all the test fuels at full load while the speed varies from 1000 rpm to 3500 rpm. For CO emissions, ethanol and biodiesel has about 34% and 8.4% of oxygen, so that DE10, DB5, DE10B5 blends contain amount of oxygen which can enhances the complete combustion, that lead to the reduction in CO emissions. The lower C content in blends that diminishes the CO formation may also be another reason. On average, CO reduced by 36.61%, 26.60% and 34.40%. HC reduced by 37.16%, 32.26% and 43.33% and smoke reduced by 45.66%, 24.11% and 29.45% with DE10, DB5, DE10B5 respectively. DE10, DB5, DE10B5 have a smaller C/H ratio than conventional diesel that may be another reason leading to the results above.
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On average, over speed range at full load NOx emissions increased by 7.39%, 2.13% with DE10, DB5 blends and reduced by 4.6% with DE10B5 by the diesel fuel. The formation of NOx emissions is mainly due to the peak temperature and the availability of oxygen in combustion chamber during combustion process. Adding small amount of ethanol and/or biodiesel to diesel, on one hand, supplement a little oxygen content into the blended diesel that may lead to increase in the NOx emissions. However, in the other hand, ethanol has higher latent heat of vaporization (840 kJ/kg) than diesel (270 kJ/kg) that may cause the lower combustion temperature in the cylinder for the ethanol blended diesel, and as a result preventing the NOx formation. Besides that, lower heating value and lower cetane number of the ethanol and/or biodiesel blended diesel fuels may be other reasons of lower combustion temperature and reduce the NOx .
Fig. 6. Comparison of CO emissions at full load
HC (ppm)
200
D DE10 DB5 DE10B5
150 100 50 0 1000
1500
2000
2500
Speed (rpm)
3000
Fig. 7. Comparison of HC emissions at full load
3500
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NOx (ppm)
400 300 D
200
DE10 DB5
100
DE10B5
0 1000
1500
2000
2500
3000
3500
Speed (rpm) Fig. 8. Comparison of NOx emissions at full load
Smoke (FSN)
5 4 3 2 1
D DE10 DB5 DE10B5
0 1000 1500 2000 2500 3000 3500 Speed (rpm)
Fig. 9. Smoke at full load
3.3 The Number of Particles in the Exhaust Gas The number of particles in the exhaust gas wasmeasured at each mode with all fuels. It showed that DE10, DB5, DE10B5 produced lower particle number than diesel fuel in most cases. On average, the particle number decreased by 28.70%, 42.85% and 19.26% with DE10, DB5, DE10B5 compared to diesel fuel (Fig. 10). This result agrees with the reduction of smoke mentioned above and one more time it shows that the combustion process with DE10, DB5, DE10B5 is better than that with conventional diesel.
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Fig. 10. Number of particles
4 Conclusions The influences of the blends including DE10, DB5, DE10B5 on performance and emission characteristic of the diesel engine had been studied by carrying out the experiments on the engine test bed. The results showed that DE10, DB5, DE10B5 reduced engine torque averagely by 6.9%, 2.02% and 1.1% at full load as compared to the diesel fuel. On the aspect of emissions, fueling these blends reduced quite clearly HC, CO and smoke emissions. The highest reduction in CO, HC and smoke could be up to 36.61%, 43.33% and 45.66%. Moreover the number of particles in the exhaust gas decreased by 28.70%, 42.85% and 19.26% with DE10, DB5, DE10B5 compared to diesel fuel over the speed range. However, NOx emissions increased by 7.39%, 2.13% with DE10, DB5 but decreased by 4.6% with DE10B5. These results demonstrate the initial possibility of application of ethanol and biodiesel blended diesel as fuels for diesel engine without any engine modification. Acknowledgments. We would like to thank Hung Yen University of Technology and Education and Hanoi University of Science and Technology for supporting this research.
References 1. Barry, D.S., Justin, R.B., Kathleen, E.H.: Grain and cellulosic ethanol: history, economics, and energy policy. Biomass Bioenerg. 31(6), 416–425 (2007) 2. Regina Delgado, Y.C.O.B., Antonio Araujo, S., Fernandes, Jr., V.J.: Properties of Brazilian gasoline mixed with hydrated ethanol for flex-fuel technology. Fuel Process. Technol. 88(4) 365–368 (2007) 3. Huang, J., Wang, Y., Li, S., Roskilly, A.P., Hongdong, Y., Li, H.: Experimental investigation on the performance and emissions of a diesel engine fuelled with ethanol–diesel blends. Appl. Therm. Eng. 29(11–12), 2484–2490 (2009) 4. Rakopoulos, D.C., Rakopoulos, C.D., Kakaras, E.C., Giakoumis, E.G.: Effect of ethanol– diesel fuel blends on the engine performance and emissions of heavy duty DI diesel engine. Energy Convers. Manag. 49(11), 3155–3162, 525 (2008)
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5. Borhanipour, M., Karin, P., Tongroon, M., Chollacoop, N., Hanamura, K.: Comparison Study on Fuel Properties of Biodiesel from Jatropha, Palm and Petroleum Based Diesel Fuel. SAE Technical Paper 2014-01-2017 (2014). https://doi.org/10.4271/2014-01-2017 6. Lapuerta, M., Armas, O., Fernández, J.R.: Effect of biodiesel fuels on diesel engine emissions. Progr. Energy Combust. Sci. 34, 198–223 (2008) 7. Tuyen, P.H., Tuan, L.A., Lan, H.L.: The influences of waste cooking oil derived biodiesel on diesel engine characteristics. In: Proceedings of the 5th South East Asian Technical University Consortium (SEATUC), Hanoi, pp. 542–545 (2011). ISSN 1882-5796 8. Labeckas, G., Slavinskas, S.: The effect of rapeseed oil methyl ester on direct injection diesel engine performance and exhaust emissions. Energy Convers. Manag. 47, 1954–1967 (2006) 9. Subbaiah, G.V., Raja Gopal, K., Hussain, S.A.: The effect of biodiesel and bioethanol blended diesel fuel on the performance and emission characteristics of a direct injection diesel engine. Iran. J. Energy Environ. 1(3), 211–221 (2010). ISSN 2079-2115 10. Yilmaz, N.: Comparative analysis of biodiesel - ethanol - diesel and biodiesel - methanol -diesel blends in a diesel engine. Energy 40, 210–213 (2012) 11. Andersson, J., Giechaskiel, B., MuñozBueno, R., Sandbach, E., Dilara, P.: Particle Measurement Programme (PMP) Light-duty Interlaboratory Correlation Exercise (ILCE_LD), Final Report of Institute for Environment and Sustainability (2007)
Inverse Kinematics Analysis of 7 DOF Collaborative Robot by Using Coordinate and Velocity Projection Methods Phuong Thao Thai(B) and Quang Hoang Nguyen Department of Applied Mechanics, School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Redundant manipulators are known to have more advantages than standard ones such as higher flexibility, obstacle and joint limits avoidance capability, and much more solutions of inverse kinematics. This paper proposed a method for finding solution of inverse kinematics of a 7-DOF manipulator by combining numerical method and projection method. The combination of these methods can improve the accuracy of the solutions, the null space of pseudo-Jacobian is also considered to avoid singularities, obstacle avoidance and joint limitation. Some simulations are given to verify the effectiveness of the proposed approach. Keywords: Inverse kinematics · Redundant manipulator · Jacobian method · Projection method
1 Introduction In a few decades, collaborative robots, commonly called as cobots, are improving rapidly in the robotics industry for their flexibility. This cobot is figured out to have the ability to physically interact with humans in a shared workspace by using sensors, intelligent controls, and other design features such as lightweight materials, rounded edges. To manipulate dexterously like a human arm, the 7-degree-of-freedom (DOF) cobot is designed with a structure of anthropomorphic arm. There are many approach of solving this 7-DOF arm inverse kinematics problem. Authors in [1, 2] consider the problem by analytical method in case of non-offset arm. In this approach, the wrist and shoulder are supposed to be spherical. Then the analytical solution can be found, by choosing the free internal motion of elbow joint around axis through shoulder and wrist. The disadvantage of this method is the problem of singularity and joint limit avoidance. Another approach in solving the inverse kinematic is based on Jacobian matrix. With this anthropomorphic arm, the number of variables is more than the number of equations derived from the position and orientation of the robot hand. Hence, pseudo-inverse matrix method is used and the solution can be achieved by integration joint velocities, acceleration or jerks. The benefit of using this method is problems of obstacle avoidance, joint limitations, singularities are solved clearly by the study of null space [7]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 479–486, 2022. https://doi.org/10.1007/978-981-19-1968-8_39
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In previous research [1, 2], the inverse kinematics based on matrix calculation is carried out with some link offsets, the general case has not been investigated carefully as 7DOF anthropomorphic arm is an “enormous” case. Additionally, by using Jacobian matrix, the accumulated error may occur due to the integration calculation. In this paper, the method based on Jacobian matrix and projection method are combined for finding solution of inverse kinematics of a general 7-DOF anthropomorphic manipulator. The combination of these methods can improve the accuracy of the solutions, the null space of pseudo-Jacobian is also considered to avoid joint limitation. Some simulations are given to verify the effectiveness of the proposed approach.
2 Kinematic Analysis 2.1 Direct Kinematics Let’s consider a 7-DOF manipulator as shown in. The direct kinematics can be solved systematic by using Denavit-Hartenberg (DH) method [6]. The link coordinate systems established with the DH convention and the corresponding DH parameters are shown in Fig. 1 and Table 1, respectively. In which qi , i = 1, 2, ..., 7 represents the joint variables. O5,6
z5 x3
x4 x2
z0
d5
q3
q2
O3,4
O1,2
d3 d1
z1
z6 q5
d7
x5
q4 z2
q7 O7
z3
x1
O0
q6 z4
y0 //x1
x0
q1
Fig. 1. 7-DOF manipulator and link-frames based on DH convention
The relative homogeneous transformation matrices Ai−1 i (qi ) are calculated by substituting the DH parameters in Table 1 into the matrix equation for each joint: ⎤ cos θi − sin θi cos αi sin θi sin αi ai cos θi ⎢ sin θi cos θi cos αi − cos θi sin αi ai sin θi ⎥ ⎥ ⎢ Ai−1 i (θi ) = ⎣ 0 ⎦ sin αi cos αi di 0 0 0 1 ⎡
(1)
The position and orientation of the k th link are given by: T0k (q) = A01 (q1 )A12 (q2 ) . . . Akk−1 (qk )
(2)
Inverse Kinematics Analysis of 7 DOF Collaborative Robot
=
481
0 (q) Rk0 (q) rOk , k = 1, . . . , 7 0 1
Results of direct kinematics are given as followings: ⎡ ⎤ ⎡ ⎤ 0 d2 sq1 (0) (0) rO1 = ⎣ 0 ⎦, rO2 = ⎣ d2 cq1 ⎦, d1 d1 ⎡ ⎤ d2 sq1 + d3 cq1 sq2 (0) rO3 = ⎣ −d 2 cq1 + d3 sq1 sq2 ⎦ d1 − d3 cq2 ⎡ ⎤ d2 sq1 + d3 cq1 sq2 + d4 (sq1 cq3 − cq1 cq2 sq3 ) (0) rO4 = ⎣ −d2 cq1 + d3 sq1 sq2 + d4 (−cq1 cq3 − sq1 cq2 sq3 ) ⎦ d1 − d3 cq2 − d4 sq2 sq3 ⎡ ⎤ d5 ((cq1 cq2 cq3 + sq1 sq3 )sq4 + cq1 sq2 cq4 ) (0) (0) rO5 = rO4 + ⎣ d5 ((sq1 cq2 cq3 − cq1 sq3 )sq4 + sq1 sq2 cq4 ) ⎦ d5 (sq2 cq3 − cq2 cq4 ) ⎤ ⎡ −d6 (((cq2 cq3 cq4 − sq2 sq4 )sq5 ⎢ +cq2 sq3 cq5 )cq1 + (cq4 sq5 sq3 ⎥ ⎥ ⎢ ⎥ ⎢ −cq5 cq3 )sq1 , ⎥ ⎢ ⎥ ⎢ ⎢ −d6 (((cq2 cq3 cq4 − sq2 sq4 )sq5 ⎥ (0) (0) rO6 = rO5 + ⎢ ⎥ ⎢ +cq2 sq3 cq5 )sq1 − (cq4 sq5 sq3 ⎥ ⎥ ⎢ ⎥ ⎢ −cq5 cq3 )cq1 , ⎥ ⎢ ⎣ −d6 ((sq2 cq3 cq4 + cq2 sq4 )sq5 ⎦ +sq2 sq3 cq5 ) ⎡ ⎤ d7 (((cq5 cq3 cq4 − sq3 sq5 )cq2 ⎢ ⎥ −cq5 sq2 sq4 )sq6 + cq6 (cq2 sq4 cq3 ⎢ ⎥ ⎢ ⎥ +sq2 cq4 ))cq1 + sq1 ((cq4 cq5 sq3 + ⎢ ⎥ ⎢ ⎥ sq5 cq3 )sq6 + cq6 sq3 sq4 )), ⎢ ⎥ ⎢ ⎥ d7 (((cq5 cq3 cq4 − sq3 sq5 )cq2 ⎢ ⎥ (0) (0) rO7 = rO6 + ⎢ ⎥ ⎢ ⎥ −cq5 sq2 sq4 )sq6 + cq6 (cq2 sq4 cq3 ⎢ ⎥ ⎢ ⎥ +sq cq ))sq − cq ((cq cq sq + 2 4 1 1 4 5 3 ⎢ ⎥ ⎢ ⎥ cq )sq + cq sq sq )), sq 6 6 3 4 5 3 ⎢ ⎥ ⎣ ⎦ d7 (((cq5 cq3 cq4 − sq3 sq5 )sq6 + +cq6 sq4 cq3 )sq2 + cq2 (cq5 sq6 sq4 − cq6 cq4 )
2.2 Inverse Kinematics In robotics, inverse kinematics is the mathematical process of calculating the variable joint parameters needed to place the end of a kinematic chain. The inverse kinematics are solved based on the constraints at the levels of position, velocity, or acceleration [7]: x − f(q) = 0
(3)
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θi
di
ai
αi
1
q1
d 1 = 0.28
0
π/2
2
q2
d 2 = 0.10
0
π/2
3
q3
d 3 = 0.33
0
−π/2
4
q4
d 4 = −0.10
0
π/2
5
q5
d 5 = 0.32
0
−π/2
6
q6
d 6 = 0.10
0
π/2
7
q7
d 7 = 0.10
0
0
x˙ − Jq q˙ = 0
(4)
x¨ − Jq q¨ − J˙ q q˙ = 0
(5)
2.2.1 Jacobian-Based Method As 7DOF cobot is a redundant arm, the solutions of (6) and (5) are given by applying pseudo-inverse: q˙ = J† (q)˙x
(6)
˙ q¨ = J† (q) x¨ − J(q)
(7)
The solution of the inverse kinematics under null space consideration can be found in acceleration level [5] as following:
† ˙ q˙ + I − J† J z0 (8) q¨ = JW (q) x¨ − J(q) W in which, z0 ∈ Rn is an arbitrary vector that guarantees the robot able to avoid obstacles, singularity, and joint collision. The vector z0 is computed as following: z0 = α
∂φ(q) ∂q
(9)
in which, φ(q) is the objective function that depends on the set of requirements. For example, to avoid singularity, the objective function is chosen as the function of manipulation measurement. φ(q) = det[J(q)JT (q) (10) The manipulation function is cancelled at singularities. Therefore, maximizing this function value will help the robot avoid singularities during operation.
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To neglect joint limitation, the objective function is selected by measuring distance to joint limitation. qi − qi ) 2 1 n ci φ(q) = − i=1 2 qiM − qim
(11)
In which, qiM (qim ) is the maximum (minimum) value of joint limitation and qi is the average value of joint limitation, ci is weight parameters. To avoid obstacles, we use the function that measure the distance to obstacles. φ(q) = min p(q) − o
(12)
In which, o is position vector of a point on the obstacle and p(q) is generalized vector of robot manipulator. † is generalized pseudo inverse matrix of Jacobian matrix and is calculated as JW following: −1 † JW = W−1 JT (q) J(q)W−1 JT (q)
(13)
W is called the weight matrix. There are several choices of matrix W, if W = I, the solution in (3) will have the minimum norm. If W = M(q), the solution is found with the optimization of kinetic energy. 2.2.2 Projection Method The problem of inverse kinematics is accumulation of error when finding the joint variables by taking integration. By using the coordinate and velocity projection, the joint variables found by the integration are adjusted and revised so that they are forced onto manifolds. Then the accuracy of the solutions is improved significantly.
Fig. 2. Block diagram for inverse kinematics
Coordinate Projection Due to the integration, the found joint variables q∗ may not satisfy the Eq. (3) and belong outside the manifold. Therefore, the coordinate projection is proposed to figure out the
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point q that belongs to the curve of the Eq. (3) and has the shortest distance to the point q∗ . Distance function V is given as following: V =
T 1 q − q∗ P q − q∗ → min, P > 0 2
(14)
The problem of this method is finding the point point q that satisfies the Eq. (3) and minimize function V . By using the calculation algorithm in [5], the joint variables are adjusted and forced onto the manifolds. Velocity Projection The method of velocity projection is similar to the coordinate projection, it is necessary to find the q˙ satisfy the Eq. (4) by adjusting q˙ ∗ to the manifolds. The distance function in this case is given as following: V =
T 1 q˙ − q˙ ∗ Q q˙ − q˙ ∗ → min, Q > 0 2
(15)
It is essential to find the point q˙ that satisfies the Eq. (6) and minimize function V . Hence, the solution of velocity level is given as following:
† † Jq q˙ ∗ Jx x˙ + I − JQ (16) q˙ = JQ The block diagram for inverse kinematics based on the combination of Jacobian matrix and projection method is shown in Fig. 2.
3 Numerical Simulation To verify the accuracy of the proposed method, some numerical simulations are done. Two trajectories have been implemented by MATLAB. The motion law along the trajectory is defined as following: sf − si π t 2π t 1 , 0 ≤ t ≤ tf s(t) = si + − sin (17) π tf 2 tf In this paper, z0 is chosen to avoid singularities, and φ(q) is calculated as in (10). The first simulation is carried out by designing the linear trajectory between two picking points: A(0.52, −0.26, 0.28) and B(0.52, 0.26, 0.478) in 5 s in 1 time. The results are illustrated in Fig. 3. The second simulation results are given in Fig. 4 with curvilinear trajectory. From the results, it can be seen that the robot configuration changes uniformly and smoothly, without any singularities and joint limitation. The position errors in x, y, z coordinates of end-effector also called as e = [e1 , e2 , e3 ] are all within 10−6 and 10−16 and decreases sharply after 2 s, so the accuracy of this approach is significantly improved.
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a. Robot configuration
b. Position error of end-effector vs time
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c. Joint variables vs time
Fig. 3. Tracking of end-effector along linear trajectory between 2 picking points
a. Robot configuration
b. Position error of endeffector vs time
c. Joint variables vs time
Fig. 4. Tracking of end-effector along curve trajectory
4 Conclusion This paper proposed a new approach for solving the inverse kinematics of the 7DOF collaborative robot. The problem of singularity, joint limitation and obstacle avoidance has been neglected by using null space matrix. The accuracy of the solution is also improved significantly by projection method. The numerical simulation are illustrated to clarify the validity of the proposed method. Acknowledgement. This work was supported in part by the National Program: Support for research, development, and technology application of industry 4.0 (KC-4.0/19-25), under the grant for the project: Research, design and manufacture of Cobot applied in industry and some other fields with human-robot interaction (code: KC-4.0-35/19-35).
References 1. Tian, X., Xu, Q., Zhan, Q.: An analytical inverse kinematics solution with joint limits avoidance of 7-DOF anthropomorphic manipulators without offset. J. Franklin Inst. (2020). https://doi. org/10.1016/j.jfranklin.2020.11.020
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2. Wang, Y., Artemiadis, P.: Closed-form inverse kinematic solution for anthropomorphic motion in redundant robot arms. Adv. Robot. Autom. 2, 110 (2013). https://doi.org/10.4172/21689695.1000110 3. Wang, J., Li, Y., Zhao, X.: Inverse kinematics and control of a 7-DOF redundant manipulator based on the closed-loop algorithm. Int. J. Adv. Robot. Syst. 7(4), 37 (2010) 4. Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: Modelling, Planning and Control. Springer, London (2009). https://doi.org/10.1007/978-1-84628-642-1 5. Nguyen, H., Thai, T.: On solving inverse kinematics of redundant robotic manipulators by using coordinate and velocity projection methods. J. Comput. Sci. Cybern. 32–41 (2012) 6. Craig, J.: Introduction to Robotics: Mechanics and Control. Pearson/Prentice Hall (2005); Du, X., George, S.M.: Thickness dependence of sensor response for CO gas sensing by tin oxide films grown using atomic layer deposition. Sens. Actuat. B 135, 152–160 (2008) 7. Nakamura, Y.: Advanced Robotics/Redundancy and Optimization. Addison-Wesley, Publishing Company, Reading (1991)
Investigating the Effect of Pulsed Fiber Laser Parameters on the Roughness of Heat-Resistant Parts in Cleaning Processes Toan Thang Vu, Thanh Dong Nguyen(B) , Thanh Tung Vu(B) , and Hong Hai Hoang School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam {dong.nguyenthanh,tung.vuthanh}@hust.edu.vn
Abstract. Metal casting molds are widely used in the field of mechanical manufacturing and mass production. Mold materials require good temperature resistance, high thermal wear resistance. However, the mold surface is susceptible to dirt from the casting material or material oxide layer. Therefore, the need to clean the surface to reuse the mold is necessary. The layer of material that adheres to the surface is also metal, so it has good adhesion, making it difficult to clean. In addition, the required cleaning process does not affect the substrate layer, the size, and roughness of the cleaned surface. In this paper, a cleaning method for SKD61 steel mold material by fiber laser in nano-second pulse emission mode is proposed. The effect of pulsed fiber laser parameters of the laser source which includes the average power, the pulse width, and the repetition rate in the cleanliness of the treated surface, and cleaning efficiency are analyzed in detail. The experimental results showed that the selection of appropriate technical parameters can completely remove the contaminants, without affecting the substrate material and improve the roughness of the treated surface. Keywords: Fiber laser · Laser cleaning · Nanosecond laser · Metal casting mold
1 Introduction Metal casting molds are complex assemblies, working in an etched environment under the influence of thermal cycles and mechanical loads. Therefore, the mold material requires good mechanical properties under high temperature working conditions. The life of the mold is determined by the number of castings achieved. During casting, molten metal is injected into the mold with high injection pressure and flow rate. At that time, the molten metal will diffuse into the mold surface. At the same time, the elements in the mold material (especially iron) will diffuse into the molten metal. These processes can result in the dissolution in the material and intermetallic compounds between the mold metal and casting metal [1–3]. When creating an intermetallic compound, the casting material will adhere to the surface of the mold. The adhesive layer affects the mold surface quality, causing dimensional errors or reducing the quality of the molded product. Removing the metal stains © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 487–495, 2022. https://doi.org/10.1007/978-981-19-1968-8_40
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layer on the mold surface by the mechanical method will be difficult due to the small coating thickness of 2–5 μm, the high hardness, and the good adhesion of the coating to the substrate [4, 5]. Meanwhile, the thermal method has disadvantages due to the influence on the mechanical properties or the geometrical parameters of the element [6, 7]. Now a day, chemical methods are commonly used using corrosive chemicals [8, 9]. This chemical is usually mainly based on a combination of strong acids (HF) and the oxidants (Hydro peroxide - H2 O2 ). Although this method is being applied in production, it has many disadvantages such as: an uneven layer of material removed, long cleaning time, chemical waste that is harmful to the environment. An ultrasonic vibration method is also used for mold cleaning [10]. However, this method is not effective with metal stains, and high adhesion. Laser cleaning of soiled materials is a method with many advantages such as high cleaning speed, controllability of the cleaning process, and the ability to clean any different materials. Excimer laser working at 248 nm is recommended to clean TiN coating because of the absorption coefficient of this coating with the highest excimer [11–13]. Excimer laser is also used to make paper or rubber injection molds [14, 15]. Especially the femtosecond pulse laser is effectively used in self-cleaning mode [16–18]. However, the laser sources used all work in the femtosecond pulse regime, when light decomposition molecules play a dominant role. While the research related to the metal mold cleaning mechanism in which the thermal expansion effect plays a dominant role is still limited. In this paper, a cleaning method for cleaning temperature resistance mechanical parts by using fiber laser in the nanosecond pulse emission regime was proposed. The mold pin element with a conical shape made of heat-resistance steel SKD 11 was used as a sample. SKD11 steel has good mechanical properties even at high temperatures up to 700 °C. The cleaning process should ensure the geometric size and surface roughness of the pin. The productivity and quality of cleaning metal stains on the pin were investigated. An influence of the set of technological parameters and the working parameters of the laser beam on the surface quality was shown through the surface roughness of the pin were studied and analyzed in detail. The depth of the removal material layer was precisely controlled by controlling the parameters of the laser source (average power, pulse width, repetition rate) and the working parameter of the laser beam (scanning speed). The experimental results show that by optimizing the technological parameters of the laser source, the ability to remove the metal stains on the heat-resistance materials and improve the surface quality of the mechanical elements after cleaning was fully possible.
2 Materials and Methods 2.1 Pulsed Fiber Laser Cleaning System The cleaning system using fiber laser is shown in Fig. 1. A 50W fiber laser source with a center wavelength of 1064 nm is used to clean the metal stains on the pin surface. Laser radiation is transmitted in an optical fiber with a length of 2 m for easy mounting during machining. The pulse width can vary between 25–250 ns. The specifications of the fiber laser surface pin cleaning system are shown in Table 1. The peak power (Ppeak )
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and pulse energy (E) of the laser source is given by: Ppeak =
Pa E = τ × fr fr
(1)
where Pa , τ , fr are the average power, pulse width, and repetition rate, respectively. The material removal mechanism occurs depending on the pulse energy of the laser beam and the absorption coefficient between the layer and substrate. For lasers working in the continuous regime, the depth of removal material, h, is determined by the optical absorption coefficient, α, and Beer-Lambert’s law: E 1 (2) h = ln α Et where h, α E, Et are the thickness of the removal layer, the optical absorption coefficient, laser fluence, and the threshold value of the fluence, respectively [19]. The maximum peak power of the system can reach 25 kW. In addition, the layer of material to be removed is metal, the thermal expansion mechanism is the main mechanism in the cleaning process. The technological parameters of the laser source such as the wavelength, the pulse width, and the repetition rate were used to improve the surface quality and roughness of the mechanical elements after the cleaning process. Table 1. Specification of laser cleaning system Average output power
50 W
Wavelength
1064 ± 5 (nm)
Pulse duration range
25–250 (ns)
Repetition frequency range
70–500 (kHz)
Maximum peak power
≥25 (kW)
Single pulse maximum energy
≥0.67 (mJ)
Output power instability
0. The constants ζ and g were selected so that the errors ex , ey and minimal average eVx , eVy . 1) Determine the constant ζ of the coefficient matrix K of the controller Set constant g = 1, position and velocity errors are investigated according to ζ as described in Fig. 4. From Fig. 4, the constant ζ selected ζ = 0.9. 2) Determine the constant ζ of the coefficient matrix K of the controller.
Fig. 5. The position and velocity error according to ζ with g = 1
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With ζ = 0.9, selected the constant g by investigating minimal of the position and velocity error as described in Fig. 6. From Fig. 6, the constant g selected g = 15 × 104 (Fig. 5).
Fig. 6. The position and velocity error according to g with ζ = 0.9
4.2 Results and Discussion From the setting parameters defined in Sect. 4.1 and the dynamic controller designed in Fig. 3, Fig. 9 is the simulation result of NURBS ξ tracing simulation on Simulink/Matlab of the linear state feedback dynamics controller. In Fig. 9, the blue line is the control trajectory, while the orange line is the desired NURBS trajectory with error data at the numbered points in Fig. 9 synthetic in Table 2. Figure 7 is the angular velocity of the two wheels when the robot tracks the NURBS trajectory and Fig. 8 compares the desired and actual values of the velocity and angular velocity when the robot moves the NURBS trajectory.
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Fig. 7. Angular velocity of the two wheels
Fig. 8. Compare the desired and actual values of velocity V G and angular velocity Ω
Figure 10 and 11 describe the position and velocity error values when controlling the robot the NURBS trajectory tracking in Fig. 2. In addition, Fig. 9, 10 show that the robot moves continuously from the inside to the outside following the desired NURBS trajectory with the maximal error of 1.7 mm (see Table 2). Figure 9, 10, and 11 show that the maximal velocity error occurs at the points where the trajectory has a maximal radius and begins to change direction. The sudden change points in Fig. 11 corresponds to the change direction when the robot is moving along
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Fig. 9. The movement trajectory of the differential drive mobile robot Table 2. Position error data in the x and y directions at the numbered points in Fig. 9 Point
1
2
3
4
5
6
7
8
9
ex (mm) ey (mm)
0.4287 0.0306
0.5096 −0.0782
0.7003 0.3030
0.5277 0.5463
0.5737 0.1908
−1.0915 0.2087
−1.1252 0.7600
−1.3677 0.5777
−1.5154 −0.7506
e (mm)
0.4298
0.5156
0.7630
0.7595
0.6046
1.1113
1.3578
1.4847
1.6911
Point
10
11
12
13
14
15
16
17
18
ex (mm) ey (mm)
−1.4002 −1.0182
−1.2168 −0.4182
0.2947 0.2059
0.3243 0.2194
0.0597 0.8973
−0.4012 0.8857
−0.4379 1.4744
−0.3765 −1.9355
−0.3814 −1.3012
e (mm)
1.7313
1.2867
0.3595
0.3915
0.8993
0.9723
1.5381
1.9718
1.3559
Note:e = (ex2 + ey2 )0.5
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Fig. 10. The error of the G-point position on the robot when the robot moves the NURBS trajectory tracking
Fig. 11. The velocity and angular velocity error when the robot moves the NURBS trajectory tracking
the NURBS trajectory tracking ξ. That means a jump point at positions where the error e = 0, which is entirely consistent with the nature of motion.
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5 Conclusions In this paper, the nonlinear dynamics control of a differential-driven mobile robot has linearized to follow the desired NURBS trajectory tracking with a small error. The proposed solution is to design a linear state feedback dynamics controller with a pole position approach based on the position and velocity error model. From the simulation results and discussion, the paper achieves the following results: (i) A linear state feedback dynamic controller has been developed for a differential-driven mobile robot that can produce high-performance tracking with minor tracking errors; (ii) This research proposes a method to determine the coefficients of the time-varying linear state feedback dynamic controller, and (iii) Used the NUBRS interpolation method to design the motion trajectories of the differential-driven mobile robot to ensure smooth curvilinear motion to avoid sudden speed changes. Thereby, it can apply the research results presented in this paper to control AGVs in the industry. Also, the problems of longitudinal slip, lateral slip and Coulomb friction during the interaction between the wheel and the road surface will be considered part of our future research goals. Acknowledgments. This research was funded by the Ministry of Industry and Trade in a ministeriallevel scientific and technological research project, conducted in 2020, code: DTKHCN.076/20.
References 1. Ahmadi, S.M., Taghadosi, M.B.: Haqshenas MAR, A state augmented adaptive backstepping control of wheeled mobile robots. Trans. Inst. Meas. Control 43(2), 434–445 (2021) 2. Thai, N.H., Trinh, L.T.K., Le, Q.D.: Roadmap, routing and obstacle avoidance of AGV robot in the static environment of the flexible manufacturing system with matrix devices layout. Sci. Technol. Dev. J. 24(3), 2091–2099 (2021) 3. Ly, T., Thai, N., Dzung, L., Thanh, N.: Determination of kinematic control parameters of omnidirectional AGV robot with mecanum wheels track the reference trajectory and velocity. In: Sattler, K.-U., Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H. (eds.) ICERA 2020. LNNS, vol. 178, pp. 319–328. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64719-3_36 4. Yu, R., Ding, S., Tian, H., Chen, Y.-H.: A hierarchical constraint approach for dynamic modeling and trajectory tracking control of a mobile robot. J. Vibr. Control 28, 1–13 (2021) 5. Andaluz, V., Roberti, F., Toibero, J., Carelli, R., Wagner, B.: Adaptive dynamic path following control of an unicycle-like mobile robot. In: Jeschke, S., Liu, H., Schilberg, D. (eds.) ICIRA 2011. LNCS (LNAI), vol. 7101, pp. 563–574. Springer, Heidelberg (2011). https://doi.org/ 10.1007/978-3-642-25486-4_56 6. Vázquez, J.A., Villa, M.V.: Path-tracking dynamic model based control of an omnidirectional mobile robot. IFAC Proc. 41(2), 5365–5370 (2008) 7. Ren, C., Ma, S.: Dynamic modeling and analysis of an omnidirectional mobile robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4860–4865 (2013) 8. Sarkar, N., Yun, X., Kumar, V.: Control of mechanical systems with rolling constraints: application to dynamic control of mobile robots. Int. J. Rob. Res. 13(1), 55–69 (1994)
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9. Zamanian, H., Javidpour, F.: Dynamic modeling, and simulation of 4-Wheel skid-steering mobile robot with considering tires longitudinal and lateral slips. Int. J. Sci. Res. Knowl. 4(2), 040–055 (2016) 10. Sidek, N., Sarkar, N.: Dynamic modeling and control of nonholonomic mobile robot with lateral slip. In: Third International Conference on Systems, pp. 35–40 (2008) ˇ 11. Cerkala, J., Jadlovska, A.: Mobile robot dynamics with friction in Simulink. In: Proceedings of the 22th Annual Conference Proceedings of the International Scientific Conference - Technical Computing Bratislava, pp. 1–10 (2014) 12. Xie, Y., et al.: Coupled fractional-order sliding mode control and obstacle avoidance of a four-wheeled steerable mobile robot. ISA Trans. 180, 282–294 (2021) 13. Sun, Z., Xie, H., Zheng, J., Man, Z., He, D.: Path-following control of Mecanum-wheels omnidirectional mobile robots using nonsingular terminal sliding mode. Mech. Syst. Signal Process. 147(15), 107128 (2021) 14. Brezak, M., Petrovi´c, I.: Path smoothing using clothoids for differential drive mobile robots. In: Proceedings of the 18th World Congress The International Federation of Automatic Control, pp. 1133–1138 (2011) 15. Scheiderer, C., Thun, T., Meisen, T.: Bézier curve based continuous and smooth motion planning for self-learning industrial robots. Procedia Manuf. 38, 423–430 (2019) 16. Thai, N.H., Trinh, T.K.L., Nguyen, T.L., Le, Q.D.: Trajectory tracking using linear state feedback controller for a mecanum wheel omnidirectional. In: Advances in Asian Mechanism and Machine Science - Proceedings of IFToMM Asian MMS 2021 (2021) 17. Thai, N., Ly, T.: NURBS curve trajectory tracking control for Differential-Drive Mobile Robot by a linear state feedback controller. In: Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H., Sattler, K.-U. (eds.) ICERA 2021. LNNS, vol. 366, pp. 685–696. Springer, Cham (2022). https://doi. org/10.1007/978-3-030-92574-1_71 18. Tzafestas, S.G.: Introduction to Mobile Robot Control. Elsevier inc., Amsterdam (2017) 19. Lantos, B., Márton, L.: Nonlinear Control of Vehicles and Robots. Springer-Verlag, London (2011). https://doi.org/10.1007/978-1-84996-122-6 20. Manas, C., Leena, V., Rangan, B.: Towards optimal computation of energy optimal trajectory for mobile robots. In: Advances in Control and Optimization of Dynamical Systems, Kanpur, India, pp. 83–87 (2014) 21. Gierszewski, D.M., Schneider, V., Lauffs, P.J., Peter, L., Holzapfel, F.: Clothoid-augmented online trajectory generation for radius to fix turns. IFAC 51(9), 174–179 (2018) 22. Walambe, R., Agarwal, N., Kale, S., Joshi, V.: Optimal trajectory generation for car-type mobile robot using spline interpolation. IFAC 49–1, 601–606 (2016) 23. Nguyen, T.H., Nguyen, T.Q.: A kinematic control algorithm for blasthole drilling robotic arm in tunneling. Sci. Technol. Dev. J. 20(k5), 13–22 (2017)
A Thoroughly Approach: Pulley Kinematic, Actuator Dynamic and Stiffness on Cable Suspended Parallel Robots Le Duc Duy and Nguyen Truong Thinh(B) Department of Mechatronics, Ho Chi Minh City University of Technology and Education, Thu Duc City, Ho Chi Minh City, Vietnam [email protected]
Abstract. Cable Suspended Parallel Robots (CSPRs) have recently made significant advancements in driving in specific fields where dynamic characteristics are essential. Moving a huge load in quick transitions while keeping great accuracy is a good illustration. As a result, in order to effectively handle CSPRs, it is need to be approached thoroughly both on kinematics, dynamics, stiffness. By extending the standard kinematic with pulley kinematic and adding the actuator dynamic to the dynamic for space-moving objects to feed-forward the predicted tension of the cables. On the other hand, the static equilibrium equation is used for real-time estimating the payload in pick-and place applications, a proportional-derivative(PD) model drives each motor. Finally, experiments are used to test the stability of dynamic modeling for control and to confirm the program. Keywords: CSPR · Cable robot · PD Control · Dynamics · Kinematics · Pulley kinematic · Stiffness
1 Introduction Robots have been used in a wide range of industries in recent decades. Many aspects of manufacturing and engineering, on the other hand, do not rely on robots, owing to the limitations of conventional robots [1]. For example, in many applications, workspace requirements and load carrying capacity are far greater than what conventional robots can provide, even when the robot’s cost is taken into account [2]. A new class of parallel robots was introduced to solve the latter issue [3]. Cable-Suspended Parallel Robots (CSPR) are structurally similar to parallel actuated robots, with the exception that the End-Effector (EE) can only be pulled, not pushed, by the cables. Figure 1 depicts a schematic representation of a CSPR in a general configuration. It is made up of a motor, a winch system, and an EE. Because of the behavior of the cables, feedback control of CSPR is much more difficult than that of parallel-actuated robots. As compared with other types, CSPRs have a large workspace, a large payload, a high movement speed, easy maintenance, and are inexpensive. Those values enable them to offer many solutions not only in industrial applications, but also in a wide range © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 624–638, 2022. https://doi.org/10.1007/978-981-19-1968-8_51
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of other fields. As a result, they may be utilized for 3D printing of massive structures [4] with a big footprint of 15 × 11 m and 6 m high, as well as surface finishing, such as painting and sandblasting [5], to enhance the efficiency of these operations and free human operators from unpleasant tasks. Furthermore, high speed pick-and-place,up to 2,5 m/s velocity and 12 m/s2 acceleration in [6]. As multi-science platform, CSPR can be a support for five-hundred-meter Aperture Spherical radio Telescope (FAST) [7], the most sensitive radio telescope, which will allow astronomers to jumpstart numerous science goals, such as neutral hydrogen line scanning in distant galaxies out to extremely large redshifts, searching for the first bright star, finding thousands of new pulsars, and so on. It should be noted that redundant actuated CSPRs are preferable to cranes for accurate pick-and-place operations and large and heavy parts since they suffer from less load swinging. Furthermore, unlike regular cranes, CSPRs can control both the position and orientation of the object. As a result, the purpose of this article is to determine a control solution for a suspended semi-industrial CSPR prototype for metal box pick and place operations. Because of the variability of the load, robust control is essential to achieve high precision and repeatability of the MP pose. Several efforts have been made to model and handle CSPR in real-time [8, 9]. All kinetostatic models proposed for CSPR should have static balancing between external forces and cable tension [10]. Most popular control techniques used for conventional robots might be applied for CSPRs with the premise of no mass and no elongation for cables. PD controller is consider for almost applications that do not require strictly special working conditions. But the examples above are usually high load and slow moving or vice versa because of the effect of dynamic features is ignored. In addition, the pulley kinematic was not concerned. Therefore, this paper present control solution that real-time mass compensating feed-forward assemble with the dynamic processing block and extend pulley kinematic.
Fig. 1. CSPR 3D model in suspended configuration
The paper is organized as follows. Section 2 presents the kinematic and dynamic modeling theory in general which purposed to experiments. Section 3 mention about
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the control scheme and controllers. Section 4 presents an analysis of the experiment. Finally, conclusions are drawn and we present future work in Sect. 5.
2 System Kinematic and Dynamic Model This section contains a kinematic and dynamic model. The prototype uses eight cables to handle the platform’s position and orientation. The length, width, and height of the structure are 5 m, 3 m, and 3 m, respectively. The cables are coiled on motorized winches with a diameter of 100 mm, and the cable is guided from the winches to the cable exit point by eight passive pulleys. Between each motor and each winch, a reduction gearbox with a 30:1 ratio is built. The moving-platform (MP) has a length of 0.5 m, a width of 0.5 m, and a mass of 153 kg. The inverse kinematics problem can be described as follows: Given a desired position and orientation of the payload (p, R), find the positions of the motor (qi , i = 1, 2, …, n) that satisfy the static equilibrium and geometry constraints. To define its position in 3-D space, the platform requires three coordinates. As a result, each joint has three unknowns. Furthermore, each cable has an unknown tension. As a result, in the case of 8 cables, the total number of unknowns is 32. At least three attachment points are required for the payload to maintain its desired position and orientation. As a result, the inverse kinematics problem is unsolvable in the cases of one and two robots. There are an infinite number of solutions for cases with three or more robots, because the number of unknowns is greater than the sum of the number of static equilibrium equations and the number of constraints. In the standard model, the well-known structural equations are derived from the force-torque equilibrium of the platform [5]. JTτ + w = 0
(1)
Figure 2 illustrates schematically n = 6 d.o.f CSPR. First, the standard model is prefered. By modeling the pulleys as fixed passage points, they are simplified. Ai denotes each cable exit point at the pulley attach to base frame, while Bi expresses the attachment point to the mobile platform. The positions of the cable attachment vector bi points from point P to point Bi and is expressed in frame Fp . The position vector are denoted bpi point from point O to point P and is expressed in frame Fb , P being the center of mass of mobile platform. Note that Fb is the global coordinate and Fp is the attached coordinates on the MP. As with other parallel mechanisms, the inverse kinematics of a cable-suspended system is relatively simple. The vector b li of ith cable is define as follow [11]: b
Li = li ui = b p + b Qp p bi − b ai
(2)
With li is lenght of ith cable and ui is the unit vector of the ith cable vector, defined as: 2 li = b Li b
bL i ui = 2 b Li
(3) (4)
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Fig. 2. CSPR geometric configuration.
And b Qp is the rotation matrix from frame Fb to frame Fp . ⎡ ⎤ cα cβ cα sβ sγ − sα cγ cα sβ cγ + sα sγ b Qp = ⎣ sα cβ sα sβ sγ − cα cγ sα sβ cγ + cα sγ ⎦ −sβ cβ sγ cβ cγ
(5)
In Eq. (4), c stands for cos(·) and s indicates sin(·). Also α, β and γ are angle of rotation around z, y and x axis respectively. By taking the first derivative of Eq. (1), ai ’ = 0 and p bi = b ωp × p bi the following equation is obtained [12]: b˙
Li = uiT b p˙ + (p bi × uiT )b ω˙ p
(6)
Note that ωi is the rotational velocity of MP expressed in the Fb. Therefore the kinematic equation for the robot has the following form: bL ˙ i = Jtw = ρ q˙ with tw = b p˙ b ω˙ T (7) D bω i
= E θ˙ , θ˙ = γ , β, α
⎤ cα cβ −sα 0 E = ⎣ sα cβ cα 0 ⎦ −sβ 0 1
(8)
⎡
(9)
With ρ denote winch radius and D denote gear reducer. The x’ vector include the linear and rotation velocity of MP expressed in F b and J is the forward jacobian matrix. ⎤ ⎡ u1 b Qp (b b1 × u1 ) ⎢ u2 b Qp (b b2 × u2 ) ⎥ ⎥ ⎢ (10) J =⎢ . ⎥ .. ⎦ ⎣ .. . um b Qp (b bm × um )
The majority of CDPR idler pulleys are fitted with single revolute joint types, which reduce cable twisting. This sort of pulley allows rotation along the zb axis while keeping
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Fig. 3. Pulley geometry.
the cable’s entrance point stationary, with the goal of aligning the cable with the rotation axis of the platform. This illustration is in Fig. 3. As for definition of the CDPR geometry, an orientation angle ζ of the pulley must be considered. Radius ri , length ji between the fixed point Ai of the revolute joint and the center hi of a pulley define its geometry. χ is the angle between di and the xpull axis of the Fpull coordinate system attached to the pulley.The vector Li in the extend model is described as follow: b (11) Li = li ui = b p + b Qp p bi − b ai − Rzbi (ξi ) b ji + Rypulli (χi )b ri Where Rzb I is the rotation matrix of magnitude I around the zb axis and Rypuli I is the rotation matrix of pulley sheave pivot around the ypuli axis. From [13], the dynamic model of the CSPR are written as. −J T τ + wg + we − H x¨ − C x˙ = 0
(12)
where: H stand for the spatial inertia matrix of the moving platform and C the matrix of the centrifugal and Coriolis wrenches.
mp I3×3 −M Xˆ p (13) H= M Xˆ p Ip where: mp presents mass of the platform. I3 × 3 is identity matrix. MXp = b Qp [mp xg , mp yg , mp zg ]T is the first momentum e xpressed in frame Fb and MXˆp is the skew-matrix associated to MXp. H stands for tension inertia matrix. We can obtain the platform’s inertia tensor It using Huygens-Steiner’s theorem, then compute H. M Xˆ p M Xˆ p mp ⎡ ⎤ Ixx Ixy Ixz With It = ⎣ Iyx Iyy Iyz ⎦ Izx Izy Izz
H =b Qp Itb QpT −
(14)
(15)
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During platform movement, centrifugal and Coriolis forces have an impact on it and define the following:
b b ωˆ ωMX ˆ p (16) C x˙ = b ωI ˆ tb ω As the C.O.M of the moving platform does not coincide, the wrench caused by gravity is defined as:
mp I3×3 wg = (17) M Xˆ p On the other hand, the actuator dynamic is written as [14]: ˙ − Iqi φ¨ + fvφ˙ + fssign(φ)
ρ τ =τ D
(18)
The relationship between the length of cable and the motor position is define as [14]: Li − Loi Li D= D ρ ρ ˙ ¨ φ˙ i = Lρi D, φ¨i = Lρi D
φi =
(19)
Thus, the Eq. 18 can be rewritten as: Iqi
L˙ i ρ L¨ i ˙ − τ =τ D + fv D + fssign(φ) ρ ρ D
(20)
3 Stiffness Analysis The stiffness of the mechanism is demonstrated in the CDPRs by the deflection of the endeffector caused by the applied wrench under static equilibrium. Redundancy in actuators is required in this type of robots due to the property of the cables that cannot push and can only pull. Redundancy in actuators generates internal forces, the consequence of which is a zero wrench on the robot’s end-effector, but these forces provide internal forces stiffness. Elastostatic equations [15] define the relationship between a change in platform position δp, and the change in external wrench δwe corresponding to that change: δwe = Kδp
(21)
The expression of the stiffness matrix K is [16]: K = J T diag(k)J +
d T J τ dp
(22)
where k is denotes the stiffness caused by the elasticity of the cable and the stiffness of the actuator. The stiffness induced by internal forces is the second factor in the preceding
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equation. The effects of cable elasticity and internal forces on overall stiffness of CDPRs may be expressed using a four spring model as [16]: K = Ka + Ke
(23)
where Ke is due to cable elasticity and Kp is related to internal forces. In terms of geometry and cable characteristics, Ke and Kp may be stated as follows [16]: n ui uiT bˆ Ti uiT ki ˆ Ke = T ˆ T ˆT i=1 bi ui ui bi ui ui b (24)
n n 0 0 I − ui uiT bˆ T − ui uiT bˆ T τi τ Kp = − i Li b ˆ i − bˆ i ui uT bˆ bˆ T − bˆ i ui uT bˆ T 0 uˆ i bˆ i i=1 i=1 i i Due to the fact that wires can only pull, they must be taut during all of the robot’s moves. This condition is crucial to the robot’s performance. Wrench Closure Workspace (WCW) is described in [11] as a collection of end-effector poses in which there exists a set of positive cable tensions such that the moving platform stays in static equilibrium for any external wrench applied to it. These positions may be calculated using Eq. 1. If a cable-suspended robot has more cables than are required to control a given set of degrees of freedom, it is said to be redundantly actuated. The ability to increase the workspace is one of the advantages of developing a redundantly operated robot. Another advantage of building a redundant system is that the extra cables can boost the robot’s lifting capability and speed. This decreases the stress on each individual cable and necessitates the use of smaller actuators. However, because to the various potential routes, calculating the inverse dynamics of the redundant manipulator is difficult. The system of Eqs. (12) is underdetermined for eight cables model (redundant case) and contains an unlimited number of solutions. The problem to solve is minimize (τT τ) under the following equality and inequality constraints: Aτ = H x¨ + C x˙ − wg τi ≥ 0 i = 1, 2, ...8
(25)
Quadratic programming (Barrette and Gosselin 2005) can be used to solve this problem. Many popular tools, such as MATLAB’s ‘quadprog’ can handle the quadratic programming problem. This technique finds the cable’s minimal norm vector of forces while meeting a set of equality requirements (equilibrium equations) and an inequality constraint (ti ≥ 0 i = 1, 2,…, 8).
4 Controller Solution In this section, we present the control model, which is based on an extended kinematics model that includes pulley kinematics, a dynamic model, and a PD controller. The expected goal is improve significantly compared to normal control methods. The model scheme is shown in Fig. 4. The dynamic block is used to feed-forward to controller due to the tensile force and friction force applied on each motor shaft. In order to real-time
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estimate the payload, it’s worth updating the mass in the compensation term to increase robustness, especially if the items in question have a wide range of forms, sizes, and weights (PDFF) [18]. With the following assumptions: The platform has a low linear and angular velocity; Due to the carrying payload, particularly the metal plate, the platform has just one wrench applied to it; The cables are stiff and unyielding. The inertial and Coriolis effect on the platform can be neglected. Then the payload is calculated by Eq. 1.
Fig. 4. Control model scheme
Where x = [px , py , pz , α, β, γ]T is 6 dimension vector includes the intended Cartesian location and orientation of the MP, the desired MP twist x’, and x” is the 6 dimension vector of MP acceleration. The wrench w due to gravity and mp is a estimate payload obtained from the tension sensors. ⎤ ⎡ ⎤ ⎡ 0 τ1 ⎥ ⎢ .. ⎥ ⎢ 0 ⎥ ⎢ . ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ −m g ⎢ ⎥ p ⎥ (26) JT⎢ ⎥ ⎢ τi ⎥ = ⎢ ⎢ gy −m p g⎥ ⎢ . ⎥ ⎢ ⎥ . ⎣ . ⎦ ⎣ m gx ⎦ p g τ8 0 The system dynamic block based on Eq. 18 where τ is obtained from Eq. 11. The τc denotes the feed-forward torque. The cable length and shaft position relationship is expressed in Eq. 19. The function of PD controller is shown as follow: τPD = Kd e˙ + Kp e
(27)
where Kp and Kd are the controller’s proportional and derivative gains, adjusted so that the robot can attain precision and stability using only the MP. The position and velocity errors of the motor shaft are defined as e and e’.
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5 Experimental Results 5.1 Prototype After the implementation of the algorithm,experiments are conducted in order to research the improvement in control CSPR when using the extend kinematic model with pulley and the PDFF controller. From there, draw conclusions about the influence of dynamic parameters on the accuracy and stability of the robot. The simulation has the following main characteristics: +m = 8 cables so that the prototype has 2 degrees of actuation redundancy. +The cable configuration shown in Fig. 5.
Fig. 5. Cable configuration
+The dimensions of the base frame are 6.0 × 3.0 × 3.0 (m). +The overall dimensions of the plat form are 26.5 × 23.5 × 22 cm, and it weighs 100 (kg). +The payload weights 200 (kg). As a result, the total mass of the moving object is 300 (kg). +The platform’s reference frame is placed in the ideal center of mass. The procedure of experiments as follow: First, the MP is in a static pose at Fb orgin, we produce a motion along x axis without changing the orientation, and a travel speed is derived from its motion equation. This is the simplest trajectory because its motion is on only one axis and do not has any acceleration, so the dynamic parameters impact on MP is not considerable. In the next step, the designed motion in on three axes and has acceleration on each axis. Thus, it can express clearly about the difference in precision when compare two trajectory. In the final step, the motion returns the origin along x
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and z axis and change the orientation of MP around y axis by 5 degree. This motion do not have acceleration on any axis but it has a longer distance than the first trajectory while two motion is carried out in the same interval of time and the changed orientation. Repeat the experiment with the control model without the system dynamic block and do not include the pulley kinematic. So we have two models to compare to the simulation. The moving platform’s mass remains constant, and the cable elasticity is negligible. The motion is shown in Fig. 6 and the equations of trajectory is shown below: ⎧ −140t ⎪ ⎨ 0≤t umax if uc < umin if umax < uc < umax ,
(11)
where δ is the amount of value that exceeds the controller’s limits, ξ is a regulator to ensure the system is stable, and is developed as bellow, u is the actual force, and uc is the desired control force. The isolator force fails to meet the control force due to actuator limitations in many cases. In this study, the following auxiliary design system is proposed to regulate the phenomenon, | ξ . −kλ ξ − Sδ+|ξ + tanh ξ |ξ | ≥ μ ξ= (12) |ξ | ≤ μ, 0 where ξ ∈ R is an auxiliary design system state, Kλ ∈ R+ , and μ is a small positive value. The auxiliary controller uau that satisfies the constraint of MRE isolator is added as, uau = −δ − ξ
(13)
where δ and ξ are defined in Eqs. 11 and 12, respectively. Finally, the controller proposed in this study consists of two components, including a robust sliding mode controller uc and the auxiliary controller uau , u = uc + uau =
1
b1
(k s S − b2 S − b3 x) − + d − δ − ξ
The update laws for the parameters are determined bellow, −1 ˙ S ks S − b2 S − b3 x + d , b1 = c1 b1
(14)
(15a)
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˙ b2 = c2 S 2 ,
(15b)
˙ b3 = c3 Sx.
(15c)
The dynamic component of the observer hysteresis state can be regulated as, ϑ = −c4 S.
(15d)
3.2 Stability Analysis Theorem 1: Consider the vibration system (1) with the sliding function given by (4) under the proposed controller (14). If there is the hysteresis observer (15d) and the update laws (15a–c) such that all signals are bounded in the system, stability is achieved. Proof. Lyapunov function candidate is selected as, V =
1 2 1 ˜2 1 ˜2 1 ˜2 1 ∼2 1 2 S + b1 + b2 + b3 + + ξ 2 2c1 2c2 2c3 2c4 2
(16)
With the time derivative of the Lyapunov function and application of the control algorithm (16), we have, . 1 1 1 1 ˙ ˜ ˜ +ξ ξ V˙ = S S˙ + b˜ 2 b˙˜ 2 + b˜ 2 b˙˜ 2 + b˜ 3 b˙˜ 3 + c1 c2 c3 c4 1 1 = S(−b1 u − b2 S − b3 x − b1 + d ) + b˜ 1 b˙˜ 1 + b˜ 2 b˙˜ 2 c1 c2 . 1 ˜ ˙˜ 1 ˙ ˜ ˜ +ξ ξ + b3 b3 + c4 c4 1 1 = S[−b1 (uc + uau ) − b2 S − b3 x − + d ] + b˜ 1 b˜˙ 1 + b˜ 2 b˙˜ 2 c1 c2 . 1 ˜ ˙˜ 1 ˙ ˜ ˜ +ξ ξ + b3 b3 + c4 c4
1 ˆ + d − δ − ξ − b2 S − b3 x − b1 + d (ks S − bˆ 2 S − bˆ 3 x) − = S −b1 bˆ 1 . 1 ˜ ˙˜ 1 1 1 ˙ ˜ ˜ +ξ ξ , + b1 b1 + b˜ 2 b˙˜ 2 + b˜ 3 b˙˜ 3 + (17) c1 c2 c4 c4
The derivative of the Lyapunov is divided into 3 parts, (18) V˙ = V˙ 1 + V˙ 2 + V˙ 3 , ∼ We apply the inequality || − ≤ − = and the observer dynamics component Eq. (15d) ϑ = −c4 S to V1 , 1 ˙˜ ˜ ˆ − + b1 V˙ 1 = Sb1 c4
Adaptive Observation Controller for an Uncertain Hysteresis System
1 ˆ ˜ ˜ ˜ = b1 S + −β|x| − γ x − || + ϑ c4 1 1 1 2 2 ˜ ˜ ˜ ˜ |x| ≤ b1 S − β|x| + γ + ϑ c4 c4 c4 b1 ˜ 2. = − (β − γ )|x| c4
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(19)
Next, the adaptive algorithms Eqs. (15a–c) is applied to V˙ 2 , 1 V˙ 2 = S −b1 (ks S − bˆ 2 S − bˆ 3 x + d ) − b2 S − b3 x + d bˆ 1 1 ˜ ˙ˆ 1 ˙ 1 ˙ + b1 b1 + b˜ 2 bˆ 2 + b˜ 3 bˆ 3 c1 c2 c3 1 = S − bˆ 1 − b˜ 1 (ks S − bˆ 2 S − bˆ 3 x + d ) − b2 S − b3 x + d bˆ 1 1 ˜ ˙ˆ 1 ˙ 1 ˜ ˙ˆ + b1 b1 + b2 b2 + b˜ 3 bˆ 3 c1 c2 c3 b˜ 1 ks S − bˆ 2 S − bˆ 3 x + d = −ks S 2 − S 2 bˆ 2 − b2 − Sx bˆ 3 − b3 − S bˆ 1 1 ˙ 1 ˙ 1 ˜ ˙ˆ + b1 b1 + b˜ 2 bˆ 2 + b˜ 3 bˆ 3 c1 c2 c3 1˙ 1˙ = −ks S 2 − b˜ 2 S 2 − bˆ 2 − b˜ 3 Sx − bˆ 3 c2 c3 −1 1˙ (20) − b˜ 1 bˆ 1 S ks S − bˆ 2 S − bˆ 3 x + d + bˆ 1 . c1 The auxiliary design system Eq. (13) and the Lemma 1 are applied to V˙ 3 , .
V˙ 3 = −Sb1 (−δ − ξ ) + b1 ξ ξ
ξ Sδ + |ξ | + tanh = b1 S(δ + ξ + b1 ξ −kλ ξ − ξ
ξ = −b1 kλ ξ 2 − b1 |ξ | − ξ tanh ≤ −b1 kλ ξ 2 − κ.
(21)
The value (β − γ ) is guaranteed to be a positive. The derivative Lyapunov V˙ ∼2 b1 V˙ ≤ −ks S 2 − (β − γ )|x| − b1 kλ ξ 2 − κ < 0 c4 ∼
(22)
The boundedness of S, b˜ 1 , b˜ 2 , b˜ 3 , and are asymptotic to zero by the Lyapunov stability criterion. Applying LaSalle’s theorem, the closed-loop system is asymptotically stable.
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Fig. 2. The El Centro earthquake excitation
4 Simulation In this section, the scaled system combined with the proposed controller is simulated to reduce vibration effectiveness. The dynamic system’s parameter values are assigned m = 3 kg, k = 20 N mm−1 c = 0.02N s mm−1 . The system is exposed to the El Centro earthquake excitation, where x¨ g is the earthquake acceleration. The design parameters are selected c1 = 1, c2 = 1, c3 = 1, c4 = 1, λ = 2. According to existing research, the parameters of MRE-based isolator are used for the simulation. The system is excited under El Centro earthquake stimulus as shown in Fig. 2.
Fig. 3. The comparison of relative displacement responses under earthquake excitation for three different strategies.
Fig. 4. The comparison of absolute acceleration responses under earthquake excitation for three different strategies.
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The relative displacement and the mass’s absolute accelerate responses are depicted in Figs. 3 and 4 for three cases: passive case, on-off skyhook controller, and proposed controller. The relative displacement reduced significantly using controllers where the proposed controller is more efficient than the skyhook controller. The efficiency is the same for the acceleration response, as shown in Fig. 4. By the proposed control method, the system also works effectively for different stimuli. The algorithm will also be applied to the actual system for future studies.
Fig. 5. Control force and current input for the system with proposed algorithm.
Fig. 6. Time history of the adaptive parameter b1 , b2 and b3 .
The actual control force calculated by using the proposed algorithm is shown in Fig. 5. It is the desired force component within the isolator’s ability. The control force archives a significant value at about 12 s and 28 s because the displacements are prominent at this time (refer to Fig. 3). The controller requires a considerable value of force to ensure a stable system. Moreover, the system parameters changed significantly at this time. The adaptive parameters are shown in Fig. 6. From the figure, the parameters achieve a stable state after 20 s. The hysteresis estimation value and estimated value are shown in Fig. 7a and 7b. We can see that the high accuracy and low error response are found from the figures. It is noticeable that predicted results are confirmed perfectly portrayed by the hysteresis observer. These results demonstrate that the proposed controller achieves high efficiency compared to conventional controllers to reduce the amount of
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(a)
(b) Fig. 7. The estimated hysteresis state and the estimated error: (a) the hysteresis state and (b) the error.
system vibration. The controller is designed to be also efficient with other earthquake stimuli. The controller is proven to be stable at all times. The mass displacement ensures asymptote to zero under the excitations. Furthermore, the auxiliary controller adjusts itself when the control force exceeds the allowable limit to ensure the system is always stable.
5 Conclusions This study proposed a novel controller to overcome semi-active systems’ disadvantages, including the force constraint, system parameter uncertainty, and hysteretic behavior. A nonlinear observer was constructed to predict the unknown hysteresis state, and the input constraint was also considered to ensure the system’s stability. The simulation results have demonstrated the effectiveness of the proposed control algorithm. The comparative results showed a significant improvement in system vibration reduction performance compared to the skyhook controller.
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21. Nguyen, X.B., Komatsuzaki, T., Truong, H.T.: Novel semiactive suspension using a magnetorheological elastomer (MRE)-based absorber and adaptive neural network controller for systems with input constraints. Mech. Sci. 11, 465–479 (2020). https://doi.org/10.5194/ms11-465-2020 22. Polycarpou, M.M.: Stable adaptive neural control scheme for nonlinear systems. IEEE Trans. Automat. Control 41(3), 447–451 (1996). https://doi.org/10.1109/9.486648
Numerical Simulation of Hot Water Flashing Flow in a Converging - Diverging Nozzle Anh Dinh Le1(B) , Quan Hoang Nguyen1 , Long Ich Ngo2 , Anh Viet Truong2 , Okajima Junnosuke3 , and Iga Yuka3 1 School of Aerospace Engineering, VNU-University of Engineering and Technology,
144 Xuanthuy, Caugiay, Hanoi, Vietnam [email protected] 2 Hanoi University of Science and Technology, 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam 3 Institute of Fluid Science, Tohoku University, Sendai, Japan
Abstract. The hot water flashing flow in a converging-diverging nozzle is studied using a homogeneous compressible water-vapor two-phase flow. Our simplified thermodynamic model is implemented for capturing the latent heat transfer of phase change. A density-based algorithm based on the explicit Harten-Yee secondorder upwind TVD scheme is performed. The numerical result is compared with Abuaf’s experimental data, for which a quantitative agreement in the flow parameters is produced. Thermodynamic effect analysis shows that the temperature depression increases as the water temperature increases. That results in an obvious decrease of the local saturated vapor pressure at high-temperature conditions. The flashing non-equilibrium effect then reduces and is reasonably predicted by the homogeneous model. The thermodynamic suppression is obvious in the flow characterized by the cavitation mechanism rather than the flashing. Additionally, the suppression effect is influenced by not only the water temperature and the flow behavior but also the non-dimensional time scale of the flow field. Keywords: Cavitation · Flashing · Homogeneous model · Thermodynamic effect · Converging-diverging nozzle
1 Introduction Cavitation and flashing know to have similar macroscopic manifestations phenomena. Cavitation is the appearance of the vapor phase inner the liquid flow, where the local pressure reduces below the liquid saturated vapor pressure because of the acceleration of flow. In some cases, the pressure does not recover over the liquid saturated pressure, the flow is thus fully evaporated and is flashed. Both cavitation and flashing can stimulate occur in the water at the high-temperature condition and are critical safety issues in the heat transfer flow system in the industry such as the thermal power station or the nuclear power station. For which, the cavitation and flashing flow have a tight interaction between turbulence and heat and mass transfer at the liquid-vapor interface. In addition, the water is typically pressurized, making it difficult in the experiment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 753–760, 2022. https://doi.org/10.1007/978-981-19-1968-8_62
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Recently, several numerical simulation approaches have been developed to correctly simulate the cavitation and flashing flow. The first approach is the two-fluid model (TFM) which solves the phase equations separately. This model can represent different flow aspects such as the non-equilibrium effect, the phase slip-velocity, the drag, and the buoyancy, etc. [1], but demands a high computational cost (computational time). In addition, the accuracy of TFM is sensitive to the model empirical parameters such as the bubble size, the thermal coefficient, or the bubble number density [1]. In the latter approach, the homogeneous model (HM), including the equilibrium [2] and the non-equilibrium assumption [3, 4], suitably manifests for simulating the cavitation and flashing flow. Regarding HM model, the liquid-vapor two-phase flow is simplified as a pseudo-single phase medium. A transport equation for the vapor phase is then derived including the mass transfer source term. This model has been satisfactorily simulated various flow types ranging from cavitation to flashing flow, including the water at either room temperature or high-temperature conditions [2–4]. Notably, the flashing flow typically occurs in the water at high temperature, the flow mechanism is thus highly influenced by the thermodynamic effect. The temperature inside the cavity is decreased (referring to temperature depression), reducing the local saturated vapor pressure and making the cavitation and flashing mechanism sometimes unclarified clearly in previous studies. In this study, we thus applied the homogeneous model, coupling the governing equations with our simplified thermodynamic model [4], to study the mechanism of the hot water flashing flow in a converging-diverging nozzle [5]. First, the numerical method is validated through the quantitative comparison with Abuaf’s experimental data of the flow parameters. Then, the thermodynamics effect on the flashing/cavitation flow mechanism that did not clarify in previous studies, is investigated.
2 Numerical Method The system equations for the homogeneous compressible water-vapor two-phase flow are given by [4]: ∂ρui ∂ρ + = 0, ∂t ∂xi ∂ρui uj + δij p ∂τij ∂ρui + = , ∂t ∂xj ∂xj ∂ρui T ∂ κeff ∂T Sh ∂ρT + = + , ∂t ∂xi ∂xi cp ∂xi cp ∂ρui Y ∂ρY + = m+ + m− . ∂t ∂xi
(1)
In this equation, p, T, ρ, and ui are the mixture pressure, temperature, density, and velocity components, respectively. Y is the vapor mass fraction and S h = −0.8Lm− + Lm+ is the heat source by phase change [4]. Here, m+ and m− are the rate of mass transfer in cavitation, calculating by the Hertz-Knudsen-Langumur equations [6]. L is the latent
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heat. The equation of state of the mixture is obtained as follow: ρ=
p(p + pc ) . Rg Y (p + pc )T + Kl (1 − Y )p(T + Tc )
(2)
With K l , pc , and T c are the water constant, the pressure constant, and the temperature constant of water, respectively. Besides, Rg is the vapor constant. Additionally, Wilcox k-ω turbulent model with Allmaras wall function is implemented [6]. The configuration of the nozzle, designed by Abuaf et al. [5], is shown in Fig. 1. The structure grid is generated over the simulation domain. Based on the grid-sensitive study, the grid of 241 × 60 points is selected for this study with y+ ranging from 30 to 400. At the inlet, the pressure Pin and temperature T in are specified. Pout is used for outlet boundary. No-slip and adiabatic conditions are implemented to the nozzle wall. The slip condition is applied to the nozzle axis. The saturated vapor pressure pv (T ) is calculated based on the local flow temperature. Then, the threshold phase-change pressure pv * (T ) is estimated by pv (T ) and the turbulent fluctuation term [6]. The detail of the numerical conditions is depicted in Table 1.
Fig. 1. The converging-diverging nozzle configuration
Table 1. Numerical simulation conditions Run
BNL133
BLN296
Pin [kPa]
349.00
764.90
Pout [kPa]
203.00
432.60
T in [K]
394.40
421.95
Pv [kPa]
206.30
461.00
0.98
0.92
σ
For simulation, the FORTRAN code using the explicit density-based Finite Difference Method (FDM) is developed. Strang splitting time scheme and the 2nd order upwind Total Variation Diminishing (TVD) scheme are used for the time integration and the inviscid terms of Eqs. (1) [6]. In addition, the centered scheme is used for the discretization of the viscous terms. The detail of the numerical method can refer to the work by Anh et al. [6].
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3 Results and Discussion Figure 2 illustrates the distribution of time-averaged cross-section vapor void fraction and the pressure in the nozzle and Abuaf’s experimental data [5] for runs BNL133 and BLN296. The numerical pressure tendency agrees quantitatively with the measured data. In that, the pressure rapidly decreases from the nozzle inlet to the throat and remains nearly constant in the converging section. Regarding the vapor void fraction distribution, a satisfactory prediction with the experimental data is produced. The discrepancy between the measured data and the numerical data may cause by the measurement error. The feature of the time-averaged vapor void fraction (a), pressure (b), Mach number (c), and temperature (d) contours in the nozzle in run BNL133 are shown in Fig. 3. The water evaporation commences occurrence firstly at the nozzle throat before flashing in the diverging section. The high void fraction occurs at the wall. The pressure decreases gradually from the entrance, reaches its minimum at the throat, and remains almost constant in the rest of the diverging section. The Mach number increases rapidly behind the throat and reaches a maximum of 0.6 in most of the converging areas where the vapor phase exists. The Mach number is small at the nozzle wall due to the occurrence of the reserved flow. Because of the water evaporation, the temperature decreases from the nozzle throat to the downstream region. The minimum temperature observes on the wall near the outlet region, where the vapor void fraction is high.
Fig. 2. Vapor void fraction and pressure distribution between simulation and Abuaf’s experimental
The results above enable the validity discussion on the thermodynamic effect on the flashing flow mechanism in this subsequence. To clarify the thermodynamic effect on the flashing mechanism, the simulation is for the water at a wider temperature range. Figure 4(a) illustrates the cross-section time-averaged pressure p, threshold phase-change pressure pv * (T ) (refers to as left axis), and temperature (refers to as right axis) distribution in the nozzle for the flow condition σ = 0.98. Notably, pv * (T ) is the threshold saturated pressure [6]. The temperature of the water is varied, which are T 0 = 353 K, 373 K, 394.4 K (run BLN133), and 405 K. The temperature remains constant along the converging section before decreasing in the diverging part where the water evaporated. The temperature depression increases proportionally with the temperature of water T 0 .
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Fig. 3. Contour of vapor void fraction (a), pressure (b), and Mach number (c), and temperature (d) distribution inside Abuaf’s nozzle in case BNL133
This decreases the local threshold phase-change pressure closer to the local flow field pressure. Notably, the pv * (T ) reduces lower than the local pressure p at T 0 = 394.4 K and T 0 = 405 K, supposing the usual cavitation behavior instead of flashing flow in the diverging section. Similar behavior can be seen in Fig. 4(b) for the flow cavitation number σ = 0.92 with the temperature of water T 0 = 390 K, 400 K, 422 K (run BNL296), and 440 K. For which the flashing flow observes at T 0 < 422K while the cavitation flow occurs at T 0 = 440 K owing to the thermodynamic effect. These results indicate that the flow mechanism is strongly influenced by the thermodynamic effect in terms of temperature depression. Then, the flashing non-equilibrium effect is significantly reduced, underlying the applicability in the prediction of this flow type by the present homogeneous model. Figure 5 depicts the cross-section time-averaged vapor void fraction distribution inside the nozzle for the flow cavitation numbers σ = 0.98 and σ = 0.92. Regarding σ = 0.98, the inverse thermodynamic suppression observes for the water below the normal boiling temperature. The vapor void fraction increases when the temperature rises from T 0 = 353 K to 373 K. The suppression effect commences significantly at T 0 > 373 K. In that, the vapor void fraction decreases as the temperature of water increases. The maximum vapor void fraction drops from 0.7 at T 0 = 373 K to 0.5 at T 0 = 405 K. Regarding σ = 0.92, the thermodynamic suppression shows a feasible impact on the vapor void fraction distribution at T 0 < 422 K where the flashing flow occurs, as depicted in Fig. 4(b). On the contrary, the vapor void fraction is largely suppressed at T 0 = 440 K, in which the flow is characterized as the usual cavitation flow. A similar behaviour can be seen with T 0 = 394.4 K and T 0 = 405 K at σ = 0.98. Notably, opposed to σ = 0.98, the suppression does not appear for T 0 up to 400 K at σ = 0.92 where the water is flashed. It supposes that the thermodynamic suppression presents only for the cavitating flow and agrees with our previous work [6].
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Fig. 4. (a). Pressure p, phase-change pressure pv * (T ), and temperature T distribution at different water temperatures and σ = 0.98. (b). Pressure p, phase-change pressure pv * (T ), and temperature T distribution inside nozzle at different water temperatures and σ = 0.92
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Fig. 5. Vapor void fraction distribution inside nozzle at different temperatures and cavitation number Table 2. Comparison of the flow time and the thermal time
Table 2 depicts the non-dimensional time scale τ /τ th of the flow field in the nozzle. For which, τ = Dth /U th and τ th , defined by Yoshida et al. [7], are the flow time and the thermal time, respectively. The non-dimensional time scale is small for T 0 ≤ 373 K at σ = 0.98 and T 0 ≤ 400 K at σ = 0.92 where either inverse or low suppression effect occurs. Importantly, such behaviors result even at the very high-temperature T 0 = 400 K at σ = 0.92 although it is visible at the same temperature at σ = 0.98, as shown in Fig. 5. The suppression effect becomes obvious as the non-dimensional time scale is high (about an order of magnitude 1) in this study. Unlike the temperature depression that appears at any temperature, the above results imply that the suppression effect is not only influenced by the water temperature and the flow mechanism but also depends on the characteristic time of the flow.
4 Conclusion The hot water flashing flow in a converging-diverging nozzle is studied using the homogeneous model approach. Our simplified thermodynamic model is implemented to account for the thermodynamic effect on the phase-change processes. The numerical result is compared with Abuaf’s experimental data and the thermodynamic effect on the flashing flow mechanism is investigated. The results are summarized as follows: – The present simulation method shows a good applicable to predicting the flashing flow with a satisfactory prediction of the flow parameters.
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– The flashing mechanism is strongly influenced by the thermodynamic effect in terms of temperature depression. The flashing non-equilibrium is significantly reduced, underlying the applicability in the prediction of this flow type by the present homogeneous model. – The thermodynamic suppression is obvious in the flow characterized by cavitation phenomena rather than the flashing. In addition, the suppression effect is not only influenced by the water temperature and the flow mechanism but also depends on the characteristic time of the flow.
Acknowledgments. This research has been done under the research project QG.21.32 “A Study on Numerical Simulation of the Vortex Ring Cavitation in Water with Consideration of Thermodynamic Effect and Concentration Air” of Vietnam Nation University, Hanoi.
References 1. Liao, Y., Lucas, D.: 3D CFD simulation of flashing flows in a converging-diverging nozzle. Nucl. Eng. Des. 292, 149–163 (2015) 2. Iga, Y., Nohmi, M., Goto, A., Shin, B.R, Ikohagi, T.: Numerical study of sheet cavitation breakoff phenomenon on a cascade hydrofoil. ASME J. Fluids Eng. 125, 643–651 (2003) 3. Schmidt, D.P., Gopalakrishnan, H., Jasak, H.: Multi-dimensional simulation of thermal nonequilibrium channel flow. Int. J. Multiph. Flows 36, 284–292 (2010) 4. Anh, L.D., Okajima, J., Iga, Y.: Modification of energy equation for homogeneous cavitation simulation with thermodynamic effect. ASME J. Fluids Eng. 141(8) (2019) 081102-1-12 5. Abuaf, N., Wu, B.J.C., Zimmer, G.A., Saha, P.: A study of nonequilibrium flashing of water in a converging-diverging nozzle: Volume 1 Experimental, U.S. Nuclear Regulatory Commission, Washington, DC 6. Anh, L.D.: Study of thermodynamic effect on the mechanism of flashing flow under pressurized hot water by a homogeneous model. ASME J. Fluids Eng. 144(1), 011206 (2022) 7. Yoshida, Y., Kikuta, K., Niiyama, K., Watanabe, S.: Effect of thermodynamic parameter on cavitation in rocket inducer, JAXA Res. Dev. Memorandum JAXA-RM-12-006E (2013)
A Case Study on Humanoid Robot Using Robotics Software in E-learning Duc-An Pham1 , Huy-Anh Bui2 , Xuan-Thuan Nguyen1 , Thi-Thoa Mac1 , and Hong-Hai Hoang1(B) 1 School of Mechanical Engineering, Hanoi University of Science and Technology, No. 1,
Dai Co Viet, Hanoi, Vietnam [email protected] 2 Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam
Abstract. Due to the COVID-19 pandemic, E-learning is developed significantly to unlock the key factors that support researchers/students. This paper presents a software solution on an E-learning system to control the humanoid robot, which is essential for students’ knowledge and experimental studies in the academic environment. In particular, the research concentrates on controlling NAO robots in the virtual space. First, by installing Choregraphe software, the programs are developed to calibrate and handle different factors that contribute to the robot’s performance, such as joint motions, collision avoidance, navigation, localization. After that, the detailed characteristics of the NAO robot are designed and displayed on the Webot 8.4 monitor. By transmitting the connection signal from the Choregraphe to Webot 8.4, the virtual environment analyzes the effectiveness and competencies of the controllers perceived by users. A case study is investigated to inspect the robot operation and the stability of the software solution. Keywords: COVID-19 · Humanoid robot · Robotics · E-learning · Virtual environment
1 Introduction E-learning [1–4] can be defined to be the utility of smart devices (laptop, tablet, and smartphones…) for studying new information and skills. It is one of the most potential fields in 4.0 education. The remarkable value of E-learning lies in its perceived usefulness and perceived ease of use. Recently, it is clear that the COVID-19 pandemic [5–7] has had a significant impact on all sectors worldwide, affecting every aspect of human lives, including but not limited to the education sector. During the Covid 19 lockdown, several schools, colleges, and universities have to discontinue face-to-face teaching. Practitioners and instructors undergo multiple challenges, and the demand for maintaining the efficiency of the educational systems was one of the main issues they have encountered. Hence, numerous governments have permitted education providers to deploy E-learning systems to facilitate engagement with learners rapidly. That is why E-learning is becoming the optimal solution to solve the challenges of physical classroom training. Since E-learning allows people to share knowledge despite geographical © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 761–770, 2022. https://doi.org/10.1007/978-981-19-1968-8_63
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boundaries and limitations, it opens a prospective chance to design an innovative global educational network. Currently, the proliferation of technology and the boom in the Internet of things (IoT) worldwide have led to the outstanding growth of combining robot teaching and E-learning [8–10]. Many robots are being used for learning goals, from simple robots to humanoid robots [11, 12]. One significant merit of this teaching combination is that the individuals could learn new skills without having a mentor offline present interacting with them. The choice of the robot is generally dictated by the area of study and the student age group. While simple robots are particularly used to teach robotics, mathematics, motion physics or computer science, humanoid robots are easier to interact with and often used to teach a depth range of computer vision, artificial intelligence, and language subjects. Therefore, in recent years, humanoid robots have been used as teaching assistants or even teachers in the classroom for some subjects across language, mathematics, and science. However, as shown in many studies, while students like interacting and learning with humanoid robots, the teachers who are a bit reluctant to use the humanoid robots in the classroom prefer the humanoid robots to take on restricted roles instead of full autonomy in the classroom. It is mostly because the teachers are unaware of the common technical capabilities of humanoid robots and are uncertain about how best to incorporate them in the classroom. Besides, the physical interaction with the real robots is limited to some extent because of the COVID 19 disease. To overcome these mention issues, real-time simulations for humanoid robots are analyzed. We leveraged the simulator by aiding an open-source environment that provided a simplification of the computational 3D features, speeding up the network simulation. Thus, the simulation could rapidly guarantee the real-time performances required in interfacing a real robotic platform. The humanoid robot in this article is concentrated on NAO model from Softbank Robotics. The remainder of this paper is divided as follows. The following section analyzed the model structure of NAO robot. The software interface and signal transmission are illustrated in Sect. 3. The fourth section presents the case study on the NAO model, followed by the brief conclusions drawn in the final section.
2 NAO Robot Modeling NAO is well known as a humanoid robot, first developed in 2006 by Aldebaran [13–19]. He stands 58 cm tall and has 25° of freedom in total. Hence, he can depict the proportions of a human and interact with people. NAO has been widely used in research laboratories, R&D departments, academic environments, and robotics courses in universities. Moreover, NAO is also developed to be applied in reality or daily life. The recently updated version of Nao tends to be used in various fields such as speech recognition, face detection, high-level communication with humans, helping people in their activities, entertainment, etc. These days, over 11 000 NAO models are used in over 60 countries, making them one of the most famous robots worldwide. The designed appearance of NAO robot is presented in Fig. 1. As shown in Fig. 1, NAO has two eye LEDs on his face, one head cap at the top of the head to protect his CPUs. NAO body is built based on complicated parts such as the neck, shoulder, hip, elbow, wrist, etc. Thus, the gesture of NAO is flexible as a human.
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Fig. 1. The general structure of NAO model
3 A Brief Overview of the Software Interface 3.1 Webots Webots [20, 21] is a professional environment to model, simulate and program different types of robots. Moreover, this platform allows the users to prototype 3D virtual space with physics properties (colours, texture, collisions, friction coefficients, density, etc.). These figures could be measurable and changeable continuously depending on the state of the environment. Hence, the users can program and calibrate their robots to exhibit the desired behaviour as expected. In addition, Webots contains an extensive library of robot models and controllers to help users optimize the designed operation and transfer control programs to a real robot. The programs can be utilized to co-operate by applying the built-in IDE or with third-party development software. The data on Webots could be packed and shared in open-source environments. Thus, Webots is well-suited to apply in research and academic fields related to robotics, especially for educational projects. 3.2 Chorgraphe - A Graphical Tool for Humanoid Robot Programming Chorgraphe [22–25] is a developed multi-platform application. Based on this program, the users could monitor and calibrate the action of NAO robots without writing a single line of code. The main features of Chorgraphe are included as follows: – Create and arrange detailed animations, behaviours for NAO robots by aiding drag and drops GUI. – Design the NAO own dialogues to interact with the human voice. – Inspect the accuracy of desired characteristics on a simulated robot or directly on an actual one based on a broker with a network IP and Port. – Monitor and control single tasks or multi-tasks of the NAO model at the same time. – Expand and enrich any combinational behaviours with Python code editing.
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3.3 Communication Between the Choregraphe Application and Webots Platform To begin with, the Choregraphe suite software is downloaded originally from SoftBank Robotics website. In this paper, the Choregraphe Version 2.5.10.7 is tested carefully and is known to work well with naoqisim. Meanwhile, version 8.4 of Webots is chosen to install and connect with the mentioned Choregraphe application. Next, the specific steps used to establish the connection between the Choregraphe and Webots are indicated as follows: (1) Launch Webots by clicking its icon and create a sample world in the File section (Fig. 2).
Fig. 2. Open a sample world in Webots
(2) Select the file robots/aldebaran/nao.wbt to open the sample (Fig. 3).
Fig. 3. Sample world library in Webots
(3) Check the info statement of the console panel at the bottom of the Webots interface. If any red errors/warnings related to naoqisim appeared in the textbox, Webots should be closed and re-opened again (Fig. 4).
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Fig. 4. The info statement panel in Webots projects
(4) Next, click the right mouse on the Choregraphe icon and run as administrator to launch the program. By default, the following panels are displayed (Fig. 5):
Fig. 5. The general interface of Choregraphe software
(5) The advanced options are displayed in the expanded panels of View menu (Fig. 6). On the menu bar of Choregraphe, select Connection > Connect to or tap the Connect to button.
Fig. 6. Connect to Webots
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(6) Double-click the desired robot icon (the name should include the name of the user computer). Change the IP series and Fixed Port in the necessary case (Fig. 7).
Fig. 7. Fix the IP series and Port code in the necessary case during connection.
(7) After that, the overview of NAO model is shown as in Fig. (8).
Fig. 8. The detailed joints of NAO robot.
4 Case Study A case study of NAO model dancing is designed to evaluate the stability of the signal connection and the performance of the robot motion. The sound recorded during the - di” - presented by robot operation process is inspired by the music song “Covid nhanh di HUST students and staff.
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Fig. 9. Design the behavior layers of NAO project.
To begin with, we designed the behaviour layers for NAO model, included as the startstop condition of the robot, flicker eye LEDs and the background music. The behaviour layers are indicated as in Fig. (9). Based on these layers, the detailed motions of NAO robot are established steps by step on the timeline. There are 37 motions in total on the whole body of the NAO robot. In particular, the rotated degrees are considered and calculated precisely for each joint. Next, these parameter values are calibrated directly as in Fig. (10), 11 and Fig. (12).
Fig. 10. Calibrate the left leg joints of NAO robot.
Fig. 11. Calibrate the left wrist joints of NAO robot.
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Fig. 12. Calibrate the head joints of NAO robot.
At the next stage, the frame rate is set as 10, and the activation mode is passive to synchronize with the background music (Fig. 13).
Fig. 13. Edit the timeline for the robot performance.
Finally, the proposed program is also applied on the real NAO model to inspect the accuracy of the robot performance. In Fig. (14a) to Fig. (14b), the movement of the real robot is recorded and compared to the simulation. It is noticeable that the NAO motion on the simulation project is similar to the performance of the real model. Furthermore, the joint errors during NAO operation are eliminated completely.
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Fig. 14. (a) The real robot movement (b) The simulation robot movement
5 Conclusion In this paper, a method of controlling virtual NAO robots in the E-learning network is proposed. It has been shown that using NAO simulation can help students and teachers get more out of the educational experience and provide cost-effective means for supplemental learning in online classes. Based on the abilities to teach, operate, and study anywhere and anytime, this approach has made a creative deployment of learning and communication that have profoundly transformed the traditional teaching and learning landscape, from physical environments to digital platforms. Furthermore, by coupling the Choregraphe software and Webots, the properties related to robot operation such as integration, sensing, cognition, manipulation, and power of the NAO models are still ensured precisely. Hence, it makes users comfortable during the interaction. In conclusion, further investments and contingency plans are needed to develop a resilient education system supporting NAO robots’ electronic and distance learning.
References 1. Alsoud, A.R., Harasis, A.A.: The impact of COVID-19 pandemic on student’s e-learning experience in jordan. J. Theor. Appl. Electron. Commer. Res. 16, 1404–1414 (2021) 2. Alyoussef, I.: E-learning system use during emergency: an empirical study during the COVID19 Pandemic. Front. Educ. 6 (2021) 3. Kumar, A., et al.: Blended learning tools and practices: a comprehensive analysis. IEEE Access 9, 85151–85197 (2021) 4. Alamo, T., Millán, P., Reina, D.G., Preciado, V.M., Giordano, G.: Challenges and future directions in pandemic control. IEEE Control Syst. Lett. 6, 722–727 (2021) 5. Altalbe, A.: Antecedents of actual usage of e-learning system in high education during COVID-19 pandemic: moderation effect of instructor support. IEEE Access 9, 93119–93136 (2021) 6. Abroshan, H., Devos, J., Poels, G., Laermans, E.: COVID-19 and phishing: effects of human emotions, behavior, and demographics on the success of phishing attempts during the pandemic. IEEE Access 9, 121916–121929 (2021) 7. Bastani, H., Drakopoulos, K., Gupta, V., et al.: Efficient and targeted COVID-19 border testing via reinforcement learning. Nature 599, 108–113 (2021)
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8. López-Belmonte, J., Segura-Robles, A., Moreno-Guerrero, A.-J., Parra-González, M.-E.: Robotics in education: a scientific mapping of the literature in web of science. Electronics 10, 1–18 (2021) 9. Younis, H.A., Mohamed, A.S.A., Jamaludin, R., Wahab, M.N.A.: Survey of robotics in education, taxonomy, applications, and platforms during covid-19. Comput. Mater. Continua 67, 687–707 (2021) 10. Carro, G., Sancristobal, E., Plaza, P.: Robotics as a tool to awaken interest in engineering and computing among children and young people. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje 16, 204–212 (2021) 11. Tuna, G., Tuna, A., Ahmetoglu, E., Kuscu, H.: A survey on the use of humanoid robots in primary education: prospects, research challenges, and future research directions. Cypriot J. Educ. Sci. 14, 361–373 (2019) 12. Newton, D.P., Newton, L.D.: Humanoid robots as teachers and a proposed code of practice. Front. Educ. 4, 1–10 (2019) 13. Aspernäs, A.: Human-like Crawling for Humanoid Robots - Gait Evaluation on the NAO robots, Master thesis, Faculty of Technology, Department of computer science and media technology. CM), Linnaeus University, Sweden (2018) 14. Shamsuddin, S., et al.: Humanoid robot NAO: Review of control and motion exploration. In: 2011 IEEE International Conference on Control System, Computing and Engineering, pp. 511–516 (2011) 15. Cao, H.-L., et al.: Robot-assisted joint attention: a comparative study between children with autism spectrum disorder and typically developing children in interaction with NAO. IEEE Access 8, 223325–223334 (2020) 16. Scianca, N., De Simone, D., Lanari, L., Oriolo, G.: MPC for humanoid gait generation: stability and feasibility. IEEE Trans. Rob. 36, 1171–1188 (2020) 17. Aldebaran, doc.aldebaran.com 2014, Récupéré sur http://doc.aldebaran.com/ 18. Assad-Uz-Zaman, M., Islam, M., Rahman, M., Wang, Y., McGonigle, E.: Kinect controlled NAO robot for telerehabilitation. J. Intell. Syst. 30, 224–239 (2021) 19. Thepsoonthorn, C., Ogawa, Ki., Y. Miyake, The relationship between robot’s nonverbal behaviour and human’s likability based on human’s personality. Sci. Rep. 8, 8435 (2018) 20. Webots: Open-source Mobile Robot Simulation Software. http://www.cyberbotics.com 21. Kashyap, A.K., Parhi, D.R., Muni, M.K., Pandey, K.K.: A hybrid technique for path planning of humanoid robot NAO in static and dynamic terrains. Appl. Soft Comput. 96, 106581 (2020) 22. Pot, E., Monceaux, J., Gelin, R., Maisonnier, B.: Choregraphe: a graphical tool for humanoid robot programming. In: RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 46–51 (2009) 23. Beiter, M., Coltin, B., Liemhetcharat, S.: An introduction to robotics with NAO, Central Career and Technical School, Erie, Pennsylvania (2012) 24. Choregraphe: Multi-platform desktop application. http://doc.aldebaran.com/2-1/ 25. Miskam, M.A., Shamsuddin, S., Yussof, H., Omar, A.R., Muda, M.Z.: Programming platform for NAO robot in cognitive interaction applications. In: 2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), pp. 141–146 (2014)
LQR Control Design in Vibration Control of a Benchmark Building Structure Subjected to Seismic Load Thi-Thoa Mac and Hai-Le Bui(B) School of Mechanical Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet Street, Hanoi, Vietnam [email protected]
Abstract. This work presents the design problem of LQR controllers in active vibration control of a benchmark building structure under the seismic load based on a multi-objective optimal solution. The objective functions include the simultaneous reduction of the peak relative displacement and the control energy of the system. The simulation results show that the designed LQR controllers have high control efficiency and guarantee the system’s stability. The Pareto front represents the trade-off level of the objectives, the maximum relative displacement, and the control energy of the system, allowing the designers to choose the appropriate configuration of the controller for the structural model. In addition, the influence of control time delay on the efficiency of the controllers is also carried out. Keywords: LQR control · Active control · Building structure · Earthquake · Multi-objective optimization · Time delay
1 Introduction The vibration control problem of mechanical structures appears in many published studies as well as in industrial applications. In which controllers based on the linear quadratic regulator (LQR) play an important role. The LQR technique is utilized in [1] for active control of stay-cable vibration mitigation, in which the results obtained from the LQR controller, passive and output feedback control methods are compared. The controller in [2] is designed based on the LQR technique in vibration control of structures using an improved tuned liquid column damper. A proposed performance index of the LQR controller, including the relative displacement (RD) and the absolute acceleration (AA) of a building structure, has been introduced in [3]. The LQR technique is applied to control a nonlinear suspension system of a quarter car system [4]. A hybrid LQR-PID controller is investigated in [5] for vibration control of building structures having an active tuned mass damper under earthquake loads. In Ref. [6], the LQR active controller is used in vibration suppression in the stone and marble cutting process. Investigations in [7] represent the active control problem of a two-degree-of-freedom model in the milling process using the LQR method and adaptive network-based fuzzy inference system. The sky-hook damper suspension of half car © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 771–780, 2022. https://doi.org/10.1007/978-981-19-1968-8_64
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models with the active control force produced by using the LQR controller is presented in [8]. The LQR method is applied in [9] to create laws in the active vibration control of the flexible riser under the vortex-induced vibration. In LQR controllers, determining weighting matrices is an important problem and should be given due attention. In Ref. [10], an energy-based method is introduced to calculate the weighting matrices of LQR controllers in active vibration control of a building structure. The LQR controller is used in [11] for vibration control of a 10-story building under seismic excitations, where gain matrices are determined using particle swarm optimization. The active control problem of vehicles using the LQR controller is presented in [12], in which weighting matrices are calculate through an unscented Kalman filter with the modified Riccati equation. An automatic selection method for weighting matrices of the LQR controller is proposed in [13] for active control of an 11 degree-of-freedom structure subjected to earthquake. The active vibration control of structures using the LQR controller is studied in [14] with uncertainties, in which weighing matrices are determined by solving a optimization problem based on the reliability. An approach to calculate the gain matrices of the LQR controller is proposed in [15] for vibration control of structures under seismic excitations. In [16], the LQR technique is applied for the control of piezoelectric composite beams, in which weighting matrices are determined by the genetic algorithm. In the present work, LQR controllers are designed to control a 3-floor building structure subjected to earthquake, where the trade-off level of the objectives, including minimization of RD of the structure and the control energy of the system, is investigated. The efficiency of the controller in the paper is also compared with that of the LQR controller in a previously published paper.
2 Controlled Model Figure 1 shows the benchmark 3-story structure subjected to seismic load x¨ 0 , where the control force u is produce from the actuator on the first floor. Parameters of masses mi , stiffnesses k i , and damping coefficients ci are given as [17]: mi = 1000 kg, k i = 980 kN/m, and ci = 1.407 kNs/m, i = 1, 2, 3. The allowable value of the control force is umax = 700 N. The state equations of the structure are: [M ]{¨x} + [C]{˙x} + [K]{x} = −{λ}¨x0 + {u} ⎧ ⎫ ⎧ ⎫ ⎧ ⎫ ⎨ x1 ⎬ ⎨u⎬ ⎨ m1 ⎬ {x} = x2 ; {u} = 0 ; {λ} = m2 ⎩ ⎭ ⎩ ⎭ ⎩ ⎭ x3 m3 0
(1)
In which the matrices of mass [M], stiffness [K], and damping [C] of the system are 3 × 3 in size. Equations (1) are represented in the state space as below (neglecting the external load): {˙y} = [A]{y} + {B}u
(2)
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where T {y} = y1 y2 y3 y4 y5 y6 T = x1 x2 x3 x˙ 1 x˙ 2 x˙ 3 ⎡
0 0 0
⎢ ⎢ ⎢ ⎢ [A] = ⎢ −k1 −k2 ⎢ m1 ⎢ k2 ⎣ m 2 0
0 0 0
0 0 0 0
1 0 0
(3) 0 1 0
0 0 1 0
k2 −c1 −c2 c2 m1 m1 m1 −k2 −k3 k3 c2 −c2 −c3 c3 m2 m2 m2 m2 m2 k3 −k3 c3 −c3 0 m3 m3 m3 m3
{B} = 0 0 0
1 m1
00
T
⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦
(4)
(5)
The seismic load x¨ 0 is obtained from data of the 1940 El Centro earthquake, in which the peak acceleration of the excitation is scaled to 0.112 g [17]. The control force u is determined through the LQR controller presented in Sect. 3.
Fig. 1. The 3-story structure.
3 Controller Design The control objectives of the system include: • The maximum relative displacement of the system needs to be minimized (usually occurs on the first floor) to ensure structural safety. (6) max y12 → min • The control energy of the system also needs to be minimized. u2 → min
(7)
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The LQR problem in this study is stated as follows [18]: For a linear time-invariant system (2), determine an optimal control satisfying the objective function in the quadratic form as follows: {y}T Q {y} + uT Ru dt → min (8) In which [Q] = [Q]T is a symmetric and positive semi-define matrix, and R is positive. The solution of the problem exists and is given by: u = −R−1 [B]T [S]{y} = −{K}{y}
(9)
The matrix [S] is the unique positive definite root of the below Riccati equation: (10) [S][A] + [A]T [S] + Q − [S]{B}R−1 {B}T [S] = 0 For the objective functions (6) and (7), [Q] and R are selected as follows: ⎡ ⎤ α00000 ⎢ 0 0 0 0 0 0⎥ ⎢ ⎥ ⎥ ⎢ ⎢ 0 0 0 0 0 0⎥ Q =⎢ ⎥; R = β ⎢ 0 0 0 0 0 0⎥ ⎢ ⎥ ⎣ 0 0 0 0 0 0⎦ 000000
(11)
The weights α and β are positive numbers expressing the trade-off degree of the objectives (6) and (7). Hence, by varying the values of the weights α and β, the construction of the Pareto front between the objectives can be performed. The diagram of the LQR controller is exposed in Fig. 2.
Fig. 2. The diagram of the LQR controller.
To guarantee the stability of the feedback system in Eq. (2), real parts of roots λ of the following characteristic equation must be negative [18, 20]: det[([A] − {B}{K}) − λ[I ]] = 0
(12)
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In addition, if the system exists the actuator time-delay τ, the control force u will have the following form [20]: u = u(t − τ ) = −{K}{y(t − τ )}
(13)
Hence, Eq. (2) becomes: {˙y} = [A]{y} − {B}{K}{y(t − τ )}
(14)
Taking the Laplace transform of Eq. (14): s[I ]{y(s)} = [A]{y(s)} − {B}{K}{y(s)}e−τ s
(15)
For the feedback system (14) to be stable, real parts of roots λ of the following characteristic equation must be negative [20]: det [A] − {B}{K}e−τ s − λ[I ] = 0 (16) In which the component e−τ s is approximated using the 2nd order Taylor polynomial series as follows: e−τ s =
(−τ s)2 + (−τ s) + 1 2
(17)
Therefore, the critical time delay τ max can be determined by increasing τ from 0 until the system becomes unstable.
4 Numerical Simulation Results The numerical simulations are performed in this section. The necessary indexes are defined as follows to facilitate the representation of simulation results and to compare the efficiency of the controllers designed in this study with that of published researches: – The peak RD of the structure: Jd =
max|di | maxd i
(18)
Ja =
max|ai | max|ai |
(19)
dt |u(t)| T umax
(20)
– The peak AA of the structure:
– Control energy of the system: Ju =
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where d i is RD of the ith story, d i = x i − x i−1 ; ai is AA of the ith story, ai = x¨ i + x¨ 0 ; d i and ai are RD and AA of the ith story in the open-loop (OL) case. T is the total simulation time, and dt is the time-step size. First, the initial controller is designed (denoted LQR). The weights α and β are selected to balance the indexes J d and J a . The indexes J d , J a , and J u obtained from different controllers are shown in Table 1. In which the LQR controller in [17] is denoted LQRr. The notations LQRd and LQRu are explained later.
Fig. 3. Peak value of RD and AA.
It can be seen from Table 1 that the LQR controller gives a much higher control efficiency than the LQRr controller [17] for both J d (about 27% reduction) and J a (about 19% reduction). The index J a of the LQR controller has lower value than that of all the controllers in [17, 19]. In which the notations MBBC and SSMC are the modified bang–bang controller and the saturated sliding mode controller, respectively. Peak values of RDs and AAs of all floors are shown in Fig. 3. The time response of RD of the 1st floor, the AA of the 3rd floor, and the control force are plotted in Fig. 4. As analyzed in Sect. 3, the objectives, including the structure’s maximum relative displacement and the system’s control energy, need to be minimized. This multi-objective optimization problem is implemented by varying the weights α and β in the objective function (8). It can be seen that the objective functions (6) and (7) can be expressed through the indexes J d and J u . The LQR controllers corresponding to these indexes are respectively denoted LQRd and LQRu.
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Fig. 4. Time responses of the system.
Table 1. The indexes J d , J a , and J u LQRr [17]
MBBC [17]
SSMC [17]
Lim et al. [17]
Du et al. [19]
LQR
LQRd
LQRu
Jd
0.657
0.381
0.388
0.396
0.41
0.480
0.418
0.772
Ja
0.584
0.548
0.560
0.543
0.53
0.472
0.484
0.708
Ju
0.344
0.715
0.175
τ max (ms)
45
14
105
The Pareto front between the indexes J d and J u is presented in Fig. 5. The results in Fig. 5 show that the indexes J d and J u have a trade-off with each other. Based on the Pareto front, the designer can choose an appropriate controller configuration to balance the objectives, including the structure’s maximum relative displacement and the system’s
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control energy. The simulation results for the LQRd and LQRu controllers are also shown in Table 1 and Figs. 3 and 4.
Fig. 5. Pareto front of J d and J u .
Hence, the index J d of the LQRd controller (designed following the maximum displacement objective of the structure) is better than that of the LQR and LQRu controllers, about 13% and 48%, respectively. The value of the index J a of the LQR controller is lower than that of the LQRd and LQRu controllers, about 2.5% and 33%, respectively. The index J u of the LQRu controller (designed according to the control energy objective of the system) is better than that of the LQR and LQRd controllers, about 49% and 76%, respectively. If the system has the control delay, the critical time-delay value τ max for the system to be stable is calculated through Eqs. (16–17). τ max of the LQR, LQRd, and LQRu controllers are also shown in Table 1. Variations of the indexes J d and J a of the LQR, LQRd, and LQRu controllers versus time delay are plotted in Fig. 6. The results in Table 1 and Fig. 6 show that the control energy (J u ) is low, the resistance to control delay is high. The LQRd controller has the highest control efficiency for the index J d but tends to lose stability when τ > 30 ms. The LQR and LQRu controllers exhibit high stability for τ < 80 ms. The results in Table 1, Fig. 5, and Fig. 6 are useful to choose the controller’s configuration suitable for the system.
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Fig. 6. Variation of J d and J a vs. time delay
5 Conclusions In the present work, the design problem of the LQR controller in active control of a benchmark building structure subjected to seismic excitation is performed. The research results show that changing the weighting matrices in the objective function of the LQR controller is necessary to determine a suitable configuration for the LQR controller through the Pareto front of optimal objectives. The designed controllers ensure the system stability and have high efficiency according to the designed goal. Design of sustainable LQR controller for building structures subjected to seismic loads taking into account nonlinear factors will be necessary studies in the future. Acknowledgments. This research is funded by Hanoi University of Science and Technology (HUST) under project number T2021-SAHEP-009.
References 1. Shi, X., Zhu, S., Nagarajaiah, S.: Performance comparison between passive negative-stiffness dampers and active control in cable vibration mitigation. J. Bridg. Eng. 22, 04017054 (2017) 2. Bhattacharyya, S., Ghosh, A.D., Basu, B.: Design of an active compliant liquid column damper by LQR and wavelet linear quadratic regulator control strategies. Struct. Control. Health Monit. 25, e2265 (2018) 3. Miyamoto, K., Sato, D., She, J.: A new performance index of LQR for combination of passive base isolation and active structural control. Eng. Struct. 157, 280–299 (2018) 4. Nagarkar, M., Bhalerao, Y., Patil, G.V., Patil, R.Z.: Multi-objective optimization of nonlinear quarter car suspension system–PID and LQR control. Proc. Manuf. 20, 420–427 (2018)
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5. Heidari, A.H., Etedali, S., Javaheri-Tafti, M.R.: A hybrid LQR-PID control design for seismic control of buildings equipped with ATMD. Front. Struct. Civil Eng. 12, 44–57 (2018) 6. Hanieh, A.A., Albalasie, A.: Comparison between elastomeric passive isolators and LQR active control in stone cutting process: modelling and simulation. Proc. Manuf. 33, 770–777 (2019) 7. Li, X., Liu, S., Wan, S., Hong, J.: Active suppression of milling chatter based on LQR-ANFIS. Int. J. Adv. Manuf. Technol. 111(7–8), 2337–2347 (2020). https://doi.org/10.1007/s00170020-06279-6 8. Rao, L.G., Narayanan, S.: Optimal response of half car vehicle model with sky-hook damper based on LQR control. Int. J. Dyn. Control 8, 488–496 (2020) 9. Song, J., Chen, W., Guo, S., Yan, D.: LQR control on multimode vortex-induced vibration of flexible riser undergoing shear flow. Mar. Struct. 79, 103047 (2021) 10. Alavinasab, A., Moharrami, H., Khajepour, A.: Active control of structures using energybased LQR method. Comput.-Aid. Civil Infrastruct. Eng. 21, 605–611 (2006) 11. Amini, F., Hazaveh, N.K., Rad, A.A.: Wavelet PSO-based LQR algorithm for optimal structural control using active tuned mass dampers. Comput.-Aid. Civil Infrastruct. Eng. 28, 542–557 (2013) 12. Dertimanis, V.K., Chatzi, E.N.: LQR-UKF active comfort control of passenger vehicles with uncertain dynamics. IFAC-PapersOnLine 51, 120–125 (2018) 13. Miyamoto, K., She, J., Sato, D., Yasuo, N.: Automatic determination of LQR weighting matrices for active structural control. Eng. Struct. 174, 308–321 (2018) 14. Li, Y., Xu, M., Chen, J., Wang, X.: Nonprobabilistic reliable LQR design method for active vibration control of structures with uncertainties. AIAA J. 56, 2443–2454 (2018) 15. Moghaddasie, B., Jalaeefar, A.: Optimization of LQR method for the active control of seismically excited structures. Smart Struct. Syst. 23, 243–261 (2019) 16. Tian, J., Guo, Q., Shi, G.: Laminated piezoelectric beam element for dynamic analysis of piezolaminated smart beams and GA-based LQR active vibration control. Compos. Struct. 252, 112480 (2020) 17. Lim, C., Park, Y., Moon, S.: Robust saturation controller for linear time-invariant system with structured real parameter uncertainties. J. Sound Vib. 294, 1–14 (2006) 18. Lin, F.: Robust Control Design: An Optimal Control Approach. Wiley, New York (2007) 19. Du, H., Zhang, N., Naghdy, F.: Actuator saturation control of uncertain structures with input time delay. J. Sound Vib. 330, 4399–4412 (2011) 20. Bui, H.-L., Le, T.-A., Bui, V.-B.: Explicit formula of hedge-algebras-based fuzzy controller and applications in structural vibration control. Appl. Soft Comput. 60, 150–166 (2017)
Detecting Crack on a Beam Subjected to Impact Load Fergyanto E. Gunawan1(B) , Tran Huu Nhan2 , Sutikno3 , and Insannul Kamil4 1 Industrial Engineering Department, BINUS Graduate Program, Master of Industrial
Engineering, Bina Nusantara University, 11480 Jakarta, Indonesia [email protected] 2 Department of Automotive Engineering, Faculty of Transportation Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam 3 Mechanical Engineering Department, Faculty of Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia 4 Industrial Engineering Department, Faculty of Engineering, Andalas University, Padang, Indonesia
Abstract. Crack often affects structural deformation nonlinearly, leading to difficulty detecting their existence using some Structural Health Monitoring methods. In this paper, we propose a damage index derived from the Euler-Bernoulli beam theory. Furthermore, we evaluate the method’s ability to detect the crack when the specimen is subjected to an impact load. To assess the proposed damage index, we consider a beam specimen containing a lateral crack. The beam has a length of 300 mm and a square-shaped cross-sectional area of 20 mm × 20 mm. The beam is made of steel, is constrained at one end, and is subjected to an impact load at the other end. The beam lateral displacement at many observation points across the beam-free surface is recorded during the beam dynamics. The records are used to compute the proposed damage index. The results suggest that the index only detects the crack when the beam deformation is affected by the crack. When the crack face is suppressed to bear the compressive stress, the resulting deformation is not much different from that of the uncracked beam. In that condition, the proposed index is not able to indicate the existing crack. Keywords: Structural health monitoring · Euler-Bernoulli beam · Cracked beam · Lateral deformation · Damage index
1 Introduction This paper discusses an issue arising in the development of the Structural Health Monitoring (SHM) system. The system allows us to monitor engineering structures in operation, informing us of the structural condition for safety consideration. In doing so, the system collects data—structural, external loads, and the environment—and infers the internal structural states. Typically, the architecture of the SHM system is shown in Fig. 1. The architecture has two parts. The first part is integrated into the monitored engineering structure on the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 781–789, 2022. https://doi.org/10.1007/978-981-19-1968-8_65
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site. The second part is positioned in a central control office. To make the SHM system work, we require data sensing, storing, and transferring systems. The second necessary part is the Structural Evaluation Subsystem. The collected data are processed, extracted, and used to infer structural integrity-related attributes, including the structural natural frequencies and deformation modes. This research is related to the second SHM part described previously. The topic of damage-sensitive features has been a subject of interest of many researchers during the last few decades.
Fig. 1. The Structural Health Monitoring Framework (adapted from Ref. [19])
The natural frequency and mode shapes are the most widely used damage features [1, 2]. However, many publications proposed using machine learning techniques during the last few years, including the convolution neural network [3]. But, the use of natural frequency is still pervasive. Reference [4] uses the attribute to predict cracks on a laminated composite. As for aerospace applications where laminated materials are widely used, References [5] discussed several relevant techniques. The other widely used method is by considering the structural data are a time series from which an Auto-Regressive and Moving Average (ARMA) model can be established [6]. With the model, any response from the structure that deviates significantly from the model prediction can be regarded as gathered from an altered structure. We propose a damage index to enrich the body of works about the damage-sensitive features. We evaluated the applicability of the index in Ref. [7] for the case of a beam with smeared damage and a breathing crack under a harmonic load in Ref. [8, 9]. In Refs. [10–12], we evaluate the index using mass-spring systems. In Refs. [13–15], we studied the use of F-statistic and its benefits in conjunction with a machine learning approach. In the present work, we look at the index performance for the case involving an impact force.
2 Research Methods When a beam is laterally loaded and undergoes a slight deformation, the lateral deformation w(t, x) of any unloaded point x at the time t follows the differential equation: EI
∂ 2w ∂ 4w = −ρA +q ∂x4 ∂t 2
(1)
The symbol E represents the beam elastic modulus, I is the moment of inertia of the beam cross-sectional area, ρ means the material density, and A represents the beam
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cross-sectional area. The last symbol q = q(t, x) is a time-varying applied load at a location. In Eq. (1), the first term denotes elastic energy, and the second indicates kinetic energy. The elastic energy constantly interchanges with the kinetic energy during the beam-free vibration in the elastic regime and intact condition. An increase in elastic energy should be accompanied by a reduction in kinetic energy and vice versa. When the beam cracks, energy should be dissipated, and the equation would be out of balance. If the beam geometry is unchanged and the material properties intact, the deformation should strictly follow the governing dynamics. Any change in the material constants or geometry, for example, due to crack, should alter the equilibrium equation. We propose that any deviation from the condition of the energy equilibrium may indicate the occurrence of damages. Mathematically, we propose. ∂ 4w ∂ 2w (2) d = EI 4 + ρA 2 − q. ∂x ∂t We denote d as the damage index as it measures the incompatibility between the elastic energy, the kinetic energy, and the applied work. To evaluate the present proposal in its capability to detect damage, we establish a data set from a model of beam vibration. The model is developed by the finite element method. The beam is a cantilever subjected to an impact load at its free end. The other end is clamped. We analyze the beam on the intact condition and the condition with a crack. See Fig. 2. The beam length is 300 mm with a square cross-sectional area of 20 mm by 20 mm. It is made of steel with an elastic modulus of 207 GPa, Poisson’s ratio of 0.3, and a density of 8050 kg/m3 . The crack exists in the beam midsection with a length of 6 mm. An impact force is applied to the free end as a concentrated force with the profile follows the first hump of the sine function. The duration is 27.544 ms. The beam dynamics are analyzed for a period of 128 ms. For every 0.5 ms, the beam displacement data at several observation points are recorded to a file for further processing.
Fig. 2. A cracked cantilever beam subjected to a concentrated impact force.
3 Results and Discussion In the previous section, we discuss our proposal for a damage indicator, an index that can monitor the occurrence of damages. The index can be computed at any point in the structure and requires only the displacement data.
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The first part of the index is a fourth-derivative of the displacement to the position. We compute the derivative numerically by using a central difference approximation scheme. The approximation computes the fourth derivative with the formula: ∂ 4w dx4
≈
wi−2 − 4wi−1 + 6wi − 4wi−1 + wi−2 h4
(3)
In the formula, the point of interest for the damage index is the point xi . The present damage index requires measurements in five points. Those five points should be in a closely-spacing row. In the present case, we take the distance between points of 1 mm. The second part of the equation is the time-derivative of the displacement. It can be computed in three steps. First, we fit the time-history data at the point of interest with a cubic spline. Second, we calculate the time derivative of the spline. Finally, we compute the result for all time instances. As mentioned briefly in the Research Method section, the sample data are obtained via a numerical simulation about a beam specimen with and without crack. The crack is located at the middle section of the beam. Critical to developing a model that accurately describes the beam dynamics with a crack. To assess the model capability in modeling the edged crack, we observe three aspects. Firstly, we consider the crack area when the region is subjected to compressive and tensile stresses. Theoretically, the crack face should have zero stress in tension, but the crack should support compressive stress. We observe that the established numerical model can show the phenomenon reasonably well. In Fig. 3, we show the stress in the beam and its deformation when the beam undergoes positive bending deformation. Similarly, in Fig. 4, we present the beam for the case of negative bending deformation. For the first condition, the cracked area is tension; thus, the crack opens, and the stress on the crack face becomes small. We also see the stress concentration at the crack tip. For the second condition, the cracked region is under compressive stress.
Fig. 3. The Von Mises stress distribution and the beam deformation when the beam deforms in a positive bending mode.
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Fig. 4. The Von Misses stress distribution and the beam deformation when the beam deforms in a negative bending mode.
The crack closes but can support the applied compressive stress. In this condition, the existing damage does not obstruct the continuity of the stress field. These results suggest that the crack has been modeled sufficiently for the current study. Secondly, for precise assessment, we selected an element where one of its edges lies on the crack face. We acquire the stress perpendicular to the crack face from the element and present the data as a function of time in Fig. 5. The results in Fig. 5 strengthen our conviction that the model has been adequately developed. The figure shows the crack face is completely compressed during the first 25 ms. Afterward, the beam vibrates harmonically, as suggested by the data. As long as the beam harmonic vibration, the crack undergoes a closing and opening phenomenon. The elemental stress reduces to zero when the crack opens and increases when the crack closes. These results are in agreement with our expectations of the model. Finally, we use the displacement data at the observation points and compute the proposed damage index. The damage index is calculated for the condition with and without cracks. Theoretically, we expect the damage index value is small when the beam has no crack. In the following, we show the index at two points. The first is at x = 0, which is located on the top surface of the beam at the midsection. The second point is also on the top surface but slightly shifted to the right of the midsection at 5 mm. The lateral displacements at those points are depicted in Fig. 6 and Fig. 7, respectively. Both measurement points are near to each other. As a result, the displacement time histories look similar. The midpoint deflects laterally around 1.2 mm maximum. A notable phenomenon shown by the two figures is that the lateral displacement data for the beam with and without crack are challenging to be differentiated. We conclude that difficult to monitor the damage by observing the beam lateral displacement. However, the existing crack becomes detectable when the lateral displacement data are used to compute the proposed damage index. Figure 8 shows the value of the index is more significant for the case with crack. For the case without crack, the damage index value is small. Theoretically, it should be zero. The small value is due to the numerical approximation in computing the space- and time-derivatives. We also witness
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Fig. 5. The stress is perpendicular to the crack face in an element on the crack face.
Fig. 6. The beam lateral displacements at x = 0 with and without the crack
Fig. 7. The beam lateral displacements at x = 5 mm with and without the crack.
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the effectiveness of the proposed damage index is degrading quickly by distancing the observation point from the damage location (see Fig. 9). We also note that the present case, the changes of the natural frequencies are small. The largest change is associated with the fourth mode with a magnitude about 5%.
Fig. 8. The computed damage index at the point x = 0 for a beam with and without the crack.
Fig. 9. The computed damage index at the point x = 5 mm for the beam with and without the crack.
4 Conclusion The Structural Evaluation Subsystem (SES) is a system within a Structural Health Monitoring (SHM) system that functions to interpret structural deformation data and to relate them to the structural integrity. A widely used method is the change of the structural
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natural frequency. However, some types of damages may not alter the frequency significantly to be detectable by the SES. In this work, we propose a damage index that is sensitive to the occurrence of a crack. As for future research, we offer to evaluate the index for various crack lengths and investigate how the crack length affects the index. The results should be compared with the change of the natural frequencies. Acknowledgments. The research is supported by Direktorat Riset dan Pengabdian Masyarakat Direktorat, Jenderal Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republic of Indonesia, via Penelitian Terapan Unggulan Perguaran Tinggi dan Penelitian Terapan with the main contract number: 309/E4.A/AK.04.PT/2021 and the derivative contract number: 3507/LL3/KR/2021 with the title: “Pengembangan Sistem Pemantauan Struktur - Tahap 1: Model Matematis untuk Prediksi Kerusakan.”
References 1. Xu, Y., Brownjohn, J.M., Hester, D.: Enhanced sparse component analysis for operational modal identification of real-life bridge structures. Mech. Syst. Signal Process. 116, 585–605 (2019) 2. Perez, M.A., Serra-Lopez, R.: A frequency domain-based correlation approach for structural assessment and damage identification. Mech. Syst. Signal Process. 119, 432–456 (2019) 3. Khodabandehlou, H., Pekcan, G., Fadali, M.S.: Vibration-based structural condition assessment using convolution neural networks. Struct. Control Health Monit. 26(2), e2308 (2019) 4. Sahu, S., Das, P.: Experimental and numerical studies on vibration of laminated composite beam with transverse multiple cracks. Mech. Syst. Signal Process. 135, 106398 (2020) 5. Giurgiutiu, V.: Structural health monitoring (SHM) of aerospace composites, in Polymer composites in the aerospace industry. Elsevier, pp. 491–558 (2020) 6. Paixao, J.A., da Silva, S., Figueiredo, E.: Damage quantification in composite structures using autoregressive models. In: Proceedings of the 13th International Conference on Damage Assessment of Structures, pp. 804–815 (2020) 7. Gunawan, F.E.: A new damage indicator for structural health monitoring: Euler-Bernoulli beam case. ICIC Express Lett. Part B Applicat. 11(3), 213–220 (2020) 8. Gunawan, F.E., Nhan, T.H., Asrol, M., Kanto, Y., Kamil, I.: A new damage index for structural health monitoring: a comparison of time and frequency domains. Proc. Comput. Sci. 179, 930–935 (2021) 9. Gunawan, F.E.: A new damage indicator in time and frequency domains for structural health monitoring: the case of beam with a breathing crack. Int. J. Innovat. Comput. Inf. Control 16(5), 1579–1591 (2020) 10. Gunawan, F.E., Soewito, B., Surantha, N., Mauritsius, T., Sekishita, N.: Numerical analysis of general-vibration-based method for structural health monitoring. In: AIP Conference Proceedings, 2227, AIP Publishing LLC, p. 040030 (2020) 11. Gunawan, F.E.: The sensitivity of the damage index of the general vibration method to damage level for structural health monitoring. ICIC Express Letters 13(10), 931–939 (2019) 12. Gunawan, F.E., Soewito, B., Surantha, N., Mauritsius, T., Sekishita, N.: Numerical analysis of general-vibration-based method for structural health monitoring. In: The 16th International Conference on Quality in Research (QiR) (2019) 13. Gunawan, F.E., Soewito, B., Surantha, N., Mauritsius, T., Sekishita, N.: The accuracy of the f statistic for structural health monitoring. In: AIP Conference Proceedings, 2227. AIP Publishing LLC, p. 020024 (2020)
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14. Gunawan, F.E., Soewito, B., Surantha, N., Mauritsius, T., Sekishita, N.: The accuracy of the f statistic for structural health monitoring. In: The 16th International Conference on Quality in Research (QiR) (2019) 15. Gunawan, F.E.: Improving the reliability of F-statistic method by using linear support vector machine for structural health monitoring. ICIC Express Letters 12(12), 1183–1193 (2018) 16. Rahutomo, R., Gunawan, F.E.: Design deep learning neural network for structural health monitoring. J. Theor. Appl. Inf. Technol. 97(5), 1634–1643 (2019) 17. Kiusalaas, J.: Numerical methods in engineering with Python 3. Cambridge University Press, Cambridge (2013) 18. de Boor, C.: A Practical Guide to Splines. Springer, New York (2001) 19. Xu, Y.L., Xia, Y.: Structural Health Monitoring of Long-Span Suspension Bridge. Spon Press, London (2021)
Thermal Analysis by Finite Element Model for Powder Screw Extruder for 3D Printing Method Quang Duy Do, Hung Quang Tran, Thien Bat Le, Lan Xuan Phung, and Trung Kien Nguyen(B) School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Nowadays, the application of 3D printing is becoming more popular in production. 3D printing is also prospective supported processing in medical applications because it can print complex scaffold structures. In this paper, the heating process on a 3D printer head with a screw extruder was simulated to determine the temperature state of polycaprolactone powder in the barrel. The result was used to control the starting temperature for the motor feeding. The effect of the design material will be compared between Aluminium alloy and Teflon plastic on barrels and extrusion shafts through the influence of materials on the ability to transfer heat and the determination of the temperature of the sensor to control the motor running time. The results show that using Teflon material is more effective in saving printing powder and avoiding powder clogging. Keywords: Additive manufacturing · 3D printing · Heating simulation · Extrusion temperature · PCL powder
1 Introduction Additive manufacturing refers to the technologies that build 3D objects by adding layerby-layer of material. Among the common additive manufacturing techniques, Fused Deposition Modeling (FDM) technology is referred to as one of the most well-known, simple, and low-cost methods. Besides applications in engineering and design, manufacturing, 3D printing based on FDM technology has potential application in fabricating scaffolds for tissue engineering. For common applications, FDM printers are often used in popular thermoplastic materials such as PLA, ABS, PCL in filament form. For tissue engineering applications, polycaprolactone (PCL) is the most used thermoplastic material in scaffold fabrication due to its biocompatibility and biodegradability. Moreover, compared to PLA, PCL decomposes more slowly, so it is possible to keep structurals stability and mechanical properties in the living body for tissue regeneration [1]. However, the most popular form of PCL material at medical grade is a powder of pellet that is not suitable for FDM machines. Thus, it is necessary to develop equipment or machine to produce PCL filament from powder or pellet form or directly use it in powder or pellet © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 790–801, 2022. https://doi.org/10.1007/978-981-19-1968-8_66
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form on the 3D printers. The customized machine can also produce the composite filament that composes of PCL and other materials to increase the biological characteristics of the base material. For this machine, the filament quality depends on nozzle diameter and extrusion speed. Moreover, it is also affected by the melt temperature set for the printing process. Temperature control and maintaining heat sources to make melt PCL material is one important controlling factor for the quality of the filament printing process [2]. Temperature analysis is a necessary step to optimize the design of the printing head and heating parameter. There have been a few studies on thermal analysis using finite element model and important investigations have been drawn for their designs. Ghany et al. investigated the temperature distribution of single-screw extruders using FEM to evaluate and propose a better screw design for extrusion heads [3]. The result shows that the important thing is the diameter of filament just before hot block, so they use Mk8 model with heat breaker is better for design. Arias et al. determined the effect of the materials and geometric design elements of the deposition printing head by FEM analysis [4]. They show that the conical nozzle is better with an increasing flow rate. When working with low speed, a small diameter nozzle will make form-flatten filaments, and when working with high speed, we need a larger nozzle diameter. In short, thermal analysis is the first and essential step in the design and manufacturing process of 3D printers. For screw-based extrusion, one of the main problems is the clogged plastic phenomenon in the barrel. The reason lies in the temperature transferred from the extruder to the powder inlet, which leads to the powder melting very early and condensing on the screw [5]. Normally, the materials used to make screws and barrels are aluminum alloys or steel. To minimize the phenomenon of plastic clogging in the barrel, a cooling area (heat sink) with a fan is added in the design of an extruder. However, with the mini-screw extruder, the problem can only be solved in a short time operation. The plastic clogging phenomenon still appears after normal printing time. In this research, the material of the barrel and screw is considered to reduce the heat transfer along the screw. The thermal process analysis using the FEM method is applied to figure out the influence of the screw and barrel material on the extrusion process. Furthermore, thermal process analysis to determine the right printing temperature to generate the filament from PCL powder. The preliminary results show that the diversification of the design material will be better results and potentially reverse the risk of plastic clogging.
2 Models and Simulation Setting 2.1 Model Design for Analysis In general, any 3D screw extrusion printer followed a common design as shown in Fig. 1. However, there were also distinct characteristics beyond size and material to adjust the temperature distribution in the barrel properly. This adjustment made the ability to continuously extrude the powder to avoid the case that the molten plastic logs the inside of the barrel. Two models with the same structure but different materials for extrusion screw and barrel were used for this research. The first one was made from Aluminum with a heat transfer coefficient of 30 W/m·K, while the second model used Teflon with a heat transfer coefficient of 0.24 W/m·K. The material selection of models to perform the
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thermal analysis was mainly according to economic and manufacturing parameters. The materials should be available on the market, low price, easy to manufacture, compatible with machines on the market. The development of a plastic powder extruder that could be directly used thermoplastic powder materials requires a fundamental understanding of the relationship between locomotive design and heat transfer on the barrel. Therefore, a design that controlled regions of the barrel reaching the permissible temperature was the main focus of the present study as it’s very important for the success of extrusion of plastic filaments directly from the powder. The design of the screw extrusion printing head included feeding hopper, barrel, extrusion shaft, heating chamber, nozzle, and temperature sensor. The copper nozzle with a diameter of 1.75 mm was connected to the heating chamber using two heat source heads and one temperature sensor. The barrel and hopper that also support the heating chamber were responsible for feeding the powder material to the extruder. The hopper at the inlet was used to guide the powder into the barrel. The screw transported the powder from the feed area from the hopper at low temperature to the melting zone near the nozzle area. The barrel was connected to the heating chamber through a threaded hole. By this design, the length of the hot zone could be varied in the most appreciated position which the temperature distribution in the areas of barrel and screw meets the set standards. For the model geometry, we used a screw shaft with the inner diameter increased from 7 mm to 10 mm along its length, while the outside diameter was kept constant at 12 mm. Threads were evenly distributed axially on the screw with a pitch of 8 mm and a total of 10 pitches. To generate the compression to extrude the powder, we use the design with a helix angle of 18.5° and a thread angle of 45° as shown in Fig. 2.
Screw
Feed Hopper
Barrel
PCL Material
P6 P5 O-ring
P4
Feeding region
P3 Heater
Nozzle
P2 P1
Heating region
Fig. 1. The extruder structure and six investigated locations on the feeding screw shaft.
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Table 1. The material properties of PCL, Teflon, and Aluminum. PCL
Teflon Aluminum
Density, ρ (g/mol)
114.14 N/A
1.73 × 105
Specific heat, C (J/mol·K)
220
N/A
24.2
Mass density D (g/cm3 )
1.145
2.2
2.7
Melting point (°C)
60
327
660.32
0.25
35
Thermal conductivity 1.42 coefficient (W/m·K)
Fig. 2. The geometrical dimension of the screw shaft.
2.2 Material Extrusion Process The plastic powder flowed into the screw system through the hopper and the extruder was transported by the screw to the extruder. The extruder had divided into three main zones: transportation zone, melting zone, extrusion zone. The heat sources were set on the heating chamber to make the powder melt since it reached the end of the screw shaft. The rotatory motion of the motor transports and compressive powder through the reduced lead screw. Heat source and heat dissipation were the main control factor for the material feeding consistency at the outlet and flow consistency at the inlet. The heat convection to ambient temperature and heat conduction from the screw significantly affected the powder melting inside the barrel. The time for powder material contacted with heat sources depend on the screw rotation speed. If the temperature was too high, the material melt at the inlet and jam the entire system. On the other hand, it also caused the material to burn at the outlet. In another hand, the low temperature would hinder the extrusion process, causing of stuck because the slow melting rate. Time for Powder’s Moving To calculate the powder’s traveling time from the inlet to the outlet, a set of barrel and screw with geometry data were summarized in Table 2. The volume inside the barrel: Vc (mm2 ) = π × (Dc /2)2 × Lc The axial volume of screw: 1 2 2 Va (mm2 ) = × π × rmax + rmin + rmax × rmin × (L − rev × width) 3
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The volume in a Pitch: 2 × Lrib Vp (mm2 ) = π × rrib
The volume of empty area (area between screw’s end and nozzle): 1 Vempty = π × rc2 × h1 + × π × rc2 + rn2 + rc × rn 3 1 2 2 + rmin.end + rmax.end × rmin.end × h1 ×h1 − × π × rmax.end 3 where, rmax.end , rmin.end were radius of a truncated cone of the screw. The volume of powder inside barrel: Vpowder mm2 = Vc + Vempty −Va −Vp If 95% inside the barrel was powder and 5% was allowed for free space, the volume of final powder is: Vf·powder mm2 = 0.95 × Vpowder The volume of plastic that came out from the nozzle is: 2 × v × t mm2 Vout = π × rnozzle (where v is print velocity)
Table 2. The main geometry dimensions of the extruders Barrel data Length, Lc (mm)
75
Diameter, dc (mm)
12
Screw data Max diameter, Dmax (mm)
10
Min diameter, Dmin
7
Length, L (mm)
87
Pitch (mm)
8
Number of pitch, N
10
Nozzle diameter, rn (mm)
1.75
The printing speed was investigated at 30 mm/s, 50 mm/s, and 70 mm/s. Then total time for the powder moving in the total length of the barrel and each pitch of the screw is calculated in Table 3.
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Table 3. The traveling time of PCL powder with three printing speeds. No Printing speed, V Total screw time Pitch time (mm/s) tT (s) tP (s) 1
30
51
5.1
2
50
30.6
3.1
3
70
21.9
2.2
2.3 Simulation Setup The thermal heating process was performed with a 50 W heat source and a temperature sensor’s limit in the range of 95 °C to 105 °C. The heating head and sensor were mounted directly on the extruder. The powder extrusion system components were set to an initial temperature of 25 °C (room temperature). The PCL was used as feeding powder. The influence of barrel material and extrusion screw material on the state of powder inside the barrel was determined at 6 points (P1 to P6 ) equidistant along the extrusion screw and one point of PCL powder in the heating zone as shown in Fig. 1. To determine the temperature and time to turn off the heating and turn on the motor since Point P1 reached the melting temperature (90 °C). In this case, the value of the sensor was always set in the range 95 ÷ 105 °C. As the sensor detected 95 °C then the heat source would be turned on, while the temperature at this point was 105 °C then the heat source would be turned off. In this study, we designed and conducted the thermal simulation using Solidworks and FFEPlus solver. The 3D model was meshed by Tetra type level 1 with an element size of 0.78 mm evenly and tolerance of 0.039 mm. The preliminary result was not too different from the model meshed with smaller elements size, therefore, this mesh type was selected for this research to reduce the calculation time.
3 Results and Discussion 3.1 The State of PCL Powder in the Barrel The temperature distribution of the extruder with two material models was revealed in Fig. 3. The temperature profile of six points long the screw shaft with two material models was plotted in Fig. 4. Table 2 presented the temperature values of the six equidistant points on the extrusion screw at the time when the temperature at point P1 reached 90 °C. These temperatures with the Teflon model were lower than in the Aluminum model. In the case of the Teflon model, three points near the inlet (points P4 , P5 , P6 ) reached 25 °C ÷ 26 °C and increased insignificantly over time. Meanwhile, these points with the aluminum material model reached 33 °C ÷ 48 °C and according to the chart, it continued to increase rapidly over time. In addition, most of the screw area experience temperatures above 80 °C on the Aluminum material model. This causes the heating of powder at the inlet resulting in the plastic extrusion process being blocked, and the PCL resin would be glued on the feeding screw.
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a)
Teflon
b) Aluminum
Fig. 3. The temperature profile of the extruder with two material models. 120 P1 P2 P3 P4 P5 P6
Teflon Model
o
Temeprature ( C)
100
80
60
40
20 0
50
100
150
200
250
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Time (second)
a) Teflon model 120
P1 P2 P3 P4 P5 P6
o
Temperature ( C)
100
Aluminum Model
80
60
40
20 0
50
100
150
200
250
300
Time (second)
b) Aluminum model Fig. 4. Temperature profiles of six points of long the screw shaft with two material models.
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Table 4. The temperature of six points with two material models at the time P1 reached 90 °C. Positions
Teflon model
Aluminum model
P1
90 °C
90 °C
P2
58 °C
65 °C
P3
36 °C
57 °C
P4
26 °C
48 °C
P5
25 °C
39 °C
P6
25 °C
33 °C
3.2 The Kickoff Temperature of the Sensor to Start the Printing Process To determine the kickoff temperature of the sensor to start the printing process (motor start running), the temperatures at the heating region (point P1 and P2 ) were the main controlling factors. This section considered the extrusion speed was 50 mm/s (total time at 30.6 s and pitch time at 3.1 s) to explain the kickoff temperature. At the pitch time of 3.1 s, the motor speed and flow speed inside the Barrel are calculated at 20 rpm and 0.65 mm/s, respectively. The simulation results presented the temperature profile of points P1 and P2 as shown in Fig. 5 for both models with sensor’s temperature setting in range 95 °C ÷ 105 °C and extrusion speed of 50 mm/s. The temperature of PCL powder at the P1 area was expected to reach 90 °C for printing. In the case of the Teflon model, Fig. 4a showed that point P1 reached 90 °C at t 90 = 130 s. Since the PCL powder experienced a movement time t T from the inlet to point P1 , the time for the motor to start running was calculated by Eq. (1): tm = t90 −tT
(1)
Then, the time for the motor to start running (t m ) was used to determine the kickoff temperature of the sensor started the printing process. The time tT was 30.6 s with an extrusion speed of 50 mm/s. Therefore, the time for the motor to start running is 99.4 s as calculated in Eq. 1. At this moment, the temperature at the sensor region was determined at 108 °C. With the Aluminum model, the time of P1 ’s temperature at 90 °C was determined at t 90 = 210 s. The time for the motor started running and the kickoff temperature was set at t m = 179.4 s and T m = 103 °C, respectively. It could be seen that using the same temperature source and movement time of powder, the time for P1 in the Aluminum model reached 90 °C was longer than that in the Teflon model. The reason was that Aluminum was better at absorbing heat than Teflon. The heat source from the heating head was partially absorbed into the Aluminum barrel and extrusion screw, so the time for the endpoint of the screw reached 90 °C was longer than that of the Teflon material model. Table 4 presents the kickoff time and sensor temperature to start feeding motor for different printing speeds for both Teflon and Aluminum models. The Teflon model showed stable data which makes possible a control scheme for the feeding motor.
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With the power of heat source was 50 W and time for the PCL powder ran each step of t R , the heat pervade could be calculated as: Qprevade = P · t (J) 140
Teflon Model
o
Temperature ( C)
120
100
80
60
40
P1
P2
20 0
50
100
150
200
250
300
Time (second)
a) Teflon model 120
Aluminum Model
o
Temperature ( C)
100
80
60
40
P1
P2
20 0
50
100
150
200
250
300
Time (second)
b) Aluminum model Fig. 5. The temperature profiles at points P1 and P2 of a) Teflon model and b)Aluminum model with the sensor temperature setting at 95 °C–105 °C
The heat for the powder reached 90 °C was expressed in the following equation: m Qreceived = mCT = × C × (Tfinal − Tstart ) (J) ρ Qpervade = Qreceived The total volume of the PCL that could be heated was determined as: m = 1810 mm3 V= D The total volume of PCL inside the barrel was 3682.25 (mm3 ). The volume powder was calculated at 1810 (mm3 ) - half of the total, which meant that the powder ran during the final step was enough time to reach the melting point (90 °C).
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Table 5. The t kickoff time and sensor’s temperature with three printing speeds. Model
t 90 (s)
t m (s)
T m (°C)
Teflon at 30 m/s
130
Teflon at 50 m/s
130
99.4
108
Teflon at 70 m/s
130
108.1
116
Aluminum at 30 m/s
210
159.0
95
Aluminum at 50 m/s
210
179.4
103
Aluminum at 70 m/s
210
188.1
93
79.0
97
3.3 Sensor’s Temperature Effect on the Melting Point By increasing the sensor temperature to 115–125 °C, the time point P1 reached to 90 °C of both material models was reduced compared to the those with the setting in 95 °C ÷ 105 °C range. 160
Teflon Model
140
o
Temperature ( C)
120 100 80 60 40 20
P1
P2
0 0
50
100
150
200
250
300
Time (second)
a) Teflon model 140
Aluminum Model
100
o
Temperature ( C)
120
80
60
40
P1
P2
20 0
50
100
150
200
250
300
Time (second)
b) Aluminum model Fig. 6. The temperature profiles at points P1 and P2 of a) Teflon model and b) Aluminum model with the sensor temperature setting at 115 °C ÷ 125 °C
The point P1 reached 90 °C at t 90 at 102 s and 138 s on the Teflon and Aluminum model as shown in Fig. 6. Based on the temperature profile of two points P1 and P2 ,
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these points on the Teflon model had temperature was more stable than those with the Aluminum model. Therefore, the model with Teflon material would reduce the heating time, eliminate the risk of clogging due to PCL adhesive inside the barrel. 3.4 The Effect of O-ring in the Extruder’s Design on the Melting Point A common problem in plastic extruders was that the PCL resin would stick at the tip of the extruder, affecting the ability to extrude the powder and plastic in the barrel as the extruder shaft operates for a long time. To solve this problem, the extruder’s design was added a Teflon O-ring acting as an insulation washer located at the area between the heating area and the barrel as shown in Fig. 1. The temperature of the PCL powder could be adjusted with the modification in the thickness of the O-ring. As shown in Fig. 7, the temperature value of point P3 in the improved model (with o-ring) was almost at the original room temperature while the temperature at point P1 remained at 90 °C. Based on temperature profile results, it could be seen that after 300 s, the temperature of point P3 increased slightly (about 30 °C), while the temperature of point P3 in the model without O-ring was up to nearly 60 °C. Thus, adding Teflon O-rings helped the screw head be isolated from the heating zone results in the reduction in the screw’s temperature. This design would help to reduce the risk of melt-PCL adhesion on the screw leading to clogging, to ensure a stable volume of PCL material at the nozzle since the extruder was working for a long time. 140
P1 P3 without O-ring
120
o
Temperature ( C)
P3 with O-ring 100
80
60
40
20 0
50
100
150
200
250
300
Time (second)
Fig. 7. The temperature profile at the point P3 with and without O-ring Table 6. The temperature of point P3 at the time P1 reached 90 °C. Positions
Without O-ring
With O-ring
P1
90 °C
90 °C
P3
36 °C
26,5 °C
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4 Conclusion This paper presented research on thermal process analysis using the FEM method on a powder screw and barrel extrusion process. The following conclusions were drawn: – The improvement was visible to use Teflon instead of an Aluminum design. The Teflon model would avoid the danger of plastic clogging due to heat transfer upward to the inlet. Through temperature simulation, it was possible to determine the time to start running the feeding motor. – The adding of a Teflon O-ring had provided positive results for head isolation. The lack of direct contact between the heating zone and the barrel prevented heat from transferring to the barrel. The O-ring also helped to control the distance from the hopper to the nozzle and set the starting extrusion time, thereby changing the travel time of the material flow. – The simulation of powder’s motion for the printing process could not be simulated, thereby also causing some difficulty in determining the plastic state and the calculation process.
References 1. Tran, T.N., et al.: Cocoa shell waste biofilaments for 3D printing applications. Macromol. Mater. Eng. 302(11), 1700219 (2017) 2. Dávila, J.L., Freitas, M.S., Inforçatti Neto, P., Silveira, Z.C., Silva, J.V.L., d’Ávila, M.A.: Fabrication of PCL/β-TCP scaffolds by 3D mini-screw extrusion printing. J. Appl. Polym. Sci. 133(15), 1–4 (2015) 3. Arias, G.G., Diaz, F.J., Ramirez, E.R., Guzman, J.V.: Thermal analysis by finite elements of hotends for 3D printing by fused filament fabrication. 1–4 (2021) 4. Ortega, E.S., Sanz-Garcia, A., Pernia-Espinoza, A., Escobedo-Lucea, C.: Efficient fabrication of polycaprolactone scaffolds for printing hybrid tissue-engineered constructs. 5–15 (2018) 5. Drotman, D., Diagne, M., Bitmead, R., Krstic, M.: Control-oriented energy-based modeling of a screw extruder used for 3D printing. 1–6 (2016)
The Effect of Printing Parameters on the Characteristics of PCL Scaffold in Tissue Engineering Application Tuan Quang Ta1 , Trung Kien Nguyen2 , Son Hoanh Truong2 , and Lan Xuan Phung2(B) 1 National Research Institute of Mechanical Engineering, Hanoi, Vietnam 2 School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi,
Vietnam [email protected]
Abstract. Three-dimensional (3D) printing is one of the most prospective techniques to fabricate scaffolds in tissue engineering because of its outstanding advantages in the ability to construct controllable and complex 3D structures. The scaffold should have a 3D porous structure and suitable mechanical properties to create a favorable environment for cell ingrowth. In this study, the effect of printing parameters for a 3D printer based on Fused Deposition Modelling (FDM) on the quality of Polycaprolactone (PCL) scaffold is investigated. The scaffold is fabricated and the main geometry characteristics of the scaffold in different printing conditions are evaluated. The results show that the filament width and pore size are significantly affected by printing parameters such as velocity, acceleration, and printing temperature of the 3D printing process. The result is the basis for further studies to optimize the accuracy of scaffolds. Keywords: PCL scaffold · 3D printing · Tissue engineering · Printing parameters
1 Introduction Tissue engineering involves the combination of scaffolds, cells, biomaterials, bioactive factors to obtain the functional and autologous tissues for restoration, improvement and maintenance of damaged tissue caused by different factors such as diseases, injuries, or birth defects. Conventional methods of tissue regeneration and healing are dependent on donated tissue along with risks of infectious disease. To solve this problem, engineered tissue has been studied for instead, in which a favorable environment is created for cell growth and tissue reproduction. These substrates are grown in vitro or in vivo to generate tissues that can be implanted in the wound. In tissue engineering, the scaffold acts as cell carriers to create an environment conducive to cell growth and tissue regeneration. It is typically a 3D porous structure. In addition to traditional methods such as solvent casting and phase separation, 3D printing is considered an effective technique in scaffold fabrication technology. Several researchers have successfully applied various 3D printing technologies [1–3]. Among © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 802–811, 2022. https://doi.org/10.1007/978-981-19-1968-8_67
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these methods, FDM technology is widely used because of the popularity of the FDM 3D printers. The typical biomaterials used for FDM printing technology should be thermoplastic materials such as Polycaprolactone (PCL), Polyethylene glycol (PEG), and Polylactic acid (PLA). Among these materials, PCL has a slower degradation rate that is suitable for long-term and controlled transplants. PCL has high toughness and biocompatibility. It is also approved by FDA for application in tissue engineering because of easily processing and degrading to nontoxic production. Thus, PCL is one of the most thermoplastic biomaterials used for bone and cartilage tissue engineering. Recently, many studies have focused on fabricating PCL scaffolds using FDM 3D printing. Kieu et al. introduced a device to fabricate biomedical plastic PCL filament used in FDM 3D printers [4]. The scaffold was fabricated using the customized filament and cell culture is used for the biocompatibility test. Siyi Wang et al. also used FDM 3D printing technology for fabricating a hollow-structured cage-shaped PCL scaffold and carried out experiments with cell culture on the scaffold [5]. Patricio T. et al. studied the technology of fabricating PCL and PCL/PLA scaffold by 3D printing method, specifically the printing parameters [6]. For the experimental process, they designed and fabricated an FDM 3D printing model using PCL biomaterial and investigate the effect of printing conditions on scaffold characteristics. The engineered tissues or organs will be transplanted into the patient’s body and substrates are required to have the geometrical shape of the damaged tissue or organ. Therefore, the scaffold should have a 3D porous structure with an adjustable pore size as well as the desired pore distribution and degenerative properties to allow cell migration and growth. Shuo Zhang et al. studied the characteristics of scaffolds for different tissue applications as shown in Table 1 [7]. In addition, the scaffold must provide adequate mechanical properties and shape stability to resist stresses and maintain the integrity of the designed structure. These characteristics of scaffold can be adjusted by setting input printing parameters before converting into G-code file for printing process. The most popular printing patterns are grid, rectilinear and concentric. By 3D printing, the distance between individual layers and thickness of the layer can be controlled to provide the internal structure in depth. The characteristics of scaffolds including filament width, pore Table 1. Pore size and porosity for tissue applications [7] Type of tissue
Pore size (μm)
Porosity (%)
Cancellous bone
500 ÷ 1000
50 ÷ 90
Cortical bone
f0 where f0 is the crossover frequency value between off state (0 A) and on state (3 A), f is the base excitation frequency.
Fig. 6. The transmissibility of the scaled suspension using MRE in the frequency domain
The experimental results are shown in Fig. 6 and 7. Figure 6 shows the transmissibility response of the sprung mass in the frequency domain with fixed current values of 0, 3 A, and on-off control. The suspension has two natural frequency values. The first natural frequency values are 13 Hz and 33 Hz, corresponding to the applied current values of 0 A and 3 A, respectively, as shown in Fig. 6. The first natural frequency can be adjustable
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Fig. 7. The top plate response of the system under swept sine excitation
from 13 Hz to 33 Hz by applying an appropriate current. The effect of the second natural frequency is small and insignificant. In the case of on-off control, the current applied to the absorber is set to 3 A and 0 A when the suspension operates in the low and highfrequency ranges, respectively. Furthermore, the suspension was remarkably improved by using the on-off control. The transmissibility shows that the resonance of the vibration response can be avoided. This fact is illustrated by comparing the time history as shown in Fig. 7. The figure shows that the feedback amplitude is consistently minimized using the on-off control scheme.
4 Conclusions In this study, the characteristics of MRE materials were investigated under the influence of magnetic field values and frequency. The stiffness is adjusted by controlling the current applied to the electromagnet. The Bouc-Wen model accurately describes the hysteresis behavior of the material. The MRE-based system is an intelligent system that can change the system’s natural frequency so that the system avoids resonance. Experimental results show that the MRE damping system combined with the stiffness control strategy achieves significant efficiency. Acknowledgments. Thank you, Dr. X.B. Nguyen, for supporting of this research.
References 1. Jolly, M.R., David Carlson, J., Munoz, B.C.: A model of the behaviour of magnetorheological materials. Smart Mater. Struct. 5, 607 (1996) 2. Nguyen, X.B., Komatsuzaki, T., Iwata, Y., Asanuma, H.: Modeling and semi-active fuzzy control of magnetorheological elastomer-based isolator for seismic response reduction. Mech. Syst. Signal Pr. 101, 449–466
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3. Nguyen, X.B., Komatsuzaki, T., Zhang, N.A.: A nonlinear magnetorheological elastomer model based on fractional viscoelasticity, magnetic dipole interactions, and adaptive smooth Coulomb friction. Mech. Syst. Signal Process. 141, 106438 (2020) 4. Komatsuzaki, T., Iwata, Y.: Design of a real-time adaptively tuned dynamic vibration absorber with a variable stiffness property using magnetorheological elastomer. Shock Vib. 2015, 11 (2015). Article ID 676508. https://doi.org/10.1155/2015/676508 5. Nguyen, X.B., Komatsuzaki, T., Iwata, Y., Asanuma, H.: Fuzzy semiactive vibration control of structures using magnetorheological elastomer. Shock Vib. 2017, 15 (2017) 6. Norouzi, M., Alehashem, S.M.S., Vatandoost, H., Shahmardan, M.M.: A new approach for modeling of magnetorheological elastomers. J. Intell. Mater. Syst. Struct. 27(8), 1121–1135 (2016) 7. Liao, G.J., Gong, X.L., Xuan, S.H., Kang, C.J., Zong, L.H.: Development of a real-time tunable stiffness and damping vibration isolator based on magnetorheological elastomer. J. Intell. Mater. Syst. Struct. 23(1), 25–33 (2011) 8. Rasooli, A., Sedaghati, R., Hemmatian, M.: A novel magnetorheological elastomer-based adaptive tuned vibration absorber: design, analysis and experimental characterization. Smart Mater. Struct. 29(11), 15 (2020) 9. Yang, J., et al.: Experimental study and modelling of a novel magnetorheological elastomer isolator. Smart Mater. Struct. 22(11), 1–14 (2013) 10. Opie, S., Yim, W.: Design and control of a real-time variable modulus vibration isolator. J. Intell. Mater. Syst. Struct. 22(2), 113–125 (2011) 11. Nguyen, X.B., Komatsuzaki, T., Iwata, Y., Asanuma, H.: Fuzzy semiactive control of multidegree-of-freedom structure using magnetorheological elastomers. In: Proceedings of the ASME 2017 Pressure Vessels and Piping Conference (PVP2017) (2017) 12. Tao, Y., Rui, X., Yang, F., et al.: Design and experimental research of a magnetorheological elastomer isolator working in squeeze/elongation–shear mode. J. Intell. Mater. Syst. Struct. 29(7), 1418–1429 (2018). https://doi.org/10.1177/1045389X17740436 13. Jansen, L.M., Dyke, S.J.: Semi-active control strategies for MR dampers: a comparative study. J. Eng. Mech. ASCE 126(8), 795–803 (2000) 14. Nguyen, X.B., Komatsuzaki, T., Iwata, Y., Asanuma, H.: Robust adaptive controller for semiactive control of uncertain structures using a magnetorheological elastomer-based isolator. J. Sound Vib. 343, 192–212 (2018) 15. Sistla, P., Figarado, S., Chemmangat, K., Manjarekar, N.S., Valappil, G.K.: Design and performance comparison of interconnection and damping assignment passivity-based control for vibration suppression in active suspension systems. J. Vib. Control 27(7–8), 893–911 (2021). https://doi.org/10.1177/1077546320933749 16. Nguyen, X.B., Komatsuzaki, T., Truong, H.T.: Novel semiactive suspension using a magnetorheological elastomer (MRE)-based absorber and adaptive neural network controller for systems with input constraints. Mech. Sci. 11, 465–479 (2020) 17. Nguyen, X.B., Komatsuzaki, T., Truong, H.T.: Adaptive parameter identification of Bouc-wen hysteresis model for a vibration system using magnetorheological elastomer. Int. J. Mech. Sci. 213, 106848 (2022)
A Computational Method of Air-Electric Equivalent in Air Spindle Truong Minh Duc1,2 , Ta Thi Thuy Huong3 , Nguyen Thanh Trung2 , and Vu Toan Thang2(B) 1 University of Economics, Technology for Industries, Hanoi, Vietnam
[email protected]
2 Hanoi University of Science and Technology, Hanoi, Vietnam
{trung.nguyenthanh2,thang.vutoan}@hust.edu.vn 3 Haiphong University, Haiphong, Vietnam
Abstract. The stable and balanced operation of the air spindle requires an extremely precise manufacturing process. Therefore, it is very important to calculate the dynamics, pressure distribution and stiffness in designing. There are many methods to calculate the air bearing pad model between shaft and bearing in the air spindle. In this paper, a very easy understanding and implementing calculation method which is air-electric equivalent for air bearings of five degrees of freedom was introduced, thereby, the relationship between stiffness, clearance and pressure distribution on air bearing pad surface was realized. Based on this calculation result, it is possible to propose a suitable design and manufacturing method in production conditions in Vietnam. Keywords: Rotary air spindle · Air-electric equivalent · Stiffness · Pressure distribution
1 Introduction In rotary air spindle, the pressure distribution on the air bearing pad surface and the clearance between shaft and bearing greatly affect to the characteristics and stability during operation, especially to the stiffness of air spindle. Many calculation methods have been proposed, in which there is the calculation used the Reynols equation combined with the Navier–Stokes equation. The application of the finite element and the finite differential method have become the two main methods to solve the problem of gas flow. In 1886, Osborne Reynolds [1] presented the Reynolds famous equation by combining the simplified Navier-Stokes equations with a continuity equation providing the mathematical basis for lubrication theory. Subsequently, Harrison [2] proposed the Harrison equation by combining the Reynolds equation with the gas equation under the assumption of isothermal conditions, known as the basic equation of gas lubrication, today. Lemon [3] proposed a simplified model to analyze the performance of static air bearings considering the effect of circumferential flow. The calculated performance © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 868–877, 2022. https://doi.org/10.1007/978-981-19-1968-8_73
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curves are in good agreement with the experimental curves. Mori et al. [4] compared several flow models with the consideration of inertial effects to explain experimental results, the flow state including entrance effects near the outlet, the development of boundary layers and the occurrence of shock waves were discussed. In addition, depending on the limit type of the static air bearing, a reasonable flow pattern should be applied to obtain accurate results. In recent years, to investigate the state of turbulent flow inside the bearing’s clearance, many turbulence models based on computational fluid dynamics such as k-ε and large vortex simulation have been applied in the study of static air bearings [5]. Currently, several numerical methods (including Engineering Simplification Algorithm ESA) [6– 8], Finite Element Method FEM and other finite methods are available. Finite difference method (FDM), Computational Fluid Dynamics (CFD) and Multi-physics Coupling Method MPCM) and experimental methods were applied to investigate features of static air bearing. Colombo et al. [7–9] investigated the performance of rectangular thrust air bearings with single and multiple holes by proposing a lumped parametric model. The accuracy of the proposed model was verified using FEM and FDM. It has been found that the analysis process using ESA is easy to operate and the existing design curve can greatly simplify the design and calculation process. However, due to the difference between the assumed linear gas flow and the real gas flow, the ESA can produce large errors for small clearance air bearings. In addition, the ESA can be only used to calculate the bearing in a stationary state, when considering the real operation speed of the spindle, ESA is no longer effective. Liu et al. [6, 10, 11] presented in detail a FEM-based algorithm to analyze the performance of static air bearings and thrust force. However, due to the complicated meshing and the calculation process, and the iterative algorithm, it is difficult to build a computational program like the general program [12]. Especially, in the recent two decades, with the rapid advancement in commercial CFD software such as FLUENT [13] and ANSYS CFX [14], CFD has become a normal tool to investigate the performance of static air bearings. Although CFD has a lot of advantages, it is time consuming. A typical CFD analysis typically includes pre-processing, solving, and post-processing. During pre-processing, it is necessary to prepare several meshes with different resolutions to ensure that the calculation results are independent of the mesh resolution, it can take about 80% of the total time to customize liquid zone in the preprocess. Furthermore, parameterization in CFD simulations is quite difficult, therefore, any change in the geometry of the static air bearings will lead to recalculation. In addition, the CFD method may encounter problems with convergence in some complicated cases. Recently, Zhou et al. [15] proposed a novel pressure distribution testing method in static thrust air bearings using a pressure-sensitive membrane. The feasibility and accuracy of the method has been experimentally verified; however, it is a prerequisite that the design of the test equipment is suitable, and the measuring instrument has adequate accuracy. Besides, the fabrication of equipment model and testing procedures can be quite expensive and time consuming. In this paper, the calculation solution used the air-electric equivalent method was proposed. This method is easy to understand and deploy the calculation of stiffness for air bearings in air spindle. This calculation process was based on setting a gas circuit
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equivalent to an electrical circuit; thus, we can turn air elements into electrical elements to form the circuit. Calculating circuit’s parameters through existed results of current laws helps to simplify the calculation of air circuits.
2 Fundamentals of Air-Electrical Calculation Method 2.1 Model of Air-Electric Conversion
Fig. 1. Structure of air bearing spindle
Figure 1 show the structure of air spindle. The torque from motor 1 is transmitted to the shaft 5. The air spindle works on the principle of supplying air through the orifice holes of the Shaft sleeve 4 and the Lower thurst plate 6 forms air film between shaft 5 with Shaft sleeve 4 and Lower thurst plate 6. This mechanism restricts five degrees of freedom, thus there is only one degree of freedom to rotate around the z-axis. In each air bearings, there are orifice holes d11 and d12 (Fig. 2), the air path D connects to the air source with stable pressure P0 . Since D > > d11 and d12 , D should be treated as a lossless path.
Fig. 2. Structure of air bearings in the cap part
This paper will give air-electric equivalent calculation method for the lower air bearings of the air spindle with the conversion model as follows:
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Fig. 3. Model of air-electric conversion
As seeing on Fig. 3, a constriction on the air flow has the effect of obstructing the air flow as a current resistance. Experiment has shown that air resistance R is a quantity inversely proportional to the square of the flow’s cross section S. 2.2 Construction of Calculating Formulas for Model of Lower Air Bearings Compressed air from the supply P0 flows into the two orifice holes d11 and d12 considered as two resistors R11 and R12 , and then following into grooves. At here, the air pressure is balanced (because a large groove is treated as a conductor with extremely small resistance).
Fig. 4. Model of air-electric equivalent
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Air pressure Source P0 flows through the orifice hole d11 and d12 , pressure down to P1 , and flow in to grooves. In the area which is created by the grooves closed. The pressure is equal to P1 then pressure down to 0 – atmosphere when its flow outside of airbearing P1 =
P0 R11 R12 R11 +R12
+ R2td
R2td
(1)
With R21 R22 R21 + R22
R2td =
(2)
Assume that two orifices are the same. d1 = d11 = d12
(3)
Resistance l l =ρ 2 π d1 S
R11 = ρ
(4)
4
R1td = 2R11 = 2R12 = ρ
8l π d12
(5)
where: R11 , R21 Resistance from r0 to rg1 . R21 , R22 Resistance from rg2 to rn . Deduce P1 =
P0 R1td 2
P1 =
+ R2td
R2td
P0 R1td 2R2td
(6) (7)
+1
Resistance from rg2 to rn rn R22 =
ρ rg2
ρ rn dr = ln 2π rz 2π rz rg2
(8)
Resistance from rg1 to r0 R21 =
rg1 ρ ln 2π z r0
(9) r
R2td
ln rrg2n ln rg10 ρ ln = 2π z ln rrn + ln rrg1 g2 0
(10)
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Flift = Flift1 + Flift2 + Flift3
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(11)
Flift1 : The lift force causes by pressured air from r0 to r0g1 . Flift2 : The lift force causes by pressured air in the grooves area. Flift3 : The lift force causes by pressured air from rg2 to rn Flift1
rg1 = Pr21 dS1
(12)
r0
Here Pr21 = i21 .R212 =
P0 R2td . .R212 R1td + R2td R21
ln rrg2n R2td = R21 ln rrg2n + ln
C1 =
r
ln
rn rg2
rn rg1 rg2 . r0
(14)
1 r dr =ρ ln 2π rz 2π z r0
(15)
r P0 1 ln C1 ρ R1td + R2td 2π z r0
(16)
R212 =
ρ r0
Pr21 =
rg1 r0
ln
=
(13)
Deduce rg Flift1 = 2AC1 π
ln r0
r rdr r0
(17)
1 2z
(18)
With P0
A=
r
8l d12
+
ln rn ln rg1 0 1 rg2 2z rn rg1 ln r . r g2
rg1 B1 =
ln r0
2 2 − r2 rg1 rg1 r 0 ln rg1 − ln r0 − rdr= r0 2 4
P0
Flift1 =
r
8l d12
+
1 2z
ln rrn ln rg1 0 g2 r ln rrn . rg1 g2
Flift2 can be written as:
0
⎡ ⎤ rn 2 − r2 r r π ⎣ ln rg2 g1 g1 0 ⎦ 2 rg1 + . ln 2z ln rn . rg1 r0 2
(19)
(20)
rg2 r0
0
2 2 Flift2 = P1 S1 = P1 π rg2 − rg1
(21)
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⎛ P0
P1 =
r
8l d12
+
1 2z
ln rrn ln rg1 0 g2 rn rg1 ln r . r g2
⎛
8l d12
+
0
ln rn 1 rg2 2z r ln rrn . rg1 g2
(22)
rg2 r0
⎞ rg1 rn ln ln r0 2 2 ⎝ 1 rg2 ⎠π rg2 − r rg1 g1 2z ln rn . rg1 ln r 0 rg2 r0
P0
Flift2 =
⎞ r ln rrg2n ln rg10 1 ⎝ ⎠ 2z ln rn . rg1
(23)
0
The same Flift1 , We have the following formula to calculate Flift3 : rn Flift3 =
Pr22 dS3
(24)
rg2
where: Pr22 = i22 .R222 = Pr22 =
P0 R2td . .R222 R1td + R2td R22
rn P0 1 ln C2 ρ R1td + R2td 2π z r
(25) (26)
where: r
Flift3 = 8l d12
+
1 2z
ln rg1 R2td = 0r C2 = R22 ln rrg2n . rg10 ⎡ ⎤ rg1 2 − r2 r r π ⎣ ln r0 P0 n g2 g2 ⎦ 2 . rg2 + ln r r 2 ln rrn . ln rg1 2z ln rn . rg1 n 0 rg2 r0 g2 ln
(27)
(28)
rn rg1 rg2 . r0
Hence, the total lift force from (20) + (23) + (28) of air bearing is defined as: ⎛ ⎞ 2 − r2 ln rrg2n rg1 rg1 0 2 rg1 . ln − ⎜ ⎟ ⎜ ln rn . rg1 ⎟ r0 2 ⎜ ⎟ rg2 r0 ⎜ ⎟ ⎜ ⎟ r g1 rn ln ln ⎜ ⎟ rg2 r0 π⎜ P0 2 2 ⎟ rg2 − rg1 + Flift = ⎜ ⎟ (29) r r g1 ⎟ ln rrn . ln r 2z ⎜ ln rn . g1 0 8l 1 g2 ⎜ ⎟ r r g2 0 + 2 ⎜ 2z r ⎟ d1 rg1 ⎜ 2 2 ln rrn . rg1 rn − rg2 ⎟ ln g2 0 rg2 ⎜ ⎟ 2 ⎝ + r0 rg2 ⎠ + ln r g1 rn 2 ln rn . rg2 r0
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2.3 Investigation of Thrust Force in Air Bearings Investigation of Thrust Force According to Clearance z Design parameters: Po = 0.04 (kg/mm2 ), rn = 25; rg1 = 15; rg2 = 20; r0 = 10; l = 2,5; d = 0,5.
Thrust force F (N)
250 200 150 100 50 0 0
5
10
15
20
Clearance z(micromet) Fig. 5. The graph of investigating thrust force according to clearance z.
Comments: - From the graph, it can be seen that; the thrust force F is inversely proportional to the clearance z: when the thrust force increases, the clearance z decreases and vice versa. - Therefore, if we want to increase the thrust force, we need to reduce the clearance z. - This means that it requires very high machining accuracy because the optimal clearance z is from 5 ÷ 12 μm. Investigation of thrust force according to rd Design parameters: Po = 0,04 (kg/mm2 ), rn = 25; z = 0,01; r0 = 10; l = 2,5; d1 = 0,5.
Thrust force F(N)
400 300 200 100 0 0
5
10
rd(mm)
15
20
25
Fig. 6. The graph of investigating thrust force according rd .
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Comments: - The cluster of air bearings and cap with rd = 14 mm in this study is close to maximum thrust force, showing the correct direction of improvement. - From the graph, it can be seen that; the thrust force will decrease gradually when rd approaches rn (radius of shaft in contact with the lower air bearings) and approaches r0 (radius of shaft assembling with hole Fig. 4).
3 Conclusion In the paper, calculation results of some researches about air spindle have been pointed out and analyzed, there are several shortcomings in the calculation results of those methods. The calculation by air-electric equivalent method proposed in this study is appropriate and simple. Some conclusions and formulas showed in the paper are: Calculate the lift that is the thrust force of air bearings by the formula (29). Investigation of thrust force according to clearance z (Fig. 5). Investigation of thrust force according to rd (Fig. 6). Investigation of thrust force according to design parameters shows that the research direction is correct and appropriate.
References 1. Reynolds, O.: IV. On the theory of lubrication and its application to Mr. Beauchamp tower’s experiments, including an experimental determination of the viscosity of olive oil. Philosoph. Trans. Royal Soc. London 177, 157–234 (1886) 2. Harrison, W.J.: The hydrodynamical theory of lubrication with special reference to air as a lubricant. Trans. Cambridge Philosoph. Soc. 22, 39–54 (1913) 3. Lemon, J.R.: Analytical and experimental study of externally pressurized air lubricated journal bearings. J. Basic Eng. 84(1), 159–165 (1962) 4. Mori, H., Miyamatsu, Y.: Theoretical flow-models for externally pressurized gas bearings. J. Lubric Technol. 91(1), 181–193 (1969) 5. Zhu, J.C., Chen, H., Chen, X.D.: Numerical simulation of the turbulent flow in ultraprecision aerostatic bearings. In: Advanced Materials Research, vol. 680, pp. 417–421 (2013) 6. Liu, T., Liu, Y., Chen, S.: Aerostatic lubrication. In: Harbin Institute of Technology press, pp. 51–66 (1990) 7. Colombo, F., Moradi, M., Raparelli, T., Trivella, A., Viktorov, V.: Multiple holes rectangular gas thrust bearing: dynamic stiffness calculation with lumped parameters approach. In: Boschetti, G., Gasparetto, A. (eds.) Advances in Italian Mechanism Science. MMS, vol. 47, pp. 421–429. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-48375-7_45 8. Viktorov, V.: Evaluation of squeeze effect in a gas thrust bearing. Mater Contact Character. VIII 116, 121 (2017) 9. Colombo, F., Raparelli, T., Trivella, A., et al.: Lumped parameters models of rectangular pneumatic pads: static analysis. Precis. Eng. 42, 283–293 (2015) 10. Dun, L., Chunye, P., Weiping, G., et al.: On the discretization of orifice compensated externally pressurized lubrication and computational convergence. Tribology 21(2), 139–142 (2001) 11. Shusen, L., Dun, L.: Analysis of the dynamics of precision centrifuge spindle system with the externally pressurized gas bearing. Chin. J. Mech. Eng. 41(2), 28–32 (2005)
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12. Shufei, Z.: The analysis of dynamic characteristic parameters of aerostatic bearing for precision motorized spindle. Southeast University (2010) 13. ANSYS Inc. ANSYS fluent software: CFD simulation. http://www.ansys.com/products/flu ids/ansys-fluent. Accessed 2021 14. ANSYS Inc. ANSYS CFX. Turbomachinery CFD simulation. https://www.ansys.com/pro ducts/fluids/ansys-cfx. Accessed 2021 15. Zhou, Y., Chen, X., Cai, Y., et al.: Measurement of gas pressure distribution in aerostatic thrust bearings using pressure-sensitive film. Tribol. Int. 120, 9–15 (2018)
CFD Simulation of Temperature Distribution of Atrium Space in a Residential Building Kieu Hiep Le, Tien Cong Do, Van Thuan Nguyen, Tien Anh Nguyen, and Viet Dung Nguyen(B) School of Heat Engineering and Refrigeration, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Recently, due to global warming and economic development, the demand for air conditioners has increased especially in countries with hot and humid climates, such as Southeast Asia. In the air-conditioned space, the air temperature, relative humidity, and velocity are needed to be accurately controlled. In this paper, a CFD simulation has been conducted to investigate and evaluate the temporal and spatial air temperature distribution in the atrium space under different cooling capacity scenarios. The results of the simulation indicated that it is possible to select the best air-conditioning system for the atrium space in the residential building, which in turn enhances the living environment and helps save the investors’ investment costs and operating costs. This study demonstrated that the CFD simulation can be used as an effective assisted tool during the design and operation of the heating, ventilating, and air conditioning systems. Keywords: Temperature distribution · Air velocity distribution · Air conditioning · CFD simulation
1 Introduction As economic development takes place, people’s living standards have been improved significantly in recent decades. Additionally, the environmental temperatures have risen significantly in recent years due to the global warming effect, which has negatively affected human living conditions. Thus, the air conditioner becomes a basic requirement for the new building, especially for the tropical regions. It was forecasted that the world air conditioner demand exceeding 110 million units in 2018 [1]. For ensuring the thermal comfort requirements of commercial and residential buildings, numerous studies have been conducted to investigate the impact of environmental conditions on the human sense. It should be noted that thermal comfort depends on a variety of factors such as personal differences in mood, culture, and other individuals’ organizational and social factors [2]. Besides other factors including relative humidity, mean radiant temperature, and two personal variables as clothing insulation and metabolic rate; the temperature and velocity of air temperature are the important criteria of thermal comfort [3]. Thus, an appropriate structure of air transport and distribution system incorporated with a suitable cooling capacity plays a key role. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 878–886, 2022. https://doi.org/10.1007/978-981-19-1968-8_74
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For low-height rooms, determining cooling capacity is relatively straightforward, but for atrium spaces, this becomes even more difficult due to a remarkable temperature maldistribution. It was observed that several used spaces will not be cooled whereas other unused regions are conditioned unnecessarily. To avoid that, an experimental study is the most reliable method to provide an accurate description of thermal comfort. However, it’s financially expensive, time-consuming, and difficult to conduct sensitive parametric studies [4]. The arrangement of air diffusers and cooling selection based on Computational Fluid Dynamic (CFD) modeling might be an alternative solution instead of experimental study. The CFD simulation can help to reduce the investment cost and save implementation time. Furthermore, the CFD simulation also allows carrying parametric studies without any additional cost. In this paper, a transient CFD simulation is conducted to analyze the temperature maldistribution for a huge atrium space. The impact of cooling capacity on the temperature evolution is examined to check the responsiveness of the designed HVAC system compared with the standard of A-class commercial and residential buildings where the temperature must reach 24 °C after 30 min. This analysis provides a novel strategy for selecting the optimal cooling capacity for similar buildings.
2 Model Description In this study, the HVAC system of a residential building with 30 layers is considered. The atrium space with dimensions of (66 m × 26 m × 30 m) L × W × H is air-conditioned by four AHU (Air Handling Units). The cooled air is transported by a fan of the AHU through the air ducts to the diffusers. The ventilation system is installed on the ceiling third floor with a height is 10 m. Therefore, diffusers are selected as jet nozzle diffuser type which can produce a very long air projection. With each supply air duct are installed 12 downward diffusers that ensure uniform supply airflow in the entire working zone. The cooling air is supplied to the conditioned space, performing heat transfer before recirculating back to the AHU, ending a cycle. Based on the architectural drawings of the building presented in Fig. 1, the geometrical model of the atrium is generated by using Ansys DesignModeler (c.f. Fig. 2). The geometrical model is imported into the Mesh tool of Ansys software to perform the meshing procedure. The influence of mesh quality is checked by performing simulations with the different number of grid cells. It was obtained that the number of grid cells is increased until it is beyond 15 million, the results become computational meshindependent. In addition, the maximum skewness and orthogonal are controlled at about 0.8 and 0.2, respectively, to achieve solution convergence. Hence, in the rest of this work, a grid with 15425602 hexahedral cells, as shown in Fig. 2, is applied to all of the case studies in the CFD simulation. For each unsteady-stated simulation, it takes from 30 to 40 h to reach convergence.
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Fig. 1. Architectural drawings of the building of atrium space.
Fig. 2. (a) Geometry of the atrium space: position of the AHU, air ducts and diffusers, (b) details of the computational grid of the air duct and diffusers.
2.1 Model Parameters The heat sources of the working space and of the AHU are the key parameters. The working zone is defined as a space with a height of 2.5 m from the floor and its area equal to the floor area of the atrium. This zone is where most commercial and entertainment activities of the building take place, so the positive heat source of this zone is primarily emitted by humans; however, equipment, restaurants, and so on may also contribute to the heat source. The heat source of working space is extracted from the Heatload Daikin software. Therefore, the heat source value of the working space is approximately chosen of 20 W/m3 . Additionally, the volumetric cooling capacity of AHUs is determined from
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the cooling capacity of AHUs as in Eq. 1 where Q is the heat load (W) and V is the heat-source volume (m3 ). The calculated results are presented in Table 1 for different cooling capacity selections. qv =
Q V
(1)
Table 1. Cooling capacity and heat source of each AHU in the different scenarios Senarios
Cooling capacity [kW]
Heat source [kW/m3]
1
100
−8.028
2
120
−9.634
3
140
−11.239
4
160
−12.845
5
180
−14.450
The fan of AHU is simplified as a 2D fan model and the pressure of each fan is set to 600 Pa, which is determined based on the pressure loss in the duct in order to ensure a required volume flow rate for cooling space. Furthermore, the heat transfer is assumed as a constant heat flux boundary condition. So, the heat flux of the roof, floor, and exterior wall are 85.2 w/m2 , 9.27 w/m2 , and 593.0 w/m2 , respectively. The initial temperature and pressure of the domain are set as the ambient temperature (36 °C) and 1 bar. 2.2 Solver Settings In the present work, the Ansys Fluent 19.2 software was used to perform the simulations. The 3D unsteady-stated RANS equations (Eq. 2) with k – ε turbulence model (Eq. 4) were numerical solved [5]. ∂τij ∂ui ∂ui ∂p ∂ 2 ui + uj =− +ν − ∂t ∂xj ∂xi ∂xj ∂xj ∂xj
(2)
∂ui =0 ∂xi
(3)
In which ui is the fluid velocity, p is the pressure (divided by the density ρ), ν is the fluid kinematic and τij is Reynolds-stress tensor. ∂K ∂ui ∂K ∂ νT ∂K + ui = −τij −ε+ ∂t ∂xi ∂xj ∂xi σk ∂xi (4) 2 ∂ K +ν ∂xi ∂xi
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∂ε ∂ε ε ∂ui ∂ νT ∂ε + ui = −Cε1 τij + ∂t ∂xi K ∂xj ∂xi σε ∂xi −Cε2
∂ 2ε ε2 +ν K ∂xi ∂xi νT = Cμ
K2 ε
(5)
(6)
In these equations, Cε1 , Cε2 , Cμ , σk and σε are the nondimensional constants. According to comparisons with physical experiments, the constants assume the following values: Cε1 = 1.44, Cε1 = 1.92, Cμ = 0.09, σk = 1.0, σε = 1.3 The SIMPLE algorithm was employed for pressure-velocity coupling. Second-order discretization schemes were used for both the convection and viscous terms of the governing equations. The PRESTO! scheme was applied for the pressure terms. The energy and momentum equation were solved simultaneously dealing with an assumption where the air is considered as an ideal gas.
3 Results and Discussion 3.1 Results The unsteady-sated CFD simulation results were obtained by using the ANSYS 19.2 software. The temperature distribution field at the O-O section, and the temperature at the locations 1 to 6 with a heigh of 1.2m from the floor, are included in the measurement values, as shown in Fig. 1. Figures 3 ,4, 5, 6 and 7 present the distribution of air temperature for the scenarios (1) to (5) after 10 min, 20 min, and 30 min respectively. Firstly, by comparison of Figs. 3 ,4, 5, 6 and 7 , we can find that the scenarios (1) & (2) have the distribution of air temperature still very high after 30 min of operation. The scenario (3) is generally acceptable with a required temperature of 26 ± 2 °C after 30 min. Thus, the scenarios (4) and (5) also completely meet the required temperature but scenario (4) only takes 20 min to reach the same state as scenario (3). Similarly, scenario (5) also only takes 20 min to achieve like the state at the scenario (4) after 30 min; the temperature can even drop to 22 °C at several locations. The decrease in temperature at locations 1 to 6 (as shown in Fig. 1) over time is shown in Fig. 8. It is generally the scenarios that the workspace temperatures (location 1–6) are almost identical. The temperature difference between points is negligible in the whole space. This means that cold air is relatively evenly distributed. The jet diffusers have therefore been installed and designed following the requirements. 3.2 Discussion Increasing the cooling capacity directly impacts human comfort, temperature control, and system energy consumption. Particularly, a small cooling capacity results in an
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unsatisfactory temperature in the air-conditioned space, causing discomfort for people and making it difficult to adjust the temperature. Besides, a large cooling capacity means a high investment and operation costs, resulting in unnecessary waste.
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Fig. 3. Temporal distribution of air temperature with the scenario (1).
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Fig. 4. Temporal distribution of air temperature with the scenario (2).
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Fig. 5. Temporal distribution of air temperature with the scenario (3).
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Fig. 6. Temporal distribution of air temperature with the scenario (4).
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Fig. 7. Temporal distribution of air temperature with the scenario (5)
Fig. 8. Temperature evolution over operation time obtained with different scenarios.
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Figure 8 shows the relationship between temperature and operating time for all 5 scenarios. From the simulation results, we can easily see that as cooling capacity increases, the temperature curve becomes steeper, which means temperature approaches the required state rapidly. The simulation results are therefore in agreement with the theory. As shown in Fig. 8, it can be seen that the first and second scenarios do not meet the temperature requirements. 30 min of operation only drop the temperature to approximately 30 °C. A temperature was reduced to about 27 °C when the capacity was increased to 140 kW/AHU. Although it is within the mall’s required temperature range, it will be difficult to adjust many of the temperature in this case. With the cooling capacity of 160 kW/AHU, the upper limit of 28 °C can be reached in about 15 min and the lower limit of 24 °C can be reached after 30 min. The cooling capacity is 180 kW/AHU at the scenario (5), and it only takes about 10 min to reach 28 °C, about 20 min to reach 24 °C, and after 30 min the temperature can drop to about 22 °C. The larger the cooling capacity, the greater the technical benefit, but also the higher the installation and operation costs; therefore, it is necessary to optimize between technical and economic issues. Consequently, with the standards of residential building and the balance of technical requirements and investor funds, 160 kW/AHU of capacity (scenario 4) was selected to be installed for the air conditioning system in this building.
4 Conclusion In this paper, transient three-dimensional CFD simulation is developed to investigate the most suitable operating conditions in an air conditioning system designed for an actual building. The results indicate that the installation of the jet nozzle diffuser designed met the requirements for uniform distribution of cool air throughout the conditioned space. In addition, the study also examines the selection of optimal cooling capacity for the air conditioning system, which not only helps to meet technical requirements but also saves the investor money. The results obtained from this study imply that CFD simulation can pave the way for optimizing jet nozzle diffuser arrangements and the cooling capacity in the air conditioning system. The results indicate that selecting a suitable cooling capacity is very essential for ensuring thermal comfort for humans. In addition, the cooling capacity is also very important in the process of temperature adjustment and energy consumption, and savings. Besides, the uniform distribution of cold air is also crucial to the efficiency of the air conditioner system. It also implies that CFD simulation can be considered as an efficient alternative tool for designing and investigating the HVAC system.
References 1. The Japan Refrigeration and Air Conditioning Industry Association World Air Conditioner Demand (2019) 2. Djongyang, N., Tchinda, R., Njomo, D.: Thermal comfort: a review paper. Renew. Sustain. Energy Rev. 14(9), 2626–2640 (2010). https://doi.org/10.1016/j.rser.2010.07.040
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3. Lin, Z., Deng, S.: A study on the thermal comfort in sleeping environments in the subtropics— developing a thermal comfort model for sleeping environments. Build. Environ. 43(1), 70–81 (2008). https://doi.org/10.1016/J.BUILDENV.2006.11.026 4. Yang, L., Ye, M., He, B.-J.: CFD simulation research on residential indoor air quality. Sci. Total Environ. 472, 1137–1144 (2014). https://doi.org/10.1016/j.scitotenv.2013.11.118 5. Alfonsi, G.: Reynolds-averaged navier-stokes equations for turbulence modeling. Appl. Mech. Rev. 62(4), 19 (2009). https://doi.org/10.1115/1.3124648
CFD Simulation of Swirl-Stabilized Pulverized Coal Flames in a Cylindrical Combustion Chamber Tien Anh Nguyen, Kieu Hiep Le(B) , Gia My Tran, and Tran Tho Dang School of Heat Engineering and Refrigeration, Hanoi University of Science and Technology, 01 – Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam [email protected]
Abstract. In this paper, a CFD model of swirl-stabilized pulverized coal combustion furnace is presented. Primary air is used to transport pulverized coal into the cylindrical combustion chamber, while secondary air enters the combustion chamber through an axial swirl generator. The purpose of this study is to examine the influence of secondary swirl intensity on the properties of pulverized coal swirling flames, such as shape, length and temperature, and pulverized coal combustion efficiency. The swirling combusting flows are calculated using the k-e model and second-order models of turbulence. Detailed gas temperature distribution, gas-phase species concentrations and coal particles burnout are determined. The results show that the overall CO and NO concentrations in the combustion chamber is lower when secondary swirl intensity increases. Also, most char escapes outside the chamber after the combustion instead of being trapped at the bottom of the chamber. Keywords: Pulverized coal burner · CFD simulation · Combustion efficiency · Burnout rate · Swirl angles
1 Introduction In pulverized coal combustion, coal particles are injected into the combustion chamber with the primary airflow. The control of the primary flow and secondary flow for pulverized coal flames is the key to optimal flame stability, and carbon conversion efficiency. It also helps to reduce of nitrogen oxide (NOx) emissions and to avoid the slagging in the chamber. The mathematical modeling technique for simulating pulverized coal flames including the NO predictions has been developed by Weber et al. [1], which help engineers in designing large-scale burners and flames and define essential information such as shape, length, and temperatures of flame, temperature distribution, and oxygen concentration. The study of Sung et al. [2] contributes to a better understanding of the structure of pulverized coal swirling flames regarding the burner air staging for NOx reduction in the furnace. For the flame temperature, as the level of swirl is increased, the maximum temperature reduces, and the flame then moves downstream. In the study © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 887–896, 2022. https://doi.org/10.1007/978-981-19-1968-8_75
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of Nicolai et al. [3], the numerical and experimental models of pulverized coal combustion have been investigated. A good agreement between numerical and experimental observation was found for the velocity fields, particle temperatures, H2 O, and CO2 concentration. It also indicates that the behavior at the beginning of the conversion phase is exhibited by the particle trajectories exhibit differences. However, the overall conversion of the particles is almost identical at the end of the chamber although the particle temperatures vary significantly between the different regions. The combustion of biomass with pulverized coal for power generation is being promoted nowadays as an alternative to fossil fuels, with the added benefits that the high combustion temperatures and long residence times in the furnace. Biomass, which is derived from agricultural or industrial production, is a renewable and clean-burning fuel, contributing appximate 43% of the total energy required in developing countries and 26% for several developed countries [4]. Furthermore, biomass co-firing can reduce net greenhouse gas emissions as well as NOx and sulfur oxide (SOx) emissions [5]. For these reasons, several studies have applied the swirled pulverized coal burner to coal-biomass co-firing models. Abbas et al. [6] evaluated a new dual fuel burner designed for the cofiring biomass with pulverized coal, in which biomass was introduced through the swirled secondary airflow. This model indicated the devolatilization rate to increase, leading to lower NO formation in the adjacent secondary air stream. However, due to the slower coal-biomass mixing, co-firing insignificant influences on the emission and combustion performance. Choi et al. [7] use a swirl pulverized coal burner to investigate the combined effects of woody biomass/bituminous coal co-firing and air-staged combustion on NO reduction and burnout performance. The results show that the co-firing flame provides a higher overall temperature compared to bituminous coal flame. However, an explicit relationship between the temperature and the woody biomass co-firing ratio were not shown. Additionally, with the air staging, NO concentrations in the woody biomass co-firing flames were rather. Table 1. Proximate and ultimate analysis of coal [7] Proximate analysis (wt. %, air-dry) Moisture
Volatiles
Fixed carbon
Ash
NCV (MJ/kg)
Carbon
Ultimate analysis (wt. %, dry) Hydrogen
Oxygen
Nitrogen
Sulfur
Ash
9.00
33.83
48.83
8.43
25.17
72.20
4.61
10.94
2.65
0.44
9.16
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lower compared to the pure coal flame, while CO concentrations illustrated the reverse tendency. In the frame of University Industry Cooperation Project between Hanoi University of Science and Technology and Power Generation Corporation 1, we attempt to investigate the co-firing process of Australian bituminous coal and Vietnamese biomass in the boiler of a thermal power plant. In order to conduct this project, firstly the pulverized coal combustion chamber is considered as a baseline. This paper presents a CFD model of swirl-stabilized pulverized coal flame. A cylindrical combustion chamber incorporated with an adjustable swirling flow generator is modeled. The influence of swirl angles on the chemico-thermodynamic of furnace is investigated by comparing the O2 , CO2 , CO, NO concentration and the burnout rate of char. Finally, conclusions of this work are drawn.
2 Model Descriptions Figure 1 shows the 125 kW pulverized coal-fired furnace geometrical model. This model is used for the overall assessment of pulverized coal combustion (and coal-biomass cofiring in the future). The cylindrical combustion chamber is down-fired and is made up of two individual cylindrical parts with an internal diameter of 600 mm and a height of 1500 mm. Primary air is used to transport pulverized coal into the furnace.
Fig. 1. Chamber and burner model.
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Secondary air enters the combustion chamber through an axial swirl generator. The structure of the burner and swirling flow generator is shown in Fig. 2. The black parts are fixed to each other and can rotate around the angle between 0° and 15°. This structure has the effect of adjusting the swirl intensity of the secondary airflow. Australian bituminous coal was used in this model. The mean particle size for coal was 107 µm. The proximate and ultimate analyses of coal, as well as the net calorific value (NCV), are listed in Table 1. The furnace was designed with a capacity of 125 kW. The mass flow of coal supplied to the furnace was 20 kg/h. Primary and secondary air was heated to 200 °C before entering the combustion chamber. The primary and secondary airflow rates were 30 m3 /h and 120 m3 /h, respectively. The model has been simulated in 4 cases: case 1, case 2, case 3, case 4, corresponding to 4 rotation angles of the swirling flow generator of 0°, 5°, 10°, 15°, respectively (Figs. 3 and 8)
Fig. 2. Structure of burner (a) and swirling flow generator (b).
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Fig. 3. Gas temperature distributions with different swirl angles at the center plane of the chamber.
Fig. 4. O2 distributions with different swirl angles at the center plane of the chamber.
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Fig. 5. CO2 distributions with different swirl angles at the center plane of the chamber.
Fig. 6. CO distributions with different swirl angles at the center plane of the chamber.
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Fig. 7. NO distributions with different swirl angles at the center plane of the chamber.
Fig. 8. Coal particle flow path tracking colored by particle temperature.
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3 Results and Discussion 3.1 Temperature Distribution 0 shows the effect of the swirl intensity of the secondary airflow on the temperature maps. The overall temperature is higher when increasing the swirl intensity. The peak values of the temperatures for flames of cases 1, 2, 3, 4 are 1601 K, 1504 K, 1622 K, and 1678 K. For higher swirl intensity, the high-temperature zone is closer to the top of the combustion chamber and tends to move to the chamber wall side in the main combustion zone. 3.2 Gas-Phase Species Concentration Figures 4 and 5 show in-furnace oxygen and carbon dioxide distributions of four cases. Since O2 and CO2 species are complementary in terms of the main reactant and product, the O2 concentration decreases, and the CO2 concentration increases with chamber height. The overall O2 concentration is lowest in case 4 (15°) because the amount of O2 is almost used when entering the combustion chamber. In case 3 (10°), the O2 concentration is still high at the top of the combustion chamber. At the area 0.3 m away from the burner, the amount of O2 is almost completely used. In cases 1 (0°) and 2 (5°), the O2 concentration is only high in the small area near the burner. Figures 6 and 7 show 2-D maps of the in-furnace CO and NO for four cases. The overall CO concentrations are higher in the high-temperature region, and CO concentrations decrease with chamber height, as shown in Fig. 6. The higher the secondary swirl intensity, the lower the overall CO concentration in the chamber. The NO maps are shown for 4 cases in Fig. 7. The overall NO concentration in the chamber has the lowest value for case 1, but is highest for case 2 and tends to decrease when swirl intensity increases. 3.3 Burnout Rate of Coal Table 2 shows the char mass fraction after the combustion process which flows with exhausted combustion products or trapped in the combustion chamber. It can be seen that most of the char escapes outside the chamber, especially for the case 1 (0°) and case 4 (15°). For case 1, the air follows a trajectory from high pressure zone to low pressure zone and is nearly unaffected by any other factors in the flow, so most particles go straight out of the fire chamber with emission. In case 4, due to the great influence of centrifugal force when following the vortex, the particles tend to move near the wall of the chamber, which makes it easier for particles to escape from the combustion chamber (Fig. 9) In addition, the char mass fraction in all 4 cases is in the range of 32–39%. Combined with the results on the distribution of O2 concentration as previously mentioned, it can be seen that the amount of supply air is not enough for combustion, so there is a lack of O2 for the coal to burn out. For example, in case 4 (15°), the char mass fraction after burning is only 22.81% when increasing the primary and secondary airflow by 33%.
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Table 2. Coal burnout and char mass fraction after burning Cases
Burnout rate (%)
Char mass fraction (%) Exhausted with combustion product
Trapped in chamber
1 (0°)
61.12
27.10
11.78
2 (5°)
67.45
18.55
14.00
3 (10°)
67.69
18.22
14.09
4 (15°)
65.11
25.60
9.29
Fig. 9. In-furnace O2 concentrations of case 4 (15°) with (right) and without (left) additional airflow.
4 Conclusion In this paper, the results of preliminary evaluation of swirl-stabilized pulverized coal flames in a cylindrical combustion chamber has been illustrated. The effects of the swirl intensity of the secondary airflow on pulverized coal combustion were evaluated in a chamber CFD model. Except for swirl intensity, other parameters such as excess air coefficient, inlet air temperature, etc. are still kept fixed. The investigation was conducted in 4 cases corresponding to 4 rotation angles of the swirling flow generator of 0°, 5°, 10°, 15°. Detailed gas temperature, gas-phase species concentrations, and coal particle burnout were determined. The results indicate as follows: – The high-temperature zone will be closer to the top of the combustion chamber and tend to move to the furnace wall side in the main combustion zone if the secondary swirl intensity increases. – A higher swirl angle leads to a lower overall CO concentration in the furnace. – Except for the non-swirl secondary flow case, the overall NO concentration in the furnace tends to decrease when the swirl angle increases.
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– Most of the char is exhausted with the combustion product after the combustion instead of being trapped at the bottom of the chamber. In the future, the coal-biomass co-firing model will be applied and the effect of excess air coefficient will be considered for detailed assessment of NOx and CO emissions. Acknowledgments. This work was supported by funding from the Power Generation Corporation 1 (EVNGENCO1) via Agreement 29/HD-KHCN-EVNGENCO1-BKHN.
References 1. Weber, R., Peters, A.A.F., Breithaupt, P.P., Visser, B.M.: Mathematical modeling of swirling flames of pulverized coal: what can combustion engineers expect from modeling? J. Fluids Eng. 117(2), 289–297 (1995) 2. Sung, Y., et al.: Optical non-intrusive measurements of internal recirculation zone of pulverized coal swirling flames with secondary swirl intensity. Energy 103, 61–74 (2016) 3. Nicolai, H.: Numerical investigation of swirl-stabilized pulverized coal flames in air and oxyfuel atmospheres by means of large eddy simulation coupled with tabulated chemistry. Fuel 287, 119429 (2021) 4. Tillman, D.A.: The Combustion of Solid Fuels and Waste. Academic, London (1991) 5. Tillman, D.A.: Biomass cofiring: The technology, the experience, the combustion consequences. Biomass Bioenerg. 19, 365–384 (2000) 6. Abbas, T., Costen, P., Kandamby, N.H., Lockwood, F.C., Ou, J.J.: The influence of burner injection mode on pulverized coal and biomass co-fired flames. Combust. Flame 99, 617–625 (1994) 7. Choi, M., Li, X., Kim, K., Sung, Y., Choi, G.: Detailed in-furnace measurements in a pulverized coal-fired furnace with combined woody biomass co-firing and air staging. J. Mech. Sci. Technol. 32(9), 4517–4527 (2018). https://doi.org/10.1007/s12206-018-0848-7 8. Hillermanns, R.: Das Strömungs- und Reaktionsfeld sowie Stabilisierungseigenschaften von Drallflammen unter dem Einfluß der inneren Rückströmungszone Dissertation, Universität Fridericiana Karlsruhe (1988)
The Process of Custom Designing Replacement Cranial Bone Patches in Human Body Thi Kim Cuc Nguyen(B) , Hoang Hong Hai, Cao Xuan Binh, and Vu Tien Dung School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. In this study, a comprehensive process for the design, fabrication, and validation of artificial cranial bone for patients with craniofacial defects was developed. The main goal of this research is to introduce a simple method for redesigning and fabricating defective parts of the skull. Computerized Tomography (CT) data were used to reconstruct a 3D point clouds of the skull. In combination with digital image acquisition techniques, the 3D model of a large artificial bone can be designed. This design was then analyzed and compared with the mechanical properties for its cortical bone using Finite Element Analysis (FEA). The simulation results of the implant structure showed that the values of deformation distribution, strain distribution, and von Mises stress were within the allowable values of the material under intracranial pressure. Designing, fabricating cranial replacement patches, and implanting them on the patient showed the suitability of the design to the patient’s damaged area, ensuring shape, stable structure, and good aesthetics. Keywords: Craniofacial implants · Segmental bone defects · Finite element analysis · Intracranial pressure · Stress distribution
1 Introduction Craniofacial defects are the reasons for the loss of facial aesthetics for the patient, affecting psychology and personality, causing difficulties in social integration, work, and quality of life. In common bone tissue damage, damage to the cranial skull is often serious and requires high aesthetics. Acquired skull defects represent a reconstructive challenge in cranioplasty. It stabilizes intracranial pressure and helps develop brain structures, especially in young people [1]. Autologous bone grafts have traditionally been one of the best for craniofacial reconstruction [3]. Bone grafts are associated with low costs and minimal risk. However, their use is limited by the limited availability of donor graft material, difficulty in determining graft formation, increased operative time, and postoperative risks [4]. Bone tissue implantation methods using alternative materials such as titanium alloys, Polymethyl methacrylate (PMMA), and Polyether-ether-ketone (PEEK), etc., has been researched and the most suitable material to be used remains controversial [4]. Currently, titanium is a popular material used in cranioplasty. However, the use of titanium alloy is associated with implant exposure, infection, magnetic © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 897–904, 2022. https://doi.org/10.1007/978-981-19-1968-8_76
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resonance imaging, and the potential of overlying soft tissue coverage for poor long-term adaptability [7]. PEEK has attracted considerable attention from orthopedic and material scientists. PEEK is suitable for load-bearing implants because of its favorable biological mechanical properties, radioactivity, compatibility, and chemical inertness. [8]. PEEK implants have the same strength and elasticity properties as cortical bone specifications. Artificial human bone tissue is typically fabricated with standard modulus shapes. Modular products have a low cost; however, they need to be tailored to suit each patient’s defect. Uncontrollable re-processing and increasing surgical time do not guarantee patient safety. These replacement pieces often fail to achieve aesthetic requirements, especially when the replacement bone fragment is in an uncovered area. Therefore, the fabrication of custom implants suitable for each patient is being investigated. The implant fabrication method using 3-Dimension (3D) technology can be used to replace human bones according to each patient’s injury. The complex shapes in this region make such reconstruction challenging. Solid works, Catia, Geomagic, etc., software allow design from a given shape and size, using the Digital Image and Communications in Medicine (DICOM) data to create a 3D model of the anatomy depicting the defect. After obtaining the 3D model, it is possible to use the designed defect compensation for the fabrication of implants that conform to the physical and mechanical requirements. The craniofacial region presents special problems for tissue engineering. The stresses and strains that the engineered tissues will experience have been mostly poorly studied.
Fig. 1. The process flow for design and fabricate of PEEK cranial implant.
For tissue transplantation to be useful in ameliorating craniofacial malformations, a clear understanding of the growth activity coordination of transplanted and native tissues are required. Bone growth in response to loading conditions has also been studied [10]. It is highly difficult to use the symmetric method to construct artificial bone fragments for the frontal cranial fragments or large defect areas. These major defects can be handled by reverse engineering. The study aims to propose a procedure for the design of the skull implant that is suitable for the patient’s lesion area to ensure geometrical and aesthetic
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accuracy. This is reduced surgical times. Implants can be designed more successfully if they match the architecture of environmental stress in which they will operate.
2 Fabrication Process of the Cranial Patch For cranial implants, the input design data is a computerized tomography (CT) scan data of each patient, and the implant products need to achieve both medical and aesthetic requirements. The experimental design of cranial implant parts from conventional 3D plastic prints gives the doctor a detailed view of the shape, geometry, and aesthetics. Since then, there are clinical assessments that help edit 3D design files for fabricating as well as transplantation later. To design from DICOM data, we propose a new design method as shown in Fig. 1. Step 1: The patient’s CT scans data is obtained through magnetic resonance imaging methods or CT 3D images containing information about bones, muscles, skin, brain, etc., on the patient’s skull. For patching of the skull, part of the bone skull is extracted from this 3D image. Step 2: The patient’s defect skull bone is built from the CT data after using a threshold to separate muscles, skin, brain, etc., and saved in a standard (.stl) file. Step 3: A cranial implant is reconstructed from the 3D data file. After having a 3D Computer-aided design (CAD) model of the patient’s skull defect, the cranial implant was designed using reverse engineering the results are as shown in Fig. 2. Step 4: The cranial prototypes and the cranial artificial parts are fabricated using a 3D printer with common materials such as poly lactic acid (PLA), Acrylonitrile Butadiene Styrene (ABS), etc. They are sent to medical professionals and reconstructive surgeons for validation of clinical diagnosis and preoperative research for patients. Step 5: Design the fixing part on the model of the reconstructed cranial implants. To facilitate fixing the 3D cranial on the patient’s cranial, a fixing part is added to the 3D cranial model. Step 6: Evaluate the design’s durability and suitability through simulation results. The model simulation is designed by the Finite Element Analysis (FEA).
Fig. 2. A right cranial implant is designed based on reverse engineering (a) Cranial implant with designed fixation parts (b)
Step 7: Create the final implant design. The implant is then fabricated by injection molding PEEK bio plastic.
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Fig. 3. Cranial model imported into ANSYS Product 19.2
In this paper to simplify the skull model, assume continuous thick skull, homogeneous skull material. The 3D model we use is the cranial model and the artificial cranial part of a female patient with a traumatic brain injury. Here the model does not take the whole skull but will be extracted a part to reduce the computational volume. Models are imported into ANSYS Products 19.2 software to simulate the stress distribution, deformation, and strain parameters exerted on the cranial artificial part as shown in Fig. 3. For the left part of the patient’s skull, the patch of the skull consists of 3 fixing parts attached to the skull by 3 cylindrical screws. 2.1 Methods and Boundary Conditions The implant material is PEEK, which is suitable for a replacement since the action of the cranial is static, without movement [11]. The dimensions (length x width x depth) are 147.59 mm × 105.94 mm x 39.22 mm of left cranial model and 123.40 mm × 103.56 mm × 36.27 mm of right cranial model. The material of fixing screw is Ti-6Al4V, which is popular in current medicine due to its high durability and compatibility with the human body [12]. To ensure the safety of the patient, the screw cannot affect the meninges, thus it must have a suitable length. Therefore, the screw is 4 mm in length with a 2 mm screw diameter. During the simulation, the screw shape is cylindrical to make it easy to simulate and compute. The model is simulated using FEA, in a static structural environment. The cranial bone should be fixed in FEA. The load set on the system is the intracranial pressure acting on the entire inner surface of the skull and the replacement patch. This research uses two load conditions to simulate intracranial pressure: 7 mmHg (minimum load) and 15 mmHg ≈ 2000 Pa (maximum load) [2]. They were applied to the entire interior surface of the cranial implant. The fixed part is the cranial model. The meshing model is divided into 3 separate parts according to the characteristics of the regions: body sizing, edge sizing, face sizing. With the meshed model, the mean Skewness value is 0.24992 and the standard deviation is 0.15622. This index is within the acceptable range for mesh quality (0–0.5).
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2.2 Material Properties According to the research in [13], the coefficient of friction between bone and Ti-6Al-4V under simulated body fluid conditions (SBF) ranges from 0.46 to 0.56. Therefore, the coefficient of friction between bone and Ti-6Al-4V in this research is 0.5 [14]. According to A. Omidi et al. the ccoefficients of friction between PEEK and Ti-6Al-4V is less than 0.3 [15]. Therefore, the coefficient of friction between the screw and the cranial implant is choosing 0.1. Table 1 shows the material parameters assigned to the finite element model, assigned to bones, screws and skull patches, respectively. Table 1. Parameter of materials E (GPa)
ρ (kg/m3 )
ν
Bone
18
1810
0.3
Ti6Al4V
107
4405
0.323
PEEK
3.85
1310
0.4
E: Elastic modulus (GPa); ρ: Density (kg/m3 );
ν: Poisson’s ratio
2.3 Simulation Results The mesh data for both cases was 7 mmHg and 15 mmHg: 641310 nodes, 396919 elements. At the maximum load of 15 mm Hg, the Von Mises Strain value at the screw holes ranges from 600 to 830 in the bone. The maximum von Mises stress is 0.24347 MPa in the cranial implant (all three local max values concentrated at the rim of the artificial cranial part). The deformation value is small: 7.2308 × 10–2 μm with bone and 0.77295 μm with the cranial implant. The fixed screws have a maximum deformation of about 7.0138 × 10–2 μm as shown in Fig. 4. At the minimum load 7 mmHg, the deformation, Von Mises strain, and stress values are all reduced across all parts. In particular, the deformation on the cranial implant decreases to 0.36329 μm, and the deformation on the skull is 7.2308 × 10–2 μm. The Von Mises strain value in the bone area around the screw holes was between 260 and 800. The maximum von Mises stress is 0.11433 MPa with the cranial implant and 11.588 MPa with the skull as shown in Fig. 5. The simulation results in two different intracranial pressure cases show three areas that have the maximum deformation value are mainly concentrated on center of the artificial cranial part. The Von Mises stress distribution and Von Mises strain distribution in the artificial cranial part are mainly distributed at the edges and center. While the Von Mises stress distribution and the Von Mises strain distribution in the bone are uniform and only have the change in the screw holes.
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Fig. 4. Front and back of the cranial implant at maximum load 15 mmHg: (a) Deformation, (b) Von Mises strain, (c) Von Mises stress.
Fig. 5. Front and back of the cranial implant at minimum load 7 mmHg: (a) Deformation, (b) Von Mises strain, (c) Von Mises stress.
3 Result and Discusion According to Roberts et al., strain values less than 200 mε induce atrophy since the bone is not sufficiently stimulated [16]. Fatigue failure occurs when strain values exceed 4000 mε. Based on the results of Robert et al., Ramos et al. suggested that the ideal strain value at the screw holes in temporomandibular joint implants is 200 to 2500 mε, to ensure optimal strain on the bone [8]. The maximum strain values reported in our study for the three screw holes in the bone were all within the optimum range of 200 to 2500 mε. The results of the craniofacial implant of a female patient who lost two large cranial fragments are shown in Fig. (6a). The design of the cranial implants with the left and right box pieces along with the fixed part is shown in Fig. (6b) and (6c). A minimum of 3 fixation parts is deeded for large cranial implants. According to surgeons, the placement of fixed parts should avoid the locations of nerves and it should be evenly distributed on the circumference of the patch.
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Fig. 6. Images of the traumatic area of the skull patient on the left and right sides.
The replacement patch suitable for the patient’s defective area is created using the proposed fabrication design method. The fabrication of a Patient-specific PEEK implant was made using the injection molding method. The technology of fabricating the cranial implant is injection molding with pellet biomedical PEEK heated to melt at about 380 to 400 °C through two halves of the mold. The graft after injection molding is drilled with evenly spaced holes to drain fluid during the implantation process and create conditions for the cranial tissues to grow both inside and outside of the cranial implant. After the simulation results were evaluated, Checking the accuracy and fit of the cranial implant with the patient’s skull is presented through 3D printing of PLA samples of defective areas and fabricated parts and help the doctor plan the pre-surgery.
4 Conclusion In this study, we propose a 3D technology application for the problem of reconstructing the missing cranial parts using digital methods. The results of simulating parameters show that the values are within the allowed threshold. The proposed cranial implant design process shows dimensional, structural, and aesthetic accuracy. This process allows the fabrication of implants to suit the requirements of biomedical products. The application of 3D technology reduces designing and fabricating time as doctors do not have to repair implant parts during surgery. The result shows that there are many prospects in designing and fabricating artificial bones for implants in the human body.
References 1. Bogu, V.P., Ravi Kumar, Y., Khanara, A.K.: Modelling and structural analysis of skull/cranial implant: beyond mid-line deformities. Acta Bioeng. Biomech. 19(1), 125–131 (2017). https:// doi.org/10.5277/ABB-00547-2016-04 2. Mohammed, M.I., Fitzpatrick, A.P., Malyala, S.K., Gibson, I.: Customised design and development of patient specific 3D printed whole mandible implant. In: Solid Freeform Fabrication 2016 Proceedings 27th Annual International Solid Freeform Fabrication Symposium An Additive Manufacturing Conference SFF 2016, January 2018, pp. 1708–1717 (2016)
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3. Feroze, A.H., Walmsley, G.G., Choudhri, O., Lorenz, H.P., Grant, G.A., Edwards, M.S.B.: Evolution of cranioplasty techniques in neurosurgery: historical review, pediatric considerations, and current trends. J. Neurosurg. 123, 1098–1107 (2015). https://doi.org/10.3171/2014. 11.JNS14622.Disclosure 4. Han, X., et al.: Carbon fiber reinforced PEEK composites based on 3D-printing technology for orthopedic and dental applications. J. Clin. Med. 8(2), 240 (2019). https://doi.org/10.3390/ jcm8020240 5. Alonso-Rodriguez, E., Cebrián, J.L., Nieto, M.J., Del Castillo, J.L., Hernández-Godoy, J., Burgueño, M.: Polyetheretherketone custom-made implants for craniofacial defects: report of 14 cases and review of the literature. J. Cranio-Maxillofacial Surg. 43(7), 1232–1238 (2015). https://doi.org/10.1016/j.jcms.2015.04.028 6. Honigmann, P., Sharma, N., Okolo, B., Popp, U., Msallem, B., Thieringer, F.M.: Patientspecific surgical implants M\made of 3D printed PEEK: material, technology, and scope of surgical application. Biomed Res. Int. 2018, 1–8 (2018). https://doi.org/10.1155/2018/452 0636 7. Wang, C., et al.: Tribological behavior of Ti-6Al-4V against cortical bone in different biolubricants. J. Mech. Behav. Biomed. Mater. 90, 460–471 (2019). https://doi.org/10.1016/j. jmbbm.2018.10.031 8. Rammos, C.K., Cayci, C., Castro-Garcia, J.A., Feiz-Erfan, I., Lettieri, S.C.: Patient-specific polyetheretherketone implants for repair of craniofacial defects. J. Craniofac. Surg. 26(3), 631–633 (2015). https://doi.org/10.1097/SCS.0000000000001413 9. Punchak, M., et al.: Outcomes following polyetheretherketone (PEEK) cranioplasty: systematic review and meta-analysis. J. Clin. Neurosci. 41, 30–35 (2017). https://doi.org/10.1016/j. jocn.2017.03.028 10. Herring, S.W., Ochareon, P.: Bone-special problems of the craniofacial region. Orthod. Craniofacial Res. 8(3), 174–182 (2005). https://doi.org/10.1111/j.1601-6343.2005.00328.x 11. Panayotov, I.V., Orti, V., Cuisinier, F., Yachouh, J.: Polyetheretherketone (PEEK) for medical applications. J. Mater. Sci. Mater. Med. 27(7), 1–11 (2016). https://doi.org/10.1007/s10856016-5731-4 12. Pal, S.: Design of Artificial Human Joints & Organs, vol. 1, pp. 1–419 (2014) 13. Kurtz, S.M., Devine, J.N.: PEEK biomaterials in trauma, orthopedic, and spinal implants. Biomaterials 28(32), 4845–4869 (2007). https://doi.org/10.1016/j.biomaterials.2007.07.013 14. Sampaio, M., et al.: Effects of poly-ether-ether ketone (PEEK) veneer thickness on the reciprocating friction and wear behavior of PEEK/Ti6Al4V structures in artificial saliva. Wear 368–369, 84–91 (2016). https://doi.org/10.1016/j.wear.2016.09.009 15. Omidi, A., Jeannin, C., Nazari, M.A., Panahi, M.S.: Analysis of temporomandibular joint prosthesis using finite element method and a patient specific design. Eng. Solid Mech. 7(1), 83–92 (2019). https://doi.org/10.5267/j.esm.2018.10.001 16. Roberts, W.E., Huja, S.S., Roberts, J.A.: Bone modeling: biomechanics, molecular mechanisms, and clinical perspectives. Semin. Orthod. 10(2), 123–161 (2004). https://doi.org/10. 1053/j.sodo.2004.01.003
Research and Design of Compact Equipment Using Phase_Shifting Deflectometry Apply in Optical Surface Measurement Nguyen Thi Kim Cuc, Vu Danh Tien, Cao Xuan Binh, and Vu Tien Dung(B) School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Recently, aspherical optics are increasingly used, because aberrations of optical systems can be reduced, applied in many applications from compact optical devices such as camera lenses to large complex telescope systems. Fabricating and testing aspherical surfaces on very large or small optics presents many challenges because the radius of curvature varies across the surface. With the measurement providing a wide range of high-resolution areas, the deflectometry method can solve the problem of measuring the profiles of optical parts or free form curved surfaces. This paper researches and designs a surface measuring compact device for optics using the phase-shifting deflectometry method to achieve high accuracy and resolution. In addition, evaluating measurement results components to optics aberration is also covered in this research. Keywords: Deflectometry · Phase shifting · Optical surface testing · Radius non-contact measurement
1 Introduction Measurement of free form surfaces in the manufacture of mechanical and optical devices is one of the difficult problems that need to be solved. In the past, the method of measuring contact by probe or measuring by light interference is often used. The probe contact method has an extended measurement time and the interaction between the probe and the surface can affect the object especially optical devices. The interference method has the advantages of high accuracy and high resolution but measures only a small area [1]. To meet the requirements of measurement speed, high resolution, and non-contact, a 3D measuring method using structured light is researched and developed [2–4]. The advantages of the structured light method are fast sample scanning, a huge number of measurement points, and large object that can be measured when using the matching algorithm. However, applied to the high-gloss surface, optical surface, this method is difficult to apply. It is often necessary to matte the part during measurement, this does not apply to optical components.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 905–912, 2022. https://doi.org/10.1007/978-981-19-1968-8_77
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Recently, with the optical surface objects, the deflectometry method has been researched and applied in the industry. This method can achieve comparable interferometry accuracy but with simple and low-cost hardware. In this paper, we introduce the research and design of the compact equipment using phase-shifting deflectometry apply to regular optical components.
2 Basic Principle The basic principle of the deflectometry method is shown in Fig. 1. The system consists of an LCD screen, a camera, and a measuring object. The LCD generates a light pattern to illuminate the test optics and the CCD camera captures the reflected light from the testing surface. Each pixel on the LCD screen is a light source to the measured object. From the relation between light source, testing object, and detector, the bi-directional surface slopes of all measuring points on the surface will be calculated. From the slope data, the wave-front of the testing surface is retrieved by software algorithms [5, 6].
Fig. 1. Deflectometry schematic diagram
2.1 Governing Equation First, the geometric relation is shown in Fig. 2. The slope data of x and y-direction can be calculated by the following equations:
wx (xm , ym ) =
xm −xs dm2s zm2s −Z(xm ,ym ) dm2s
+
wy (xm , ym ) =
ym −ys dm2s zm2s −Z(xm ,ym ) dm2s
+
+
+
xm −xc dm2c zm2c −Z(xm ,ym ) dm2c
(1)
ym −yc dm2c zm2c −Z(xm ,ym ) dm2c
(2)
where wx and wy are testing surface slopes of x and y-direction; x m and ym are points on the testing surface, x s and ys are the coordinates of corresponding pixel position on
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Fig. 2. Geometric relation between light source, camera, and testing surface
the light source plane; x c and yc are camera aperture coordinates; d m2s and d m2c are the distances from the surface under test to LCD and camera aperture respectively; z is the surface height of test mirror; zm2s and zm2c are the z-directional distance from the surface under test vertex to LCD and camera aperture respectively. The parameter Z(xm ,ym ) is the surface height that needs to be measured in the test. However, the surface under test is close to its nominal value compared to the distance zm2s and zm2c . Therefore two Eqs. (1) and (2) can rewrite in simply form: wx (xm , ym ) =
xm − xc 1 xm − xs ( + ) 2 zm2s zm2c
(3)
wy (xm , ym ) =
ym − yc 1 ym − ys ( + ) 2 zm2s zm2c
(4)
Secondly, from the slope data, the testing wavefront can be retrieved by software integration with Southwell algorithms [7–9]. Finally, the wavefront retrieved results can be analyzed with Zernike polynomial to compare the optical aberration of the testing mirror with other methods [10]. 2.2 Data Capture and Processing In the deflectometry method, the LCD generates the phase-shifting pattern, and the CCD camera captures the reflected light from the testing surface. By phase matching algorithms, the geometric relation from each pixel on LCD to mirror pixel and CCD pixel can determine for slope calculation with Eqs. (3) and (4) [11]. The data processing of the deflectometry method is shown in Fig. 3. In the step of configuration, the chessboard is used to calibrate for removing camera distortion as shown in Fig. 4.
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Fig. 3. Deflectometry method data processing flowchart
Fig. 4. Camera distortion calibaration with chessboard. (a) Chessboard coordinate, (b) Screen coordinate
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3 Experiment Setup Figure 5 shows the compact deflectometry system design with a fixed testing mirror position. An Odroid LCD screen with a resolution of 1024 × 768 is used as the light source is fixed on the housing can be finely adjustable by tilting knob and rotating state. And the camera in the system is the Point Gray Camera model: CM3-U3 with 1288 × 964 resolution, 12 bit ADC, 30 frame rate.
Fig. 5. Compact deflectometry system design
Fig. 6. Phase-shifting pattern reflected by testing surface
The exact position of the camera and LCD screen are determined by reference spheres which can be measured on Coordinates Measuring Machine (CMM).
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4 Result and Discussion A concave mirror with 76 mm diameter with 1840 mm radius of curvature (Fig. 6) according to the manufacturer’s specification is used to evaluate the measuring system (Table 1). Table 1. Measurement results Times (i)
Results of radius of curvature R(i) (mm)
1
1842.725487
2
1841.473626
3
1844.654568
4
1844.484611
5
1841.787286
6
1843.542837
7
1842.713265
8
1841.567589
9
1844.463141
10
1841.864356
The R-value of the measurement is calculated according to the formula: R = R ± σ × tα
(5)
where: R is the average result of 10 measurements: 1 10 R(i) = 1842.927676 mm R¯ = 10 i=1 σ is the standard deviation of measurement results: 10 2 ¯ R−R(i) ) = 1.274727 mm i=1 ( σ = 10−1
t α according to the Student integral method, with a reliability of 95%, and the number of measurement times is 10: t α = 2.262. Instead (5): R = 1842.928 ± 2.883 mm. Using the CMM Mitutoyo Crystal-Apex S544 with 100 points on the testing surface, thereby calculating the radius of curvature by Minimum Zone Element method, Rt = D/2 = 3682.9172/2 = 1841.4586 mm. Compare measurement results with an experimental system with the phase-shifting deflectometry method and CMM results: δ= where:
|R − Rt | × 100% |Rt |
(6)
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δ is the relative error. Instead (6): δ = 0.08%. The accuracy and reliability of the measuring system are affected by the system calibration parameters and measurement conditions [12]. The testing spherical surface aberration error when removing the small order terms of piston, tilt, and power shows in Fig. 7.
Fig. 7. Testing surface aberration error after low Zernike polynominal order removal
The aberration error at the edge of the test surface is about 60 μm because of the machining process of the optical part. In addition, optical devices often use clear aperture with 80–90% of the internal area. In the clear aperture, aberration error in the μm range, achieving the same accuracy as the interference methods.
5 Conclusions The paper shows research and design of compact equipment to measure optical surface by phase-shifting deflectometry with advantages of measuring time, non-contact and high accuracy. The radius of curvature result uses a compact deflectometry system with concave mirror objects to approximate the manufacturer’s actual parameters and the measurement results on CMM with a relative error of only 0.08%. And the high orders of the optical aberration can be analyzed comparable to interferometer method accuracy. The deflectometry is suitable for optical measuring objects and high-gloss surfaces which can not be done by the 3D structured scanning method. Acknowledgments. This research is based upon work supported by Hanoi University of Science and Technology - the project T2021-TT-006.
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References 1. Malacara, D.: Optical Shop Testing. John Wiley & Sons, N.Y. (2006). https://doi.org/10.1002/ 9780470135976 2. Gupta, M., Agrawal, A., Veeraraghavan, A., Narasimhan, S.G.: Structured light 3D scanning in the presence of global illumination. In: CVPR 2011, pp. 713–720 (2011). https://doi.org/ 10.1109/CVPR.2011.5995321 3. Cuc, N.T.K., Nguyen, P.-T., Pham, D.A., Vu, T.T., Cao, X.B.: Non-contact 3D measurement of freeform reflective surface In: Lecture Notes in Mechanical Engineering (International Conference on Material, Machines and Methods for Sustainable Development) (2020) 4. Cuc, N.T.K., Vinh, N.V., Hung, N.T., Khai, P.K.: Optimal parameters selection for 3Dmechanical surface measuring equipment based on the structured light Gray code. J. Sci. Technol. Tech. Univ. 122, 22–27 (2017) 5. Peng, S.: SCOTS: A reverse Hartmann test with high dynamic range for giant magellan telescope primary mirror segments. In: Proceedings of SPIE - The International Society for Optical Engineering (2012). https://doi.org/10.1117/12.926719 6. Trumper, I., Choi, H., Kim, D.W.: Instantaneous phase shifting deflectometry. Opt. Express. 24, 27993–28007 (2016) 7. Southwell, W.H.: Wave-front estimation from wave-front slope measurements. J. Opt. Soc. Am. 70, 998–1006 (1980) 8. Phuc, P.H., Manh, N.T., Rhee, H.-G., Ghim, Y.-S., Yang, H.-S., Lee, Y.-W.: Improved wavefront reconstruction algorithm from slope measurements. J. Korean Phys. Soc. 70(5), 469–474 (2017). https://doi.org/10.3938/jkps.70.469 9. Zhong, J., Weng, J.: Phase retrieval of optical fringe patterns from the ridge of a wavelet transform. Opt. Lett. 30, 2560–2562 (2005) 10. Hoffmann, M., Ernst, A., Bergen, T., Hettenkofer, S., Garbas, J.: A robust chessboard detector for geometric camera calibration. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP, vol. 4, pp. 34–43 (2017). https://doi.org/10.5220/0006104300340043 11. Manh, N.T., Kang, P., Ghim, Y.-S., Rhee, H.-G.: Nonlinearity response correction in phaseshifing defectometry. Meas. Sci. Technol. 29, 045012 (2018) 12. Cuc, N.T.K., Vinh, N.V., Hung, N.T.: Improving the accuracy of the calibration method for structured light system. J. Sci. Technol. 127, 2354–1083 (2018)
Numerical Study of Ultraviolet Germicidal Effect Against SARS-CoV-2 Virus Nguyen Duy Minh Phan1(B) , Ngo Quoc Huy Tran1 , Le Anh Doan1 , Quang Truong Vo1 , Duy Chung Tran2 , Thi Thanh Vi Nguyen1 , Van Sanh Huynh1 , Tran Anh Ngoc Ho1 , Duc Long Nguyen1 , and Cuong Mai Bui1 1 Faculty of Mechanical Engineering, The University of Danang - University of Technology
and Education, 48 Cao Thang, Danang, Viet Nam [email protected] 2 Faculty of Electrical and Electronic Engineering, The University of Danang - University of Technology and Education, 48 Cao Thang, Danang, Viet Nam
Abstract. This paper presents a numerical investigation of Ultraviolet (UV) disinfection performance against the recently explored SARS-CoV-2 Virus. The UV lighting source is of a vertical lamp supposed to be put in popular mobile UV-C devices. The Finite Volume Method (FVM) and Discrete Ordinates (DO) model are adopted to deal with UV irradiance. Various results for the formation of an effective disinfection zone, detailed disinfection rate, and UV exposure duration are discussed and analyzed in detail. Results show that the bactericidal influence against SARS-CoV-2 viruses, which is strongest in the horizontal central plane through the UV lamp, can be significantly increased with the upgrade in the lamp power utilized. Furthermore, the UV exposure duration is found to have a considerable effect on the disinfection performance. Specifically, the disinfection rate is greatly improved, resulting in a remarkable expansion in the effective disinfection zone with a longer exposure period. Furthermore, the exposure duration required for 90% total viruses being eliminated with different lamp wattages are reported. Keywords: UV-C · Disinfection · SARS-CoV-2 · CFD
1 Introduction Ultraviolet (UV) rays whose electromagnetic spectrum (EM) ranges between X-rays and the visible light can be typically categorized into four types: UV-A (320–400 nm); UV-B (280–320 nm); UV-C (200–280 nm) and V-UV (100–200 nm). Among them, as claimed by the International Commission on Illumination (CIE) and the Center for Disease Control (CDC), both UV-B and UV-C show their effectiveness in air and surface disinfection and water treatment [1]. However, the latter is more frequently employed thanks to its wide spectrum of microbial pathogens inactivated [2]. The UV-C rays destroy the Deoxyribonucleic acid (DNA) of the pathogens after attacking microorganisms’ cell walls and absorbed by proteins and nucleotides, leading to efficient prevention of their reproduction [1]. Actually, UV-C radiation has been widely utilized for numerous © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 913–924, 2022. https://doi.org/10.1007/978-981-19-1968-8_78
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disinfection purposes; this can be attributable to its excellent germicidal performance, safety to humans and the environment, and economic efficiency [3]. The latest COVID-19 pandemic spreading around our world has been raising various disease prevention and healthcare challenges. In order to reduce virus transmission, especially in closed rooms of healthcare centers, hospitals, and offices, the Ultraviolet germicidal irradiation (UVGI) approach is noticed to have a great performance [4, 5]. For instance, the UV-C irradiation of 222 nm wavelength was stated to possibly eliminate 88.5% and 99.7% of total SARS-CoV-2 viruses with, respectively, the doses of 1 mJ/cm2 and 3 mJ/cm2 [5]. Moreover, Buchan et al. [6] recently found that UV-C lighting could improve the disinfection efficiency by more than 80% compared to being only based on traditional convection approaches. Taking advantage of this germicidal superiority, many indoor UV disinfection systems/devices have been recently launched and upgraded continuously [7]. Amongst them, mobile devices (e.g., autonomous or manually controlled robots) are more preferred due to their multi-function, simple installation and maintenance, and considerably greater disinfection zone and efficient bactericidal process compared to fixed ones (e.g., hanging lamps) [8]. To improve the design of the aforementioned devices and assess their operating performance, numerical tools become important. Indeed, the Computational Fluid Dynamics (CFD) approach shows its efficiency in the prediction of UV irradiation field and germicidal effects in air disinfection [9], water treatment [10], and ventilation systems [11]. It is, however, necessary to note that there exist very few available works on the UV disinfection against the recently discovered Coronavirus and the factors affecting this inactivation. In this work, we are, therefore, aiming to numerically study the germicidal influences of a UV lamp on this virus type; furthermore, effects of lamp wattage and UV exposure time are also investigated. This work would contribute to the improvements of UV mobile devices with one single vertical lamp (Fig. 1) that are supposed to be well suitable for small or medium rooms.
Fig. 1. Design of a simple UV-C mobile device.
The rest of this paper is organized as follows: Sect. 2 introduces the theory background including the governing equations and the numerical approach; simulated results are presented and analyzed in Section 3; Section 4 provides concluding remarks and some suggestions for the future works.
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2 Theory Background 2.1 UV Irradiance The UV irradiance is dealt with the radiative transfer equation (RTE) as: d I (r, s) + (α + σs ) I (r, s) ds 4π 4 σs 2 σT + = αn I r, s s · s d , π 4π
(1)
0
(W/m2 );
r, s and s’ being, respectively, the position, direcwith I the radiation intensity tion, and the scattering direction vectors; α and σ s are, in turns, the absorption and scattering coefficients; n is the refractive index; σ = 5.669 × 10–8 W/m2 K4 is the Stefan-Boltzmann constant; T is the local temperature (K); Φ is the phase function; Ω’ is the solid angle. 2.2 Numerical Approach 2.2.1 Discrete Ordinates Model Discrete Ordinates (DO) model for non-gray radiation is used to discretize Eq. (1) as [12]: ∇ · (Iλ (r, s)s) + (αλ + σs )Iλ (r, s) σs = αλ n Ibλ + 4π
4π
2
Iλ (r, s’)(s · s’)d .
(2)
0
Here, I λ and I bλ are, respectively the spectral intensity and the black body intensity; α λ is the spectral absorption coefficient. Discretization parameters include pixelation set of 3 × 3 and division set of 15 × 15; the modeling factors are tested to be sufficient for this problem [12, 13]. 2.2.2 Simulation Implementation Figure 2 shows the computational domain and boundary conditions applied. This domain is built imitating a closed room with lamp centered. Additionally, we use unstructured tetrahedral mesh for calculations; an example of the near-field grid system is presented in Fig. 3. For the sake of solution stability, a mesh convergence study has been conducted. As can be seen in Fig. 4 and Table 1, mesh M2 consisting of 1.1 million elements produces nearly the same simulated results for disinfection rate with one obtained by higher refinement. However, the computational cost for the latter (i.e., M3) is considerably larger (see Table 1). It is noted that positions whose results are revealed in Table 1 are introduced in Fig. 5. The resolution of M1 is, therefore, employed for all of our simulations. The calculations are solved in a commercial software built in the Finite Volume Method (FVM) named Ansys Fluent.
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Fig. 2. Computational geometry and boundary conditions applied.
Fig. 3. Near-field mesh used in (a) xy central and (b) xz central planes.
Fig. 4. Variations in the boundary of r d = 90% of a 15 W-lamp after 5 s exposed in (a) xy central and (b) xz central planes with different mesh resolutions.
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Table 1. UV disinfection rate produced by a 15 W-lamp after 10 s exposed at different positions with various mesh resolutions. Mesh
Elements
A
F
Time
M1
750 K
25.74
21.32
3h
M2
1.1 M
34.3
26.24
4.2 h
M3
1.5 M
34.56
26.68
5.2 h
Fig. 5. Definitions of characteristic planes, lines and points.
2.3 Disinfection Rate The correlation between initial radiation intensity, I 0 (W/m2 ), of the lamp and its wattage, P (W), is [1]: I0 =
P , 2π rl
with r (m) and l (m) being the lamp radius and length, respectively. The disinfection rate, r d , is expressed as [14]: rd = 1 − e−kIt × 100%.
(3)
(4)
where I (W/m2 ) is the radiation intensity, t (s) is the exposure duration and k (m2 /J) is the microbial susceptibility to the UV radiation. In this study, the SARS-CoV-2 virus with k = 0.08528 m2 /J is of our interest. To evaluate the germicidal performance, we define an efficient disinfection zone Z90 within which less than 10% of total SARS-CoV-2 viruses survive after an exposure period, i.e., r d = 90%. It is noted that this zone is varied when the criterion set differs; specifically, it becomes extended when the criterion for r d is decreased (see Fig. 6).
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Fig. 6. Different criterions for the efficient disinfection region; the number stands for the disinfection rate r d . The lamp is of 15 W, the exposure duration is 10 s.
3 Results and Discussion 3.1 UV Disinfection Performance In this part, the UV-C bactericidal influences against the SARS-CoV-2 viruses are discussed and analyzed in detail. Furthermore, the lamp wattage is varied in P = 5-30 W to study its impacts. The exposure duration is kept to be of t e = 10 s.
Fig. 7. UV disinfection performance of a 15 W-lamp after 10 s exposed in (a) the xy central and (b) the xz central planes.
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Figure 7 illustrates the formation of Z90 of a 15 W-lamp in both the xy and xz central planes. The largest Z90 is determined to be the horizontal central plane, indicating the disinfection being the most efficient there. This area considerably narrows at planes near the upper and lower parts of the lamp. In addition, for both vertical and horizontal directions, the further the distance to the lamp, the significantly lower the disinfection rate is noticeably found. For example, along the z-centerline, r d produced by a 15 W lamp is dramatically reduced from ~96% at the position of 9r to ~30% at the one of 19r further from the lamp (see the black line in Fig. 8). Additionally, Fig. 8 also reveals the variation in disinfection rate along centerlines when the lamp wattage differs. As can be observed, the germicidal performance can be greatly improved with the larger lamp power. For instance, along the y-centerline, the disinfection rate can be increased by 200% with a 30 W-lamp compared to that obtained with a 15 W-one, probably resulting in the expansion of Z90 and bactericidal effectiveness, especially the region near the lamp. This can be attributed to the increase in radiation intensity and then the UV lighting coverage region when the lamp power is increased (see Fig. 9). It is noted that the intensity in the surface of a 30 W-lamp is two times larger than that of a 15 W-one.
Fig. 8. UV disinfection rate produced by various lamp wattages after 10 s exposed along (a) the y-centerline and (b) the z-centerline.
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Fig. 9. UV intensity of various lamp wattages along (a) the y-centerline and (b) the z-centerline.
3.2 Exposure Duration The exposure duration, i.e., the length of the germicidal period, is considered to play a crucial role in the UV disinfection process. In this part, several values of it, i.e., t e = 2 s, 5 s, 10 s, and 20 s, are tested to investigate its influences against SARS-Cov-2 virus. Results for disinfection performance of a 15W-lamp with various t e are presented in Fig. 10. As expected, the longer it is exposed, the larger Z90 and the greater disinfection rate around the lamp are seen. This implies that when a low wattage is employed, the bactericidal effects can be still acceptable in case the exposure duration is large. Furthermore, it seems that an increase in the exposure duration slightly improves the smoothness of germicidal distribution. In addition, detailed disinfection rates distributed on y and z-centerlines are provided in Fig. 11. It exists that on the latter, at the distance of 19r from the lamp, r d can be increased by 2.9 times with a 20 s-period rather than finishing the exposure after only 5 s. Tables 2 and 3 reveal the minimum exposure durations required for r d reaching 90% at different positions along y-centerline and z-centerline, respectively. Generally, the duration needed is larger with the further distance to the lamp for all the wattages studied. Specifically with a 15 W-lamp, on the horizontal centerline, the exposure process at the position of y = 2 m should be lengthened by ~ 1645.5 folds compared to that at y = 1 m. At positions very far from UV lighting source, the duration is noted to be extremely long. For example, the values can be still as large as, respectively, 16.47 h and 219.9 h for y = 2 m and z = 2 m (a surrounding wall) despite the fact that the highest wattage, i.e., 30 W, is used. Moreover, the greater the wattage is, the shorter the exposure duration at the same distance is suggested. Additionally, it is interesting to see that for a fixed
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position, the hours of exposure expected is a approximately linear function of the lamp power.
Fig. 10. UV disinfection performance of a 15 W-lamp with various exposure durations in xz central plane.
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Fig. 11. UV disinfection rate of a 15 W-lamp with various exposure durations along (a) the y-centerline and (b) z-centerline. Table 2. Required UV exposure time (hour) against SARS-CoV-2 virus at different positions along y-centerline with various lamp wattages. y 1m
10 W
15 W
20 W
25 W
30 W
0.03
0.02
0.02
0.01
0.01
1.5 m
1.59
1.06
0.79
0.64
0.53
1.75 m
9.08
6.05
4.54
3.63
3.03
49.35
32.91
24.69
19.76
16.47
2m
Table 3. Required UV exposure time (hour) against SARS-CoV-2 virus at different positions along z-centerline with various lamp wattages. z
10 W
15 W
20 W
25 W
30 W
0.5 m
0.02
0.02
0.01
0.01
0.01
1m
1.39
0.92
0.69
0.55
0.46
33.18
22.13
16.6
13.28
11.07
1.75 m 148.06
98.86
74.2
59.39
49.5
1.5 m 2m
651.03 436.9
328.77 263.54 219.9
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4 Conclusions In this work, the UV disinfection effectiveness against SARS-CoV-2 was numerically investigated. The UV radiative transfer equation was tackled with the Finite Volume Method and Discrete Ordinates Model. With the largest efficient disinfection zone, the germicidal effects were found to be the most efficient in the horizontal central plane. Additionally, the disinfection rate was noted to become significantly lower at a further distance from the UV lamp. Moreover, the larger the lamp power, the higher the radiation intensity, and then the greater bactericidal influences were observed. In addition, the exposure duration was determined to have a strong impact on the UV disinfection performance. In detail, the bactericidal rate was noticed to get higher with the lengthened exposure period, resulting in a larger effective disinfection region in the room. Furthermore, generally, the exposure duration was indicated to be longer with the further distance from the lamp and/or the lower lamp power used. Especially, for r d being of 90% at surrounding walls, the exposure should take an extremely long time, i.e., more than 219.9 h. For future work, we would consider the effects of airflow and temperature variation in the room. Furthermore, utilization and design optimization are also of our interest. Acknowledgments. This research is funded by the People’s Committee of Danang city, under contract number 22/HÐ-SKHCN (2021).
References 1. Kowalski, W.: Ultraviolet Germicidal Irradiation Handbook: UVGI for Air and Surface Disinfection. Springer Science & Business Media (2010) 2. Narita, K., et al.: Ultraviolet C light with wavelength of 222 nm inactivates a wide spectrum of microbial pathogens. J. Hosp. Infect. 105, 459–467 (2020) 3. UV Lights and Lamps: Ultraviolet-C Radiation, Disinfection, and Coronavirus, (n.d.). https://www.fda.gov/medical-devices/coronavirus-covid-19-and-medical-devices/uvlights-and-lamps-ultraviolet-c-radiation-disinfection-and-coronavirus 4. Heilingloh, C.S., et al.: Susceptibility of SARS-CoV-2 to UV irradiation. Am. J. Infect. Control. 48, 1273–1275 (2020) 5. Kitagawa, H., et al.: Effectiveness of 222-nm ultraviolet light on disinfecting SARS-CoV-2 surface contamination. Am. J. Infect. Control. 49, 299–301 (2021) 6. Buchan, A.G., Yang, L., Atkinson, K.D.: Predicting airborne coronavirus inactivation by farUVC in populated rooms using a high-fidelity coupled radiation-CFD model. Sci. Rep. 10, 1–7 (2020) 7. Shining a light on COVID-19. Nat. Photonics. 14 337–337 (2020). https://doi.org/10.1038/ s41566-020-0650-9 8. Yang, J.-H., Wu, U.-I., Tai, H.-M., Sheng, W.-H.: Effectiveness of an ultraviolet-C disinfection system for reduction of healthcare-associated pathogens. J. Microbiol. Immunol. Infect. 52, 487–493 (2019) 9. Heidarinejad, M., Srebric, J.: Computational fluid dynamics modelling of UR-UVGI lamp effectiveness to promote disinfection of airborne microorganisms. World Rev. Sci. Technol. Sustain. Dev. 10, 78–95 (2013)
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10. Sobhani, H., Shokouhmand, H.: Effects of number of low-pressure ultraviolet lamps on disinfection performance of a water reactor. J. Water Process Eng. 20, 97–105 (2017) 11. Capetillo, A., Noakes, C.J., Sleigh, P.A.: Computational fluid dynamics analysis to assess performance variability of in-duct UV-C systems. Sci. Technol. Built Environ. 21, 45–53 (2015) 12. Ho, C.: Evaluation of reflection and refraction in simulations of ultraviolet disinfection using the discrete ordinates radiation model. Water Sci. Technol. 59, 2421–2428 (2009) 13. Atci, F., Cetin, Y.E., Avci, M., Aydin, O.: Evaluation of in-duct UV-C lamp array on air disinfection: a numerical analysis. Sci. Technol. Built Environ. 27, 98–108 (2020) 14. Mitscherlich, E., Marth, E.H.: Microbial Survival in the Environment: Bacteria and Rickettsiae Important in Human and Animal Health. Springer Science & Business Media (2012)
Thermal Efficiency and Exhaust Emission of an SI Engine Using Hydrogen Enriched Gas from Exhaust Gas Fuel Reforming Based on Ni-Cu/Al2 O3 Catalysts Nguyen The Luong1(B) , Tran Van Hoang2 , Pham Minh Tuan1 , and Le Anh Tuan1 1 Department of Internal Combustion Engine, School of Transportation Engineering, Hanoi
University of Science and Technology, No.1 Dai Co Viet Street, Hanoi, Vietnam [email protected] 2 Faculty of Mechanical Engineering, University of Economics - Technology Industrial, No-456 Minh Khai-Vinh Tuy ward- Hai Ba Trung District-Ha Noi, Hanoi, Vietnam
Abstract. Exhaust gas fuel reforming has been identified as a thermochemical energy recovery technology with the potential to improve gasoline engine efficiency and, as a result, reduce CO2 emissions in addition to other gaseous emissions. Endothermic catalytic reforming of gasoline and a fraction of the engine exhaust gas is used to recover energy from the hot exhaust stream. The reformate containing hydrogen has a higher enthalpy than the gasoline fed to the reformer and is recirculated to the intake manifold. The highest hydrogen selectivity and the gasoline conversion is observed over the Ni-Cu/Al2 O3 catalysts at 550 °C which is higher than Ni/Al2 O3 catalysts. This paper investigates the influence of addtived hydrogen enriched gas fuel produced by Ni-Cu/Al2 O3 catalysts to thermal efficiency and emissions of SI engine. As the results is compared with the engine using gasoline fuel and engine addtived hydrogen enriched gas fuel based on Ni/Al2 O3 catalysts. The Structural, morphological characterizations and Catalytic activity are also examined. The average thermal efficiency, CO, HC, and NOx results show that CO and HC emissions of the engine when adding hydrogen-rich gas with a Ni-Cu/Al2 O3 catalyst are lower than CO and HC emissions of the engine when adding hydrogen with a Ni/Al2 O3 catalyst and using only gasoline fuel, with CO decreasing by − 48.06%t and −15.75%, and HC decreasing by 30.50% and 11.22%, respectively. While thermal efficiency and NOx emissions tend to be opposite, for thermal efficiency increased by 6.38% and 2.21%, respectively, for NOx emissions increased by 29.49% and 7.12%. Keywords: SI. Engine · Ni-Cu/Al2 O3 catalysts · Ni/Al2 O3 catalysts · Gasoline · Thermal efficiency and emission
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 925–937, 2022. https://doi.org/10.1007/978-981-19-1968-8_79
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1 Introduction In the near future, hydrogen (H2) has the potential to be used as an energy carrier. Many studies have been conducted in an attempt to develop internal combustion engines that can run on hydrogen fuel. As a substitute fuel for internal combustion, hydrogen has many advantageous properties, including high diffusivity, flammability, and octane number. Steam reforming of fuel or water electrolysis can both produce hydrogen. In order to apply on internal combustion engine (ice), hydrogen-rich gaseous fuel was produced by exhaust gas assisted reforming of gasoline or diesel fuel with steam (onboard production) [1, 2]. This technique can potentially provide a feasible and practical engine system. It does not require a secondary fuelling system that is undesirable for the driver/operator. In the field of spark-ignition engine, hydrogen, or hydrogen-rich gas blending to complete gasoline replacement have been investigated; Berntsson et al. [3] investigated the combustion characteristics of the hybrid combustion concept that consisted of hydrogen spark ignition combustion and n-heptane HCCI combustion using a single cylinder engine with optical access. Rosati et al. [4] investigated the flame development of hydrogen spark-ignition and compression-ignition and compared with that of gasoline spark-ignition using an optical engine. Kan et al. [5] performed both experimental and computational investigations on the combustion and emissions of the hydrogenrich syngas in a spark ignition engine. The spark timing was able to be advanced to −52° after top-dead center (aTDC) at wide-open throttle operation with the syngas, of 17% hydrogen and 49% nitrogen. The simulation results indicated that the spark timing must be advanced further to achieve the highest thermal efficiency at lower hydrogen composition. Kim et al. [6] concluded that 5% hydrogen addition in a turbocharged direct-injection engine improved the lean-burn combustion with enhanced excess air ratios up to 1.5. Their results showed that 3% hydrogen blending in a gasoline engine achieved the thermal efficiency improvement as high as 12% under the lean-burn operation due to significantly reduced knock tendency. Ji et al. [7] suggested the use of pure hydrogen combustion for starting the engine at the restart process instead of a complete engine stop. The pure-hydrogen idling reduced the vehicle fuel consumption level by 0.69 L per 100 km under the New European Driving Cycle (NEDC) mode in their simulation study. Several studies attempted to achieve substantial CO2 reduction by adding hydrogen into a gas-fueled spark-ignition engine. Hydrogen addition not only increased the brake thermal efficiency, but also improved the performance under lean operations. Owing to the higher flame speed, 3% hydrogen blend achieved faster and shorter combustion. Luo et al. observed that knocking was initiated at an engine speed and average cylinder temperature of 3000 rpm and 1000–1100 K, respectively, which are higher than those of typical gasoline engines [8]. Despite these unfavorable phenomena, the use of hydrogen can achieve high efficiency in spark-ignition engines owing to its high knock-resistance tendency. Hydrogen-rich gaseous fuel involves hydrogen generation by direct catalytic interaction of liquid fuels with steam (steam reforming). Krumplet et al. reported the activity of different transition metals (Fe, Cu, Co, Ag, Ru, Ni, Pt, Pd, Rh) for converting liquid fuels in to hydrogen rich products, all metals exhibited 100% conversion above 700 °C [9–12], they also reported that at below 600 °C, conversion drops more quickly for
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first-row transition metals (particularly Ni and Co) than for second-row (Ru) and thirdrow (Pt, Pd). The second and third-row transition metals exhibit a higher H2 selectivity (>60%) than the first-row transition metals at temperatures above 650 °C [9]. Nickel has been widely used as catalysts for steam reforming of gasoline fuels because its high electronic conductivity, thermal stability, activity and low cost [13]. However, it showed low conversion of gasoline fuels at low temperatures and easily deactivated at high temperature [14, 15]. Addition of a second metal (Co, Mo, W, Re, Pd) to the Ni–alumina catalyst also resulted in better activity at much lower operating temperatures [16–21]. The activity, durability and hydrogen selectivity of nickel catalysts doped with a thermal stabilizer and activity promoter, such as lanthanum and cerium oxide respectively were reported [17], a lower carbon deposition and a higher thermal stability of metallic Ni particles under reaction conditions were also observed [17]. Ni catalysts containing Cu have already been found to have significantly different catalytic activity and yields distribution as compared to monometallic Ni catalysts in many reactions [18]. On-board generation of hydrogen with Ni/Al2 O3 catalyst has been studied on SI. Engines, however engine efficiency and exhaust emission of an SI engine using hydrogen rich gas from exhaust gas fuel reforming based on Ni-Cu/Al2 O3 Catalysts have not been reported. Therefore, the aim of this study, gasoline and on-board hydrogen rich gas from exhaust gas fuel reforming based on Ni-Cu/Al2 O3 catalysts will be supplied by the intake pipe, mixed and burned together in the combustion chamber in order to improve the engine efficiency and exhaust emission of the gasoline engine.
2 Experiments and Method 2.1 Engine Experiments were carried out on a Toyota Vios engine 4-stroke, 4-cylinder, 1.5-L engine, whose specifications are listed in Table 1. It is originally a fuel injection engine, it has been modified in order to have more electronically control the injection of gasoline to feed for engine and catalyst, the hydrogen-rich-gas in the intake port (Fig. 1). An ECU of Motohawk (model 0565-128) is an electronic control system developed at the our Laboratory, based on Matlab/Simulink which allows full control and free programming for injection timing and duration of both fuels, spark timing and lambda regulation, this ECU is used to control engine and catalyst (Table 2). Fuel injector
Fuel injector
Mixer
Spark plug
Reformer Catalyst
Throttle Exhaust gas analyzer
Control unit
Fig. 1. Test schematic
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Toyota vios 1.5
Stroke
4
Compression ratio
9.5
Number of valves
4
Max power output
107 kw at 6000 rpm
Max torque
141 Nm at 4200 rpm
Max speed
Table 2. Conversion and product selectivity for steam reforming of gasoline on Al2 O3 supported catalysts, T = 550 °C, gasoline feed rate was 3% fuel of engine, S/C molar ratio: 0.9 Catalysts
Conversion (%)
Product selectivity (%)
Iso-octane
Water
H2
CO
CO2
CH4
18 wt% Ni0.5 -Cu0.5 /γ-Al2 O3
42.6
12.2
70.6
7.4
15.3
6.7
18 wt% Niγ-Al2 O3
20.6
7.8
68.5
9.5
17.8
4.2
2.2 Catalyst Selection For this work, two exhaust gas reforming catalysts have been selected. One as a reference based on Ni (18% wt.) supported on a Al2 O3 , the other based on Ni0.5 -Cu0.5 (18% wt.) supported on proprietary. During the γ-Al2 O3 washcoat deposition, The γ-Al2 O3 slurry was prepared by mixing 23 mass% of the Al (NO3 )3 binder solution, 23 mass% of γ-Al2 O3 powder, and 54 mass% of distilled water, followed by vigorous stirring for 8 h at room temperature. The dip and spin coating were used to coat Al2 O3 washcoat layer on metallic substrate (400 cpsi) amount Al2 O3 of 210 g is used. The catalyst was dried at room temperature for 30 min and heated at 523 K for 2 h, followed by sintering at 923 K for 2.5 h. In the Ni and Ni-Cu layer deposition was prepared by mixing 23 mass% of the Al (NO3 )3 binder solution, 23 mass% of milled NiO or CuO-NiO powder and 54 mass% of distilled water, followed by vigorous stirring for 8 h at room temperature or high-energy ball milling (wet milling) instead of the stirring process. The dip and spin coating process, drying and sintering procedure was repeated above. The structures, the morphological aspects and the compositions of catalysts were analyzed by X-ray diffractometry (XRD) using Cu Kα radiation (RIGAKU RINT2100CMT), scanning electron microscopy (SEM, Hitachi, SU6600 EVACSEQ and JOEL JSM7600F) and EDX (energy dispersive X-ray, X-Max50). The gas mixture was analyzed by a gas chromatograph, thermal Trace GC RGA, equipped with a thermal conductivity detector. Conversion, selectivity and formation rates of products were calculated by an internal standard analyzing method as reported [18].
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2.3 Test Fuels A commercial premium gasoline (PV Gas) used to feed for SI. Engine and on broad hydrogen rich gas from exhaust gas fuel reforming. 2.4 Test Bench Specification The test bench (AVL, Austria) is equipped with AC dynamometer APA100, fuel consumption 733 S, cooling water 554. The exhaust gas was sampled using a gas emission bench (AVL, CEBII, Austria). The emission bench consisted of several gas detectors for CO, CO2 , NOx , and oxygen (O2 ) measurements. Two infrared detectors (IRD) were employed for CO and CO2 measurements, whereas chemi-luminescence and paramagnetic detectors were used for NOx and O2 measurements, respectively. The exhaust sampling line was heated for wet-based measurement used in NOx detection, Bosch LS 4.9 sensor is used to measure lambda of engine. 2.5 Test Procedures The experiments were carried out under the following conditions. Engine tests at low torque of 15% WOT and low speed of 1500 rpm, then it tests at medium torque of 50% WOT and speed of 3000 rpm, coolant temperature of 80 ± 2 °C, room temperature 35 ± 2 °C, and engine room humidity of 60 ± 5% were maintained during the experiment. The levels of 3% gasoline engine was used to supplied for SRG catalysts, the composition of SRG was produced to feed directly for engine. The amount of gasoline of engine was reduced by 3% when SRG was added during the engine test. Spark timing sweeps were carried out for each combination operation condition in order to determine the minimum spark advance for best torque (MBT) requirements. Specific fuel consumption (SFC), emissions, and combustion stability were assessed at each operation condition. The brake thermal efficiency was calculated by [19]. bp ηbrake = mfuel .LHV fuel where bp is measured brake power, and LHVfuel is lower heating value of fuel.
3 Results and Discussion 3.1 Structural and Morphological Characterizations Figure 1 showed XRD patterns of 18 wt% Ni0.5 -Cu0.5 /Al2 O3 and 18 wt% Ni/Al2 O3 catalysts. For 18 wt% Ni0.5 -Cu0.5 /Al2 O3 , the reflection of CuO and cubic NiO were evidently observed (Fig. 1a). Only cubic NiO phase was observed (Fig. 1b) for pure of Ni/Al2 O3 . The peaks of γ-Al2 O3 is observed due to the preparations of catalysts while no peak of FeCrAl steel was observed. Figure 2 showed a SEM micrograph of Ni0.5 -Cu0.5 /Al2 O3 catalysts. For the cross sections (Fig. 2a), the thickness of coating layer was about 20 μm, the particles become finer with homogeneous size distributions, where they appear interlocked with each other exhibiting tighter packing. The fine particles are densely spread on the γ-Al2 O3
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Diffraction intensity (a.u.)
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Monoclinic CuO -Al2O3 Cubic NiO
a
b
20
30
40
2 (o)
50
60
70
Fig. 2. XRD patterns of different catalysts (a) 18 wt% Ni0.5 -Cu0.5 /Al2 O3 /FeCrAl steel, (b) 18 wt% Ni/Al2 O3 / FeCrAl steel
washcoat surface. The surface of catalyst showed in Fig. 2b, the high distributions of CuO-NiO nanoparticles on large spongy clusters of γ-Al2 O3 were observed, where there exist aggregations of packed particles from a few nanometers to few hundred nanometers in size, which may cause agglomeration of powders indicating sintering of catalysts. 3.2 Catalytic Activity Conversion and products selectivity for steam reforming of gasoline on Al2 O3 /FeCrAl catalysts at low temperature of 550 °C were summarized in Table 1. It is presented that gasoline conversion over 18 wt% Ni0.5 -Cu0.5 /γ-Al2 O3 /FeCrAl catalyst were much higher than the those of 18 wt% Ni/γ-Al2 O3 , the similar products selectivity were observed for all catalysts. The best catalytic performance of Ni0.5 -Cu0.5 /γ-Al2 O3 catalyst demonstrated in Table 1 is consistent with TPR results, of which the superior reducibility of Ni-Cu/γ-Al2 O3 catalyst was reached at low temperature [17]. 3.3 Engine Test 3.3.1 Efficiency The thermal efficiency was shown in Fig. 3, with the air-fuel ratios at 1500 rpm and 15% WOT. Because the amount of gasoline was reduced by the amount of that, used in the reforming experiments, in LHV when SRG was added during the engine test, the thermal efficiency represents the total thermal efficiency including that of reformer. For lean operation (k > 1.3), thermal efficiency of engine with SRG catalysts is much better than that of gasoline. Highest efficiency peaks at λ = 1.3 with the addition of gasoline fuel into catalysts and at λ = 1.2 with gasoline only. The thermal efficiency of engine with SRG based on Ni-Cu/Al2 O3 catalyst was higher than that with SRG based on Ni/Al2 O3 catalyst, the increase in efficiency can be achieved by higher the conversion for steam reforming of gasoline on Ni-Cu/Al2 O3 supported catalysts.
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Fig. 3. SEM micrograph of cross section (a) and surface (b) for 18 wt% Ni0.5 -Cu0.5 /gAl2 O3 /FeCrAl steel catalysts
Thermal efficiency is partially dependent on combustion efficiency. Combustion efficiency is high when using lean equivalence ratio and decreases for rich operation as there is insufficient oxygen to complete combustion. However, due to the worsening of combustion stability, thermal efficiency decreased for lean operation. The reason for the increase in efficiency with added hydrogen is the increase in burn rate and combustion efficiency, particularly for lean operation (λ = 1.1–1.3). However, under ultra-lean conditions (k > 1.4), thermal efficiency of gasoline with added hydrogen enriched gas is lower than that. As the fast burn speed of hydrogen and SRG’s greater ratio of specific heats should result in less heat transfer out of the cylinder. At low WOT of 15%, low exhaust gas temperature of engine was observed, so the gasoline conversion efficiency of the Ni-Cu/Al2 O3 catalyst to hydrogen enriched gas is higher than that of the Ni/Al2 O3 catalyst.
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Figure 4 shows the thermal efficiency with air–fuel ratio at 3000 rpm and 50% WOT. The thermal efficiency includes the speed dependent friction power needed by the engine. As seen in Fig. 4, thermal efficiency is slightly higher than 1500 rpm and 15% WOT at equivalent λ and with hydrogen enriched gas. Opening the throttle decreases pumping loss, which results in significantly higher thermal efficiency for lean operation. At higher of WOT, exhaust gas temperature of engine became higher, the gasoline conversion efficiency of the Ni-Cu/Al2 O3 catalyst and Ni/Al2 O3 catalyst were significantly improved. Thereby, the thermal efficiency of engine with SRG based on Ni-Cu/Al2 O3 catalyst was similar than that based on Ni/Al2 O3 catalyst.
Brake thermal efficiency (%)
34
Hydrogen- gasoline by Ni-Cu/Al2O3 Hydrogen- gasoline Ni/Al2O3 Gasoline
32 30 28 26 24 22
20 1
1.1
1.2
λ
1.3
1.4
1.5
Fig. 4. The influence of air–fuel ratio on brake thermal efficiency at 1500 rpm and 15% WOT
The average thermal efficiency results show that, at low speed and WOT. The thermal efficiency of the engine when adding hydrogen-rich gas with a Ni-Cu/Al2 O3 catalyst is higher than the thermal efficiency of the engine when adding hydrogen with a Ni/Al2 O3 catalyst and using only gasoline fuel, which is 7.00% and 3.45%, respectively. At higher of speed and WOT The average result was 5,75 và 0,98% respectively. Figure 5 depicts the temperature of the exhaust gas as a function of engine speed and WOT. At 1500 rpm and 15% WOT with all fuels, the onset of misfire, which results in a dramatic drop in exhaust gas temperature, most likely limits lean limit operation. Exhaust gas temperatures are generally reduced by advancing the ignition timing, and they are further reduced by the addition of hydrogen-enriched gas. The exhaust gas temperature of engines using hydrogen-rich catalysts remains higher than that of engines running solely on gasoline. The increase in temperature caused by the addition of hydrogen is due to an increase in burn rate and combustion efficiency. More than fuel was supplied at higher speeds and WOT, as a result, the temperature of the engine’s exhaust was found to be higher.
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Brake thermal efficiency (%)
34
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Hydrogen- gasoline by Ni-Cu/Al2O3 Hydrogen- gasoline Ni/Al2O3 Gasoline
32 30 28 26 24 22
20 1
1.1
1.2
1.3
λ
1.4
1.5
Fig. 5. The influence of air–fuel ratio on brake thermal efficiency at 3000 rpm and 50% WOT
3.3.2 Emissions Figure 6 shows The influence of air–fuel ratio on CO emission at other operating conditions of 1500 (15% WOT) and 3000 rpm (50% WOT. At 1500 rpm and 15% WOT, the engine power is low, the engine is still cold, so CO emissions are quite high at the value λ = 1. CO decreases sharply when the engine is working with light lambda (λ = 1.2 ÷ 1.3), continuing to increase the lambda (λ > 1.3), the CO tends to increase slightly. The cause of this phenomenon is that when the engine tests at lean lambda operation conditions, the combustion efficiency decreases. Hydrogen- gasoline by Ni-Cu/Al2O3, 15% WOT, 1500rpm Hydrogen- gasoline Ni/Al2O3, 15% WOT, 1500rpm Gasoline, 15% WOT, 1500rpm Gasoline, 50% WOT, 3000rpm Hydrogen- gasoline by Ni-Cu/Al2O3, 50% WOT, 3000rpm Hydrogen- gasoline Ni/Al2O3, 50% WOT, 3000rpm
Exhaust gas temperature (oC)
670
620 570 520
470 420 370
320 1
1.1
1.2
λ
1.3
1.4
1.5
Fig. 6. The influence of air–fuel ratio on Exhaust gas temperature at 1500 (15% WOT) and 3000 rpm (50% WOT)
At higher speeds (3000 rpm) and WOT (50%), the engine power was more stable, the engine temperature is improved, and the CO of the engine was much lower in comparison to the CO of the engine at low speeds and WOT.
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Compare CO of gasoline engines and engines using hydrogen enriched gas, the addition of hydrogen enriched gas improves the combustion process, thereby lowering the CO of the engine with the engine using rich hydrogen- by catalyst was observed. Compare the CO produced by the engine using the Ni-Cu/Al2 O3 catalyst to the CO produced by the engine using the Ni/Al2 O3 catalyst. At higher speeds (3000 rpm) and WOT (50%), the temperature of exhaust emission was high, resulting in a high gasoline conversion efficiency by both catalysts. Thereby The CO difference between engines using Ni-Cu/Al2 O3 and Ni/Al2 O3 catalysts is negligible. At low speed (1500 rpm) and WOT (15%), the temperature of exhaust emission was lower, gasoline conversion efficiency of Ni-Cu/Al2 O3 catalyst was higher than that of Ni/Al2 O3 catalyst, the more hydrogen enriched gas supplied to the engine, as the result, the better the combustion of engine was observed, it is indicated that CO of engine with Ni-Cu/Al2 O3 catalyst is lower than that with Ni/Al2 O3 catalyst. 6000
Hydrogen- gasoline by Ni-Cu/Al2O3, 15% WOT, 1500rpm Hydrogen- gasoline Ni/Al2O3, 15% WOT, 1500rpm Gasoline, 15% WOT, 1500rpm Gasoline, 50% WOT, 3000rpm Hydrogen- gasoline by Ni-Cu/Al2O3, 50% WOT, 3000rpm Hydrogen- gasoline Ni/Al2O3, 50% WOT, 3000rpm
CO concentration (ppm)
5000
4000
3000
2000
1000
0 1
1.1
1.2
λ
1.3
1.4
1.5
Fig. 7. The influence of air–fuel ratio on CO emission at other operating conditions of 1500 (15% WOT) and 3000 rpm (50% WOT)
In general, the unburned hydrocarbons in the exhaust are mainly caused by three mechanisms: (a) misfiring or incomplete combustion, which occurs in highly rich or lean situations, or when the air–fuel mixture contains a large amount of burned exhaust or nitrogen to make the flame propagate incompletely in the combustion chamber, (b) flame quenching effect, which takes place near the combustion chamber surface area or clearance and (c) deposits or oil membranes, which absorb fuel [14]. Figure 8 showed the variation in HC emissions versus air–fuel ratio for 1500 rpm (15% WOT) and 3000 rpm (50% WOT), respectively. The minimum HC emission occurs in the condition of stoichiometric to slightly lean combustion which conditions there is sufficient air to make burned HC participate in oxidation reactions. However, if combustion conditions are too lean, HC emission increases because combustion becomes incomplete. Introducing hydrogen enriched gas decreases HC emissions. For a constant air–fuel ratio, the concentration of unburned hydrocarbons is higher at 1500 rpm than at 3000 rpm. In terms of the effect of hydrogen enriched gas on HC emission, the addition of hydrogen enriched gas reduces HC due to the lower carbon content of the fuel, a shorter quenching distance,
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HC concentration (ppm)
and higher combustion temperatures. However, lean combustion slows the burn speed, and at cooler cylinder temperatures, the quench zone is larger. Under lean conditions, there is a large increase in HC emissions, possibly due to a larger quench zone and partial burning. Compare the HC from the engine using the Ni-Cu/Al2 O3 catalyst to the HC from the engine using the Ni/Al2 O3 catalyst. At higher speeds (3000 rpm) and WOT (50%), the HC difference between engines using Ni-Cu/Al2 O3 and Ni/Al2 O3 catalysts is negligible due to the high temperature. At low speed (1500 rpm) and WOT (15%), the temperature of exhaust emission was low, gasoline conversion efficiency of Ni-Cu/Al2 O3 catalyst was higher than that of Ni/Al2 O3 catalyst, the more hydrogen enriched gas supplied to the engine, as the result, the better the combustion of engine was observed, it is indicated that HC of engine with Ni-Cu/Al2 O3 catalyst is lower than that with Ni/Al2 O3 catalyst (Fig. 7). Nitrogen oxides form in atmospheric oxygen and nitrogen at high temperatures in a reaction related to combustion. Engine NOx usually peaks around of λ = 1.2–1.3, where combustion temperatures are high and there is an abundance of oxygen. From the peak production, the concentration decreases for stoichiometric or richer conditions (although combustion temperatures are still high), due to a decreasing amount of oxygen. In leaner conditions, the decrease in NOx is primarily due to lower combustion temperatures. Figure 8 showed that the production of NOx peaks around λ = 1.2 for all kinds of fuel. Generally, the addition of hydrogen increases NOx due to its higher flame temperatures. Lean combustion can reduce the peak combustion temperature and inhibit the activation of thermal NOx mechanisms [19, 20]. Therefore, the concentration of NOx is only dependent on the excess air ratio. With the increased speed and WOT, its higher flame temperatures, so NOx of engine increased for both fuels. Hydrogen enriched gas addition into engine improved the combustion thereby higher NOx was observed with the engine. Hydrogen- gasoline by Ni-Cu/Al2O3, 15% WOT, 1500rpm Hydrogen- gasoline Ni/Al2O3, 15% WOT, 1500rpm Gasoline, 15% WOT, 1500rpm Gasoline, 50% WOT, 3000rpm Hydrogen- gasoline by Ni-Cu/Al2O3, 50% WOT, 3000rpm Hydrogen- gasoline Ni/Al2O3, 50% WOT, 3000rpm
3500
2500
1500
500 1
1.1
1.2
λ
1.3
1.4
1.5
Fig. 8. The influence of air–fuel ratio on HC emission at other operating conditions of 1500 (15% WOT) and 3000 rpm (50% WOT)
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It is evident that there is a trade-off between CO, HC and NOx when selecting the air/fuel ratio. Increased λ results in lower NOx but higher CO and HC. Hydrogen enriched gas addition affects this trade-off between HC and NOx emissions without the drastic increase in HC, since it allows leaner operation (Fig. 9). Hydrogen- gasoline by Ni-Cu/Al2O3, 15% WOT, 1500rpm Hydrogen- gasoline Ni/Al2O3, 15% WOT, 1500rpm Gasoline, 15% WOT, 1500rpm Gasoline, 50% WOT, 3000rpm Hydrogen- gasoline by Ni-Cu/Al2O3, 50% WOT, 3000rpm Hydrogen- gasoline Ni/Al2O3, 50% WOT, 3000rpm
NOx concentration (ppm)
4000
3000
2000
1000
0 1
1.1
1.2
λ
1.3
1.4
1.5
Fig. 9. The influence of air–fuel ratio on NOx emission at other operating conditions of 1500 (15% WOT) and 3000 rpm (50% WOT)
The average CO, HC, and NOx results at low and high speeds, as well as WOT show that the CO and HC emission of the engine when adding hydrogen-rich gas with a Ni-Cu/Al2 O3 catalyst is lower than the CO and HC emission of the engine when adding hydrogen with a Ni/Al2 O3 catalyst and using only gasoline fuel, with CO respectively −48,06% and −15,75%. For the HC respectively 30.50% and 11,22%. while NOx emissions tend to be the opposite, NOx increase respectively 29.49% and 7,12%.
4 Conclusion This paper investigated the influence of addtived hydrogen enriched gas fuel produced by Ni-Cu/Al2 O3 catalysts to thermal efficiency and emissions of SI engine and to compare with the engine using gasoline fuel and engine addtived hydrogen enriched gas fuel based on Ni/Al2 O3 catalysts. The Structural, morphological characterizations and Catalytic activity were also observed. The average thermal efficiency, CO, HC, and NOx results show that CO and HC emissions of the engine when adding hydrogen-rich gas with a Ni-Cu/Al2 O3 catalyst are lower than CO and HC emissions of the engine when adding hydrogen with a Ni/Al2 O3 catalyst and using only gasoline fuel, with CO decreasing by −48.06%t and −15.75%, and HC decreasing by 30.50% and 11.22%, respectively. While thermal efficiency and NOx emissions tend to be opposite, for thermal efficiency increased by 6.38% and 2.21%, respectively, for NOx emissions increased by 29.49% and 7.12%. Acknowledgments. This research has been supported by Ministry of Science and Technology, Vietnam; Research Center for Engines, Fuels and Emissions at Hanoi University of Science and Technology is acknowledged for engine measurement.
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References 1. Quader, A.A., Kirwan, J.E., Grieve, M.J.: Engine performance and emissions near the dilute limit with hydrogen enrichment using an on-board reforming strategy; [SAE Technical paper No. 2003-01-1356] (2003) 2. Kirwan, J.E, Quader, A.A, Grieve, M.J.: Fast start-up on-board gasoline reformer for near zero emissions in spark-ignition engines [SAE Technical paper No. 2002-01-1011] (2002) 3. Berntsson, A., Denbratt, I.: Spark assisted HCCI combustion using a stratified hydrogen charge. In: Presented at the 7th International Conference on Engines for Automobile (2005) 4. Rosati, M.F., Aleiferis, P.G.: Hydrogen SI and HCCI combustion in a direct-injection optical engine. SAE Int. J. Engines 2(1), 1710–1736 (2009) 5. Kan, X., Zhou, D., Yang, W., Zhai, X., Wang, C.-H.: An investigation on utilization of biogas and syngas produced from biomass waste in premixed spark ignition engine. Appl. Energy 212, 210–222 (2018) 6. Kim, J., Chun, K.M., Song, S., Baek, H.-K., Lee, S.W.: Hydrogen effects on the combustion stability, performance and emissions of a turbo gasoline direct injection engine in various air/fuel ratios. Appl. Energy 228, 1353–1361 (2018) 7. Ji, C., Yang, J., Liu, X., Wang, S., Zhang, B., Wang, D.: Enhancing the fuel economy and emissions performance of a gasoline engine-powered vehicle with idle elimination and hydrogen start. Appl. Energy 182, 135–144 (2016) 8. Luo, Q., Sun, B.: Inducing factors and frequency of combustion knock in hydrogen internal combustion engines. Int. J. Hydrogen Energy 41(36), 16296–16305 (2016) 9. Krumplet, M., Krause, T.R., Carter, J.D., Kopasz, J.P., Ahmed, S.: Fuel processing for fuel cell systems in transportation and portable power applications. Catal. Today 77, 3–16 (2002) 10. Cunha, A.F., et al.: Sorption enhanced steam reforming of ethanol on hydrotalcite-like compounds impregnated with active copper. ChERD 91, 581–592 (2013) 11. Cunha, A.F., et al.: Steam reforming of ethanol on a Ni/Al2 O3 catalyst coupled with a hydrotalcite-like sorbent in a multilayer pattern for CO2 uptake. Canadian J. Chem. Eng. 90, 1514–1526 (2012) 12. Cunha, F., et al.: Steam reforming of ethanol on copper catalysts derived from hydrotalcite-like materials. Ind. Eng. Chem. Res. 51, 13132–13143 (2012) 13. Cheekatamarla, P.K., Finnerty, C.M.: Reforming catalysts for hydrogen generation in fuel cell applications. J. Power Sources 160, 490–499 (2006) 14. Wang, L., Murata, K., Inaba, M.: Control of the product ratio of CO2/(CO+CO2) and inhibition of catalyst deactivation for steam reforming of gasoline to produce hydrogen. Appl. Catal. B: Environ. 48, 243–248 (2004) 15. Wang, L., Murata, K., Inaba, M.: Steam reforming of gasoline promoted by partial oxidation reaction on novel bimetallic Ni-based catalysts to generate hydrogen for fuel cell-powered automobile applications. J. Power Sources 145, 707–711 (2005) 16. Wang, L., Murata, K., Matsumura, Y., Inaba, M.: Lower-temperature catalytic performance of bimetallic Ni−Re/Al2 O3 catalyst for gasoline reforming to produce hydrogen with the inhibition of methane formation. Energy Fuels 20, 1377–1381 (2006) 17. Navarro, R.M., Alvarez-Galvan, M.C., Rosa, F., Fierro, J.L.G.: Hydrogen production by oxidative reforming of hexadecane over Ni and Pt catalysts supported on Ce/La-doped Al2 O3 . Appl. Catal. A: Gen. 297, 60–72 (2006) 18. Tuan, L.A., Luong, N.T., Ishihara, K.N.: Low temperature catalytic performance of NiCu/Al2 O3 catalysts for gasoline reforming to produce hydrogen applied in SI. engines. Catalysts 6(3), 45 (2016) 19. Heywood, J.B.: Internal Combustion Engine Fundamentals. McGrawHill, New York (1988)
Photocatalytic Activity of Tungsten-Loaded Titanium Dioxide Photocatalysts Against Dyes and Bacteria in Water System Saepurahman1(B) , Muhammad Eka Prastya1 , Mohd Azmuddin Abdullah2 , Keiichi N. Ishihara3 , and Tjandrawati Mozef1 1 Research Center for Chemistry, National Research and Innovation Agency (BRIN), Gd. 452
Kawasan Puspiptek Serpong, Tangerang Selatan, Banten 15314, Indonesia [email protected] 2 SIBCo Medical and Pharmaceuticals Sdn. Bhd., No. 2, Level 5, Jalan Tengku Ampuan Zabedah, D9/D, Seksyen 9, 40000 Shah Alam Selangor, Malaysia 3 Graduate School of Energy Science, Kyoto University, Kyoto 6068501, Japan
Abstract. Advanced oxidation processes, such as photocatalysis complies with Green Chemistry principles. Photocatalysis can be used to reduce colour and kill pathogens in water for drinking. In this study, tungsten-loaded titanium dioxide photocatalysts have been prepared using wet impregnation method. The photocatalytic activity under UV-C irradiation was investigated against cationic methylene blue (MB) and anionic methyl orange (MO) dyes and three gram-positive bacteria (Bacillus subtilis, Bacillus cereus and Staphylococcus aureus); and three gram-negative bacteria (Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii). Both MB and MO were not decolourized under UV-C only. This was changed when titanium dioxide photocatalyst was added. Titanium dioxide containing 1 mol% of tungsten showed further improvement in the decolourization rate. Adding more tungsten, however, only improved the decolourization rate of MB, but not MO. The photocatalysts showed antibacterial activities against E. coli and B. cereus at 66.7 ppm concentration and UV-C exposure for 2.5 min, but not at 667 ppm concentration and the UV-C exposure for 7.5 min, due to the effects of UV-C shielding by the photocatalyst. The tungsten-loaded photocatalysts displayed more antibacterial activities than the pristine titanium dioxide. In the presence or absence of the photocatalysts, both S. aureus and P. aeruginosa were killed after 2.5 min UV-C exposure, but the photocatalysts showed little or no activities against A. baumanii and B. subtilis. The photocatalytic treatment of water for drinking under UV-C irradiation therefore exhibited synergistic effects where the photocatalyst decolourized the water and the UV-C killed the bacteria. Keywords: Titanium dioxide · Tungsten · Photocatalysis · Dyes · Antibacteria
1 Introduction One of the 17 SDGs initiative is clean water and good sanitation with the target of achieving universal and equitable access to safe and affordable drinking water for all by © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 938–952, 2022. https://doi.org/10.1007/978-981-19-1968-8_80
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2030 [1]. Increasing in human population and rapid industrialization have led to water scarcity. In certain areas of the world, water quality is poor that makes it suitable only for bathing and washing. As a result, communities rely on bottled water for drinking. As an alternative to the industrially made bottled water, deprived communities opt to gallon refilling station or a water depot to refill their water gallons. Water depots use simple technology comprising of adsorption, low pressure reverse osmosis and UV disinfection processes to make water suitable for drinking. The UV disinfection stage is crucial as it it sterilizes the water and kills water borne pathogens that may cause diseases. The UV disinfection unit consists of only a stainless steel tube with an embedded germicidal UV-C lamp. According to WHO guidelines for acceptable drinking water quality, E. coli and total coliform bacteria must not be detectable in any 100 mL of sample [2]. The emergence of antibiotic resistant bacteria and recent finding on the presence of COVID-19 virus in wastewater and sewage have put communities at risks of exposure to contaminated water resources [3, 4]. There is a great need to improve the existing UV disinfection system as bacterial infections are the major causes of chronic diseases such as tuberculosis, diarrhea, and typhoid fever. The use of antibiotics, though low-cost and effective to treat bacterial infections, are now facing the emergence of multidrug-resistant bacteria, rendering it becoming useless from uncontrolled use over prolonged period of time. The major groups of antibiotics that are currently in clinical applications have three bacterial targets:- DNA replication machinery, cell wall synthesis, and translational machinery [5]. Bacterial resistance overcomes these modes of action by expressing enzymes that could modify or degrade antibiotics, alter the cell components, and trigger the efflux pumps. These in turn provide simultaneous resistance against numerous types of antibiotics [6]. Nanoparticles (NPs) have demonstrated broad-spectrum of antibacterial properties against both Gram-negative and Gram-positive bacteria. Interestingly, most of the antibiotic resistance mechanisms can be interfered, reversed, or halted by the NPs. The mode of action of NPs is based on direct contact with the bacterial cell surfaces, without the need to penetrate the cell [7]. There is a high hope that the NPs would be less prone to promoting bacterial resistance than the antibiotics. The Ag NPs, for example, exhibit concentration-dependent antimicrobial activity against P. aeruginosa, and E. coli, while ZnO NPs inhibit S. aureus [8]. The antimicrobial mechanism of action of the NPs may involve metal ion release, oxidative stress induction, or non-oxidative mechanisms. These mechanisms can occur individually, or simultaneously [9]. The Ag NPs have been suggested to neutralize the surface electric charge of the bacterial membrane and change its penetrability, ultimately leading to bacterial damage [10]. The antioxidant defence system may be disrupted leading to the generation and accumulation of reactive oxygen species (ROS), causing disruption to the bacterial cell membrane. Moreover, the generation of ROS inhibits the antioxidant defence system and causes mechanical disruption to the bacterial cells membrane. The major causes of antibacterial activities of the NPs are the perforation and disruption of the bacterial cell membrane, induction of intracellular antibacterial effects, and generation of ROS [10].
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Another physical criteria in the WHO guideline for acceptable drinking water quality is colour where the maximum allowable concentration of colour is 15 TCU (True Colour Unit) [2]. While colour may not give direct health effect, it is a direct indicator of pollution. Decolourization of water is therefore important to meet the standards. Advanced oxidation processes such as photocatalysis using titanium dioxide to decolourize water complies with Green Chemistry principles which aims to minimize or eliminate of waste in eco-friendly manner [11]. Tungsten-loaded titanium dioxide photocatalyst has been shown to increase the degradation of methylene blue, higher than the pristine titanium dioxide [12, 13]. This study aimed to evaluate the potential of tungsten-loaded titanium dioxide photocatalysts to decolourize methyl orange and methylene blue and to exhibit anti-bacterial activities against three gram-positive bacteria (B. subtilis, B. cereus and S. aureus); and three gram-negative bacteria (E. coli, P. aeruginosa, A. baumannii) under UV-C irradiation.
2 Materials and Methods 2.1 Materials TiO2 Degussa P25 (Degussa AG, Germany) and ammonium metatungstate (AMT, ≥ 99.0% as WO3 ; Fluka, Germany) were used as the raw material and source of WO3 . Methylene blue, methyl orange, and NaCl were obtained from Sigma Aldrich. Mueller Hinton Broth and Mueller Hinton Agar were obtained from Oxoid UK. Three Gramnegative (Escherichia coli BW25113, Pseudomonas aeruginosa PA14, Acinetobacter baumannii DSM30008) and three Gram-positive (Bacillus subtilis DSM10, Bacillus cereus, Staphylococcus aureus Newman) bacterial isolates were used to test the antibacterial activity of the photocatalysts. Each bacterial isolate was obtained from the collection of Laboratory of Microbiology, Research Center of Chemistry, BRIN. The bacterial stock (in −80 °C) were re-cultured at 30 °C for 24 h, in a Mueller Hinton Broth. Subsequently, the bacterial suspensions were streaked onto a sterile plate Mueller Hinton Agar to obtain fresh bacterial culture. 2.2 Preparation of Tungsten-Loaded Titanium Dioxide Photocatalysts Tungsten loaded TiO2 was prepared using the previously reported protocol [12, 13]. Briefly, Degussa P25 was weighed and dispersed in water and then a calculated amount of ammonium metatungstate solution was added. The suspension was stirred overnight to equilibrate the adsorption-desorption processes. The suspension was then evaporated to dryness using a water bath set at 80 °C. The powder was further dried in an oven at 120 °C overnight. The powder was then ground using mortar and pestle and then calcined at 450 ºC for 2 h with a ramp of 3.5 °C/min under static air. The photocatalyst was denoted based on tungsten loading. For example, 1-w-450 indicates that the photocatalyst contains tungsten at 1 mol% WO3 loading.
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2.3 Dyes Decolourization Studies 50 mg of photocatalyst was mixed with 10 mL of distilled water and then ultrasonicated for 10 min using an ultrasonicator (Granbo). The dispersion was transferred into a crystallizing dish with a diameter of 9 cm containing 40 mL of dye solution at concentration 25 ppm. This gave catalyst concentration of 1 g/L, total volume of 50 mL and dye concentration of 20 ppm. Two-types of dye, the cationic methylene blue (MB) and the anionic methyl orange (MO) were tested. The mixture was put inside a 59.5 × 49.5 × 50 cm3 of UV-C disinfection box equipped with a 15 W of Philips TUV 15W/G15 T8 generating a UV-C light. The crystallizing dish was put under the lamp at 12.5 cm distance from the lamp. The suspension was stirred using a magnetic stirrer (DLab). Eight hundred microliter of the suspension was taken at certain times where it was centrifuged at 7000 × rpm (Dlab) for 5 min to separate the photocatalyst from the liquid. The concentration of dye in the suspension was determined using an Agilent Cary 60 UV-Vis Spectrophotometer, at λ = 665 nm and λ = 464 nm for MB and MO, respectively. 2.4 Antibacterial Activities Antibacterial assays were performed following the previously reported method with a slight modification [14]. A stock dispersion of photocatalyst in water was prepared by mixing 50 mg of photocatalyst in 5 mL of distilled water and ultrasonicated for 10 min. This produced a stock photocatalyst dispersion at concentration of 10,000 ppm. A bacterial suspension in a sterile saline (0.85% NaCl) was prepared from a fresh bacterial culture. The concentration of the bacteria was adjusted to reach a McFarland standard of 0.5 which is equivalent to a bacterial concentration of 1 × 108 CFU mL−1 . A suspension containing bacteria and photocatalysts with a total volume of 2 mL was added into every well of a 24-wells sterile microtiter plate. The volume of photocatalyst dispersion added was adjusted to obtain concentrations of 667 and 67 ppm. The microtiter plate was then placed inside the UV-C disinfection chamber. The distance between the lamp and the microtiter plate was set at 8 cm. In the first study, the microplate containing bacteria and photocatalyst at concentration of 667 ppm was exposed to the UV-C light for 7.5 min. After exposure, the suspension in the well was homogenized. One loop of the suspension was streaked onto a sterile plate of MH agar using a sterile loop (diameter of 6 mm). The streak was directed inward from the perimeter of the petri dish meaning that the streak located near the edge of the petri has more bacteria than the one located at centre of the petri dish. The MH agar plate was divided into equally eight segments to accommodate samples tested. The agar plate was then incubated at 30 °C for 24 h prior to visual observation where the bacterial cell viability was evaluated qualitatively (see Fig. 3 (b-d)). If no bacterial mortality took place, the streak will all be covered with bacterial colonies. If antibacterial activity took place the streak will appear clean or partially be covered with bacterial colonies. The number of the colonies formed along the streaks is classified into 0, 1, 2, 3, and 4 where the lowest number, 0, means 0–20% of the streak covered with bacterial colonies while the highest number, 4, means 80–100% of the streak was covered with bacterial colonies. Bacterial mortality due to UV-C only was also studied
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using a bacterial suspension in one of the wells where water was added in place of photocatalyst dispersion. (see Fig. 3(a)). The effect of the photocatalysts to the bacterial viability in the absence of the UV-C irradiation was also studied (see Fig. 3 (e- h). In the second study, the amount of the photocatalyst dispersion was reduced to 66.7 ppm while maintaining the UV-C exposure of 7.5 min ((see Fig. 4 (A-D)) while in the third study both the photocatalyst concentration and UV-C exposure was reduced to 66.7 ppm and 2.5 min ((see Fig. 4 (E-H)), respectively. The results of the antibacterial assays were from at least two independent experiments. 22 20 18 16
0-w-450 1-w-450 3-w-450 6.5-w-450 100-w-450 Photolysis
MO (ppm)
14 12 10 8 6 4 2 0 0
10 20 30 40 50 60 70 80 90 100 110 120 130
time (min)
Fig. 1. MO concentration as a function of time
3 Results Figure 1 shows the changes in the MO concentration as function of time. Under UV-C irradiation only (photolysis), the MO was not decolourized. In the presence of UV-C and 0-w-450, the MO was decolourized. Unlike pure TiO2 (0-w-450), pure WO3 (100w-450) had no significant photocatalytic activity. Adding a small amount (1 mol%) of tungsten into the pristine TiO2 , however, significantly improved the decolourization rate where complete MO decolourization was reached after 2 h of UV-C exposure. Adding more tungsten to 3-w-450–2 and 6.5-w-450 did not improve the photocatalytic activity of 1-w-450, although it was still better than 0-w-450. This suggests that TiO2 containing 1 mol% of tungsten was the optimum photocatalyst for MO degradation. Figure 2 shows the decolourization of MB as a function of time. Similar to MO, the MB was nearly unaffected by the UV-C only (photolysis). The MB concentration, however, dropped to 60% in the presence of 0-w-450 within the first 10 min. This was not seen for 100-w-450 suggesting that the sample is not active for both MO and MB degradation. The MB concentration dropped to 90% in the presence of 1-w-450 within the first 10 min. Adding more tungsten to 3-w-450–2 and 6.5-w-450 further improved the MB decolourization.
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22 20 18 16
0-w-450 1-w-450 3-w-450 6.5-w-450 100-w-450 Photolysis
MB (ppm)
14 12 10 8 6 4 2 0 0
10 20 30 40 50 60 70 80 90 100 110 120 130
time (min)
Fig. 2. MB concentration as a function of time
Fig. 3. Anti-bacterial activity against E. coli under UV-C exposure for 7.5 min (a-d) and 0 min (eh) in the presence of water (a, h), and photocatalysts 0-w-450 (b, g), 1-w-450 (c, f), and 6.5-w-450 (d, e) at 667 ppm concentration.
Figure 3 (a-d) shows visual appearance of the MH agar after anti-bacterial activity against E. coli under a UV-C irradiation for 7.5 min. The UV-C alone could efficiently kill most of the E. coli as seen in section (a). The presence of photocatalysts 0-w-450 (b), 1-w-450 (c), and 6.5-w-450 (d) at 667 ppm concentration did not increase the activity against E. coli. The (b) section contained slightly less E. coli colonies than (c) and (d) sections. Figure 3 (e-h) displays the visual appearance of the MH agar after inoculation from the wells which had not been exposed to UV-C irradiation. As can be seen, all sections contained E. coli colonies suggesting that the UV-C is the main contributor for the killing of E. coli. The photocatalysts (b, c, and d) did not appear to have toxic effects on the E. coli, at the concentrations tested, without the presence of the UV-C. Since
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Fig. 4. Anti-bacterial activity against E. coli under UV-C exposure for 7.5 min (A-D) and 2.5 min (E-H) in the presence of water (A, H), and photocatalysts 0-w-450 (B, G), 1-w-450 (C, F), and 6.5-w-450 (D, E) at 66.7 ppm concentration.
the effects were not observed at 667 ppm, the concentration of the photocatalyst was reduced to one-tenth of the initial concentration while maintaining the UV-C exposure time.
Fig. 5. Anti-bacterial activity against P. aeruginosa. Legends are similar to Fig. 4.
As shown in Fig. 4 (A-D), the sections containing photocatalysts (B), (C), and (D) had more E. coli colonies than the one with water only (A). However, there are a significant difference in the section (B), (C), and (D) where the two latter sections (C, D) contain less colonies than the former (B). This suggests that photocatalysts 1-w-450 and 6.5-w-450
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Fig. 6. Anti-bacterial activity against A. baumannii. Legends are similar to Fig. 4.
Fig. 7. Anti-bacterial activity against S. aureus. Legends are similar to Fig. 4.
exhibited higher activities against E. coli activity than the pristine 0-w-450. Since the UVC exposure time of 7.5 min was considered too long to evaluate the possible antibacterial activities of the photocatalyst due to the stronger effects of the UV-C, the UV-C exposure time was reduced to 2.5 min while photocatalyst concentration maintained at 67 ppm and the result is shown in Fig. 4 (E-H). The UV-C exposure for 2.5 min was not sufficient to kill E. coli (H), but the anti-bacterial activity against E. coli of the photocatalyst was seen better at this UV-C exposure (E, F, and G). Similar to (B), (C), and (D), the anti-E. coli activity of 6.5-w-450 was better than 0-w-45–2 and 1-w-450.
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Fig. 8. Anti-bacterial activity against B. subtilis. Legends are similar to Fig. 4.
Figure 5 shows the anti-bacterial activity against P. aeruginosa of the photocatalysts under a UV-C exposure for 7.5 min (A-D) and 2.5 min (E-H) in the presence of water (A, H) and photocatalysts (B-G) at concentration 67 ppm. All sections (A, H) were clear suggesting that all P. aeruginosa were killed, even at 2.5 min UV-C. The effects of the photocatalysts towards P. aeruginosa could not be confirmed. With A. baumannii (Fig. (6)), no colonies were observed (A-D) under UV-C exposure for 7.5 min. Decreasing the exposure time to 2.5 min showed different results where the section in the presence of water only (H) was clear, while the sections (E, F, and G) showed partially filled A. baumannii colonies. This implies that the photocatalysts had no significant anti-bacterial activities against A. baumannii. Although sections (E and F) contained less colonies than section (G), suggesting some photocatalyst effects on A. baumannii activity, they were still lower than the control (water). For S. aureus (Fig. (7)), similar to the trend seen for P. aeruginosa, all sections (A-H) were clear suggesting that all bacteria were killed even after short UV-C exposure of 2.5 min. Hence, the effects of photocatalysts on S. aureus could not be verified. Figure (8) shows the activities against B. subtilis. Exposure to UV-C for 7.5 min (A-D) almost killed all the B. subtilis. Reducing the exposure time to 2.5 min made significant differences where section (H) contained as many colonies as sections (E and F), while section (G) contained more B. subtilis colonies. This suggests that the photocatalysts had little or no effects on the antibacterial activities against B. subtilis. In the case of B. cereus (Fig. (9)), the colonies were present in sections (A, H) suggesting that the bacteria were more resistant to the UV-C effects. The bacterial growth was however significantly affected by the presence of photocatalysts, after 7.5 min UV-C exposure (B-D). Based on the colony formation, the photocatalyst 1-w-450 (F and C) had greater effects on the activities than 0-w-450 (G and B) and 6.5-w-450 (E and D).
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Fig. 9. Anti-bacterial activity against B. cereus. Legends are similar to Fig. 4.
Table 1. Antibacterial activities in the presence of photocatalysts at 667 ppm No.
Bacteria
#
UV-C exposure for 7.5 min
No UV-C exposure
Water
0a
1a
6.5a
Water
0a
1a
6.5a
1
0
4
4
4
4
4
4
3
2
0
4
4
4
4
4
4
4
1
0
4
4
4
4
4
4
3
2
0
4
4
4
4
4
4
4
1
0
4
4
4
4
4
4
4
2
0
4
4
4
4
4
4
4
1
0
4
4
4
4
4
4
4
2
0
4
4
4
4
4
4
4
0
4
4
4
4
4
4
4
Gram negative 1 2 3
Escherichia coli Pseudomonas aeruginosa Acinetobacter baumannii
Gram positive 4
Staphylococcus aureus
5
Bacillus subtilis
1 2
0
4
4
4
4
4
4
4
6
Bacillus cereus
1
2
4
4
4
4
4
4
4
2
2
4
4
4
4
4
4
4
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No.
Bacteria
#
UV-C exposure for 7.5 min
UV-C exposure for 2.5 min
Water
0a
1a
6.5a
Water
0a
1a
6.5a
1
0
2
4
1
4
4
4
3
2
0
4
1
1
4
4
3
3
3
0
4
1
1
4
4
3
3
0
0
0
0
0
0
0
0
Gram negative 1
Escherichia coli
2
Pseudomonas aeruginosa
1 2
0
0
0
0
0
0
0
0
3
Acinetobacter baumannii
1
0
0
0
0
0
1
0
0
2
0
0
0
0
0
1
0
0
3
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
2
0
0
0
0
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0
0
1
0
0
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2
1
2
0
1
1
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3
0
1
1
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Gram positive 4 5
6
Staphylococcus aureus Bacillus subtilis
Bacillus cereus
The number 0–4 indicates the colony density along the streak: 0 = 0–20%, 1 = 21–40%, 2 = 41–60%, 3 = 61–80%, and 4 = 81–100%.; a is the initial tungsten content of the photocatalyst.
Table 1 and Table 2 summarize the antibacterial activities of the photocatalysts at 667 and 67 ppm concentration, respectively. Almost all bacteria, except B. cereus, were killed after exposure to the UV-C irradiation for 7.5 min, without the presence of photocatalysts. Table 2 suggests that the antibacterial activities of the photocatalyst against P. aeruginosa and S. aureus could not be verified. This was because the activities from the UV-C alone were high that the contribution of the photocatalysts in killing the bacteria were unclear. The photocatalysts also showed little or no effects against B. subtilis and A. baumannii, as the activity obtained with the colony formed in the presence of the photocatalysts was similar to the one in the control (water). The photocatalysts exhibited weak activities against E. coli but much stronger activities against B. cereus (Table 2), especially when exposed to the UV-C in the presence of 1-w-450. In general, the antibacterial activity of the photocatalysts was observed against specific bacterial
Photocatalytic Activity of Tungsten-Loaded Titanium Dioxide
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strain, but not as strong as the effects of UV-C alone. The synergistic effects of UV-C exposure and photocatalysts could be further optimized for optimal effects against dye degradation and pathogenic bacteria for future use in water treatment.
4 Discussions This study aims to evaluate the potential of tungsten-loaded titanium dioxide photocatalysts for dyes decolourization and to induce bacterial mortality. Dyes decolourization studies show that the UV-C had no significant effect to the decolourization of MO and MB. While both dyes decolourized in the presence of photocatalysts, MO is more resistant to decolourization than MB. This resistance is probably related to the different chemical structure of MO which contains azo group which is more difficult to break as compared to the thiazine ring in the MB [15, 16]. The decolourization rate increases in the presence of TiO2 . The UV-C interacts with the TiO2 photocatalyst to form hydroxyl radicals and other oxygen containing radicals which then attack the dye leading to the change of its structure and ended off to decolourization. While adsorption of dyes onto the photocatalyst surface is the prerequisite of the photocatalytic decolourization, the decolourization can also be due to the adsorption of the dyes in the aqueous solution onto the photocatalyst without the need of a UV-C irradiation. Hence, does not involve the change in the chemical structure of the dyes. The decolourization of MB increased when more tungsten was added up to 6.5-w450 as previously reported [12, 13]. The higher decolourization rate for MB at higher tungsten loading can be related to the higher adsorption capacity for MB due to the negatively charged surface of the 6.5-w-450 at normal pH. The coulombic attraction between the cationic dye and the negatively charged surface of 6.5-w-450 leading to higher decolourization rate. For MO, the decolourization increased when the photocatalysts was modified by 1 mol% of tungsten. Further increase in the tungsten loading, however, did not further improved the decolourization rate. This is likely because, MO, is an anionic dyes and that coulombic repulsion between the negatively charged 6.5-w-450 and the dye does not give benefit to the photocatalytic decolourization. Since the cationic and anionic dyes degrade fast using photocatalyst having different tungsten loading, a combination of 1-w-450 and 6.5-w-450 might be used to treat samples containing both types of dyes. The antibacterial assay was performed using a normal saline (0.85% NaCl). This was to minimize bacterial growth and also to maximize UV-C penetration and while at the same time minimize the photocatalysts interaction with the growth medium. Normal saline was reported to protect the bacterial cells from cells damage/lysis [17]. The antibacterial assay was done using two different variables: - photocatalyst concentration, and exposure time. While it is expected that the higher the concentration of photocatalyst the higher the bacterial mortality; this was not the case. Although the bacterial culture was exposed to UV-C for long time (7.5 min), at 667 ppm of photocatalyst concentration, no significant bacterial mortality was observed for the six bacteria tested. This was in contrast to segment derived from the well containing only water (control) (see Table 1 and Fig. 3 (a). The photocatalysts behave as a shield protecting the bacteria from the
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damaging UV-C irradiation. The shielding effect of bacteria from UV irradiation has also been reported for aluminium nano-plasmonic which exhibited the nano-shielding effect to protect E. coli [18]. Visual appearance of the 2 mL bacterial culture containing 667 ppm photocatalyst showed a milky white appearance, hence it was diluted to 66.7 ppm while maintaining the exposure time to 7.5 min. While this certainly help to improve the UV-C penetration, the bacterial culture had a translucent appearance, the long exposure to UV-C is long enough to kill many bacteria, and that the bacterial mortality due to the photocatalyst was obscured. For this reason, the exposure time was reduced to 2.5 min. The short exposure time allows some bacteria to survive, hence, by comparing the visual appearance of the control segment and the segments containing the photocatalysts, the antibacterial activity of the photocatalyst can be estimated. While generally the use of UV-C irradiation (254 nm) in short period of time (10– 25 min) is sufficient to kill bacteria, fungi, and virus because of its higher DNA damaging energy, which results in the formation of photo-dimers in the genomes and disrupts the cells multiplications [19], the B. cereus can withstand such harsh condition. It is still viable after 7.5 min of UV-C exposure. This is possibly related to the formation of endospores. It has been reported that bacterial endospore produced by Bacillus class on the generative stage is known to be resistant against numerous stresses, such as heat and ultraviolet radiation [20]. The presence of photocatalysts in the UV-C water disinfection system may increase the mortality of the bacteria. In aqueous system, TiO2 absorbs photons, resulting in the formation of active hydroxyl (·OH) and other reactive oxygen species (ROS) such as O2 − and H2 O2 in the presence of O2 and H2 O. These ROS, particularly H2 O2 , not only penetrates the cell membranes, but also produce another oxidative active hydroxyl through a reaction inside bacterial cells that finally leads to the cells damage [21]. Other studies have demonstrated that once TiO2 photocatalyst are mixed with the bacterial cultures, the TiO2 is attached to the bacterial cells and disrupts the cell wall under UV light exposure. The damaged cell wall allows TiO2 NPs to enter into the cells and releases the cell contents from the cells, resulting in cell death [22]. The antibacterial activity the tungsten-loaded titanium dioxide as reported here are in agreement with previous studies which indicated that bacteria including S. aureus, E. coli, P. aeruginosa, Lactobacillus acidophilus, Streptococcus spp., Listeria monocytogenes, Serratia marcescens, Salmonella spp., Clostridium perfringens, P. stutzeri„ Vibrio parahaemolyticus, and coliform bacteria were killed by photoactivated TiO2 NPs [23, 24]. In this study, we identified that tungsten-loaded titanium dioxide photocatalysts have a minor anti-E. coli activity but strong anti- B. cereus activity as seen in Table 1. Further analysis revealed that the E. coli mortality was slightly enhanced in the presence of 6.5-w450 while the B. cereus mortality was best when 1-w-450 was added. While both bacteria are classified into Gram-negative for E. coli and Gram-positive for B. cereus, the positive and negative labelling refers to the thickness of the peptidoglycan layer in bacterial membrane; the Gram-negative has a thinner peptidoglycan whereas the Gram-positive a thicker peptidoglycan layer, and not the relative charge of the bacterial membrane; it was reported that the bacteria membrane carries negative charged [25]. Hence, 1-w-450 is expected to adhere closer to the bacteria due to the coulombic attraction as compared
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to the 6.5-w-450 which repulses the bacteria. While the UV-C and UV-C in combination with photocatalyst may kill the bacteria, the bacterial mortality can also be due to the toxicity of the photocatalyst. In this study, we found, however, that the photocatalysts are nontoxic as seen in Table 1. The TiO2 NPs are an effective antibacterial once they are activated by UV light [26]. While the UV-C is effective in killing the bacteria in water, it is ineffective to decolourize colour. Hence, the presence of photocatalyst in an existing water UV disinfection system has a synergistic effect to the improvement of drinking water quality.
5 Conclusions The UV-C alone was ineffective to decolourize dyes. The presence of tungsten-loaded titanium dioxide photocatalysts, however, improved the decolourization rate of cationic MB and anionic MO dyes. While the photocatalysts displayed antibacterial activities towards specific bacterial strains especially B. cereus, UV-C alone was already effective. The synergistic effects of UV-C and photocatalysts for water treatment for drinking had a big potential for industrial application. The UV-C could kill the bacteria, while the presence of photocatalysts could effectively decolourize the dyes. Acknowledgement. The authors thank JASTIP NET 2021 program for funding this research.
References 1. THE 17 GOALS Sustainable Development, (n.d.). https://sdgs.un.org/goals. Accessed 4 Nov 2021 2. World Health Organization, Guidelines for Drinking-water Quality, 4th edn. (2011) 3. Lahrich, S., Laghrib, F., Farahi, A., Bakasse, M., Saqrane, S., El Mhammedi, M.A.: Review on the contamination of wastewater by COVID-19 virus: impact and treatment. Sci. Total Environ. 751 (2021) 4. Medema, G., Heijnen, L., Elsinga, G., Italiaander, R., Brouwer, A.: Presence of SARSCoronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in The Netherlands. Environ. Sci. Technol. Lett. 7, 511–516 (2020) 5. Hsueh, P.R.: New Delhi Metallo-β-lactamase-1 (NDM-1): an emerging threat among Enterobacteriaceae. J. Formos. Med. Assoc. 109, 685–687 (2010) 6. Knetsch, M.L.W., Koole, L.H.: New strategies in the development of antimicrobial coatings: the example of increasing usage of silver and silver nanoparticles. Polymers (Basel) 3, 340– 366 (2011) 7. Wang, L., Hu, C., Shao, L.: The antimicrobial activity of nanoparticles: present situation and prospects for the future. Int. J. Nanomedicine. 12, 1227 (2017) 8. Ramalingam, B., Parandhaman, T., Das, S.K.: Antibacterial effects of biosynthesized silver nanoparticles on surface ultrastructure and nanomechanical properties of Gram-negative bacteria viz. Escherichia coli and Pseudomonas aeruginosa. ACS Appl. Mater. Interfaces 8, 4963–4976 (2016) 9. Leung, Y.H., et al.: Mechanisms of antibacterial activity of MgO: non-ROS mediated toxicity of MgO nanoparticles towards Escherichia coli. Small 10, 1171–1183 (2014)
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10. Jung, W.K., Koo, H.C., Kim, K.W., Shin, S., Kim, S.H., Park, Y.H.: Antibacterial activity and mechanism of action of the silver ion in Staphylococcus aureus and Escherichia coli. Appl. Environ. Microbiol. 74, 2171–2178 (2008) 11. Ravelli, D., Dondi, D., Fagnoni, M., Albini, A.: Photocatalysis. A multi-faceted concept for green chemistry. Chem. Soc. Rev. 38, 1999–2011(2009) 12. Saepurahman, Abdullah, M.A., Chong, F.K.: Preparation and characterization of tungstenloaded titanium dioxide photocatalyst for enhanced dye degradation. J. Hazard. Mater. 176, 451–458 (2010) 13. Saepurahman, Abdullah, M.A., Chong, F.K.: Dual-effects of adsorption and photodegradation of methylene blue by tungsten-loaded titanium dioxide. Chem. Eng. J. 158, 418–425 (2010) 14. Astuti, R.I., Prastya, M.E., Batubara, I., Budiarti, E., Ilmiyawati, A..: Antiaging and antioxidant bioactivities of asteraceae plant fractions on the cellular functions of the Yeast Schizosaccharomyces pombe. Adv. Pharmacol. Pharm. Sci. 2021, 12 (2021) 15. Konstantinou, I.K., Albanis, T.A.: TiO2 -assisted photocatalytic degradation of azo dyes in aqueous solution: kinetic and mechanistic investigations: a review. Appl. Catal. B Environ. 49, 1–14 (2004) 16. Houas, A., Lachheb, H., Ksibi, M., Elaloui, E., Guillard, C., Herrmann, J.M.: Photocatalytic degradation pathway of methylene blue in water. Appl. Catal. B Environ. 31, 145–157 (2001) 17. Kim, N.H., Rhee, M.S.: Phytic acid and sodium chloride show marked synergistic bactericidal effects against Nonadapted and Acid-adapted Escherichia coli O157:H7 Strains. Appl. Environ. Microbiol. 82, 1040–1049 (2015) 18. Kunz, J.N., et al.: Aluminum plasmonic nanoshielding in ultraviolet inactivation of bacteria. Sci. Reports 71(7), 1–10 (2017) 19. de Alba, A.E.M., Rubio, M.B., Morán-Diez, M.E., Bernabéu, C., Hermosa, R., Monte, E.: Microbiological evaluation of the disinfecting potential of UV-C and UV-C plus ozone generating robots. Microorganisms 9, 172 (2021) 20. Begyn, K., et al.: Directed evolution by UV-C treatment of Bacillus cereus spores. Int. J. Food Microbiol. 317, 108424 (2020) 21. Fujishima, A., Rao, T.N., Tryk, D.A.: TiO2 photocatalysts and diamond electrodes. Electrochim. Acta. 45, 4683–4690 (2000) 22. Long, M., Wang, J., Zhuang, H., Zhang, Y., Wu, H., Zhang, J.: Performance and mechanism of standard nano-TiO2 (P-25) in photocatalytic disinfection of foodborne microorganisms – Salmonella typhimurium and Listeria monocytogenes. Food Control 39, 68–74 (2014) 23. Swetha, S., Santhosh, S.M., Balakrishna, R.G.: Synthesis and comparative study of NanoTiO2 over Degussa P-25 in disinfection of water. Photochem. Photobiol. 86, 628–632 (2010) 24. Ripolles-Avila, C., Martinez-Garcia, M., Hascoët, A.-S., Rodríguez-Jerez, J.J.: Bactericidal efficacy of UV activated TiO2 nanoparticles against Gram-positive and Gram-negative bacteria on suspension. CyTA - J. Food. 17, 408–418 (2019) 25. Liu, L., et al.: Self-assembled cationic peptide nanoparticles as an efficient antimicrobial agent. Nat. Nanotechnol. 47(4), 457–463 (2009) 26. Kermanizadeh, A., et al.: In vitro assessment of engineered nanomaterials using a hepatocyte cell line: cytotoxicity, pro-inflammatory cytokines and functional markers. Nanotoxicology 7, 301–313 (2013)
Free Vibration Analysis on Stepped Composite Cylindrical Shells Reinforced by Inner/outer Ring Stiffeners Nguyen Quoc Hung(B) , Nguyen Manh Cuong, and Ta Van Cuong Hanoi University of Science and Technology School of Mechanical Engineering, Hanoi, Vietnam [email protected]
Abstract. The Continuous Elements Method (CEM) is employed to analyze vibration of stepped composite cylindrical shells reinforced by four inner/outer ring stiffeners. The functions of displacement are expressed by Levi series. Base on assembled dynamic stiffness matrix of the structure to find the structure’s displacement. To confirm the reliability and accuracy of this method, the results on harmonic response and natural frequencies of this approach compared to results of a model built from Finite Element Method (FEM). Through comparative analyses, it is obvious that the present approach has a good stable property, short computation time, requires less resources of computer, even in medium and high frequency. Keywords: Continuous element method · Stepped composite cylindrical shells · Dynamics stiffness matrix
1 Introduction Composite materials are used in many shell structures of revolution such as vessels, airplanes, missile due to the mechanical properties of these materials in terms of strength and stiffness in the required directions compared with ordinary metal materials. Therefore, free-vibration study of a composite cylindrical shell with ring stiffeners is an important topic. There are many analytical studies of vibration analyses of composite shells such as Li, Pang, Miao and Li [4] studying in vibration of a stepped composite cylindrical shell using Rayleigh-Ritz method, Kim and Lee [5] studying in vibration of a ring-stiffened composite cylindrical shell using Rayleigh-Ritz method and Nam, Cuong, Thinh, Dat, Trung [6] using CEM for analyzing a composite cylindrical shell with ring stiffeners. All above-mentioned works, it is important to remark the lack of study on CEM for stepped composite cylindrical shells. In this paper, the CEM is used for analyzing a structure of stepped composite cylindrical shell with stiffeners. The CEM is based on the exact closed from solution of their governing differential equations of motion for harmonic oscillation. From relationship between a vector of loads and a vector of displacements, find the vector of displacements by multiplying the vector of load by
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 953–968, 2022. https://doi.org/10.1007/978-981-19-1968-8_81
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inverse assembled dynamic stiffness of the structure. The main object of this studies is to present a procedure to obtain the dynamic stiffness matrix assembled from 9 dynamic stiffness matrices by including the important effects of shear deformation and rotatory inertia. Nine matrices include a stepped composite cylindrical shell divided into five cylindrical shells with different thicknesses and four inner/outer stiffeners have the same thickness. All above continuous elements based on FSDT is proposed for free vibration analysis with clamped, free boundary conditions. The achieved numerical results are compared to the results calculated by FEM and the.
2 Theoretcal Fomulations 2.1 Description of the Model Consider a conical shell with parameters denoted as Fig. 1.
Fig. 1. Geometry and coordinate system of conical shell
Let’s investigate the model with (s, θ , z) coordinate with s is the coordinate along the shell generators, θ is the circumferential coordinate and z is the coordinate perpendicular to the surface of shell. The shell has displacements denoted by u, v, w in the s, θ , z respectively. The general formulation of cylindrical shell and ring stiffeners is R(s) = R1 + s sin α
(1)
where α = 0, the structure is a cylindrical shell, with α = 90o , the structure is an outer ring stiffeners and α = −90o the structure is an inner ring stiffeners.
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2.2 Kinematic Relations Base on the previous model and the assumption of moderately thick theory, the displacements of an arbitrary point of cylindrical shell and ring stiffener for first order shear deformation theory are expressed in terms of displacements and rotation components of middle surface. These are calculated by u(s, θ , z, t) = u0 + zφs (s, θ , t) v(s, θ , z, t) = v0 + zφθ (s, θ , t) w(s, θ , z, t) = w0 (s, θ , t)
(2)
where u, v, w are the displacement components in s, z, θ directions, φs and φθ are rotations of a transverse normal about θ and s axes, respectively. Strain-displacement relations can be written as ∂φs ∂u0 +z ∂s ∂s ∂v z 1 ∂φθ u sin α + w cos α + + φs sin α + εθ = R(s) ∂θ R(s) ∂θ ∂w γsz = φs + ∂s ∂w 1 γθz = − v cos α + φθ R(s) ∂θ ∂u ∂φs 1 z ∂φθ γsθ = − v cos α + φθ + − φθ sin α + R(s) ∂θ R(s) ∂θ ∂s εs =
(3)
2.3 Lamina Constitutive Relations Consider a composite shell consisting of N orthotropic uniform thickness layers. The fibers of layers make an angle with the x axis. The stress–strain relations of the k th layer neglecting the transverse normal strain and stress are calculated by: ⎧ ⎫ ⎡ ⎫ ⎤⎧ ⎪ εs ⎪ σs ⎪ Q11 Q12 0 0 Q16 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎢Q Q ⎪ ⎥⎪ ε ⎪ ⎪ ⎨ θ ⎪ ⎨ σθ ⎪ ⎬ ⎢ 21 22 0 0 Q26 ⎥⎪ ⎬ ⎢ ⎥ τθz = ⎢ 0 0 Q44 Q45 0 ⎥ γθz ⎥⎪ ⎪ ⎪ ⎢ ⎪ ⎪ ⎪ γxz ⎪ ⎪ τxz ⎪ ⎪ ⎣ 0 0 Q54 Q55 0 ⎦⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩τ ⎭ Q61 Q62 0 0 Q66 ⎩ γxθ ⎭ xθ where Qij is the transformed stiffness of k th layer.
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2.4 Stress, Forces and Moment Resultants Stress and moment resultants are expressed by (Ns , Nθ , Nsθ , Ms , Mθ , Msθ ) h/2 = [σs , σθ , σsθ , zσs , zσθ , zσsθ ]dz −h/2
(Qs , Qθ ) = K
h/2 −h/2
(5)
[τsz , τθz ]dz
where K = 5/6 is the shear correction factor. A12 ∂u0 ∂υ0 + u0 sin α + + w0 cos α NS = A11 ∂s R ∂θ ∂φS ∂φθ B12 +B11 + φS sin α + ∂s R ∂θ A22 ∂v0 ∂u0 Nθ = A12 + + w0 cos α ∂s R ∂θ (6) ∂φS ∂φθ +B12 + B22 φS sin α + ∂s ∂θ ∂v0 1 ∂u0 + − υ 0 sin α NSθ = A66 ∂s R ∂θ 1 ∂φS ∂φθ sin α cos α ∂υ0 cos α sin α cos α ∂u0 + φθ − + + υ0 +B66 R ∂θ ∂s R 2R2 ∂θ 2R ∂s 2R2 B12 w0 cos α ∂u0 ∂v0 MS = B11 + u0 sin α + + ∂s R ∂θ R D12 ∂φS ∂φθ + +D11 φS sin α + ∂s R ∂θ B22 ∂u0 ∂v0 + u0 sin α + + w0 cos α Mθ = B12 ∂s R ∂θ D22 ∂φS ∂φθ + φS sin α + +D12 ∂s R ∂θ ∂v0 ∂u0 sin α + − υ0 MSθ = B66 ∂s R∂θ R cos α ∂u0 cos α ∂υ 1 ∂φS ∂φθ 1 1 + + + + sin αφθ + 2 cos α sin αυ0 +D66 − 2R2 ∂θ 2R ∂s R ∂θ ∂s R 2R − cos α ∂w0 1 ∂w0 Qθ = KA44 υ0 + + φθ , QS = KA55 + φS R R ∂θ ∂s
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2.5 Force Resultant The equations of motion using the first-order shear deformation shell theory (FSDT) for laminated cylindrical shells and ring stiffeners are calculated by ∂NS 1 ∂NSθ sin α + (NS − Nθ ) + = I0 u¨ 0 + I1 ϕ¨s ∂s R R ∂θ ∂NSθ 1 ∂NSθ 2 sin α cosα + NSθ + + Qθ = I0 v¨ 0 + I1 ϕ¨θ ∂s R R ∂θ R 2 sin α ∂MS 1 ∂MSθ + (Ms − Mθ ) + − Qs = I1 u¨ 0 + I2 ϕ¨s ∂s R R ∂θ ∂MSθ 1 ∂MSθ 2 sin α + MSθ + − Qθ = I1 v¨ 0 + I2 ϕ¨θ ∂s R R ∂θ
(7)
1 ∂Qθ sin α cos α ∂QS + + Qs − Nθ = I1 w¨ 0 ∂s R ∂θ R R where I1 =
k+1 N z
ρ (k) z i dz (i = 0, 1, 2).
k=1 zk
3 Continuous Element for Vibration Analysis of Composite Cylindrical Shells and Ring Stiffeners 3.1 State Vectors State vectors for symmetrical circumferential mode (for both cylindrical shell and ring stiffener) include force, moment resultants and displacement components, these are expressed by levi series as follows: ⎧ ⎫ ⎫ ⎧ u(s), ⎪ uo (s, θ , t), ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ w(s), ⎪ wo (s, θ , t), ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ∞ ⎨ ⎨ φ (s, θ , t), ⎬ φθ (s), ⎬ θ = cos(mθ )eiωt ⎪ Ns (s), ⎪ ⎪ ⎪ ⎪ Ns (s, θ , t), ⎪ (8) ⎪ ⎪ m=1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ Qs (s, θ , t), ⎪ Qs (s), ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ ⎭ ⎩ Ms (s, θ , t) Ms (s) ⎧ ⎫T ⎫T ⎧ v(s), ⎪ vo (s, θ , t), ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ∞ ⎪ ⎨ φ (s), ⎪ ⎬ ⎨ φs (s, θ , t), ⎬ s = sin(mθ )eiωt ⎪ ⎪ ⎪ Nθ (s, θ , t), ⎪ N (s), θ ⎪ ⎪ ⎪ ⎪ m=1 ⎪ ⎪ ⎪ ⎪ ⎩ ⎭ ⎭ ⎩ Mθ (s, θ , t) Mθ (s)
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in which m is circumferential mode of the shells. State vectors can be abbreviated as ym ={um ,vm ,wm , φsm , φθm , Nsm ,Nsθm ,Qsm ,Msm , Msθm }T . From (8), (7) and (6), we obtain a system of differential equations in s-coordinate which can be expressed in matrix form dy = Am ym ds
(9)
with Am is a 10 × 10 matrix. 3.2 Dynamic Stiffness Matrix K Because the system of differential equations is linear so to obtain dynamic transfer matrix Tmc of cylindrical shells and Tmr of ring stiffeners, we can use matrix exponential. We obtain L
Tmc
= e0
Am ds
R2- R1
Tmr
=e
Am ds
(10)
0
Then the transfer matrices above are separated into four blocks as follows T11 T12 Tm = T21 T22 m
(11)
after some manipulations of (11), the dynamic stiffness matrix K(ω)m is determined by K(ω)m =
−1 −1 T11 −T12 T12 −1 −1 T21 − T22 T12 T11 T22 T12
(12) m
4 Dynamic Stiffness Matrix for Ring Composite Inner/Outer Ring-Stiffened Cylindrical Shells 4.1 Dynamic Stiffness Matrix Assembly Figure 3 is model used for analysis. The model includes a cylindrical shell divided into 3 parts of equal length L and different thickness to form a cylindrical shell with 2 steps. The ring stiffeners has the width cr and the thickness br . Above we mentioned the dynamic stiffness matrix for each element (cylindrical shell, ring stiffener) but to obtain dynamic stiffness matrix for whole system including 3 cylindrical shells with 2 inner/outer ring stiffeners, it need to assemble matrices of elements. The conditions for assembling above elements as follows [6]. The assembly order of the above model is cylinder – ring – cylinder – ring … as follows Fig. 2 where K c (ω), K r (ω) are dynamic stiffness matrices of cylindrical shells and ring stiffeners, respectively Fig. 3.
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Fig. 2. The assembling procedure to of the first three element shells
Fig. 3. Model of inner/outer ring-stiffened cylindrical shells
4.2 Determination of Natural Frequencies The relationship between displacements and forces leads to the equation K(ω)Um = Fm
(13)
where Um is the vector of displacements Fm is the vector of loads defined as Um = Fm =
um (0), vm (0), wm (0), φsm (0), φθm (0),
T
um (L), vm (L), wm (L), φsm (L), φθm (L) Nsm (0), Nθm (0), Qsm (0), Msm (0), Msθm (0), Nsm (L), Nθm (L), Qsm (L), Msm (L), Msθm (L)
T
(14)
In Eq. (14), ω and Fm are given. The vector of displacements is calculated by Um = Fm /K(ω)
(15)
so for a given frequency range, we can plot the graph of harmonic response curve. From the graph, it’s easily to specify the natural frequencies.
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5 Numerical Results and Discussion 5.1 Modal Analysis In this section, the results on natural frequencies of CEM are compared with results of Cmodehenchen Guo [6] using domain decomposition method for vibration analysis of cylindrical shell without ring stiffeners. The geometrical properties and material properties of cylindrical shell with 2 steps are R = 1 m, h1 /R = 0.05, h1 /h2 = 2, L/R = 6 and E1 = 210 GPa, E2 = 10 GPa, G12 = G12 = 6 GPa, v = 0.3, (Material 1). To demonstrate the accuracy of CEM more clearly Table 4, the results of natural frequencies of this approach are compared to results of Wang and Lin [8] using analytical method. The model used in Wang and Lin [8] was a cylindrical shell reinforced by an outer ring stiffener. The material and geometry properties are shown in Table 2. All results are shown in Tables 2, 3. Table 1. Comparisons of dimensionless natural frequencies = ωR ρ(1 − μ212 )E2 for the three-layered [0/90°/0] two-stepped cylindrical shells with two boundary conditions. Mode
C-F
C-C
Chenchen Guo [7]
Present
Chenchen Guo [7]
Present
1
0.089
0.088
0.189
0.185
2
0.099
0.097
0.273
0.227
3
0.208
0.206
0.319
0.269
4
0.226
0.222
0.338
0.315
5
0.265
0.262
0.361
0.334
6
0.319
0.317
0.387
0.357
7
0.344
0.343
0.446
0.441
Free Vibration Analysis on Stepped Composite Cylindrical Shells Reinforced
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Table 2. Geometrical and material properties of the composite cylindrical shell with an outer ring stiffener Characteristics of cylindrical shell
Geometrical properties
Shell radius R (m)
0.3
Shell thickness h (m)
0.03
Shell length L (m)
5
Characteristics of inner/outer ring stiffeners
Geometrical properties
Ring depth br (m)
0.03
Ring width cr (m)
0.09
Material properties of cylindrical shell
Geometrical properties of ring stiffener
E1 = 150 GPa, E1 = E2 = 9 GPa G12 = G23 = 7.1 GPa, G23 = 2.5 GPa
Er = 70 GPa, Gr = 2.6 GPa
ν12 = 0.3, ρ = 1600 kg/m3
ν12 = 0.23, ρ = 2710 kg/m3
Table 3. The comparison of resonant frequencies in some circumferential modes of this paper’s results to Wang and Lin [8]. The boundary condition of this model is clamped-clamped (C-C) Mode
Frequency (rad/s) Wang and Lin [8]
Present
Error (%)
A
B
(|A-B|/A)*100
ω10
3636.3
3606.5
0.81%
ω11
700.3
696.2
0.58%
ω21
1575.2
1528.1
3%
ω12
2196.7
2373.7
8.05%
ω22
2288.8
2457.9
7.4%
5.2 Harmonic Responses of Composite Cylindrical Shells Reinforced by Inner/Outer Ring Stiffeners The advantage of CEM will be demonstrated through comparison with FEM. The harmonic response graph will clearly show the accuracy advantage of CEM. A model like Fig. 3 was modeled in APDL (a FEM software) with different meshes. A concentrate force F = 1eiωt is applied at one free end of this model. The geometrical and material properties of this model are described in Table 4. The boundary conditions chosen for the analysis are free-free (F-F) and clamped – free (C-F). In the examples of this paper, cylindrical shells and ring stiffeners have the same layer configuration and material. Case 2 is a cylindrical shell with two steps reinforced by an inner ring and an outer ring. The material of this model is Material 2. The results is shown in Table 5 and Fig. 4.
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Table 4. Geometrical and material properties (Material 2) of the stepped composite cylindrical shell with an inner ring stiffener and an outer ring stiffener Geometry properties
Geometrical properties
Shell radius R (m)
0.2
Shell thickness h (m)
0.018 – 0.021 – 0.018
Shell length L (m)
0.3
Ring depth br (m)
0.02
Ring width cr (m)
0.021
Material properties of cylindrical shell and ring stiffeners E1 = 134.4 GPa, E1 = E2 = 8.96 GPa, G12 = G23 = 5.73 GPa, G23 = 4.48 GPa, ν12 = 0.25, ρ = 1600 kg/m3
Table 5. Comparison of natural frequencies (Hz) of free – free cylindrical shell reinforced by an inner ring stiffeners and an outer ring stiffeners of CEM and of FEM (Material 2) Mode
Frequency (Hz) CEM
FEM 80 × 60 × 3
Error (%)
A
B
(|A-B|/A)*100
2.1
498
486
2.4
2.2
539
515
4.48
3.1
1319
1316
2.27
3.2
1342
1339
2.23
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000
Frequency (Hz)
-50
20log10(|w|)
-75 -100 -125 -150 -175 -200 -225 CEM
FEM 48x36x1
80x60x2
Fig. 4. The harmonic responses of free – free cylindrical shell reinforced by an inner ring stiffener and an outer ring stiffeners in CEM and FEM (Material 2) [90/0/90]
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Table 6. Comparison of natural frequencies (Hz) of clamped – free cylindrical shell reinforced by an inner ring stiffeners and an outer ring stiffeners of CEM and of FEM (Material 2) Mode
Frequency (Hz) CEM
FEM 80 × 60 × 2
Error (%)
A
B
(|A-B|/A)*100
1.1
303
303
0
2.1
536
521
2.66
2.2
769
742
3.51
3.1
1328
1324
0.3
3.2
1525
1479
3.02
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000
Frequency (Hz)
20log10(|w|)
-75 -100 -125 -150 -175 -200
CEM
FEM 48x36x1
FEM 80x60x2
Fig. 5. The harmonic responses of clamped – free cylindrical shell reinforced by an inner ring stiffener and an outer ring stiffener in CEM and FEM (Material 2) [90/0/90]
5.3 The effect of the Layer configuration on the Frequency of Cylindrical Shell It can be seen from Fig. 6 that the increase of thickness of shell and rings leads to increase in the natural frequency. The model used is the same as Fig. 3. The material of this model is Material 2 and its geometry properties is follow Table 6.
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Table 7. Natural frequencies in 5 cases of thickness of cylindrical shell and ring stiffeners Mode
Layers 0/90/0/90/0/90/0/90/0/90
0/90/0/90/0/90/0/90
0/90/0/90/0/90
0/90/0/90
0/90
1.1
312
316
320
324
328
1.2
932
950
970
988
1008
2.1
294
520
720
892
1034
2.2
580
770
960
1130
1272
2.3
1090
1288
1498
1688
1852
3.1
538
1234
1738
2104
2368
3.2
740
1420
1906
2254
2500
3.3
1018
1648
2142
2512
2784
Frequency (Hz)
Finally, it is very important to note from Fig. 4 and Fig. 5 that CEM gives more exact and better results compared to FEM because it is necessary to get a finer meshing for FE curves to reach the curve results of CEM. 3000 2750 2500 2250 2000 1750 1500 1250 1000 750 500 250 0 1
2
3 4 0/90 0/90/0/90/0/90 0/90/0/90/0/90/0/90/0/90
5
6 7 0/90/0/90 0/90/0/90/0/90/0/90
8
Fig. 6. The effect of layers configuration on the cylindrical shell with clamped-free boundary condition (Material 2)
Free Vibration Analysis on Stepped Composite Cylindrical Shells Reinforced
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6 Conclusion and Developments In this study, a new Continuous Element has successfully been constructed for stepped composite cylindrical shell reinforced by ring stiffeners. The developed formulation has been proved to be exact and reliable through comparisons with other approaches and with the Finite Element Method. The obtained harmonic responses demonstrated the advantages of our solution in term of precision and speed, the capacity of storage in all low, medium and high frequencies. In addition, the method can be easily applied to many different structures is the assembly of cylindrical shells, conical shells, and ring stiffeners. The materials of the cylindrical shell and the ring stiffeners in this study are considered to be the same, however, this method allows to change the cylindrical shell material to be different from the ring stiffeners material arbitrarily. The influences of composite layer schemes and boundary conditions have been investigated and discussed. The proposed Continuous Element models can be developed to solve the problems of composite cylindrical/conical shell reinforced by a large number of ring stiffeners or combined cylindrical - conical ring-stiffened shells with/without contact with elastic foundation and fluid.
Appendix Equation (9) is written more clearly as follows dum = c4 sin αum + mc4 vm + c4 cos αwm + c5 sin αφsm ds D11 B11 +mc5 φθm − Nsm − Msm c1 c1 m dvm sin α D66 B66 = um − vm − Nsθm + Msθm ds R R c10 c10 dwm 1 = −φsm + Qsm ds kA55 d φsm = c2 sin αum + mc2 vm + c2 cos αwm + c3 sin αφsm ds B11 A11 +mc3 φθm − Nsm + Msm c1 c1
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m d φθm sin α B66 A66 = φsm − φθm + Nsθm − Msθm ds R R c10 c10 dNsm c6 sin α mc6 sin α um + = −I0 ω2 + vm 2 ds R R2 mc6 sin α cos α c7 sin α 2 φsm + w + −I ω + m 1 R2 R2 mc7 sin α sin α c2 sin α + φθm − (1 + c4 )Nsm − Msm 2 R R R mc6 sin α dNsθm kA44 cos2 α m2 c6 2 =− um + −I0 ω + + 2 vm ds R2 R2 R m cos α mc4 2 sin α mc2 Nsθm − Msm + (kA33 + c6 )wm − 2 Nsm − R2 R R R c6 sin α cos α dQsm m cos α = um + (kA44 + c6 )vm ds R2 R2 m2 kA44 c6 cos2 α c7 sin α cos α wm + + −I0 ω2 + + φsm 2 2 R R R2 c7 cos α kA44 c4 cos α φθm − + Nsm +m − 2 R R R sin α c2 cos α Qsm − Msm − R R dMsm = (2c8 sin2 α − I1 ω2 )um + 2mc8 sin αvm + 2c8 sin α cos αwm ds −(2c9 sin2 α − I2 ω2 )φsm − 2c5 sin αNsm + Qsm 1 m Msm − Msθm −2 sin α c3 + R R dMsθm kA44 cos α 2 2 = mc8 sin αum + m c8 − − I1 ω vm ds R mkA44 − I0 ω2 wm + mc9 sin αφsm + mc8 cos α − R 2 sin α Msθm +(m2 c9 + kA44 − I2 ω2 )φθm − mc5 Nsm − mc3 Msm − R where 2 c1 = A11 D11 − B11 , c2 = (A12 B11 − A11 B12 )/Rc1 , c3 = (B11 B12 − A11 D12 )/Rc1 , c4 = (B11 B12 − A12 D11 )/Rc1 , c5 = (B11 D12 − B12 D11 )/Rc1 , c6 = (A12 c4 + B12 C2 + A22 /R)/R,
c7 = (A12 c5 + B12 c3 + B22 /R)/R, c8 = (B12 c4 + D12 c2 + B22 /R)/R, 2 − A66 D66 c9 = (B12 c5 + D12 c3 + D22 /R)/R, c10 = B66
Free Vibration Analysis on Stepped Composite Cylindrical Shells Reinforced
Aij = Aij =
N 1 N
967
K
Qij (zk+1 − zk ) (i, j = 1, 2, 6) , K
Qij (zk+1 − zk ) (i, j = 4, 5) ,
1
1 K 2 Qij (zk+1 − zk2 ) (i, j = 1, 2, 6) , 2 N
Bij = Dij =
1 3
1 N
K
3 Qij (zk+1 − zk3 ) (i, j = 1, 2, 6)
1
where zk−1 and zk are boundary of k th layer.
References 1. Reddy, J.N.: Mechanics of Laminated Composite Plates and Shells: Theory and Analysis, 2nd edn. CRC Press, New York (2003) 2. Reddy, J.N.: Theory and Analysis of Elastic Plates and Shells, 2nd edn. CRC Press, New York (2007) 3. Thinh, T.I., Cuong, N.M.: Dynamic stiffness matrix of continuous element for vibration of thick cross-ply laminated composite cylindrical shells. Compos. Struct. 98, 93–102 (2013) 4. Li, H., Pang, F., Miao, X., Li, Y.: Jacobi-Ritz method for free vibration analysis of uniform and stepped circular cylindrical shells with arbitrary boundary conditions: a unified formulation. Comput. Math. Appl. 77, 427–440 (2019) 5. Kim, Y.-W., Lee, Y.-S.: Transient analysis of ring-stiffened composite cylindrical shells with both edge clamped. J. Sound Vib. 252(1), 1–17 (2002) 6. Nam, L.T.B., Cuong, N.M., Thinh, T.I., Dat, T.T., Trung, V.D.: Continous Element Formulation for Composite Ring-Stiffened Cylindrical Shells. Vietnam J. Sci. Technol. 56, 515–530 (2018) 7. Guo, C., Liu, T., Wang, Q., Qin, B., Shao, W., Wang, A.: Spectral-Tchebychev technique for the free vibration analysis of composite laminated stepped and stiffened cylindrical shells with arbitrary boundary conditions. Compos. Struct. 272, 114193 (2021) 8. Wang, R.T., Lin, Z.X.: Vibration analysis of ring-stiffened cross-ply laminated cylindrical shells. J. Sound Vib. 295, 964–987 (2006) 9. Thinh, T.I., Nguyen, M.C., Ninh, D.G.: Dynamic stiffness formulation for vibration analysis of thick composite plates resting on non-homogenous foundations. Compos. Struct. 108, 684–695 (2014) 10. Casimir, J.B., Khadimallah, M.A., Nguyen, M.C.: Formulation of the dynamic stiffness of a cross-ply laminated circular cylindrical shell subjected to distributed loads. Comput. Struct. 166, 42–50 (2016) 11. Kouchakzadeh, M.A., Shakouri, M.: Free vibration analysis of joined cross-ply laminated conical shells. Int. J. Mech. Sci. 78, 118–125 (2014)
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12. Hung, N.Q., Nguyen, M.C., Cuong, T.V.: Vibration analysis on stepped composite cylindrical shells reinforced by inner/outer ring stiffeners In: The 15th National Conference on Solid Mechanics, Thai Nguyen, Vietnam (2021) 13. Qu, Y., Chen, Y., Long, X., Hua, H., Meng, G.: Free and forced vibration analysis of uniform and stepped circular cylindrical shells using a domain decomposition method 74, 425–439 (2013)
Subcooled Flow Boiling and Its Application in Designing LNG Vaporizers Tuan Le and Kieu Hiep Le(B) School of Heat Engineering and Refrigeration, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Subcooled flow boiling has various applications in industrial processes, which includes the regasification of liquefied natural gas at the gas terminal. This process can be done with ambient air vaporizers (AAV), which utilizes ambient air around as a heat source. An important factor that needs to be taken care of when designing AAV is the quality of natural gas at the end of vaporizing region. In this study, a subcooled flow boiling model was proposed using Mixture Model and Heat Transfer in Fluid interfaces in COMSOL Multiphysics. The flow boiling model was validated with Umekawa’s data which used water as a working fluid. Then, it was used to determine the required length of the heated tube for the natural gas exited at the outlet to have volume fraction equaled 1. Besides, a heat transfer analysis was carried out to study the flow boiling heat transfer coefficient along the heating length with different inlet conditions. The study showed for a heating length of 1.9 m, with the operating pressure of 0.4 MPa, and ambient temperature of 20 °C, inlet velocity of 0.06 m/s would make LNG vaporized completely. The study also showed that higher inlet mass flux or higher inlet velocity required more heating length for the AAV to fully vaporize LNG. Keywords: LNG · Flow boiling · Ambient air vaporizers · CFD modeling
1 Introduction Subcooled flow boiling and its application have been encountered in numerous industrial processes. This phenomenon happens when a heat flux is applied to a heated channel surface. As wall temperature rises beyond the local saturation temperature of the inner fluid, heat transfer mode would turn from single-phase to two-phase due to the occurrence of flow boiling. The term “subcooled” indicates the condition in which bulk liquid temperature may still be lower than the saturation temperature. During the analysis of heat exchangers used at gas stations, flow boiling characteristics of fluid inside provide essential data for their development, operation, and manufacture. Since demands for fuel that has low greenhouse gas emissions and efficient energy conversion are thriving throughout the world, liquefied natural gas (LNG) has received considerable attention. For long-distant transportation, natural gas is liquefied to LNG at approximately −162 °C at atmospheric pressure. After being received at the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 969–979, 2022. https://doi.org/10.1007/978-981-19-1968-8_82
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gas terminal, LNG enters a regasification process by going through vaporizers. There are various type of vaporizers. In this study, we investigated the ambient air vaporizer (AAV) which is a heat exchanger utilizing ambient air as the external heat source instead of seawater or intermediate fluid. The AAV is considered an effective and eco-friendly way to transform LNG to natural gas because it neither affects the water environment nor releases exhaust emissions. As LNG enters the inlet, it will absorb heat from the wall and go through the flow boiling process. The natural gas at the outlet is expected to meet the local pipeline requirements. For the purpose of designing and optimizing vaporizers in the regasification process, various studies were carried out to investigate the flow boiling heat transfer behavior of LNG inside the tube as well as factors that may influence the outlet condition of natural gas. Because of many involved factors and the complexity of the problem, these studies were often presented under the CFD method. Jeong et al. [1] studied the convection heat transfer of air outside the finned tube with the working fluid was nitrogen. The model was built under the assumption that there was no phase change of fluid inside and only focused on the overheated region. An extensive numerical study was carried out by Liu et al. [2] about LNG ambient air vaporizers. The model was coupled with both natural convection outside and two-phase flow boiling inside and was then used to investigate LNG heat transfer flow boiling in the finned tube as well as the influence of air temperature, inlet flow, and location of finned tube. In addition, Liu et al. [3] also studied the influence of frost formation on AAV. A detailed model was conducted by Sun et al. [4] to study the supercritical flow boiling and predict two phase flow behavior of LNG inside vaporizers. It can be seen from previous studies about LNG vaporizers that they all had expensive computational time because the whole vaporizers as well as fluid-solid coupled method and related factors (i.e. air flow outside, fin thickness, frost formation) were taken into account. The LNG flow inside had to go through vaporizing region and overheated region in order to achieve the required state at outlet [1]. In this study, we would focus on the vaporizing region, where two phase flow and mass exchange between two phases played an important role. One important factor when designing AAV is the vapor volume fraction. It is expected to equal 1 at the end of vaporizing region. Determining the length of the heated tube and investigating the temperature distribution would provide helpful information for optimal design. In this study, we would focus on the development of a vertical subcooled flow boiling model which utilized LNG. The model was first validated with Umekawa’s experimental data which used water as working fluid due to the shortage in LNG data. After that, it was used to determine the outlet condition of natural gas and study the heat transfer performance of LNG inside. The CFD analysis in this study was performed using the commercial software, COMSOL Multiphysics version 5.5.
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2 Model Description 2.1 Setting up Subcooled Boiling Model Figure 1 shows the geometry of 2D-axis symmetry boiling model. As subcooled liquid enters the inlet, it is heated up by a constant heat flux applied to the wall surface. The model consists of an inlet region, a heating region, and an outlet region. Apart from heating length, other parts of the wall were considered adiabatic walls. In CFD module, COMSOL provided three interfaces to simulate dispersed flow in Multiphase Flow: the Bubbly Flow, the Mixture Model and the Euler-Euler Model. Of three listed models, the Euler – Euler was the most accurate option to simulate multiphase flow model because it took account of two separate set of Navier-Stokes equations, one set for each phase. However, because it was computationally expensive, the Mixture Model was often advised to use as a prior step though weaknesses of this model compared to the Euler-Euler were accuracy and versatility [5]. In this specific study, a predefined Mixture Model was chosen because it could deal with two-phase flow consisting of a liquid continuous phase and a vapor dispersed phase. In this study, the Heat Transfer in Fluids interface and Mixture Model interface were taken into account. Here, some assumptions would be made in the two-phase flow slip model. We developed a homogenous model with a vapor dispersed phase and liquid continuous phase. The homogenous model was considered the simplest approach to model two-phase flows by treating them as single-phase flows with average properties [6]. The liquid phase and vapor phase velocity were assumed to be equal. Other mixture properties like density, thermal conductivity, viscosity, and heat capacity can be defined below, respectively. ρ = φL ρL + φG ρG
(1)
k = φL kL + φG kG
(2)
μ = φL μL + φG μG
(3)
Cp = XL CpL + XG CpG
(4)
During the analysis of phase change, volume fraction φ was an important factor that had a strong influence on the properties of the mixture. φL , φG were the fraction of pipe volume at cross-sectional area occupied by liquid and vapor phase, respectively. Mass fraction of liquid XL and mass fraction of vapor XG were used to calculate Cp in Eq. (4) can be defined as XL =
φL ρL φG ρG ; XG = ρ ρ
(5)
For the wall boiling model, instead of using empirical correlation to define qw , a simple heat flux boundary was applied to the tube surface. −n · q = qw
(6)
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Fig. 1. 2D-axis symmetry flow boiling model inside a tube
2.2 Conservation Equation Three governing equations, known as the Navier-Stoke equations, are fundamental in simulating fluid dynamics. The mass, momentum, and energy conservations were also described in this study. In addition, mass exchange on the liquid-vapor interface must be taken into consideration in the equations. COMSOL provided the Mass Transfer function by specifying a mass transfer rate from dispersed phase to continuous phase, called mdc . User-defined source terms would be implemented here using Bo’s approach [7]. Based on the control equation of void fraction, he introduced a time scale representation to take account of the rates of bubbles’ generation and collapse, the change of bubble size, as well as the interactions between bubbles and heated wall, liquid, and with each other. The control equation was presented as: ∂φG + ∇(j · φG ) = ω ∂t
(7)
Source term ω considered the combined effect of evaporation at the thermal boundary layer and condensation at subcooled core. It was defined by the following equations: ω = ωevap + ωcond
(8)
φG − φG ωevap = max 0, tevap φG − φG ωcond = min 0, tcond
(9)
(10)
Subcooled Flow Boiling and Its Application
φG =
1.1 Xeq 1−X )ρG Xeq + ( ρeq L
973
(11)
where φG was determined by thermodynamic vapor quality Xeq . Equation (11) defined φG in saturated region. On the other hand, φG was set to 0 in subcooled region. tevap , tcond were the time scales for φG to return to φG by the effect of evaporation and condensation respectively. As the source term ω was calculated, the mass transfer rate mdc was specified and was implemented in Navier-Stokes equations, subsequently. The density of each phase was assumed to be constant. In Mixture model, the mass, momentum, and energy conservation equations were described below, respectively [5]. 1 1 (12) − ∇.j = mdc ρL ρG ∂j ρ + ρ(j.∇)(ρL − ρG ) j slip .∇ j ∂t (13) = −∇.p + ∇.τ + ρg + F ∂T + ρCp u∇T = Qsource + Qp + qw (14) ρCp ∂t The transport equation of vapor volume fraction was described as: ∂φG + j.∇φG + ∇.j slip ∂t ρ.mdc =− + ∇.(Dmd ∇φG ) ρG ρL A k-ε turbulence model was used to deal with turbulent flow. μT ∇k + Pk − ρε ρ(j · ∇)k = ∇ · μ + σk μT ε ε2 ρ(j · ∇)ε = ∇ · μ + ∇ε + Cε1 Pk − Cε2 ρ σε k k
(15)
(16) (17)
For k - ε turbulence model, COMSOL only provided the Wall Functions property for Automatic Wall Treatment method. When using this function, a variable need to consider which is the wall lift-off δ + . This variable represented a gap between computational region and physical wall. The wall lift-off was presented as: 1√ h ρCμ4 k, 11.06 (18) δ + = max 2μ The mesh used in this study was refined until it got the optimal results. The Mapped type mesh was used to better control the number of elements in heating region of the tube. Furthermore, a customized boundary layer with 5 layers, stretching factor of 1.2, thickness adjustment factor of 2.5 is set at the tube surface. The total number of elements in the geometry was approximately 19000. The mesh statistics were inspected and showed that it had the minimum element quality of 0.934. It indicated that the mesh had good quality to run the simulation.
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3 Validation and Discussion 3.1 Validation with Umekawa’s Result The boiling model was validated with the experimental study conducted by Umekawa et al. [8]. Umekawa et al. carried out an experiment to investigate the influence of void fraction profile in flow boiling inside a vertical tube. It was measured along the heating length and was compared with several correlations. Besides, Umekawa et al. evaluated void fraction data against thermodynamic equilibrium quality in some cases. The data obtained from his study showed a good quality compared to various correlations. Therefore, it can be chosen for validation. The apparatus in Ref. [8] used a temperature controller unit to keep inlet temperature at 80 °C before entering a vertical tube. The studied tube had an inner diameter of 5 mm, and the heating length was 200 mm. The surrounding wall was heated by a constant heat flux of 2441 kW/m2 . The operating pressure was set at 0.49 MPa. The experiment was conducted at a mass flux of 600 kg/m2 s and the working fluid was water.
Fig. 2. Comparison between simulation data and experimental data extracted from [8].
The simulation had the same parameters as Umekawa’s experiment. The inlet and outlet length were both set at 10 mm. The results obtained were compared against experimental data in Fig. (2). It can be seen that the simulation trendline was slightly less steep than the experimental data. However, the onset of the nucleate boiling point was similar. Overall, the simulation result showed a good agreement with the experimental value. 3.2 Application of Flow Boiling Model in Designing LNG Vaporizer After validated with Umekawa’s result, the subcooled boiling model was used to calculate with LNG and to determine the vapor volume fraction of natural gas at the outlet of vaporizing region. The gas condition that exits this region is expected to have volume
Subcooled Flow Boiling and Its Application
975
fraction of 1, which means LNG is fully vaporized into natural gas. Determining the length of the heated tube provides information for the optimal design of the whole AAV. One factor needed to be taken care of is the flow boiling mechanism when switching the model from constant heat flux boundary to convective heat flux boundary. As presented in Umekawa’s results, the experiment in his study was carried out at a fixed high heat flux along the tube. The dominance of nucleate boiling over convective boiling in his study leaded to a sudden increase in flow boiling heat transfer coefficient h, and a gradual decrease was maintained along the tube until it reached gas phase. In our study, a convective flow boiling is carried out, the heat flux varies and depends on ambient environment, so we need to take care of both nucleate boiling at the lower part of the tube and convective boiling at the upper part. The heat transfer coefficient h now depends more on the quality of gas rather than heat flux as in Umekawa’s experiment. The vaporizer investigated in this study had an inner diameter of 6mm. The operating pressure of the simulation was chosen from Umekawa’s range of experimental data set, and it was set at 0.4 MPa. Real-life ambient air vaporizers are strongly influenced by climate condition. The air temperature often varies according to the location of gas terminal. In this study, the ambient air outside was evaluated in tropical region which had the monthly average temperature of 20 °C. Under the assumption of a forced-air draft AAV with longitudinal finned tube surface was investigated, the enhanced heat transfer coefficient of ambient air was 250 W/m2 K. A convective heat flux boundary condition provided by COMSOL was specified to take account of the convection heat transfer of ambient air outside AAV. For fluid inside, Oman LNG data was used [9]. The LNG composition consists of 90% methane, 6.35% ethane, 0.15% propane, 2.5% butane, and 1% nitrogen. Thermophysical properties of LNG and NG for simulation were calculated using REFPROP [10]. We investigated with the same inlet velocity of 0.06 m/s and the heating length varied from 0.5–2.0 m. The simulation results are presented in Fig. 3. As can be seen, with a heating length of 1.9 m, LNG can be vaporized completely inside the heating zone. Thus, a tube with a diameter of 6 mm and length of 2.1 m, i.e., including the heating zone of 1.9 m and adiabatic zones of 0.2 m, is used to design the AAV. In the rest of the paper, the simulation results are conducted with this designed tube. The vapor volume fraction increases drastically from zero to 0.8 in the first 0.4 m in 4 cases. In the length from 0.5–2.1 m, the volume fraction increases slowly. With the heating length of 1.9 m and inlet velocity of 0.06 m/s, volume fraction of the natural gas reached 1 at the end of the tube. This indicated that the required length for LNG fully transformed into natural gas was 1.9 m. Figure (4) shows the liquid volume fraction, vapor volume fraction, and temperature distribution along the tube. As LNG enters the tube inlet at −162 °C and flowed upward, the temperature rises along the flow direction to approximately −10 °C and then remains unchanged at the last 0.5 m of tube length. This indicates that the overheated region of AAV, which is considered a single-phase heat transfer region of gas, needs to have enough length to increase the temperature from −10 °C to meet the temperature requirement of the pipeline.
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Fig. 3. Vapor volume fraction of LNG obtained with different heating length.
Fig. 4. Distribution of liquid volume fraction, vapor volume fraction, and temperature along the tube.
The mean axial LNG temperature is computed from the temperature distribution and plotted together with the wall temperature in Fig. 5a. The flow boiling convective heat transfer coefficient along the heating length of the tube is calculated as α=
q Tw − Tf
(19)
where q is the heat flux from channel wall (W/m2 ), Tw and Tf are the wall and LNG temperature, respectively. To verify the convective heat transfer coefficient obtained
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280 260
Temperature [K]
240 220 200 180 160 Wall temperature LNG temperature
140 120 100 0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Length [m] (a)
Heat transfer coefficient [W/m 2K]
2500 Simulation Dittus – Boelter and Shah correlation
2000 1500 1000 500 0 0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Length [m] (b)
Fig. 5. Temperature and flow boiling heat transfer coefficient of LNG along the heating length.
from the CFD solution, the empirical correlations are recalled. In the liquid region, the heat transfer convective region is calculated based on Dittus – Boelter’s correlation [11]. 0.4
hsp,l = 0.023Rel0.8 Pr l
λl D
(20)
In the two-phase region, it is calculated based on Shah’s correlation [12]. With his database, the boiling heat transfer coefficient is calculated with an enhancement factor ψ . htp = ψ.hsp,l
ψ= ψ0 =
(21)
ψ0 for low subcooling sub ψ0 + T Tsat for high subcooling
(22)
230.Bo0.5 with Bo ≥ 0.3 × 10−4 1 + 46.Bo0.5 with B < 0.3 × 10−4
(23)
The boiling number Bo is defined as: Bo =
q Ghlg
(24)
978
T. Le and K. H. Le
where G is the mass flux (kg/m2 s), hlg is the latent heat of vaporization (J/kg). Rel is the Reynold number for liquid. htp and hsp,l are the heat transfer coefficient of two-phase and liquid single-phase respectively (W/m2 K). Both numerical and correlated heat transfer coefficient are presented in Fig. 5b. As shown in Fig. (5), both numerical and correlated heat transfer coefficients of LNG have a similar order of magnitude. It can be seen that when the LNG enters the heating zone, the heat transfer coefficient reaches a high magnitude, but it reduces significantly when the vapor fraction reaches 0.5. It can be explained that at the lower part of the heating zone, the boiling process occurs intensely. At the upper part, the vapor occupied the tube and the single-phase heat transfer process takes place. Although a discrepancy between the numerical and correlated heat transfer coefficient still needs to be revisited by performing experimental study in the future, the qualitative agreement presented in Fig. 5.b implies that the CFD model can be used to predict the heat transfer coefficient of the LNG flow-boiling process. A sensitivity analysis is performed to investigate the impact of the operating condition on the heat transfer. In this study, the LNG inlet mass flux is varied in range from 200 kg/m2 s to 800 kg/m2 s and the obtained results are presented in Fig. 6.
Fig. 6. Comparison of vapor volume fraction with different inlet mass flux
In Fig. (6), vapor volume fraction at different inlet mass flux is shown. Higher inlet mass flux or higher inlet velocity requires more heating length for the AAV to fully vaporize LNG. At the mass flux of 800 kg/m2 s, the onset of nucleate boiling starts increasing near the end of the tube which indicates that further increasing mass flux would be inappropriate for the boiling phenomenon to happen in the designed tube.
4 Conclusion To determine the required length for vaporizing LNG in an AAV and the state of natural gas at outlet, a subcooled flow boiling model was proposed in this numerical study. The Mixture Model interface and Heat Transfer in Fluid interface in COMSOL Multiphysics 5.5 were used to set up the cases. The model was used to calculate the vapor volume fraction and temperature distribution of a LNG vaporizer with inner diameter of 6 mm,
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operating temperature of 0.4 MPa and LNG inlet velocity of 0.06 m/s. It took 1.9 m of heating length to vaporize LNG completely. The temperature at the end of vaporizing region was −10 °C. A sensitive heat transfer analysis was conducted with different inlet mass flux varied from 200–800 kg/m2 s. Further improvements for the boiling model would be considering the effect of finned tube surface and heat transfer through fins to increase the accuracy of the model.
References 1. Jeong, H., Lee, Y., Ji, M., Bae, K., Chung, H.: Natural convection heat transfer estimation from a longitudinally finned vertical pipe using CFD. J. Mech. Sci. Technol. 23(6), 1517–1527 (2009) 2. Liu, S., Jiao, W., Wang, H.: Three-dimensional numerical analysis of the coupled heat transfer performance of LNG ambient air vaporizer. Renew. Energy 87, 1105–1112 (2016) 3. Liu, S., et al.: Dynamic heat transfer analysis of liquefied natural gas ambient air vaporizer under frost conditions. Appl. Therm. Eng. 110, 999–1006 (2017) 4. Sun, B., et al.: Modeling of cryogenic liquefied natural gas ambient air vaporizers. Ind. Eng. Chem. Res. 57(28), 9281–9291 (2018) 5. COMSOL, CFD Module User’s Guide, ver 5.5 6. Kandlikar, S.: Handbook of Phase Change: Boiling and Condensation, p. 220 (1999) 7. Bo, T.: CFD homogeneous mixing flow modelling to simulate subcooled nucleate boiling flow. No. 2004-01-1512. SAE Technical Paper (2004) 8. Umekawa, H., et al.: The influence of the heating condition on the void fraction in a boiling channel. Phys. Procedia 69, 599–606 (2015) 9. Mokhatab: Handbook of Liquefied Natural Gas, 1st edn., p. 4 (2014) 10. Reference Fluid Thermodynamic and Transport Properties-REFPROP Version 8.0, NIST Standard Reference Database (2007) 11. Dittus, F.W., Boelter, L.M.K.: Heat transfer in automobile radiators of tubular type. Univ. Calif. (Berkeley) Publ. Eng. 2, 443–460 (1930) 12. Shah, M.: A general correlation for heat transfer during subcooled boiling in pipes and annuli. ASHRAE Trans. 83(1), 205–217 (1977)
Patent Map for Forecasting Technology Trends and Policymaking Case Study: IoT in Agriculture Pha N. Pham(B) and Trong-Hieu Nguyen Ministry of Science and Technology, National Institute of Patent and Technology Exploitation, Hanoi, Vietnam [email protected]
Abstract. Effective use of patent information can significantly reduce the time and money invested in research and development. However, currently in Vietnam, the exploitation of patent information is still restricted. Therefore, this paper provides an overview and the effectiveness of a patent map and a patent mapping process proposed by the research team. The article also presents a case study applying a patent map to analyze technology trends and make policy in IoT-enabled agriculture. The results show that IoT in agriculture has been heavily invested in research and development in the past five years. Since this technology is in its early stages of development, there are plenty of opportunities to focus investments in research and development. The research results show eight technology sectors that are receiving the most attention for research and development. The top five research technology trends are indicated, as well. Keywords: Patent map · Patent analysis · Agriculture · IoT-enabled agriculture · IoT
1 Introduction In the past few decades, companies have tended from tangible to knowledge-based intangible assets such as intellectual property rights, business secrets, trademarks, etc. Preliminary estimates for several OECD countries show that firms now invest as much in intangible assets related to innovation (R&D, software, skills, know-how, and branding) as they invest in traditional capital such as machinery, equipment, and buildings [1]. Intangible assets are becoming more and more valuable, helping to improve the competitiveness of businesses and economies. Among intangible assets, patents are one of the most important. A report from the World Intellectual Property Organization (WIPO) shows that up to 90%–95% of the inventions can be found in patent documents, and 80% of these technologies do not appear in other resources, such as journals, magazines, and encyclopedias. According to the WIPO survey, a company can save up to 60% of the time and 40% of investigating budget for R&D activities if it can use patent documents effectively [2, 3]. Patent databases are © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 980–990, 2022. https://doi.org/10.1007/978-981-19-1968-8_83
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valuable sources of information containing cutting-edge technologies, processes, and products from all around the world. Exploiting information from patents helps catch up with world development trends, find ideas for improving goods and production processes, and capture information about competitors’ technologies to build technology and product development strategies is an economical and fruitful approach for enterprises. Patent documents are often long, rich in technical and legal jargon, and are difficult to read and analyze for non-specialists. Therefore, finding and exploiting patent information in a useable way as well as synthesizing patent information in industries and fields to provide information on technology and market trends helps businesses, as well as countries, have a basis for making their policies is not an easy thing. There is a need to have an intuitive solution, systematizing patent information, a simple and easy-tounderstand solution that helps a large number of users to access what was previously only available to experts usability. The patent map was born in that context.
2 Patent Map and Application For a long time, patent information was used mainly to find one or several patent documents that are close to the technology being researched and developed to help determine the novelty of the research results, using patent information as a basis for developing new technologies, or using information from patent documents as a basis for determining infringement of intellectual property rights. However, the information contained in patents is very rich, especially when using information from a group of patent documents that will give new information about a technology area, about the market, about the potential technology development, etc. In recent years, patent analysis and creating patent maps are not only important for businesses, organizations but have been recognized as one of the important tasks at the government level in many developed countries. In the most general way, a patent map, also known in the same sense as the patent landscape reports [4], is defined as the patent information collected for a particular use, assembled, analyzed, and described in terms of a visual representation such as a graph, chart, or table [5]. Understandably, a patent map turns complex and difficult-to-exploit patent information into an easy-to-understand and easy-to-grasp visual form, helping to expand the user and exploit patent information. Currently, creating a patent map has become effortless with specialized software. Patent information should be collected comprehensively so as not to omit data and eliminate noise that affects the analysis results as much as possible. The patent information is then systematized, visualized, and analyzed. The patent map is fruitful for management organizations and research institutions. It provides valuable information when making policies and identifying research trends at government institutions, consultants, research institutes, and universities. For example, the Japanese government often uses an analytical method based on Patent Maps when preparing the Annual Report on Japan’s Economy (Economic White Paper) and Annual Report on the Promotion of Science and Technology (White Paper on Science and Technology) [5]. A survey conducted by the Institute of Intellectual Property (IIP) (Japan) shows that 85% or more of major Japanese companies use patent maps in one way or another. The
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patent maps might need by all divisions of companies, including the corporate control department, technology development department, and intellectual property management department [5].
3 Patent Mapping Process The patent mapping process of this study, as shown in Fig. 1, is divided into three stages. The first stage is to identify suitable types of maps through needs identification and analysis of demand characteristics. The second stage of patent mapping is data collection, analysis, and processing of patent data. The information contained in a patent document varies widely but falls into two categories: structured information, which is semantically consistent information and format between internationally recognized patents, such as patent numbers, filing date, owner, author, etc. and unstructured information are free texts, they have different lengths and content such as claims, patent descriptions, patent abstracts, … Therefore, data analysis and processing divide into two processes: (1) quantitative analysis is to use structured patent information, usually on the first page of the patent document, then classify, arrange, and make statistics of the information; (2) Qualitative analysis is the
Fig. 1. Proposed patent mapping process.
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use of technical information (unstructured) of the patent document, using text mining algorithms, processing, and analyzing valuable information. The results of qualitative and quantitative analysis can be combined to create a map that has more dimensions and more useful information. The third stage of patent mapping is to visualize the results and generate the patent map. Results visualization is the process of displaying the information given by data processing and analysis in the form of graphs, charts, or tables. Conventionally, patent maps have two forms, the first is for management organizations, with the function being to analyze general information about the industry, technology area, market, status, and development potential of technology to have a basis for giving overall orientations and strategies. This type of patent map is built through visualized information and analyzing this information to form appropriate reports through automated software or experienced experts. The second type of patent map serves businesses or research institutions. This map needs specific information in a narrow range of technologies or patents that directly concern research and development or analysis technology information of existing or potential competitors/partners. This patent map needs more information about important patents, which are the original patents to support research and development, the patents that have the most impact on the technology sector, or patents close to the technological development orientation of the enterprise or research organization. It is necessary to identify the technology, determine the core technology features from these patents, and extract useful information for the enterprise or research organization.
4 IoT in Agriculture Today, the trend of global competition is increasing in all fields as well as agriculture. Therefore, innovation for success is inevitable. Keeping pace with the tendency of the fourth industrial revolution, IoT in agriculture (examples in Fig. 2) is increasingly interested and promoted in research and development. Some of the benefits of IoT-enabled agriculture can be summarized as follows.
Fig. 2. IoT enabled agriculture [6].
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Currently, the world is facing climate change, so the weather is harsh and erratic. IoT-enabled agriculture brings outstanding efficiency in such situations. IoT controls products and farm conditions such as weather, humidity, temperature, air quality, soil quality, etc. in real-time, gets insights faster, and combines with intelligent prediction to provide optimal solutions or prevent adverse effects. Using IoT in agriculture will help the system accurately and optimally allocate resources for the production process based on synchronously collected data. As a result, physical resources such as land, water, and energy are used optimally and efficiently. In addition, IoT and automation systems in agriculture also can cut down labor costs. IoT enabled agriculture to control and optimize the number of fertilizers and pesticides and control the harvest time to ensure clean, better quality, and safe products. The system also helps connect and synchronize with the country’s IoT system and bring fresh products to consumers. Vietnam is one of the agricultural producing countries with large output and is heavily affected by climate change. Vietnam is determined to carry out industrialization and apply the achievements of the industrial revolution 4.0. Therefore, the trend of smart agriculture and especially the application of IoT in agriculture is inevitable. Patent mapping and analysis of patents for IoT in agriculture provide a panoramic picture and forecast technology development trends in the world, which will be an important basis for orienting research and strategy to develop this field in Vietnam.
5 Patent Map Analysis for IoT in Agriculture The research team has collected nearly 3000 patent documents in the field of IoT in agriculture from several powerful patent databases, using the specialized patent mapping tool that the team has successfully built to create the patent map. In this paper, patent map-based analysis help identifies trends in technology development and policymaking. 5.1 Overall Development Trend Figure 3 shows the publication patents by year. It is easy to realize that this is a very new research trend in the world. Studying in this field has been only interested within the last 15 years. The tendency of patent publication is rocketed in the past 5 years from 2017 with only 111 patent documents published to the years 2020 and 2021 there were 943 and 1021 patent documents published, respectively.
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Fig. 3. Publication patents by year.
The legal status of patents is shown in Fig. 4. Pending patents accounted for a massive proportion with 60.37%. Meanwhile, expired patents only account for a meager percentage of 1.60%. The ratio of granted patents is 29.13%. The percent of lapsed patents and revoked patents is 5.70% and 3.83%, respectively.
Fig. 4. The legal status of patents.
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The total number of pending and protected patents accounted for nearly 90%, especially the number of pending patents accounted for more than 60%, which shows that IoT technology in agriculture is emerging and in the early stages of technology development. From the data of publication patents by year and the legal status of patents, it can be concluded that IoT in agriculture is within emerging and growth phases of the technology cycle and is a field getting of great interest in the world currently. There is an enormous growth potential for researchers. 5.2 Countries and Assignees Figure 5 shows the countries where patents were publication and statistics the number of published patents in each country. From the figure, the top four countries in the patent publication include China, the United States, India, and South Korea, with 701, 581, 478, and 270 published patents, respectively.
Fig. 5. Publication country of patents.
Figure 6 shows the number of patent documents of the top 20 assignees, Fig. 7 shows the combination of the research age on the technology and the number of patent families held by the assignees. It is easy to see that LG Electronics Inc. currently grips a superior number of patent documents compared to the remaining companies, organizations, and individuals with a total of 540 patent documents, more than fourteen times compared to the second assignee Huawei Technologies Co., LTD. The number reflects that LG Electronics Inc. has focused on investing and developing very strongly in IoT-enabled agriculture and is the leading company in this field. Figure 7 shows more detail about the research and development of IoT in agriculture by the leading owners. LG Electronics Inc. is with a superior number of patents compared to the remaining companies mentioned above. In addition, the company also has a research age of up to 17 years, just behind IBM Corporation (32 years) and Samsung Electronics Co., LTD. (20 years); however, those companies own only 29 and 10 patent documents, respectively. Beijing Xiaomi Mobile Software Co., Ltd., Sony Semiconductor Solution Corporation, and Salesforce.com, Inc. are emerging as young companies in IoT-enabled Agriculture with 1, 2, and 3 years of research but strongly focusing on investment in research and development of this field with 13, 10 and 15 patent documents, respectively.
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For a better understanding of the technology and product development trends of each of these companies, a deeper analysis of the patents held by these companies is required. However, these are in-depth studies in follow-up research that are not covered in this paper.
Fig. 6. Patent assignees.
Fig. 7. Patent assignees by research age and number of patent families.
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5.3 Technology Forecasting Figure 8 shows a breakdown of patents applied in specific technology areas. Technology is divided into 35 sectors according to the WIPO division in reference [7]. Patents in the field of IoT in agriculture are used in 33 out of 35 technology sectors. The most important and prominent are eight technology sectors including digital communication (965 patent documents), telecommunications (848 patent documents), other special machines (551 patent documents), IT methods for management (509 patent documents), computer technology (438 patent documents), control (419 patent documents), measurement (287 patent documents), and electrical, machinery, apparatus, energy (143 patent documents). As can be seen, IoT in agriculture focuses on four main technology areas: communication (digital communication and telecommunication), using information technology for management, service tools (machines and computer technology), measurement and control. The field of communication in the area receives leading interest. However, there are also areas of technology that have not received much attention as transport, environmental technology, food chemistry, etc. These are potential places for researchers to exploit in the future.
Fig. 8. Patents in technology sectors.
Usually, to identify the technologies included in patents, people often use the patent classification such as International Patent Classification (IPC) or Cooperative Patent Classification (CPC) for grouping technology. However, these classifications are generally intended for use by intellectual property professionals only. Therefore, the research team used text mining algorithms to generate concepts and concept clusters in the patent map, so that more people can read and understand the technological trends being developed. Figure 9 shows patent maps with two layers of technological concept clusters (the area of the representation domain represents the number of patent families). IoT in agriculture is currently being developed in eight major technology areas (first layer of technology concept clusters), including wireless communication, processor, terminal, user equipment, base station, transceiver, wireless devices, and internet of thing (or IoT).
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The significant technology trends to be mentioned in the wireless communication sector in agriculture include a processor (294 patent families), a wireless device (198 patent families), terminal (182 patent families), base station (162 patent families), transceiver (160 patent families), user equipment (157 patent families), wireless signal (74 patent families), etc. The important technology trends to be mentioned in the processors used in IoTenabled agriculture include user equipment (144 patent families), terminal (141 patent families), a base station (131 patent families), transceiver (128 patent families), wireless devices (102 patent families), etc.
Fig. 9. Patent concept clusters.
The big technology trends to be mentioned in the terminal in IoT-enabled agriculture include a base station (115 patent families), user equipment (108 patent families), transceiver (97 patent families), wireless signal (63 patent families), transmission (37 patent families), setting information (37 patent families), etc. Figure 9 shows that the five most important technology areas in IoT-enabled agriculture are processors in wireless communication, wireless device, terminal in wireless communication, a base station in wireless communication, and a transceiver in wireless communication.
6 Conclusions The article introduced an overview of the patent map and patent mapping process proposed by the research team. The field of IoT in agriculture is an important field and an inevitable trend of Vietnam. This paper conducts general and specific reports on IoT in agriculture. IoT in agriculture has been invested heavily in research and development for the past five years. As this technology is in the early stages of development, there are plenty of opportunities to focus investments for research and development. Four
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countries that possess a massively published patent include China, the United States, India, and South Korea. LG Electronics Inc. is the leader in both research results and development experience in technology. IoT research in agriculture focused on eight key technology sectors in which communication technologies are gaining the most attention. Some potential technology fields as transportation, environmental technology, food chemistry but not yet focused. Through analysis of the technology concept clusters map, the five most important technology areas of IoT-enabled agriculture include processors in wireless communication, wireless device, terminal in wireless communication, a base station in wireless communication, and transceiver in wireless communication. Acknowledgments. This research is carried out within the framework of project no.04.2020M008 funded by the Ministry of Science and Technology (MOST), Vietnam. The authors would like to thank MOST for the support.
References 1. OECD: Ministerial report on the OECD Innovation Strategy (2008) 2. Wang, F.-S.: Field study of patent strategies from patent map on big data: an empirical case of big data application platform in Taiwan. In: International Conference on e-Commerce, e-Administration, e-Society, e-Education, and e-Technology, Nagoya, Japan (2014) 3. Chiu, Y.-J., Ying, T.-M.: A novel method for technology forecasting and developing r&d strategy of building integrated photovoltaic technology industry. Math. Prob. Eng. 2012, 24 (2012) 4. Trippe, A.: Guidelines for Preparing Patent Landscape Report, World Intellectual Property Organization (WIPO) (2015) 5. Suzuki, S.-I.: Introduction to Patent Map Analysis, Japan Patent Office, Asia-pacific Industrial Property Center (2011) 6. E-agriculture, IoT in Agriculture Solution from AgTech Startups, Food and Agriculture Organization of the United Nations. https://www.fao.org/e-agriculture/news/lot-agriculture-soluti ons-agtech-startups 7. Schmoch, U.: Concept of a Technology Classification for Country Comparisons, Final Report to the World Intellectual Property Organisation (WIPO) (2008)
Modelling for Sliced Avocado Drying in Modified Air Thi Thu Hang Tran(B) School of Heat Engineering and Refrigeration Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. A numerical model of avocado slice drying in modified air is presented in this work. In this model, Fick’s law is applied, and effective diffusivity is determined by experiments. The simulation results are successfully validated by experiment of single sliced avocado samples. By this model, partial distributions of moisture and temperature inside the sample are examined. Results show that the moisture content is significantly maldistributed while spatial temperature gradient can be negligible. Two drying periods which are constant and falling drying periods can be clearly seen. In the first drying stage, sample temperature is maintained almost constant because heat transfer is enough for only evaporation while in the second period, temperature raises due to the increase in the internal mass transfer resistance. Keywords: Modelling · Avocado drying · Modified air · Effective diffusivity · Fick’s law
1 Introduction Recently, carbon dioxide or nitrogen or air with adjustable components has been replaced for hot drying air to avoid the oxidation reaction of drying material [1]. This reduces the browning of drying material and improves the keeping of bioactive ingredients. Besides, this drying method also helps to shorten drying time and to reduce energy consumption compared with normal air [2–4]. Drying often includes complicated heat and mass transfers in both microscale and macroscale. Modeling for drying kinetics of sliced food has been mentioned in several publications. In which, most of them are experimental models [5–8]. These models often describe averaged moisture content versus time based on experimental data. Experimental models are simple, but it is only sufficient for design and analysis purposes. To develop completely drying kinetics to examine the transportation kinetics inside the sample, mathematical models need to be built. In terms of numerical models for drying of sliced food or single layer food, there have been several publications [9–11]. In which, most of them were built by using Fick’s law, then they were validated successfully. However, these models were implemented for typical material dried in hot air and applied for convectional drying or hybrid dryers. For modified air drying, from the author knowledge, there have been no mathematical models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 991–1000, 2022. https://doi.org/10.1007/978-981-19-1968-8_84
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This work belongs to a series of research for modified air drying to extend the application ability of modified air for agricultural products of Vietnam. In which, one mathematical model for modified air drying is presented. The model accounts for spatial distributions of moisture and temperature inside the layer. The results are validated by comparison with experimental data. Distributions of temperature and moisture are examined at different drying conditions to evaluate the effects of drying conditions on the drying kinetics.
2 Model Description 2.1 Mass and Energy Conservation Equation The object of model is one single thin layer of avocado, in which the thickness is much smaller than the length and the width of sample. Thus, gradients of moisture and temperature with thickness is higher than other dimensions, or model is considered as one dimension model. The sample is also assumed as isotropic and homogenous, and the effective diffusivity is dependent on moisture distribution. The shrinkage is negligible in this model. The moisture transport in the product is described as the moisture density change is due to the water diffusion flux as [12]: ∂(ρ0 X ) − ∇ · −Deff ∇(ρs X ) = 0 ∂τ
(1)
w In which, ρ s is solid density (kg/m3 ), τ is time (s). X = M Ms is dry basic moisture content (kg water/kg solid), Deff is effective moisture diffusivity (m2 /s). Similarly, the heat transfer equation inside the product is [10]:
ρeff ceff
∂t − ∇ · λeff ∇t = 0 ∂τ
(2)
where ρ eff , ceff , and λeff are the effective density (kg/m3 ), effective heat capacity (J/kgK) and effective thermal conductivity (W/mK) of drying product. Boundary conditions at the sample surface are written as: −ρs .Deff · −λeff ·
∂X = −β · ρg Ysurf − Yg,b ∂x
∂t = −α tf − tsurf + hv · β · ρg Ysurf − Yg,b ∂x
(3) (4)
where α (W/m2 K) and β (m/s) are the convective heat and mass transfer coefficients. t f is the gas temperature (o C), t surf is the surface temperature of sample (°C), Δhv is the evaporation enthalpy (J/kg). Y surf and Y d,b are the moisture content on the sample layer surface and gas. Ysurf =
Pv,surf μv · μg P0 − Pv,surf
(5)
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Yg,b =
Pv,g μv · μg P0 − Pv,g
993
(6)
In which, μv and μg are the mass of 1 kmol vapor and gas correspondingly. P0 is total gas pressure, Pv,surf , and Pv,g are vapor pressures on the sample surface and in the bulk gas. Partial vapor pressure on the sample surface is determined by sorption isotherms [13]: 1 if X ≥ Xirr Pv,surf = (7) X X + a) − a · (1 Pv,sat (T ) Xirr Xirr if X < Xirr X irr = 0.411 is the irreducible moisture content, a = 0.9 is experimental factor, Pv,sat is partial saturation vapor on the surface.
3984.2 (8) Pv,sat = 133.32 exp 18.5848 − 233.426 + t 2.2 Constitutive Relationships The density and heat capacity of the dry solid are calculated based on the assumption of perfect mixing ρs =
1 − xw,0 xw,0 ρw (t0 )
+
xp ρp (t0 )
+
xf ρf (t0 )
+
xc ρc (t0 )
cs (t) = xp cp (t) + xf cf (t) + xc cc (t)
(9) (10)
The effective density, effective heat capacity of the wet product are determined by ρeff = ρs (1 + X ) ceff (t) =
(11)
ρs · cs (t) + ρs · X · cw (t) ρs (1 + X )
(12)
The effective thermal conductivity is computed based on the Omic law with parallel configuration λeff (t) = εp .λp (t) + εf λf (t) + εc λc (t) + εw λw (t)
(13)
In Eqs. 9–13, xi and εi are mass and volumetric fractions of component i. The mass fraction of the moisture, protein, fat and carbohydrate of fresh avocado are presented in Table 1, 2, 3 and 4. In Eqs. 3 and 4, the heat and mass transfer coefficients are computed from Sherwood and Nusselt numbers as [15]. Sh = 2 + 0.6 · Re1/2 · Sc1/3
(14)
1/3
Nu = 2 + 0.6 · Re1/2 · Pr
(15)
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T. T. H. Tran Table 1. Components of fresh avocados. Component
Percentage, %
Water
87.65
Protein
0.95
Fat
7.35
Carbohydrate
0.5
Table 2. Component density, kg/m3 . Component
Density, kg/m3
Water
9.972 × 102 + 3.14 × 10−3 t – 3.757 × 10−3 t2
Protein
1.3299 × 103 – 5.184 × 10−1 t
Fat
9.256 × 102 – 4.176 × 10−1 t
Carbohydrate
1.599 × 103 – 3.105 × 10−1 t
Table 3. Component heat capacity, kJ/kgK. Component
Heat capacity, kJ/kgK
Water
4.1289 – 9.086 × 10−5 t + 5.473 × 10−6 t2
Protein
2.008 + 1.2089 × 10−3 t – 1.313 × 10−6 t2
Fat
1.9842 + 1.4733 × 10−3 t – 4.8 × 10−6 t2
Carbohydrate
1.5488 + 1.9625 × 10−3 t – 5.94 × 10−6 t2
Table 4. Component thermal conductivity, W/mK. Component
Thermal conductivity, W/mK
Water
0.57109 + 1.76 × 10−3 t − 6.7036 × 10−6 t2
Protein
0.17881 + 1.196 × 10−3 t – 2.718 × 10−6 t2
Fat
0.1807 – 2.76 × 10–4 t – 1.7749 × 10−7 t2
Carbohydrate 0.2014 + 1.3874 × 10−3 t – 4.331 × 10−6 t2
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The mass and energy conservation equations are solved numerically by the volume element method by running a Matlab code. The detailed description of the numerical methodology can be found in Ref. [16].
3 Results and Discussion 3.1 Model Validation To validate the proposed model, a serial of drying experiments is performed with the experimental apparatus shown in Fig. 1. A detailed description of the experimental setup can be found in [17]. Experiments are carried out for thin sliced Booth avocado (from Daklak – Vietnam) (2 mm of thickness). The drying conditions are presented in Table 5.
Fig. 1. Schematic diagram of experiment system.
The effective moisture diffusivity which is unknown in Eq. 1 is estimated by using the inversed method by minimizing the error between the numerical and experimental moisture content evolutions. Table 5 shows the effective moisture diffusivity at different drying temperatures.
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Velocity (m/s)
Deff. 10–12 (m2 /s)
60
0.04
13
80
0.04
69.29
120
0.04
125.3
140
0.04
209.1
Fig. 2. Moisture content versus time obtained with drying temperature of 60 °C
Results of moisture content versus time of simulation and experiment are presented in Figs. (2) and (3). It can be seen the good agreement between simulation and experiment. All simulation data are near the average empirical value and are in the range of experimental data. The model will be applied to simulate the temperature and moisture distribution in the next section.
Fig. 3. Moisture content versus time obtained with drying temperature of 120 °C
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3.2 Spatio-temporal Temperature and Moisture Content Distributions Spatial distributions of moisture content and temperature inside the sample are shown in Figs. (4), (5) and (6). In all cases, surface moisture content reduces dramatically to equilibrium value for several minutes at low temperature or several seconds at high temperature. However, the moisture content at the center changes slowly. For example, at 60 °C, it takes approximately 700 s to commence the dehydration at the sample center (Fig. 7).
Fig. 4. Distributions of moisture content obtained with drying temperature of 60 °C
Fig. 5. Distributions of moisture content obtained with drying temperature of 120 °C
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Fig. 6. Distributions of temperature obtained with drying temperature of 60 °C
Fig. 7. Distributions of temperature obtained with drying temperature of 120 °C
In contrast to moisture, the temperature distribution is almost uniform. The spatial distribution of temperature is very small at both high and low drying temperatures. This can be explained by the high thermal conductivity and thin thickness. There are two drying stages that can be observed. Firstly, the temperature remains almost constant. Because in this period, the heat from the gas is enough only for evaporation from the surface of the avocado slices so drying rate is constant in time. After that, it is more and more difficult for internal moisture to move to the surface and evaporation decreases versus time. Thus, temperature increases gradually to the gas time.
4 Conclusion In this work, one novelty numerical model is built and validated. In this model, spatial distributions of moisture and temperature are mentioned. In which, effective diffusivity is determined by experiment by a simple equation. There is good agreement between experiment and simulation. Simulation results show that the gradient of moisture inside
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the layer avocado layer is high; however, the temperature is almost uniform. These distributions should be checked by experiments in the future. This model is the first step to evaluate the application ability of carbon dioxide as the drying ambient of sliced food. In the next step, the simulation model will be simplified to combine with Computation Fluid Dynamic (CFD) tool to simulate the pilot dryer. Acknowledgments. This research is funded by Vietnam Ministry of Education and Training under grant number B2022-BKA-11.
Nomenclature α β c D Δhv λ ρ t τ μ X x Y b
Heat transfer coefficient, W/m2 K Mass transfer coefficient, m/s Heat capacity, J/kgK Diffusivity, m2 /s Evaporation enthalpy, J/kg Thermal conductivity, W/mK Density, kg/m3 Temperature, °C Time, s Mass of 1 kmol, kg/kmol Dry basic moisture content, kgw/kgs Position Moisture content of gas, kgw/kg dry air Bulk
Subscribe eff g 0 sat surf v
Effective Gas Initial Saturation Surface Vapor
References 1. Mujumdar, A.S., Law, C.L.: Drying technology: trends and applications in postharvest processing. Food Bioprocess. Technol. 3, 843–852 (2010) 2. Braga, A.M.P., Pedroso, M.P., Augusto, F., Silva, M.A.: Volatiles identification in pineapple submitted to drying in an ethanolic atmosphere. Dry. Technol. 27, 248–257 (2009) 3. Kudra, T., Poirier, M.: Gaseous carbon dioxide as the heat and mass transfer medium in drying. Dry. Technol. 25, 327–334 (2007) 4. Santos, P.H.S., Silva, M.A.: Kinetics of L-ascorbic acid degradation in pineapple drying under ethanolic atmosphere. Dry. Technol. 27, 947–954 (2009)
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5. Midilli, A., Kucuk, H., Yapar, Z.: A new model for single-layer drying. Dry. Technol. 20, 1503–1513 (2002) 6. Özdemir, M., Seyhan, F.G., Özde¸s Bodurb, A., Onur Devres, Y.: Effect of initial moisture content on the thin layer drying characteristics of hazelnuts during roasting. Dry. Technol. 18(7), 1465–1479 (2000) 7. Rapusas, R.S., Driscoll, R.H.: The thin-layer drying characteristics of white onion slices. Dry. Technol. 13, 1905–1931 (1995) 8. Yaldýz, O., Ertekýn, C.: Thin layer solar drying of some vegetable. Dry. Technol. 19, 583–597 (2001) 9. Akpinar, E., Midilli, A., Bicer, Y.: Single layer drying behaviour of potato slices in a convective cyclone dryer and mathematical modeling. Energy Convers. Manage. 44, 1689–1705 (2003) 10. Agrawal, S.G., Methekar, R.N.: Mathematical model for heat and mass transfer during convective drying of pumpkin. Food Bioprod. Process. 101, 68–73 (2017) 11. Doymaz, ˙I.: Evaluation of some thin-layer drying models of persimmon slices (Diospyros kaki L.). Energy Convers. Manage. 56, 199–205 (2012) 12. Kucuk, H., Midilli, A., Kilic, A., Dincer, I.: A review on thin-layer drying-curve equations. Dry. Technol. 32, 757–773 (2014) 13. Mujumdar, A.S.: Handbook of Industrial Drying. CRC Press, Boca Raton (2014) 14. ASHRAE, ASHRAE Hanbook refrigeration, ASHRAE, 1791 Tuillie Circle, N.E, Atlanta, GA 30329 (2014) 15. VDI-Gesellschaft Verfahrenstechnik und Chemieingenieurwesen, VDI Heat Atlas, 2nd edn. Springer, New York (2010).https://doi.org/10.1007/978-3-540-77877-6 16. Hampel, N., Le, K.H., Kharaghani, A., Tsotsas, E.: Continuous modeling of superheated steam drying of single rice grains. Dry. Technol. 37, 1583–1596 (2019) 17. Nguyen, H.N., van de Steene, L., Le, D.D.: Kinetics of rice husk char gasification in an H2 O or a CO2 atmosphere. Energy Sources Part A Recovery Utilization Environ. Eff. 40, 1701–1713 (2018)
Experimental Study on Sliced Avocados Drying in Modified Air Van Thuan Nguyen1 , Thi Thu Hang Tran1(B) , Viet Dung Nguyen1 , and Hong Nam Nguyen2 1 School of Heat Engineering and Refrigeration Engineering, Hanoi University of
Science and Technology, Hanoi, Vietnam [email protected] 2 Energy Department, University of Science and Technology of Hanoi, Hanoi, Vietnam
Abstract. Avocado which is an attractive healthy fruit has always been of interest for the application of post-harvesting technologies. In this work, an experimental study of modified air for sliced avocado is carried out to examine the effect of operating conditions on the drying kinetics and product appearance. From the product quality evaluation, the results show that despite the moderate drying temperature, the modified air drying gives a good appearance product. However, at a temperature above 100 °C, there is inflation occurring on the surface and the color is brown. Besides, it is observed that the drying is faster at higher temperatures. The empirical formula for normalized moisture content versus time is successfully built and validated. Keywords: Avocado drying · Modified air · Product quality · Empirical model
1 Introduction Avocados have a range of health benefits, including improving digestion, decreasing the risk of depression, and protecting against cancer [1]. However, avocados are the climacteric fruit [2] with the fresh preservation time is only several days under the proper conditions [3]. In the production chain, this fruit is often ripened by ethylene then wrapped in newspaper to deliver daily [2]. These treatments can be expensive and be effective for only using fresh fruit. Drying is one of the most common postharvest processing techniques which can help to prolongate the preservation time. However, it should be noted that the avocado slices can turn brown rapidly after a short time exposing them to the air. It is due to the reaction between the polyphenol oxidase enzyme with the oxygen [4]. The brown part of avocado might look unappetizing and can taste bitter. This harmful oxidation reaction can be avoided if the concentration of oxygen in the drying agent reduces. Thus, vacuum freeze-drying is the most effective thermal dehydration technique for avocado preservation [2, 5, 6]. Although the nutrition and taste of the product can be guaranteed, this freeze-drying cannot be applied in the industrial sector due to its disadvantages such as long time time, and high energy consumption, high initial investment cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1001–1011, 2022. https://doi.org/10.1007/978-981-19-1968-8_85
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The modified air-drying technique where the oxygen gas is replaced by inert gases. Indeed, the modified air drying agent is carbon dioxide (CO2 ), nitrogen (N2 ), or a gas mixture with a low oxygen ratio. Thus, it can be expected that the modified drying technique can be promisingly applied for the avocado dehydration process. Additionally, the modified air drying also yields high product quality and low energy consumption [7]. This method has been applied for a range of product likes strawberry, ginger, pineapple, guava, and papaya [8–12]. However, there is a lack of systematic research on the drying kinetic, so this limits the application ability of modified air drying. In this work, an experimental system is built up to perform the drying process of avocado slices at high temperatures in the CO2 environment. From the sample mass evolutions, the drying kinetic is analyzed. Additionally, the empirical drying model is proposed to predict the drying process at various drying temperatures and velocities. The product quality is assessed by the color change and the morphology of the sample. Based on the compromising of drying time and product quality, a suitable drying condition is proposed for industrial applications.
2 Experiment Methodology and Data Evaluation 2.1 Experiment Description The diagram of the experiment system is illustrated in Fig. 1. This setup is built for a universal proposed which can also be used for analyzing the gasifications, pyrolysis processed [13]. The main part is a cylindrical drying chamber made by a ceramic tube with a diameter of 75 mm and a length of 1110 mm. The CO2 gas is stored in the tank and flows into the chamber via a mass flow rate controller. By adjusting the mass flow rate, the CO2 velocity is controlled. The drying agent is heated in preheating section to the designed temperature before interacting with the drying sample. Furthermore, the outer surface of the tube is covered by an electrical heater which can help to maintain a uniform temperature in the chamber. Both preheating and drying chamber are insulated for preventing heat leakage to the ambient. The drying product is put on one perforated disk which enables the drying agent to flow with the low-pressure drop. An accurate balance with an uncertainty of 0.1 mg is used to record continuously sample mass. The mass evolution of the sample is logged in the PC. The experiment procedure is performed by following steps: • The fresh avocado which comes from Daklak province, Vietnam is purchased from the local supermarket. It is cleaned with water and is stored in the fridge. • The drying chamber is heated to the set-up temperature. • CO2 with the specific mass flow rate is heated up to the drying temperature, then flows into the drying chamber. The drying process commences when the temperature discrepancy is smaller than 0.5 °C. • An avocado slice with a thickness of 2 mm is put on the disk and the balance is on. The disk is lifted to the center of the drying chamber. • The mass of the sample is logged with an interval time of 5 s. The drying process ends when the mass of the sample does not change further.
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Fig. 1. Schematic diagram (a) and the image (b) of the experiment system (reprinted of [13]).
• The dried product is collected to analyze the surface by Scanning Electron Microscope (SEM) and Canon camera. The color of the dried product is analyzed by using a digital colorimeter WR-10QC. Each drying experiment is repeated at least 5 times to eliminate random error. Additionally, the solid mass fraction of fresh and ripen avocados is determined by using a GMP 500 drying chamber. The fresh sample is dried at 120 °C in 2 h and the mass of the remained sample is evaluated. It results in a solid mass fraction of 12.35% which is used for the rest calculation of this paper.
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Drying experiments were performed with four drying temperatures T g of 60 °C; 80 °C; 120 °C; 140 °C and two gas velocities ω of 0.03 m/s; 0.04 m/s. The drying temperatures are chosen to evaluate the drying process for both low temperature (60 °C and 80 °C) and elevated temperature (120 °C and 140 °C) regimes. The velocities are limited at low values to keep the internal and external heat and mass transfer resistances in the same order of magnitude. 2.2 Data Analyzing From the mass evolution of the sample, the temporal moisture ratio is computed as MR =
Mt − Me M0 − Me
(1)
where, M0 , Mt , and Me denote the initial, temporal, and equilibrium moisture mass of the sample, respectively. In terms of product color, the difference between the color of the fresh and dried samples is determined by using the digital colorimeter WR-10QC. The sample color is characterized in CIE L(brightness) * a (green) * b (red) coordinates. The color difference is expressed as (2) E = (L − L0 )2 + (a − a0 )2 + (b − b0 )2 where the L, a, b are obtained with the dried sample and L 0 , a0 , b0 are measured with the fresh sample.
3 Results and Discussion 3.1 Drying Kinetic The influences of gas temperature on evaporation at the same drying velocity are presented in Figs. 2 and 3. It can be seen that the drying time varies in range from 25 min to 90 min depending on the drying conditions. Naturally, the drying process is faster at a higher temperature and drying ceases for a shorter time. Additionally, the drying speed becomes lower with the increase of drying time. It implies that the internal mass transfer resistance grows up when moisture content decreases. Additionally, the faster drying velocity also accelerates drying because the heat and mass transfer on the sample surface is enhanced if the gas velocity increases. However, at high temperatures, the effect of velocity is not clear due to the difference between gas temperature and sample temperature being high enough to control the heat and mass transportations.
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Fig. 2. Evolution of MR versus time at ω = 0.03 m/s.
Fig. 3. Evolution of MR versus time at ω = 0.04 m/s.
3.2 Product Quality It is already known that drying time is very fast incorporated with high drying temperatures. However, avocados are a sensitive thermal material. Thus, the influences of temperature on the color and surface characteristics are evaluated. Figure 4 shows the pictures of a dried product obtained from SEM. Although the SEM picture does not directly reflect the product quality, the sample morphology may help to indirectly predict the porosity and the hardness of the dried product. As can be seen, there are cavities on the surface of the sample dried at 60 °C and 80 °C while the smooth surface is monitored in samples dried at 120 °C and 140 °C. However, samples dried at high temperatures have several breakages observed on the surface. These can be explained by the slow evaporation and the slight shrinkage of solid parts, these phenomena give the small hollows on the surface at low drying temperature. However, at high drying temperatures, the fast heat flow rate transferred to the sample makes fast moisture evaporated on and inside the sample. Thus, the dried crust may appear early on the surface and it makes an increase in mass transfer resistance from the inside sample to the surface. A large amount of vapor accommodated inside the sample forces the surface inflation results in the smooth skin and corruption of the surface.
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Fig. 4. Product surface (scale: 300:1): (a) T g = 60 °C; (b) T g = 80 °C; (c) T g = 120 °C; (d) T g = 140 °C.
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Fig. 5. Images of (a) fresh and dried samples: (b) T g = 60 °C; (c) T g = 80 °C; (d) T g = 100 °C; (e) T g = 120 °C.
Images and corresponding color differences of samples are presented in Table 1 and Fig. 5. The higher drying temperature gives the higher difference between the original sample and the dried products. However, this difference at a temperature below 100 °C is not significant. The products in these cases are observed still bright. At temperatures higher than 100 °C, the brownness becomes much more serious if the temperature increases. It may be explained that samples are burned at elevated temperatures. Combination the analysis results of drying kinetic, SEM image, and color, the drying temperature T g of 800 C can be considered as a suitable condition for avocado drying.
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This temperature offers relatively fast evaporation and good product quality. However, to evaluate completely the application ability of modified air drying, experiments need to carry for both modified hot air, normal hot air, and freezing drying. Table 1. Difference between the color of fresh and dried samples. E
Samples Fresh sample
0.000
T g = 60 °C
14.870
T g = 80 °C
12.857
T g = 120 °C
26.948
T g = 140 °C
46.074
4 Empirical Modeling for Drying Kinetics Since the experiments are performed with specific conditions, a mathematical model should be developed to generalize the obtained data and to extend its applicability for the real dryer evaluation. In this work, the Lewis model is employed to describe the evolution of the moisture ratio over drying time. In this model, the dependence of moisture ratio MR on the drying time is expressed as MR = exp(−kτ )
(3)
where k is a constant factor. Table 2. Values of k and the model goodness obtained with the gas velocity of 0.03 m/s. Temperature
K
R2
T g = 140 °C
0.0021
0.9813
T g = 120 °C
0.0017
0.9897
T g = 80 °C
0.0007
0.9922
T g = 60 °C
0.0006
0.9953
The value of k is determined by running an optimization routine where the sum of squared error between predicted and experimental data is minimized. The optimized results are presented in Tables 2 and 3. As can be seen, the magnitude of k varies when the drying conditions change. Thus, to extend the applicability of the proposed model, a correlation between k factor and the temperature and velocity of the drying agent is proposed as k = −5.8 × 10−4 + 2.01 × 10−5 Tg − 3.7 × 10−3 ω
(5)
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Table 3. Values of k and the model goodness obtained with the gas velocity of 0.04 m/s. Temperature
K
R2
T g = 140 °C
0.0023
0.9836
T g = 120 °C
0.0016
0.9917
T g = 80 °C
0.0009
0.9919
T g = 60 °C
0.0005
0.9968
The validity of Eq. 5 is checked by the discrepancy between the experimental and predicted moisture ration evolutions. The exemplary comparison is shown in Figs. 6 and 7. Furthermore, a parity plot that shows the overall discrepancy is presented in Fig. 8. The good agreement between the numerical and experimental observation implies that the Lewis model with the k factor computed by Eq. 5 can be used reliably.
Fig. 6. Comparisons of experiment and model obtained with T g = 80 °C and ω = 0.04 m/s.
Fig. 7. Comparisons of experiment and model obtained with T g = 140 °C and ω = 0.04 m/s.
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Fig. 8. Parity plot showing the experimental and predicted moisture ratio of all experiments.
5 Conclusion To prolongate the storage time and maintain the product quality of avocado, an experimental study of the drying process of avocado slices in the pure CO2 environment is conducted in this paper. The results show that dried products at a temperature below 100 °C have a good appearance with small cavities on the surface while at high-temperature products may be burned but the surface is smooth. In terms of drying kinetics, drying is faster at higher drying temperature and gas velocity. However, at high temperatures the effect of gas velocity is small. To evaluate completely the application ability of modified air, experimental comparisons between normal air drying, modified air, freezing drying should be carried out. Besides, numerical or semi-empirical modeling also should be implemented to examine the drying kinetics as well as the instruction morphology of different products dried in modified air. Acknowledgments. This research is funded by the Vietnamese Ministry of Education and Training under grant number B2022-BKA-11.
References 1. Dreher, M.L.: Whole fruits and fruit fiber emerging health effects. Nutrients 10, 1833 (2018) 2. Mujaffar, S., Dipnarine, T.-A.: The production of a dried avocado (Persea americana) powder. In: Proceedings of the International Conference on Emerging Trends in Engineering & Technology (IConETech-2020), Faculty of Engineering, The University of the West Indies, St. Augustine, pp. 44–54 (2020) 3. Chen, J., Liu, X., Li, F., Li, Y., Yuan, D.: Cold shock treatment extends shelf life of naturally ripened or ethylene-ripened avocado fruits. PLoS ONE 12, e0189991 (2017) 4. Kahn, V.: Polyphenol oxidase activity and browning of three avocado varieties. J. Sci. Food Agric. 26, 1319–1324 (1975) 5. M.C. Castañeda-Saucedo, M.C., et al.: Effect of freeze-drying and production process on the chemical composition and fatty acids profile of avocado pulp. Revista chilena de nutrición 41, 404–411 (2014)
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6. Souza, D.S., Marques, L.G., Edelvio de Gomes, B., Narain, N.: Lyophilization of avocado (Persea americana Mill.): effect of freezing and lyophilization pressure on Antioxidant Activity, Texture, and Browning of Pulp. Drying Technol. 33, 194–204 (2015) 7. Mujumdar, A.S., Law, C.L.: Drying technology: trends and applications in postharvest processing. Food Bioprocess Technol. 3, 843–852 (2010) 8. Braga, A.M.P., Pedroso, M.P., Augusto, F., Silva, M.A.: Volatiles identification in pineapple submitted to drying in an ethanolic atmosphere. Drying Technol. 27, 248–257 (2009) 9. Cam, I.B., Basunal Gulmez, H., Eroglu, E., Topuz, A.: Strawberry drying: Development of a closed-cycle modified atmosphere drying system for food products and the performance evaluation of a case study. Drying Technol. 36, 1460–1473 (2018) 10. Hawlader, M.N.A., Perera, C.O., Tian, M.: Comparison of the retention of 6-Gingerol in drying of ginger under modified atmosphere heat pump drying and other Drying Methods. Drying Technol. 24, 51–56 (2006) 11. Hawlader, M.N.A., Perera, C.O., Tian, M., Yeo, K.L.: Drying of guava and papaya: Impact of different drying methods. Drying Technol. 24, 77–87 (2006) 12. Santos, P.H.S., Silva, M.A.: Kinetics of L-Ascorbic acid degradation in pineapple drying under ethanolic atmosphere. Drying Technol. 27, 947–954 (2009) 13. Nguyen, H.N., van de Steene, L., Le, D.D.: Kinetics of rice husk char gasification in an H 2 O or a CO 2 atmosphere. Energy Sour. Part A Recovery Utilization Environ. Effects 40, 1701–1713 (2018) 14. Midilli, A., Kucuk, H., Yapar, Z.: A new model for single-layer drying. Drying Technol. 20, 1503–1513 (2002)
Determining the Residual Stress of K Welded Pipe Joint by Hole Drilling Method Nguyen Hong Thanh1(B) and Nguyen Tien Duong2 1 Nam Dinh University of Technology Education, Nam Dinh, Vietnam
[email protected] 2 Hanoi University of Science and Technology, Hanoi, Vietnam
Abstract. Residual stresses can be present in all structures in general and welded structures in particular. They can be useful or harmful. It is a useful effect, If the residual stresses produce action opposite to stresses from a service loading. The effect of harmful occurs more often and we must measure this magnitude of the residual stresses. Hole-drilling is the most popular method to measure residual stress. This method measures released strains because of drilling a small hole. To measure this released strain is used a strain gauge around the hole. But the hole is not drilled exactly centrically towards strain gauge rosette. The ASTM E837-08 standard presents necessary instruction and procedure to be followed in the measurement of strains and the stress calculation. This paper introduces the process to prepare a pipe weld specimen in K-joint and measures the stresses of this welded joint by using a strain rosette. The base equations related to calculating the residual stresses are shown. Keywords: Hole drilling method · K-joint · Pipe welding · Residual stress · Strain rosette
1 Introduction Residual stresses are stresses that remain in the structure after the service load has been removed. Residual stresses can result from a variety of processes including plastic deformations, temperature gradients, or structural changes. The geometry and stiffness are two main factors that affect the stresses in welded components. In many manufactured engineering parts, residual stresses cause serious problems that affect the working life of the welded structure. To understand harmful residual stresses, we must measure the strain in the welded joint and then determine the residual stress levels. The hole-drilling method was executed in a welded specimen of K joint of A572 Grade 50 steel. Hole drilling method for stress measurement [1, 2] is a popular method for most materials. It is one of the most popular methods of using semi-destructive techniques to measure the residual stress distribution on the thickness of the weld in both strength and direction. This is a technique for high accuracy and good reliability, convenient when preparing samples and how to do it. First, consider the material to be isotropic, continuous © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1012–1022, 2022. https://doi.org/10.1007/978-981-19-1968-8_86
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and elastic. Second, the material can be machined such as drilling holes without affecting deformation measurements. This method determines macro stress. However, to avoid the local yield limit due to stress concentration around the drill hole, the magnitude of the residual stress shall not exceed 60 ÷ 70% of the local yield limit. The reliability of measured residual results depends on the quality of the measuring device. Finally, the measured resolution range must lie around the measuring hole position. The analysis of the measurement hole depth shall not exceed 0.5 × d0 (d0 is the diameter of the hole). The damage of the test piece is small, local, hole drilling and usually within the permitted range or can be repaired. The measurement principle involves drilling holes (about 1.8 mm in diameter and about 1 mm depth) at the position where the excess stress is measured Fig. 1. The hole drilling will inhibit the reduction of residual stress, deformation and be measured by sensors. So, the sensors must be attached around the position to be measured (Fig. 1). Through measurement of deformation around the drilled hole, residual stress is calculated and determined through measuring device. If the structure exists the residual stresses, there will be a different degree of deformation at the machined locations, which provides data to calculate residual stress. To determinate the residual stress, it is necessary to first drill into a sample with a depth equal to the hole diameter and smaller than the thickness of the sample. If the depth is greater than the diameter of the hole, it is difficult to ensure the accuracy of the measurement. The deformation measurement of machining holes at different locations is realized by laser interference or laser interferometry [3].
Fig. 1. Hole drilling method.
In this paper, the K welded joint of pipes are performed by the Gas Metal Arc Welding (GMAW) process [5]. The residual stresses of this joint are measured by the Hole-Drilling Method [4]. The welding parameters for this welded joint are presented.
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2 Materials and Methods 2.1 Material Parameters The material used in K type pipe joint is the structural carbon steel. The chemical composition of A572 Grade 50 steel are: C (0.18%), Mn (1.6%), Si (0.55%), S (0.035%), P (0.035%) and it has the melting temperature of 1500 °C. This steel has the yield strength of 355 MPa, elastic modulus E = 210GPa (at 20 °C), poisson coefficient of 0.33 [6]. Table 1. Parameters of K pipe joint Parameter
Description
Unit
Value
D
The outer diameter of the chord pipe
mm
219
t
The thickness of the chord pipe
mm
12.7
Dbtens , Dbcomp
The diameter of the branch pipe
mm
102
tb
The thickness of the branch pipe
mm
6
θ
Branch angle
Degree
47
g
Gap
mm
50
D/t
Chord wall slenderness
–
17.24
Db /t b
Branch slender-ness
–
17
Db /D
Width ratio
–
0.47
The size of K joint of pipes is designed according to the standard of AISC [7] (Fig. 2). The dimensions of this joint are described in Table 1.
Fig. 2. K type pipe welding joint.
2.2 Welding Parameters Because of the branch pipe thickness is 6 mm, bevelled edge so that the weld is performed by 2 layers with 3 passes (Fig. 3).
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Fig. 3. Welding layers arrangements
The Table 2 gives the welding parameters of GMAW process in the layers and passes of K welded connection. Table 2. Welding parameters of GMAW Layers/(Pass) I h (A) U h (V) V h (mm/s) Dd (mm) Layer 1 (1, 2) 120
22
5
1
Layer 2 (3, 4) 130
24
5
1
Layer 2 (5, 6) 125
23
5
1
2.3 Experimental Procedure The hole drilling method is carried out when the element containing residual stresses is cut in some way. The stresses with force components taking action on the cut surface will reduce and the stresses within the remaining material will redistribute to maintain the balance of inner force [4]. Drilling a hole in a localized area can also release the stress which cause strain on surface of the welded joint. The material properties of the K welded pipe mustn’t change after hole drilling. 2.4 Hole Drilling Device and Drilling Method The hole drilling is fastened to the sample with nuts [3, 4]. The test piece is also clamped and the drilling position is adjusted through the eyepiece as shown in Fig. 4. At this locking position, the eyepiece is replaced and in this alignment, the air turbine assembly is fasten, see Fig. 4 [4]. Drilling is performed at the center of a strain rosette using an air turbine capable of operating the drill at a speed of approximately 40,000 rpm. To control the depth of the drill hole, an adjusting screw is used through the micrometer scale. The hole is drilled at a regular depth [1, 2]. A locking collar gets used to hold the drill. Tensile data is recorded at regular depth from the three channels of the strain rosette. It represents the strain along the respective direction [4]. From each channel of the strain rosette, strains are obtained using a data acquisition device. The resulting strain gets used to calculate the stress values [8].
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Fig. 4. RS 200 drilling machine.
Three standard rosettes of types are used in hole drilling method, and the arrangements of these types are shown in Fig. 5 as Type A, Type B, and Type C [2]. Marking for hole drilling location is indicated at the center location of the rosettes.
Fig. 5. Standard rosettes.
2.5 Strain Gauge Installation Strain data is fixed firmly on the test weld using suitable sticky. Cyanoacrylate based sticky are most widely used [2, 3]. These wires are connected to the data acquisition equipment to read the experimental data. Step of depth to be drilled is followed with
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reference to ASTM E837-08 standard. Strain data is recorded at regular steps from threechannel of strain rosette. It represents strain along the corresponding direction [2]. To measure strains at three different angles were used type A strain gauge. The installed strain rosette is shown in Fig. 6. Strain measurements were carried out at the HAZ zone.
Fig. 6. Installed rosette gauge.
2.6 Calculation of Residual Stresses
Fig. 7. Strain gauge rosette arrangement for determining residual stress.
The typical alignment of strain gauges is used to measure the three normal strains εA , εB and εC is shown in Fig. 7. The strain rosettes are used to measure normal strains along the x and y axes. It can be noted that the strain components εx , εy and γ xy at the measuring point is got from the normal strain measurements made along any three lines drawn through the measured point. The measured strains are εA , εB and εC in this case [3, 4]. Usually, α A , α B and α C are the angles which form with the x axis. It is advantageous to arrange strain A in a
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straight with x axis so that α A = 0. It makes solving problems easier. The εA , εB and εC are the respective strain measurements made from the strain gauges as Fig. 5. The relationship between the measured strains and the strains in the x and y-axis are written from the strain transformation equations [3, 4]. The following transformation relations for strain gauges εA , εB and εC are respectively [4]. εA = εx Cos2 αA + εy Sin2 αA + γxy Sin αA Cos αA
(1)
εB = εx Cos2 αB + εy Sin2 αB + γxy Sin αB Cos αB
(2)
εC = εx Cos2 αC + εy Sin2 αC + γxy Sin αC Cos αC
(3)
The Cartesian components of strain εx , εy and γ xy may be decided from the simultaneous solution of Eq. 1, Eq. 2 and Eq. 3 [4]. The principal strains are determined by Eq. 4 and Eq. 5. 2 1 1 2 εx − εy + γxy ε1 = εx + εy + (4) 2 2 2 1 1 2 ε2 = εx + εy − εx − εy + γxy (5) 2 2 γxy tan 2θ = (6) εx − εy The angle θ are calculated from Eq. 6, in which, θ 1 refers to the angle between the x-axis and the axis of the maximum principal strain εl , and θ 2 is the angle between the x-axis and the axis of the minimum principal strain ε2 [4]. σ1 =
E (ε1 + νε2 ) 1 − ν2
(7)
σ2 =
E (ε2 + νε1 ) 1 − ν2
(8)
The principal stresses are calculated from the above principal strains using Eq. 7 and Eq. 8. Thus residual stress is decided using the above suggested calculation method [8].
3 Results and Discussion The specimen of K pipe joint after finishing welding (Fig. 8) is drilled the hole to measure the strains and the residual stresses.
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Fig. 8. Specimen after welding
The Fig. 9 shows the position of drilled hole in the chord pipe. It is far from the main axis of chord pipe about 30 mm and the first branch pipe is 22.5 mm.
Fig. 9. The position of drilled hole.
After cleaning the location to be measured, the measuring devices are installed and holes are drilled Fig. 10.
Fig. 10. Drilling the hole.
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The obtained results are presented in the Table 3. These results show that the σ X stress component is very high. The average stress of σ X is 122.4 MPa. The τ XY shear stress component is very small. The average shear stress of τ XY is only −7.9 MPa. The average stress of σ Y is 45.2 MPa. Table 3. Result of residual stress Depth (mm)
Residual stress (MPa) σ max
σ min
σX
σY
τ XY
0
0
0
0
0
0
0.04
122
16
93
45
−48
0.08
119
−9
74
35
−61
0.10
94
−3
83
8
−30
0.12
180
55
160
74
46
0.16
110
35
110
34
−2
0.18
149
47
133
63
38
0.22
90
30
73
48
−28
0.30
116
62
116
62
3
0.36
233
93
233
93
4
0.40
304
129
249
184
81
0.50
122
14
118
18
20
0.60
290
−47
289
−46
−11
0.80
70
−57
−51
64
−27
0.90
97
−112
34
−49
−96
Aver
149.7
18.1
122.4
45.2
−7.9
in which: σ max is the maximum principle stress. σ min is the minimum principle stress. σ X is the stress in X direction. σ Y is the stress in Y direction. τ XY is the shear stress. σ x is the average residual stress. Aver. is the average value.
The Fig. 11 presents the main residual stress distribution (σ max and σ min ) at position 1 which is determined by the hole drilling method. We can see that the main stress changes in depth of the drilling hole. The highest maximum principle stress is 304 MPa at a depth of 0.4 mm. It is equal 85.6% of the yield strength of considered material (355 MPa). The average maximum principle stress is 149.7 MPa.
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Fig. 11. Principle stresses distribution
The residual stress distributions in X and Y directions are given in Fig. 12.
Fig. 12. X and Y stresses distribution.
The values of these stress components also depend on the depth of the drilling hole. The highest σ X stress is 289 MPa at a depth of 0.6 mm. It is equal 81.4% of the yield strength of base material. The highest σ Y stress is 184 MPa at a depth of 0.4 mm. The principal stress is calculated based on σ X , σ Y and τ XY . The σ X stress is nominal residual stress in the X direction which is perpendicular to the main pipe axis. Therefore, the principal residual stress and the σ X stress have different values at the same depth. At a deep 0.6 mm, the σ X stress is the greatest but the other stress components are very small. At a deep 0.4 mm, the σ X stress is not maximal (just quite high) but the other stress components (σ Y and τ XY ) are very high. So the principal stress is the largest at a deep of 0.4mm.
4 Conclusions It is noticed from this study that the hole drilling method is straightforward in measuring the residual stress in the K pipe welded joint. The stress component in X directions is highest. The stress component in Y directions is significant. The τ XY shear stress component is insignificant. The residual stresses on the surface of the chord pipe are smaller than that at the depth of from 0.4 mm to 0.6 mm. It is noticed that in the HAZ zone, the residual stress in the K pipe welded joint is very high. The maximum residual stress is more than 80% of the yield strength of base material.
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Acknowledgments. The research was funded by Nam Dinh University of Technology Education. The author thank to School of Mechanical Engineering, Military Technical Academy.
References 1. SINT: Technology - System for Measuring Residual Stress By The Hole-Drilling Method (1998) 2. ASTM - Standard E 837-85: Standard test method for determining residual stresses by the hole driling strain-gage method 3. Rossini, N.S.: Methods of measuring residual stresses in components. Mater. Des. 35, 572–588 (2011) 4. Senthilmurugan, A., Arasu, K.: Residual stress measurement using hole drilling method. Int. Res. J. Eng. Technol. 03(04), 448–450 (2016) 5. Parmar, R.S.: Welding Engineering and Technology. Khanna Publishers, Delhi (2013) 6. AWS-D1.1 M: 2010 - An American National Standard (2010) 7. Paker, J.: Hollow Structural Section Connections. American Institute of Steel Construction (2012) 8. Jeyakumar, M.: Residual stress evaluation on Butt-welded in 718 plates. Can. J. Basic Appl. Sci. 1, 88–99 (2013)
The Effect of Microstructure and Nano Additive Lubrication on the Specific Grinding Energy and Surface Roughness in Ti-6Al-4V Grinding Hung Phi-Trong1,2 , Trung Nguyen-Kien2 , Chung Luong-Hai3 , and Son Truong-Hoanh2(B) 1 Mechanical and Power Engineering Faculty, Electric Power University, Hanoi, Vietnam 2 School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi,
Vietnam [email protected] 3 Mechanical Engineering Department, Vinh University of Technology Education, Vinh, Vietnam
Abstract. Ti-6Al-4V (Ti64) alloy is largely employed in aerospace applications because of its better combination of high strength and good ductility than many other α + β Ti-alloys. The microstructures of Ti64 are various and strongly affect to its mechanical properties and fatigue behavior. The mill annealing (Ti64-Elo) with fully elongated alpha microstructure is optimized microstructure for the manufacturability and dimensional stability, while the beta anneals of fully lamellar microstructure (Ti64-La) is developed to improve the mechanical property such as the ductility, strength, creep resistance, fracture toughness and fatigue strength. This experimental study estimates the influence of Ti64 microstructure on the grinding performance through the surface roughness and specific energy in the surface grinding process with the cBN tool under dry and nano additive lubricating environment. The grinding process of Ti64-Elo produces lower surface roughness and specific grinding energy than those of Ti64-La. The nanofluid also contributes better grinding performance than conventional dry grinding. Keywords: Surface roughness · Specific grinding energy · Ti-6Al-4V · cBN grinding wheel · Nanofluid
1 Introduction Titanium alloy exhibits unique properties, such as excellent corrosion resistance, high strength-to-weight ratio, high melting point, and good mechanical properties at high temperatures. However, titanium alloys present poor thermal properties, high hardness, high chemical reactivity with almost cutting tool materials, and work hardening during machining. These properties lower the grindability of this alloy and make it to be listed in the group of hard-to-cut materials. Many theoretical and experimental studies have been done to investigate the microstructure and increase the grinding properties of titanium alloys in cutting processes generally and grinding process specially. Ahmed et al. studied © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1023–1032, 2022. https://doi.org/10.1007/978-981-19-1968-8_87
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a simple method for assessing the influences of cooling speed from the high temperature on phase transformation seen in α + β titanium alloy [1]. The cooling speeds higher than 410 °C/s indicated a microstructure with full martensite, a great transformation was seen with a cooling speed from 20 to 410 °C/s. With a slower cooling rate, this structure was sequentially replaced by diffusion-controlled Widmanstatten α generation. Dabrowski reported on a research of the phase transformation kinetics in the cooling of Ti64 alloy with α + β crystal structure from the β region [2]. He used metallographic inspection of specimens cooled at different speeds in the region of diffuse transformations and showed alternations from β lamellar structure under a cooling speed from 0.015 to 7.3 °C/s to the granular structure with a cooling speed of 0.012 °C/s. Singh et al. produced Ti64 alloy from powder mixture by warm compression, sintering in a vacuum, and hot extrusion [3]. The alloy with 0.34 wt% oxygen density was presented to different annealing treatments. The results showed that the α/β annealing achieved the highest impact strength which was close to the standard values of the ingot Ti64. In terms of the grinding process, many researchers prove that dry and wet grinding has inadequate cooling and lubrication efficiency on the grinding process of titanium alloy in particular and other hard-to-cut materials in general. Therefore nano-particles are added to the fluids to increase the machining performance. Nanofluid are a solution consisting of nano-particles, such as MoS2 , TiO2 , ZnO, Al2 O3 , C60, CNT, and diamond [4]. Using nanofluids (NFs) would contribute favorable advantages to the machining process of hard-to-machine materials as nanofluids have high thermal conductivity and more heat is dispersed from the cutting region in comparison with the conventional fluids. Furthermore, they can maintain stability in lubricating and cooling at high temperatures in the machining zone. There were some grinding processes using nanofluid that have been experimentally investigated. The study of Li et al. estimated the grinding temperature under minimum quantity lubricant (MQL) cooling of nodular cast iron, Ni-based alloy and C45 steel when the base oil is palm oil [5]. The Carbon nanotube (CNT) nano-particles of 2 and 2.5% volume fractions were added to the base fluid. The outcomes indicated that the grinding of C45 steel presented the highest temperature of 363.9 °C among the experimented alloys. Using the 2% nanofluid (363.9 °C) provided slightly higher grinding temperature than the nanofluid of 2.5% fraction (352.9 °C). Kumar et al. used the small quantity coolinglubrication (SQCL) environment with sunflower oil as the base oil for grinding process of hardened AISI52100 steel using a vitrified alumina wheel [6]. His result proved that the wheel wear rate was considerably reduced. The Ft /Fn ratio was lowered and stabilized and surface quality was favorable with the nano-SQCL environment. In the study of Setti et al., MQL condition was also employed with nanofluid as cutting fluid mixed by adding 0.05, 0.1, 0.5, and 1% volume fraction of CuO and Al2 O3 nano-particles into the water in the surface grinding of Ti64 [7]. The results demonstrated that the application of nanofluid reduced the grinding temperature and tangential forces. The Al2 O3 nanofluid in MQL mode also accelerate the chip removal from the cutting zone. The present work is implemented to estimate the grinding performance of Ti64 workpieces with two microstructures under dry and nanofluid cutting. The surface roughness and specific grinding energy are measured and evaluated. The effect of feeding rate and cooling environments on grinding performance is also studied. The received results can
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be employed for choosing the suitable cutting conditions for the surface grinding of Ti–6Al–4V alloy.
2 Experimental Setup In the grinding tests, Ti64 alloy workpieces with two microstructures are used. The Ti64-Elo workpieces are received at mill anneal temperature of 704–788 °C in two hours then air cool, while Ti64-La samples are beta-annealed at 1066 °C (higher than β-transus temperature) for 2 h and furnace cool. Figure 2 demonstrates the significant difference in Ti64-Elo and Ti64-La workpiece microstructure. Metallographic examination of samples reveals variations in the α phase morphology varied from granular with mill annealing Ti64-Elo (Fig. 2a) to lamellar with beta annealing Ti64-La (Fig. 2b). The heat treatment temperature and cooling rate are the main cause for these differences in these microstructures. The hardness of Ti64-La microstructures is higher than those Ti64-Elo (376 HV and 349 HV, respectively). The grinding operation is carried out in plunge surface grinding mode on HS Super MC500 high-speed vertical machining center as presented in Fig. 3. A resinoid cBN grinding wheel with a diameter of 100 mm is used in all tests. An impregnated diamond dresser is utilized in the dressing of the wheel. Dry cutting and synthetic oil in water under flood cooling mode are applied. Table 1 provided detailed cutting parameters of the grinding experiments (Fig. 1). 1525 Temperature(°C)
Ti64-La 1025
Ti64-Elo β-transus
525
Air Cool
Furnace Cool
25 0
2
4 6 Time (hour)
8
10
Fig. 1. Mill annealing process of Ti64-Elo and beta annealing process of Ti64-La
The study is conducted with 8 sets of grinding experiments, consisting of four workpiece speeds and two grinding environments. For each experiment, 10 cutting passes are performed at depth of cut at 0.01 mm. The grinding forces are measured during ten cutting passes by a piezoelectric Kistler 9139AA dynamometer, while the main spindle vibration is controlled using a Brüel & Kjær Triaxial DeltaTron Accelerometer. The surface roughness of the Ti64 samples is taken by a Mitutoyo surface roughness tester. The synthetic base oil is CIMTECH 3150-VLZ lubricant, which is produced by CIMCOOL Fluid Technology, Korea. The xGnP-M25 with a surface area of 120–150 m2 /g, the thickness of 6 nm, and diameter of 25 μm, and are provided by XG Sciences, Inc, USA. A new evaluation of frictional and tribological characteristics of exfoliated
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Fig. 2. Crystal structure of (a) Ti64-Elo and (b) Ti64-La (Alpha - darker phase, beta - lighter phase)
Table 1. Grinding parameters Grinding mode
Plunge surface grinding, up-grinding
Grinding machine tool
High-speed machining center MC500
Grinding wheel
Resinoid cBN120 D100
Workpiece
Ti64-Elo and Ti64-La (16 mm × 10 mm × 9 mm)
Grinding parameters
Wheel velocity (vc ): 30 m/s; Feeding rate (vw ): 1000 ÷ 10000 mm/min; Cutting depth (ap ): 0.01 mm; Grinding width (b): 4.5 mm
Coolant
Dry environment and 10% synthetic oil + xGnP based fluid (Flood Cooling)
graphite nanoplatelet (xGnP) as an additive in cutting fluids is conducted. xGnP has a crystal structure of carbon atoms in the form of a honeycomb. It is extremely slender and named a two-dimensional material. It owns tribological characteristics that no conventional materials are matched. In addition to its well-known physical and mechanical characteristics, xGnP has very high thermal conductivity (5000 W/m.K), interlayer slide
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behavior, high chemical inertness, and smooth surface, which are considerably frictional and tribological properties. The xGnP shows low friction behavior due to its inter-layer slide under weak van der Waals force. Additionally, xGnP naturally is oleophilic and hydrophobic. As a result, it has a high affinity to oil while strongly resisting water and oxygen, which then can be employed as an additive in cutting oil [8].
3 Results and Discussion 3.1 Influence of Grinding and Lubricating Condition on the Surface Roughness The surface finish is an important factor to predict precisely the machining performance, and surface roughness is one of the most significant factors in estimating the surface finish quality. The surface roughness of a machined sample is dominantly influenced by the dressing condition, grain size, cutting condition, lubricating and cooling condition, and the material removal rate. The roughness value Ra and Rz of the Ti64 samples is plotted in Fig. 4. The slightly higher surface roughness Ra and Rz are achieved with the increase of the workpiece speed in the range of 1000 to 10000 mm/min for both Ti64-Elo and Ti6-La microstructure. In addition, the experimental results show an oscillation of the roughness values with the workpiece speed in the range of from 1000 to 6000 mm/min under dry condition. The higher grinding temperature, more loads on abrasive grains, and larger grinding force tend to enhance the abrasive wear and make the worse surface finish [9]. The occurrence of chip re-precipitated on the ground surface at high workpiece speed will also result in higher Ra and Rz values. Moreover, the higher plastic deformation of the processed surface due to the ascending force will induce a higher surface roughness [4]. The high workpiece speed will results in higher cutting temperature that will cause a higher reaction and adhesion of Ti64 on the cBN grinding wheel.
Fig. 3. The experimental surface grinding set-up
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Surface Roughness, Ra (μm)
1.2
vc = 30 m/s ap = 0.01 mm 1.0
0.8
a) 0.6
Dry grinding, Ti64-Elo Dry grinding, Ti64-La
Synthetic oil + xGnP, Ti64-Elo Synthetic oil + xGnP, Ti64-La
0.4 0
2000
4000
6000
8000
10000
12000
Workpiece Infeed Speed, vw (mm/min) 6.5
vc = 30 m/s ap = 0.01 mm
Surface Roughness, Rz (μm)
6.0 5.5 5.0
b)
4.5 4.0 3.5
Dry grinding, Ti64-Elo Dry grinding, Ti64-La
Synthetic oil + xGnP, Ti64-Elo Synthetic oil + xGnP, Ti64-La
3.0 0
2000
4000
6000
8000
10000
12000
Workpiece Infeed Speed, vw (mm/min)
Fig. 4. Influence of cooling condition and feed rate on surface roughness Ra (a) and Rz (b) in grinding process of Ti64-Elo and Ti64-La
In general, the average roughness when employing synthetic oil + xGnP based fluid is lower than in the dry environment. The better lubricating and cooling properties of xGnP nano-particles in a synthetic oil-based fluid are the reasons for the reduction in the surface roughness of machined Ti64 samples [10]. Titanium tends to strongly chemically react with cBN grits and creates swarfs re-precipitated on the processed titanium surface. However, the cutting with nano-fluid would introduce the xGnP nanoparticles between the Ti64 surface and the cBN grains. Table 2 presented the outcomes of Energy Dispersive X-ray Spectroscopy of the specimen ground. The higher weight percent of B and N elements on the surface under dry grinding in comparison with synthetic oil-based xGnP nanofluids proved the effect of the lubricating membrane in inhibiting the direct touch between the wheel and machined surface. The self-cleaning action which is visually indicated in Fig. 5 also assists in rejecting chips from the cutting zone by removing re-precipitation on the surface of grinding wheel. This was one of the major reasons producing the high roughness value. On fine surfaces, this debris is relocated (Fig. 5a) and on rough ones, they stick to the surfaces and are discarded from the surfaces when the drops roll away (Fig. 5b) [7].
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Table 2. The weight percentage of B and N elements on Ti64-Elo and Ti64-La machined surface Ti64-Elo
Ti64-La
Dry
Synthetic oil + xGnP
Dry
Synthetic oil + xGnP
B (wt%)
12.56
3.59
9.44
3.3
N (wt%)
0
0
0
0
Fig. 5. The self-cleaning action on a fine and rough surface
The influence of the microstructure on the surface roughness is not prominent. The surface roughness of Ti64-La is a little higher than Ti64-Elo. The minimum and maximum roughness Ra of Ti64-Elo samples for all tested conditions are 0.716 and 1.012 μm, respectively, while those of Ti64-La are consecutively 0.796 and 1.082 μm. This is attributed to the fact that before being ground. The higher hardness value of Ti64-La makes shearing and fracturing the main cutting mechanism in the material removal process, leading to the occurrence of keen peaks and as a result higher roughness. On the other hand, in Ti64-Elo grinding process, the plastic deformation of the ground surface layer happens majorly due to the lower hardness value of this microstructure [11]. The slightly roughness variation between two microstructures would be result from the small difference in the hardness of these microstructures.
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3.2 Influence of Grinding and Lubricating Condition on the Specific Grinding Energy Specific grinding energy (es ) can be used as an estimating factor of the lubricating ability of the cutting fluid. The sufficient lubrication in the grinding zone lowers the grinding forces and friction between the sample and the abrasive grains, resulting in relatively low es . The abrasive grains create an abrasive mechanism through rubbing, ploughing, and cutting with various specific grinding energies consisting of sliding energy, ploughing energy, and cutting energy. The specific grinding energy is correlated with tangential force Ft and can be calculated as following equation [12]: es =
Ft .vc vw .ap .b
(1)
The material removal rate Qw is the volumetric material can be removed in a unit of time and computed as: Qw = vw .ap .b
(2)
Furthermore, the largest undeformed chip thickness (agmax ) of a grinding wheel is represented as follows [13]: ag max =
4 vw C.Nd vc
ap ds
1/2 (3)
where C is a constant number; ds describes the grinding wheel diameter; Nd is the density of active cutting edge. In this study, the approximate Nd value for the resinoid cBN wheel is 12.5 mm−2 , while the constant number value is 6.928. Figure 6 present the chart of the calculated data of es versus Qw . The results show a reducing trend from 200 to 40 J/mm3 compared to that of steels from 80 to 40 J/mm3 [13]). However, the specific energy when workpiece speed vw is 10000 mm/min of both Ti64 microstructures and cooling environments is relatively higher than those of the griding process at a low feed rate. It is attributed to the main spindle oscillation at the highest feed rate value, the tangential force Ft and specific energy es also increase. The average specific energy of Ti64-Elo is also a little lower than Ti64-La because Ti64Elo has a lower hardness value than Ti64-La. Since a material has a high hardness, the constraints on its molecules are stronger, then the received grinding forces would be also higher. As the Qw value is small, the value of specific grinding energy is strongly influenced by the rubbing and ploughing action between the sample and grinding wheel due to the small value of the largest undeformed chip thickness. Then, the rubbing and ploughing process is gradually replaced by cutting interaction, the grinding energy trends to reduce rapidly as the vw and Qw value increased. The energy firstly fast declines, then slowly reduce without depending on cooling environments and sample microstructures [14]. In wet grinding, the specific grinding energy under synthetic oil + xGnP based fluid generally has lower values than those with the dry environment due to the lower tangential force of the nanofluid environment.
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3
Specific Grinding Energy, es (J/mm )
200
Dry Grinding, Ti64-Elo Dry Grinding, Ti64-La Synthetic Oil + xGnP, Ti64-Elo Synthetic Oil + xGnP, Ti64-La
180 160 140 120 100 80 60 40 20 0
100
200
300
400
500
Material Removal Rate Qw (mm3/min)
Fig. 6. Influence of the material removal rate on specific energy in grinding process of Ti64-Elo and Ti64-La
4 Conclusions The grinding experiments are implemented on two microstructures of Ti64 alloys under various cutting parameters and lubricating conditions with a resinoid cBN grinding wheel. The main conclusions are then deduced as follows: • The roughness value of Ti64-La samples with lamellar structure and higher hardness shows a slight increase compared to Ti64-Elo. • The higher surface roughness is generally achieved with enhancing workpiece infeed speed. The ground surface in dry grinding has a higher roughness value than nanofluid cooling conditions. • The specific energy in the xGnP nanofluid environment is lower than dry cutting. In general, the specific grinding energy value of Ti64-Elo is lower compared to Ti64-La.
References 1. Ahmed, T., Rack, H.J.: Phase transformations during cooling in α+β titanium alloys. Mater. Sci. Eng. A 243, 206–211 (1998) 2. Dabrowski, R.: The kinetics of phase transformations during continuous cooling of the Ti6Al-4V alloy from the single-phase β range. Arch. Metall. Mater. 56(3), 703–707 (2011) 3. Singh, A.P., Yang, F., Torrens, R., Gabbitas, B.: Heat treatment, impact properties, and fracture behaviour of Ti-6Al-4V alloy produced by powder compact extrusion. Materials 12, 3824 (2019) 4. Lee, P.H., Kim, J.W., Lee, S.W.: Experimental characterization on eco-friendly micro-grinding process of titanium alloy using air flow assisted electrospray lubrication with nanofluid. J. Clean. Prod. 201, 452–462 (2018) 5. Li, B., et al.: Numerical and experimental research on the grinding temperature of minimum quantity lubrication cooling of different workpiece materials using vegetable oil-based nanofluids. Int. J. Adv. Manuf. Technol. 93, 1971–1988 (2017). https://doi.org/10.1007/s00 170-017-0643-0
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6. Kumar, K.M., Ghosh, A.: Assessment of cooling-lubrication and wettability characteristics of nano-engineered sunflower oil as cutting fluid and its impact on SQCL grinding performance. J. Mater. Process. Technol. 237, 55–64 (2016). https://doi.org/10.1016/j.jmatprotec. 2016.05.030 7. Setti, D., Sinha, M.K., Ghosh, S., Rao, P.V.: Performance evaluation of Ti–6Al–4V grinding using chip formation and coefficient of friction under the influence of nanofluids. Int. J. Mach. Tools Manuf. 88, 237–248 (2015) 8. Singh, H., Sharma, V.S., Singh, S., Dogra, M.: Nanofluids assisted environmental friendly lubricating strategies for the surface grinding of titanium alloy: Ti6Al4V-ELI. J. Manuf. Process. 39, 241–249 (2019) 9. Li, Z., Ding, W., Liu, C., Su, H.: Grinding performance and surface integrity of particulatereinforced titanium matrix composites in creep-feed grinding. Int. J. Adv. Manuf. Technol. 94(9–12), 3917–3928 (2017). https://doi.org/10.1007/s00170-017-1159-3 10. Shabgard, M., Seyedzavvar, M., Mohammadpourfard, M., Mahboubkhah, M.: Finite difference simulation and experimental investigation: effects of physical synergetic properties of nanoparticles on temperature distribution and surface integrity of workpiece in nanofluid MQL grinding process. Int. J. Adv. Manuf. Technol. 95(5–8), 2661–2679 (2017). https://doi. org/10.1007/s00170-017-1237-6 11. Sadeghi, M.H., Haddad, M.J., Tawakoli, T., Emami, M.: Minimal quantity lubrication-MQL in grinding of Ti–6Al–4V titanium alloy. Int. J. Adv. Manuf. Technol. 44, 487–500 (2009) 12. Zhang, X., et al.: Performances of Al2O3/SiC hybrid nanofluids in minimum-quantity lubrication grinding. Int. J. Adv. Manuf. Technol. 86(9–12), 3427–3441 (2016). https://doi.org/ 10.1007/s00170-016-8453-3 13. Zhou, H., Ding, W., Liu, C.: Material removal mechanism of PTMCs in high-speed grinding when considering consecutive action of two abrasive grains. Int. J. Adv. Manuf. Technol. 100(1–4), 153–165 (2018). https://doi.org/10.1007/s00170-018-2685-3 14. Zhenzhen, C., Jiuhua, X., Wenfeng, D., Changyu, M.: Grinding performance evaluation of porous composite-bonded CBN wheels for Inconel 718. Chin. J. Aeronaut. 27(4), 1022–1029 (2014)
Experimental Investigation of Performance of Cellulose Cooling Pad Nguyen Viet Dung, Nguyen Tien Hung, Nguyen Ba Chien(B) , and Nguyen Dinh Vinh(B) School of Heat Engineering and Refrigeration, Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi 10000, Vietnam {chien.nguyenba,vinh.nguyendinh}@hust.edu.vn
Abstract. In this paper, an experimental analysis of the refrigeration effect of the evaporative cellulose cooling pads was investigated. The experimental test was performed with the air velocity ranging from 0.5 m/s to 4 m/s, the water temperature ranged from 8–10 °C and the temperature and humidity of ambient ranged from 28–30 °C and 65–75%, respectively. The temperature of chilled water temperature, temperature, and humidity of the inlet air, and the ratio of waterair flow rate have a strong influence on the performance of the cooling pad. A correlation was also developed to predict the cooling capacity of the cooling pad, which shows good agreement with the experimental data. Keywords: Cooling pad · Evaporative cooling · Porous foam · Green building · HVAC
1 Introduction The 2020 report of International Energy Agency points out the energy assumption in the building sector represents nearly 55% of global electricity consumption and CO2 emissions from the operation of buildings increase to 28% of total global energy-related CO2 emissions. Green building designs, therefore, have become popular in recent decades as one of the efforts of humans to prevent global warming. On the other hand, the conventional heating, ventilation, and air conditioning (HVAC) systems may cost up to 50% energy consumption of the building. Thus, passive cooling such as evaporative cooling is considered as one of the alternative solutions for conventional air conditioning systems in green buildings [1]. Several studies on evaporative cooling technology have been published in the literature [2–7] to evaluate their suitability and performance in operation. Franco et al. [7] investigated the evaporative cooling boxes for greenhouses. They evaluated the pressure drop, heat and mass transfer coefficient, water consumption, and saturation efficiency at various flow rates. Ghani et al. [8] interviewed the application of cooling pads for greenhouse agriculture to reduce the energy demand in this sector. The author noted that the
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1033–1041, 2022. https://doi.org/10.1007/978-981-19-1968-8_88
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combination of fogging methods, natural and forced ventilation, and evaporative cooling could become an integrated greenhouse climate control. Pandelidis et al. [6] investigated the plate materials for evaporative air coolers. The study shows that, for evaporative air coolers, synthetic materials are better suited to the role of plate materials. Matinez et al. [9] reported the experimental thermal and fluid-dynamic behavior of a new type of evaporative pad. The authors evaluated the effects of operation parameters of cooling pad such as water flow rate, air velocity, and thickness of pad on the efficiency of systems. The experimental results show that the maximum saturation efficiency of this type of pad is 80.5% and the maximum pressure drop in the airflow is less than 17 Pa. The exergy efficiency is dependent from the pad thickness. The authors also proposed a new overall exergy efficiency to optimize the operation of the cooling pad in air conditioning applications. Research by Nada et al. [10] presented the performance of a new bee-hive structure cooling pad. Numerous parameters have been considered including air velocity, inlet air temperature, water flow rate, water temperature and cooling pad thickness. The data illustrated that the saturation efficiency, exergy efficiency and overall exergy efficiency depend on the pad thickness and water flow rate. The overall exergy efficiency could reach 74%. They also proposed new dimensionless experimental correlations for predicting the system performance. The above review has shown that the operation and efficiency of cooling pads de on several factors such as pad material, range of application, and climate conditions. Thus, more experimental data on the performance of cooling pads are worth demonstrating. In this study, we investigate the performance of a cellulose cooling pad under the variation of testing conditions for air-conditioning applications. Finally, a new correlation to predict the cooling capacity is also developed in this work.
2 Experimental Model 2.1 Apparatus
Fig. 1. The schematic of the experimental apparatus
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Fig. 2. Photo of pad structure
The experimental model is shown in Fig. 1. The model mainly consists of three cooling pads, a water supply system, a variable fan, a control system, and a data acquisition system. The chilled water is delivered by a DC pump from the water bath to the nozzles. To avoid maldistribution, a matrix nozzle 2 × 3 is placed on top of the cooling pads. In the airside, the flow is circulated by a variable fan. Hot air enters the inlet, flows through three cooling pads, and then supplies to the testing room. As shown in the model, the temperature sensors are set up at 5 positions along with the airflow. The dry bulk temperatures of the airflow are measured by Pt1000 type. Concurrently, the wet bulk temperatures of airflow are determined by the same sensor type except that, it is covered by a wet-wick jacket. The bottom of the wick is flooded in a distilled water bottle to maintain the humidity. The pressure drop of the airflow through the cooling pads is determined by a differential pressure transmitter. To minimize the disturbance of flow, the length of the outlet tube is set as five times of tube diameter. The temperatures of supply and return chilled water flow are also measured by the Pt1000 sensors. A level of water is set at the bottom of the cooling pads to avoid the fluctuation of measured temperature and to prevent the shortcut of airflow. A mass flow meter is used to determine the flow rate of supply water. The testing model is driven by a BACnet system to synchronize the operation of the fan, pump as well as to collect the data from the sensors. The testing pad in present study is a bee-hive cellulosic one. The corrugated angle of the cross-layer is 45°/45°. The height, width, and depth of each pad are 500 mm, 200 mm, and 100 mm, respectively. A photo sample of the cooling pad is depicted in Fig. 2. 2.2 Data Reduction In this work, the relative humidity is determined based on the wet bulk and dry bulk temperature as follows: φ=
ewb − N (1 + 0.00115 × twb )(tdb − twb ) edb
(1)
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where tdb and twb are dry-bulk and wet-bulk temperatures. edb and ewb are convert factors of dry-bulk and wet-bulk temperature and are defined as follows: 17.502×tdb
edb = 6.112 × e 240.97+tdb
(2)
17.502×twb
ewb = 6.112 × e 240.97+twb
(3)
N is an empirical factor and equal 0.6687451584. The ratio of cooling supply water and the circulation air is defined as follows: Rcp =
m ˙w m ˙a
(4)
where m ˙ w and m ˙ a are mass flow rate of chilled water supply and air, respectively. The cooling capacity of the cooling pad system is defined by the flow rate and the enthalpy of air as follows: qE = m ˙ a × (ha,o − ha,i ) where h is the enthalpy of air. The velocity of airflow is determined as follows: √ u = K. P
(5)
(6)
where K is calibration factor and P is pressure drop of airflow. Table 1. Uncertainty of sensors Parameter
Uncertainty
Unit
Temperature
±0.1
°C
Pressure
±2
% (full scale)
Mass flow rate
±2
% (full scale)
The summary of uncertainty is illustrated in Table 1.
3 Results and Discussions Figure 3 shows the effect of chilled water supply temperature on the cooling capacity and absolute humidity. In the airside, the inlet temperature, humidity, and velocity of airflow are set at 28 °C, 62%, and 4 m/s, respectively. The data depicts that both cooling capacity and absolute humidity strongly depend on the chilled water temperature. The cooling capacity increases when the water temperature decreases. With the change of water temperature from 8 °C to 6 °C, the cooling capacity increased about 10%. On
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Fig. 3. Variation of cooling capacity and absolute humidity
Fig. 4. Effect of water-air ratio on cooling capacity
Fig. 5. Effect of chilled water temperature on cooling capacity
the contrary, the absolute humidity decreases linearly when chilled water temperature decreases. Similar trends are also reported in the study of Nada et al. [10]. The effect of the ratio of water-air on the cooling capacity is depicted in Fig. 4. The inlet temperature and humidity of airflow are fixed at 29 °C and 63%, respectively. When the velocity of airflow is 3 m/s and the temperature of chilled water supply is
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7 °C, the cooling capacity increases when the ratio increases and reaches a maximum value at the ratio of about 1.42. When the ratio is larger than 1.42, the cooling capacity decreases with the increase of the water-air ratio. A similar graph is observed in the experimental results when the velocity increases to 4 m/s and the temperature of the chilled water supply is 8 °C. In this case, the maximum cooling capacity shifts to the left and reaches to higher value compared to the previous case. It means both the effect of inlet velocity and chilled water temperature should be strongly considered in the relation to the water-air ratio to obtain the optimal cooling capacity. Figure 5 illustrates the effect of chilled water supply temperature on the cooling capacity. In this test, the airflow temperature, humidity, and velocity are fixed. The maximum value of cooling capacity rises when the chilled water temperature decreases. When temperatures of water are 7 °C and 8 °C, the optimal points are found at the same value air-water ratio. However, when the temperature of water decreases to 6 °C, the optimal point reaches at lower air-water ratio value. Hence, it is important to balance the water temperature and ratio. In addition, more chilled water data with lower temperatures are still needed in case the cooling pads are used in other applications than air conditioning (Fig. 6).
Fig. 6. Comparison between the experimental data and proposed correlation
In this work, to predict the cooling capacity of this pad type, a new correlation is developed. The above discussions point out that the cooling capacity strongly depends on the inlet temperature of air and chilled water as well as the ratio of water-air. Consequently, the cooling capacity correlation is determined as the function of those parameters. The general form is defined as follows: QE = f (Ta,i , Tw,i , Rcp )
(7)
Base 2213 data points, the new cooling capacity of pad are calculated using a regression method. The final form is determined as follows:
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QE = Qa,i × (C1 + C2
Ta,i,db Ta,i,db + C3 + C4 Rcp ) Tw,i Ta,i,wb
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(8)
where C1, C2, C3 and C4 are the adjustment factors and are illustrated in Table 2. Figure 4 shows the comparison between the proposed correlation and the experimental data with a mean deviation of 2.56%. Table 2. Cooling capacity correlation factors Factor
Values
Unit
C1
−1.255
[–]
C2
5.209
[–]
C3
−3.843
[–]
C4
0.043
[–]
4 Numerical Model In order to deploy the cooling pad to the real air conditioning applications, a CFD model is also developed to estimate the airflow velocity. The calculation domain is based on the experimental models. Using the simplified model, the pressure drop could be defined as a combination of Darcy’s Law and an additional inertial loss term [11] as follows: 1 μ (9) u + C2 ρu2 δ P = − α 2 where μ is the air viscosity, α is the permeability of the cooling pad, C2 is the pressure jump coefficient, u is the velocity and δ is the thickness of the cooling pad. Using the experimental data, the values of permeability and pressure jump coefficient are calculated as follows: C2 = −0,893
(10)
1 = 614110 α
(11)
The sample of velocity contour is shown in Fig. 7. The comparison of supply air velocity between the CFD simulation and experimental results is illustrated in Fig. 8. The maximum deviation is about 10%. However, the model predicts well the gradient of flow, thus it is applicable to use with some minor revisions.
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Fig. 7. Contour of velocity
Fig. 8. Comparison of velocity
5 Conclusion In this study, the experimental performance of the cooling pad has been evaluated. The results are summarized as follows: – The cooling capacity of the cooling pad strongly depends on the temperature and velocity of inlet air, the temperature of chilled water, and the ratio of water-air. – The optimal point should be carefully considered under the effect of all the above parameters. – A CFD model has been carried out to predict the velocity of airflow. The simplified porous model is applicable to use. – Finally, to predict the cooling capacity, a new correlation is proposed and archives the mean deviation of about 2.5%.
Acknowledgments. This research is funded by Ministry of Education and Training (Vietnam) under project number B2020-BKA-04 and ITC joint-stock company (Vietnam).
References 1. Oropeza-Perez, I., Østergaard, P.A.: Active and passive cooling methods for dwellings: a review. Renew. Sustain. Energy Rev. 82(Sept), 531–544 (2018)
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2. Dhamneya, A.K., Rajput, S.P.S., Singh, A.: Thermodynamic performance analysis of direct evaporative cooling system for increased heat and mass transfer area. Ain Shams Eng. J. 9(4), 2951–2960 (2018) 3. Xuan, Y.M., Xiao, F., Niu, X.F., Huang, X., Wang, S.W.: Research and application of evaporative cooling in China: a review (I) - research. Renew. Sustain. Energy Rev. 16(5), 3535–3546 (2012) 4. Alamdari, P., Saedodin, S., Rejvani, M.: Do non-metallic material and radiation shields affect the operation of direct evaporative cooling systems? Int. J. Refrig. 114, 98–105 (2020) 5. Bishoyi, D., Sudhakar, K.: Experimental Performance of a direct evaporative cooler in composite climate of India. Energy Build. 153, 190–200 (2017) 6. Pandelidis, D., Pacak, A., Cicho´n, A., Gizicki, W., Worek, W., Cetin, S.: Experimental study of plate materials for evaporative air coolers. Int. Commun. Heat Mass Transf. 120(Nov), 2021 (2020) 7. Franco, A., Valera, D.L., Peña, A.: Energy efficiency in greenhouse evaporative cooling techniques: cooling boxes versus cellulose pads. Energies 7(3), 1427–1447 (2014) 8. Ghani, S., et al.: Design challenges of agricultural greenhouses in hot and arid environments – a review. Eng. Agric. Environ. Food 12(1), 48–70 (2019) 9. Martínez, P., Ruiz, J., Martínez, P.J., Kaiser, A.S., Lucas, M.: Experimental study of the energy and exergy performance of a plastic mesh evaporative pad used in air conditioning applications. Appl. Therm. Eng. 138(Dec), 675–685 (2018) 10. Nada, S.A., Fouda, A., Mahmoud, M.A., Elattar, H.F.: Experimental investigation of energy and exergy performance of a direct evaporative cooler using a new pad type. Energy Build. 203, 109449 (2019) 11. Ansys Fluent 2012
Design and Construction for Computational Models of Ultrasonic Transducers Anh-Duy Truong, Van-Son Dinh, Van-Sang Pham(B) , and Manh-Tuan Ha(B) School of Transportation Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam {sang.phamvan,tuan.hamanh}@hust.edu.vn
Abstract. The study aims to construct a realistic computational model of an ultrasonic transducer, by utilizing the Harmonic Response and Harmonic Acoustics analysis systems in ANSYS, alongside with using Piezo & MEMS ACT Extension. Materials, physical dimensions, and contacts between elements, meshing methods, mesh sizing, and boundary conditions are studied and applied to match the manufacturing process of physical transducers. In addition, 4 variations of the model with different operating frequencies and beam spread angles are built to compare the resulted ultrasonic beams. The result for this shows that transducers operating in lower frequencies (under 250 kHz) create narrow and propagating beams, with max Sound Pressure Level (SPL) of 125 dB concentrated at the beam center. Higher frequencies (above 350 kHz) do not create propagating beams but have SPL spread in all direction, with the average the figure of 100 dB. Larger beam angles result in wider sound fields but shorter reach distances. Keywords: Ultrasonic transducer · Piezoelectricity · Computational model · Harmonic Response · Harmonic Acoustics
1 Introduction Ultrasonic transducers utilize the propagating characteristics of ultrasound to conduct Nondestructive Testing, Ultrasonic Imaging and Distance Measurement [1]. The principal structure of an ultrasonic transducer can be seen in Fig. 1 [2]. The piezoelectric element (or piezo layer) acts as an ultrasonic source, thanks to a phenomenon known as The Piezoelectric Effect. Specifically, by placing two poles of a piezoelectric material within an alternating electric field, the material will compress and stretch accordingly, creating a harmonic motion. Currently, Polycrystalline ceramic is one of the most widely known piezoelectric materials [3]. The piezoelectric effect can be understood as a linear electromechanical interaction between the mechanical and the electrical states. The constant for such a linearly proportional relation is defined as the piezoelectric coefficient matrix d. The piezoelectric coupled field equation can be written as [4]: D = d T + εT E © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1042–1063, 2022. https://doi.org/10.1007/978-981-19-1968-8_89
(1)
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Fig. 1. Structure of an ultrasonic transducer
S = sE T + d t E
(2)
where D is the electric displacement matrix (C/m2 ), E is the electric field matrix (V/m), S is the strain matrix, T is the stress matrix (N/m2 ). The piezoelectric elastic compliance matrix under constant electric field condition is denoted as sE (M2 /N), while ε T (F/M) is the piezoelectric permittivity matrix. The piezoelectric permittivity is a measure of the charge stored on an electrode material at a given voltage. The permittivity of vacuum is ε0 = 8.55e − 12 (F/M). The relative permittivity is the ratio εT ε0 , and it is denoted as K. The chosen piezoelectric material for this study is PZT-5A because of its wide popularity. The matching layer is used to minimize the mismatch in acoustic properties of the piezoelectric material and the targeting media. There are several matching techniques, but this study focuses on the traditional method, due to its simplicity in calculation [5]. The traditional method applied the properties of acoustic impedance. Acoustic impedance is the measure of the opposition that a system presents to the acoustic flow resulting from an acoustic pressure applied to the system [6]. It is given by Z (MRayl): Z = ρv
(3)
in which ρ is the density (kg/m3 ), and v (m/s) is the speed of sound travelling through the medium. The speed of sound in a medium or material can be calculated by: v=
E(1 − ϑ) ρ(1 + ϑ)(1 − 2ϑ)
(4)
where E is the Young’s modulus and ϑ is the Poisson’s ratio [7]. By knowing the acoustic impedance of the piezoelectric material (Z p ) and the propagating medium, which is water in this study (Z w ), the acoustic impedance of the matching layer (Z m ) can be calculated by [8]: (5) Zm = Zp Zw In this study, BaSO4 -Bisphenol A is chosen for the matching material, as its acoustic impedance (Zm = 6.26) [9] matches the discrepancy in that of PZT-5A (Zp = 25.7, calculated using Eq. (3), (4) and [10]) and water (Zw = 1.48) [8].
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The backing layer and insulation are used to support the fragile piezoelectric element and prevent unwanted ultrasonic waves from escaping the transducer. In this study, fiber glass is chosen for the backing material, while resin epoxy is chosen for the insulation [11, 12]. The housing material can be neglected since the housing acts as a fixed support for the whole system during simulation. Regarding physical dimensions, a typical ultrasonic transducer is manufactured based on the half-length and quarter-length configuration. This means that the thickness of the piezo element is half the wavelength (λ/2), while the matching layer has the thickness of a quarter of a wavelength (λ/4). The wavelength (λ) can be derived from the center operating frequency (f c ) of the transducer and the speed of sound in which the ultrasound travels [12]. The diameter of the piezoelectric layer determines the characteristics of the output ultrasonic beam. Ultrasonic beam is the field of sound pressure emitted from a circular transducer surface [13], and it is characterized by the near field distance (N) and beam spread angle (θ ). The near field distance – the length of the area where diffractions from different sound waves create fluctuations in sound intensity near the transducer. The area behind this point, where sound waves combine to create a uniform field of sound pressure, is called the far-field [13]. The near field distance is given by: N=
D2 2λ
(6)
The far-field beam spread angle can be seen in this Fig. 2:
Fig. 2. Beam spread angle
And this can be calculated by [13]: θ = sin−1
1.22λ D
(7)
2 Objectives and Methods The objectives of this study are twofold. First is to design and construct the computational model of an ultrasonic transducer that behaves similarly to a physical one. Second is to compare the influences on resulted ultrasonic beams by changing elements’ thicknesses and diameters.
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To achieve these goals, the study utilizes the Harmonic Response and Harmonic Acoustic analysis systems included in ANSYS Workbench. The Harmonic Response system determines the steady state response of the transducer’s structure while the piezoelectric layer is being activated, and the Harmonic Acoustics system determines the steady-state response of the surrounding fluid medium based on the simulation results taken from Harmonic Response [14]. The connection between these analysis systems is shown in Fig. 3, and details on the analysis setups are described in Sect. 3.3 and 3.4. It is also necessary to install the Piezo & MEMS ACT Extension from the ACT Appstore, as it allows ANSYS to simulate the piezoelectric coupling effect. More information on the extension can be found in [15].
Fig. 3. Connection between analysis systems in ANSYS Workbench
Before going through these analysis systems, a 3D model of the transducer is built. To achieve a realistic model, realistic physical structure and suitable materials is set. Correct contacts between elements and boundary conditions are also considered. Then, with such model, 4 variations are built and compared their influences on resulted ultrasonic beams. Details on the input of materials and the construction of models are shown in Sect. 3.1 and 3.2.
3 Modelling with ANSYS Mechanical 3.1 Input of Materials Material properties for BaSO4 -Bisphenol A and PZT-5A are added manually to the Engineering Data tab. These properties can be found in [9] and [10]. Other materials are already existed in ANSYS Material Library. 3.2 Preparing the Geometric Models The geometric model, constructed using Space Claim, follows the principal structure of a typical transducer mentioned above (Fig. 4a). This model is then enclosed in a rectangular box with the dimension of 90 × 90 × 150 (mm) (shown in Fig. 4b) acting as a fluid environment. As discussed in the Objectives and Methods, four variations of the model are built for comparison. From [12] and Eq. (7), input parameters that determine the elements’ thicknesses and diameters using for each model are shown in Table 1.
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Center frequency (f c )
Beam spread angle (θ )
1
200 kHz
10°
2
200 kHz
20°
3
400 kHz
10°
4
400 kHz
20°
Fig. 4. a) Sectional cut of the geometric model and b) the enclosure
3.3 Configuration for Harmonic Response For all 4 models, only the transducer is put into Harmonic Response analysis, which mean the enclosure is suppressed. Contact types for each element pair are shown in Table 2, while Meshing Methods and Sizing are shown in Table 3 and 4. Notedly, meshing for the housing element can be skimmed through, since it acts as a fixed support (Fig. 5). Definitions within the Harmonic Response system is summarized in Table 5, whereas Table 6 includes details for the Piezoelectric Body enabled by the Piezo & MEMS ACT Extension. Lastly, for Analysis Setting, the Harmonic Response analysis runs with a Full Solution Method for 20 intervals. Based on the center frequency of the model (f c ), different frequency ranges are applied.
Design and Construction for Computational Models Table 2. Contact types between elements Element pair
Contact type
Piezo – Backing
Bonded
Piezo – Matching
Bonded
Matching – Insulation
Bonded
Matching – Housing
Bonded
Housing – Insulation
Bonded
Housing – Backing
Bonded
Piezo – Insulation
No separation
Backing - Insulation
No separation
Table 3. Meshing methods for each element Element
Method
Setting
Piezo
Sweep
No of division: 5
Backing
Sweep
No of division: 13
Matching
Sweep
No of division: 7
Insulation
Multizone
Element size: 1e−3 to 2e−3
Housing
Multizone
None
Table 4. Additional sizing Element
Sizing type
Element size
Piezo and Backing
Body
1e−3 to 2e−3
Matching
Body
0.5e−3 to 1e−3
Insulation (upper and lower face)
Face
1e−3 to 2e−3
Fig. 5. Mesh overview
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Definition
Scoped geometry
Setting details
Fixed support
All housing’s surfaces
None
Piezoelectric body
Piezo body
See Table 6
Voltage
Upper Piezo surface
Vol (real): 10 V
Voltage
Lower Piezo surface
Vol (real): 0 V
Table 6. Setting details for Piezoelectric Body Category
Setting details
Material definition
Simplified
Polarization
Y
PIEZ e31
−4.78
PIEZ e33
9.79
PIEZ 15
0.81
DPER ep11
1600
DPER ep33
1800
Table 7. Frequency range for different models Model
Range
1
150–250 kHz
2
150–250 kHz
3
350–450 kHz
4
350–450 kHz
3.4 Configuration for Harmonic Acoustics After finishing the Harmonic Response analysis, the simulation process moves to the Harmonic Acoustics analysis. Here, only the enclosure geometry is considered, and it is set to Acoustic Region in the analysis setup. All 6 outer surfaces of the enclosure are set to Radiation Boundary, and additional Ports are set to evaluate transmission loss of the transducer (Fig. 6). To simulate the acoustic response of the water element enclosed a transducer, the results of Frequency Response from Harmonic Response are imported into Velocity Import, with the scope set to the 3 faces of the transducer that contacts the fluid environment (Fig. 7).
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Fig. 6. Ports setup for transmission loss evaluation
Fig. 7. Import velocity for Harmonic Acoustics
For Analysis Setting, the Harmonic Acoustics analysis also run within the same frequency range as in Harmonic Response, with the solution intervals of 20, and Solution Method is set to Full.
4 Results and Discussion 4.1 Results from Harmonic Response For the Harmonic Response analysis, the study monitors the Normal Elastic Strain frequency response of the matching layer’s surface that is in direct contact with water. From this result, the resonance frequency – where the matching layer experiences the highest amount of strain, can be determined. The frequency response of the 4 models can be seen in Fig. 8. There is a clear spike in the amplitude of strain when the matching layer resonates. For Model 1, the matching layer resonates at 175 kHz, with the strain of 1.22E−6 (m/m), while that of Model 2 is 1.99E−6 at 225 kHz. For Model 3 and 4, the figures are 5.10E−7 at 425 kHz, and 1.68E−6 at 390 kHz, respectively.
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Fig. 8. Frequency response of the lower matching surface in 4 models
In addition to the clear peak in the amplitude of strain, each model also has a second resonant frequency, with Model 1 is at 205 kHz, Model 2 is at 185 kHz, Model 3 is at 405 kHz, and Model 4 is at 335 kHz. The different mode shape of the matching surfaces that are in contact with water at the 1st and 2nd resonant frequencies are shown in Fig. 9, 10, 11 and 12. Visually, mode shapes of higher frequencies have more nodes and ripples in their matching layer’s surfaces.
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Fig. 9. Mode shape of the matching layer at 2 resonant frequencies (Model 1 at a) 175 kHz and b) 205 kHz)
4.2 Results from Harmonic Acoustics Regarding the Harmonic Acoustics analysis, the results plot out Far-Field Sound Pressure Level (SPL) at different frequencies within the chosen frequency range. The result of Far-field SPL for each operating frequency is a polar graph, representing SPL at different angle within an infinite sphere. Since the transducers face downward, only the lower half of the graphs are plotted. The SPL polar graphs also provide information about the beam spread angle, as it can be calculated by measuring the angle in which the SPL concentrated. In addition, the 3D SPL field is also provided for visual evaluation. First, the Far-field SPL of resonant frequencies are shown below:
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Fig. 10. Mode shape of the matching layer at 2 resonant frequencies (Model 2 at a) 225 kHz and b) 185 kHz)
For Model 1, Far-field SPL is highest at 135.72 dB when the matching layer reaches the 1st resonant frequency of 175 kHz (Fig. 13a). At this frequency, most of the sound energy cluster together to create a narrow ultrasonic beam, with the highest SPL located in the center of the beam. The beam spread angle can also be calculated from this graph by taking the center angle and minus the angle where the beam starts to form (θ = 270°–259° = 11°). This is 1° more than the figure calculated by Eq. (7). The 2nd resonant frequency has the highest Far-field SPL of 113.7 dB at 270°, with a slightly higher beam spread angle of 14°. For non-resonant frequencies, Model 1 has the highest Far-field SPL ranging from 97 dB to 122 dB at various angles, thus forming different beams with different spread angles (Fig. 14).
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Fig. 11. Mode shape of the matching layer at 2 resonant frequencies (Model 3 at a) 425 kHz and b) 405 kHz)
For Model 2, the Far-field SPL at the 1st resonant frequency (225 kHz) is 127 dB at 240° and 300°, which correlates to a 30° beam spread angle (10° more than the figure calculated by Eq. (7)). This is not the highest Far-field SPL recorded within the 150–250 kHz frequency range, as this figure ranges from 93 dB to 144 dB. The second resonant frequency in Model 2 has the Far-field SPL of 102.67 dB, with a 13° beam spread angle (Fig. 16). Regarding Model 3, the highest Far-field SPL ranges from 102 dB to 135 dB. At the 1st resonant frequency of 425 kHz, this number is 118.81 dB. The polar graph for Far-field SPL at 425 kHz is shown in Fig. 17.
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Fig. 12. Mode shape of the matching layer at 2 resonant frequencies (Model 4 at a) 390 kHz and b) 335 kHz)
Here, sound energy scatters across the whole lower half of the polar graph, with no concentrated beam being formed. However, in almost all angles, SPL stay above 100 dB. No beam can be seen in the graph of the 2nd resonant frequency and all other non-resonating ones. For Model 4, the same irregular distribution of Far-field SPL occurs. Highest SPL ranges from 98 dB to 129 dB. Below are the polar graphs of Far-field SPL at the 1st and 2nd resonant frequencies: Next, the study looks at transmission loss – the amount of sound energy dissipated while travelling through water after a fixed distance (Fig. 20).
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Fig. 13. (a) Far-field SPL of Model 1 at the 1st resonant frequency (175 kHz) and b) 3D SPL field of Model 1 at the 1st resonant frequency (175 kHz).
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Fig. 14. Far-field SPL of Model 1 at the 2nd resonant frequency (205 kHz)
Model 1 experiences the least energy losses at its 1st resonant frequency (175 kHz). At the 2nd resonant frequency (205 kHz), this model has a higher-than-average amount of losses. At 240 kHz, Model 1 also experiences the least transmission loss. Interestingly, Model 2 follows a relatively similar pattern, with transmission loss dips at 175 kHz, and peaks at 180 kHz, 205 kHz, and 245 kHz. Moreover, no significant figures for transmission loss are seen in Model 2’s resonant frequencies. Model 3 and Model 4 share the same pattern, with transmission loss increases with higher frequencies. 4.3 Evaluation of Computational Models Model 1 and 2 operating under lower frequencies create simple mode shapes, while with a considerably fewer number of nodes (1 to 2), compared to the complex mode shapes seen in Model 3 and 4. This is similar to the physical behavior of a typical disc membrane, where higher resonant frequencies resulted in more complex vibration. The resulted transmission loss also follows the same the pattern with experimental data, where it showed that attenuation – the reduction in amplitude of the ultrasound beam as a function of distance, increase nonlinearly with higher frequencies [16].
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Fig. 15. a) Far-field SPL of Model 2 at the 1st resonant frequency (225 kHz) and b) 3D SPL field of Model 2 at the 1st resonant frequency (225 kHz).
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Fig. 16. Far-field SPL of Model 2 at the 2nd resonant frequency (185 kHz)
4.4 Discussing the Influence of Beam Spread Angle As mentioned in Sect. 1, the diameter of the piezoelectric layer determines the beam spread angle of the output ultrasonic beam. Hence, models with similar beam spread angle will share the same diameter and vice versa. Based on Table 7, two comparison pairs are inspected. First, regarding Model 1 and 2, the influence of beam spread angle can be seen by comparing Fig. 13a and Fig. 15a. While Model 1 has a more concentrated beam, with the highest SPL at the center, Model 2 has a more spread-out beam shape, with lower SPL at the center. This indicates that Model 1, with a narrower beam spread angle, can propagate further into water with its focused beam, compared to Model 2. Beside the difference in the spread of sound beams, beam spread angle (or diameter) does not have impacts on other behaviors of the models. Both models have relatively simple mode shapes as seen in Fig. 9 and 10. Both also share the same pattern in transmission loss within their frequency range. With Model 3 and Model 4, there are no ultrasonic beam being created, as seen in Fig. 17, 18 and 19, and both models have SPL scattered. These models share the same complex mode shapes as seen in Fig. 11 and 12, and transmission loss pattern.
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Fig. 17. (a) Far-field SPL of Model 3 at 1st resonant frequency (425 kHz) and b) 3D SPL field of Model 3 at 1st resonant frequency (425 kHz).
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Fig. 18. Far-field SPL of the 2nd resonant frequency (405 kHz)
4.5 Discussing the Influence of Operating Frequencies As discussed in Sect. 3.3, models operate within the same frequency range have a relatively similar pattern in mode shapes and transmission loss, regardless of the differences in beam spread angle. However, when comparing Model 1 to Model 3, and Model 2 to Model 4, the lower frequencies (in this case are 150–250 kHz) have more defining ultrasonic beams, with less average energy being lost when travelling through water. Higher frequencies, on the other hand, while experience more losses in energy, have higher SPL spread across the whole lower half of the polar graphs. This is explained by the difference in mode shapes of the matching layers. Since nodes act as source of vibration, models with a single node in the center of the matching layer (as seen in Fig. 9 and 10) transform most of its energy to sound waves, while the high number of nodes in other models will cause wave interference, resulting in waves cannot travel for great distances.
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Fig. 19. Far-field SPL of Model 4 at the a) 1st resonant frequency (390 kHz) and b) 2nd resonant frequency (355 kHz) and c) 3D SPL field of Model 4 at 1st resonant frequency (390 kHz)
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Fig. 20. Transmission loss in 4 models
5 Conclusion The study has successfully in creating working models of ultrasonic transducers with realistic behaviors, albeit with errors. Results from comparing 4 variations shows that: • Narrower beam spread angles allow ultrasonic beams to travel further into the fluid environment. Wider angles, while experience reduced propagation, can have wider field of sound energy. • Transducers operating in lower frequencies can produce soundwaves that travel far into the propagating environment. In contrast, higher frequencies prevent deep propagation, but allow for denser, wider, and more consistent sound energy around the lower matching surface.
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Based on these characteristics, different operating frequencies will serve different applications. For example, while lower frequencies can be used in depth sensing, higher ones can be used in ultrasonic imaging. Beam spread angle can be adjusted for larger field of view of further reach, which is useful in ultrasonic searching or fish finding. Acknowledgement. This research is funded by Hanoi University of Science and Technology (HUST) under grant number T2021-PC-041.
References 1. Mix, P.E.: Introduction to Nondestructive Testing: A Training Guide, p. 457. Wiley, Hoboken (2005) 2. PI Ceramic, Generating Ultrasound with Piezo Components. https://www.piceramic.com/en/ expertise/piezo-technology/generating-ultrasound-with-piezo-components/ 3. Carter, R., Kensley, R.: Introduction to Piezoelectric Transducers, pp. 4–5 (2018) 4. Chen, P.-S.: Analysis and Design of Piezoelectric Mirco-actuator, pp. 11–12 (2006) 5. Rathod, V.T.: A review of acoustic impedance matching techniques for piezoelectric sensors and transducers. MDP I, 13–16 (2020) 6. Kinsler, L.E., Frey, A.R., Coppens, A.B., Sanders, J.V.: Fundamentals of Acoustics, p. 126. Wiley, Hoboken (2000) 7. Crecraft, D.: Ultrasonic instrumentation: principles, methods and applications. J. Phys. E: Sci. Instrum. 16(3), 181 (1983) 8. Song, S.H., Kim, A.: Omni-directional ultrasonic powering for millimeter-scale implantable devices. IEEE Trans. Biomed. Eng. 62(11), 2717–2723 (2015) 9. Trogé, A., O’Leary, R., Hayward, G., Pethrick, R., Mullholland, A.: Properties of photocured epoxy resin materials for application in piezoelectric ultrasonic transducer matching layers. J. Acoust. Soc. Am. 128(5), 2704–2714 (2010) 10. Piezo.com: Materials Technical Data (Typical Values), vol. 1 (2018) 11. Lau, S., et al.: Multiple matching scheme for broadband 0.72Pb(Mg1/3Nb2/3)O3– 0.28PbTiO3 single crystal phased-array transducer. J. Appl. Phys 105, 094908 (2009) 12. Iowa State University: Non Destructive Evaluation Techniques. https://www.nde-ed.org/NDE Techniques/Ultrasonics/EquipmentTrans/characteristicspt.xhtml 13. Gohari, H.J.: Focusing of Ultrasonic Beam, pp. 25–29 (1997) 14. ANSYS Inc. ANSYS Help. https://ansyshelp.ansys.com 15. Senousy, M., Roche, D.: ACT_Piezo & MEMS Extension_Lecture (2019) 16. Lee, K.I., Humphrey, V.F., Kim, B.N., Yoon, S.W.: Frequency dependencies of phase velocity and attenuation coefficient in a water-saturated sandy sediment from 0.3 to 1.0 MHz. Acoust. Soc. Am. 121(5), 2553–2558 (2007)
A Study on Forced-Air Thermal Dissipation in Lithium-Ion Batteries Using Numerical Method Khai H. Nguyen, Tung X. Vu, Le Thi Thai, and Van-Sang Pham(B) School of Transportation Engineering, Hanoi University of Science and Technology, No 1. Daicoviet Road, Hai Ba Trung Dist, Hanoi 100000, Vietnam [email protected]
Abstract. The battery temperature management system is crucial to battery performance, affecting the entire functionality of the electric vehicle’s powertrain system (EVs). Air-cooling is extensively utilized in EVs due to its advantages in structure design, high reliability, and safety. In this work, we investigate the heat dissipation of a battery module using the forced-air method. The simulation results showed that the best thermal dissipation operational state for the battery system is at a lower vehicle speed of 36 km/h. Also, since the battery generates more heat at 72 km/h, the optimal working condition in this regime is SOC greater than 36%. Keywords: Battery · Thermal dissipation · Forced-air
1 Introduction Climate change has caused problems for the transportation industry in recent years. Traditional vehicles do not just consume a lot of petroleum, but they already release a lot of solid suspended particles, hydrocarbons, nitrogen oxides, and sulfur oxides, all of which are major pollutants in the environment. New energy vehicle development can efficiently address issues such as fossil fuel shortages, automotive emissions, and environmental damage [1–6]. Since the environmental benefits, high efficiency, safety, and long-term endurance, electric automobiles, nowadays, are receiving increasing attention [7–10]. However, despite the benefits, BEVs confront major obstacles. When temperatures rise over an acceptable level, Lithium-Ion Batteries (LIBs) degrade quickly, increasing the risk of fire and explosion. During charging and discharging, lithium-ion batteries generate heat both inside the cells and in the interconnection points, resulting in excessive battery temperature rise and temperature non - uniformity. In order to remove the excessive heat in the battery, much research has been conducted to develop an efficient BTMS for EVs and HEVs. Wang et al. [11] discussed the design considerations for BTMSs and described four types of BTMS cooling methods: air cooling, liquid cooling, PCM, and heat pipe cooling. Liquid cooling was suggested as being the most suitable method for large-scale battery applications at high charging/discharging C-rate and in high-temperature environments. A similar review was © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1064–1079, 2022. https://doi.org/10.1007/978-981-19-1968-8_90
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conducted by Xia et al. [12]. Kim et al. [13] reviewed heat generation phenomena and critical thermal issues of lithium-ion batteries. The different BTMS cooling methods were proposed and categorized. Except for the air and liquid cooling techniques, the paper also presented refrigerant two-phase cooling, PCM-based cooling, and thermoelectric element cooling. It reported that the heat pipe system needs extra cooling plates with additional weight and volume to enlarge the contact areas with the battery cells. Liu et al. [14] summarized the development of BTMS systems including air, liquid, boiling, heat pipe, and PCM-based cooling with a particular focus on the PCM cooling techniques. Thanks to that, it was suggested that the improvement of BTMS systems should be focused on the performance and safety enhancement of Li-ion batteries. Despite the preceding research looking at various cooling ways for batteries used in EVs and other applications, there is still a gap in the literature about air-cooled BTMSs for EVs and HEVs. The air-cooling BTMS is one of the most essential cooling options for making EVs and HEVs more efficient and safer. Its low cost and simple construction make it appealing for a variety of commercial EV and HEV BTMS applications. For all of these things, in this work, we will examine the effect of forced-air cooling on the battery package, which will provide information on a battery system’s pros and cons in usage.
2 Battery Package Modeling 2.1 Battery Model Construction In order to examine the thermal states of the EV battery, we need to evaluate a specific battery model. In this paper, we refer to the Tesla Roadster Model S because it is not only a pioneer in EV vehicles but also the availability of Tesla patents in the public domain, which was useful in gaining a good grasp of battery module structure as well as its thermal mechanism. A Tesla Model S battery pack is made up of 18650 (65 mm × 18 mm) individual Lithium-Ion cylinder cells, coupled in both series and parallel. This battery pack consists of 16 identical battery modules arranged in a 74p6s pattern. The 74p denotes a set of 74 cells that are connected in parallel and 6 s implies that six of these ‘74p groups’ are linked in a sequence [Fig. 1]. Thus, it generated an energy capacity of 5.3 kWh per module.
Fig. 1. A sketch of the tesla model S battery package
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A battery module contains the cooling system, module management system, wiring, and other components are integrated within the battery module [20]. In the Tesla Model S, each module is made up of 444 cells coupling in a staggered grid to guarantee the working of the thermal management system. As a suggestion, this paper estimates the thermal dissipation of such an arrangement. However, due to the cell arrangement periodic property of the module as well to reduce the number of complexity in mathematics equations, we just analyze a group of 3 cells coupling in series, put in staggered grid [Fig. 2]. The parameters that affect temperature distribution such as the cell’s distance, the feed-flow rate, state of charge, will also be calculated.
Fig. 2. The battery module arrangement in Tesla Model S. The cells are grouped up 6 segment coupling in series (the black arrow indicates series connector, the cells orange and blue color depict positive and negative side, respectively). Each series includes 74 cells connected in parallel. (i) The typical group of cell arrangements in a module. (ii) - The estimating model.
2.2 Mathematical Model 2.2.1 Cell Heat Generation Modeling During battery operation, the Newman-Tiedemann -Gu mathematical model [15–17] is utilized to describe the behaviors of the battery. Herein, the cell voltage follows Eq. 1, discharge progress described by Eq. 2, charge progress governs by Eq. 3 and Eq. 4. The total cycle of charge-discharge generates the amount of heat obeys Eq. 5. Vcell = V (soci , T ) −
i γ (soci , T )
SOCi = 1 − (1 − SOC) SOCi = SOC
CAh.m−2 , 0 CAh.m−2 , i
CAh.m−2 , 0 CAh.m−2 , i
(1) (2) (3)
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SOC =
∫ idt CAh.m−2 , 0
;i =
I A
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(4)
dU Q = I . Uoc − Vcell − T dT
(5)
where A is the electrode area (m2 ). CAh.m−2 , 0 is nominal cell capacity (Ah-m−2 ). CAh.m−2 , i is cell capacity at current density i (Ah-m−2 ). i - is current density (A-m−2 ). I-is current (Amps). SOC is the state of charge (fraction). T is the temperature (K or o C). Uoc is equilibrium voltage (V) and γ is admittance term (Ohm−1 .m−2 ). 2.2.2 Battery Thermal Dissipation Modeling Regarding the transport equation indicating the three-dimensional flow and heat transfer for Newtonian fluids, it is governed by Eq. 6, treated by integrating over a control volume V, and Gauss divergence theorem follows Eq. 7. ∂(ρφ) + div(ρφU ) = div( grad φ) + Sφ ∂t d ρφdV + ρvφ.da = ∇φ.da + Sφ .dV dt A
A
(6) (7)
V
A
where φ represents the scalar property being transported, including velocity components, pressure, energy. is diffusion coefficient, described by Arrhenius-Eq. 8. = Deff = D0 e
−Ea RT
(8)
Four terms in Eq. 7 show transient term, convective flux, diffusive flux, and the source term respectively. Herein, the transient term implies the rate of change of property φ in the control volume V. The convective flux indicates the total velocity of decrease of the fluid property across the volume boundary causing convection. Diffusive flux signifies the rise of the property within the control volume due to diffusion. And the source term depicts the generation/destruction of φ. 2.3 Numerical Solver and Mesh Resolution In this work, the multi-solver is utilized to handle the interaction between cell heat generation and surface cell heating, as well as thermal convection from the heated cell surface and feed-flow. Four primary solver control models include Implicit Unsteady, Reynolds-Averaged Navier-Stokes (RANS), Battery, and Segregated Fluid Temperature. In the implicit method, nonlinear systems of algebraic equations are solved interactively for the value of all sub-volume at the new time simultaneously. Because highfrequency pressure fluctuations are not of concern in the cooling system, this approach is ideal for estimating. Also, due to the battery’s discharge time, this simulation takes a long duration, so that large time steps must be set-up. The RANS turbulence model
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gives a closure relationship for the Reynolds-Averaged Navier Stokes equations to solve for the transport of mean flow quantities. States of flow are obtained by decomposing the instantaneous quantities into a mean value and a fluctuating component (variable φ in Eq. 7). A battery solver is used to control the battery physics properties such as state of charge or heat generation. In the segregated fluid temperature model, the total energy equation is solved with temperature as the solution variable. The equation of state is then used to calculate enthalpy from temperature. Thus, the heat transfer problem between the battery surface and the air-feed flow is described using this model. In this work, the estimating model is meshed automatically by a polyhedral mesher, the base’s size 5 mm. Total time takes 171 s, acquired 1849.99 MB. The number of cells are 126087, faces are 666509, and vertices are 620067 [Fig. 3]. All cases are conducted on Desktop Dell VOSTRO 64-bit, 8 GB Ram, processor Intel Core i3-4170 CPU @ 3.70 Ghz.
Fig. 3. Battery model meshing. (a) Casing mesh and (b) Cells mesh.
2.4 Boundary Conditions In this model, to examine the effect of forced airflow on the battery dissipation performance, we shall determine multiple cases. Where the boundary condition is applied includes velocity = 20 m/s at case inlet, zero gradient pressure at case outlet. Battery region for the cells, Ohmic heating for the connector, and strap. And wall boundary conditions remain for the case pod.
3 Results and Discussion In this work, we model three cells that correspond to a nominal battery module. Along with that, our estimation method uses three different vehicle speeds to identify the thermal states. The three vehicle-operation regimes are 10 m/s (36 km/h), 20 m/s (72 km/h), and 30 m/s (108 km/h). During the defining battery process, a parameter known as C-rate is typically used to quantify the rate at which a battery charges or discharges. The C-rate (units of h−1 ) is computed by dividing the current (A) by the nominal capacity (Ah) following: Current(A) −1 h (9) C − rate = Capacity(Ah)
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The three equivalent C-rates of the model are derived as Fig. 4 refers to the Tesla Range Plotted Relative To Speed publication and the formula Eq. 9.
Fig. 4. Three of estimating battery discharge rates. In which 2.56C, 3.65C, and 7.3C respond to 495 (s), 990 (s) and 1406 (s), respectively.
It is noticed that the temperature nonuniformity of all of the cylindrical battery cells will have a serious impact on the battery’s reliability, cycle life, electrochemical properties, and safety. According to the IEC 62133-2:2017 standard requirements, the allowable discharge battery temperature ranges from –20 °C to 60 °C. For lithium-ion battery temperatures, a temperature range of 25 °C to 40 °C was recommended, with a maximum temperature differential of about 5 °C [18–22]. As a result, the paper will estimate the thermal properties of the battery in the following sections, utilizing this requirement as a reference to examine the battery’s thermal dissipation capabilities. 3.1 Examination Thermal State of Model 2.56C Rate Discharge The simulation of the battery at 2.56C (I = 19.2 A, U = 20 m/s) discharge rate is shown in Fig. 5. The surface temperature of cells in a group does not differ significantly, and the influence of varied cell spacing configurations has essentially little effect. During the 20 m/s EVs constant speed, the battery surface temperature fluctuates in the range of 26 °C to 35 °C, which is within the recommended temperature range for EVs [18–22]. Besides, as can be observed, the highest cell differential temperatures ranging from 1.05 °C to 0.4 °C representing 1 mm, 2 mm, and 4 mm cell spacing in each test instance [Figs. 6], the effect of cell spacing in three circumstance-examinations is not significant. This is trivial when the vehicle’s thermal behavior is taken into account. As a result, the forced-air approach method is ideal for EV cooling design in this regime. Despite the low specific heat capacity of air cooling (1.0035 J.g−1 .K−1 ), the advantages of the forced-air approach in EV thermal management in this regime offered significant benefits. Supplementarily, there is an interesting circumstance here in which increasing the cell distance appears to make the system hotter [Fig. 6a]. This can be explained by the pressure difference and velocity field, which are displayed in Fig. 7.
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Fig. 5. Surface temperatures of 2.56C discharge rate at full discharge state (SOC = 0%). (a) A 1 mm cell spacing, (b) A 2 mm cell spacing, and (c) A 4 mm cell spacing.
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Fig. 6. The plot of 2.56 C discharge rate. (a) Surface average temperature and (b) differential temperature between cells in a circumstance.
Following that, the gradient pressure in the cases between inlet and outlet boundaries are approximately 3000 (Pa), 2200 (Pa), and 1700 (Pa) corresponding to 1 mm, 2 mm, and 4 mm, respectively [Fig. 7b, d, f]. Which causes the different velocity values inside the case. Specifically, the pressure is larger, the velocity is higher [Fig. 7a, c, e]. As a result, with the same contacting surface, the heat transfer in the narrower cell spacing is larger because the faster velocity yields the higher coefficient of heat convection (Q = α.F.(tsurface - tflow ) - Newton Heat Law). Therefore, the average temperature of cells distance by 1 mm will be the lowest. Despite that, cell temperature homogeneity was positive as cell distance increased. To explain this phenomenon, the strap connector is considered. In the model, the strap connector is used to link cells in one examine-circumstance. This is a factor that impacts velocity distribution within the case in the forced-air system. When cell spacing narrows,
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this strap acts as an obstacle to flow movement, resulting in non-uniform cell velocity. As a result, heat dissipation is non-uniform. When cell spacing is larger, on the other hand, airflow can encroach on all available space within the case, as well as the area region between cells, where the strap’s influence is minimal. Consequently, the larger cell spacing will have the best temperature uniformity [Fig. 6b].
Fig. 7. The visualization of the battery velocity and pressure at 2.56C discharge rate for the 3cell system mimics the actual operating in the vehicle. Where figures a, c, e indicate the velocity coolants of estimating-case, it is 1 mm, 2 mm, 4 mm cell-spacing respectively. The figures d, e, f show the pressure field at the corresponding arrangement, 1 mm, 2 mm, 4 mm serially.
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3.2 Examination Thermal State of Model 3.65C Rate Discharge Estimating the 3.65C discharge rate of the model, the battery temperature simulation results are presented in Fig. 8a, b, c. Comprehensively, following the increase of vehicle speed, the needed current rises, so that the battery heat generation goes up [Eq. 5]. Here, corresponding to 3.65C, the 27.4 A current value is extracted which ensures the vehicle speed at 30 m/s (72 km/h) constantly. After computing the interaction between the surface battery and flow-air, as well as heat convection within the case, the acquired data indicated the increase in average battery temperature at three circumstances, roughly 28 °C and 44 °C [Fig. 9a]. This thermal range is higher in comparison to the prior operation mode. On the other hand, in this regime, the temperature differential between cells rose to 1.9 °C [Fig. 9b] for 1 mm cell spacing and 0.8 °C for 4 mm cell spacing. For all of these factors, although it still guarantees the IEC 62133-2:2017 standard, the appropriate design to apply in the vehicle’s heat dissipation at this speed should be carefully considered. It is the consistency of surface temperature and cell uniformity temperature, but also space-optimizing and safety. As aforementioned, the influence of input flow rate, which is controlled directly by gradient pressure from the RANS method, causes the differential temperature inside the case. The velocity and pressure distribution within the case is depicted in Fig. 10. The narrower the space, the higher the velocity (or/also gradient pressure); therefore, the amount of heat dissipated rises. In addition, according to the report data [Fig. 9a], the battery surface temperature can reach 44 °C, however, this is the highest temperature reached during the vehicle cycle life. Plot 9a shows that the battery temperature exceeds the safety point (T = 40 °C) after the 600th second (SOC = 35.8%). As a result, it is suggested that the battery SOC be kept at or above 36% during this regime to ensure the safety mode.
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Fig. 8. The 3,65C visualization of battery temperature at full discharge state (SOC = 0%). (a) Cells spacing 1 mm, (b) cells spacing 2 mm, and (c) cells spacing 4 mm.
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Fig. 9. The plot of 3.65C discharge rate for (a) average surface temperature and (b) differential temperature between cells in a model.
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Fig. 10. The visualization of the battery velocity and pressure at a 3.65C discharge rate. Where figures a, c, e indicate the velocity coolants of estimating-case, it is 1 mm, 2 mm, 4 mm cell-spacing respectively. The figures d, e, f show the pressure field at the corresponding arrangement, 1 mm, 2 mm, 4 mm serially
3.3 Examination Thermal State of Model 7.3C Rate Discharge In this regime, the research will investigate battery thermal stages at 30 m/s (108 km/h) vehicle operation speed, which is commonly referred to as highway velocity. Figure 11 describes the results of the thermal battery simulation. Equivalent to 7.3C of battery discharge time, the amount of running current is roughly 55 A. As a result, at 60% of SOC discharged [Fig. 11d], the battery temperature exceeded the acceptable temperature of 60 °C for all of the tests. This temperature rose to 72 °C at 10% of SOC [Fig. 11a, b, c]. There is a risk of fire damage in the battery.
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The reason is since the current utilized is too high, causing rapidly excessive heat production within the battery. Thus, the battery cannot efficiently cool three of the cells under the flow rate of 20 m/s (Q = 0.02 m3 /s). Temperature homogeneity across the cells is not ideal, ranging from 2 °C to 4.53 °C, according to the battery differential temperature Fig. 11f. For all of these things, before switching to highway velocity, our advice is to increase the input flowrate or decrease the input air-cooling temperature by enhancing air density.
Fig. 11. The battery surface temperature at 7.3C discharge rate. (a), (b), (c) The visualization surface temperature at 10% of SOC. (d) The SOC following the 7.3C time discharge. (e) The plot of surface temperature following SOC in battery cycle life. (f) The cells differential in a case.
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4 Conclusion The forced-air cooling approach recently was found to be the most popular optimization method for battery thermal management of EVs and HEVs. By using that approach method, this work examined thermal dissipation in a model of battery mimicking a module of multiple-cell. The results showed that in the low-speed vehicle (36 km/h), the thermal of the battery is ensured. Likewise, with the same input of flowrate of 20 m/s airspeed, the surface temperature of the battery is inversely proportional to cell distance, also the cell uniformity temperature grows as cell distance increases. Conversely, the ability of air-cooling thermal management is hampered at 72 km/h due to increased heat generation; we propose keeping the battery’s SOC at a higher 36% in this regime to ensure the vehicle’s safety. Moreover, at highway speed (108 km/h), air-cooling systems require a larger flowrate or should reduce the temperature of the input air feed flow before entering the cooling process. Acknowledgments. This research is funded by Hanoi University of Science and Technology (HUST) under project number T2021-PC-013.
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13. Kim, J., Oh, J., Lee, H.: Review on battery thermal management system for electric vehicles. Appl. Therm. Eng. 149, 192–212 (2019) 14. Liu, H., Wei, Z., He, W., Zhao, J.: Thermal issues about Li-ion batteries and recent progress in battery thermal management systems: a review. Energy Convers. Manag. 150, 304–330 (2017) 15. Sharma, A., Zanotti, P., Musunur, L.P.: Enabling the electric future of mobility: robotic automation for electric vehicle battery assembly. IEEE Access 7, 170961–170991 (2019) 16. Gu, H.: Mathematical analysis of a Zn/NiOOH cell. J. Electrochem. Soc. 130(7), 1459–1464 (1983) 17. Newman, J., Tiedemann, W.: Potential and current distribution in electrochemical cells: interpretation of the half-cell voltage measurements as a function of reference-electrode location. J. Electrochem. Soc. 104(7), 1961–1968 (1993) 18. Kim, U.S., Shin, C., Kim, C.S.: Modeling for the scale-up of a lithium-ion polymer battery. J. Power Sources 189, 841–846 (2009) 19. Park, C., Jaura, A.K.: Dynamic Thermal Model of Li-Ion Battery for Predictive Behavior in Hybrid and Fuel Cell Vehicles, SAE Technical Paper, 0148-7191 (2003) 20. Rao, Z., Wang, S., Zhang, G.: Simulation and experiment of thermal energy management with phase change material for aging LiFePO4 power battery. Energy Convers. Manag. 52(12), 3408–3414 (2011) 21. Park, H.: A design airflow configuration for cooling lithium-ion batteries in hybrid electric vehicles. J. Power Sources 239, 30–36 (2013) 22. Yang, X.-H., Tan, S.-C., Liu, J.: Thermal management of Li-ion battery with liquid metal. Energy Convers. Manag. 117, 577–585 (2016)
A Study on Anti-pedal Error System in Car Based on Hydraulic Approach Sy-Le Ho, Xuan-Giao Nguyen, Van-Hop Pham, Tien-Bang Nguyen, and Van-Thuan Truong(B) Department of Fluid Power and Automation Engineering, School of Transportation Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Pedal error in car is a serious problem in traffic. Over the years, many researchers and manufacturers have researched and developed different types of accelerator and brake pedals to overcome the wrong pedaling error. This study aims to a solution of dealing with this problem and without changing driving behavior. Basing on a hydraulic approach, a design of model is established and implemented via simulation and experiment. The obtained result indicates that the model may be a considerable solution for the aforementioned problem. Keywords: Pedal error · Accelerator and brake pedal · Driving behavior
1 Introduction Accidents caused by accelerator and brake pedal errors are common in Vietnam and the world. We can hear the news about faulty pedaling accidents every day on the media and social networks. But in Vietnam, there are not yet detailed statistics. According to the National Highway Traffic Safety Administration (NHTSA), each year in the United States there are 16,000 accidents involving the wrong pedaling error while driving, which means an average of 44 accidents a day [1]. Meanwhile, in Japan, this number is about 6,000 resulting in many injuries and deaths [2]. In 2009, the Institute for Research and Analysis of Traffic Accident Data, whose headquarter is in Tokyo, recorded 6,700 accidents, 37 deaths, and more than 9,500 injuries related to this mistaken pedaling error [3] (Fig. 1). Many people believe that only inexperienced drivers make mistakes on the accelerator and brake pedals. But NHTSA shows that experienced ones also meet this error. While 93% of accidents are caused by people with completely normal mental and health, no illness, no drugs, only 1% cases are due to drivers using stimulants. Especially with experienced drivers, they do not recognize this error, still think they are doing it correctly, and keep pedaling the accelerator for a very long time (more than 12 s in many cases) until the car is stopped by a large obstacle. The researchers said that driver’s foot manipulation is a “blind spot” operation. The car controllers do not see their feet but rely on feeling to determine the position, direction, amplitude, force, and speed of the pedal [4]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1080–1089, 2022. https://doi.org/10.1007/978-981-19-1968-8_91
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Fig. 1. Statistics of accidents caused by faulty accelerator pedaling by age group [1]
A sample survey of 2,393 accidents caused by mistaking the accelerator for the brake in North Carolina of the USA from 2004 to 2008 shows that women always account for a higher percentage than men in all age groups. National Motor Vehicle Crash Causation Survey data shows a similar proportion, and on average, women cause up to two-thirds of faulty pedaling accidents [4] as in Table 1. Table 1. The proportion of misstep on accelerator by gender and age group by NHTSA Age
Gender Male
Female
76
37%
63%
The increase in accidents involving pedaling error has posed a difficult question for researchers. On the market today, there is a device to prevent the wrong accelerator pedal, but it has not met the needs of users because of its price and is only suitable for certain types of vehicles. Therefore, we have studied the topic “ANTI ACCELERATOR PEDAL ERROR SYSTEM” with the desire to overcome the risk caused by pedaling wrongly. Due to shortcomings in the implementation solution, many published studies have not been applied in practice. Our team has found four domestic and foreign inventions related to this idea. The two projects of “Combination of accelerator and brake on the same pedal” of a Japanese engineer [5] and an Indian student group [6] both lead drivers to change their driving habits. Working with this type of pedal for a long time also hurts their feet. Besides, the two ideas “Building a device to detect the wrong pedal” of
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Mr. Nguyen Long Uy Bao [7] and Nam Son Garage [8] show their disadvantages. The accelerator pedal does not work effectively, the risk of the device being confused when determining the wrong pedal is high. Even if the faulty pedal error is detected, the car still can not slow down safely. Therefore, the group’s research project focused on developing a new method of detecting and correcting the wrong accelerator pedal error, overcoming the disadvantages of previous ideas, and increasing the practical applicability of the system. This study focuses on detecting exactly when the drivers misplaces the accelerator pedal, then the device will simultaneously pull the brake and disconnect the accelerator to make the vehicle stop and ensure the safety of the driver and people around. This is an interesting research topic for engineering with a practical vision in a specific field. The research is not limited to the reported parameters collected, but also clearly analyzed and evaluated. A model is built up basing on hydraulic theory. The research is implemented in both numerical simulation and experiment. The results show that it seems to be effective for solving pedal-error problem.
2 Fundamentals of Work 2.1 Proposed Model The model consists of a hydraulic cylinder which piston is connected to the accelerator pedal, the cylinder bore is fixed to the chassis. When a driver steps on accelerator pedal, the oil is pushed out of the cylinder through the throttle and solenoid valve into the oil reservoir. On the travel distance of the oil, a pressure sensor is arranged to measure the oil pressure and then send a signal to the microprocessor. In case of the wrong accelerator pedal: The driver steps on the brake very quickly with a large force but mistakenly steps on the accelerator, the oil pressure increases and flows to the oil reservoir. The sensor measures the oil pressure and then transmits the analog signal to the microprocessor. When the pressure exceeds the predetermined pressure, the microprocessor will command both two relays to supply power to the solenoid valve and electric motor. Immediately the solenoid valve closes, blocks the oil to the reservoir, and the accelerator pedal was locked and could not be moved. At the same time, the brake pedal helps the vehicle slow down, the foot pedal force is calculated from the oil pressure, the brake pedal pull corresponds to the driver’s accelerator pedal force. When the driver takes his foot off the pedal, the oil pressure drops, the relay switches off the valve and the electric motor, then the accelerator and brake pedals return to their normal positions. In normal case: The pressure sensor reports to the microprocessor that the pressure is within the allowable range, the microprocessor does not command the two relays, so the accelerator and brake pedals work normally. In case the driver intentionally accelerates: Based on the vehicle’s operating status data such as speed, average speed, vehicle density, or traffic conditions, the system decides whether to activate or not. This depends on sensor data and further research to distinguish between intentional acceleration or faulty pedaling. The driver can also actively turn off the system when necessary.
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2.2 Working Data Collected Accelerator Pedal Force: The accelerator pedal force is mainly concentrated in the range of 20-36N, usually at 28N [9]. Braking Force: Free braking force is quite small (20 ÷ 50)N, small value with cars, great value for trucks [10]. Maximum braking force is about (500 ÷ 700)N, depending on the condition of each person [10]. For women, the maximum pedaling force may be smaller at around 400N [11]. Brake Response Time: + Active braking situation: the driver is alert, and his awareness of good braking ability is significant. The best-estimated reaction time is 0.7 s, in which 0.5 s is for realizing the risk and 0.2 s is for pedal switching. + Normal situation: the driver detects a common road signal such as a speeding sign or a traffic light. Reaction time is slower, about 1.25 s. This is due to an increase in perception time to over one second with a fixed pedal shift travel time of about 0.2 s. + Unexpected situation: the driver encounters an unusual obstacle, such as a pedestrian or another vehicle crossing the road at a close distance. He would need more time to analyze the event and respond. The reaction time depends on the distance to the obstacle and its approach direction. The best-estimated brake response time is 1.5 s for blocks from both sides of the vehicle and a few tenths faster for straight-forward ones. While the perception time is 1.2 s, the foot movement time extends to 0.3 s [12]. 2.3 Model Parameters The model parameters used in this study is in following Table 2. Table 2. Parameters of research model Parameters
Value (unit)
Cylinder diameter
16 (mm)
Cylinder stroke
50 (mm)
Accelerator pedal force
20 ÷ 36 (N)
Oil pipe line
6 (mm)
Mass density of hydraulic oil
887 (kg/m3 )
Oil velocity in pipe line
0.5 ÷ 1.5 (m/s)
The throttle flow control valve is an important equipment in the system. According to hydraulic theory, governing equation of the throttle valve is: 2p (1) Q = μS ρ
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with Q is flow rate, P is pressure drop of valve, μ is coefficient of orifice, ρ is mass density of hydraulic oil. A sensor is placed before the throttle valve, transform pressure signal into electrical signal to microprocessor. Based on preset pressure in microprocessor, the signal is recognized that whether it is pedal error or not.
3 Established Model and Control Diagram The Fig. 2 describes the 3D model (front and back view of design) built up in Solidworks of system. The configuration of accelerator and brake pedal is similar to a conventional one. It helps drivers don’t need to change driving behaviors. In which, (1) is brake pedal, (2) is accelerator pedal, (3) is footbrake zipper, (4) is floorboard, (5) is oil reservoir, (6) is pressure sensor, (7) is directional control valve, (8) is throttle valve, (9) is oil pipe, (10) is hydraulic cylinder, (11) is electric motor and (12) is electrical cabinet.
Fig. 2. 3D model of accelerator and brake pedal
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The Fig. 3 shows the control diagram of system, where P and P0 are pressure signal and preset pressure, respectively. For easily investigating dynamic response of system, an equivalent model is built up in Automation Studio software as shown in Fig. 4. In which, a double acting cylinder is used instead of one side cylinder in real system. The hydraulic circuit uses a double acting cylinder instead of a one side cylinder as in reality because the authors simulate the force and speed of the accelerator pedal by injecting hydraulic oil into the top chamber cylinder of the double acting cylinder, this does not affect the experimental results. The pressing force is supplied by a hydraulic unit with the same characteristics of the force generated by drivers.
Fig. 3. Control Diagram of system
The research is executed by both simulation and experiment. An experimental system is assembled as in Fig. 5. The work used a Arduino microprocessor which has structural configuration indicated in Fig. 6.
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Fig. 4. An equivalent model built in Automation Studio software
Fig. 5. Experimental system
Fig. 6. Arduino microprocessor used in system
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4 Results and Discussion According to above fundamentals and established model as well, this part presents some research results and discussion. In operation principle, the system works well following control diagram as shown in Fig. 7.
Fig. 7. Operation testing
Table 3. The effect of throttle valve opening on pressure and accelerator pedaling speed in the case of normal pedaling and wrong pedaling Throttle Opening (%)
Normal pedaling
Pedaling error
Pressure (bar)
Pedal velocity (cm/s)
Pressure (bar)
Pedal velocity (cm/s)
13
0
43.65
0
10
7.89
0.3
25.65
6.8
20
2.36
1.8
12.65
9.6
30
0.89
2.0
09.05
13.9
40
0.5
2.2
6.43
18.6
50
0.38
2.2
4.69
21.3
60
0.32
2.2
3.26
23.6
70
0.27
2.2
2.56
24.2
80
0.24
2.2
2.35
25.8
90
0.22
2.2
1.55
25.8
100
0.2
2.2
1.36
25.8
0
Table 3 describes the effect of throttle valve opening on pressure and accelerator pedaling speed in the case of normal pedaling and wrong pedaling. We can see the
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difference between 2 states of drivers. Difference of stepping force values leads to difference in pressure signals obtained in sensor as in Fig. 8. The signal is processed in controller and suitable command is applied on system response. The smaller the throttle valve opens, the greater the pressure before the valve, the smaller the accelerator pedal speed, and vice versa. From the survey, the team was able to select the optimal throttle valve opening (80–100%) so that the pressure in the pipeline is large enough to help the sensor detect the wrong pedal error without hindering the switch of the pedal.
Fig. 8. The normal pedal pressure graph
Fig. 9. The pressure graph when accelerator pedal error
In the two above graphs, when the pressure is negative, the oil is sucked back by the cylinder thanks to the elastic force of the accelerator pedal. In Fig. 9, we can see that the pressure pulse spikes when a sudden pedal force is applied and then decreases to the pressure range maintained by the movement of the accelerator pedal.
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5 Conclusion The paper presents a study on anti-pedal error system in car based on a hydraulic approach. In general view, research results are satisfied the purpose of work. A device for anti pedal error is developed successfully without requirement of driving behaviors. Adopting hydraulic and electrical control principles, the equipment is accurate and durable. The structure of the accelerator pedal is similar to it in conventional one, so it is easy to install the device in current vehicles. However, these are preliminary results of research, which is the basis for prospective studies as system optimization, artificial intelligence application in pressure signal processing and so on.
References 1. Peake & Fowler. Accidents caused by pedal errors (2016). https://www.peakefowler.com/acc idents-caused-by-pedal-errors/ 2. Suzuki, T.: Method for detecting operation mistakes with accelerator pedal. Int. J. Autom. Eng. 9(1), 16–22 (2018) 3. Factsand Details. New Brake and Accelerator Pedal Designs (2010). https://factsanddetails. com/japan/cat23/sub184/item2793.html 4. NHTSA. “Pedal Application Errors”, (Report No. DOT HS 811 597). National Highway Traffic Safety Administration, Washington, DC 5. Naruse, M.: Combination Brake-accelerator Pedal Solves Accidental Acceleration Problem (2010). https://www.impactlab.com/2010/08/05/combination-brake-accelerator-pedalsolves-accidental-acceleration-problem/ 6. Asst. Professor, B.Tech. “Design and fabrication of brake and accelerator on single pedal”, ISSN NO: 2347-3150, pp. 431–440 (2016) 7. Check out the anti-mistake system of Vietnamese people’s brake and accelerator pedals. https://thanhnien.vn/soi-he-thong-chong-nham-chan-phanh-chan-ga-cua-nguoi-viet-pos t1263745.html 8. Device to prevent wrongly pedaling the car’s accelerator. https://www.doisongphapluat.com/ da-phat-minh-ra-thiet-bi-chong-dap-nham-chan-ga-xe-o-to-a300500.html 9. Deng, T.-M., Fu, J.-H., Shao, Y.-M., Peng, J.-S., Xu, J.: Pedal operation characteristics and driving workload on slopes of mountainous road based on naturalistic driving tests. Safety Sci. 119, 40–49 (2019) 10. Mortimer, R.G.: Foot brake pedal force capability of drivers. Ergonomics 17(4), 509–513 (1974) 11. OpenStax. Diagnostics of automotive systems (Page 27/37). Automotive technical diagnostic textbook. OpenStax CNX 12. Broen, N.L., Chiang, D.P.: Braking Response Times for 100 Drivers in the Avoidance of an Unexpected Obstacle as Measured in a Driving Simulator, pp. 900–904. Dynamic Research, Inc. Torrance, California (1996)
High-Speed Focus Detection System Using Diffractive Beam Sampler and Position-Sensitive Detector Xuan Dat Tran1,2 , Xuan Binh Cao1,2(B) , and Le Phuong Hoang1 1 Square Lab, Hanoi University of Science and Technology (HUST), Ha Noi 10000, Vietnam
[email protected] 2 School of Mechanical Engineering, Hanoi University of Science
and Technology (HUST), Ha Noi 10000, Vietnam
Abstract. In laser processing, the focus detection needs to be rapid and precise. This paper proposes a technique that provides a rapid-response, precise detection, and high-resolution focus inspection system based on geometrical optics and advanced optical instruments. A diffractive beam sampler (DBS) and a positionsensitive detector (PSD) are used in the system to precisely signal the focus position with high resolution. The reflected image position is quickly detected by the PSD and the focal position is maintained persistently when the target surface is shifted along the optical axis. The proposed system is easy to implement and inexpensive. Moreover, with high numerical apertures, this system is appropriate for practical use in industrial production and applications other. Keywords: Focal position detection · Laser micromachining · Diffractive beam sampler · Single-slit masks · Position-sensitive detector
1 Introduction In laser processing, the requirements for accuracy and product quality always come first. The incorrect focal position will lead to poor machined surface quality, which causes great loss to the manufacturing industry. Therefore, the topic of the focus inspection is researched and published by many scientists and engineers. The quality of the focus condition determined the profile measurement [1]. A precision focus inspection system would also improve the reproducibility of current fabrication methods, such as direct laser patterning on non-planar surfaces [2], two-photon nanopatterning [3], and laser microgroove using CCD cameras [4]. A wide variety of autofocus systems are based on the following mechanisms: maximizing the reflected light signal detected behind a pinhole [5], viewing the specimen in the same beam path as the laser [6–8], and optical triangulation by means of a position-sensitive device [9]. All of these approaches employ auxiliary lasers in addition to working lasers with comparatively low numerical aperture optics.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1090–1097, 2022. https://doi.org/10.1007/978-981-19-1968-8_92
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Liu et al. [10] employed a bisected laser beam for autofocusing. The bisected laser beam is used with two achromatic lenses [11], two cylindrical lens assemblies [12], a tunable optical zoom system [13], and a high-speed optical rotating diffuser [14]. However, the detection range verified in their system is restricted in the range −200 to 200 μm, which is too small in laser technology. The system for in situ real-time focus detection uses a double hole mask and advanced camera for higher resolution and fidelity [15] but the signal response is not rapid. The system uses a single-slit mask and position-sensitive detector for the rapid signal response but this system is limited to the resolution and numerical aperture [16]. Therefore, a real-time focus control system with the ultrarapid signal response, precise detection, and high-resolution is still a challenging goal to achieve. In this paper, we propose an autofocusing system that combines the diffractive beam sampler, a position-sensitive detector, and a single-slit mask. The proposed system has detection resolution, ultrarapid response, and a high-numerical-aperture. The slit image position on the detector signals the focus position rapidly and accurately while the specimen is shifted along the optical axis. This paper is organized as follows: Sect. 2 gives the working principle of the system. Section 3 performs simulation on the 3DOptix and obtains results. Section 4 discusses the proposed method. Section 5 presents concluding remarks.
2 Working Principle of the Focus Determination System The setup of this system for an out-of-focus reflecting surface is shown in Fig. 1. This system uses the diffractive beam sampler (DBS) to create an outgoing beam exit from the DBS with a propagation angle [17]. The laser beam originates from a laser beam source with wavelength λ and is diffracted from the beam sampler. The beam sampler becomes a secondary laser source for three beams: (−1), (0), and (+1). However, we use a mask with a long and narrow rectangular off-axis slit. This mask blocks the beam (0), (+1) and allows only part of the beam (−1) to pass through. Then, the beam passes through the beam splitter, the objective lens (with focal length f ), gets reflected on the specimen surface, is redirected through the objective lens. The incoming beam intersects at point I2 and the refracted beam intersect at point I3 . Then, the beam will be reflected at the beam splitter and finally arrives at the PSD. The image in the single slit of the mask cannot be displayed on the photosensitive area of the PSD interface screen but we can determine the position of the incident beam via the output voltage of the PSD in a linear manner. The PSD offers an ultrafast response for the image position under the interaction between the laser beam and the sensitive PSD microsensors. According to Fig. 1, the reflected beam (−1) makes an angle B with the optical axis. The sum of the objective-lens-beam-splitter distance and the beam-splitter-PSD distance is denoted by p, and v is the position of the slit image on the detector. According to this optical path, we will determine the dependencies of v and u via moving the specimen.
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Fig. 1. Geometrical optics of the system. LS: Laser source, DBS: Diffractive beam sampler, M: Mask, BS: Beam splitter, OL: Objective lens, S: Specimen, PSD: Position-sensitive detector.
According to [16], we can obtain with laser beam):
≈ 0 (where w0 is the beam waist of the
−1 2 2 2 πw − 1 + λf 0 a f
b =1+ f a f
→
π w02 λf
b ≈1+ f
a f
(1)
1 af →b= −1 a−f
Similarly, the intersection at point I3 is the image of the intersection at point I2 . By substituting d and –c (I3 is the virtual image of I2 ), we obtain the following: d =1+ f −c f
→d =f +
−c f
−1 2 2 2 πw − 1 + λf01
−c f
(2)
f −1
According to Fig. 1, the intersection at point I2 is the image of the intersection at point I1 over the specimen, which can be considered as a perfect mirror. Therefore, we have the following: c = b − 2u
(3)
On the other hand, we consider the following ratios: h a = tanβ = tanα c b a → h = (b − 2u) tanα b h d p−d = →v=h v p−d d
(4)
(5)
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By substituting Eqs. (2) and (4) into (5), we obtain the following: a p 2ap − f (a + p) v=− − 1 tanα − 1 2u − f f a−f
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(6)
By differentiating Eq. (6) with respect to u we obtain the changes in v that result from perturbations in u as follows: p a −1 − 1 δu (7) δv = −2tanα f f where δv is the change in the slit image position on the detector (scalar), δu is the change in the sample position (scalar). The values ϑ which is the resolution of the system indicate the ratio between δv and δu.
3 Simulated Setup and Results 3.1 Simulated Setup
Fig. 2. The system is set up on the 3DOptix software. LS: Laser source, DBS: Diffractive beam sampler, M: Mask, BS: Beam splitter, OL: Objective lens, S: Specimen, D: Detector.
Figure 2 shows a photograph of the simulated setup using a DBS and mask with a single slit. To determine the focal position, a Cobolt laser with a wavelength of 355 nm was used [18]. The laser beam passed through the DBS (Edmund Optics, Transmission grating beamsplitter with grooves 70 lines/mm, the aperture 12.7 mm, and order −1 [18]) with a propagation angle α of 2.848°. When the beam hits the mask, only a narrow beam with a slit-like cross-section passes through. The beam was manipulated on the specimen surface by an objective lens (Thorlabs, TL4X-SAP 4X Super Apochromatic Microscope Objective [18]) with a focal length of 50 mm. We used a metallic mirror as the specimen. To keep the focal distance constant throughout the simulation, the specimen was moved along the optical axis. The optical components were arranged such that the distance between the DBS and the beam splitter was 50 mm, the distance between the beam splitter and the objective lens was 100 mm and the distance between the beam splitter and the PSD was 200 mm. The mask was arranged between the DBS
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and the beamsplitter. The setup and calibration procedure were performed simply on the 3DOptix software. In the simulation system, the PSD is replaced by the white detector because there is not the PSD in this software. As shown in Fig. 3(a), the specimen is located at the focal
Fig. 3. Photograph of the slit image on the detector. (a) The specimen position located at the focal position. (b) The specimen position located in front of the focal position. (c) The specimen position located behind the focal position
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position; thus, the position of the slit image on the detector is incident upon the y-axis of the detector as the reference position. In Fig. 3(b), the specimen is located in front of the focal position; thus, the position of the slit image is on the right of the reference position. In Fig. 3(c), the specimen is located behind the focal position; thus, the position of the slit image is on the left the reference position. 3.2 Results We conducted simulations in the 3DOptix software to verify the efficiency of this system. The sampled fraction of the beam was tested by moving the specimen at various positions by placing the specimen at a distance z above or below the focus in increments of 0.5 mm. The results are shown in Fig. 4. The black and red lines correspond to the theoretical and simulated results, respectively.
Fig. 4. The slit image position according to the specimen position.
From our simulated setup, we have the following parameters: a = 150 mm, p = 300 mm, f = 50 mm, and α = 2.848°. We obtain ϑ = 0.99523 using Eq. (7). With simulated results, we calculated the resolution ϑ = 0.99351. These results show our simulation data and theoretical results agree with each other quite well. Furthermore, the numerical aperture of the laser beam is calculated using the following equation: NA =
λ π w0
(8)
With λ = 355 nm and w0 = 1 nm, we obtained NA = 113. However, the propagation angle of the DBS is α = 2.848° = 0.049 rad, which is much larger than the numerical aperture of the laser beam. Obviously, our method provided stringent focusing conditions with high numerical apertures.
4 Discussion As expected, when the specimen deviates from the focal position, the position of the reflected ray on the detector shifts further in both directions relative to the reference
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position at the focal position according to the equations offered in Sect. 2. This indicates the change in the relative position of the sampling beam on the detector to the location of the specimen. The proposed method uses the PSD so the response time is rapid [16]. The focal position is precisely signaled based on the position of the slit image detected by the PSD with a detection error. This error is identified after we complete the experiment. Furthermore, our system is a real-time detection and autofocus system. When the target surface is shifted from the focus, the computer can automatically move the surface to the focus again by the reference voltage of the PSD. This method does not directly perform laser processing. However, the proposed method can be used for the detection of the focal position in high-precision laser processing. Thus, a high-power laser source for machining and a low-power laser source for focus detection in the same system would be difficult for engineers and scientists.
5 Conclusion In this paper, a novel system for focus detection that integrates a DBS, PSD, and a single-slit mask to give a rapid response, high inspection resolution, and high numerical aperture was proposed. The focal position is detected based on geometrical optics and advanced optical instruments. The theoretical calculations and simulation on the 3DOptix demonstrate the validity of the method. This system has many advantages and needs further improvement to increase its applicability in the manufacturing industry.
References 1. Rhee, H.G., Kim, D.L., Lee, Y.W.: Realization and performance evaluation of high speed autofocusing for direct laser lithography. Rev. Sci. Instrum. 80, 073103 (2009) 2. Luo, J., Liang, Y., Yang, G.: Realization off autofocusing system for laser direct writing on non-planar surfaces. Rev. Sci. Instrum. 83, 053102 (2012) 3. Jung, B.J., Kong, H.J., Jeon, B.G., Yang, D.Y., Son, Y., Lee, K.S.: Autofocusing method using fluorescence detection for precise two-photon nanofabrication. Opt. Express. 19, 22659– 22668 (2011) 4. Cao, B.X., Hoang, P.L., Ahn, S., Kim, J.O., Noh, J.: High-precision detection of focal position on a curved surface for laser processing. Precis. Eng. 50, 204–210 (2017) 5. Luo, J., Liang, Y., Yang, G.: Dynamic scan detection of focal spot on nonplanar surface: theoretical analysis and realization. Opt. Eng 50(7), 073061 (2011) 6. Fox, M.D.T., French, P., Peters, C., Hand, D.P., Jones, J.D.C.: Applications of optical sensing for laser cutting and drilling. Appl. Opt. 41(24), 4988–4995 (2002) 7. Hand, D.P., Fox, M.D.T., Haran, F.M., Peters, C., Morgan, S.A., McLean, M.A.: Optical focus control system for laser welding and direct casting. Opt. Lasers Eng. 34(4–6), 415–427 (2003) 8. Haran, F.M., Hand, D.P., Peters, C., Jones, J.D.: Focus control system for laser welding. Appl. Opt. 36(21), 5246–5251 (1997) 9. Neumann, B., Damon, A., Hogenkamp, D., Beckmann, E., Kollmann, J.: A laser-autofocus for automatic microscopy and metrology. Sens. Actuat. 17(1–2), 267–272 (1989) 10. Liu, C.S., Hu, P.H., Lin, Y.C.: Design and experimental validation of novel optics-based autofocusing microscope. Appl. Phys. B. 109, 259 (2012)
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11. Liu, C.-S., Lin, Y.-C., Hu, P.-H.: Design and characterization of precise laser-based autofocusing microscope with reduced geometrical fluctuations. Microsyst. Technol. 19(11), 1717–1724 (2013). https://doi.org/10.1007/s00542-013-1883-z 12. Liu, C.-S., Jiang, S.-H.: Design and experimental validation of novel enhanced-performance autofocusing microscope. Appl. Phys. B 117(4), 1161–1171 (2014). https://doi.org/10.1007/ s00340-014-5940-9 13. Liu, C.S., Wang, Z.Y., Chang, Y.C.: Design and characterization of high-performance autofocusing microscope with zoom in/out functions. Appl. Phys. B. 121, 69–80 (2015) 14. Liu, C.S., Jiang, S.H.: Precise autofocusing microscope with rapid response. Opt. Laser. Eng. 66, 294–300 (2015) 15. Cao, B.X., Hoang, P.L., Ahn, S., Kim, J.O., Kang, H., Noh, J.: In-Situ real-time focus detection during laser processing using double-hole masks and advanced image sensor software. Sensors 17, 1540 (2017) 16. Cao, B.X., Hoang, P.L., Ahn, S., Kang, H., Kim, J., Noh, J.: High-speed focus inspection system using a position-sensitive detector. Sensors 17, 2842 (2017) 17. Cao, B.X., Hoang, P.L., Ahn, S., Kim, J., Sohn, H., Noh, J.: Real-time detection of focal position of workpiece surface during laser processing using diffractive beam samplers. Opt. Lasers Eng. 86, 92–97 (2016) 18. 3DOptix. https://design.3doptix.com/index.html
An Experimental Study on Position Control of Pneumatic Cylinder Using Programmable Logic Controller and Pneumatic Proportional Valves Duc Thinh Pham, Dinh Son Tran, and Xuan Bo Tran(B) Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. This paper evaluates experimentally the position control ability of a pneumatic cylinder by using pneumatic proportional valves and a programmable logic controller. An experimental system consisting of a double acting pneumatic cylinder, two pneumatic proportional valves and a programmable logic controller is considered. A proportional-integral-differential controller is applied to control the cylinder position. The controller is evaluated by experimental system with step and square wave input signals. The evaluation results show that the used control method can provide good control performances with the absolute position accuracy less than 3% and the rise time less than 3 s. Keywords: Position control · Pneumatic cylinder · Proportional control valve · Programmable logic controller
1 Introduction Pneumatic systems are power transmission systems that convert fluid power to mechanical power by using compressed air. The pneumatic systems are used in many applications in the industrial field because of their advantages [1–3]. Providing large force and/or torque, reliability, ease to install and maintenance are some of the highlights of the advantages of pneumatic transmission systems. The pneumatic systems are also used in high-temperature and toxic working environments. In addition, air sources are available, and the exhausted air does not harm our environment. However, control of the pneumatic systems is often difficult because the dynamics of the pneumatic systems are highly nonlinear. The air compressibility, the characteristics of the pneumatic valves, and the force friction in the actuators are the main factors causing the nonlinearities of the systems. To precisely control the position of a pneumatic cylinder, pneumatic proportional valves are often used [4, 5]. These valves allow continuous control of the air flow to the cylinder and thus the cylinder speed can be easily changed. Advanced or nonlinear controllers are often used in combination with the proportional valves to improve the control quality of the cylinder position [6–8]. However, when the nonlinear or advanced © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1098–1105, 2022. https://doi.org/10.1007/978-981-19-1968-8_93
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controllers are applied, more advanced programming devices than a programmable logic controller (PLC) need to be used. Advanced programming devices are often not common in the market and are not highly standardized, leading to difficulties in industrial applications. This paper investigates the ability of precise position control of a pneumatic cylinder using pneumatic proportional valves and a PLCs device. To conduct this study, we design a pneumatic system using a pneumatic cylinder and two pneumatic proportional valves. A proportional-integral-differential controller is used and programmed on the PLCs device. Experimental evaluation with different reference position inputs is given to show the ability of the proposed method.
2 Experimental System Figure 1 shows schematic of the experimental pneumatic servo system. The system consists of a double acting cylinder (SMC, CM2L25-300). The stroke, bore diameter, and road diameter are 0.3 m, 0.25 m, and 0.1 m, respectively. Figure 2 shows the photo of the experimental system. The piston movement was controlled by the change of the compressed air in two-cylinder chambers. The air in the cylinder chambers was independently controlled by two proportional valves (SMC, model VEF3121-1-02). Each valve is connected to each cylinder chamber. Valve 1 was connected to the left chamber and Valve 2 was connected to the right chamber. The valves can provide the amount of 1400 l/m. Figure 3 shows the characteristics of the proportional valve. These valves have 3 ports: 1(P), 2(A) and 3(R). In the proposed experimental system, port 1(P) Pneumatic cylinder Load Valve 1
Valve 2
Position sensor x
Amplifier 1
ADC
Amplifier 2
PLC u2
DAC
u1 Air preparation unit Compressor
PC
Fig. 1. Schematic of pneumatic servo system
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was connected to the air source, port 2(A) was connected to the cylinder chamber, and port 3(R) was connected to the atmosphere. The input signals to control the valves are current, which varies from 0 to 1 A. When the input signal is given from a value greater than 0.5 to 1 A, port 1(P) is connected to port 2(A) and thus the air from the air source is fed into the cylinder chamber. When the input signal is given from 0 to a value less than 0.5 A, port 2(A) is connected to port 3(R) and thus air from the cylinder chamber is exhausted to the atmosphere.
Fig. 2. Image of the experimental system.
When the input signal is 0.5 A, all the valve ports are closed and therefore no air is supplied into or discharged from the cylinder chambers. The air flowrate through the valve is proportional to the amperage supplied. The piston will perform forward stroke when Valve 1 is supplied with a current between 0.5 and 1 A and Valve 2 is supplied with a current between 0 and 0.5 A. Otherwise, the piston will perform a reverse stroke when Valve 1 is energized. power is in the range of 0 to 0.5 A and Valve 2 is supplied with a current between 0.5 and 1 A. The piston will be stationary when the current signals to the two valves are 0.5 A. Two valve amplifiers (SMC, model VEA250) were used to convert voltage signals to the current signals. The piston position was measured by a position sensor (NOVOTECHNIK, LWH0300), which has a measuring range of 300 mm with
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an accuracy of 0.5%. This position sensor was mounted parallelly with the cylinder. The rod end of the piston was connected to an external load which slides on a guiding bar. A programmable logic controller (SIEMENS, PLC Model S7-1200) was used to communicate with the position sensor and the proportional valves. The signal from the position sensor was sent to the PLC through an analog-to-digital converter which is built into the PLC. The PLC calculates the control law based on the PID control algorithm and sends the control signal to the proportional valves through a digital-to-analog converter (SIEMENS, Model SM 1232). A personal computer (HP, Elitebook 8570p) was used to collect experimental data and to program the PID controller on the PLC through TIA Portal software. The compressed air was supplied from an air compressor to the cylinder through an air preparation unit. System pressure was set at 4 bar. The external load acting on the piston was 0.5 kg. The sampling time was set at 0.1 s.
Fig. 3. Characteristic of proportional valve [10].
3 Controller Design In this study, a PID controller is used to control the position of the pneumatic cylinder. The PID controller is the basic controller and is supported by the PLC programming device. The PID controller function is described as [5]: u(t) = Kp · e(t) + Ki ·
e(t) · dt + Kd ·
de(t) u(t) = Kp · e(t) + Ki · dt
e(t) · dt + Kd ·
de(t) dt
(1)
where u(t) is the control law. K p is the proportional gain and K i is the integral gain. K d is the derivative gain. e(t) is the error of the piston position and is caculated by the following equation:
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e(t) = x(t) − xd (t)e(t) = x(t) − xd (t)
(2)
where x is the piston position which is measured by the position sensor and x d is the reference position. Basing on the characteristic of the proportional valves and the amplifiers mentioned above, the compressed air supplying into the cylinder chamber is equal to amount of air that is released from another cylinder chamber. Therefore, the proportional valve control signal is calculated from the control law u as follows: u1 = 2.5 + u u1 = 2.5 + u u2 = 2.5 − u u2 = 2.5 − u
(3)
4 Experimental Results and Discussion To investigate the position controlling ability of the pneumatic cylinder using the proposed method, we evaluated the controller with step and square wave signal inputs. The supply pressure was set at 4 bar and the external load was set at 0.5 kg. Figure 4a, b, and c shown the results of the control performances of the three step inputs: x d = 0.11 m, x d = 0.18 m, and x d = 0.25 m, respectively. The value of K p , K i , and K d were determined by using trial and error method. The most adaptable parameters were chosen as: K p = 2, K i = 14, and K d = 0.01. In all three cases, at the beginning from 0 to 2.4 s, the reference positions were at 0 m. Then, the reference positions were switched to 0.11 m, 0.18 m, and 0.25 m, respectively. The control result for the step input x d = 0.11 m is shown in Fig. 4a. The rise time is 0.12 s and the overshoot is 0.03 m. The piston reaches the stable state after 0.8 s with the error remaining at 0.39 mm. In this case, the big overshoot appears with the result of about 30% reference position. This can be explained by the fast movement of piston in the short distance; thus, inertia force causes piston to move further than the reference position. Overshoot could be minimized by adjusting the parameters of the PID controller. In this study, we fixed the K p , K i , and K d in all cases. By keeping them constant, we study the adaptation of the controller for the different reference inputs. For the desired positions of 0.18 m and 0.25 m, the control performances are better than those of 0.11 m, as shown in Fig. 4b and Fig. 4c. For the desired position of 0.18 m, the rise time is 0.2 s and the overshoot value is 0.2 mm. The position error in stable state is 0.7 mm. For the desired position of 0.25 m, the rise time is 0.7 s and overshoot phenomenon does not appear. The position error in stable state is 0.49 mm. The results obtained in Fig. 4 show that using the proportional valves and the PID controller implementing on PLCs, it is possible to control the position of the pneumatic cylinder with relatively good accuracy. However, each controller parameter set is suitable for only one desired position or range of the cylinder. To investigate further the ability of the controller, we experimented the system with square wave signal, which have random frequency and have amplitude ranging from 0.06 to 0.24 m as shown in Fig. 5. The initial position is random. In this case, the control performance is good in all cycles of the test. The piston tracks the desired position relatively good. The rise time is fast, and the overshoot value is small. The rise time is about 0.7 s, and the maximum value of overshoot is about 3.7 mm.
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Fig. 4. Control results of three step signal inputs.
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Fig. 5. Control result performance of square wave input signal.
5 Conclusion This study implemented a controller PID on PLC to control the position of a pneumatic cylinder by using pneumatic proportional valves. The PID controller was designed and programmed on PLC S7-1200 through Tia Portal software. The method was evaluated in a wide range of desired position inputs. The results shown that the proposed controller provides good control performances. Piston can track the desired input with relatively small errors. The rise time in all cases is not exceeded 1 s. The maximum position errors in stable states are not exceeded 3%.
References 1. Beater, P.: Pneumatic Drives: System Design, Modelling and Control. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-69471-7 2. Saravanakumar, D., Mohan, B., Muthuramalingam, T.: A review on recent research trends in servo pneumatic positioning systems. Precis. Eng. 49, 481–492 (2017). https://doi.org/10. 1016/j.precisioneng.2017.01.014 3. Liu, S., Bobrow, J.E.: An analysis of a pneumatic servo system and its application to a computer-controlled robot. ASME J. Dynam. Syst. Meas. Control 110, 228–235 (1988) 4. Wang, J., Wang, D.J.D., Pu, J., Moore, P.R.: Modelling study, analysis and robust servocontrol of pneumatic cylinder actuator systems. IEE Proc. Control Theory Appl. 148(1), 35–42 (2001) 5. Tressler, J.M., Clement, T., Kazerooni, H., Lim, M.: Dynamic behavior of pneumatic, systems for lower extremity extenders. In: Proceedings of IEEE International, Conference on Robotics and Automation, pp. 3248–3253 (2002) 6. Karponis, D., Koya, Y., Miyazaki, R., Kanno, T., Kawashima, K.: Evaluation of a pneumatic surgical robot with dynamic force feedback. J. Robot. Surg. 13(3), 413–421 (2018). https:// doi.org/10.1007/s11701-018-0878-2 7. Tran, X.B., Nguyen, V.L., Nguyen, N.C., Pham, D.T., Phan, V.L.: Sliding mode control for a pneumatic servo system with friction compensation. In: Sattler, K.-U., Nguyen, D.C., Vu, N.P., Tien Long, B., Puta, H. (eds.) ICERA 2019. LNNS, vol. 104, pp. 648–656. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37497-6_75
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8. Gross, D., Rattan, K.: Pneumatic cylinder trajectory tracking control using a feedforward multilayer neural network. In: Proceedings of IEEE Aerospace and Electronics Conference, Dayton, 14–17 July 1997 (1997) 9. Nagrath, I.J., Gopal, M.: Control Systems Engineering, 3rd edn. New Age Publisher, New Delhi (2007) 10. https://stevenengineering.com/Tech_Support/PDFs/70PCVEF.pdf
Control Strategies for Beer Recovery Process from Surplus Yeast Lan Anh Dinh Thi1(B) , Viet Dung Nguyen1 , Van Nhat To1 , Thu Ha Nguyen1 , and Thanh Ha Tran2,3 1 Faculty of Automation Engineering, School of Electrical and Electronic Engineering,
Hanoi University of Science and Technology (HUST), Hanoi, Vietnam [email protected] 2 ELANI Technology Solutions Corporation, Hanoi, Vietnam 3 Institute of Science Technology and Training OMEGA, Hanoi, Vietnam
Abstract. In beer production, a large amount of surplus yeast is released into the environment. In fact, surplus yeast contains 40–70% of beer which can be recovered. While a small amount of surplus yeast is used for animal feeds, the rest requires an expensive treatment process before it is released into the environment. The solution to this problem is to use a high-speed centrifuge to separate the beer from the surplus yeast with low investment costs and fast return on investment. For this the process, the automatic control system plays an important role to keep the system efficiently operated as well as provide the high quality of recovered beer. This paper presents control strategies which are designed and applied to the process including PI controller, fuzzy PI controller, fuzzy PD controller, MPC and supervisory controller. A plant model is derived from the real production process used for the design of centrifuge controllers. A supervisory control is also designed and built which controls and monitors the operation of the whole beer recovery process. The performances of the centrifuge controllers are compared to select the one with the best quality control. Keywords: Disc stack centrifuge · Beer recovery · Surplus yeast · PID controller · Fuzzy control · Model Predictive Control
1 Introduction In the brewing production process, breweries have to come up with the best products that have to be economically optimal. At the same time, the waste discharged into the environment must be treated so that it does not affect the environment. One of the methods to reduce the waste that is to use a beer recovery system from yeast. The amount of beer is about 60% from yeast and the remaining yeast can be used as animal food. The quality of the recovered beer is assessed through parameters such as the remaining yeast cells, the O2 pickup and the microbiological stability.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1106–1120, 2022. https://doi.org/10.1007/978-981-19-1968-8_94
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The technology used in the system is the centrifugal separation technology. This technology has many advantages such as high efficiency, no microbial invasion and easy cleaning. The beer recovery process is as follows [1]: Yeast is taken to mix with degassed water to ensure 45.6% before entering the centrifuge. The input signals of flow of yeast and degassed water are sent to PLC through the sensors. The centrifuge used is BRUX 510, a continuous discharging disk stack nozzle centrifuge provided by Alfa Laval [3, 4]. Separator products include concentrated yeast and recovered beer. In the document [1], a monitoring control system for the beer recovery system from surplus yeast was presented, which used a PI controller to control the output yeast concentrate. In this paper, the PI controller will be compared with Fuzzy controllers and Model Predictive Control (MPC). Fuzzy controllers are designed for this systems based on fuzzy theory [6, 7]. MPC uses Model Algorithmic Control (MAC) method for modeling and also computing controller for this process [5]. The simulations employ Matlab Simulink Toolbox [9] to simulate and compare the system quality of the controllers. The model of the disc centrifuge is built in [2]. It should be noted that two methods of fuzzy controller and MPC are used in this paper because of the following: (1) fuzzy theory applied to the controller design has some advantages compared to the traditional controller design such as being able to manually adjust the controller’s parameters. (2) MPC has been intensively studied and widely applied in process control areas since the 70s of the 20th century. This work considers a process to recover beer from yeast residues, the authors expect to study more about MPC method and apply it to compare the performances of MPC with fuzzy controller so that the best quality can be chosen for the process. A supervisory control is also designed and built which controls and monitors the operation of the whole beer recovery process [1]. This paper studies a beer recovery system that can be applied in practice with the goal of minimizing environmental pollution caused by surplus yeast and recovering a valuable amount of beer.
2 Design of Control Strategies for Beer Recovery Process from Surplus Yeast 2.1 Problem Beer recovery process from surplus yeast should control the sequential process and centrifuge. The centrifuge needs to determine angular speed of rotor to be able to separate the beer from surplus yeast. The rotational speed of the centrifuge has been determined to be ω = 148.9 rad/s [1]. Regarding the sequential control of the process for separating beer from surplus yeast, it is necessary to follow a certain process. First, the controller starts the process, opens the water supply valve to clean centrifuge. Then, the controller will open the valves so that the mixture of surplus yeast and degassed water are pumped into the centrifuge. The final stage of the operation is CIP (Clean In Place) which cleans and makes the plant ready for the next batch.
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The centrifuge control should maintain the density of the concentrated yeast at a desired value. The volume fraction of the concentrated yeast in the outlet needs to be at rate of 75%. The control object in this paper is the disc stack centrifuge. The control variable of the centrifuge controller is the density of the concentrated yeast which is calculated through a quantity called dry fraction. Dry fraction means the percentage by volume or weight of the fluid that corresponds to a completely dry yeast [2]. In Fig. 1, r1 and r2 are outer and inner radius of disc; θ is half the included angle of the discs; a is clearance between discs; z is direction of flow axis; y is perpendicular axis to the z axis. The rotational speed of the centrifuge is derived from these parameters [1].
Fig. 1. Schematic diagram of a disc stack centrifuge [2]
The structure of the disc centrifuge includes: inlet – where the liquid to be cleaned enters, outlet – where the liquid after clean comes out, stacked discs – to increase the separation surface area, solids discharge port after separation, a motor to rotate the centrifuge. The process for beer recovering is presented in [1]. Surplus yeast with 66.3% volume fraction is taken from tank and mixed with degassed water. There is a linear pump on each pipe to ensure that volume fraction of yeast before flows into centrifuge reaches 45.6%. Yeast flowrate and degassed water flowrate are measured and sent to PLC by sensors. The total inlet flowrate is 30 hl/h. The separator used in the system is BRUX 510, a continuous discharging disc stack nozzle centrifuge provided by Alfa Laval. 2.2 Control Design for Centrifugal Separation Process Controlling the output quality is an important task of the system. The concentrated yeast should have a stable density. The density of the concentrated yeast is related to the dry fraction according to Eq. (1), given in [2]:
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ρH =
ρ1 ρ2 xH (ρL − ρ1 ) + ρ1
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(1)
where ρ 1 is the density of 100% yeast when dried (ρ 1 = 1.46 kg/m3 ); ρ 2 is the density of water (ρ 2 = 1 kg/m3 ); ρ L is the density of recovery beer (ρ L = 1.006 kg/m3 ); ρ H is the density of concentrated yeast (ρ H = 1.07 kg/m3 ). After calculation, the dry fraction of fluid x H is the control variable that the controller has to maintain its values at 21.1%. The mass balance of the yeast is as in Eq. (2): dxH 1 = (xI qI − xH qH − xL qL ) dt m
(2)
where m is the mass of the volume containing yeast and beer; x I is the dry fraction of the mixture of yeast and degassed water; qI is the mass flow of the mixture of yeast and degassed water, qH is mass flow of the concentrated yeast, x L is the dry fraction of the recovery beer, qL is the mass flow of the recovery beer. It is assumed the process is ideal, i.e. x L .qL ≈ 0. By taking the Laplace transform for Eq. (2), we get the following transfer function which was taken from [1]: G(s) =
k1 k2 s + 1
(3)
k 1 and k 2 are the coefficients of G(s) where k 1 = x I /qH = 1.16; k 2 = m/qH = 54.64. The Eq. (3) has first-order form. PI Controller: The object is the first-order form, so the selected controller is PI controller. PI controller is commonly used in industry because of its simple structure but high reliability. The transfer function of PI controller is Gc (s) = k p *(1 + 1/(T I *s)). The parameters of PI controller are T I = k 2 and k p = 100. In Fig. 2, the plant is G(s); r is the set-point value; e = r – y called the error which is the difference between r and the output of the plant y; u is the signal of the controller.
Fig. 2. PI controller structure
Fuzzy Controller: In addition to traditional methods to design a PID controller, there are other approaches to design a PID controller such as using fuzzy theory. The structure of a fuzzy PID controller is depicted in Fig. 3. For this structre, r, y, e and the plant G(s)
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are identical as in Fig. 2. In addition, there are ie and de, in which de is the error derivative and ie is the error integral. The PID fuzzy controller uses values such as e, de, and ie as inputs to the fuzzy system, then through the fuzzy system, the control signal u is found to enter the object – it is the same as a proportional-integral-derivative controller (PID controller) [6]. In this paper, a fuzzy PI controller and a fuzzy PD controller are designed and compared with the PI controller.
Fig. 3. Fuzzy PID controller structure
For the sake of understandability, we briefly summarize some basic concepts of Fuzzy Logic Control Theory [6] in the following. These concepts include fuzzy sets, some basic calculations of fuzzy sets, linguistic variables, fuzzy clause, fuzzy rule and defuzzification. • Fuzzy sets: The fuzzy set is represented as a membership function. All values belonging to the set will results in the value of the membership function greater than or equal to 0 or less than or equal to 1. The membership function form is used in this paper is a triangle function. • Basic calculations of the fuzzy sets are intersection and union. The intersection of two sets is defined as follows: A ∩ B = {x|x ∈ A and x ∈ B}. The membership function of the set A ∩ B will be calculated by μA∩B (x) = min(μA (x), μB (x)). The union of two sets is defined as follows: A ∪ B = {x|x ∈ A or x ∈ B}. The membership function of the set A ∪ B will be calculated by μA∪B (x) = max(μA (x), μB (x)). • Linguistic variables are defined by five components: linguistic variable name, linguistic variable value, universe set U, language variable value generation rule, language variable value association rule with fuzzy sets defined on the universe set U. • Fuzzy clauses/rules are constructed from experience. Fuzzy rules are always written in the following form: “IF x is Ai THEN y is Bi ” i = 1, …, n where x = (x 1 , x 2 , …, x k ), where x, y are linguistic variables; Ai , Bi are the values of language variables. The part “IF x is Ai ” is called the conditional clause. The part “THEN y is Bi ” is called the result clause. The proposition can be abbreviated R : A → B. When knowing the input, based on the fuzzy clause, we can infer the result R with the dependency function depending on the conditional clause and the result clause. The selected fuzzy inference mechanism is MAX-MIN. The MAX-MIN is defined: membership functions
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are determined according to the MIN composition rule and the union between compositional clauses is obtained according to the MAX rule. The MAX rule is the fuzzy “OR”. The fuzzy “OR”is written as μA∪B (x) = max(μA (x), μB (x)). The MIN rule is the fuzzy “AND”. The fuzzy “AND” is written as μA∩B (x) = min(μA (x), μB (x)), where μA is read as “the membership in class A” and μB is read as “the membership in class B”. • Fuzzy rule is a set of many fuzzy clause. From fuzzy propositions and association rules, we obtain a fuzzy set. • The process of determining the clear value from the membership function is called defuzzification. There are three methods of defuzzification: maximum, centroid, bisector. The centroid method will choose the center of the membership function. In this paper, the centroid method is used. Fuzzy PD Controller: The inputs of the fuzzy PD controller are e and de. The fuzzy set of e is {Negative, Zero, Positive} and the fuzzy set of de is {Negative, Zero, Positive}. The fuzzy set of output u is {Negative, Small Negative, Zero, Small Positive, Positive}. The membership function of a fuzzy set is in the form of a triangular function as in Eq. (4): ⎧ ⎪ 0, x < a ⎪ ⎪ ⎨ x−a , a ≤ x < b (4) μA (x) = b−a c−x ⎪ ⎪ c−b , b ≤ x < c ⎪ ⎩ 0, x ≥ c The range of e is [−δ δ] with δ = 24. The range of de is [−λ λ] with λ = 400. The range of u is [−α α] with α = 20000. The membership functions of inputs and output are given in Fig. 4.
Fig. 4. Membership functions of the fuzzy PD controller
The rule set of the fuzzy PD controller is described in Table 1:
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E
Negative
Zero
Positive
Negative
Negative
Negative
Small negative
Zero
Zero
Zero
Zero
Positive
Small positive
Positive
Positive
The selected fuzzy inference mechanism is MAX-MIN, the defuzzification method is centroid. Fuzzy PI Controller: The inputs of the fuzzy PI controller are e and ie. The fuzzy set of e is {Negative, Small Negative, Zero, Small Positive, Positive} and the fuzzy set of ie is {Negative, Positive}. The fuzzy set of output u is {Negative, Small Negative, Zero, Small Positive, Positive}. The range of e is [−δ δ] with δ = 24. The range of ie is [−λ λ] with λ = 10. The range of u is [−α α] with α = 20000. Membership functions of inputs and output are given in Fig. 5. The fuzzy PI controller has the following rules: – – – – – – –
Rule 1: IF e is Negative THEN u is Negative OR, Rule 2: IF e is Small Negative THEN u is Small Negative OR, Rule 3: IF e is Zero THEN u is Zero OR, Rule 4: IF e is Small Positive THEN u is Small Positive OR, Rule 5: IF e is Positive THEN u is Positive OR, Rule 6: IF ie is Negative THEN u is Small Negative OR, Rule 7: IF ie is Positive THEN u is Small Positive.
Fig. 5. Membership functions of the fuzzy PI controller
The step responses of the PI controller, the fuzzy PD controller and the fuzzy PI controller after simulations in MATLAB are shown in Fig. 6.
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Fig. 6. Compared step responses
The step response of the fuzzy PD controller has static error (Fig. 7). The step response of the fuzzy PI controller with no static error but with an overshoot Δh = 6%, the response time of the fuzzy PI controller is T 1 = 0.6 s (Fig. 6). The step response of the PI controller follows the set-point with no static error. The response time of the PI controller is T 2 = 2 s. The results are acceptable. As the plant is the first-order form, the I component in the PI controller will eliminate static error while the PD controller will have static error.
Fig. 7. Static error of the fuzzy PD controller
In the case of noise at time t = 3 s, the magnitude of the noise affecting the output (about 5–10% of the set value), the simulation results are shown in Fig. 8. When there is interference from outside, the output of the fuzzy PI controller according to the set value, the output of the fuzzy PD controller has stactic errror.
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Fig. 8. Simulation results when there is noise d
Model Predictive Control (MPC): Predictive control uses the existing information of the object model in the past of the object to predict the control signal and output signal in the future. MPC is based on calculating input values for the process by solving an objective function optimization problem. The structrute is described in Fig. 9 (the plant is G(s)) and basic components of the predictive control are [5]: – Using a mathematical model to predict the output of the process at future times (called horizon); – Calculating the control signal sequence by minimizing an objective function; – Using the receding strategy, i.e. at each time, only the first control signal in the calculated sequence of control signals is applied (Fig. 10).
Fig. 9. Structure of MPC
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The basic principle of MPC is briefly explained here, a detailed presentation of the MPC can be found in [5] The future outputs for a determined horizon N, called the prediction horizon, are predicted at each instant t using the process model. These predicted outputs y(t + k|t) for k = 1, …, N depend on the known values up to instant t (past inputs and outputs) and on the future control signal u(t + k|t), k = 0, …, N−1, which are those to be sent to the system and calculated. The set of future control signals is calculated by optimizing a determined criterion in order to keep the process as close as possible to the reference trajectory ω(t + k). This criterion usually takes the form of a quadratic function of the errors between the predicted output signal and the predicted reference trajectory. The control signal u(t|t) is sent to the process whilst the next calculated control signals are rejected.
Fig. 10. MPC strategy [5]
One of the modeling methods, which is widely used in literature as well as in practice, is the MAC method. MAC is based on the values of the weighting function gk . Output value yk at times is calculated as follows (5): yk = gk ∗ uk =
∞
gj uk−j
(5)
j=0
The future output value yk+i is calculated as follows: yk+i =
∞
gj uk+i−j +
i
j=i+1
= ci +
gj uk+i−j
j=0
i
gj uk+i−j
(6)
y = Gp + c
(7)
j=0
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⎡
g0 0 ⎢ g1 g0 ⎢ where G = ⎢ .. ⎣ .
⎤ 0 0⎥ ⎥ , . . .. ⎥ . . ⎦ · · · g0 ···
gN gN −1
T
y = yk yk+1 . . . yk+N , c = [c0 c1 . . . cN ]T , T
p = uk uk+1 . . . uk+N The chosen objective function is usually of the second order form, where ω is trajectory, Rk and Qk are coefficient matrix positive: Jk = eT Qk e + pT Rk p = (y − ω)T Qk (y − ω) + pT Rk p ⇒ Jk = pT G T Qk G + Rk p + 2(c − ω)T Qk Gp
(8) (9)
The optimal solution of the Eq. (9) is: −1 p∗ = − G T Qk G + Rk G T Qk (c − ω) ⇒ uk =
1 0 . . . 0 p∗
MPC simulation results using MATLAB with N = 3, Qk = 8000, Rk = 0.005.
Fig. 11. Result of MPC
(10) (11)
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Fig. 12. Simulation results of fuzzy controller and MPC with no noise
The result in Fig. 11 is an output that follows the set-point. The step response shows neither static error nor overshoot. The response time is T 3 = 0.6 s. In comparison with the step responses of the PI controller, the fuzzy PI and the fuzzy PD controller, the performance of the step response of MPC is better. Figure 12 shows that the output quality of the fuzzy controller is not as good as MPC in case of noise (Fig. 12). The result of MPC has not static error and overshoot (Fig. 13). Thus, the selected control strategy is MPC which provides the best of quality control.
Fig. 13. Simulation results of fuzzy controller and MPC with noise
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2.3 Design of a Control and Monitoring System The main task of this part is to build and design a supervisory control system to control and monitor the entire operation of the beer separation process. The technological process of the system includes three cycles: Clean – Run – CIP and is described by the algorithm below (Fig. 14):
Fig. 14. Sequential operation of the Beer recovery process
First, when the operator presses the “Start” button to start the system, it switches to the Clean mode and valve V5 is opened to supply water to clean the entire system. After 30 min, the system switches to Run mode. In Run mode, surplus yeast is introduced from valve V2 and combined with degassed water at valve V3, valves V6 and V8 are opened to dilute surplus yeast with a concentration of 45.6%. The surplus yeast after dilution is put into the centrifuge and rotated. At the same time, valves V5 and V6 open to add clean water and CO2 respectively. The recovered beer passed through valve V15 and the concentrated yeast passes through valve V1, then it returns to the process again. This process will end when the concentration of yeast concentrate reaches 75% or the operator presses the “Confirm” button. The last mode is CIP. This is a cycle than cleans and disinfects the system. Valve V1 is opened to supply CIP solution. At the same time, valves V6, V8, V9, V10, V11 and V14 are opened so that the CIP leads to the entire pipe in the system. Valves V12 and V13 open to recover CIP solution for future use. CIP mode lasts 90 min and then the whole process is over. CIP is the process of cleaning and disinfecting the pipeline system as well as equipment in the production line. This is a
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very important step in the food processing industry. The CIP process has the participation of many substances such as clean water (hot or cold depending on technology), high concentration NaOH, phosphoric acid, peracetic acid, … The control and monitoring system is simulated through TIA Portal V15.1 [8]. During operation, when there is an “Error” signal, the system will switch to the “Alarm” state, turn on the warning light and end the process. Errors often occur in centrifuge during rotation. The operator needs to wait until the rotor comes to a completed stop and then performs the inspection and repairing. Every day, the system automatically generates a report as an Excel-based template with necessary for the implementation of maintenance and repair.
3 Results The designed controllers are implemented in a real process and results of a beer separation system from surplus yeast are estimated: • Input: Yeast flow rate 20–60 hl/h with yeast concentration after dilution from 25–45%. • Output: High quality recovered beer, low residual yeast, less than 100,000 yeast cell/ml. Concentrated yeast reaches concentrations above 75%. The system is simulated and integrated with the monitoring system through the HMI interface and report as shown in Fig. 15 and Fig. 16.
Fig. 15. The main HMI of the beer recovering system
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Fig. 16. Production report
4 Conclusions The recovery of beer from surplus yeast is a good solution and can be widely applied. The controllers for the centrifuge and the supervisory control to control and monitoring the entire system have been proposed in this paper. The performances of the centrifuge controllers are compared to select the one with a better quality control. The simulation results of the controllers show that the quality of MPC is the best for controlling the yeast concentrated. A report will be output after each operation process so that the results of the process can be recorded. The process of recovering beer from surplus yeast can further develop by considering an energy monitoring system. This system evaluates the process of energy consumption and offers suitable solutions to save energy.
References 1. Van Thanh, D., Anh, D.T.L., Anh, N.T., Nam, N.H.: A supervisory controlled system for beer recovery from surplus yeast process. Ta.p chí KHOA HO.C VÀ CÔNG NGHÊ., pp. 61–66 (2018) 2. Svensson, A.: Control Strategy in a Centrifugal Separation Process. KTH Electrical Engineering (2010) 3. Alfa Laval: Don’t waste a drop. 4. Alfa Laval: Alfa Laval – disc stack separator technology 5. Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, Cham (1999). https://doi. org/10.1007/978-1-4471-3398-8 6. Chen, G., Pham, T.T.: Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems. CRC Press LLC, Boca Raton (2001) 7. Nguyen, H.T., Prasad, N.R., Walker, C.L., Walker, E.A.: A First Course in Fuzzy and Neural Control. Chapman & Hall/CRC, Boca Raton (2003) ´ T.V.: thiêt ´ kê´ hê. thông ´ HMI/SCADA vo´,i TIA PORTAL, Nhà xuât ´ ban Khoa ho.c và k˜y 8. Hiêu, thuâ.t (2019) 9. Documentation, S.: Simulation and Model-Based Design, MathWorks (2020). https://www. mathworks.com/products/simulink.html ij
Robust Control of Quadcopter in Case of Releasing Liquid and Encountering Uncertainties Nguyen Son Hoang1 and Manh-Tuan Ha2(B) 1 Aeronautical Engineering Department, University of Science and Technology of Hanoi,
Hanoi, Vietnam 2 Aerospace Engineering Department, Hanoi University of Science and Technology, Hanoi,
Vietnam [email protected]
Abstract. This paper illustrates the dynamics model and controller design of the quadcopter in the case of releasing liquid gradually. In the context of this paper, two ways of changing the mass of the liquid are taken into account. First, the fluid is released vertically in flight, and the second involves spraying the liquid horizontally. On the other hand, disturbances, as well as noises, are added to match the actual conditions. This paper also gives the dynamics equations of the quadcopter in the above cases using the Newton Euler method. To deal with the uncertainties, the controller is designed based on the Backstepping controller strategy, which is the recursive control that the design process can be “started” at a known-stable system and the designer will “back out” a new controller that progressively stabilizes each outer subsystem. Finally, the control algorithm and dynamic model are simulated by Matlab to demonstrate the feasibility of the system. In general, the quadcopter is stable under the disturbances and uncertainties after applying the Backstepping control strategy. Keywords: Quadcopter · Gradual mass drop · Newton Euler method · Backstepping control
1 Introduction Over the past few years, unmanned aerial vehicle (UAV) has gained public attention by manifold civil application domains in an array of fields including aerial photography, delivery of goods, agriculture, asset inspection, mining, firefighting. A UAV is defined as an unmanned, airborne vehicle that uses aerodynamic forces to provide lift, can fly autonomously or is remotely controlled in combination with onboard GPS that can recover, reuse or carry the payload. We can categorize UAV into two types, fixed-wing and multirotor. The fixed-wing type uses wings that can generate lift caused by the aircraft’s forward airspeed and the shape of the wing. They have great flight speed but are very difficult to maneuver in tight spaces. Contrary to fixed-wing, multirotor flies slower but has the ability to maneuver © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1121–1149, 2022. https://doi.org/10.1007/978-981-19-1968-8_95
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well in many types of terrain. Hence, multirotor is more commonly used than fixed wings. Today’s UAVs multirotor can do a lot of different jobs, go to places that are difficult for humans to reach. During the movement, it is likely that the mass of the UAV multirotor would change, causing an imbalance for the whole system because mass is a parameter that appears in all the dynamic equations of the system. The result is often that the UAV multirotor shakes and fails to follow the predetermined trajectory leading to low performance of the task. This issue often happens in different fields such as agriculture, firefighting. For example, during crop spraying, a large amount of pesticides is dropped vertically while moving. In firefighting operation activities, the UAV multirotor is used to spray extinguish agents to the high buildings where the rescue team is hard to reach. To solve this problem, Hexacopter could be a possible solution to deal with uncertainties as it can work well with six degrees of freedom instead of four as for quadcopter. However, the cost to manufacture a hexacopter is quite high plus the design is complicated but the application is equivalent to that of a quadcopter. Therefore, designing the robust control for the quadcopter, in this case, is essential in order to reduce quadcopter cost as well as help it fulfill its mission in severe conditions. During the development of quadcopter, there are various genres of controllers which were designed by researchers for different purposes from simple controls to complicated one. In 2004, Samir Bouabdallah and Roland Siegwart applied PD, PID control for the quadcopter [1]. Two years later, P.Castillo, R.Lozano and A.Dzul also used LQR control for the linearized model [2]. Although these controllers do not perform well when dealing with uncertainties, it has become the foundation for other robust control systems in the future. As a result, there are numerous controllers using Lyapunov stability theory in the following years like H infinity control [3], Sliding control [4]. They have achieved their purpose by adapting very quickly to disturbances, nevertheless requires sophisticated computation in design as well as manufacture in practical condition. It is easy to see that there are not many research projects related to stabilizing quadcopter while releasing the liquid. Linear Parameter Varying nonlinear-based control system [5] is an interesting article in which the system adapted well under variation of mass. However, this controller is quite complicated because it uses may different controllers such as PD, back stepping control, H infinity control which requires complicated calculation. Another study [7] used Gain-Scheduled PID and model predictive control technique but the system only works for altitude control or PID self-tuning method as Oualid Doukhi, Abdur Razzaq Fayjie, and Deok Jin Lee did [6]. Some scientists also used advanced techniques like control strategy based on neural networks to cope with some external disturbances that can affect quadcopter dynamics. This work will approach the problem with another control strategy which is using Backstepping control along with Lyapunov stability theory in order to eliminate external disturbances. The Backstepping control method is a recursive design procedure where the design process start at a known-stable system and design back out a new controller that can stabilize outer subsystem by linking the choice of a control Lyapunov function with the design of a feedback controller and guarantees global asymptotic stability of strict feedback systems. [8] This controller is extremely useful in dealing with uncertainties such as mass change, wind effect, drag force. Otherwise, in practical conditions, the
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information about the environment is not available and there are many unpredicted parameters. This paper will focus mainly on survey the response of the system to the impact of uncertain factors. In the past few years, many projects have been carried out in order to apply Backstepping controller for the quadcopter. In 2005, A recursive controller for quadcopter was proposed which mark the foundation for the development of this algorithm in the future [9] Later, Nguyen Xuan Mung and Sung Kyong applied the observer to the Backstepping control in order to simulate the trajectory of the system which is considered as the breakthrough with the prediction of noises and uncertainties. [10] However, the above papers merely used these controllers so as to simulate the motion of the quadcopter in space without applying disturbances or uncertainties such as mass change. Therefore, the main objective of this paper is applying those disturbances into the dynamic model using Backstepping controller and observe the adapt of the system. The structure of this paper is described as follows. In the second part, a mathematical dynamic model of the quadcopter is illustrated including the total forces and torque moments acting on the system. Along with that, the method of calculating the moment of inertia of the quadcopter system and the body is also considered. Otherwise, the effects of wind on the system and the reaction propulsive force caused by spraying liquid horizontally are also taken into account. Otherwise, the Backstepping controller for the subsystems is used for three subsystems. In part four, we use Matlab to simulate the whole system with disturbance factors in two cases dropping the liquid vertically and spraying horizontally. Last part is the conclusion and orientation for further research in the future.
2 Modelling of Quadcopter Dynamics The main content of this part is to find out the dynamic model of the system from which to build the basis for the controller design using the Newton Euler method [11]. Firstly, two reference frames related to the quadcopter are taken into account. An inertial frame of reference is a frame of reference whose origin is at a predetermined position relative to the Earth, with 3 axis points to the North, East and center of earth ˆ eˆ x , eˆ y , eˆ z) . The body frame of reference is the frame of reference attached to Ea : (O, the center of mass of the object Eb : (Ob , eˆ 1 , eˆ 2 , eˆ 3 ). Figure 1 illustrates two reference frames with respect to the quadcopter.
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Fig. 1. Reference frames related to the quadcopter
The quadcopter dynamic model includes two main parts: translational system which consists of horizontal as well as vertical movement along with altitude changes and rotational system involves roll, pitch, and yaw movement. This model is built based on the following assumption. First assumption: The quadcopter system is symmetric and rigid. Second assumption: The propeller is rigid and the drag force is neglected. Third assumption: The aerodynamics force acting on the system is constant (Fig. 2).
Fig. 2. Quadcopter configuration
2.1 Translation Model of Quadcopter The matrix Rb/i below depicts the transformation from the body frame to the inertial frame in which c(t),s(t) stand for cos and sine, respectively. ⎡
⎤ cφcψ sφsθ cψ − cϕsψ cψsθ cψ + sφsψ ⎢ cθ sψ sΨ sθ sΦ + cΨ cΦ sΨ sθ cΦ − cΨ sΦ ⎥ ⎢ ⎥ ⎢ −sθ ⎥ cθ sφ cθ cφ
(1)
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The relationship between velocity in the body frame and position derivative of the quadcopter in the inertia frame as below: ⎡ ⎤ ⎡ ⎤ x˙ u ⎣ y˙ ⎦ = Rb/i ⎣ v ⎦ (2) z˙ w where T x˙ y˙ z˙ is the velocity of the system in the inertia frame. [u v w]T is the velocity of the quadcopter in the body frame. The Newton second law below is used to describe the total force act on the system T fx fy fz ∈ R3 FB = m˙v = m(ωB × vB + v˙ B )
(3)
T T where vB = u v w and ωB = p q r . Transfer (3) to equation form: f x = m(u˙ + qw − rv) f y = m(˙v − pw + ru) f z = m(w˙ + pv − qu) Assuming that the external forces are absent, we have: ⎡ ⎤ fx ⎣ f y ⎦ = mgRb/i .ˆez − T eˆ 3 fz
(4)
(5)
2.2 Rotational Motion of Quadcopter Firstly, the Euler equation gives the total torque applied to the quadcopter which is equivalent to the variation of the angular moment. Euler angle is defined as the angle between the coordinate of two reference frames (O, eˆ x , eˆ y , eˆ z) and (Ob , eˆ 1 , eˆ 2 , eˆ 3 )
I .ω˙ B + ωB × (I ωB ) = τ
(6)
where I is the diagonal inertia matrix with I x , I y , I z are the moments of inertia with T respect to x, y, z-axis, respectively, τ = τφ τθ τψ . ⎤ Ix 0 0 I = ⎣ 0 Iy 0 ⎦ ∈ R3 0 0 Iz ⎡
Converting (6) to the equation form of the dynamic model of the rotational motion in the body frame. τ φ = p˙ Ix − qrIy + qr Iz
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τ θ = q˙ Iy + prIx − pr Iz τ ψ = r˙ Ix − pqIy + pqIy
(7)
The total external moment acting on the body frame contains the roll actuator action T moment which is the control torque generated by rotor speeds τ = τφ τθ τψ and the gyroscopic torque because of the rotational effect of four rotors: ⎡ ⎤ ⎡ ⎤ τ pφ jr θ˙ Ω r ˙ r⎦ τ gyro = ⎣ τ pθ ⎦ = ⎣ jr φΩ τ pψ 0 ⎡ ⎤ ⎡ ⎤ ˙ r τφ jr θΩ ˙ r⎦ τ T = τ − τ gyro = ⎣ τ θ ⎦ − ⎣ jr φΩ (8) τψ 0
2.3 Actuator Dynamic T Mi = T τ 1 τ 2 τ 3 is considered as the input matrix for the quadcopter to manipulate the behavior of the system, d is the drag coefficient of rotors. The position in the inertia of the quadcopter is controlled by varying the thrust force Fi which is defined as the force produced by each rotor. ⎤ ⎡ ⎤ ⎡ F1 + F2 + F3 + F4 T ⎥ ⎢τ1 ⎥ ⎢ F4 − F2 ⎥ ⎥ ⎢ (9) Mi = ⎢ ⎢τ2 ⎥ = ⎣ ⎦ F3 − F1 ⎢ ⎥ ⎢τ ⎥ d (F2 + F4 − F1 − F3 ) 3 With b is the thrust factor of the rotor, thrust magnitude Fi is determined by (10) [12]. Fi = b 2i
(10)
2.4 Dynamic Equation of Quadcopter System Using (4) (7) (8) and apply the Second Newton law, the dynamics equation of translation motion and rotational motion of quadcopter expressed in the Earth frame is deduced as below: x¨ =
(sψ sφ + cψ sθ cφ)T m
(11)
y¨ =
(sψ sθ cφ − cψ sφ)T m
(12)
(cθ cφ)T −g m
(13)
z¨ =
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φ¨ = θ˙ ψ˙
Iy − Iz l Jr − θ˙ Ω + τ1 Ix Ix Ix
(14)
θ¨ = ψ˙ φ˙
l Iz − Ix Jr ˙ + τ2 + φΩ Iy Iy Iy
(15)
Ix − Iy l + τ3 Iz Iz
(16)
ψ¨ = θ˙ φ˙
where Ω = Ω1 − Ω2 + Ω3 − Ω4 is the total residual rotor angular speed. i with i = 1,2,3,4 is the angular speed of the corresponding rotor. 2.5 Moment of Inertia Evaluation In this problem, changing the mass will also change the moment of inertia of the system, thus in order to accurately simulate the motion of the system, the moment of inertia of the system is developed in this part. Assume that the liquid contains n boxes of n1 , n2 , . . . nn with corresponding masses m1 , m2 , . . . mn attached to the bottom of the quadcopter. These boxes are shaped like a cuboid whose base is a square with sides of length “a” and height “h”. There are n boxes of n1 , n2 , . . . nn with corresponding masses m1 , m2 , . . . mn attached to the bottom of the quadcopter. First Case: The Quadcopter Drops the Liquid Vertically When simulating, the boxes with masses mi are released which means that the moment of inertia corresponding to mi will be subtracted from the moment of inertia of the whole system. It can be seen from Fig. 3 (xk , yk , zk ) is the coordinate system attached to the box system and (xq , yq , zq ) is the coordinate system attached to the quadcopter. From that, the moment of inertia of the whole system can be calculated as [5].
a
Fig. 3. Moment of inertia of quadcopter in case of dropping liquid vertically
The moment of inertia of the boxes system about the zk and zq axes are: 1 I( ni=1 mi )/zn = I( ni=1 mi )/zq = ( mi )a2 6 n
i=1
(17)
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The moment of inertia of the boxes system about the xk and yk axes are: I( ni=1 mi )/xn = I( ni=1 mi )/yn 1 1 ( = mi )a2 + ( mi )( h2 ) 12 12 n
n
n
i=1
i=1
i=1
(18)
Applying the Huygens-Steiner theorem, we can deduce the moment of inertia of the boxes system about xq and yq axes as follows: I( ni=1 mi )/xq = I( ni=1 mi )/yq 1 mi )( h2 ) 2 n
= I( ni=1 mi )/yn +
n
i=1
=
1 ( 12
n
mi )a2 +
i=1
7 ( 12
i=1
n
mi )(
i=1
n
h2 )
(19)
i=1
Finally, the moment of inertia of the whole system including the quadcopter and k boxes corresponding to the three axes x q , yq , zq of the quadcopter are: Isystem/xq = IXX
n n n 1 7 2 2 + mi a + mi h 12 12 i=1
i=1
(20)
i=1
Isystem/yq
n n n 1 7 2 2 = Iyy + mi a + mi h 12 12 i=1 i=1 i=1 n 1 Isystem/zq = Izz + mi a2 n 6 i=1
Second Case: The Quadcopter Sprays the Liquid Horizontally In this case, we assume that there are n boxes of n1 ,n2 , . . . nn with corresponding masses m1 ,m2 , . . . mn discharged horizontally (Fig. 4).
Fig. 4. Moment of inertia of quadcopter in case of spraying liquid horizontally
The moment of inertia of the boxes system about the yq axis is I n mi )/yq = I( ni=1 mi )/yn (n i=1 n n
2 1 1 = 12 mi h2 + 12 mi a i=1
i=1
i=1
(21)
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The moment of inertia of the boxes system about the zq and xq axes are: I( n
i=1 mi )/xq =I (
n
1 i=1 mi )/xn + 2
n
i=1 mi )(
n
a2 )
ni=1 n n 1 7 = mi h2 + mi a2 12 12 i=1
i=1
I( ni=1 mi )/zq = I( ni=1 mi )/zn +
(22)
i=1
1 mi )( a2 ) 2 n
n
i=1
i=1
n n n 1 7 ( = mi )a2 + ( mi )( a2 ) 12 12 i=1
i=1
(23)
i=1
The moment of inertia of the whole system including the quadcopter and k boxes corresponding to the three axes x q , yq , zq of the quadcopter are: Isystem/xq = IXX
n n n 1 7 2 2 + mi h + mi a 12 12 i=1
i=1
(24)
i=1
Isystem/yq
n n n 1 1 2 2 = Iyy + mi h + mi a 12 12 i=1
i=1
i=1
Isystem/zq = IZZ
n n n 1 7 + mi a2 + mi a2 12 12 i=1
i=1
i=1
2.6 The Dynamic Model of Wind Disturbance In real condition, there are various disturbance effects acting on the system. In order to simulate those uncertainties so that the system is more practical, a series of winds is applied to the quadcopter system. We can consider the force of the wind acting on the quadcopter as drag. The equation below calculated the drag force [13]: Fd =
Cd Vbw ρ A 2
where Cd is the drag coefficient. ρ is the air density (kg/m3 ). A is the surface area of the quadcopter exposed to the wind (m2 ).
(25)
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Vw b is the relative velocity of the quadcopter with the wind which is calculated by the equation below: ⎡ ⎤ x˙ i i ⎣ Vw = V − V = y˙ ⎦ − V iw w b b z˙
(26)
In this paper, we can assume that the contact surface area is the same in all directions of the quadcopter. As a result, the torque is neglected. Apply (21) and (22) on the Eq. (9), (10), (11) [14]: Fdx (sψsφ + cψsθ cφ)T − m m (sψsφ + cψsθ cφ)T = m i sign VwX Cd x˙ − Vwi y ρAX
x¨ =
−
(27)
2m
Fdy (sψsθcφ−cψsφ)T − m m i sign VWy Cd y˙ −Vwi y ρAy (sψsθcφ−cψsφ)T
y¨ =
=
=
m
−
2m
cos φ)T z¨ = (cos θ m − g − Fmdz i i sign VW Cd z˙ −VW ρ Az (cos θ cos φ)T m
−g−
(28)
Z
(29)
Z
2m
where Vbi is the velocity of the quadcopter in the inertia reference frame (m/s), Vwi is the velocity of wind in the inertia reference frame (m/s). 2.7 Jet Discharge Propulsion Let’s assume spraying the liquid horizontally case of quadcopter. In this case, the liquid ejected from the nozzle not only makes the quadcopter change in mass and moment of inertia but also the net force applied on the whole system. In order to accomplish the predefined mission, the quadcopter needs to eject a large amount of liquid with high speed resulting in the production of great force. Jet propulsion is produced when thrust is generated by aast moving jet of fluid in accordance with Newton’s Law of motion. The propulsive force or thrust induced by the jet can be expressed as (30) F = ρl Q Vbl − Vib where ρl is the liquid density kg/m3 , Vbl is the velocity out of the jet, Vib is the velocity of the system. Q is the flow volume m3 /s is calculated by the equation below: Q = AVbl
(31)
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Because the quadcopter only eject liquid horizontally so we just assume that the thrust acts only in the y direction, from (27) (29) (30), we consider the dynamic equation of the quadcopter: y¨ = (sψsθcφ−cψsφ)T m
−
i i sign VW Cd y˙ −VW ρAy y y
2m ρ Q V l −˙y + l mb
(32)
3 Control Design 3.1 Control Design Strategy The part depicts the process of designing a controller for a quadcopter based on Backstepping control strategy [15, 16]. The input signal includes the desired value of the altitude, the position. In order to move the vehicle in longitudinal and lateral axis, the corresponding rotation and pitch angle have to be adjusted by modifying the propellers rotational speeds while the altitude as well as yaw controller working independently. The actual trajectory is measured and adjusted the virtual control law of the Backstepping control strategy. To facilitate the design of the controller of the quadcopter, the whole system will be divided into 3 subsystems. As shown in Fig. 5, subsystem 1 contains the position controller, subsystem 2 contains the rotational controller, subsystem 3 contains the yaw controller and the altitude controller. The objective of this paper is focusing on designing position as well as altitude controller so that the quadcopter still equilibrates and follows a predefined trajectory.
Fig. 5. Control strategy of the quadcopter system
Firstly, we assume the following state vectors: φ˙ X X˙ φ x1 = , x2 = , x3 = , x4 = ˙ θ Y y˙ θ
(33)
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⎤ ⎡ ⎤ ⎡ T Ω1 ⎥ ⎢ τ1 ⎥ ⎢ ψ˙ ψ ⎥, x8 = ⎢ Ω2 ⎥ x5 = , x6 = ˙ , x7 = ⎢ ⎦ ⎣ ⎣ Z τ2 Ω3 ⎦ z τ3 Ω4
x7 and x8 are considered as the control input of the system while x1 x2 x3 x4 x5 x6 is the output of the system. 3.2 Backstepping Control Method To apply the Backstepping controller, the dynamics system needs to be written in a appropriate form. Based on (27) (29) (32) (33), the state equation of quadcopter applying Backstepping control is rewritten as below: Position control (subsystem 1): x˙ 1 = x2 , x˙ 2 = f 1 + g1 (x5 , x7 )α 1 (x3 ) Rotational control (subsystem 2): x˙ 3 = x4 , x˙ 4 = f 2 (x4 , x6 , x8 ) + g2 α 2 (x7 ) Altitude, yaw control (subsystem 3): x˙ 5 = x6 , x˙ 4 = f 3 (x3 , x4 , x6 ) + g3 (x3 , x5 , x7 )α 3 (x7 ) where the matrices g1 , g2 , g3 are defined as below: ⎧ sin ψ cos ψ ⎪ T ⎪ g = ⎪ 1 ⎪ m −cos ψ sin ψ ⎪ ⎪ ⎪ ⎪ 1 ⎨ Ix 0 g2 = 0 I1y ⎪ ⎪ ⎪ ⎪ 1 ⎪ 0 ⎪ ⎪ I ⎪ ⎩ g3 = Cos φ Cos θ z 0 m And the vectors α 1 , α 2 , α 3 are defined as below: ⎧ ⎪ Sin φ ⎪ ⎪ α = 1 ⎪ ⎪ Cos φ Sin θ ⎪ ⎪ ⎨ τ α2 = 1 ⎪ τ ⎪ 2 ⎪ ⎪ ⎪ T ⎪ ⎪ α3 = ⎩ τ3
(34)
(35)
(36)
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fx fφ f , f2 = , f 3 = z where: As well as the vector f 1 = fy fθ fψ ⎡ ⎤ ⎡ ⎤ fx 0 ⎣ fy ⎦ = ⎣ 0 ⎦ fz −g ⎤ ⎡ ⎤ ⎡ ˙ ˙ Iy −Iz r ˙ θ θ ψ IX − JIIΩ fφ x ⎥ I −I J Ω ⎣fθ ⎦ = ⎢ ⎣ φ˙ φ˙ z Iy X + IIy r φ˙ ⎦ I −I fψ θ˙ φ˙ x y
(37)
(38)
Iz
The scope of this method is to force the subsystem to follow the predetermined trajectory which is set up by the users. For this purpose, we need to build control laws for the system and then constrain all input parameters to stick to this predefined plan. This virtual law will be used to transmit to the rotors speed calculator. 3.3 Position Controller We are considering the virtual system: x˙ 1 = V 1
(39)
The position tracking error is the difference between the desired value and the actual value: Z1 = X 1d − X 1
(40)
Considering derivatives of z1 : .
z˙ 1 = x1d − x1 = x1d − x2
(41)
The control design is based on an augmented Lyapunov function candidate which is V1 =
1 T z z1 with z1 → ∞ so V 1 → ∞ 2 1
(42)
V 1 is globally positive definite. Taking the derivative of the Lyapunov function with respect to time: . V˙ 1 = z1 z˙ 1 = z1 x1d −x2 = z1 (˙x1d − V 1 ) (43) In order to achieve globally asymptotically stable, the time derivative of the Lyapunov-candidate-function must be globally negative definite. Hence, the virtual control input v1 is constructed as below: v1 = x˙ 1d + C 1 z1
(44)
where C1 is the positive definite matrix, the Eq. (42) is rewritten as: V˙ 1 = −C 1 ZT1 Z1 < 0
(45)
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We consider new virtual control for the system x˙ 2 = f1 + g1 (x5 , x7 )α1 (x3 ) with α1 (x3 ) = v2 . Applying the same technique, the time derivative of velocity tracking error must be considered z2 = x2 − V 1 = x2 − x˙ 1d − C 1 z1 = x˙ 1 − x1d − C 1 z1 Where x˙ 1 − x˙ 1d = z˙ 1
(46)
Let us consider the following positive definite Lyapunov candidate function: V2 =
1 T z z2 + V 1 with z2 → ∞ so V 2 → ∞ 2 2
(47)
Taking time derivative of V2 we have, . V˙ 2 = zT1 z1 +z 2 T z2 . T T T = −C 1 z1 z1 − z1 z2 + z2 x˙ 2 − x˙ 1d − C 1 z1
(48)
Substitute x˙ 2 = f1 (x2 ) + g1 (x5 , x7 )α1 (x3 ) with α1 (X3 ) = v2 into (47): +ZT2
V˙ 2 = −C 1 ZT1 Z1 f 1 + g1 (X 5 , X 7 )V 2 − X˙ 1d − C 1 Z˙ 1 − Z1
(49)
The virtual control vector v2 must be constructed so that the time derivative of Lyapunov-candidate-function is negative definite: v2 =
−C 2 z2 + C 1 z˙ 1 + z1 − f 1 + x¨ 1d g1 (x5 , x7 )
(50)
With g1 (x5 , x7 ) is nonzero because the entire thrust of the rotors needs to be greater than zero to counter the earth’s gravity Substitute (49) into (48), we have V˙ 2 = −C 1 zT1 z1 − C 2 zT2 z2 < 0 which is globally asymptotically stable. 3.4 Rotational Controller We are considering the virtual system: x˙ 3 = V 3
(51)
The angular tracking error is the difference between the desired value and the actual value: z3 = X 3d − X 3
(52)
Z˙ 3 = X˙ 3d − X˙ 3 = X˙ 3d − X 4
(53)
Considering derivatives of z3 :
The control design is based on an augmented Lyapunov function candidate which is V3 =
1 T z z3 with z3 → ∞ so V 3 → ∞ 2 3
(54)
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V 3 is globally positive definite. Taking the derivative of the Lyapunov function with respect to time: V˙ 3 = z3 z˙ 3 = z3 (x3d − x4 ) = Z3 (x3d − V 3 )
(55)
In order to achieve globally asymptotically stable, the time derivative of the Lyapunov-candidate-function must be globally negative definite. The virtual control input v3 is constructed as below: V 3 = x3d + C 3 Z3
(56)
where C3 is the positive definite matrix, the Eq. (54) is rewritten as: V˙ 3 = −C3 ZT3 Z3 < 0
(57)
We consider new virtual control for the rotational system x˙ 4 = f 2 (x4 , x6 , x8 ) + g2 a2 (x7 ) with α 2 (x7 ) = V 2 . Next, the angular velocity tracking error must be taken into account: .
z4 = x4 − V 3 = x4 − x˙ 3d − Cz3 = x˙ 3 − x3d −C 3 Z3 Where x˙ 3 − x˙ 3d = z˙ 3
(58)
Let us consider the following positive definite Lyapunov candidate function: V4 =
1 T z Z4 + V 3 with Z4 → ∞ so V 4 → ∞ 2 4
(59)
Taking time derivative of V4 we have,
V˙ 4 = −C 3 ZT3 Z3 − ZT3 Z4 + ZT4 x˙ 4 − x3d − C 3 Z˙ 3
Substitute x˙ 4 = f 2 (x4 , x6 , x8 ) + g2 α 2 (x7 ) with α 2 (x7 ) = V 4 into (41): V˙ 4 = −C 3 ZT3 Z3 + zT4 f 2 (x4 x6 x8 )+ .. g2 V 4 − x3d −C 3 z˙ 3 − Z3
(60)
(61)
The virtual control vector v4 must be constructed so that the time derivative of Lyapunov-candidate-function is negative definite: v4 =
−C 4 z4 + C 3 z˙ 3 + z3 − f 2 (x4 , x6 , x8 ) + x¨ 3d g2
(62)
3.5 Yaw and Altitude Control Apply the same technique as rotational control and position control, we obtain the virtual control laws of the quadcopter in yaw and altitude control are: V 5 = x˙ 5d + C 5 Z5 V6 =
−C 6 Z6 + C 5 z˙ 5 + z5 − f 3 (x3 , x4 , x6 ) + x¨ 5d g3 (x3 , x5 , x7 )
(63) (64)
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3.6 Closed Loop Control System Finally, by using (44) (50) (56) (62) (63) (64), the system (33) become stable under parameter varying, disturbance as well as uncertainties by following these virtual control laws: V 1 = x˙ 1d + C 1 z1 −C 2 Z2 +C 1 z˙ 1 +z1 −f 1 +¨x1d V2 = g1 (x5 ,x7 ) ˙ +C Z V 3 = xx3d 3 3 −C 4 z4 +C 3 z˙ 3 +z3 −f 2 (x4 ,x6 ,x8 )+¨x3d V4 = g2 V 5 = X 5d + C 5 Z5 −C 6 z6 +C 5 z˙ 5 +z5 −f 3 (x3 ,x4 ,x6 )+¨x5d v6 = g (x3 ,x5 ,x7 )
(65)
3
where the control gain matrices are selected as below: 35.25 0 4.24 0 C1 = , C2 = 0 25.49 0 67.07 51.7 0 33.46 0 , C4 = C3 = 0 40.08 0 44.84 16.03 0 16.85 0 , C6 = C5 = 0 24.65 0 24.56
(66)
4 Simulation and Result In this part, the entire control system and dynamic model are implemented on Matlab to demonstrate the feasibility of the algorithm. By applying these control laws (64) back to the state vectors (33), we obtain the closed loop control algorithm of this robust system T with X(t) = x1 (t) x2(t) x3(t) X 4(t) x5(t) x6(t) is the control output of the system and Y(t) = x7(t) x8(t) is the control input of the system. √ In addition, to make the system more realistic, a sequence of noise W(t) = QR(t) will be added to the state system evert 0.0004 s, where R is the random number generated by Matlab and ⎤ ⎡ 10−6 0 0 0 0 0 ⎢ 0 10−6 0 0 0 0 ⎥ ⎥ ⎢ ⎥ ⎢ −6 0 0 0 ⎥ 0 10 ⎢ 0 Q=⎢ ⎥ −6 ⎢ 0 0 0 ⎥ 0 0 10 ⎥ ⎢ ⎣ 0 0 0 0 10−6 0 ⎦ 0 0 0 0 0 10−6 T X(t) = x1(t) x2(t) x3(t) x4(t) x5(t) x6(t) + W(t) (67) T T T Series of winds with velocity Vwi x Vwi x Vwi x = 19 7 11 (m/s) and 18 5 10 are applied to the object to demonstrate the robustness of the system over time periods
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as shown below where the winds will impact the quadcopter at intervals of 2 s and 4 s. These values correspond with numbers 3, 5, 8 in Beaufort’s scale (Gentle breeze, fresh breeze and fresh gale). The yaw angle is unchanged in this experiment, hence, considering this parameter is out of scope in this paper. The quadcopter is moving at a slow speed, so in order to stabilize the system, the desired roll and pitch angle are adjusted to approximately 0. The main purpose of this article is to stabilize the system and force it to move in the predefined trajectory with minimum error. Figures below demonstrates the speed of the wind in the x, y, z directions (Figs. 6, 7 and 8, Table 1).
Fig. 6. Wind velocity in x direction
Fig. 7. Wind velocity in y direction
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Fig. 8. Wind velocity in z direction
Table 1. Quadcopter parameters Parameter
Definition
Value
Unit
m
Quadcopter mass without payload
1.023
[kg]
M
Quadcopter mass with payload
2.123
[kg]
g
Gravity of Earth
9.8
[m/s2 ]
b
Thrust factor
3.13 × 10–5
[kg.m]
d
Drag coefficient of rotor
7.5 × 10–7
[Kg.m.s2 ]
Jr
Rotor inertia
3.13 × 10–6
[Kg.m2 ]
l
Arm length
0.23
[m]
Ixx
Quadcopter inertia on x axis
0.0094999
[Kg.m2 ]
Iyy
Quadcopter inertia on y axis
0.0094999
[Kg.m2 ]
Izz
Quadcopter inertia on z axis
0.018576
[Kg.m2 ]
4.1 First Case: The Quadcopter Drops the Liquid Vertically Figures 13, 14, show the references in blue line and the tracking trajectory in the red line. A 3 dimensional (3D) representation is shown in Fig. 12. The total time for this experiment is 40 s. The tracking result is demonstrated as below: At t = 0, the quadcopter stays at the origin O (0.1, 0.1, 0.1) then goes up vertically and reaches the altitude of 2 m after 8 s, while the values of 2 remaining axes are constant. At the end of this period, the system reaches a point (0.1 0.1 2). From t = 8, the quadcopter hovers in the air and only changes direction in the x and y direction. We simulate the path of the quadcopter. The path goes to point (10, 0, 2) at t = 16 because the quadcopter only moves in the x direction.
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At t = 16 to the end of the experiment, the quadcopter goes in a square path and finally comes back to point (10, 0, 2) at t = 40 (Figs. 9, 10, 11, 15, 16, 17, 18, 19 and 20).
Fig. 9. Gradual mass reduction of quadcopter system
Fig. 10. Moment of inertia of the quadcopter in case 1 respect to x axis
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Fig. 11. Moment of inertia of the quadcopter in case 1 respect to y axis
Fig. 12. Moment of inertia of the quadcopter in case 1 respect to z axis
Fig. 13. Trajectory of the quadcopter in 3D space
Robust Control of Quadcopter in Case of Releasing Liquid
Fig. 14. Trajectory of the quadcopter in x and y direction
Fig. 15. Trajectory in z direction
Fig. 16. Tracking error in x direction
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Fig. 17. Tracking error in y direction
Fig. 18. Tracking error in z direction
Fig. 19. Tracking error in roll angle
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Fig. 20. Tracking error in pitch angle
4.2 Second Case: The Quadcopter Sprays the Liquid Horizontally In this case, the quadcopter will be simulated as a horizontal liquid drop while moving in a spiral trajectory. Figures 24, 25 illustrate the trajectory of the quadcopter with blue line referring to the desired value and red line referring to tracking value. The trajectory of the quadcopter in 3D space is demonstrated in Fig. 23. The propulsive force (29) is also applied to the quadcopter system in this case (Figs. 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 and 31).
Fig. 21. Moment of inertia of the quadcopter in case 2 respect to x axis
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Fig. 22. Moment of inertia of the quadcopter in case 2 respect to z axis
Fig. 23. Moment of inertia of the quadcopter in case 2 respect to y axis
Fig. 24. Trajectory of the quadcopter in 3D space
Robust Control of Quadcopter in Case of Releasing Liquid
Fig. 25. Trajectory of the quadcopter in x and y direction
Fig. 26. Tracking error in z direction
Fig. 27. Tracking error in x direction
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Fig. 28. Tracking error in y direction
Fig. 29. Tracking error in z direction
Fig. 30. Tracking error in pitch angle
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Fig. 31. Tracking error in roll angle
4.3 Comments on Results Overall, in spite of receiving a wide range of noises, uncertainties, disturbances as well as mass variation over time, in both cases the quadcopter remains stable and follows a predetermined trajectory therefore it is clear that the Backstepping control fulfills its purpose. In case 1 the position controller performs better than other controllers with a maximum error of about 0.05 m for the x direction and approximately 0.1 m for the y direction. In case 2 the error is larger than case 1 with the maximum error around 0.1 m in the x direction and 0.3 m in the y direction. This happens because of the more complicated trajectory as well as the propulsive force of the water acting on the system. For the altitude controller, the system also responds very well with a maximum error of about 0.06 m to 0.08 m in both cases. About the rotational control, the system is well adaptive under disturbance with only 0.01 rad error from the desired path. In general, when exposed to uncertainties, the quadcopter tends to deviate from a predefined trajectory but, the controller still gives quick feedback to force the system back to equilibrium. To prove the robustness of the system, we can compare it with another study on the same topic [5]. It is easy to see that in most cases the maximum error is between 0.05 m and 0.1 m compared to approximately 0.2 m in paper [5]. That proves the system has worked quite well and the quadcopter is balanced enough to perform its task. 4.4 Conclusions and Future Work This paper has described the response of the quadcopter system to disturbances, and uncertainties by using a Backstepping control. Perturbations here include wind, the change in mass of the quadcopter system when liquid is released. The controller used the regression Backstepping method combined with the Lyapunov stability theory to design virtual control laws so that it can force the system to follow a predetermined trajectory
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despite the disturbances. In general, the controller has shown quite high efficiency with small error relative to other studies in the same topic. However, this article still has some limitations that need to be overcome in the future as many external factors such as air resistance, aerodynamic forces are eliminated. Future work includes designing an observer for the quadcopter system combined with a Backstepping controller to predict factors of uncertainty. In addition, Backstepping control can be combined with H 2 /H infinity to apply to the rotation of the system, which has not been given much attention in this paper. Finally, it is crucial to apply these theories to a real-world hardware flying device to demonstrate their applicability in human life.
References 1. Bouabdallah, S., Noth, A., Siegwart, R.: PID vs LQ control techniques applied to an indoor micro quadrotor. In: Intelligent Robots and Systems, 2004 (IROS 2004)–Proceedings. 2004 IEEE/RSJ International Conference on 28 September–2 October 2004, vol. 3, pp. 2451–2456 (2004) 2. Castillo, P., Lozano, R., Dzul, A.: Stabilization of a mini rotorcraft with four rotors. IEEE Control Syst. Mag. 25(6), 45–55 (2005) 3. Salazar-Cruz, S., Palomino, A., Lozano, R.: Trajectory tracking for a four rotor mini-aircraft. In: Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC 2005. 44th IEEE Conference on 12–15 December 2005, pp. 2505–2510 (2005) 4. Madani, T., Benallegue, A.: Backstepping sliding mode control applied to a miniature quadrotor flying robot. In: IEEE Industrial Electronics, IECON 2006–32nd Annual Conference on 6–10 November 2006, pp. 700–705 (2006) 5. Pham, T.H., Ichalal, D., Mammar, S.: LPV and nonlinear-based control of an autonomous quadcopter under variations of mass and moment of inertia. In: 3rd IFAC Workshop on Linear Parameter-Varying Systems, November 2019, pp. 176−183. Eindoven, Netherlands (2019). hal-02444790. https://doi.org/10.1016/j.ifacol.2019.12.371 6. Doukhi, O., Fayjie, A.R., Lee, D.J.: Intelligent controller design for quad-rotor stabilization in presence of parameter variations. J. Adv. Transp. 2017, 10 (2017). Article ID 4683912, https://doi.org/10.1155/2017/4683912 7. Sadeghzadeh, I., Abdolhosseini, M., Zhang, Y.M.: Payload drop application of unmanned quadrotor helicopter using gain-scheduled pid and model predictive control techniques. In: Su, C.Y., Rakheja, S., Liu, H. (eds.) Intelligent Robotics and Applications. ICIRA 2012. LNCS, vol. 7506, pp. 386–395. Springer, Berlin, Heidelberg (2012). https://doi.org/10.1007/ 978-3-642-33509-9_38 8. Vaidyanathan, S., Jafari, S., Pham, V.-T., Azar, A.T., Alsaadi, F.E.: A 4-d chaotic hyperjerk system with a hidden attractor, adaptive backstepping control and circuit design. Arch. Control Sci. 28(2) 239−254 (2018) 9. Bouabdallah, S., Siegwart, R.: Backstepping and sliding-mode techniques applied to an indoor micro quadrotor. In: Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on. IEEE, pp. 2247–2252 (2005) 10. Xuan-Mung, N., Hong, S.K.: Robust backstepping trajectory tracking control of a quadrotor with input saturation via extended state observer. Appl. Sci. 2019(9), 5184. https://doi.org/ 10.3390/app9235184 11. Sabatino, F.: Quadrotor control: modeling, nonlinearcontrol design, andsimulation (2015)
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12. Rible, G.P.S., Arriola, N.A.A., Ramos, M.C.: Fail-safe controller architectures for quadcopter with motor failures. In: 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pp. 384–391 (2020). https://doi.org/10.1109/ICCAR49639.2020.910 8038. 13. NASA Glenn Research Center. https://www.grc.nasa.gov/WWW/K-12/airplane/ldrat.html 14. Sierra, J.E., Santos, M.: Corrigendum to wind and payload disturbance rejection control based on adaptive neural estimators: application on quadrotors. Complexity 2020, 1 (2020). Article ID 4189272, https://doi.org/10.1155/2020/4189272 15. Tripathi, V.K., Behera, L., Verma, N.: Design of sliding mode and Backstepping controllers for a quadcopter. In: 2015 39th National Systems Conference (NSC), pp. 1–6 (2015). https:// doi.org/10.1109/NATSYS.2015.7489097 16. Madani, T., Benallegue, A.: Backstepping control for a quadrotor helicopter. In: IEEE International Conference on Intelligent Robots and Systems, pp. 3255−3260 (2006). https://doi. org/10.1109/IROS.2006.282433 17. Bouabdallah, S.: Design and control of quadrotors with application to autonomous flying, January 2007. https://doi.org/10.5075/epfl-thesis-3727
Structural Durability Analysis of Offshore Wind Turbine Tower with Monopile Foundation According to ICE 61400 Standards Vu Dinh Quy, Le Thi Tuyet Nhung(B) , and Nguyen Viet Hoang Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Research into the production of energy from other sources is promoted worldwide, including the development of modern wind turbines. At that time, a series of countries promoted the exploitation of energy sources from nature, including wind energy. Therefore, offshore wind turbines are created to replace onshore wind turbines to fully exploit the energy generated by the wind. The design of a wind turbine according to international standards is a challenge for the wind power industry in our country. This study will address IEC 61400 onshore and offshore wind turbine design issues. Then, the process of load calculation and validating the tower durability according to IEC 61400 standard was analyzed. Finally, the influence of wind speed on the durability of turbine structure was investigated through numerical calculation on ANSYS software. Keywords: Offshore wind turbine · IEC 61400 standard · Dynamic simulation · Structural simulation
1 Introduction The size of large wind turbines has increased significantly over the past three decades, from the 75 kW rated power with 17 m diameter rotors for earlier designs up to 5 MW commercial turbines with 125 m rotors [1]. However, as a result of the growth in the scale and flexibility of wind turbine blades, these blades are increasingly susceptible to aerodynamic-elasticity problems due to fluid-structure interactions (FSI) cause. Accurate FSI simulation of wind turbine blades is crucial in the development of large wind turbines. For 1.5 MW wind turbine, a methodology comprising four components, i.e. wind turbine model, CFD modeling, FEA modeling, and one-way FSI coupling was presented by Lin Wang et al. (2016) [2]. In addition, for being licensed in the market, these turbines need to comply the standards of design, manufacturing, and installation. In the European market and US market, wind turbines are designed according to IEC 61400 [3]. Currently, in Vietnam, two design standards for wind turbines have been used: TCVN 10687-1:2015-Wind turbine design requirements and TCVN 10687-24:2015-Wind turbine lightning protection [4]. These two design standards of Vietnam also refer to IEC 61400-1:2014 and IEC © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1150–1158, 2022. https://doi.org/10.1007/978-981-19-1968-8_96
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61400-24:2010. Therefore, in the design calculation for wind turbines for the international market, ICE Standard 61400 is the common standard in the world. Part3-IEC 61400-3:2009 is the design standard applicable to offshore wind turbines. The calculation of tower structure with an offshore wind turbine is different from inland wind turbine by considering the force of waves acting on the tower. For this topic, some authors have also used the FEA method to calculate the 3 MW tower structures [5, 6]. Le et al. calculated the one-way FSI modeling for a 1.5 KW Win pact wind turbine blade at wind speeds of 8 m/s, 12 m/s, and 16 m/s [7]. They gave the results of validating the blade structure and the aerodynamic forces acting on the turbine tower. In this paper, the turbine tower structure model was built and analyzed for durability with input load calculated from ICE 61400 and a previous study [7]. The wave and aerodynamic loads acting on the pier of the tower are calculated according to ICE standard 61400-3:2009.
2 FEA Modelling 2.1 Geometry The geometry of offshore wind turbine shown in Fig. 1. The turbine structure consists of two main components: Rotor nacelle assembly (blades + hub + nacelle) and support structure (tower + pile). The tubular steel tower consists of three sections; the monopile has a diameter of 5 m, a wall thickness of 0.07 m and an embedded length of 30 m [5]. The dimensions of the pile, transition piece and tower are shown in Fig. 2. The specifications of the wind turbine are summarized in Table 1.
Fig. 1. Schematic drawing of the offshore wind turbine founded on a monopile foundation [5]
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Fig. 2. The geometry of the offshore wind turbine support structures: (a) tower; (b) transition piece; (c) monopile [5] Table 1. Main parameters of Wind PACT 1.5 MW wind turbine Parameters (unit)
Value
Rated Power Pr (MW)
1.5
Wind class
IEC IA
Hub height hHUB (m)
90
Number of blades
3
Rotor radius R (m)
35
Rotor Weight (ton)
41
Nacelle Mass (ton)
143
Blade Weight (ton)
8.4
2.2 Materials Properties The material’s parameters considered for the modeling of wind turbine tower, monopile, soil layer, and water layer are given in Table 2, Table 3, and Table 4 respectively. 2.3 Loads on Wind Turbine In general, the depth of water in the Vietnam Sea for offshore wind turbines is about 10 m. Main loads on the structure included rotor weight, aerodynamic loads transferred from the rotor, wind load on the tower, hydrostatic load, wave loads. These loads were estimated following the standards IEC 61400-3:2014. The wind load on the hub height was provided by the CFD calculation in the previous study in the case of a critical wind
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Table 2. Soil parameters Parameters (unit)
Value
Density (kg/m3 )
1100
Young’s Modulus (MPa)
60
Poisson’s Ratio
0.25
Internal friction angle (deg)
35
Dilation angle ψ (deg)
41
Cohesion c (kPa)
0.1
Soil Depth (m)
20
Table 3. Structural steel parameters Parameters (unit)
Value
Density (kg/m3 )
7850
Young’s Modulus (GPa)
200
Poisson’s Ratio
0.3
Tensile yield strength (MPa)
250
Tensile ultimate strength (MPa)
460
Table 4. Water parameters Parameters (unit)
Value
Density (kg/m3 )
1030
Bulk Modulus (MPa)
2200
Significant wave height (m)
6.1
Peak spectral wave period (s)
8.5
Current velocity (m/s)
1.6
Water depth (m)
10
speed of 16 m/s [7]. The hydrostatic load was estimated by Eq. 1. Wave loads were calculated using Morison’s equation Eq. 2 that was based on the linear Airy wave theory in Appendix C2 of IEC 61400-3:2014 [3]: Fh = ρw gh Fwave (z) =
1 1 ρw π D2 CM u˙ (z, t) + ρw DCd u(z, t)|u(z, t)| 4 2
(1) (2)
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where Fwave is the horizontal wave load on a vertical element dz of the monopile at level z; CM is the inertia coefficient (1.6); CD is the drag coefficient (1); ρ is the mass density of the seawater (1030 kg/m3 ); D is the diameter of each section; u˙ (z, t) và u(z, t) is the acceleration and the wave-induced in the horizontal direction in Airy wave theory respectively. The level z is measured from the still water level, and z-axis points upwards. For the wind speed of 16 m/s, the values of loads were determined as follow (Table 5): Table 5. Wind turbine load on the hub height Parameters (unit)
8 m/s
12 m/s
16 m/s
Horizontal shear force Fx (kN)
399.4
449.0
485.1
Vertical force Fz (kN)
1885.5
1885.5
1885.5
Moment My (kN m)
926.6
1041.7
1125.3
Torque Mz (kN m)
71
94.2
466.9
3 Results and Discussion 3.1 Validation of Structure Model The model was calculated with the same load’s condition in the study of Lokesh et al. [6] to verify the accuracy of FEA modeling. The boundary conditions applied were: Fx = 2092 kN, Fz = 492.5 kN, My = 1142.6 kNm, Mz = 721.7 kNm. The meshing of the model is done in ANSYS Workbench software where the Minimum Edge length of mesh is 106.12 m, here the number of nodes generated is about 236 000 and the total number of elements formed is about 272000 (Fig 3). The soil boundary layer created is 10 times the diameter of the monopile. The bottom of the soil layer and the monopile is fixed to the ground. In the structural simulation results of the turbine tower, the two components of water and soil have been hidden to show the results more clearly. The deformation and principal stress in the pile are presented in Figs. 4 and 5 for the service limit state load condition. It is clear that the maximum deformation is at the tower head and the deformation. Along with the horizontally applied load, the stresses are developed, the maximum stress is developed at the top of the tower shown in Fig. 5. The distribution of stress and deformation is matched with the simulation results of Lokesh et al. [6]. The results shown in Table 6 indicate that the maximum stress in present study is smaller than Lokesh’s result. The error between two simulation is 5.1%. In term of maximum deformation, this model is a slightly lower than the value of Lokesh’s simulation. The different between them is 5.2%. Those small errors indicate that the accuracy of the present work is acceptable.
Structural Durability Analysis of Offshore Wind Turbine Tower
Fig. 3. Load application on offshore wind turbine [6]
Fig. 4. Deformation on wind turbine tower
Fig. 5. Principle Stress on wind turbine tower
Table 6. Comparison of simulation results Parameters (unit)
Lokesh et al. [6]
Present model
Error (%)
Max. Principle stress (MPa)
17.5
16.6
5.1
Max. Deformation (mm)
0.38
0.36
5.2
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3.2 Influence of Wind Speed in Structural Strength Figure 6 shows the distributions of deformation in the tower under the ULS design load at the wind speed equal to 16 m/s. The maximum deflection is located at the top of the tower and decreases along the length.
Fig. 6. Deformation on wind turbine tower (16 m/s)
Fig. 7. Von-Mises stress on the tower (16 m/s)
Von-Mises stresses in the tower in the cases of 16 m/s are presented in Fig. 7 for the ULS load condition. The maximum Mises stress computed is about 128 MPa.The stress concentration is detected at the lowest point of the pile.
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Table 7. Stress and deformation of pile at different wind speed Parameters (unit)
8 m/s
12 m/s
16 m/s
Max. Principle stress (MPa)
106.7
119.1
128.1
Max. Deformation (mm)
521.1
582.8
632.7
Table 7 summarizes the deflections and Mises stress in tower structure at three wind speeds. The largest deformation in the simulation of the tower structure at the wind speed of 8 m/s is 521.1 mm. This deformation is located at the top of the tower and increases as the wind speed increases. When the wind speed is doubled (from 8 m/s to 16 m/s), the displacement increases by 21.3%. Von Mises stress is the most important in the case of 16 m/s. This stress rises 20% when the wind speed climbs from 8 m/s to 16 m/s. 3.3 Validation of Turbine Tower Strength According to ICE 64100-3 Standard In current design codes, the maximum deflection on top of the tower is usually subjected to constraints to meet the serviceability requirement (ICE 64100-3). The maximum tower deflection is set to be Lw /200, where Lw is twice the height between the midline and the (Fig. 1). dallow =
200 = 1(m) 200
(3)
The results shown in Table 7 indicate that the deflections are satisfied under the service limit state. Condition at the critical wind speed (16 m/s). The equivalent stress Von-Mises σ should not exceed the allowable stress σ allow under the ultimate limit state. The allowable stress is given in ICE 64100-3: σallow =
σy 345 = 314(MPa) = γm 1.1
(4)
According to Table 7, the Von Mises stress is satisfied under the ULS condition at the critical wind speed (16 m/s). The tower has sufficient strength and safety for wind turbine operation.
4 Conclusion This paper presents a study of the load acting on the supporting structure of an offshore wind turbine 1.5 MW. The typical load case DLC 1.1 (one of 22 load cases) in ICE 614003 standard with normal wind state was investigated. The aerodynamic input forces were provided by the CFD calculation in the previous study [7]. Wave and hydrostatic forces were calculated according to ICE 61400-3 standard. The structural computational model of the turbine tower was verified with the study of Lokesh et al. [6]. The small error of
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5% in the stress calculation allows us to confirm the accuracy of the simulation. The structure is calculated with velocities of 8 m/s, 16 m/s from which it is concluded that the dangerous speed for the tower structure is 16 m/s. The validation according to ICE 61400-3 at this speed shows that the cylindrical structure is durable with high safety. The next step of the study will be to calculate the fatigue test for the offshore 1.5 MW wind turbine, and other working load cases according to ICE 64100-3.
References 1. Premalatha, M., Abbasi, T., Abbasi, S.: Wind energy: increasing deployment rising environmental concerns. Renew. Sustain. Energy Rev. 31, 270–288 (2014) 2. Wang, L., Quant, R., Kolios, A.: Fluid structure interaction modeling of horizontal-axis wind turbine blades based on CFD and FEA. J. Wind Eng. Indust. Aerodyn. 158, 11–25 (2016) 3. IEC 61400. International. Standard. Wind turbines 4. TCVN 10687. Vietnam National Standard for Wind Turbine 5. Ma, H., Yang, J., Chen, L.: Numerical analysis of the long-term performance of offshore wind turbines supported by monopiles. Ocean Eng. 136, 94–105 (2017) 6. Lokesh, R.S., Mohana, R.: Simulation and numerical analysis of offshore wind turbine with monopile foundation. IOP Conf. Ser. Mater. Sci. Eng. 872, 012046 (2020) 7. Nhung, L.T.T., Quy, V.D., Minh, N.N.: Research on structural durability of wind turbines ` thu´, 22 according to ICE 61400 standards. Tuyên tâ.p hô.i nghi. khoa ho.c co, ho.c thuy khí lân (2021). ISSN 1859-4182 ij
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Developed Programmable Logic Controllers with PI-Iterative Learning Control Algorithm a Case Study for BioGas-Based Generators Anh Hoang1 , Duc Tung Trinh1 , Thanh Trung Cao2 , and Hung Dung Pham1(B) 1 Department of Electrical of Equipment, Ha Noi University of Science and Technology, Ha
Noi, Vietnam [email protected] 2 Department of Automation Control, Ha Noi University of Science and Technology, Ha Noi, Vietnam
Abstract. The increasing energy consumption in Vietnam, a fast-growing country, has put significant pressure on conventional power generation such as thermal and hydroelectricity. In order to meet future energy need, renewable energy sources such as wind turbine, solar farm and biogas generator are the development direction in years to come in Vietnam. Among the above energy sources, biogas energy has great potential, especially for a developed livestock industry in Vietnam. In this paper, a control system has been developed for stabilizing the generator speed (output frequency of biogas generators) under all environmental influences (temperature, pressure…), the number of livestock and the uncertainties of electrical load. This paper presents a stable speed controller for biogas generator using PIILC (Iterative Learning Control) algorithm, embedded in Industrial Programmable Logic Controller (PLC). The results show that the control system can provide a stable frequency from 49.8 Hz to 50.2 Hz. Keywords: Renewable energy · Electrical generator · Composed speed control · Iterative learning control · System identification
1 Introduction In the last few decades, iterative learning control (ILC) has received special research attention. In comparison with conventional control methods, the controller design in ILC can be done without any knowledge of mathematical model of processes. Therefore, ILC employs intelligent control concept [1, 2]. Owens et al. (2012) and Bristow et al. (2006) have outlined the development of ILC [3, 4]. ILC is widely applied in practical fields such as batch process, robotics, chemical plants, and many other fields [5–10]. One of the most noticeable limitations in traditional ILC is that during the iteration of the system, the time interval for each cycle is fixed, which is not flexible enough for more complex applications. However, efforts have been made to overcome this current weakness [11, 12]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1159–1169, 2022. https://doi.org/10.1007/978-981-19-1968-8_97
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The essence of ILC is that the system inputs will be fine-tuned from the previous test to the next. ILC will use information from the past test to adjust the modulation signal control for the next attempt until the control quality is satisfactory. The most important factor in successful input conditioning in ILC is the selection of the appropriate learning function. This learning function will be used throughout the trials to update the input to the system. Therefore, the topic of choosing a learning function reasonably has attracted a large number of researchers [13–21]. Bouakrif (2011) studies the D-type learning function. Kim & Kim (1996), Luo et al. (2010) and Madady (2013) investigate PItype and PID-type learning functions [15–17]. P-type learning functions were studied rigorously around the 2010s. On the other hand, in 1997, Chen et al. indicate that the last track error correction issue has not been well studied and the solution should be to include the current repeat tracking error in the ILC update rule. In response to this, in 2000, Wang proposes basic schemas to be compared from both analytical and implementation points of view. The predictive ILC scheme is designed based on the causal pair of the action performed and its resulting state variables. Later, the effect of iterative learning control (ILC) on a plant containing resonance has been evaluated using an industrial gantry robot (Ratcliffe et al. 2005) [20]. Earlier, Chien and Luu (2005) proposed a type of iterative cybernetics. The new P-type uses the concept of forgetting factor and current error modification to monitor the robust output of uncertain nonlinear time-varying systems. Facing the overload pressure of traditional forms of energy, developing countries are now shifting their development direction of sustainable forms of renewable energy such as wind power, solar power, and power biogas to potential forms of energy. Among which biogas power is valued as one of the most potential energy in Vietnam. Therefore, biogas generators have been developed, leading to the need for controllers to stabilize the frequency of power generators. Some successful applications have been reported worldwide [22–25]. Kumar et al. (2017) achieve stable control of voltage and speed through the fuzzy control method. Other researchers propose the design of a Compose Speed Controller using PID-Fuzzy, and then conducts the simulation (Pedro et al. 2019; Silki et al. 2018; Pedro et al. 2021). By simulating and comparing with conventional PID controllers, the above reports have all achieved impressive results, yet only in simulations. To overcome these circumstances, the article will propose an iterative controller combined with traditional controller PI applied to biogas generators. The PI-ILC controller must quickly help the system operate stably, respond quickly to changes in load, and stabilize noise through the generated frequency in the range of 49.8–50.2 Hz. The article is structured as follows. Following the literature review,the next section builds a mathematical model of a biogas generator. Section 3 analyses the design of a PI controller followed bya model of the combination of a PI controller and an ILC controller. Then, the article presents the learning function formula and calculates the parameters of the learning function of the ILC. Numerical simulations and experimental results will be shown in Sect. 4. The final section provides conclusions and suggestions for future works.
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2 Mathematical Model of Biogas Generator The experimental modeling method is based on initial information about the process, observing the output signal under the corresponding input stimulus, and then collecting data to determine the model. In this work, we will present the experimental structure for data collection and then determine the mathematical model of the biogas generator object. Figure 1 shows the layout of the equipment during the experiment. The biogas generator set in this study is composed of an internal combustion engine (ICE) EP100T 4cycle, 6-cylinder, vertical, turbo-type air intake system, and an SAE J149 Gross generator with standby power of 110 kW. The ICE rotation speed varies from 0 to 3000 rpm, the optimum operating point according to the engine’s application is 1500 rpm. At that optimum speed, the machine can provide prime power of 94 kW. The ICE and SAE J149 Gross generator is directly coupled to compose the generator set used in this work. An encoder (SS) is mounted coaxially with the axis of rotation of the motor. The encoder has 1024 pulses per revolution of the motor and is responsible for determining the actual rotational speed of the generator motor. A throttle valve V2 is used to adjust the fuel input for the engine’s operation in a biogas generator. The estimation process includes the following steps: data preparation, model selection, estimation of transfer function and model validation.
Fig. 1. Experimental structure to collect response of biogas generator.
Data Preparation: the process of collecting data about the response of the subject under the corresponding input stimulus will be conducted as follows. Firstly, sets the stimulus for the input, specifically in this case, the opening angle α1 of the throttle valve V2 then start the generator motor. Next, wait for time T(s) for the machine to operate stably. Finally, set throttle valve opening angle α2 , collect data. The whole process will be done by the central controller PLC S7. Data were collected with a sampling period of 1 ms. Model Selection: According to [22], a continuous transfer function with the form of second order inertia is chosen as follows: k (1) S(s) = (1 + T1 s)(1 + T2 s)
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Estimation of Transfer Function: we conduct object identification through data about the object’s behavior. We use Matlab Identification Toolbox in this work to perform object identification. Figure 2 shows the sample data used for the identification, increasing the opening angle α2 after step 1. Model Validation: after successfully identifying the system, we need a parameter to determine whether the newly recognized model fits with the data obtained from the experimental process. The criteria for evaluating the quality of the newly identified model are outlined in [26]. Where ysim and ym are representations of simulation and measurement outputs, respectively. norm(ysim − ym ) (2) Fit = 100 1 − norm(ym − mean(ym ) The model has been recognized with the results shown in Fig. 3 and Eq. (3). We can see that the estimated model closely matches the actual measurement output. The fit of the estimated model is 86.5%. The model has been identified with the following results: S(s) =
120 (1 + 0.75s)(1 + 0.5s)
Fig. 2. Data of the experimental process.
(3)
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Fig. 3. Estimated and mesured output biogas generator
3 Controller Design In this section, a PI controller will be designed using the magnitude optimization method for the object model defined in the previous part. Then a P- type ILC will be used. Finally, select and calculate the learning function parameters for the object according to the combined model of PI-ILC. 3.1 PI Controller Design
Consider a plant as in Eq. (1). A PI controller is chosen as R(s) = kp 1 + the open-loop system is: GOL =
kkp (1 + TI s) TI s(1 + T1 s)(1 + T2 s)
1 TI s
. Then,
(4)
According to the design of the usual Magnitude Optimal Criterion, to determine the zero TI of the PI controller, the cancellation of the zero between the dominant process time constant T1 and the zero TI of the controller is TI = T1 [27]. Let TR = TkpI and TI = T1 , the closed-loop system is: GCL =
k TR s(1 + T2 s) + k
The magnitude of (5) in the frequency domain is: k2 2 |GCL (jω)| = k 2 + TR − 2kTR T2 ω2 + TR 2 T2 2 ω4
(5)
(6)
According to the conventional design via the Magnitude Optimum criterion, the controller must satisfy is |GCL (jω)| ≈ 1 in the broader possible frequency range. Condition |GCL (jω)| ≈ 1: TR 2 − 2kTR T2 = 0 ⇐⇒ TR =
TI = 2kT2 kp
(7)
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3.2 PI-ILC Controller Design The block diagram of the proposed control is shown in Fig. 4. It contains two controllers that are PI controller and ILC P-type controller combined. In which the ILC controller acts as a preprocessing stage.
Fig. 4. Block diagram of the PI-ILC controller.
Equation (1) represent the system model as a state-space model on the discrete-time domain as follows: xk (i + 1) = Axk (i) + buk (i) (8) yk (i) = cT xk (i) In this work, the selected learning function has the form P-type represented as follows: uk+1 (i) = uk (i) + Kek (i)
(9)
where uk (i), uk+1 (i) represent the index I of signal control in the k trial in the past and k + 1trial in the present. K is the learning coefficient, and ek (i) is the tracking error between the reference rk (i) and output signals yk (i) determined as follows: ek (i) = rk (i) − yk (i) Combining Eq. (8) and Eq. (10) we have: ⎡ ek (i) = r(i) − ⎣cT Ai xk (0) −
i−1
(10) ⎤
cT Aj buk (i − j − 1)⎦
j=0
= ek−1 (i) − cT bKek−1 (i − 1) + ... + cT bK
(11)
Hence, by rewriting Eq. (11) for all t = 1, 2, . . . , N we have: ⎛ ⎜ ⎜ ⎜ ⎝
ek (1) ek (2) .. . ek (N )
⎞
⎛
⎟ ⎜ ⎟ ⎜ ⎟=⎜ ⎠ ⎝
... 0 ... 0 .. .. . . −cT AN −1 bK −cT AN −2 bK . . . 1 − cT bK 1 − cT bK . . . −cT AbK 1 − cT bK .. .. . .
⎞⎛ ⎟⎜ ⎟⎜ ⎟⎜ ⎠⎝
ek−1 (1) ek−1 (2) .. . ek−1 (N )
⎞ ⎟ ⎟ ⎟ (12) ⎠
Developed Programmable Logic Controllers with PI-Iterative Learning
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⎛
⎞
⎛
Eq. 1 − cT bK . . . T −c AbK 1 − cT bK .. .. . .
ek (1) ⎜ ⎜ ek (2) ⎟ ⎜ ⎜ ⎟ set ε k = ⎜ . ⎟ and φ = ⎜ ⎝ ⎝ .. ⎠ ek (N ) −cT AN −1 bK −cT AN −2 bK have: ε k = φε k−1
... 0 ... 0 .. .. . . . . . 1 − cT bK
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⎞
(12)
⎟ ⎟ ⎟, we ⎠
(13)
As we have shown in [28], the necessary convergence conditions for the learning process need to be satisfied with all 0 ≤ t ≤ T : ek+1 (t) < ek (t)
(14)
Therefore, the required convergence of the learning process in the sense of (13), i.e. of monotonous decreasing of Euclid norm of all tracking errors εk : will be satisfied, if the following inequality of induced matrix norm holds [29]: (15) 1 − cT bK < 1 Determining the parameter K helps the converged system satisfy the set requirements defined by Eq. (15). Combine the formula presented at Eqs. (3), (7), (15), we have defined the set of 1 parameter settings for the controller as follows: kp = 160 , TI = 0.75 and 0 < K < 3.5. In the next section, the results of the simulation and experiment will be presented.
Fig. 5. Output response of biogas generator.
4 Simulation and Practice Test The simulation and experimental results in the case of using only the PI controller are shown in Fig. 5. Next, the test results using the combined controller PI-ILC in Figs. 6 and
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Fig. 6. Response speed output after 25 trials.
7. Respectively the experimental results meet the output speed of the biogas generator after the 1, 5, 10, 25 trials. Figure 6 is the result of the biogas generator speed being clarified so that we can observe after 25 trials the generator speed has almost completely followed the reference signal.
Fig. 7. Response speed output after 1, 5, 19, 25 trials.
The tracking error output of the biogas generator is shown in Fig. 8, respectively, the difference between the output value and the reference value after 5, 10, 25 trials. The error value decreases after each trial and approaches the same operating allowable limit for the output rate response requirement stated in the original proposal. Compare Fig. 5 and Fig. 9 shows that the PI-ILC controller quickly brought the system to working value. Specifically, the settling time for the PI controller is 38(s), and the PI-ILC controller is 22(s).
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Fig. 8. Tracking error between signal out model and signal reference after the trials.
Fig. 9. Response speed output after 5 trials.
5 Conclusion An intelligent controller for biogas generators is proposed in the article. This controller is established based on combining a PI controller and an ILC controller. Though ILC does not need the mathematical model of biogas generators, we still need it to find the parameter K of learning function, then the output tracking errors of the closed loop system converges asymptotically to zero. Simulation and experimental results have authenticated the performance of proposed controller as expected.
References 1. Xu, J.X., Yan, R.: Iterative learning control design without a priori knowledge of the control direction. Automatica 40, 1803–1809 (2004) 2. Moore, K.L.: Iterative Learning Control for Deterministic Systems. Springer, London (2012). https://doi.org/10.1007/978-1-4471-1912-8 3. Owens, D.H., Amann, N., Rogers, E.: Iterative learning control-an overview of recent algorithms. Math. Comp. Sci. 5, 425–438 (1995)
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4. Bristow, D.A., Tharayil, M., Alleyne, A.G.: A survey of iterative learning control. IEEE Control. Syst. Mag. 26, 96–114 (2006) 5. Tayebi, A.: Adaptive iterative learning control for robot manipulators. Automatica 40, 1195– 1203 (2004) 6. Bondi, P., Casalino, G., Gambardella, L.: On the iterative learning control theory for robotic manipulators. IEEE J. Robot. Autom. 4, 14–22 (1988) 7. Lee, J.H., Lee, K.S.: Iterative learning control applied to batch processes: an overview. Control. Eng. Pract. 15, 1306–1318 (2007) 8. Liu, T., Gao, F., Wang, Y.: IMC-based iterative learning control for batch processes with uncertain time delay. J. Process Control 20, 173–180 (2010) 9. Mezghani, M., Roux, G., Cabassud, M., Le Lann, M.V., Dahhou, B., Casamatta, G.: Application of iterative learning control to an exothermic semibatch chemical reactor. IEEE Trans. Control Syst. Technol. 10, 822–843 (2002) 10. Kim, W.C., Chin, I.S., Lee, K.S., Choi, J.: Analysis and reduced-order design of quadratic criterion-based iterative learning control using singular value decomposition. Comput. Chem. Eng. 24, 1815–1819 (2000) 11. Shi, J., He, X., Zhou, D.: Iterative learning control for nonlinear stochastic systems with variable pass length. J. Franklin Inst. 353, 4016–4038 (2016) 12. Shen, D., Li, X.: A survey on iterative learning control with randomly varying trial lengths: model, synthesis, and convergence analysis. Annu. Rev. Control. 48, 89–102 (2019) 13. Bouakrif, F.: D-type iterative learning control without resetting condition for robot manipulators. Robotica 29, 975–980 (2011) 14. Saab, S.S.: Stochastic P-type/D-type iterative learning control algorithms. Int. J. Control 76, 139–148 (2003) 15. Luo, A., Xu, X., Fang, L., Fang, H., Wu, J., Wu, C.: Feedback-feedforward PI-type iterative learning control strategy for hybrid active power filter with injection circuit. IEEE Trans. Indust. Electron. 57, 3767–3779 (2010) 16. Kim, D.I., Kim, S.: An iterative learning control method with application for CNC machine tools. IEEE Trans. Ind. Appl. 32, 66–72 (1996) 17. Madady, A.: An extended PID type iterative learning control. Int. J. Control Autom. Syst. 11, 470–481 (2013) 18. Chen, Y., Wen, M., Sun, M.: A robust high-order P-type iterative learning controller using current iteration tracking error. Int. J. Control 68, 331–342 (1997) 19. Wang, D.: On D-type and P-type ILC designs and anticipatory approach. Int. J. Control 73, 890–901 (2000) 20. Ratcliffe, J.D., Hätönen, J.J., Lewin, P.L., Rogers, E., Harte, T.J., Owens, D.H.: P-type iterative learning control for systems that contain resonance. Int. J. Adapt. Control Signal Process. 19, 769–796 (2005) 21. Chien, C.J., Liu, J.S.: A P-type iterative learning controller for robust output tracking of nonlinear time-varying systems. Int. J. Control 64, 319–334 (1996) 22. Pedro, D., Lucas, D.C., Jarbas, S., William, B.: Novel composed speed controller applied to biogas generator set. In: 2019 IEEE PES Innovative Smart Grid Technologies ConferenceLatin America (ISGT Latin America), pp. 1–6 (2019) 23. Kumar, M.K., Kailas, T.S., Ilango, K., Nair, M.G.: Fuzzy control based biogas IC engine generator system in a residential building. In: 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy), pp. 1–5 (2017) 24. Silki, J., Monika, J., Deepti, J., Deepika, M.: Fuzzy-pi based seig for rural electrification. In: 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–6 (2018)
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25. Diniz, P., da Costa, L., da Silveira, J., Barroso, G., Barcellos, W.: Performance evaluation of controllers applied to power generator set operating with wastewater biogas. Electr. Eng. 103(1), 753–768 (2020) 26. Ljung, L.: Experiments with identification of continuous time models. IFAC Proc. Vol. 42, 1175–1180 (2009) 27. Papadopoulos, K.G.: PID Controller Tuning Using the Magnitude Optimum Criterion. Springer, Heidelberg (2015) 28. Dzung, N.T., Phuc, P.H., Dich, N.Q., Phuoc, N.D.: Iterative learning control for V-shaped electrothermal microactuator. Electronics 8, 1410 (2019) 29. Trung, C.T., Ha, N.T., Quyen, T.K., Phuoc, N.D.: Convergence parameters for D-type learning function. In: Sattler, K.-U., Nguyen, D.C., Ngoc Pi, V., Long, B.T., Puta, H. (eds.) ICERA 2020. LNNS, vol. 178, pp. 262–269. Springer, Cham (2021). https://doi.org/10.1007/978-3030-64719-3_30
Sub-nanometer Displacement Measurement Using Heterodyne Interferometer and Down-Beat Frequency Technique Nguyen Thanh Dong(B) , Nguyen The Tai, Do Viet Hoang, Nguyen Thi Phuong Mai, Vu Thanh Tung, and Vu Toan Thang(B) School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam {dong.nguyenthanh,thang.vutoan}@hust.edu.vn
Abstract. The paper presents mechanical displacement measurements with subnanometer order for a double-path heterodyne interferometer using a laboratorymade phase meter based on a single phase-locked loop. We use a Zeemanstabilized He-Ne laser source in the interferometer, whose beat frequency is in the megahertz regime. It requires a high-cost, high-sampling rate analog-to-digital converter (ADC) for the megahertz-regime interference signals. A down-beat frequency technique is implemented to obtain these interference signals for a lowcost, low-sampling rate ADC. The result measured by the laboratory-made phase meter is compared with that of a reference one. The results of both phase meters show that a mechanical displacement of ~2.6 nm is confirmed. The noise reduction of the laboratory-made phase meter under 1 nm is more efficient than that of the reference one. It demonstrates that the interferometer combined with the downbeat technique is capable of achieving sub-nanometer mechanical displacement measurements. Keywords: Displacement measurement · Heterodyne interferometer · Down-beat technique · Single phase-locked loop
1 Introduction Over the last decade, nano and picometer technology has utilized high-precision highresolution positioning sensors and systems to measure or manipulate objects at the subnanometer or even picometer level [1–3]. Interferometry is one of the most candidates for acquiring such measurements and manipulations because of its traceability to the length standard of the meter. Many types of interferometry such as homodyne [4] and heterodyne [5, 6] interferometers, homodyne interferometers with frequency modulation [7] or phase modulation [8] have been developed. The motivation of the paper is to execute step-wise displacement measurements within the sub-nanometer scale or at the smallest available resolution (i.e., steps of 20 pm or even down to 10 pm). Displacement interferometry is usually based on the Michelson configuration or some variant of that basic design. Interferometers of this type measure the displacement © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1170–1176, 2022. https://doi.org/10.1007/978-981-19-1968-8_98
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proportional to the phase change interpolated from a sinusoidal interference signal with frequency f . Therefore, processing the measured signal from the avalanche photodiode (APD) is critical for obtaining the measurement results. A conventional heterodyne interferometer often has a beat frequency of the interference signal in the megahertz regime due to traditional Zeeman-stabilized He-Ne heads (i.e., Zygo and Agilent interferometers [9–11]). Such interference signals require a high-cost high-sampling frequency analogto-digital converter (ADC) equipped with a digital signal processor/field-programmable gate array (FPGA) [12–14]. It will limit the broad applications of the conventional heterodyne interferometer in industry. This paper presents mechanical displacement measurements with sub-nanometer order using a conventional double-path heterodyne interferometer and a laboratory-made phase meter based on the single phase-locked loop (PLL) [6]. We show a traditional Michelson setup and a down-beat frequency technique, in which the beat frequency is downed from 3.6 MHz into 20 kHz. The laboratory-made phase meter equipped with an ADC, whose sampling frequency is 250 kHz, is used for these displacement measurements. The measurement results are compared with that of a reference phase meter [15]. The results give a mechanical displacement of ~2.6 nm. A comparison of the noise reduction between the phase meters is estimated under the same system and measurement conditions. The PLL phase meter gives more efficient noise reduction than the reference one. It shows that the interferometer can achieve sub-nanometer mechanical displacement measurements. The paper is organized as follows. Section 2 describes the principles of the heterodyne interferometer and the down-beat technique. Section 3 provides experiments and results. Section 4 shows the conclusions.
2 Methodology 2.1 Heterodyne Interferometer Figure 1 shows the schematic of a heterodyne interferometer. A target mirror is driven by a piezoelectric stage (PZT), and the heterodyne interferometer measures its displacement. The interferometer consists of a frequency-stabilized He-Ne laser, which has two slightly different and orthogonally polarizing frequencies f 1 and f 2 , a beam splitter (BS), a double-pass configuration (it includes a polarization beam splitter (PBS), a retroreflector, two quarter-wave plates (QWP), two mirrors (a fixed mirror (FM) and a target mirror (TM)), two polarization plates (PPs) and two photodetectors (PD). After BS, the laser beam is separated into two beams, reflected and transmitted beams. The reflected beam passes through PP towards PD1, and an interferometer signal I ref is generated. The transmitted beam comes to interferometer in which PBS splits the incident light into reflected S-polarized (vertical polarization, f 1 ) and transmitted P-polarized (horizontal polarization, f 2 ) beams. In the reference arm, the S-polarized beam passes through QWP1, reflected by FM and retroreflector respectively, becomes P-polarized beam, toward FM second time, passes through QWP1, becomes S-polarized beam and return PBS. In the measurement arm, the P-polarized beam passes through QWP2, reflected by TM and retroreflector respectively, becomes S-polarized beam, toward TM second time, passes through QWP2, becomes P-polarized beam and return PBS. By adjusting
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PP to the same polarization plane of the beams f 1 and f 2 , the interference signal I meas is generated at PD2. The interferometer signals are described as Iref = I1 cos(2π ft),
(1)
Imeas = I2 cos(2π ft + δ),
(2)
where I 1 , I 2 , f , and δ are the amplitude of the two signals, signal frequency, and the phase difference, which is related to the displacement of the TM, respectively. For discrete signal processing, the phase δ is shifted over an interval time of 0 to tN , given by δ=
t=t N
dδ
(3)
t=0
where dδ is the instantaneous phase difference obtained over dt. Here assume that the TM velocity is not changed over dt and dt = 1/f s is the sampling time interval of the ADC. For a double-path heterodyne interferometer, dδ can be calculated as 8π n dL, (4) λ where dL, n, and λ are displacements of TM over dt when the mirror is in uniform motion, the refractive index of air, and the laser source wavelength in vacuum, respectively. Substituting Eq. (4) to Eq. (3), we have the relationship between δ and the value of displacement measurement mirror L dδ =
δ=
t=t N =N dt t=0
8π n 8π n dL = L. λ λ
(5)
If dδ is larger than 2π rad over dt, the ambiguity should be mixed in Eq. (5). 2.2 Down-Beat Technique In the setup, a down-beat frequency technique is implemented to obtain these interference signals using a low-cost, low-sampling rate ADC. I ref and I meas are mixed with a pure electronic signal I* generated from a function generator (FG), which is given by (6) I ∗ = A · cos 2π f ∗ · t , where A and f * are the signal amplitude and frequency, respectively. The products are then passed through by low-pass filters (LPFs) to remove higher-frequency terms ∗ and (f + f ∗ ) and maintain lower-frequency terms (δf = f –f *). The reference Iref ∗ measurement Imeas signals after the LPFs are expressed as ∗ Iref = I ∗ · Iref = 0.5AI1 cos 2π (δf )t , (7) ∗ Imeas = I ∗ · Imeas = 0.5AI2 cos 2π (δf )t + δ .
(8)
Finally, the interference signals are fed into peripheral component interconnect (PCI) equipped with the ADC at f s . A laboratory-made phase meter based on the PLL is employed to determine δ [6].
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Fig. 1. Schematic of the heterodyne interferometer. BS, beam splitter; PBS, polarization beam splitter; QWPs, quarter-wave plates; PZT, piezoelectric stage; PPs, polarization plates; PD1 and PD2, photodetectors; LPF, low-pass filter; PCI, peripheral component interconnect; PC, personal computer.
3 Experiments and Results Figure 2 shows the photograph of the experimental setup. In the setup, we use a He-Ne head (Zygo ZMI 7705, beat frequency = 3.6 MHz) [10] as a light source for the interferometer. To minimize the environmental effects, the measurement mirror is set close to PBS as much as possible. In addition, we use a commercial double-path plane mirror interferometer (Agilent 10706B) to limit the effects, reduce the length of the dead-path and feedback of beam light. A travel translation stage (Thorlabs NFL5D20P/M, sensitivity = 267 nm/V, range = 20 μm) [16] is driven by a voltage driver (Thorlabs KPZ101) that is used to generate mechanical displacements of the target mirror. The interference signals are collected by two PDs (Thorlabs PDA36A2) for signal processing. The whole setup is located on an antivibration table with an enclosure, and the experimental conditions are shown in Table 1.
Fig. 2. Photograph of the double-path heterodyne interferometer. BS, beam splitter; TM, target mirror; PZT, piezoelectric; PD1 and PD2, photodetectors.
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As far as we have mentioned in our down-beat technique, the measurement and reference signals are directly mixed with the purely electronic signal (f* frequency and peak-to-peak amplitude = 600 mV) generated from a function generator (HM80305) by two mixers (Mini-circuits ZAD-1+). The products are digitized and filtered two digital LPFs with a cutoff frequency of 48.83 kHz (Moku: Lab, sample rate 488.3 kHz, windowing Blackman). The outputs of the digital LPFs are lower frequency components that have a beat frequency δf = 20 kHz. The intensities of the interference signals are shown in Eqs. (7) and (8). The outputs are then down-sampled to 244 kHz and fed into the PCI (NI-PCI 6143) equipped with an ADC (resolution = 16 bits, sampling frequency = 250 kHz). The single PLL phase meter is used to find δ and a corresponding displacement L. The final sampling frequency of δ and L is 100 Hz. The displacement of TM is created with a 0.01 V step signal driven by the PZT stage. A theoretical corresponding step displacement of ~2.67 nm (= 0.01 V × 20 μm/75 V) is determined. The actual displacement of TM is measured by the interferometer equipped with our laboratory-made phase meter. To compare with the results, we use a reference phase meter (Moku:Lab, resolution = 1 mdeg, input and output sampling frequency = 500 MHz and 122 Hz, respectively) [15]. The measurement results determined by the phase meters are then compared with the displacement step of 2.67 nm. A distance of 24 nm in steps of ~2.67 nm (0.01 V per step) is generated using the PZT stage to induce a shift for the TM. Figure 3 shows the measurement results of both phase meters. The results show that a distance of 24 nm in steps of ~2.6 nm is determined by both phase meters. These measurement results are slightly different from the reference data (2.67 nm/0.01 V) derived from [16] since it may be possible that the PZT table with an extra load is actuated. The standard deviations on each step segment displacement are 1.3 nm and 0.14 nm for the reference and laboratory-made phase meters, respectively. Noise reduction of our phase meter is better than that of the reference phase meter. It helps the interferometer be capable of attaining sub-nanometer mechanical displacement measurements. In the near future, it will be a direction of our research to measure sub-nanometer movements or possibly reach 10 pm. Table 1. Instruments and conditions for the experiments. (1) He-Ne heterodyne laser * Beat frequency
3.6 ± 0.3 MHz
* Wavelength
633 nm
(2) PZT stage * Sensitivity
267 nm/V
* Travel range
20 μm
(3) Sample rate PCI
100 Hz
(4) Environmental conditions * Temperature
25 °C
* Humidity
67%
* Pressure
1012 hPa
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Fig. 3. The measurement results of the phase meter. PM, phase meter.
4 Conclusions This paper presents mechanical displacement measurements with sub-nanometer order using a double-path heterodyne interferometer and a laboratory-handmade phase meter replied on the single PLL. A down-beat frequency technique is implemented to acquire the interference signals correctly, whose frequency is in the megahertz range, for a low-cost ADC with hundreds of kilohertz sampling rates. A comparison of the results between the laboratory-handmade phase meter supported with the down-beat frequency and a reference phase meter is executed for the interferometer. The results indicate the interferometer can achieve a mechanical displacement of ~2.6 nm. The noises over each step are 0.14 nm and 1.3 nm for the PLL and reference phase meters, respectively. The PLL phase meter has more efficient noise reduction than the reference one. It demonstrates that this interferometer and the down-beat technique are capable of attaining sub-nanometer mechanical displacement measurements. However, unexpected effects such as environmental variations, vibrations, etc., affecting our results are not investigated for the interferometer. The investigation is one of future works. We will also develop a modified interferometer with two spatially separated beams to minimize periodic errors and outside effects and reach a picometer-order displacement measurement. Finally, creating a low-cost, high-resolution phase meter is one of the future works. Acknowledgments. This work was funded by the Vietnam Ministry of Education and Training under Project Number B2022-BKA-09.
References 1. Kuhnel, M., et al.: Towards alternative 3D nanofabrication in macroscopic working volumes. Meas. Sci. Technol. 29, 114002 (2018)
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2. Gorges, S., et al.: Integrated planar 6-DOF nanopositioning system. IFAC-PapersOnLine 52(15), 313–318 (2019) 3. Manske, E., Jager, G., Hausotte, T., Muller, A., Balzer, F.: Nanopositioning and nanomeasuring machine NPMM-200-sub-nanometer resolution and highest accuracy in extended macroscopic working areas. In: Proceedings of the 17th International Conference of the European Society for Precision Engineering and Nanotechnology, pp. 81–92 (2017) 4. Lawall, J., Kessler, E.: Michelson interferometry with 10 pm accuracy. Rev. Sci. Instrum. 71, 2669–2676 (2000) 5. Nguyen, T.-D., Higuchi, M., Vu, T.-T., Wei, D., Aketagawa, M.: 10-pm-order mechanical displacement measurements using heterodyne interferometry. Appl. Optic. 59, 8478–8485 (2020) 6. Nguyen, T.-D., Duong, Q.-A., Higuchi, M., Vu, T.-T., Wei, D., Aketagawa, M.: 19-picometer mechanical step displacement measurement using heterodyne interferometer with phaselocked loop and piezoelectric driving flexure-stage. Sens. Actuat. A Phys. 304, 111880 (2020) 7. Vu, T.T., Higuchi, M., Aketagawa, M.: Accurate displacement-measuring interferometer with wide range using an I2 frequency-stabilized laser diode based on sinusoidal frequency modulation. Meas. Sci. Technol. 27, 105201 (2016) 8. Duong, Q.A., Vu, T.T., Higuchi, M., Wei, D., Aketagawa, M.: Iodine-frequency-stabilized laser diode and displacement-measuring interferometer based on sinusoidal phase modulation Meas. Sci. Technol. 29, 065204 (2018) 9. ZYGO. ZMI 7702 Laser head specifications. https://www.lambdaphoto.co.uk/pdfs/Zygo/ LAMBDA_zmi-7705-laser-head-specs.pdf. Accessed 5 Nov 2021 10. ZYGO. ZMI 7705 Laser head specifications. https://www.lambdaphoto.co.uk/pdfs/Zygo/ LAMBDA_zmi-7705-laser-head-specs.pdf. Accessed 4 Nov 2021 11. Keysight. 5517C Laser head key specifications. https://www.keysight.com/zz/en/product/ 5517C/5517c-laser-head.html#KeySpecifications. Accessed 5 Nov 2021 12. National instruments. PXI Multifunction I/O module. https://www.ni.com/en-n/shop/har dware/products/pxi-multifunction-io-module.html. Accessed 5 Nov 2021 13. Entegra. XA-160M dual 160 MSPS 16 bit ADC, dual 615 MSPS 16 bit DAC. https://www. entegra.co.uk/ii-products/xa-160m/. Accessed 5 Nov 2021 14. Vadatech. AMC522 Datasheet. https://www.vadatech.com/product.php?product=168& catid_prev=0&catid_now=200&parentcat=448&parentarc=2. Accessed 5 Nov 2021 15. Liquid Intruments. Moku:Lab’s Phasemeter user manual. https://www.liquidinstruments. com/products/integrated-instruments/phasemeter-mokulab/. Accessed 4 Nov 2021 16. Thorlabs. Single-axis flexure translation stages: 5 mm travel. https://www.thorlabs.com/new grouppage9.cfm?objectgroup_id=720. Accessed 4 Nov 2021
Economic Analysis of a Grid-Connected Rooftop PV System for a Factory in Phnom Penh Vannak Vai1(B) , Samphors Eng1 , Chhith Chhlonh1 , and Hideaki Ohgaki2 1 Department of Electrical and Energy Engineering, Energy Technology and Management Unit,
Research and Innovation Center, Institute of Technology of Cambodia, Phnom Penh 120406, Cambodia [email protected] 2 Institute of Advanced Energy, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
Abstract. Solar rooftop PV systems are an alternative resource due to its potential and environmentally friendly. It is getting more attractive for electricity utilities and consumers, not only in rural areas but also in urban areas. This paper addresses with techno-economic analysis of a grid-connected rooftop PV system for a factory, which located in Sangkat Koh Roka, Khan Prek Pnov, Phnom Penh, Cambodia. Hybrid Optimization of Multiple Energy Resources (HOMER) Software is used to perform a system design. The system design of this study consists of electrical load, MV/LV transformer, rooftop PV panels, and inverter. Firstly, load consumption with a proposed hypothesis is collected at the selected site. Then, the components in the systems are designed with HOMER Pro to simulate and evaluate performance. The optimal sizing of the grid-connected rooftop PV system is obtained based on the net present cost (NPC) and cost of energy (COE). The simulation results provide a techno-economic analysis for further development in solar rooftop PV systems in Cambodia. Keywords: Cost of energy · HOMER · Net present cost · Optimization · Rooftop PV system
1 Introduction Renewable energy sources (RES) provide an efficient solution to global warming and fuel depletion. Particularly, a solar PV energy source is more interested for utilities and consumers as a green and alternative resource due to their potential and environmentally friendly. Many researchers have been focusing on hybrid energy systems with renewable and conventional energy sources for electrification in both rural and urban areas using optimization tools [1–8]. The modeling, analysis, and optimization of grid-connected and islanded solar PV systems for the residential sector considering utility tariff are investigated in [1]; the authors have analyzed the optimal solution with the help of PVGIS, PVWatts, and HOMER tool. The authors in [2] aim to study the techno-economic feasibility analysis of hybrid diesel, PV, battery; the optimal system design with the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1177–1186, 2022. https://doi.org/10.1007/978-981-19-1968-8_99
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lowest cost of energy (COE) and net present cost (NPC) is provided using HOMER. This HOMER is also implemented in [5] to search for optimal sizing and sensitivity analysis of hybrid wind/PV microgrid; the authors have presented a feasibility analysis of renewable energy-based off-grid and grid-connected with the highest potential of wind and solar. The authors in [7] have also used HOMER to design a hybrid AC/DC microgrid for an islanded residential application by providing the COE, NPC, and CO2 emission of the system designs. In addition, many case studies have been investigated [9–12] with PV and storage for electrification in Cambodia. Several technical and regulations for consumers with only grid and grid-connected PV systems have been established and must be followed. The maximum capacity of installed PV for owners is 50% of the grid contract capacity with zero injection into the grid [13]. Also, the PV installation affects the electricity tariff to the owners [14]. Moreover, the excess electricity production from the rooftop PV system goes unused due to Cambodian regulation. However, most authors in literature reviews focused on hybrid energy systems without different payment tariff options as established relevant to solar PV installation connected to medium voltage systems which are challenging in solar application in Cambodia. The main objective of this paper is to address with a comparative study on technoeconomic analysis of only grid and grid-connected rooftop PV systems with different payment options over the project lifetime in the factory, Phnom Penh, Cambodia using HOMER Pro software. The rest of this paper is structured as follows: the methodology of system designs with the cost analysis concept is detailed in the second section. The case study including a proposed system design is provided in the third section. The simulation results and discussion of system designs with different payment options are described in the fourth section. Finally, section five provides the conclusions and future perspectives.
2 Methodology This paper aims at performing a techno-economic analysis of the only grid and gridconnected solar rooftop PV system with different electricity tariffs under Cambodian regulation, which includes the COE, NPC, and operating cost. The design and analysis of the systems are performed with HOMER Pro software [15] which is developed by National Renewable Energy Laboratory (NREL) in the United States (USA). In this paper, load consumption, rooftop PV panels, global horizontal irradiation (GHI) data, inverter, component specifications, economic parameters are required as input data into HOMER. To achieve a research objective, several steps are proposed as shown in Fig. 1. The electrical load consumption and component costs in the proposed system design at the selected site are needed in the first step. Next, different tariff payment options with and without rooftop PV integration are studied. Finally, a comparative study on techno-economic analysis with different options using HOMER is provided.
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Fig. 1. Proposed methodology.
2.1 Cost Analysis Concept by HOMER Pro The main key constraints for economic analysis of the system configurations with HOMER such as the cost of energy (COE), net present cost (NPC), capital recovery factor (CRF), an annual discount rate are considered and provided as follows. • Annual real discount rate (i): it is helpful to change between one-time and annualized costs. This annual rea discount rate is used to calculate a discount factor and to perform annualized costs. This rate is calculated as follows: i=
i − f 1+f
(1)
where i is the normal discount rate (%) and f is the expected inflation rate (%). • Capital recovery factor (CRF): this factor is used to find the present value of the annuity over the project lifetime. The factor is calculated as follows: CRF(i, N ) =
i × (1 + i)N (1 + i)N −1
(2)
where, N and i are the project lifetime (years) and the annual real discount rate, respectively. • Net Present Cost (NPC): it presents the cost of installation and operating of the system design over the project lifetime. The NPC provides the sum of the initial capital cost and the cash flow of year t over the factor. It consists of capital cost, replacement cost, operation cost, maintenance cost, etc. This value is the main economic output, it ranks all feasible system designs in the optimization results. NPC is formulated as given in Eq. (3). N CFt (3) NPC = CF0 + t=1 (1 + i)t where, i, N , CF0 , and CFt are the annual real discount rate (%), project lifetime (years), initial capital cost (USD), and cash flow of t − year respectively.
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• Cost of energy (COE): it is the average cost per kWh (USD/kWh) of useful electric energy produced by the systems. This cost is found by a ratio of total annual cost (Cann,total ) to total annual useful electric energy generation. The COE and Cann,total are calculated as given in Eq. (4) and Eq. (5). COE =
Cann,total Eprimary
Cann,total = NPC × CRF(i, N )
(4) (5)
where, Eprimary is the annualized primary served electrical load in kWh per year, Cann,total is the total annualized cost (USD).
3 Case Study: A Factory in Phnom Penh This section describes a case study for a comparative study of a traditional grid and grid-connected rooftop PV system. The study area, electrical load consumption, and solar radiation data are provided in this section. 3.1 Studied Site The proposed grid-connected rooftop PV system will be installed on the rooftop of the P.K. Light Block factory as depicted in Fig. 2. The factory is located in Sangkat Kork Roka, Khan Preak Phnov, Phnom Penh, Cambodia. The latitude and longitude of the selected site are 11o 37 N and 104o 48.8 E, respectively [16]. Based on the available space on the rooftop of the factory, a PV sizing of 225 kWp is selected which is 50% of contract capacity as more detailed in Sect. 3.4 is proposed for the factory.
Fig. 2. The selected site of P.K. Light Block factory in Phnom Penh, Cambodia.
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3.2 Load Profile The load profile at the selected site of the factory is taken from load recording as provided in [16]. The daily average load profile of the factory is shown in Fig. 3. The average energy consumption in this paper is about 5233.074 kWh/day. The peak load power (i.e., 427.87 kW) occurs during the daytime in the afternoon. The energy consumption is dropped down to the off-peak load power (i.e., 30.93 kW) at nighttime. The scaled annual average is 5233 kWh/day.
Fig. 3. Hourly load profile at the P.K. Light factory.
3.3 Solar Radiation Solar radiation resource: the solar radiation data at the selected site of the factory is obtained from NASA of worldwide energy resource through HOMER Pro software [15], with given latitude and longitude as provided in the study area. The monthly average solar global horizontal irradiance (GHI) and clearness index are depicted in Fig. 4. The estimated annual average solar radiation is 4.96 kWh/m2 /day.
Fig. 4. Average daily solar radiation and clearness index on different months.
3.4 Electricity Tariff at the Selected Site Utility regulation for consumers with only grid and grid-connected PV systems has been established in Cambodia. Table 1 provides information related to the capacity charges and electricity tariffs for commercial, administration, and other consumers connected to the medium voltage system [14].
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Tariff Option Description
Items
Tariff Unit
Option 1
General tariff payment
0.158 USD/kWh
Option 2
Time of Use and capacity Capacity charge charge tariff payment Energy charge during peak hours (7 am to 9 pm)
5.80
USD/kW/Month
0.150 USD/kWh
Energy charge during 0.124 USD/kWh off-peak hours (9 pm to 7 am) Option 3
For a consumer who Capacity charge installs a solar PV system Energy charge for 24-h consumption
5.80
USD/kW/Month
0.150 USD/kWh
3.5 Simulation Model To perform a simulation of different configurations based on the tariff options, the components, and specifications, as well as cost, are used as input data in HOMER Pro. The grid-connected rooftop PV system design using HOMER which contains rooftop PV panels, inverter, electrical load, and grid connection is shown in Fig. 5.
Fig. 5. Schematic of the proposed grid-connected rooftop PV system design in HOMER Pro.
3.6 Components and Economic Parameters To evaluate the performance of system designs over the project lifetime of 25 years, components specification in the systems are needed. Table 2 summarizes components
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cost including the capital cost, replacement cost, operation and maintenance cost, capacity, and lifetime [16]. In this paper, the discount and inflation rates are 12% [10] and 3.1% [17] respectively. Table 2. Components and specification in the proposed system Description
Specification
1. PV system Capacity (kW)
1
Capital (USD)
600
Replacement cost (USD)
600
O&M cost (USD/year)
10
Lifetime (year)
25
Degrading factor (%)
80
2. Inverter Capacity (kW)
1
Capital (USD)
300
Replacement cost (USD)
300
O&M cost (USD/year)
3
Lifetime (year)
10
Efficiency
90
3. Grid Contract capacity (kW)
450
4 Results and Discussion The feasibilities of grid-connected system design with several indicators respected to Cambodian regulation for consumers are provided in Table 3. As shown in Table 3, the COE of the grid-connected PV system design (i.e., option 3) from the simulation result is 0.156 USD/kWh. The renewable energy fraction contributed to the system is 14.7%. The total annual energy consumption is 1,910 MWh/year, in which the energy supply of 281.139 MWh/year is from the rooftop PV system and the rest is purchased from the grid. Also, the monthly average energy production from the rooftop PV and grid for option 3 is shown in Fig. 6. Moreover, it can be noticed that the system without the rooftop PV system with two different categories of the electricity tariff for the same electrical load is more expensive (i.e., 0.158 USD/kWh and 0.162 USD/kWh) than the grid-connected rooftop PV system. The NPC for different only grids (option 1 and option 2) and grid-connected rooftop PV systems are 3.05 USD (option 1), 3.13 USD (option 2), and 3.02 USD, respectively.
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The discounted cash flow of the grid-connected rooftop PV system which consists of the capital cost, replacement, operating and salvage is provided in Fig. 7. Additionally, energy sharing from the rooftop PV systems will be increased with the deployment of batteries due to the unuse of excess electricity production.
Fig. 6. Monthly average energy production for option 3 in HOMER Pro.
Fig. 7. Disounted cash flow of the proposed grid-connected rooftop PV system design in HOMER Pro.
Table 3. Simulation results of the proposed system designs by HOMER Pro Grid
PV (kW)
Inverter (kW)
NPC (MUSD)
COE (USD/kWh)
Operating cost (kUSD/year)
Initial capital (kUSD)
Renewable fraction (%)
Option 1
0
0
3.05
0.158
302
0
0
Option 2
0
0
3.13
0.162
308
0
0
Option 3
225
144
3.02
0.156
236
178
14.7
5 Conclusion This study provides the simulation result of economic analysis for different electricity supply schemes to the factory in Phnom Penh, Cambodia. This paper focuses on a comparative analysis between the only grid and the grid-connected rooftop PV system
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with different electricity tariffs under Cambodian regulation. Also, the simulation result of the system designs using HOMER Pro provides the main indicators such as the cost of energy (COE) and net present cost (NPC). Moreover, the excess electricity production from the rooftop PV system goes unused due to the Cambodian regulation for zero injection into the grid. The optimal system design consisting of the rooftop PV system and grid provides more economic compared to the traditional grid. It can be concluded that the rooftop PV system connected to the traditional grid is cost-effective for factory owners. However, a hybrid energy system with energy storage and sensitivity analysis will be considered to minimize the unused electricity production from the rooftop PV system. The Feed-in Tariff (FIT) scenario and PV disposal cost will be also investigated in future work. Acknowledgments. The authors would like to express appreciation of the Japan-ASEAN Science, Technology and Innovation Platform (JASTIP), the Joint Usage/Research Program on ZeroEmission Energy Research, Institute of Advanced Energy, Kyoto University (ZE2021A-39), and Department of Electrical and Energy Engineering, Institute of Technology of Cambodia, for providing financial support for this research work.
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11. Vai, V., Bun, L.: Study on the impact of integrated PV uncertainties into an optimal LVAC topology in a rural village. ASEAN Eng. J. 10(1), 79–92 (2020) 12. Vai, V., Alvarez-Herault, M.-C., Raison, B., Bun, L.: Optimal low-voltage distribution topology with integration of PV and storage for rural electrification in developing countries : a case study of Cambodia. J. Mod. Power Syst. Clean Energy 8(3), 531–539 (2020) 13. EAC: On general condition for connecting solar generation sources to the electricity supply system of national grid or to the electrical system of a consumer connected to the electricity supply system of national grid, Phnom Penh, Cambodia (2018) 14. EAC: Report on power sector of the Kingdom of Cambodia (2020) 15. HOMER Energy: About HOMER Energy LLC–Creators of Hybrid Renewable Microgrid System Design Software (2021). https://www.homerenergy.com/company/index.html 16. Pet, P., Vai, V.: Etude d’un système d’énergie solaire photovoltaïque de 500KW pour l’usine de P.K.Light Block. Eng. Thesis, Institute of Technology of Cambodia (2021) 17. ADB: Asian development outlook 2021: Financing a green and inclusive recovery (2021)
Effect of Turning Vanes on Heat Exchange Characteristics of Cooling Channel in Turbine Blade Tien-Dung Nguyen, Hai-Quang Do, Cong-Hung Hoang, Minh-Hieu Nguyen, Mai-Anh Thi Bui, Cong-Truong Dinh(B) , and Hong-Quan Luu School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi 11615, Vietnam [email protected]
Abstract. To improve the power and thermal efficiency of a gas turbine, increasing the turbine inlet gas temperature is the preferred method. Therefore, the introduction of blade cooling technologies is extremely important to ensure engine performance. One of the methods used is the convection method. The study investigates the influence of the turning vanes on three factors: the first is the heat exchange of the plane on the same channel, the pressure loss between the inlet and outlet of the channel. The object-focused study is an experimental model that simulates the channeling in a turbine blade at a laboratory in New York. Computational Fluid Dynamic (CFD) numerical simulation with the k – ω SST model were used to simulate this turbine. The results obtained from the simulation process are compared with the parameters drawn from the experiment and used as a reference for improvement. The results in this article are considered to be consistent with the experimental results and some new results open up ideas in optimizing and improving the heat exchange efficiency of turbine blades. Keywords: Gas turbine · Cooling channel · Turning vane · RANS analysis · Heat transfer performance
1 Introduction In aircraft engines, the turbine is an indispensable part that converts the energy of the gas flow after the combustion chamber into mechanical energy to drive other parts. In order to improve the thermal efficiency and power of the turbine, the turbine inlet temperature is constantly increased. This results in the blades of the turbine having to operate in an increasingly high temperature environment. Especially the tip of the wing must be exposed to harsh environments because of the high temperature and the eddy currents of the tip of the wing. Blade tip cooling is a pressing issue while turbine inlet temperatures are increasing. The cooling method therefore plays an important role in the thermal design of the turbine. Convection cooling technology has been widely applied in turbines for several decades. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1187–1199, 2022. https://doi.org/10.1007/978-981-19-1968-8_100
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The typical wing tip cooling system is the ring cooling channel, which consists of Ushaped ducts located in the wing. Complicated geometric designs lead to variations in heat transfer and unavoidable pressure loss. There have been many experimental and numerical simulation studies to evaluate the effect of the internal cooling structure on the heat exchange capacity and pressure drop. Han et al. [1] performed experiments in a ribbed channel and drew conclusions about the influence of channel form ratio, ribbed texture and Reynolds number of inlet flow on heat exchange efficiency. Liu et al. [2] also experimental investigation of turbulent flow in a two-pass channel with different U-shaped bends and it can be seen from the clear differences in the Reynolds stress for different bend sections that the fluctuations caused by the mixing of the main flow and the vortices are significantly stronger than those at the boundary. Son et al. [3] used PIV (Particle image velocimetry) method to study the effect of turbulent currents on wall heat exchange in U-shaped ducts. of the fluid is the most important determinant of heat exchange efficiency. The top of the channel and the outside of the second channel are the two most influential locations, where the heat transfer coefficient is the highest in the whole channel. Su et al. [4] studied the influence of form ratio (AR) on heat exchange capacity in two-way conduction channel. Three form ratios were selected for the study (AR = 1:1, 1:2, 1:3), the results show that when the form ratio is 1:2, the highest heat transfer coefficient is achieved because the appearance of the Dean vortex. Shevchuk et al. [5] have shown that 180° rotation in the U-channel accelerates the flow, resulting in enhanced heat exchange at the outlet. Xie et al. [6], Griffith et al. [7] investigated the flow and heat transfer characteristics of internal cooling channels with columnar protrusions and hemispherical concave surfaces. The resulting column-shaped convex and hemispherical concave surfaces improve the heat exchange efficiency of the channel and the degree of heat exchange is affected by the size, structure, arrangement of the indentations and Reynolds number of the cooling air. Venkata et al. [8] performed an experiment to study the effect of several types of diversion plates on the pressure loss distribution in a U-shaped channel.The results show that when compared with the conduction channel. smooth, 90-degree deflector can reduce pressure loss by up to 40%. Bunker [9] measures the heat exchange of the internal cooling mechanism for the tip using 4 different column arrangements with the experimental model in Fig. 1. As a result, the heat transfer coefficient can be increased. up to 2.5 times while the pressure loss is at an acceptable level. The experimental model of Bunker is also the object of study in this study. In addition, there are experimental studies and numerical simulations on the effect of fluid flow and heat exchange when using turning vanes. Rao et al. [10] experimentally investigated the influence of the diversion plate on the pressure loss at the outlet of the channel. Experiments have shown that the pressure loss is greatly influenced by the shape and position of the plate, the total pressure loss can be reduced by up to 20% with the right shape and location. Schnieder et al. [11] studied a U-channel with a 45° ribbed matrix and a channel width-to-height ratio of 1:2. They concluded that the pressure loss could be greatly reduced by using turning vanes. The result was also reported by Choi et al. [12] that a discrete current adapter installed in the head-turning area can locally control the cooling effect on the head surface. Luo and Razinsky [13] used numerical simulation to analyze the flow in channel with diversion plate. The results showed that the diversion plate exerts a positive
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influence on the decoupling phenomenon and reduces the pressure loss. Liou et al. [14] studied the flow and heat exchange characteristics of the U-channel. They showed that the increased heat exchange at the junction is due to the occurrence of split flow and turbulence. The numerical simulation results of Xie et al. [15] show that the presence of guide ribs/vanes provides a 20%–65% higher heat transfer capacity of the tip wall compared to the tip wall.
2 Numerical Analysis 2.1 Description of Geometry Figure 1 shows the computational domain with basic dimensions similar to the experimental model of Bunker [1]. The input cross-section of the U-shaped model is the same. The height and width are H = 139.7 mm and W = 69.9 mm respectively, so the length and width ratio of both inlet and outlet is 2:1 and the hydraulic diameter of the model is 93.13 mm. The length of the model is L = 913.3 (equal to 10 times the hydraulic diameter). The width of the space between the inlet and outlet is 25.4 mm. The intersection is an ellipse with eccentricity e = 0.62 and a large diameter of 16.15 mm. The turning vanes are designed to be a quarter circle with diameters Cs and Cl , respectively. Table 1 shows the calculated Cs and Cl distances for the fixed C length. To evaluate the effect when the parameters change, each parameter in turn is kept constant at the test value.
Fig. 1. The position of the parameters Table 1. Ratio of values of Cs /C and Cl /C Cs /C 0.3 Cl /C
0.3 0.3
0.3 0.3
0.3 0.3
0.2
0.25 0.3
0.35 0.4
0.45 0.5
0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.55 0.55 0.55 0.55 0.55 0.55 0.55
2.2 Numerical Method Structured meshes have been created by ANSYS - ICEM for the computational domain. An example of the structural grid used in this article is shown in Fig. 2. The grids are
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divided closely at the wall area. The type O grid is used once in the case of a smooth channel and five times in the case of a channel with turning vanes as shown in Fig. 2. The grid cells near the wall have been adjusted to have element distance near the wall in the main channel to be 1 • 10−5 (m) and at the cooling wall to be 8 • 10−6 (m) to ensure y+ is less than 4.
Fig. 2. Overview of Grid model.
Figure 3 shows that the mesh quality of the channel is very good, according to Determinant 2 × 2 × 2 standards, the lowest mesh quality is even at 0.8 and the highest reaches the maximum number. For the aerodynamic analysis, the 3-D Reynold-Averaged Navier Stokes (Rans) equations were solved using ANSYS CFX 19.1, ANSYS CFXPre, CFX-Solver, CFX-Post were used to determine the boundary conditions, solve the equations and process the corresponding results. Simulation is performed using Intel X5675 3.07 GHz CPU. For each different mesh case, the simulation uses the k – ω SST model.
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Fig. 3. Mesh quality distribution
In this paper, the suitable boundary condition is selected to best match the experimental model of Bunker shown in Fig. 4 and the parameters of the boundary condition are shown in Table 2. The top wall is set at a fixed temperature and the remaining walls are adiabatic walls. The condition of the inlet stream is to have a fixed temperature and flow rate. The outlet flow is set to the average static pressure. All walls are frictionless. The ideal gas is selected for this simulation.
Fig. 4. Position of boundary conditions
In order to achieve good accuracy results for the cooling channel simulation, it is extremely important to evaluate the stability of the parameters. The value of heat flux density in the plane on the same channel is checked during the whole simulation. When the heat at the steady point and the heat distribution do not change after the iterations, the simulation is considered stable. In addition, the convergence of the simulation is also determined by the RMS value below 1 • 10−6 and the parameter values not exceeding 0.01%. Usually each simulation takes about 2000 iterations to converge. Table 2. Value of boundary conditions Inlet temperature
Inlet Mach number
Inlet turbulence intensity
Outlet average static pressure
Top wall temperature
300 K
0.1 M
5%
1 atm
320 K
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2.3 Parameter definition The Fanning friction coefficient is determined by: f =
P Dh 2ρU 2 L
Where ρ is the air density at the inlet, U is the average inlet velocity, Dh is the hydraulic diameter of the rectangular duct inlet, L is the channel length, P is the pressure drop of rectangular channel. The Reynolds number based on the hydraulic diameter of the channel is defined as: Re =
ρU Dh μ
where μ is the dynamic viscosity. The heat transfer coefficient is defined as: qw h= Tw − Tb where qw is the heat flux of the wall, T w is the end temperature of the wall, T b is the local temperature. Nusselt number is defined as: Nu =
hDh λ
where λ is the thermal conductivity of the air. The reference of Fanning friction coefficient is used with the Blasius correlation: f0 = (0.79 In Re − 1.64)−2 /4 The reference Nu0 is calculated by using the Dittus-Boelter correlation: Nu0 = 0.023Re0.8 Pr 0.3 Performance Evaluation Criteria is defined by: PEC = (
Nu f 1/3 )( ) Nu0 f0
3 Result and Discussion To select the optimal mesh type, the study will compare the number of Nusselt at the top of the channel compared with Bunker’s experimental.
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Figure 5 shows that grid 3 and grid 4 have the smallest error compared to the experimental results of Bunker [1]. Grid 3 is about 300000 elements less than grid 4 and the simulation running time is less, so the study chooses grid 3 as the standard grid to perform the following simulations.
Fig. 5. Grid deviation
The average y+ value of the turning vanes surface is 0.8 (as shown in Fig. 6) and the distribution of y+ value on the wall is guaranteed to be less than 4 (Fig. 6a). This makes computing results more reliable and accurate for surface heat transfer research problems. Combined with the results of the convergent mesh test, the most appropriate mesh size will be used for change of conformation cases to ensure the most objective comparison of results. To improve the heat exchange capacity at the top surface of the channel, the study will investigate the effect of the turning vanes at different radius on the heat exchange efficiency. Figure 7 shows that, when increasing the radius of the large turning vane, the lowvelocity portion near the top of the channel gradually decreases, leading to an increase in heat transfer capacity. In addition, with a suitable radius, the turning vanes help the cooling air less collide with the wall on both sides, reducing pressure loss for the whole channel.
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Fig. 6. y+ distribution
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Fig. 7. Mach distribution with changing of the large turning vane’s radius
Fig. 8. Dimensionless pressure distribution with changing of the large turning vane’s radius
Figure 8 shows that, as the radius of the large turning vane increases, the pressure area in the interior of the turning vanes also increases. There is also an area of high pressure at the corners of the channel due to the angular rotation currents.
Fig. 9. Nusselt distribution (Nu /Nu0 ) with changing of the large turning vane’s radius
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Figure 9 shows that the best heat transfer area is located on the cooling air. This area grows from case 0.3–0.3 to case 0.3–0.55 and gradually decreases in the last case. This can be explained because the velocity distribution in the last two cases is uneven, leading to a decrease in the flow of cooling air flowing through the surface and reducing the ability to heat transfer. Figure 10 shows that increasing the large turning vane radius over a certain range can increase the heat transfer. However, increasing the radius of the large turning vane also leads to an increase in pressure loss.
Fig. 10. Heat performances with changing of the large turning vane’s radius
Figure 11 shows that by gradually increasing the radius of the small turning vane, the separation phenomenon at the position of the separation wall increases, leading to pressure loss.
Fig. 11. Mach distribution with changing of the small turning vane’s radius
Figure 12 shows that the radius of the small turning vane increases, the pressure area inside the turning vanes also increases. In addition, the area of low pressure on the outlet
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side of the channel also increases gradually because increasing the radius of the small turning vane and the separation phenomenon occurs more.
Fig. 12. Dimensionless pressure distribution with changing of the small turning vane’s radius
Figure 13 shows that the best heat transfer area is located on the cooling air side. In the first three cases, the contact of the coolant with the top side of the duct is less affected. Therefore, the heat transfer at the top surface of the channel is almost constant. In the following 4 cases, the radius of the small turning vane increases, leading to an increase in the flow of cooling air flowing through the surface and increasing the heat transfer capacity.
Fig. 13. Nusselt distribution (Nu /Nu0 ) with changing of the small turning vane’s radius
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Fig. 14. Heat performances with changing of the small turning vane’s radius
Figure 14 shows that by increasing the radius of the small turning vane, the change in heat transfer is insignificant. Moreover, with the right radius, it reduces the pressure loss of the channel and helps to increase the efficiency.
4 Conclusion In this paper, the effects of the diversion plate on the heat exchange efficiency of the Uchannel in the turbine blade were investigated. Numerical simulations were performed using the CFX module in the commercial software ANSYS. The obtained results were the evaluation of the parameters of the numerical model compared with the experimental model, the study of the influence of two current shields on the heat exchange efficiency of the channel showed that when increasing the radius of the plates, current to a reasonable value, the heat exchange capacity of the channel is improved and the maximum value is reached, the influence of the small current plate on the pressure loss of the channel can be drawn out, helping to improve overall channel efficiency. The future development direction is to optimize the parameters of the studied turning vanes and simulate the new flow plate geometry to get the maximum heat transfer performance. Acknowledgement. This study is funded by Hanoi University of Science and Technology (HUST) under grant number T2021-PC-039.
References 1. Han, J.-C.: Heat Transfer and Friction in Channels With Two Opposite Rib-Roughened Wails. J. heat Transf. Asme Trans. 106, 774–781 (2013) 2. Liu, R., Li, H., You, R., Tao, Z.: Experimental investigation of turbulent flow in a two-pass channel with different U-shaped bends. AIP Adv. 10(6) (2020). https://doi.org/10.1063/5.001 1444
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3. Son, S.Y., Kihm, K.D., Han, J.C.: PIV flow measurements for heat transfer characterization in two-pass square channels with smooth and 90° ribbed walls. Int. J. Heat Mass Transf. 45(24), 4809–4822 (2002). https://doi.org/10.1016/S0017-9310(02)00192-8 4. Su, G., Chen, H.C., Han, J.C., Heidmann, J.D.: Computation of flow and heat transfer in rotating two-pass rectangular channels (AR = 1:1, 1:2, and 1:4) with smooth walls by a Reynolds stress turbulence model. Int. J. Heat Mass Transf. 47(26), 5665–5683 (2004). https:// doi.org/10.1016/j.ijheatmasstransfer.2004.07.019 5. Shevchuk, I.V., Jenkins, S.C., Weigand, B., von Wolfersdorf, J., Neumann, S.O., Schnieder, M.: Validation and analysis of numerical results for a varying aspect ratio two-pass internal cooling channel. J. Heat Transf. 133(5) (2011).https://doi.org/10.1115/1.4003080 6. Xie, G., Sundén, B., Wang, L., Utriainen, E.: Enhanced internal heat transfer on the tip-wall in a rectangular two-pass channel (AR = 1:2) by pin-fin arrays. Numer. Heat Transf. Part A Appl. 55(8), 739–761 (2009). https://doi.org/10.1080/10407780902864680 7. Griffith, T.S., Al-Hadhrami, L., Han, J.-C.: Heat transfer in rotating rectangular cooling channels AR = 4 with dimples. J. Turbomach. 125(3), 555–563 (2003). https://doi.org/10.1115/ 1.1571850 8. Ratna, V., et al.: Pressure drop distribution in smooth and rib roughened square channel with sharp 180° bend in the presence of guide vanes. Int. J. Rotating Mach. 10(2), 99–114 (2004). https://doi.org/10.1155/S1023621X04000119 9. Bunker, R.S.: The augmentation of internal blade tip-cap cooling by arrays of shaped pins. J. Turbomach. 130(4), 041007 (2008). https://doi.org/10.1115/1.2812333 10. Rao, D.V., Babu, C., Prabhu, S.: Effect of turn region treatments on the pressure loss distribution in a smooth square channel with sharp 180° bend. Int. J. Rotating Mach. 10(6), 459–468 (2004). https://doi.org/10.1080/10236210490503996 11. Schnieder, M., Höcker, R., von Wolfersdorf, J.: Heat transfer and pressure loss in a 180-turn of a rectangular, rib-roughened two passage channel. In: Proceedings of the 5th World Conference on Experimental Heat Transfer, Fluid Mechanics and Thermodynamics, Thessaloniki, Greece (2021) 12. Choi, S.M., Jung, E.Y., Chung, H., Cho, H.H.: Numerical investigation of the effects of discrete guide vanes on the control of heat transfer on the tip surface of a turbine blade. Int. J. Therm. Sci. 112, 142–152 (2017). https://doi.org/10.1016/j.ijthermalsci.2016.10.008 13. Luo, J., Razinsky, E.H.: Analysis of turbulent flow in 180 deg turning ducts with and without guide vanes. J. Turbomach. 131(2) (2009).https://doi.org/10.1115/1.2987239 14. Liou, T.M., Chen, C.C.: Heat transfer in a rotating two-pass smooth passage with a 180° rectangular turn. Int. J. Heat Mass Transf. 42(2), 231–247 (1998). https://doi.org/10.1016/ S0017-9310(98)00148-3 15. Xie, G., Zhang, W., Sunden, B.: Computational analysis of the influences of guide ribs/vanes on enhanced heat transfer of a turbine blade tip-wall. Int. J. Therm. Sci. 51, 184–194 (2012). https://doi.org/10.1016/j.ijthermalsci.2011.08.004
Designation and Simulation of Dehumidification Integrated Air Conditioning Ngo Minh Ðuc and Ta Van Chuong(B) School of Heat Engineering and Refrigeration, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. In hot and humid climates, controlling indoor humidity is an important part of improving comfort, durability, and health. Air conditioning dehumidifies as they cool, but they cannot independently control both temperature and humidity. In order to solve the problem, this study has proposed a novel energy-saving model of dehumidification integrated air conditioning (DHIAC) and developed a simulation program to simulate the operation of the DHIAC in the range of temperature from 20 to 30 °C and humidity from 40 to 80%. A new method of determining the heat transfer coefficient of the indoor unit according to the temperature, humidity, and air velocity entering the indoor unit has been developed to increase the accuracy of the simulation. The simulation has evaluated the energy efficiency of the DHIAC in different operating conditions, thereby analyzing the applicability and energy saving of the DHIAC when used in different climates. The simulation results show that the coefficient of performance (COP) of the DHIAC ranges from 2.5 to 4.95, depending on operating conditions. Due to its simple structure and low cost, the DHIAC can be widely applied to protect people from problems related to temperature and humidity. Keywords: Air conditioning · Dehumidification · Simulation · Fin tube heat exchanger · COP
1 Introduction Air conditioning reduces heat-related mortality by 80%, increases work productivity and improves academic performance. The number of air conditioners sold today has tripled since 1990 to nearly 100 million units per year. On average, 10,000 air conditioners are sold in the world every hour [1]. By 2050, the demand for air conditioners will triple today, increasing from 1.6 billion units to 5.6 billion units [2]. In recent years, in addition to cooling, the demand for dehumidification has also increased. When humidity is too high, it can make living conditions unpleasant, and it can also do lasting damage to the property. High humidity creates excess moisture and condensation that potentially leads to mould or rot. In addition, high humidity can damage electronic devices. Therefore, in rooms containing electronic components, the air humidity is controlled very low [3]. Sales of dehumidifiers are increasing year by year. It is expected to reach $3.5 billion by 2022 [4]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1200–1214, 2022. https://doi.org/10.1007/978-981-19-1968-8_101
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Some popular dehumidification technologies today can be mentioned as cooling dehumidification [4, 5], liquid-absorbing dehumidifying [6], electrochemical dehumidifying [7], adsorbing dehumidifying, dehumidifying rotor [8], and desiccant membrane [9]. Dehumidification technology is widely used in central air conditioning systems but rarely studied and implemented in household air conditioners. In modern household air conditioners, there is a “Dry” mode to dehumidify. This mode works independently of the cooling mode of the air conditioner, so it cannot control the comfortable environment as required. In addition, the reduced air velocity in dry mode reduces the overall heat transfer coefficient of the device, reduces volumetric efficiency and isotropic efficiency, thus reducing the COP of the device. As a result, the “Dry” mode does not accurately control humidity, which wastes the device’s power consumption. Several methods of combining air conditioning (ACU) with dehumidification (DHU) have been investigated. Y.K. Yadav [10] investigated a hybrid solar conditioning system consisting of conventional vapour compression and liquid desiccant cycles. The results showed that 80% of energy savings could be achieved under the condition of 90% of latent heat load. Guozhong Ding et al. [11] established a two-dimensional heat and mass transfer model of cross-flow dehumidifier for household split air conditioner. The influence of air, solution inlet conditions on the dehumidification performance of the system has been discussed in the paper. Chen et al. [12] designed an independent dehumidification air-conditioning system with a hot water-driven liquid desiccant and a chiller for an office building in Beijing. Wei Su and Xiaosong Zhang [13] propose a combined absorption refrigeration air conditioning system combined with liquid dehumidification. Koukouni P. Kone et al. [14] study the dehumidification performance of a residential air conditioning system with a variable speed mode. After assessment of the dehumidification performance, the variable speed model was able to maintain relative humidity between 50% to 52%. Research results show that using liquid desiccant is an effective and energy-saving dehumidifying solution, especially for central air conditioning systems. However, the application of liquid desiccant in residential air conditioning applications has been limited. This is primarily due to large system size and complexity, issues of desiccant solution leakage and equipment corrosion [15]. As a result, in this paper, a novel energy-saving air conditioner combined with dehumidification has been developed. With a simple structure, low cost, and low energy consumption, this device can be widely developed to help create a comfortable environment for people. In addition, power consumption for heating, cooling, ventilating, and controlling humidity in buildings can range from 24 to 60% of the total electricity consumed [4]. Accordingly, the utilisation of advanced technologies in buildings would substantially reduce the energy demand and improve the environmental impact.
2 Methodology 2.1 Operating Principle In this study, the dehumidification process is based on the cooling principle. The cooled air has a temperature lower than the dew point temperature, causing water vapour in the air to condense. When the air velocity through the indoor unit is high, the cooling
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capacity is mainly to help cool the air. When the air velocity decreases, the sensible heat decreases while the latent heat increases, resulting in dehumidification. It should be noted that while this process decreases the absolute humidity, it actually increases the relative humidity of the air to 100%. In order to reduce the relative humidity, a heating process is needed [16]. According to this principle, the dehumidification integrated air conditioning (DHIAC) controls temperature and humidity by changing the air velocity according to the operating mode. The DHIAC can operate in 3 modes: Mode 1 - cooling, Mode 2 - heating, and Mode 3 - dehumidification. The operating mode of the DHIAC will be controlled by the air temperature and humidity signals. The temperature tolerance is set to ±2 °C, and the humidity tolerance is ±10%. A flowchart of the control algorithm used in the DHIAC is shown in Fig. 1. Air temperature is a pre-controlled parameter. When the air temperature is higher than the maximum set temperature, the DHIAC works in Mode 1 - cooling mode until the temperature has reached the minimum value set (Fig. 2). If the air temperature is lower than the lowest setting value, the DHIAC will operate in Mode 2 - heating mode until the air temperature reaches the maximum setting value (Fig. 3). Then, the humidity control process is activated, and the humidity in the room is compared with the set maximum humidity value. If the humidity in the room is higher than the setting, the DHIAC will operate in Mode 3 - dehumidifying mode until the humidity reaches the minimum setting value (Fig. 4). Thanks to the flexible switch between the three modes mentioned, temperature and humidity in the room are maintained within a comfortable range. A schematic diagram of DHIAC is shown in Fig. 5. The device is capable of cooling, heating and dehumidifying. The control signals will be taken from the temperature and humidity sensors. Setting the optimal control mode will create a comfortable environment with low energy costs.
Fig. 1. Flowchart of the control algorithm
Mode 1 – Cooling: The heat exchanger inside the room acts as the evaporator. The outdoor unit acts as the condenser. The air velocity through the indoor unit is at the operating level of the cooling mode (Fig. 2) [11]. If T room(i) > T SP + ΔT DB → D(i) = ON If TSP − ΔTDB ≤ Troom ≤ TSP + ΔTDB → D(i) = D(i-1) If T room(i) < T SP − ΔT DB → D(i) = OFF
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Fig. 2. Control parameter in cooling mode
Mode 2 – Heating: The 4-way valve switches the step and reverses. The heat exchanger inside the room acts as the condenser, and the heat exchanger outside acts as the evaporator (Fig. 3). If T room(i) > T SP + ΔT DB → D(i) = OFF If T SP − ΔT DB ≤ T room ≤ T SP + ΔT DB → D(i) = D(i-1) If T room(i) < T SP − ΔT DB → D(i) = ON
Fig. 3. Control parameter in heating mode
Mode 3 – Dehumidification: The operating mode is similar to Mode 1, but the air velocity through the indoor unit is reduced (Fig. 4). If RH room(i) > RH SP + ΔRH DB → D(i) = ON If RH SP − ΔRH DB ≤ RH room ≤ RH SP + ΔRH DB → D(i) = D(i-1) If RH room(i) < RH SP − ΔT DB → D(i) = OFF
Fig. 4. Control parameter in dehumidification mode
2.2 Specifications of the Heat Exchangers The dehumidification integrated air conditioning used for simulation is a small capacity typically used for civil applications. In order to have a large heat exchanger area with
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Fig. 5. Schematic diagram of DHIAC
compact size, the evaporator and condenser were selected as finned tube heat exchangers [17] with specifications as shown in Table 1. The sketch of the evaporator and condenser is shown in Fig. 6 and Fig. 7, respectively. Table 1. Specifications of the heat exchangers No
Information
Cooling coil
Heating coil
1
Refrigerant
R410a
R410a
2
Heat exchanger type
Fin tube
Fin tube
3
Pipe layout type
Alternate
Alternate
4
Tube material
Cu
Cu
5
Fin material
Al
Al
6
Outer diameter [mm]
9,52
9,52
7
Inner diameter [mm]
8,92
8,92
8
Fin thickness [mm]
0,015
0,015
9
Fin pitch [mm]
1,7
1,7
10
Tube pitch [mm]
25
25
11
Deep row pitch [mm]
21,65
21,65
12
Tube length[mm]
400
400
13
Coil height [mm]
150
400
14
Row number
4
4
15
Coil thickness [mm]
86,6
86.6
16
Air flow [CMH]
_
1080
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Fig. 6. Sketch of the evaporator
Fig. 7. Sketch of the condenser
2.3 Overall Heat Transfer Coefficient An evaporator simulation program (ESP) was developed to evaluate the influence of temperature, humidity, and air velocity through the evaporator on the overall heat transfer coefficient of the evaporator (Fig. 8). The program has been built using EES (Engineering Equation Solver) software [18]. EES is a general equation-solving program that can numerically solve thousands of coupled non-linear algebraic and differential equations. The program can also be used to solve differential and integral equations, do optimization, convert units, check unit consistency, and generate publication-quality plots. A major feature of EES is the high accuracy thermodynamic and transport property database that is provided for hundreds of substances in a manner that allows it to be used with the equation solving capability [18]. The overall heat transfer coefficient of the evaporator depends on the temperature, air humidity, and the air velocity passing through the evaporator is determined by Eq. (1). U = α(T ,RH ) · α(v) · Uo
(1)
where: α (T,RH) is the correction coefficient for temperature and humidity; α (v) is the correction coefficient for air velocity; U is the overall heat transfer coefficient; U o is
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Fig. 8. Evaporator simulation program
the overall heat transfer coefficient at standard conditions (at temperature 25 °C, 60% humidity, and 2.5 m/s velocity). By adjusting the mean temperature difference between the refrigerant and the air at each fixed humidity, we can determine the variation of the overall heat transfer coefficient according to the air temperature and humidity (Fig. 9). The simulation results show that the higher the humidity or temperature, the greater the overall heat transfer coefficient.
Fig. 9. Overall heat transfer coefficient according to air temperature and humidity
Using Eq. (1) with α (v) = 1, we can calculate the correction coefficient for temperature and humidity of overall heat transfer coefficient as shown in Table 2.
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Table 2. Correction coefficient for temperature and humidity Temp [o C] Relative humidity [%] 40
45
50
55
60
20
0.723 0.774 0.831 0.895 0.968
21.11
0.722 0.774 0.833 0.899 0.975
22.22
0.719 0.774 0.834 0.903 0.983
23.33
0.718 0.773 0.836 0.908 0.99
24.44
0.716 0.773 0.837 0.911 0.997
25.56
0.713 0.772 0.838 0.915 1.003
26.67
0.711 0.771 0.839 0.918 1.009
27.78
0.708 0.77
0.84
0.921 1.015
28.89
0.706 0.768 0.84
0.923 1.021
30
0.703 0.767 0.84
0.926 1.026
Temp [o C] Relative humidity [%] 65
70
75
80
20
1.051 1.147 1.26
21.11
1.062 1.164 1.283 1.425
1.393
22.22
1.074 1.181 1.307 1.459
23.33
1.085 1.197 1.331 1.493
24.44
1.096 1.213 1.355 1.527
25.56
1.107 1.23
26.67
1.117 1.246 1.402 1.595
27.78
1.127 1.261 1.425 1.629
28.89
1.136 1.276 1.448 1.664
30
1.145 1.291 1.47
1.378 1.561
1.698
From the data in Table 2, we obtain a graph describing the relationship between the correction coefficient for temperature and humidity according to the temperature and humidity of the air (Fig. 10). Using “Curve Fitting – Matlab”, we can build a mathematical expression to determine the correction coefficient for temperature and humidity α(RH,T) , which is described as Eq. (2). α(T ,RH ) =
0, 5145 + 0, 00121 · RH − 0, 008136 · t 1 − 0, 005934 · RH − 0, 01037 · t
(2)
In the dehumidification mode, the air velocity through the indoor unit will be lower than in the case of cooling mode. The standard air velocity through the indoor unit will be reduced to 1.5 [m/s]. Therefore, in this study, the air velocity through the indoor unit will be changed from 1 to 2.5 [m/s] to build a correction coefficient for air velocity
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Fig. 10. Graph of correction coefficient for temperature and humidity
[13]. In this case, the air temperature and humidity will be maintained under standard conditions (t = 25 °C, φ = 60%). The results of the correction coefficient for air velocity are shown in Table 3. Table 3. Correction coefficient for air velocity v [m/s]
1
1.167
1.333
1.5
1.667
U [W/m2 K]
41.42
43.73
45.39
46.55
47.38
αv
0.855
0.903
0.937
0.961
0.978
v [m/s]
1.833
2
2.167
2.333
2.5
U [W/m2 K]
47.91
48.25
48.42
48.48
48.42
αv
0.99
0.996
1
1.001
1
The results in Table 3 show that the overall heat transfer coefficient increases as the air velocity through the indoor unit increases. However, the higher the air velocity, the smaller the amplitude of this increase (Fig. 11). Using the software “Curve Fitting Matlab”, we can determine the expression of the correction coefficient for air velocity as Eq. (3).
Fig. 11. Expression of correction coefficient for air velocity
Designation and Simulation of DHIAC
αv = 1, 038 −
0, 1811 v2
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(3)
2.4 Simulation Model of DHIAC The schematic diagram of dehumidification integrated air conditioning is shown in Fig. 5. To study DHIAC’s operation under different conditions, all major components of the DHIAC, such as compressor, condenser, and evaporator, were simulated. 2.4.1 Evaporator Model In the evaporator, the refrigerant receives heat energy from the air to change the enthalpy and performs evaporation. The cooling capacity Qe [kW ] can be calculated through the variation of enthalpy or the heat transfer equation between refrigerant and air by Eq. (4) and Eq. (5) [19]. Qe = mr (h1 − h4 )
(4)
Qe = αv · α(RH ,T ) · Ue · Ae · LMTDe
(5)
where: α (v) is the correction coefficient for air velocity, α (T,RH) is the correction coefficient for temperature and humidity, U is the overall heat transfer coefficient at standard conditions [W/m2 K], Ae is the heat exchange area of the indoor unit [m2 ], LMTDe is the average temperature difference between the air and the refrigerant [K]; mr is refrigerant mass flow rate [kg/s]; The average temperature difference between the air and the refrigerant at the evaporator is determined through the inlet air temperature, the outlet air temperature, and the evaporator temperature of the refrigerant by Eq. (6) [19]. LMTDe =
(ta,in − te ) − (ta,out − te ) t −te ln ta,in a,in −te
(6)
2.4.2 Condenser Model In the condenser, the refrigerant transfers heat to the air, condensing from the superheated vapour state to the saturated liquid state. The heat transfer coefficients in the superheater and condenser regions are different. So, the condenser is divided into two zones, namely the de-superheating zone and the condenser zone. The heat exchange between refrigerant and air can be determined by Eq. (7) and Eq. (8) [19]. Qc = mr (h2r − h3 )
(7)
Qc = Uc Ac LMTDc + Ude−sup Ade−sup LMTDde−sup
(8)
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2.4.3 Compressor Model Compressors can be considered the essential part of the system, the primary power consumption device, complicated structure and often the leading cause of system failure. In the operating parameters of the compressor, the volumetric efficiency and the isentropic efficiency significantly affect the power consumption of the compressor. According to [20, 21], they are determined by Eq. (9) and Eq. (10), respectively. ηv = av −
bv − cv pc pe
(9)
ηs = as −
bs − cs pc2 pe2
(10)
The real compression capacity of the compressor can be determined through the volumetric efficiency and the isentropic efficiency by Eq. (11) and Eq. (12) [20]. ηv Vs n v1
(11)
mr (h2s − h1 ) ηs
(12)
mr = Nr =
During operation, the compressor always exists losses related to electrical and mechanical. Therefore, the electric power consumption of the compressor is determined by Eq. (13) [20]. Nel =
Nr ηme · ηel
(13)
where: ηme is the mechanical efficiency of compressors; ηel is the electrical efficiency of compressors. These efficiencies are dependent on the type of compressor and are virtually unchanged during compressor operation. Specific values of the above efficiencies for some compressors have been studied and published [18]. 2.4.4 Expansion Valve Model In the expansion valve, the enthalpy of the refrigerant is constant. It is determined by Eq. (13) [19]. h3 = h4
(14)
where h3 and h4 are the enthalpies of the refrigerant at the inlet and outlet of the expansion valve [kJ/kg]. 2.4.5 System Simulation The system of equations from Eq. (4) to Eq. (14) simulates the main processes occurring in parts of DHIAC. However, to simulate DHIAC, it needs structural parameters, operating parameters and many other descriptive equations. Due to the length of paper
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limitation, some equations and operational parameters used in the simulation are not presented. Based on the presented content, using the EES software [18], the DHIAC simulation software has been built. The software interface is shown in Fig. 12. The software will be used to study the operation of DHIAC as well as analyze its energy efficiency.
Fig. 12. DHIAC simulation software
The coefficients of performance (COP) of DHIAC is defined with the typical definition by Eq. (15) [19]. COP =
Qe Nel
(15)
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3 Results and Discussion The DHIAC simulation software can calculate the vital operating parameters of DHIAC, such as cooling capacity, electric power consumption, and COP of DHIAC under different operating conditions. The simulation results show that DHIAC’s COP varies widely depending on temperature, humidity, and air velocity entering the device. COP of DHIAC in cooling mode and dehumidification mode when the temperature and relative humidity of the air change is shown in Fig. 13 and Fig. 14, respectively.
Fig. 13. COP of DHIAC in cooling mode
Fig. 14. COP of DHIAC in dehumidification mode
According to simulation results, the higher the humidity in the room, the greater the COP of DHIAC. The results are entirely consistent with the theoretical analysis. As the humidity increases, the overall heat transfer coefficient of the indoor unit increases. The temperature difference of the air through the indoor unit is reduced. The logarithmic
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temperature difference between the refrigerant and air is decreases. They increase the COP of DHIAC. Therefore, when setting the humidity control in the room, the higher the value (within comfort conditions), the more energy is saved. As the air temperature increases, the evaporating temperature of the refrigerant increases, then the COP of DHIAC increases [22, 23]. When setting the temperature, it is recommended to choose a temperature that is just enough to ensure comfortable conditions or as high as possible, so the COP of the device will increase. DHIAC’s COP in cooling mode is higher than dehumidification mode due to the higher air velocity. The results show that DHIAC’s COP in cooling mode has a value from 2.9 to 4.95, while the COP in the dehumidification mode has a value from 2.5 to 4.1. The DHIAC’s COP is high under all operating conditions, proving it is an efficient air temperature and humidity control technology with high energy savings.
4 Conclusions This study proposed a dehumidifying integrated air conditioner (DEIAC) capable of creating a comfortable environment with a temperature of 20–30 °C and a humidity of 40–80%. Thanks to its compact structure and low cost, it has potential for applications in both household and industrial. The DEIAC creates a comfortable environment with low energy consumption using three flexibly controlled cooling, heating, and dehumidifying modes. A DEIAC simulation software is also built to study the operation of DEIAC under various operating conditions. In order to improve simulation accuracy, the overall heat transfer coefficient of the heat exchanger is corrected using correction coefficients for temperature, humidity and air velocity. The effects of temperature, relative humidity and air velocity on the COP of DHIAC were investigated. The research results show that when the air temperature increases or the relative humidity increases, the COP of DHIAC increases. Therefore, when setting temperature and humidity control points, it is recommended to set the temperature and relative humidity to a high level (within the comfortable environment) to save energy. The air velocity through the indoor unit also affects the COP of DHIAC. The COP increases as the velocity increases. However, when the air velocity reaches 2.5 m/s, the COP increases very slowly. Therefore, the air velocity through the indoor unit should not be set higher than 2.5 m/s. Under all operating conditions, the COP of DEIAC is from 2.5 to 4.95, demonstrating DEIAC’s high energy savings. Acknowledgements. This research is funded by the Hanoi University of Science and Technology (HUST) under project number T2021-PC-xxx.
References 1. Davis, L., Gertler, P., Jarvis, S., Wolfram, C.: Air conditioning and global inequality. Glob. Environ. Change 69, 102299 (2021) 2. Osunmuyiwaa, O.O., Payneb, S.R., Ilavarasanc, P.V., Peacock, A.D., Jenkinsa, D.P.: I cannot live without air conditioning! The role of identity, values and situational factors on cooling consumption patterns in India. Energy Res. Soc. Sci. 69, 101634 (2020)
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3. International Standard, ISO 14644 – Cleanroom and associated controlled environments 4. Chu, W.-X., Chiu, C.-H., Wang, C.-C.: Improvement on dehumidifier performance using a plastic assisted condenser. Appl. Therm. Eng. 167, 114797 (2020) 5. Queslati, A., Megriche, A.: Performance analysis of a new humid air dehumidifier. Energy Procedia 119, 453–465 (2017) 6. Mujahid Rafique, M., Gandhidasan, P., Bahaidarah, H.M.S.: Liquid desiccant materials and dehumidifiers – a review. Renew. Sustain. Energy Rev. 56, 179–195 (2016) 7. Qi, R., Li, D., Zhang, L.Z., Huang, Y.: Experimental study on electrolytic dehumidifier with polymer electrolyte membrane for air-conditioning systems. Energy Procedia 142, 1908–1913 (2017) 8. Golubovic, M.N., Hettiarachchi, H.D.M., Worek, W.M.: Evaluation of rotary dehumidifier performance with and without heated purge. Int. Commun. Heat Mass Transf. 34, 785–795 (2007) 9. Li, C.-H., Yang, T.-F., Jhang, J.-B., Li, W.-K., Yan, W.-M.: Physical characteristics analysis and performance comparison of membranes for vacuum membrane dehumidifiers. Case Stud. Therm. Eng. 26, 101213 (2021) 10. Yadav, Y.K.: Vapour-compression and liquid-desiccant hybrid solar space-conditioning system for energy conservation. Renew. Energy 6(7), 719–723 (1995) 11. Ding, G., Chen, X., Huang, Z., Ji, Y., Li, Y.: Study on model of household split air conditioning solution dehumidifier. Appl. Therm. Eng. 139(5), 376–386 (2018) 12. Chen, X.Y., Li, Z., Jiang, Y., Qu, K.Y.: Field study on independent dehumidification airconditioning system - I. Performance of liquid desiccant dehumidification system. ASHRAE Trans. 111, 271–276 (2005) 13. Sua, W., Zhang, X.: Thermodynamic analysis of a compression-absorption refrigeration air-conditioning system coupled with liquid desiccant dehumidification. Appl. Therm. Eng. 115(25), 575–585 (2017) 14. Kone, K.P., Fumo, N.: Dehumidification performance of a variable speed heat pump and a single speed heat pump with and without dehumidification capabilities in a warm and humid climate. Energy Rep. 6, 1696–1701 (2020) 15. Elmer, T., Worall, M., Shenyi, W., Riffat, S.: An experimental study of a novel integrated desiccant air conditioning system for building applications. Energy Build. 111(1), 434–445 (2016) 16. Wu, S.: Materials for Energy Efficiency and Thermal Comfort in Buildings. Woodhead Publishing Series in Energy, pp. 101–126 (2010) 17. Bejan, A., Kraus, A.D.: Heat Transfer Handbook. Wiley, Hoboken, New Jersey (2003) 18. F-Chart Software: Engineering Equation Solver. S.A. Klein (2002) 19. An, N.N., Van Chuong, T.: Simulation of a small unidirectional air conditioner. Therm. Energy Rev. 119 (2014) 20. An, N.N., Van Chuong, T.: Develop a method to determine the characteristic parameters of refrigeration compressors from experimental data. Therm. Energy Rev. 114 (2013) 21. Van Chuong, T., An, N.N., Uy, N.Q.: Development and validation of a simulation model for heat pump water heater. J. Mech. Eng. Res. Dev. 44(2), 62–70 (2021) 22. Yan, H., Xia, Y., Deng, S.: Simulation study on a three – evaporator air conditioning system for simultaneous indoor air temperature and humidity control. Appl. Energy 207(1), 294–304 (2017) 23. Atmaca, ˙I., Senol, ¸ A., Ça˘glar, A.: Performance testing and optimization of a split-type air conditioner with evaporatively-cooled condenser. Eng. Sci. Technol. Int. J. 32, 101064 (2022)
Nummerical Analysis on Lift and Drag of a Finite-Thickness Circular Arc Hydrofoil in Different Camber Thanh Van Nguyen1 , Anh Dinh Le2 , and Anh Viet Truong1(B) 1 School of Transportation Engineering, Hanoi University of Science and Technology,
Hanoi, Vietnam [email protected] 2 School of Aerospace Engineering, VNU-University of Engineering and Technology, Vietnam National University (Hanoi), Hanoi, Vietnam
Abstract. The effect of geometrical parameters on the hydraulic and flow characteristics of a 2D hydrofoil with a circular arc camber has been studied with the aim to develop design of impeller blades of turbomachines as well as other types of equipment that uses airo/hydrodynamic wings. The lift and drag characteristics of the several models of finite-thickness circular-arc hydrofoil are investigated in different camber (leading edge angle β is from 5° to 20°) and the angle of attack (AoA) α is in the range from −12° to 20° at a high Reynolds number (Re = 106 ). The SST k – ω turbulent model is validated with experimental data of NACA4212 for usage in present study. In the result, the effect of leading-edge angle β on the flow characteristics of the circular-arc hydrofoil (namely CAβ) with different camber are carried out. The lift and drag coefficients can be determined by a linear function of the β at a certain value of AoA correspondingly. The stall phenomena are also determined in accompanying with the separation flow when the AoA α > 6° and α < −3° by the flow separates at the leading edge. The the lift coefficient CL can reach a maximum value when AoA is around 7°–9°. The flow characteristics will be useful for developing and applying in design of wing and impeller’s blade of turbomachines. Keywords: Circular-arc hydrofoil · Lift and drag · Axial-flow pump · Turbomachines
1 Introduction The circular-arc camber of hydrofoil is very popular in Vozenhexenski-Pekin’s design method [1] and used widely in the pump manufacturing industry. However, the hydrodynamic characteristics of this type of hydrofoil are still limited and require further investigation. In hydraulic machines and equipments, such as, axial flow pumps, propeller, axial propulsion devices etc., the design of the hydrofoil is the most important. A. Bruining [2] presents experimental results of measuring the lift and drag force of the 2D arc-shaped airfoil with a camber of 10% C and a thickness of 3 mm, angles of attack © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1215–1227, 2022. https://doi.org/10.1007/978-981-19-1968-8_102
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(AoA) is from −10° to +90° at high Reynolds number (up to 105 ). A stall occurs when the AoA is in between 12° and 16°. In the range of 6° to 8°, the Lift/Drag ratio achieves its highest value. In study of R. Flay et. al. [3], the flow behavior characteristics of a circular arc with a large camber (by 21% C ÷ 22%C) were clarified and the Reynolds number is up to 4.15 × 105 and the AoA is from −5° to 25°. The study indicated that the lift coefficient changes dramatically with the increasing of Re with the transition in the boundary layer on the upper surface of the wing from laminar to turbulent. When the stall phenomenon occurs, the lift generated is almost constant, but the drag begins to increase very quickly. In terms of theoretical research, A.A. Lomakin [1] gives the equation for calculating the lift coefficient for the undivided envelope flow around the circular arc camber. The lift coefficient CL depends only on the angle of incidence and the curvature of the arc. But the equation, however, does not on accounting of thickness effect, inlet conditions and flow characteristics under the effect of separating flow and stall. In this study, we focus on clarifying the influence of the key geometrical parameters of the circular-arc hydrofoil with a finite thickness by the commercial CFD software ANSYS. The paper presents the influence on the lift and drag in different camber - the curvature arc parameter of hydrofoil corresponding to changing the leading-edge angle β0 (from 5° to 20°). The inflow condition is the water flow at Re = 106 , T = 300 K and the angle of attack (AoA) is from −12° to 20°.
2 Numerical Simulation Method 2.1 Model Configuration and Meshing Before making simulation of the flow around the circular arc hydrofoil, we have to validate the simulation method based on the experimental data of NACA4412 (maximum thickness of 12% C at 30% chord length, a maximum camber is 4%C at 40% chord length and the chord length is setu by 1 m). The model of the finite-thickness circular arc hydrofoil has 74.5 mm in length, leading edge angle β is x°, a thickness δ is constant by y mm, and is denoted by the symbol CAβxδy. The computation domain is rectangular, with a long side of c = 15C and a wide side of a = 10C. The Inlet and Outlet boundaries are set far enough apart to ensure that the calculation results are not influenced. The domain and the dimensions are messed and shown for NACA4412 in Fig. 1 and the circular arc hydrofoil CAβxδy in Fig. 2. The mesh is created in an H-grid style and is created in ICEMCD. The number of survey grid layers is from 100 to 200 layers. For accurating results, the boundary layer height must be smooth and Y+ = 1. The initial boundary layer height is 2.3e−5.
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Fig. 1. Computational domain of NACA4412.
Fig. 2. Computational domain of CAβ48δ4
2.2 Numerical Models and Governing Equations The simulation method validation is described in Sect. 3.1 based on the experimental data of NACA4412. The distribution of the source, viscosity and pressure terms is using the second order upwind scheme algorithm. The second order implicit scheme discretizes convection terms. We utilize the method of solving pressure and velocity simultaneously to solve the incompressible flow equation (Pressure-Velocity Coupling). In simulation of the flow around the hydrofoil, the finite volume method and the SST k-ω turbulent model is used. The Pressure Based Couple algorithm is used. The constants in the SST k-ω model are: α k1 = 0.92; α ω1 = 0.5; β 1 = 0.075; α 1 = 0.31; β * = 0.09. The numerical simulation of flow over a hydrofoil requires solving equations of conservation of mass and momentum [5]. For an incompressible flow, the equations are provided as follows [6],
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Continuity equation: ∂ui =0 ∂xi
(1)
∂ ∂ui 1 −∂p ∂ + ui uj = tij + τij + ∂t ∂xi ρ ∂xi ∂xi
(2)
tij = 2ϑS ij ; τij = 2ϑt S ij
(3)
Momentum equation:
where:
Sij =
∂uj 1 ∂ui 1 ∂ui ; S ij = Sij − + δij 2 ∂xj ∂xi 3 ∂xj
(4)
Additional transport equations are solved when the flow is turbulent. Turbulence models are investigated in this study, namely, the spalart allmaras, standard k-ε, realizable k-ε, standard k-ω, shear stress transport (SST) k-ω and transition SST model. Detail of the model can refer to Ansys Fluent Guide. 2.3 Boundary Conditions For the purpose of validating the turbulent model of simulation, the inlet boundary condition for the case of NACA4412 is air flow at Re = 1e6, medium temperature T = 300 K, density ρ = 1.225 kg/m3 , dynamic viscosity μ = 1.82e−5 kg/ms and the AoA is from −9° to 18°. Another validation is a comparison of simulation result with experimental data of a circular arc wing [1] for double-checked results. The inlet is the water flow through a circular arc hydrofoil at Re (2 · 105 ÷ 106 ) and the angle of attack is 0°. For the main study, the inflow boundary condition was water flow at temperature T = 300 K, density ρ = 997 kg/m3 , dynamic viscosity μ = 0.00103 kg/ms, and a wide angle of attack (AoA) is 12° to 20°, and Re = 106 . All wall boundaries are no-slip walls.
3 Results and Dicussion 3.1 Validation of Simulation Method Evaluation of the mesh independence of the CAβ48δ4 and the NACA4412 profile is shown in Fig. 3. In general, the number of mesh elements is close to 1300000 to assure accuracy and save computing time. In order to validate the present computational method by using SST k-ω model, we make a comparison between the calculation results and the experimental data of the NACA4412 hydrofoil. At the value of Re is about 106 , the measurement results of lift coefficient CL from the experiment of Loftin and Smith [7] and the static-pressure coefficient distribution Cp from the experiment of Pinkerton [8] will be used in comparison
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with our calculation. Figure 4 and 5 show that the simulation results by the SST k-ω model are in a very good fitted with the experimental data, especially at the maximum and minimum value of Cp . For case of the CAβ48δ4 hydrofoil, the simulation results give the lift and drag coefficient are very good agreement in comparison with experimental data of Patrick et al. [4]. Figure 6 shows that the drag coefficient is quietly in the same value in both result of simulation by the SST k-ω model and the experimental data. The value of the lift coefficient is about 2.5% different to the experimental data.
Fig. 3. Mesh Independnece Test
Fig. 4. Relationship between CL and angle of attack AoA on NACA4412 in comparison
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Fig. 5. The static pressure distribution coefficient Cp at α = 12° in comparison
Fig. 6. Validate k-ω model through experimental data on the CAβ48δ4 hydrofoil
The validation above clearly shows that the solution method and the SST k - ω turbulent model can be used effectively for further study in flow characteristics through the high curvature circular-arc hydrofoil. 3.2 The Flow Characteristics of a Finite-Thickness Circular-Arc Hydrofoils CAβxδy Evaluation of mesh convergence on the CAβxδy model is shown in Fig. 7. The thickness for meshing independent test was performed. In Table 1, the thickness is 0.05%C to ensure that the hydrofoil is considered as a finite thickness circular-arc foil. As presented in Sect. 3.1, we can see that the number of elements is approximately 130000 which ensuring the accuracy and reduce computing time.
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The finite-thickness circular arc profiles have arc curvature of 5°, 8°, 10°, 12°, 15°, 18°, 20° respectively. Their symbols are CAβ5, CAβ8, CAβ10, CAβ12, CAβ15, CAβ18, CAβ20, respectively.
Fig. 7. Meshing independence test
Table 1. Thickness for meshing independence test
3.2.1 Lift and Drag of CAβxδy In Fig. 8, the value of CL is as the nearly linearly function of AoA in the range from − 3° to + 3° in generally. Specially, with the foil model having a low camber (curvature arc β ≤ 12°), this linear relation is extended with AoA from −6° upto + 6°. The CL can reache its maximum value when the AoA reaches 6°–9° which depending on the camber. The CL value decreased slightly when the AoA is from 9° to 12°. After that, it changes very slightly in the range of the AoA from 12° to 20°. For the drag, it is easy to see that the CD coefficient is distributed as a 2nd order function of the angle of attack as shown in Fig. 9. In the range of the AoA from +6° to +12°, the camber seems not affect to the value of the drag.
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The lift coefficient is increasing proportionally with the camber angle β and the CL /CD can get its maximum value corresponding to the drag coefficient is in from 0.02 to 0.04, as seen in Fig. 10. The Lift / Drag ratio increases when the leading eage angle increases from 5° to 20° at the same angle of attack, as shown in Fig. 11. In general, the effective AoA is in the range from 0° to 5° for the high Lift/Drag ratio.
Fig. 8. Lift coefficient curves of CAβx at Re = 106
Fig. 9. Drag coefficient curves of CAβx at Re = 106
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Fig. 10. Relationship bettwen CL and CD of CAβx at Re = 106
Fig. 11. The Ratio Lift/Drag Curves of CAβx
Interesting result of present study is that with different camber – curvature arc of CAβx hydrofoil, the lift and drag are both in a first order funtion of the leading-edge angle β° - curvature arc as shown in Fig. 12.
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Fig. 12. Lift and drag performance of CAβx hydrofoil with the thickness is 0.05%C
3.3 Flow Characteristics Figure 13 shows images of flow fields at various angles of attack from 3° to 15°. There is no separation when the AoA is smaller 6°. When the AoA α reaches 6°, the flow started to separate from the leading egde. When the AoA is increasing higher than 6°, the separated flow will generate and reattach to the upper wall, expending towards the trailing edge. When continuing to increase the angle of attack (9° to 12°), the stall phenomenon occurs with the formation of large circulation flow on the wake throughout the entire upper wall of hydrofoil. This explains the gradual decrease in CL value as shown in Fig. 8. In the positive value of the angle of attack, the flow will separate earlier in the upper side of the hydrofoil with a low-curvature arc camber at the same AoA. On the contrary, in the negative value of the angle of attack, the separation occurs on the lower side of the hydrofoil and comes earlier with a low-curvature arc camber at
Nummerical Analysis on Lift and Drag of a Finite-Thickness Circular Arc Hydrofoil
Fig. 13. Streamlines of flow around Caβ hydrofoil at different AoA
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Fig. 14. Streamline distribution on CAβ hydrofoil at −30
the same AoA. Generally, the stall effects is gradually developing when increasing the camber of the circular-arc hydrofoil (Fig. 14).
4 Conclusion In this study, the effects on the flow characteristics of the main geometrical parameters – camber of the finite-thickness circular arc hydrofoil CAβ has been estimated. The camber is nominated by leading-edge angle β. The Shear Stress Transport k-ω model gives an effective calculation results. Some remarkable conclusion as below: – The lift- and drag coefficients can be determined by a linear function of the leadingedge angle β – a nominal parameter of camber, at a certain value of the AoA correspondingly. – The the lift coefficient CL can reach a maximum value when AoA is around 7°–9°. The Lift/Drag ratio reaches its maximum value when the drag coefficient is from 0.02 to 0.04. For the effective working, the highest value of Lift/Drag ratio is when the AoA is in the range from 0° to 5°. – The stall phenomena which accompanying with the separation flow at the leading edge occurs when the AoA α > 6° and α < −3° that affects to the lift and drag performance strongly. The flow characteristics will be useful for applying in design improvement of wing or hydrofoil and impeller’s blade of turbomachines. In future study, the other geometrical factor will be continuely clarified for developing the application of the circular arc camber in hydrofoil design. The cavitation phenomena is also considered carefully.
References 1. Lomakin, A.A.: Centrifugal and Axial Pumps. Mashinostroenie Publ., Moscow Leningrad (1966)
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2. Bruining, A.: Areodynamic characterictis of curved plate airfoil section at reynolds numbers 60.000 and 100.000 and angle of attack from −10 to +90 degrees. Report LR-281 (1979) 3. Flay, R., Piard, A., Bot, P.: Areodynamic of a highly cambered circular arc aerofoil experimental investigations. In: Conference on Innovation in High Performance Sailing Yachts, CVET and Ecode Navale, Lorient, France, pp.150–162 (2017). ffhal – 01583557ff 4. Bot, P.B., Rabaud, M., Thomas, G., Lombardi, A., Lebret, C.: Sharp Transition in the Lift Force of a Fluid Flowing Past Nonsymmetrical Obstacles: Evidence for a Lift Crisis in the Drag Crisis Regime 5. Kapsalis, P.-C., Voutsinas, S., Vlachos, N.S.: Comparing the effect of three transition models on the CFD predictions of a NACA0012 airfoil aerodynamics. J. Wind Eng. Ind. Aerodyn. 157, 158–170 (2016) 6. Fluent12 Theory guide 7. Loftin, L.K., Smith, H.A.: Aerodynamic characteristics of 15 NACA airfoil sections at seven Reynolds numbers from 0.7 × 106 to 9 × 106 . National Advisory Committee for Aeronautics, Technical Note 1945, NASA, Washington, DC, USA (1949) 8. Pinkerton, R.M.: The Variation with Reynolds Number of Pressure Distribution over Airfoil Section. Report No. 63, pp. 66–84 (1939) 9. https://wright.grc.nasa.gov/airplane/models.html
Experimental Two-Phase Flow Boiling Heat Transfer Coefficient and Pressure Drop of Refrigerant in a Horizontal Micro-fin Tube Viet Dung Nguyen1 , Quoc Dung Trinh1 , Tuan Anh Vu1 , Ba Chien Nguyen1(B) , and Jong-Taek Oh2(B) 1 School of Heat Engineering and Refrigeration, Hanoi University of Science and Technology,
Hanoi, Vietnam [email protected] 2 Department of Refrigeration and Air Conditioning Engineering, Chonnam National University, San 96-1, Dunduk-Dong, Yeosu, Chonnam 59626, Republic of Korea [email protected]
Abstract. In this work, we investigated the two-phase flow boiling heat transfer coefficient and pressure drop of refrigerant in a micro-fin tube. The working refrigerant is R410A. The data were conducted under the following conditions: heat flux ranged from 9 to 12 kW m−2 , mass flux ranged from 200 to 320 kg m−2 s−1 , and the evaporation temperature of 6 °C. The test section was made of a micro-fin tube with equivalent diameters of 7.94 mm. The effects of mass flux and heat flux on heat transfer coefficient and pressure drop were illustrated. The present experimental data report the strong effect of mass flux and vapor quality on both heat transfer coefficient and pressure drop of R410A in the micro-fin tube. Keywords: R410A · Pressure drop · Heat transfer coefficient · Micro-fin tube
1 Introduction Microfin tube heat exchangers have been widely used in recent decades, especially in refrigeration systems due to their high heat transfer effectiveness. On the other hand, R410A is still used in many refrigeration and heat pump systems. Hence, studies on the two-phase heat transfer characteristics of R410A in micro-fin tubes are necessary to improve the performance of those systems. The two-phase flow boiling heat transfer coefficient of R410A in micro-fin tubes was demonstrated in several studies in the open literature [1–4]. Kim et al. [1] reported that the boiling heat transfer coefficients of R410A in micro-fin tubes are 10%–150% higher than those of smooth tubes and the heat transfer coefficients were affected by heat flux and mass flux. Wellsandt and Vamling [2] demonstrated the heat transfer coefficient of R410A in a 9.53 mm outer diameter micro-fin tube and validated their data with some existing heat transfer coefficient correlations developed for micro-fin tubes. However, all of them were overpredicted the data. Hu et al. [3] investigated the boiling heat transfer © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1228–1237, 2022. https://doi.org/10.1007/978-981-19-1968-8_103
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characteristics of R410A/oil mixture in a micro-fin tube and reported that the mixture enhances the heat transfer coefficient up to 30% at the vapor quality lower than 0.8. Kim [4] reported the boiling heat transfer coefficient of R410A in 5.1 mm outer diameter micro-fin tubes. The data show that the heat transfer coefficients enhance 1.39–1.79 times and the experimental data are predicted well by the Koyama et al. correlation [5]. Yang and Hrnjak [6] proposed a new flow pattern map for flow boiling of R410A in horizontal micro-fin tubes. This result could improve the prediction of flow patterns in micro-fin tubes. However, due to the variation in microfin tube types, experimental data are still needed to improve the prediction. In this study, we demonstrate the experimental two-phase flow boiling heat transfer coefficient of R410A in the horizontal micro-fin tube with an equivalent diameter of 7.94 mm. The effects of mass flux and heat flux on heat transfer coefficient and pressure drop are analyzed. The data are also compared with some existing heat transfer coefficient correlations in the literature.
2 Experimental Model 2.1 Apparatus The experimental apparatus is shown in Fig. 1. The model mainly consists of a refrigerant loop, a water loop, and a data acquisition system. The refrigerant loop includes a magnetic gear pump, a sub-cooler, a mass flow meter, an evaporator, a test section, a condenser, and a receiver. The refrigerant is delivered into the test section by a magnetic gear pump. The mass flow rate of refrigerant is measured by a Coriolis mass flow meter and can be adjusted by changing the pump speed. The quality of the refrigerant at the inlet test section can be controlled by pre-heater. The test section is heated by a water loop as shown in the model. The vapor at the outlet of the test section is condensed by a condenser unit then is delivered to the receiver. The experimental apparatus is insulated with foam and rubber to minimize heat transfer between the system and the environment. The detail test section is depicted in Fig. 2. The test tube is a copper-micro fin type. The main dimensions are listed in Table 1. The effective length is 1200 mm. As shown in the figure, the test section is divided into four separated subsections with a length of 300 mm. In each one, the T-type thermocouples are attached at the top, side, and bottom points at the midpoint of the sub-section. Thermocouples and pressure transducers are also set up at the adiabatic pipes between two consecutive subsections. To visualize the flow pattern, two sight glasses are installed at the beginning and the end of the test tubes. The physical properties of the refrigerant are obtained from REFPROP [7]. The temperature, pressure, and mass flow rate are recorded using data acquisition.
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Sight Glass
Sight Glass
Water Jackets
Chilled Unit
Liquid Tank Condensing Unit Flow Meter Gear Pump
Fig. 1. Experimental apparatus
Fig. 2. Test section
Table. 1. Tube dimensions Parameter
Value
Equivalent diameter (mm)
7.94 mm
Fin height
0.21 mm
Fin angle
15o
Helix angle
17o
Number of fins
75
2.2 Data Reduction The data were collected and were analyzed by the data reduction program. The heat fow rate of each subsection, Qn , is calculated from the mass flow rate and temperature of
Experimental Two-Phase Flow Boiling Heat Transfer Coefficient
water flowing inside the water jackets as follows, Qn = Wn cp Tn,in − Tn,out − Qloss
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(1)
where Wn , cp , Tn are the mass rate of flow, specific heat, and temperature of cooling water in subsection n, respectively. Qloss is the heat loss on the test section which was determined when calibrating the system. The local heat transfer coefficient inside the channel can be evaluated as the ratio of the heat flux to saturation minus the inside wall temperature: h=
Q A × (Twi − Tsat)
(2)
The saturation temperature at the inlet was defined based on the local measured pressure. The saturation temperature at the outlet of each regime was calculated by subtracting the inlet value to the saturation temperature drop. In our study, the saturation temperature of points between inlet and outlet could be obtained as the linear function of two known values. The inside tube wall temperature, Twi which was the average temperature of the top, left sides, and bottom wall temperatures, was determined based on the steady-state one-dimensional radial conduction heat transfer through the wall with internal heat generation. The vapor quality, x, at the measurement locations, z, were determined based on the thermodynamic properties: x=
i − if ifg
(3)
The total pressure drop of two-phase fluid is generally calculated by the sum of the static head pstatic, the momentum pmomentum and the frictional pressure drop pfrictional. Δptotal = Δpstatic + Δpmom + Δpfrict
(4)
Since the horizontal tube was used in this study, the pressure head can be neglected. The refrigerant is evaporated from liquid at the saturation temperature to the vapor-liquid mixture at mass quality x with a linear change of test distance over the tube length. Hence the momentum pressure drop can be calculated following equation: 2 x υg (1 − x)2 dp + a = G 2 υf −1 (5) − dz α υf 1−α For horizontal tube, void fraction α is defined by Steiner equation that was modified from the Rouhani-Axelsson model as follow: 0.25 −1 1.18(1 − x) gσ ρf − ρg x x 1−x α= + + (6) (1 + 0.12(1 − x)) ρg ρg ρf Gρf0.5 In this work, the uncertainties are analyzed following the ISO guidelines [8]. Table 2 shows the estimated uncertainty associated with direct and indirect parameters at a 95% confidence interval.
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Uncertainty
Temperature (˚C)
±0.35
Absolute pressure (%)
±0.2
Different pressure (kPa)
±2.5
Mass flux of refrigerant (%)
±0.2
Mass flux of water (%)
±0.2
Heat flux (%)
±3
Vapor quality (%)
±5
Heat transfer coefficient (%)
±10
3 Results and Discussions The influence of mass flux on the experimental pressure drop gradient of R410A in the micro-fin tube is illustrated in Fig. 3. The heat flux was set at 9 kW/m2 and mass flux is ranged from 200 kg/m2 s to 320 kg/m2 s. The results show the significant effects of mass flux and vapor quality on pressure drop. The increase of friction at the higher mass fluxes leads to the increase of pressure drop. In addition, the pressure drop also rises when vapor quality increases due to the increase of velocity of the fluid phase. 10 8
dp/dz [kPa/m]
G [kg/m2 s] 200 320
R-410A D=7.94mm Ts=6℃ q=9kW/m2
6 4 2
0 0
0.2
0.4
0.6
0.8
1
x
Fig. 3. Effect of mass flux on pressure drop
Figure 4 depicts the experimental pressure drop gradient with the variation of heat flux. The pressure drop gradient keeps constant with the change of heat flux. The decline of the pressure drop trend is observed at the vapor quality of 0.8. The behavior occurs since the liquid film disappears near the dry-out regime, which reduces the friction. It also explains the reason the trend decreases more steeply with higher heat flux.
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3 R-410A Di=7.94mm Ts=6℃ G=200kg/m2s
dp/dz[kPa/m]
2.5 2 1.5 1
q [kW/m2] 9 12
0.5
0 0
0.2
0.4
0.6
0.8
1
x
Fig. 4. Effect of heat flux on pressure drop
Figure 5 shows the effect of mass flux on the heat transfer coefficient of R410A in the micro-fin tube. The heat flux was fixed at 12 kW/m2 and the mass fluxes were set as 200 kg/m2 s and 320 kg/m2 , respectively. As seen on the graph, the experimental heat transfer coefficient increases with the increase of mass flux. However, when the vapor quality is over 0.5, the effect of mass flux is insignificant. Also, in this regime, the heat transfer coefficient depends weakly on the vapor quality. The peak and drop of heat transfer coefficient are observed at a vapor quality of 0.8 because of the dry-out. The effect of heat flux on the heat transfer coefficient of R410A is shown in Fig. 6. In this test, the mass flux was fixed at 260 kg/m2 s and the heat flux was ranged from 9 kW/m2 to 12 kW/m2 . The data show that the heat transfer coefficient is strongly affected by heat flux. At the low-quality regime, the heat transfer rises when increasing the heat flux. When the vapor quality reaches 0.5, the effect of heat flux is reduced caused by the suppression of nucleate boiling heat transfer. In this work, the experimental heat transfer coefficients are also validated with some well-known correlations [9–13] as illustrated in Table 3. All presented correlations are under-predicted. It may be explained that since all the above correlations are developed for smooth tubes while with the same diameter as smooth tubes, micro-fin tubes enhance heat transfer coefficients. Among five correlations, the one proposed by Gungor and Winterton shows the best prediction against the present data. The summary of the comparison is shown in Table 4.
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V. D. Nguyen et al. Table 3. Flow boiling heat transfer coefficient correlations
Correlations
Equations
htp = 30Re0.857 Bo0.714 lo Lazarek and Black [10] Relo =
ψ=
Shah correlation [9]
k l ; Dh
GDh φ ; Bo = ; μl Gilg
hTP ; Co = hl
0.8 0.5 ρv 1 x−1 ρl G2
q ; FrL = 2 Ghlv ρl gD 0.4 kl Pr hl = 0.023Re0.8 l l Di Bo =
htp = Max(F, S)hl ;
htp = F · hl + S · hnb 0.4 hl = 0.023Re0.8 l Pr l
Gungor and Winterton [11]
kl Di
0.86 1 F = 1 + 24000Bo1.16 + 1.37 Xtt hnb = 55Pr0.12 q2/3 (− log10 pr )−0.55 M −0.5 −1 S = (1 + 0.55F 0.1 Re0.16 lo )
Tran et al. [12]
−0.4 h = 8.4 × 10−5 (Bo2 Wel )0.3 ρρvl htp = S.hnb + F · hconv,tp hnb = 55Pr0.12 q2/3 (− log10 pr )−0.55 M −0.5 ;
Bertsch et al. [13]
hconv,tp = hconv,l · (1 − x) + hconv,v · x; S =1−x
F = 1 + 80 · x2 − x6 · e−0.6Cf
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Table 4. Comparison between the experimental heat transfer coefficient and existing correlation Correlations
MD (%)
AD (%)
Lazarek and Black [10]
50.39
−49.78
Shah [9]
48.48
−45.8
Gungor and Winterton [11]
27.4
−23.6
Tran et al. [12]
32.46
−29.69
Bertsch et al. [13]
55.12
−55.12
12
h[kW/m2K]
10 8
6 4
R-410A D=7.94mm Ts=6℃ q=12kW/m2
2
0 0
0.2
G [kg/m2s] 200 320 0.4
0.6
0.8
1
x
Fig. 5. Effect of mass flux on heat transfer coefficient 12
R-410A D=7.94mm Ts=6℃ G=260kg/m2s
h[kW/m2K]
10 8
q [kW/m2] 9
12
6 4 2
0 0
0.2
0.4
0.6
0.8
1
x
Fig. 6. Effect of heat flux on heat transfer coefficient
4 Conclusion In this work, the experimental two-phase flow boiling heat transfer coefficients and pressure drop of R410A in a micro-fin tube have been investigated. The heat transfer coefficients are increased with the increase of heat flux and mass flux. The two-phase flow pressure drop of R410A also increases when the mass flux increase. The present
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experimental data are also compared with some well-known correlations in the open literature. It is noted that a modified heat transfer coefficient correlation developed for a specific micro-fin tube is still needed to improve the prediction. Acknowledgments. This research is funded by the Ministry of Education and Training (Vietnam) under project number B2020-BKA-04, Hanoi University of Science and Technology (HUST) under project number T2020-PC-210, and Chonnam National University, South Korea.
References 1. Kim, Y., Seo, K., Chung, J.: Evaporation heat transfer characteristics of R-410A in 7 and 9.52 mm smooth/micro-fin tubes. Int. J. Refrig. 25, 716–730 (2002). http://www.sciencedi rect.com/science/article/pii/S0140700701000706. Accessed 23 Aug 2013 2. Wellsandt, S., Vamling, L.: Evaporation of R407C and R410A in a horizontal herringbone microfin tube: heat transfer and pressure drop. Int. J. Refrig. 28(6), 901–911 (2005). https:// doi.org/10.1016/j.ijrefrig.2005.01.006 3. Hu, H., Ding, G., Huang, X.-C., Deng, B., Gao, Y.-F.: Experimental investigation and correlation of two-phase heat transfer of R410a/oil mixture flow boiling in a 5-mm microfin tube. J. Enhanc. Heat Transf. 18(3), 209–220 (2011). https://doi.org/10.1615/JEnhHeatTransf.v18. i3.30 4. Kim, N.-H.: Evaporation heat transfer and pressure drop of R-410A in a 5.0 mm O.D. smooth and microfin tube. Int. J. Air-Conditioning Refrig. 23(01), 1550004 (2015). https://doi.org/ 10.1142/S2010132515500042 5. Koyama, S., Yu, J., Momoki, S., Fujii, T., Honda, H.: Forced convective flow boiling heat transfer of pure refrigerants inside a horizontal microfin tube. In: Convective Flow Boiling, p. 6. CRC Press (1996) 6. Yang, C.M., Hrnjak, P.: A new flow pattern map for flow boiling of R410A in horizontal micro-fin tubes considering the effect of the helix angle. Int. J. Refrig. 109, 154–160 (2020). https://doi.org/10.1016/j.ijrefrig.2019.09.013 7. Lemmon, E.W., Bell, I.H., Huber, M.L., McLinden, M.O.: NIST Standard Reference Database 23: Reference Fluid Thermodynamic and Transport Properties-REFPROP, Version 10.0, National Institute of Standards and Technology (2018), https://doi.org/10.18434/T4JS3C 8. ISO: ISO GUM, Guide to the Expression of Uncertainty in Measurement, first ed., 1993, corrected and reprinted, 1995, International Organization for Standardization, Geneva, Switzerland, 1995 (1993) 9. Shah, M.: A new correlation for heat transfer during boiling flow through pipes. ASHRAE Trans. 82, 66–86 (1976). http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle: A+new+correlation+for+heat+transfer+during+boiling+flow+through+pipes#0. Accessed 03 Sept 2013 10. Lazarek, G., Black, S.: Evaporative heat transfer, pressure drop and critical heat flux in a small vertical tube with R-113. Int. J. Heat Mass Transf. 25(7) (1982). http://www.sciencedi rect.com/science/article/pii/0017931082900709. Accessed 04 Sept 2013 11. Gungor, K., Winterton, R.: A general correlation for flow boiling in tubes and annuli. Int. J. Heat Mass Transf. 29(3), 351–358 (1986). https://doi.org/10.1016/0017-9310(86)90205-X
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12. Tran, T., Wambsganss, M., France, D.: Small circular-and rectangular-channel boiling with two refrigerants. Int. J. Multiph. Flow 3(4), 485–498 (1996). http://scholar.google.com/sch olar?hl=en&btnG=Search&q=intitle:e+r+g+a+m+o+n#4. Accessed 28 Aug 2013 13. Bertsch, S.S., Groll, E., Garimella, S.V.: A composite heat transfer correlation for saturated flow boiling in small channels. Int. J. Heat Mass Transf. 52(7–8), 2110–2118 (2009), https:// doi.org/10.1016/j.ijheatmasstransfer.2008.10.022
Surrounding Environment Detection of an Intelligent Wheelchair Using Improved Convolutional Neural Networks Hai-Le Bui, Tuan Truong Cong, Pham Anh Quan, and Thi Thoa Mac(B) School of Mechanical Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet Street, Hanoi, Vietnam [email protected]
Abstract. Electric wheelchairs, as mobile auxiliary equipment, are more intelligent by applying image processing, deep learning technique to improve the quality of life and self-independence of the disabled/elder people. In this paper, we have proposed an approach for object/person detection base on improved Yolov3 and image processing. We develop the dataset for training process. Our wheelchair is equipped a camera to detect the object on its path such as: pillar, bin, stair, people, door. The wheelchair experiment with person is implemented in our campus corridor. The results proved that our wheelchair system has ability to detect and recognize the trained object/people in the surroundings with a very high average accuracy. Keywords: Intelligent wheelchair · Detection · Yolov3 · CNN · Image processing
1 Introduction Nowadays, more than one billion people, or 15% of the world’s population are people with disabilities. The disability prevalence is higher for developing countries such as Vietnam. Vietnam has approximately 5.2 million people with different types of disabilities as presented in Fig. 1. People has difficulty in mobility accounts for the highest volume, 22.4% of the total as shown in Fig. 1 [1]. Therefore, the need for intelligent wheelchair daily has become a high priority. This demand is receiving growing concern from researchers, during the last years, and several approaches are being proposed to allow an easier life to the people belonging to those groups and elderly people [2]. With the rapid development of technology, wheelchairs are integrated with joystick, head gesture or pinch, sensor fusion, image processing. Recently, various wheelchair platform are being developed noncomputer-vision-based and vision-based approaches such as voice command control [3, 4], eye gaze [5], or follow the movements of the tongue [6, 7]. In [8], the wheelchair is controlled by head movement by using a camera. Besides, wearable electroencephalogram (EEG) signal acquisition device [9], braincomputer interface (BCI) has been properly implemented to the intelligent wheelchair [10–12]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1238–1245, 2022. https://doi.org/10.1007/978-981-19-1968-8_104
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Fig. 1. Disablility type in Vietnam [1]
Although the wheelchair is a convenient, accidents have occurred because of a decreasing in physical ability and an insufficient risk recognition of the user. Therefore, the surrounding environment recognition to avoid the above accident factors of the wheelchair is a prequisisent requiment [13]. Nowaday, deep learning is widely used in numerous applications in daily life and industrial fields because of its learning ability and adative ablility with surrouding change [14]. Using the advantage of deep learning, in [13], a proposed approach based on Yolo v2 is present to detect sidewalks, crosswalks and traffic lights, and other objects. However, this method is perform in off-line process with the not high accuracy with the the detection rate is 0.587. In this paper, we presented an improved Yolov3 to realtime recognize surrounding environment of an intelligent wheelchair when moving in the building such as pillar, stair, door, people, bin with the very high accuracy. We also proposed the methodololy to estimate the distance between the wheelchair and the detected object for the avoidance purpose. This paper is organized as follows. In Sect. 2, we explain the system configuration. The proposed methodology is introduced in Sect. 2. Section 3 presents the proposed methodology. In Sect. 4, we give a description of experiment results and discussions. Finally, conclusion and future work are provided in Sect. 5.
2 System Configuration This work aims to develop and intelligent wheelchair that an detect the surrounding. The real proto of type of the intelligent wheelchair is shown in Fig. 2 which includes power blocks, input block, process block on local network and output block. The wheelchair has dimension of 84 cm × 62 cm × 95 cm and the maximum load capacity is 100 kg. It is quipped camera with the resolution of 1920 × 1080 and 25 fps, MPU 6050 sensor and HC-SR04 Ultrasonic sensors. In addition, the wheelchair can be controlled via webserver. Raspberry Pi 3 is used as micro computer for the system to send the signal to control left and right motors.
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Fig. 2. Prototype and hardware structure of the wheelchair
3 Methodology In this paper, we present a method for detecting objects (such as pillar, bin, door, stair, people) when the wheelchair moves in the building. The method uses image processing and improved Yolov3. It is divided into the leaning process and the detecting process. The system includes Darknet Architecture 53 (base network) used for feature extraction (Fig. 3).
Fig. 3. Darknet - 53 structure [15]
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The network predicts four coordinates od each bounding box, t x , t y , t w , t h . The cell is located (offset) from the top left corner of the image by (cx , cy ) and the bounding box has with and height pw , ph (as shown in Fig. 4), the predictions are validated as follows: bx = σ (t x ) + cx by = σ ty + cy bw = pw etw bh = ph eth
(1)
Fig. 4. Bounding boxes with dimension priors and location predictor [15]
Yolov3 predicts many bounding boxes on each image, therefore, we used non-max suppression to obtain only one bounding box as shown in Fig. 5. For more detail, please refer to [15].
Fig. 5. Non-max suppression for reducing bounding box
The outputs of base network are the inputs of extra layers to predict labels and coordinates of bounding box as shown in Fig. 6. The input images have dimension 416 × 416. At the 82nd convolutional layer: The image is divided into 13 × 13 grid cells (the image has been divided by 32). Here, the grid cells will be responsible for finding large objects in the image. At the 94th convolutional layer: The image is divided into 26 × 26 grid cells (the image is divided by 16). And is responsible for finding medium
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Fig. 6. The improved Yolo detection structure
sized objects. Similarly, at convolution layer 106, the image is divided into 52 × 52 grid cells (image is divided by 8) and is responsible for finding small objects. The camera on the wheelchair will capture the image on the wheelchair path. To validate the training process, we use the loss function. The loss function is calculated as a total of localization loss (Lloc ) and confidence loss (Lcls ) as follows: 2
S B
Lloc = λcoord
i=0 j=0
2 2 2 √ obj ij wi − wˆ i + xi − xˆ i + yi − yˆ i +
√ hi −
2 hˆ i
2 B obj S S obj 2 obj i pi (c) − pˆ i (c) ij + λnoobj − ij Ci − Cˆ i Lcls = 2
i=0 j=0
2
i=0 c∈C
L = Lloc + Lcls
where: x i ,yi,, : the location of the center of the anchor box xˆ i, yˆ i : the location of the center of the predicted anchor box wi ,hi : which is the width and height of the anchor box wˆ i , hi : which is the width and height of the predicted anchor box obj 1ij : is 1 when there is an object in the cell i, else 0
obj
1i : is 1 when there is a particular class is predicted, else 0 Ci : confidence score of whether there is an object or not Cˆ i : confidence score of the prediction pi (c): the classification loss pˆ i (c): Conditional probability prediction
(2)
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4 Experiment Results and Discussion The wheelchair is tested in the D6 building, Hanoi university of science and technology. The working evironment of the wheelchair includes doors, pillars, bins, people, stairs as shown in Fig. 7. The CCD camera with a frame rate of 25 fps at a resolution of 1920 × 1080 pixels is equipped on the top of the wheelchair as depicted in Fig. 8. Figure 8 and 9 shows the object/human detection results in corridor with pillars based on the proposed approach. It can be seen that all trained objects/people are detectioned bay our wheelchair system. Figure 10 shows the loss function of the tranning process. It can be observed that the loss dramatically decrease to an approximated zero value after 100 batches.
Fig. 7. Test wheelchair scinario
Fig. 8. Object detection results in corridor
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Fig. 9. Humnan detection results in corridor
Fig. 10. The loss function of tranning process
5 Conclusion and Future Work In this work, we have sucefully developed the model for object/person detection base on Yolov3 for an intelligent wheelchair interm of supporting disable/elder people. The wheelchair is equipped a camera to detect the object on its path such as: pillar, bin, stair, people, door. We build the image for the dataset for the traning system. The running experiment with person is carried out in the campus corridor. The experiment results proved that our wheelchair system has ability to detect and recogize the trainned object/people with a very high average accuracy. The approach has a great potential of intelligent wheelchair for disable/elder people where low-power consumption, limited-resource hardware and high accuracy are prequisisent. This work can be extend in the future such as interact with dynamic working enviroment with intelligent con. Acknowledgments. This research is funded by Hanoi University of Science and Technology (HUST) under project number T2021-SAHEP-009. The authors would like to gratefully thank Vu Van Cuong for his assistance in technical work in our lab.
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References 1. https://www.globaldisabilityrightsnow.org/infographics/disability-vietnam 2. Chatterjee, S., Roy, S.: A low-cost assistive wheelchair for handicapped & elderly people. Ain Shams Eng. J. 12, 3835–3841 (2021) 3. Nishimori, M., Takeshi, S., Ryosuke, K.: Voice controlled intelligent wheelchair. In: SICE Annual Conference 2007. IEEE (2007) 4. Ali, A.: Design of voice controlled smart wheelchair. Int. J. Comput. Appl. 131(1), 32–38 (2015) 5. Plesnick, S., Domenico, R., Patrick, L.: Eye-controlled wheelchair. In: IEEE Canada International Humanitarian Technology Conference-(IHTC), Canada (2014) 6. Joshi, S.N.: Tongue motion controlled wheel chair. Int. Org. Res. Dev. 7(2), 14–19 (2020) 7. Liao, L., Wu, Y., Xiang, Y., Yan, X., Shi, J., Bai, J., et al.: Control system of powered wheelchairs based on tongue motion detection. In: IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*.CC) (2016) 8. Patil, H.: Design and making of head motion controlled wheel chair. Int. J. Res. Eng. Sci. Manage. 3(6), 80–84 (2020) 9. Liyanage, S.R., Bhatt, C.: Wearable electroencephalography technologies for brain–computer interfacing. In: Wearable and Implantable Medical Devices, pp. 55–78. Elsevier (2020) 10. Huang, X., Xue, X., Yuan, Z.: A simulation platform for the brain-computer interface (BCI) based smart wheelchair. In: Sun, X., Wang, J., Bertino, E. (eds.) ICAIS 2020. LNCS, vol. 12239, pp. 257–266. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57884-8_23 11. Jameel, H.F., Mohammed, S.L., Gharghan, S.K.: Electroencephalograph-based wheelchair controlling system for the people with motor disability using advanced brainwear. In: 2019 12th International Conference on Developments in eSystems Engineering (DeSE), pp. 843– 848. IEEE (2019) 12. Na, R., et al.: An embedded lightweight SSVEP-BCI electric wheelchair with hybrid stimulator. Digit. Sig. Process. 116, 103101 (2021) 13. Sakai, Y., Huimin, L., Tan, J.-K., Kim, H.: Recognition of surrounding environment from electric wheelchair videos based on modified YOLOv2. Fut. Gener. Comput. Syst. 92, 157– 161 (2019) 14. Mac, T.T.: Application of improved Yolov3 for pill manufacturing system. IFACPapersOnLine (accepted) 15. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. https://pjreddie.com/media/ files/papers/YOLOv3.pdf
Smoothed Particle Hydrodynamics Simulation of a Wave Making System Huy Nguyen Tran and Thinh Xuan Ho(B) Computational Engineering Study Program, Vietnamese-German University, Binh Duong, Vietnam [email protected]
Abstract. This paper presents a study on a meshfree particle method named Smoothed Particle Hydrodynamics (SPH) applied to a wave making system. SPH is based on a Lagrangian approach, in which motion of particles (fluid and/or solid) is tracked. The wave making system consists of a triangular wedge and a water channel with an inclined wall as its main components. The wedge moves up and down at a certain frequency to create the wave. In order to have an optimal model, model parameters such as the kernel function, size of the support domain, and the artificial viscosity coefficient are examined. Results of the water surface elevation are compared with available experimental data. Good fit is achieved for the optimal case. The wave development and breaking on the inclined wall is finely captured. Keywords: SPH · Meshfree particle method · Wave making
1 Introduction Smoothed Particle Hydrodynamics (SPH) was developed in 1977 for modeling astrophysical problems [1, 2]. Monaghan [3] was the first to apply it to fluid flows in 1994. In this method, a material domain is represented by elements or particles, which possess properties of the material, and motion of each and every of them is followed according to a Lagrangian frame. As opposed to conventional mesh-based methods such as finite difference, finite volume, and finite element methods, SPH does not require a mesh for approximation, it instead uses the particles as interpolating points; SPH is thus considered a meshfree particle method. As particles do not have a fixed connectivity, treatment of large deformation is relatively easier. In addition, time history of field variables at any point in the domain can be naturally obtained, which makes it easy to identify free surfaces, moving interfaces, and deformable boundaries. In the current work, we adopt this method to simulate a wave making system including a wavemaker and water channel. Different model parameters are examined in order to find an optimal model for such a problem. This is an initial step of a large project where wave–floating object interaction for energy harvesting is simulated.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1246–1254, 2022. https://doi.org/10.1007/978-981-19-1968-8_105
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2 Background Theory 2.1 SPH Methodology Smoothed Particle Hydrodynamics (SPH) method starts from the following identity: f (x) = f x δ x − x d x , (1)
where f (x) is a function of the position vector x, and δ x − x is the Dirac delta function. This equation is exact aslong as f (x) is defined and continuous in Ω. If the delta function is replaced by W x − x , h , it becomes an approximation: f (x) =
f x W x − x , h d x .
(2)
Here W is the smoothing or kernel function, and h is the smoothing length. W has a bell shape with its value decreasing monotonically when x moves away from x and equal zero when beyond a distance of κh, i.e., a support domain. Eq. (2) is the so-called kernel approximation. The first derivative of f (x) is defined as f x · ∇W x − x , h d x . (3) ∇ · f (x) = −
Considering a problem domain Ω filled with a set of particles as depited in Fig. 1, particle approximation of Eqs. (2) and (3) can be written, respectively, as follows: N (4) f (xi ) = Vj f xj W xi − xj , h , j=1
∇ · f (xi ) = −
N j=1
Vj f xj · ∇W xi − xj , h .
(5)
where Vj = mj /ρj is the volume of particle j, and N is the total number of particles in the support domain.
Fig. 1. SPH particles in a 2D domain. Particles j are neighbors of i in the support domain.
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Fig. 2. Mesh for linked-list algorithm
2.2 Governing Equations of the Fluid Flow Mass and linear momentum conservations are as follows: dρ = −ρ∇ · u dt
(6)
1 1 du = − ∇p + ∇ · τ + g dt ρ ρ
(7)
Their respective forms in SPH methodology are the following [4, 5]. d ρi =− mj uij · ∇i Wij dt j
pj pi d ui =− mj 2 + 2 + ij ∇i Wij + g dt ρi ρj j
(8)
(9)
Here p, μ, u, τ, and g are respectively pressure, viscosity, velocity vector, shear stress tensor, and gravity. ij is the artificial viscosity introduced into the momentum equation to represent viscous effects, in place of the shear stress tensor. It is given as −α c¯ μ ij ij ρ¯ij , uij · xij < 0 (10) ij = 0, uij · xij ≥ 0 μij =
huij · xij
(11)
x2ij + η2
where cij = ci + cj /2 is the average speed of sound, ρ ij = ρi + ρj /2, xij = xi − xj , uij = ui − uj , and η2 = 0.01h2 . The coefficient α is to be tuned for specific problems. Pressure is determined using equation of state [6]: ρ0 c02 pi = 7
ρi ρ0
7 −1
(12)
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Fig. 3. Solid wall by dummy (top) and repulsive (bottom) particles
where ρ0 = 1000 kg/m3 for water and c0 is the speed of sound. The fluid is assumed weakly compressible (WC-SPH) in this work. In addition, SPH particles move according to mj d xi = ui − ε uij W xi − xj , h dt ρ ij
(13)
j
where ε < 1, typically in the range of 0.3–0.5. Eqs. (8, 9, 13) can be solved using Symplectic algorithm, which involves two stages. In the first stage, particle position and density are calculated at the middle of a time step, and in the second stage, velocity and position are calculated at the end of the time step.
3 Computational Techniques 3.1 Search of Neighboring Particles In the calculation presented above, each particle i requires a list of neighbors j within its support domain; and i runs from 1 to the total particle number, N 0 . Searching for the neighboring particles is time-consuming, and can account up to 95% of the total computational time. To search for a neighbor, for each particle i, calculate the distance x i j from it to each and every particle j with j going from 1 to N 0 , and check if x i j ≤ κh. However, this way, namely all-pair search, is extremely time-consuming. One common way to reduce the searching load is to use linked-list algorithm. It uses a temporary and coarse mesh with uniform cells as illustrated in Fig. 2. For a 2D problem, the cell size is of κh × κh; and neighbors of particle i can be found in max nine surrounding cells. The respective number is 27 for a 3D problem. Therefore, search is performed in these cells only. Accordingly, the computational cost is reduced to O(N 0 × logN 0 ) as compared to O(N 0 × N 0 ) of the all-pair search. Further reduction can be achieved via parallel computing using Graphics Processing Unit (GPU) or Message Passing Interface (MPI). The latter is adopted in this work.
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3.2 Boundary Conditions For a free surface, in general, no special treatment is required, except that a zero pressure can be ascribed to its particles. Free surface can be easily detected via density, i.e., it is smaller for the surface particles than for others. The reason is that the support domain near the surface is truncated or incomplete with no particles above it.
Fig. 4. Schematic diagram of the wave making system
It is however complicated to model a solid wall, for this, fictitious particles are commonly used. One way is to treat them as dummy particles, which are placed inside the wall so that the support domain is complete. They satisfy the mass and momentum equations as fluid particles, however, move according to the wall (stationary or moving like wave-makers). When a fluid particle approaches the wall, its density and pressure increase, which then pushes itself apart. Another way is to use mirror image (i.e. ghost particles) of fluid particles over the wall’s surface. When a fluid particle moves toward the wall, it interacts with its own image moving in an opposite direction; wall penetration of fluid particles can thus be prevented. Alternative to the above approach is the use of repulsive particles. Specifically, wall particles are placed on the wall’s surface; they exert a repulsive force on fluid particles in their vicinity. The force can be defined based on Lennard–Jones molecular potential [3]. Actually, the force is attractive when particles are far apart and repulsive when they are close to each other. That way, wall penetration can also be avoided. Schematic representation of a solid wall is demonstrated in Fig. 3. One can refer to, e.g., [4, 7, 8] for a more detailed description.
4 Simulation of a Wave Making System 4.1 Simulation Domain A schematic diagram of the wave making system is shown in Fig. 4. It is of a plunger-type design and the same as a laboratory system constructed by [10]. It consists of a triangular wedge and a water flume as its main components. The wedge has a base of 418 mm, a height of 566 mm, and a width of ~750 mm (same as the width of the channel). It moves up and down at a certain frequency, striking the water, and creating a wave. The mean submerged height of the wedge is 375 mm. Detailed description of the system can be found in [10]. The simulation domain comprises of water, the wedge, and left, bottom,
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Fig. 5. Particle arrangement
Surface elevation (m)
0.12
C = 1.5
C = 1.2
C = 1.0
Exp
0.08 0.04 0 -0.04
Time (s)
-0.08 15
16
17
18
19
20
Fig. 6. Water elevation at x = 5.34 m for different values of the smoothing length.
and inclined walls. For the sake of computational cost, only 2D aspects are considered. Simulation is performed on GPU using DualSPHysics, an open-source code [9]. The domain is discretized into elements/particles of x by x, which are arranged in a simple cubic (SC) configuration. Repulsive particles are used to represent solid surfaces. Fig. 5 presents an example of particle arrangement. Convergence of particle size is tested by reducing the size from x = 0.02 to 0.01, 0.005, and 0.0025 m, corresponding to simulation systems of min 21 thousand and max 1.4 million particles. Results for the water surface elevation are compared with experimental data. As further reduction does not give any significant improvement, x = 0.0025 m is chosen. The objective of this work is to examine effects of model parameters on the flow field, typically the water surface elevation along the flume, and hence to choose appropriate ones for such a problem. The elevation is determined via density, i.e. ρi = j mj Wij (see Eq. (4) where f is replaced by ρ); for points on the free surface it is about 500 as only half of the support domain is filled with water particles. 4.2 Simulation Results A quintic or Wendland smoothing function is adopted in this work [4]: q 4 W (x, h) = α 1 − (2q + 1) 2
(14)
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Fig. 7. Water elevation at x = 5.34 m for different values of the viscosity coefficient.
where 0 ≤ q = x ij /h ≤ 2, α = 7/4πh2 for a 2D problem, and W diminishes when q > 2 or x ij > 2h. The support domain’s radius is thus κh = 2h.
Fig. 8. Snapshots of the wave breaking on the slope.
√ The smoothing length h is examined; it is given as h = C 2 x where C equals to 1.0, 1.2, and 1.5. The respective support domain contains 21, 37, and 57 particles initially arranged in a simple cubic pattern. Results for the water elevation at x = 5.34 m from the front wall √ are shown in Fig. 6 in comparison with experimental data available in [10]. h = 1.5 2 x is seen to give a best fit, which is then chosen for other investigations. Note that a more rigorous examination of the influence of h on the flow needs to be carried out as a large support domain tends to “smooth” out the flow field, making it (and probably temperature or concentration field) erroneously more uniform. However, we leave this for future work. In addition, a cubic spline function is adopted with the same support domain, i.e. C = 1.5. It is found that the Wendland outplays the cubic spline. Furthermore, the artificial viscosity coefficient α in Eq. (10) is investigated. Four values of α are realized, that is, α = 0.005, 0.01, 0.015, and 0.02. Other √ model parameters are as the following: particle size x = 0.0025 m, h = 1.5 2 x, and Wendland smoothing function. Results for the surface elevation are presented in Fig. 7. It is noticed in general that the elevation is over estimated with small values of α, which represents lower viscosities, and α = 0.01 provides the best fit with the experiment.
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For the case where all the model parameters are optimized, snapshots of the wave developing and breaking on the slope are shown in Fig. 8. Type √ of wave breaker can be determined via the Iribarren number given by ξ0 = tan θ/ H /L with L = (g/2π )T 2 , θ being bottom slope angle (17°), H the wave height (~0.1 m), L the deep-water wavelength (~1.6 m), g the gravity, and T the period (60 RPM) [11]. As ξ0 = 1.1, it is of a plunging breaker type, a typical one when the ocean floor is steep.
5 Summary A meshfree particle method named Smoothed Particle Hydrodynamics (SPH) has briefly been introduced for computational fluid dynamics. It was then adopted to simulate a wave making system that included the water, the moving wedge, and solid walls. This is for the first time, to the best of our knowledge, a plunger-type wave making system, which involves harsh fluid–structure interaction is simulated. Model parameters such as the smoothing function, smoothing length or size of the support domain, and the artificial viscosity coefficient were examined and optimized. This was done by tuning the parameters and repeating the simulations several times so that, except for the parameter being examined, all the others had already been optimized. Results for the surface elevation was compared with experimental data available in [10]; good fit was achieved. For the case where all the model parameters were optimized, wave development and breaking on the slope were finely captured. For future work, we plan to study interaction between the wave and floating objects for wave energy harvesting. The simulation work is to help design an appropriate experiment schedule that can reduce the workload and cost. Acknowledgments. We thank Dr. Ha Phuong for valuable discussions on the experimental system.
References 1. Gingold, R.A., Monaghan, J.J.: Smoothed particle hydrodynamics: theory and application to non-spherical stars. Mon. Not. Roy. Astron. Soc. 181, 375–389 (1977) 2. Lucy, L.B.: A numerical approach to the testing of the fission hypothesis. Astron. J. 82, 1013–1024 (1977) 3. Monaghan, J.J.: Simulating free surface flows with SPH. J. Comput. Phys. 110, 399–406 (1994) 4. Liu, G.R., Liu, M.B.: Smoothed Particle Hydrodynamics, pp. 399–406. World Scientific (2003) 5. Tran-Duc, T., Ho, T., Thamwattana, N.: A smoothed particle hydrodynamics study on effect of coarse aggregate on self-compacting concrete flows. Int. J. Mech. Sci. 190, 106046 (2021) 6. Monaghan, J.J., Kos, A.: Solitary waves on a cretan beach. J. Waterw. Port Coastal Ocean Eng. 125(3), 145–155 (1999) 7. Violeau, D., Rogers, B.D.: Smoothed particle hydrodynamics (SPH) for free-surface flows: past, present and future. J. Hydraul. Res. 54, 1–26 (2016)
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8. Ye, T., Pan, D., Huang, C., Liu, M.: Smoothed particle hydrodynamics (SPH) for complex fluid flows: recent developments in methodology and applications. Phys. Fluids 31, 011301 (2019) 9. DualSPHysics: A computer code based on SPHysics. https://dual.sphysics.org/ 10. Vu, L.T.Y., Phuong, H., Thinh, H.X., Liem, D.T., Thanh, T.Q., Son, T.D.: Design and fabrication of wave generator using an oscillating wedge. Sci. Tech. Dev. J. Eng. Tech. 2(SI1), SI103–SI111 (2019) 11. Iribarren, C.R., Nogales, C.: Protection des ports. In: Proceedings XVIIth International Navigation Congress, Section II, Communication, Lisbon, vol. 4, pp. 31–80 (1949)
Evaluation of Tensile and Fatigue Strength of ANSI 304 Steel Pipe Welds Xuan Chung Nguyen1 and Tuan-Linh Nguyen2(B) 1 Vietnam - Japan Center, Hanoi University of Industry, Hanoi, Vietnam 2 Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam
[email protected]
Abstract. ANSI 304 austenitic steel is alloy steel with high Ni and Cr content, and this steel has high strength, heat resistance, corrosion resistance, is not magnetized, and excellent weldability. However, regarding the welding process of ANSI 304 steel, the heat-affected area is very sensitive and leads to hot cracking of the weld metal. The higher hot crack forming characteristics of the Austenitic group than other alloys, along with a higher coefficient of thermal expansion, lower coefficient of thermal conductivity, welding mode, etc., are the factors that strongly affect the quality of ANSI 304 steel welds. Tensile strength and fatigue strength of welds are two important parameters characterizing the life and safety of the structure. In this paper, an experimental method is used to evaluate the influence of welding materials, welding mode, and weld geometry parameters on the tensile strength of welds when welding ANSI 304 steel pipes. Thereby, find the optimal set of welding mode parameters to fabricate the test samples. Using the Weibull distribution function and Loga normal distribution function, the fatigue graph and fatigue regression equation are built to evaluate the fatigue strength of ANSI 304 steel pipe welds. Keywords: ANSI 304 steel · Austenitic · Welding mode · Tensile strength · Fatigue strength
1 Introduction ANSI 304 steel pipe is widely used in harsh environments such as in the pipeline and petrochemical industry. Due to the high cost of this steel along with high-reliability requirements during use, the correct selection of welding mode plays an important role in avoiding economic losses. In which, fatigue strength is an important criterion that determines the reliability of the weld. Through the survey, there have been some studies related to this issue, such as in the study [1] by the authors Vikas Chauhan and RS Jadoun, who studied “Optimizing the parameters of MIG welding for stainless steel ANSI 304 and low carbon steel using the Taguchi design method”. The authors made some conclusions that the Taguchi method can be used to determine the influence of welding mode parameters such as welding current, voltage, and speed on the tensile strength of the weld. In which, the two parameters that most affect the tensile strength © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1255–1267, 2022. https://doi.org/10.1007/978-981-19-1968-8_106
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are voltage and welding speed. The effect of the parameters on the final tensile strength can be ranked in descending order as follows: voltage, welding speed, and amperage. The results also show that Argon gas is considered an effective shielding gas with the least spatter during welding. Pawan Kumar et al. [2] concluded that voltage does not affect maximizing weld bead width, minimizing weld reinforcement, and maximizing deposition rate significantly within the design limits of parameters. Wire feed rate and welding speed are found to significantly affect the maximizing weld bead width, minimizing weld reinforcement, and maximizing deposition rate significantly within the design limits of parameters. The parameters for maximizing weld width, deposition rate, and minimizing weld reinforcement using Gas Metal Arc Welding obtained are voltage at 33 V, welding speed at 56 cm/min, and wire feed rate at 14.5 m/min. K. Krishnaprasad et al. [3] studied the fatigue crack growth of welds of different materials between stainless steel and carbon steel. In this study, the authors used a pair of materials, SS316L stainless steel plate and IS2062 Grade A carbon steel, using TIG welding and SS309 auxiliary welding wire. With cracking initiated in the base metal region, the weld metal region, and the heat-affected zones. As a result, the crack growth rate was found to enhance the weld and the base metal stress. The fatigue crack growth rate in the heat-affected zone was the lowest in stainless steel, while in carbon steel, the crack growth rate was faster. Moreover, the scattering range of the data was found to be narrower. The crack resistance was found at different locations of the weld, and the crack resistance was found to have the lowest value at the weld and the highest value in the weld heat-affected zone. Wichan Chuaiphan et al. [4] studied the feasibility of welding two different materials between AISI 304 stainless steel and AISI 201 carbon steel plates with a thickness of 15 mm. The welding process applied his electrode arc welding without melting in an inert gas atmosphere and manual arc welding. In this study, we conducted an experimental study to evaluate the fatigue strength of ANSI 304 steel pipe welds by TIG welding because TIG welding can produce welds with good mechanical properties and anticorrosion.
2 Experimental Setup 2.1 Experimental Modeling The block diagram of the system for measuring the tensile and fatigue strength of the weld is shown in Fig. 1. The results of the tensile test were used to find the best welding routine, that achieve the maximum tensile strength. That routine is then used for welding fatigue test specimens. The obtained results are the fatigue curve graph and the experimental regression equation.
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Fig. 1. Block diagram of the measuring system
2.2 Equipment and Test Workpieces 2.2.1 The Welding Machine Specifications of TIG welding machine OTC 300P-Daihen-Japan, Fig. 2, are shown in Table 1.
Fig. 2. TIG welding machine OTC 300P
Table 1. Specifications of TIG welding machine OTC 300P No
Parameters
Value
1
Power
18 kVA
2
Input power
AC - 3 phase/380 V
3
Output power
AC/DC
4
Welding amperage
50 to 350 A
5
Dimensions of the machine
600 × 750 × 750 mm
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The welding parameters using the TIG welding method are shown in Table 2. Table 2. Welding mode parameters Number of welding layers
Welding method
Diameter of Welding welding rod amperage (mm) (A)
1
141
1.6
50 ÷ 70
Voltage (V)
Current/electrode type
Flow rate of shield gas (L/min)
10 ÷ 12
=/(−)
6÷8
2.2.2 Experimental Workpieces The ANSI 304 pipe workpiece has the size as shown in Fig. 3. The chemical composition is shown in Table 3. Using a 308 stainless steel welding filler material, the diameter of the welding rod is 1.6 mm, and the electrode is 100%W, the diameter of the tungsten electrode is 2.4 mm, using argon 99.99% as the shield gas. The test specimen after welding is shown in Fig. 4. Using the butt welding method with machined samples.
Fig. 3. Test specimens drawing before welding
Fig. 4. Test specimens
Table 3. Chemical composition of ANSI 304 steel [5] Standard
Steel brand
C
Cr
Ni
Mn
Other
AISI
304
0.08
18.0 ÷ 20.0
8.0 ÷ 10.5
2.00
–
Finished welds need to be checked by radiographic method to check whether the weld has met the technical requirements or not.
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The radiographic method is used to check for defects on the surface or inside of a weld. A crack or defect in the weld between two sections of the pipe will lead to a false result if included in the testing of the next steps. The results of welding film using an X-ray film camera are shown in Fig. 5.
Fig. 5. The results of welding film using an X-ray film camera
2.2.3 The Tensile Testing Machine Tensile strength of specimens is determined using hydraulic tensile testing machine CY - 6040A12 Taiwan, Fig. 6. Tensile testing machine specifications are shown in Table 4. The pipe samples after welding are inserted into the core. Then, mount on the machine for tensile test. The specimen is pulled to break to measure the tensile strength. Tested with 9 Test specimens on the machine, the fracture position of all in the weld metal. Table 4. Specification of tensile testing machine CY - 6040A12 No
Parameters
Value
1
Maximum load
100 t
2
Accuracy
±1%
3
Maximum test speed
50 mm/min
4
Workpiece diameter range
12 to 55 mm
2.2.4 The Fatigue Testing Machine The fatigue testing machine is based on a two-point bending principle, using a servo motor to adjust rotational speed, Fig. 7. The machine’s specifications are as shown in Table 5. The experimental workpiece works under repeated stress conditions according to cycle. When the weld is subjected to force, the inside of the weld will appear microcracks, they grow to a certain extent that will destroy the connection of the weld. Based on that, we tested the fatigue strength of the weld when the experimental workpiece
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Fig. 6. Tensile testing machine CY - 6040A12
was bent to both sides continuously during the fatigue test period. As such, the test specimens will be subjected to cyclically repetitive stress conditions. At the weld will form micro-cracks, they grow to a certain extent that will destroy the weld.
Fig. 7. Fatigue testing machine of the weld
Table 5. Specification of fatigue testing machine No
Parameters
Value
1
Power
1 kW
2
Load range
0.5 to 10 kg
3
Maximum workpiece length
250 mm
4
Workpiece diameter range
10 to 20 mm
5
Control parameters: Force, speed, position
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3 Taguchi Experimental Design to Evaluate the Tensile Strength of Welds Conduct a test for 9 ANSI 304 workpieces with different welding currents and clearances, using Taguchi L9 orthogonal table [6–8] with three input parameters as welding amperage (Ih ), welding speed (Vh ), and joint clearance (a), each parameter varies according to the three test levels. The parameter used to evaluate is the tensile strength (σk ), as shown in Table 6. Table 6. Taguchi L9 orthogonal table a (mm)
σk (N/mm2 )
No
Ih (A)
Vh (cm/min)
1
50
3
0.5
663
2
50
4.5
1.0
648
3
50
6
1.5
634
4
60
3
1.0
620
5
60
4.5
1.5
614
6
60
6
0.5
605
7
70
3
1.5
592
8
70
4.5
0.5
580
9
70
6
1.0
572
To consider the effect of the inputs Ih , Vh , and a on the tensile strength, the signal factor SN (Signal to noise ratio) is used: SNi = 10 log
y2i Si2
(1)
In which: Mean value of measurements for an experiment: Ni 1 yi,u Ni
(2)
i 1 (yi,u − yi ) Ni − 1
(3)
yi =
u=1
Variance value: N
Si2 =
u=1
Enumeration: i - Experimental (i = 1 ÷ 9)
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u – Experimental time (u = 1 ÷ 3) Ni – Number of tests for experiment i (Ni = 1). In the case of the largest requirement for the value of σk , SNi is determined by the formula: Ni 1 1 SNi = −10 log (4) Ni yu2 u=1
Using formula (4), calculate the SNi shown as Table 7. Table 7. SNi coefficient calculated for σk Experimental Ih (A) Vh (cm/min) a σk (mm) SN i 1
50
3
0.5
56.430
2
50
4.5
1.0
56.232
3
50
6
1.5
56.042
4
60
3
1.0
55.848
5
60
4.5
1.5
55.763
6
60
6
0.5
55.635
7
70
3
1.5
55.446
8
70
4.5
0.5
55.269
9
70
6
1.0
55.148
The SN coefficient is calculated for each metric and level as follows: SNP1,1 =
(SN1 + SN2 + SN3 ) 3
SNP1,2 =
(SN4 + SN5 + SN6 ) 3
SNP1,3 =
(SN7 + SN8 + SN9 ) 3
SNP2,1 =
(SN1 + SN4 + SN7 ) 3
SNP2,2 =
(SN2 + SN5 + SN8 ) 3
SNP2,3 =
(SN3 + SN6 + SN9 ) 3
Evaluation of Tensile and Fatigue Strength
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(SN1 + SN6 + SN8 ) 3 (SN2 + SN4 + SN9 ) = 3 (SN3 + SN5 + SN7 ) = 3
SNP3,1 = SNP3,2 SNP3,3
Table 8. The SN coefficient is calculated for each metric and level of Ih , Vh , and a Level
σk SN coefficient of Ih
SN coefficient of Vh
SN coefficient of a
1
56.235
55.908
55.778
2
55.749
55.754
55.742
3
55.288
55.608
55.751
0.947
0.300
0.027
Rank
1
2
3
Fig. 8. Graph of the influence of each main parameter on the tensile strength of the weld
Based on the influence ratings of welding parameters as shown in Table 8 and Fig. 8, it can be seen that the Ih has the greatest influence on tensile strength σk compared to Vh and a. When the welding amperage is low and the joint gap is small, the weld does not overheat. It does not harden, making the weld more flexible and, therefore, the tensile strength higher. When the welding current and the gap are larger, it often causes overheating, making the weld harden, organizing the crystal lattice in a rough form, causing the phenomenon of easy breakage. As a result, the tensile strength decreases. The graphs in Fig. 8 and Fig. 9 also show that to achieve the maximum tensile strength in the investigated region, Ih = 50 A, Vh = 3 cm/min, and a = 0.5 mm. This welding routine is used to weld 12 test specimens of ANSI 304 steel pipes to assess the fatigue strength of the weld.
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Fig. 9. Graph of interactive effects of parameters on tensile strength of the weld
4 Result and Discussion Fatigue test data at low-stress levels will be more scattered because fatigue cracking is controlled by crack initiation and is less suitable for regression analysis when the values deviate towards the slope according to the regression results. Therefore, stress ranges must be chosen to produce lifetimes from 10,000 to 1,000,000. This will avoid the transition domain of the fatigue curve. The above analysis selects the number of test pieces equal to 12 samples with stress levels according to the increment of the load of the testing machine (Increment of the load by 0.5 kg). By carrying out the test on the fatigue testing machine, the results are shown in Table 9. Fatigue test data were processed using Minitab 16.0 software. Enter data into the spreadsheet of the software, check the fit of the lifetime data with the Weibull distribution and the Loga normal distribution with 95% confidence [9–13]. We have the graph of the data according to two distributions as Fig. 10 and Fig. 11. Comparing the two graphs, we find that the fatigue test data of the above weld is more consistent with the Loga normal distribution because, with the Weibull distribution, the scale parameter equals 261.339 is an inappropriate parameter. The residual standard error of the regression is shown in Fig. 12. The analysis of variance data is as shown in Table 10. The coefficients of the regression equation: S = 0.0806445 R - Sq = 86.9% R - Sq(adj) = 85.6% log C = 3.644 m=
1 = 3,91 0,2557
The fatigue curve equation is as follows: log N = 3,644 − 3,91 log σ
(5)
Evaluation of Tensile and Fatigue Strength Table 9. Measurement data on fatigue testing machine No 1
Stress (MPa) 86.95693215
Cycle N (Rotation) 1200000
2
115.9425762
850000
3
144.9282203
620000
4
173.9138643
400000
5
202.8995084
280000
6
231.8851524
160000
7
260.8707965
125000
8
289.8564405
85000
9
318.8420846
45000
10
347.8277286
25000
11
376.8133727
13500
12
405.7990167
3500
Fig. 10. Fatigue reliability of the test sample (Weibull graph)
Fig. 11. Fatigue reliability of the test sample (Loga standard graph)
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Fig. 12. Graph of the residual standard error of the regression
Fig. 13. Weld fatigue curve graph
Table 10. Analysis of variance Source
SS
MS
F
P
1
0.430084
0.430084
66.13
0.000
Error
10
0.065035
0.006504
Total
11
0.495120
Regression
DF
The results comparing the theoretically calculated value with the experimental value verified in the experimental domain of the mechanical properties are quite consistent, which confirms the reliability of the theory and research results. Through Fig. 13 and Eq. (5), it can be seen that the stress is inversely proportional to the number of cycles. However, the experimental fatigue curve shape is not the same as the theoretical curve. Because of the position of the welding connection, there are differences in mechanical properties. The number of cycles leading to part failure is recorded on the test table and analyzed based on the fatigue curve graph of the material. From the fatigue curve graph, we can
Evaluation of Tensile and Fatigue Strength
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calculate the fatigue strength of the material. In fact. The number of cycles N obtained (under the same stress conditions) is not the same for each test. The number of cycles N in practice is usually less than the number of cycles N in theory.
5 Conclusion The width and depth of penetration of the welded joint depend mainly on Vh and Ih . If welding with small Vh and increasing Ih , then weld width increases. TIG welds with filler metal have a finer structure and better weld quality than conventional welds. The tensile strength of the weld depends on the welding parameters to varying degrees. Welding amperage has the greatest influence then on welding speed, and the weld gap has the least influence of the three parameters. In the experimental range, the welding parameters Ih = 50 A, Vh = 3 cm/min, and a = 0.5 mm have been found to achieve the maximum tensile strength. Experimentally, the fatigue curve equation has been built, showing the relationship between stress and the number of cycles. These results predict the fatigue strength of ANSI 304 steel pipe welds and control welding parameters for production applications, especially mass production.
References 1. Chauhan, V., Jadoun, R.S.: Parametric optimization of MIG welding for stainless steel (ss-304) and low carbon steel using Taguchi design method. Int. J. Recent Sci. Res. 6(2), 2662–2666 (2015) 2. Sangwan, S.S., Kumar, P., Kumar, M.: Statistical optimization of the gas metal arc welding parameters in hard facing with consideration of multiple weld qualities. J. Crit. Rev. 7(19) (2020) 3. Krishnaprasad, K., Prakash, R.V.: Fatigue crack growth behavior in dissimilar metal weldment of stainless steel and carbon steel. World Acad. Sci. Eng. Technol. 56, 873–874 (2009) 4. Chuaiphan, W., Srijaroenpramong, L.: Optimization of gas tungsten arc welding parameters for similar welding between AISI 304 and AISI 201 stainless steels. Def. Technol. 15(2), 170–178 (2019) 5. Standard Specification for Chromium and Chromium-Nickel Stainless Steel Plate, Sheet, and Strip for Pressure Vessels and General Applications Niken. ASTM A240/A240M 6. Roy, R.K.: Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement. Wiley (2001) 7. Montgomery, D.C.: Design and Analysis of Experiments, 10th edn. Wiley (2019) 8. Arora, J.S.: Introduction to Optimum Design. Elsevier (2012) 9. Hobbacher, A.: Fatigue Design of Welded Joints and Components. Abington Publishing (2014) 10. Suzuki, T.: Fatigue life prediction of welded structures based on crack growth analysis. Nippon Steel Technical Report No. 102, 2013 11. Dahlberg, M., Hannes, D., Svensson, T.: Evaluation of fatigue in austenitic stainless steel pipe components. Report number: 2015:38. ISSN 2000-0456 12. Kumar, H.: A review on fatigue life estimation of welded joint. Int. J. Sci. Technol. Eng. 4(7) (2018) 13. Khatib, H.: Fatigue strength analysis of welded joints using an experimental approach based on static characterization tests. Contemp. Eng. Sci. 9(11), 513–530 (2016)
A Study on Influence of MQL Parameters on Cutting Heat Generated During Machining Based on Numerical Simulation Van-Hung Pham1 , Thi-Thu-Ha Nguyen2 , Van-Huy Nguyen1 , Trung-Kien Quach1 , Trong-Nghia Nghiem1 , and Tuan-Anh Bui1(B) 1 School of Mechanical Engineering, Hanoi University of Science and Technology,
No. 1 Dai Co Viet Road, Hanoi, Vietnam [email protected] 2 Military Industrial College, Thanh Vinh, Phu Tho, Phu Tho, Vietnam
Abstract. In mechanical processing, to reduce the negative effects of Metal Working Fluid (MWF), it is possible to use the method of minimum lubrication (MQL Minimum Quantity Lubrication) or Near Dry Machining (NDM). The MQL method is developed from the Micro-fog lubrication method used in the lubrication and cooling of high-speed spindle of CNC machine tools, providing very high lubrication efficiency. For the cutting of difficult-to-machine metals such as titanium alloys, a mist of air-oil mist will be sprayed on the back face of the cutting tool, greatly reducing the heat, and thus increasing the tool life. This writing presents a numerical simulation study on the influence of some basic parameters of the MQL, including the flow rate of the coolant from 2 to 150 ml/hr; feed rate from 0.05 to 0.14 mm/tooth; cutting speed from 50 to 300 mm/min to the heat generated during machining. The research results are the basis for choosing a suitable set of MQL parameters when machining titanium alloy, contributing to improving the quality of detailed machining. Keywords: Numerical simulation · Minimum Quantity Lubrication · Machining · Cutting heat
1 Introduction Machining is a technological process that plays a very important role in mechanical manufacturing. To speed up machining, fluids are used to lubricate and cool during cutting (referred to as “metalworking fluids” or “cooling fluids”). At that time, the cutting force can be reduced, and the heat generated during the cutting process can be reduced or eliminated at the same time. The most used method is overflow irrigation with some disadvantages, such as: cost of purchase and cost of handling the cooling solution after use; toxicity and non-biodegradable properties; affect human health and the environment. To reduce the amount of metalworking fluids used, it is necessary to choose liquids that are not harmful to the environment and human health, scientists have introduced the Minimum Quantity Lubrication (MQL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1268–1278, 2022. https://doi.org/10.1007/978-981-19-1968-8_107
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An MQL system usually consists of the following main components: Air compressor; Cooling liquid storage tank; Pipeline; Flow control system and nozzle. The MQL method uses a micro-fog technique that sprays a very small amount of the cutting liquidpressurized air mixture, at a rate of less than 1000 ml/hr, directly into the cutting area. The volume of cutting fluid is 10,000 times less than the overflow technique. Therefore, an MQL system can bring economic efficiency up to 15%. When used in cutting machining, a very small amount of cutting lubricant is introduced into the cutting area, to reduce friction and heat between the cutting tool and the work surface. The MQL method involves misting or injecting a very small amount of the cutting lubricant into the working area, thereby reducing costs and industrial pollution. Currently, in Vietnam, this MQL method has begun to receive research attention, but it is still scattered. There have been some initial studies on the MQL method, performed alone in a few laboratories, on some commonly used mechanical materials to compare machined surface quality and tool life, did not pay attention to cost reduction and industrial environmental safety. There are many types of metalworking fluids used in MQL systems. Typically, MQL uses a mixture in the form of an emulsion oil in different proportions in water to cool and lubricate the cutting edge of the tool [1]. Metalworking fluids for MQL have the following requirements: Must be biodegradable; High stability and high lubricating efficiency will be able to meet the requirements of durable machining with a low consumption of oil. Vegetable-based oils is one of the most widely used metalworking fluids in MQL because of their high biodegradability [2]. The advantages of using vegetable base oils over conventional metalworking fluids are greater pressure resistance, which can increase cutting speed, reduce losses due to evaporation and misting, etc. Similarly, synthetic esters have the same properties as vegetable-based including low viscosity, high boiling and burning points. Some studies also show that MQL processing with synthetic ester oils is superior to oils and mineral oils [3]. As such, both cooling oils can be better substitutes for other metalworking fluids. More importantly, those cooling oils are non-toxic and highly biodegradable, making them environmentally and health-friendly options for machining with MQL. Figure 1 shows a MQL system of K. Sundara Murthy et al. used for machining in a milling machine.
Fig. 1. MQL system model of K. Sundara Murthy [4]
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There have been many publications on the use of MQL method in machining, the researchers mostly focus on the effectiveness of MQL method in improving surface accuracy, reducing cutting force and wear of cutting tool. Campatelli performed a test analysis of the machining MQL environmental impact for the AISI 1040 steel turning operation; cutting tools: CNMG 432; cutting depth 1.2 mm; cutting speed 200 m/min; MQL lubricants: Biocut-3000 at 60 ml/hr shows that the use of MQL can prolong tool life reducing production costs and machining time [5]. Kaynak [6] turned Inconel 718 material in three different cooling and lubricating environments (MQL, cryogenic and dry), which showed significant reduction in cutting forces at low cutting speeds. Tool wear when turning with the MQL method is also significantly reduced compared to dry turning. Pereira et al. [7] performed a comparative analysis of the machining efficiency in the dual-cooling method and near-dry cooling conditions. The results of the mixed cooling method study show that the tool life has been extended by more than 50% and a significant increase in cutting speed by up to 30% compared with dry machining. Studies also show that a combination of minimal cooling and lubrication methods is the solution for establishing a balance between engineering and the environment. Kyung-Hee Park [8] experimentally machining Ti-6Al-4V alloy with several different lubricating solutions such as cryogenic, MQL, LAM has shown that MQL and Cryogenic, especially the combination of Cryogenic and QML, will help reduce cutting force and reduce tool wear.
2 Influence of MQL Parameters on Machining Quality Studies on the influence of MQL parameters on the surface quality of machine parts are also interested. Indeed, Alborz Shokrani et al. [9] studied the evaluation of surface quality when machining Ti-6Al-4V alloy with different lubrication methods such as overflow; MQL; Cryogenic (cooling) and Hybrid cryogenic MQL (MQL combined cooling), show that, within the experimental range, the MQL method gives the best machined surface roughness. Rabiei et al. [10] also showed that using the MQL system when machining high hardness steels, such as HSS and 100Cr6, the surface quality is higher with the overflow technique. Mozammel Mia has built a mathematical model of the relationship between average surface roughness (Ra) [11], energy consumption (Esp - Specific Energy) and MQL when experimentally machining 4140 steels (AISI) with a tool end milling (Fig. 3). The results show that with a cutting speed of 32 m/min, a feed-rate of 22 m/min and a minimum MQL of 150 mL/hr will give minimal surface roughness. Kedare S. B [12] used the MQL method (with a flow rate of 900 ml/h) to ensure a surface roughness equivalent to that of a conventional coolant (2 l/min). Alborz Shokrani proposed to use hybrid MQL (hybrid) [9] to improve tool life when milling Ti-6Al-4V alloy, the author conducted machining experiments using 04 different coolant solutions: Flooding, MQL, Cryogenic (cooling) and Hybrid (combining Cryogenic with MQL). Research results show that MQL and Hybrid Cryogenic MQL are both effective in extending tool life up to 30 times. Ashutosh Khatri [13] studied the effects of different quenching lubrication methods when milling Titanium Ti-6Al-4V alloy (Fig. 4), and showed that, when machining Titanium Ti-6Al-4V alloy with The MQL method has lower tool wear than the dry coolant or flood coolant methods. On the other hand, when studying chip samples collected after turning titanium with MQL,
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Gupta et al. [14] found that two types of chips were formed, the ribbon and the small spiral. The chip surface is smooth, flat, sparkling, and shiny. This is explained by the temperature drop at the tool cutting edge when MQL is present. K. Sundara Murthy [4] built a multiobjective optimization model to evaluate the influence of the technology regime on tool age and surface roughness with minimal lubrication conditions. From the overview analysis of the above research situation, it can be seen that: (i) Using the coolant MQL method has better penetration at the cutting zone than other methods, the surface roughness and the cutting force are smaller. Tool life can be extended up to about 88% compared to dry machining while maintaining surface quality; (ii) When machining by milling, the MQL system usually uses a single nozzle. Metalworking fluids are usually vegetable-based or synthetic ester-based oils, which are non-toxic, highly biodegradable, environmentally friendly and do not harm operator health; (iii) Although published studies prove the ability and superiority of the MQL method, however, some issues related to MQL have not been elucidated such as its effectiveness in processing materials that are difficult in machining as titanium alloy (Ti-6Al-4V), the ability to remove chips in machining. Besides, the problem of optimizing technological parameters related to MQL in machining is also not to be announced. Therefore, more research is needed in reducing heat, expanding workpiece materials, easy chip removal, determining the optimal cutting mode and MQL parameter, and controlling it when machining conditions change. Table 1 is a summary of research results on MQL to machining quality that have been published. Table 1. Summary of research results on MQL to machining quality Documents
Cutting tool
MQL
Results
Mozammel Mia (2017) [15]
Carbide AISI 4140 endmill 12mm
V = 32 (m/min) VG-68 f = 22 ISO-grade (mm/min)
Q = 150 (ml/hr)
Ra = 0.67 (μm) Fn = 6.5 N
Mohammadjafar Hadad (2013) [16]
High speed steel for lathe
AISI 140 (340 HV)
V = 50.2, 100.4, 141.4 (m/min) f = 0.09, 0.22 (mm/rev) t = 0.5, 1, 1.5 mm
Este (RS-1642)
Q = 30 (ml/hr) P = 3 bar
T < 350° in comparison without lubrication, Ra = 3–4 (μm)
Kyung-Hee Park R245-12T3M, (2017) [8] face mill 50 mm
Ti-6Al-4V
V = 47.7, 76.4, 100, 120 (m/min) f = 0.15 (mm/rev) t = 2 mm
Vegetable oil (with and without nano graphit)
Q = 180 (ml/hr) P = 5 bar ϕ = 1 mm
Wearmin = 0.153 mm with Q = 9 (ml/min)
Nilanjan Banerjee (2014) [17]
C45 (AISI-1045)
V = 76, 190, 237 (m/min) f = 0.08, 0.27, 0.4 (mm/rev)
Accu-Lube LB 8000
Q = 50 (ml/hr)
HSMS: μ = 3.32 * V−0.45 − 0.24 * S T = 743 °C (V = 76), 818 °C (V = 190)
Chip SNMG 120408-TF, TiAlN carbide coating
Material of workpiece
Cutting parameters
Coolant solution
(continued)
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V.-H. Pham et al. Table 1. (continued)
Documents
Cutting tool
Material of workpiece
Cutting parameters
Coolant solution
MQL
Results
GuRaj Singh (2018) [18]
TiAlN carbide coating for Bridgeport BMC 1500
Inconel 718 superalloy
V = 100, 150, 200 (m/min) f = 0.1, 0.15 (mm/tooth) t = 0.5, 1.0 μm
Rhenus FU 60 with 1:20 H2 O
Q = 30 (ml/h) P = 6 bar D = 25mm α = 30°
VB = 0.1, 0.28, 0.4 (mm) ~ V VBmax = −0.433 + 5.23e−3 Vc − 1.77fz + 0.16ae
Xiufang Bai (2018) [19]
For milling machine Dema ML1060B
Ti-6Al-4V
f = 500 (mm/min) t = 0.25 mm
Oil cottonseed + nano particles
Q= 85(ml/hr) p = 0.4 Mpa D = 30mm α = 30°
Fx = 277.5 N, Fy = 88.3 N Ra = 0.594 μm (Al2 O3)
Anish Gupta (2017) [20]
Grinding, D126 C75
Inconel 751
V = 1413 Cimtech (m/min) D14 MQL f = 0.4 (m/min) oil t = 10 μm
Q = 60, 80, Droplet diameters; 100 (ml/hr) Temperature p = 2, 4, concentration 6 bar α = 30°
Masato Okada (2014) [21]
Carbide coating for Milling machine
Ti64
fz = 0.1 mm/tooth v = 50, 100, 200, 300 m/min tR = 0,25 mm tA = 5 mm
Vegetable oil
Q = 12 (ml/hr) p= 0.5 MPa
Z.Q Liu (2014) [22]
Titan coating for milling machine
Ti64
fz = 0.05 mm/tooth V = 60, 150 m/min tR = 1 mm tA = 5 mm
MQL oil: LB-1
Q=5 T = 225–295 (ml/hr) p = 0.5 Mpa D = 25 mm α = 135°
M.J. Bermingham (2015) [23]
Carbide, milling machine
Ti64
fz = 0,14 mm/tooth V = 69 m/min tR = 1 mm tA = 6 mm
Vegetable oil with 50 PSI
Q=4 (ml/hr)
Z Q Liu (2011) [24]
Milling machine PVD Titan
Ti64
fz = 0.05 mm/tooth V = 150 m/min tR = 1 mm tA = 5 mm
Vegetable oil
Q= T= 2-4-6-8-10 276–270–261–254–252 (ml/hr) p= 0,5MPa α = 135° D = 20 mm
M Jamil (2021) [25]
Milling machine
Ti64
fz = 0.1 mm/tooth V= 110–185 m/min tR = 0.5 mm tA = 8 mm
Ra = 0.25–0.2–0.2–0.15 μm T = 450–525–600–620
T = 400 Vbmax = 50 μm
Q = 150 T = 330–384 (ml/hr) p = 6 bar D = 25 mm
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3 Numerical Simulation of the Influence of MQL Parameters on Heat During Machining Based on the research results listed in Table 1, it can be seen that the above studies mainly focus on selecting some MQL parameters to achieve the best machining quality, reduce heat at the cutting area, or increase the life of tools. These data are used as the basis for establishing an empirical regression function describing the influence of some of the most basic parameters of MQL on the heat generated during machining titanium alloy. Thereby helping users quickly select the appropriate set of MQL parameters for a particular machining process. The MQL parameters have been studied by the above authors, which can be listed as follows: – – – – – – – –
Flow of cooling fluid: Q = 2; 4; 5; 6; 8; 10; 12; 150 (ml/hr); Feed rate: fz = 0.05; 0.1; 0.14 (mm/tooth); Cutting speed: V = 50; 60; 69; 100; 110; 150; 185; 200; 300 (m/min); Radial cutting depth: ae = 0.25; 0.5; 1 (mm); Axial depth of cut: ap = 5; 6; 8 (mm); Injection pressure: p = 0.5 MPa; Nozzle distance: 25 mm; Spray angle: 45o .
Using mathematical tools and supporting software, the relationship between the heat generated when cutting and the quantities obtained is as follows: – With feed rate fz = 0.05 mm/tooth, cutting speed V = 150 m/min, ae = 1 mm, ap = 5 mm, and Q = 2–4–6–8–10 ml/hr, the heat function T depends on the flow Q obtained as: T = 318.23 × Q−0.06
(1)
– When machining with Q = 4 ml/h; V = 69 m/min; ae = 1 mm; ap = 5 mm and fz = 0.05–0.1–0.14 mm/tooth, the interpolation function for cutting heat T depends on the feed-rate fz as follows: T = 875.01 × fz0.4276
(2)
– With Q = 12 ml/hr; fz = 0.1 mm/tooth; ae = 0.25 mm; ap = 5 mm; V = 50–100–200– 300 m/min, the cutting heat interpolation function T depends on the cutting speed V as follows: T = 222.56 × V 0.1833
(3)
– With Q = 5 ml/hr; fz = 0.1 mm/tooth; V = 100 m/min; ap = 6 mm; ae = 0.25– 0.5–1 mm, the cutting heat interpolation function T depends on the radial depth tR as follows: T = 449.83 × tR0.2925
(4)
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– With Q = 5 ml/h; fz = 0.1 mm/tooth; V = 100 m/min; ae = 1 mm; ap = 5–6–8 mm, the cutting heat interpolation function T depends on the axial depth of cut as follows: T = 186.45 × tA0.3682
(5)
– Based on interpolation functions depending on each parameter, the generalized cutting heat function T can be obtained as follows: T = 258.4 × Q−0.06 × fz0.4276 × V 0.1833 × ae0.2925 × ap0.3682
(6)
0 36
300
140
320
(a)
120
34
0
80
0
36
0
60
32
Q[ml/h]
100
0
38 40 20 60
360
0
34
80
100
400
380
400 120
420
140
160
180
V[m/min])
(b) Fig. 2. Graph of cutting heat T depends on flow Q and cutting speed V with fz = 0.1 (mm/tooth); ae = 1 (mm); ap = 6 (mm)
Based on the empirical regression function describing the relationship between the cutting heat and the basic parameters of the MQL system, the relationship graphs are built to have an overall view of the influence of each MQL parameter. to the cutting zone temperature. Assessing the strong or low influence of the main factors on the heat generated, it is the basis for choosing the most suitable set of parameters for processing a particular material. Figures 2, 3 and 4 depict the relationship between cutting heat and coolant flow and cutting speed, flow and feed rate, and cutting speed and feed rate, respectively. The temperature strongly depends on the cutting speed and the feed
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1275
340
320
300
280
140
260
(a) 120
36
0 34
32
30
0
280
60
0
80
0
Q[ml/h]
100
40 20 0.055
0.06
0.065
380
360
340
320
0.05
0.07
0.075
0.08
400 0.085
0.09
0.095
0.1
fz[mm/tooth])
(b) Fig. 3. Graph of cutting heat T depends on flow Q and feed rate fz with V = 150 (m/min); ae = 1 (mm); ap = 6 (mm)
rate, while if the coolant flow is increased, the cutting zone temperature will decrease accordingly. In addition, the larger flow rate will increase the degree of heat loss into the solvent and surrounding environment, reducing the heat at the cutting zone. This is also the reason to choose the right flow Q when machining a material for which the cutting mode has been calculated in advance. Similarly, the feed rate is also an important factor affecting the degree of heat generation in the cutting zone, whereby, with a certain flow rate, the cutting zone temperature will increase in proportion to the feed rate. This relationship is nonlinear and is shown in Fig. 3. A large feed requires a larger volume of material to be removed per unit time, i.e., the working intensity of the tool must be higher, resulting in a higher temperature generated here. Figure 4 also shows the influence of cutting speed and feed rate on the heat generated in the cutting zone. Accordingly, it is necessary to choose an appropriate flow rate to ensure that the temperature of the cutting area is within the allowable limit, without affecting the machining quality as well as the tool life. For example, with the Q flow limit of an MQL system of 150 ml/hr, ae = 1 mm, and ap = 8 mm, for the cutting temperature to be below 400 °C, the most feasible working range of the cutting mode parameters is selected as fz = 0–0.116 (mm/tooth) and V = 0–140 (m/min).
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(a) 0.1
50 0
46
0
0.095
48
44
0.09
0
520 0 500
fz[mm/tooth]]
0.085
42 0
0.08
46 0
40
0.075
480
44
0
0
460
0.07 0.065
420
38 0
400
0.06 0.055 0.05 60
440
36
34
0
0
80
420
380 100
120
400
140
160
180
V[m/min])
(b) Fig. 4. Graph of the cutting heat T depends on the cutting speed V and the feed rate fz with Q = 1 (ml/hr); ae = 1 (mm); ap = 8 (mm)
Table 2. Calculation and experimental results of heat generated during cutting Q (ml/h)
fz (mm/tooth)
V (m/p) 60
ae (mm)
ap (mm)
Tcalculation (°C)
Tactual (°C)
Error (%)
1
5
249
225
9.6
5
0.05
295
295
0
4
0.14
69
1
6
431
400
7.1
2
0.05
150
1
5
311
303
2.5
4
299
297
0.6
6
292
287
1.7
8
287
279
2.7
150
10 150
0.1
110 185
0.5
8
283
277
2.1
297
330
11.1
326
384
17.7
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Table 2 describes the results of calculation of cutting heat based on numerical simulation and actual temperature measurement results. The simulated value matches the measurement results with a rather small error. This small error is accepted in the technical calculation. Therefore, the problem of numerical simulation of the influence of some MQL parameters on the heat generated in the cutting zone has met the requirements. This is the basis for choosing a suitable set of MQL parameters when processing a material, ensuring the heat generation of the cutting zone within the allowable range. This contributes to ensuring the quality of the work part as well as the life of tools.
4 Conclusion Based on published experimental results, a study on the influence of basic parameters of MQL on the heat generated at the cutting area was carried out. Simulation results clarify the relationship between cutting speed, feed rate and lubricant flow to cutting heat. Accordingly, cutting speed and feed rate are the main parameters causing heat in the cutting zone. Coolant flow is a decisive parameter to reduce the temperature of the cutting zone, contributing to improving the machining quality and the life of the cutting tool. The simulation results can be used as a basis for selecting the most suitable set of MQL parameters for specific machining conditions, ensuring that the cutting temperature is within the allowable limits. When machining titanium alloys, to ensure that the cutting temperature is less than 400 °C, a flow rate Q of about 150 ml/hr, ae = 1 mm, and ap = 8 mm are required. Then the most feasible working range of the cutting mode parameters is selected as fz = 0–0.116 (mm/tooth) and V = 0–140 (m/min).
References 1. Osman, K.A., Ünver, H.Ö., Seker, ¸ U.: Application of minimum quantity lubrication techniques in machining process of titanium alloy for sustainability: a review. Int. J. Adv. Manuf. Technol. 100(9–12), 2311–2332 (2018). https://doi.org/10.1007/s00170-018-2813-0 2. Boswell, B., Islam, M.N., Davies, I.J., Ginting, Y.R., Ong, A.K.: A review identifying the effectiveness of minimum quantity lubrication (MQL) during conventional machining. Int. J. Adv. Manuf. Technol. 92(1–4), 321–340 (2017) 3. Martan, J., Beneš, P.: Thermal properties of cutting tool coatings at high temperatures. Thermochim. Acta 539, 51–55 (2012) 4. Sundara Murthy, K., Rajendran, I.: Optimization of end milling parameters under minimum quantity lubrication using principal component analysis and grey relational analysis. J. Braz. Soc. Mech. Sci. Eng. 34(3), 253–261 (2012). https://doi.org/10.1590/S1678-587820120003 00005 5. Campatelli, G.: Analysis of the environmental impact for a turning operation of AISI 1040 steel. In: IPROMS Conference, pp. 6–17 (2009) 6. Kaynak, Y.: Evaluation of machining performance in cryogenic machining of Inconel 718 and comparison with dry and MQL machining. Int. J. Adv. Manuf. Technol. 72(5–8), 919–933 (2014). https://doi.org/10.1007/s00170-014-5683-0 7. Pereira, O., Rodríguez, A., Fernández-Abia, A.I., Barreiro, J., López de Lacalle, L.N.: Cryogenic and minimum quantity lubrication for an eco-efficiency turning of AISI 304. J. Clean. Prod. 139, 440–449 (2016). https://doi.org/10.1016/j.jclepro.2016.08.030
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8. Park, K.-H., Suhaimi, M.A., Yang, G.-D., Lee, D.-Y., Lee, S.-W., Kwon, P.: Milling of titanium alloy with cryogenic cooling and minimum quantity lubrication (MQL). Int. J. Precis. Eng. Manuf. 18(1), 5–14 (2017) 9. Shokrani, A., Al-Samarrai, I., Newman, S.T.: Hybrid cryogenic MQL for improving tool life in machining of Ti-6Al-4V titanium alloy. J. Manuf. Processes 43, 229–243 (2019). https:// doi.org/10.1016/j.jmapro.2019.05.006 10. Rabiei, F.: Performance improvement of minimum quantity lubrication (MQL) technique in surface grinding by modeling and optimization. J. Clean. Prod. 86, 447–460 (2015) 11. Mia, M.: Mathematical modeling and optimization of MQL assisted end milling characteristics based on RSM and Taguchi method. Measurement 121, 249–260 (2018). https://doi.org/ 10.1016/j.measurement.2018.02.017 12. Kedare, S.B.: Effect of minimum quantity lubrication (MQL) on surface roughness of mild steel of 15hrc on universal milling machine. Procedia Mater. Sci. 6, 150–153 (2014) 13. Khatri, A., Jahan, M.P.: Investigating tool wear mechanisms in machining of Ti-6Al-4V in flood coolant, dry and MQL conditions. Procedia Manuf. 26, 434–445 (2018). https://doi.org/ 10.1016/j.promfg.2018.07.051 14. Gupta, M.: Machining parameters optimization of titanium alloy using response surface methodology and particle swarm optimization under minimum-quantity lubrication environment. Mater. Manuf. Process. 31(13), 1671–1682 (2016) 15. Mia, M., Bashir, M.A., Khan, M.A., Dhar, N.R.: Optimization of MQL flow rate for minimum cutting force and surface roughness in end milling of hardened steel (HRC 40). Int. J. Adv. Manuf. Technol. 89(1–4), 675–690 (2016) 16. Hadad, M., Sadeghi, B.: Minimum quantity lubrication-MQL turning of AISI 4140 steel alloy. J. Clean. Prod. 54, 332–343 (2013) 17. Banerjee, N., Sharma, A.: Identification of a friction model for minimum quantity lubrication machining. J. Clean. Prod. 83, 437–443 (2014) 18. Singh, G., Gupta, M.K., Mia, M., Sharma, V.S.: Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques. Int. J. Adv. Manuf. Technol. 97(1–4), 481–494 (2018) 19. Bai, X., Li, C., Dong, L., Yin, Q.: Experimental evaluation of the lubrication performances of different nanofluids for minimum quantity lubrication (MQL) in milling Ti-6Al-4V. Int. J. Adv. Manuf. Technol. 101(9–12), 2621–2632 (2018) 20. Gupta, A., Swami, P., Balan, A., Kuppan, P., Oyyaravelu, R.: Numerical modeling and heat transfer analysis of minimum quantity lubrication grinding of Inconel 751. Mater. Today Proc. 5(5), 13358–13366 (2018) 21. Okada, M., Hosokawa, A., Asakawa, N., Ueda, T.: End milling of stainless steel and titanium alloy in an oil mist environment. Int. J. Adv. Manuf. Technol. 74(9–12), 1255–1266 (2014) 22. Zhiqiang, L.: Green machining of Ti-6Al-4V under minimum quantity lubrication (MQL) condition. In: Paulo Davim, J. (ed.) Machining of Titanium Alloys, pp. 113–129. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43902-9_5 23. Bermingham, M.J., Sim, W.M., Kent, D., Gardiner, S., Dargusch, M.S.: Tool life and wear mechanisms in laser assisted milling Ti-6Al-4V. Wear 333, 151–163 (2015). https://doi.org/ 10.1016/j.wear.2014.11.001 24. Liu, Z.Q.: Investigation of cutting force and temperature of end-milling Ti-6Al-4V with different minimum quantity lubrication (MQL) parameters. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 225(8), 1273–1279 (2011) 25. Jamil, M., et al.: Sustainable milling of Ti-6Al-4V: a trade-off between energy efficiency, carbon emissions and machining characteristics under MQL and cryogenic environment. J. Clean. Prod. 281, 125374 (2021)
A Numerical Simulation and Design of an Inspection System to Evaluate the Quality of Home Appliance Products Trong-Thanh Nguyen1 , Hoang-Vuong Pham2 , Xuan-Nam Tran1 , Phu-Minh Pham1 , and Tuan-Anh Bui1(B) 1 School of Mechanical Engineering, Hanoi University of Science and Technology (HUST), No.
1 Dai Co Viet Road, Hanoi, Vietnam [email protected] 2 University of Transport and Communications (UTC), No. 3 Cau Giay Street, Lang Thuong Ward, Dong Da District, Hanoi, Vietnam
Abstract. One of the important factors that make up a product’s brand is the quality of the product after a long time of use. The process of testing and evaluating product quality before reaching the user is extremely necessary. This is a criterion that large and small enterprises are interested and is also one of the technical topics with great potential for development for scientists as well as industrial machine manufacturers. For factories and production lines of household appliances, this is a problem that businesses need to find a reasonable solution. The durability of the rice cooker handle, the sturdiness of the super-speed kettle handle and home appliances have not been optimally tested before being released to the market. This article presents preliminary calculation, numerical simulation, and design of a system to optimize the process of testing and evaluating the quality of household appliances by applying the compressed air system together with the controller integrated PLC programming to replace human labor, improve productivity and quality and bring great income to manufacturers. Keywords: Numerical simulation · PLC · Pneumatic system · Quality evaluation · Household product
1 Introduction Recently, the rapid growth in quantity along with the variety of designs of household products shows the great potential of companies in the household goods industry. The quality and durability of home appliances are among the top issues of these companies. With household appliances, the firmness of the grip positions after a long period of use is not guaranteed, which will affect the quality of the product when it is put on the market. According to the standard TCVN 5394: 1991 on the strength test of the handle of the rice cooker, when applying a load equal to three times the weight of the pot when containing the corresponding nominal amount of water, lift the handle by hand, rice cooker is not broken [1]. However, for some small and medium-sized companies, the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1279–1291, 2022. https://doi.org/10.1007/978-981-19-1968-8_108
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process of checking and evaluating the quality of the handles of household appliances has not been optimized, still being used by human, sometimes giving inadequate and accurate results. On the market, there are no system to check the quality of the handles of household appliances in the most optimal way. To improve productivity, labor efficiency, and save costs for business owners, an automatic home appliance grip testers are studied and manufactured. Mechanisms for lifting and lowering can be used cylinder-piston, winch, and cable hoist or other mechanically engaged cams. However, mechanisms such as winches or cams are often large and cumbersome and making it difficult to fully automate. The pneumatic systems have been widely applied in various industrial applications due to the increasingly rapid automation process [2]. They show many advantages over other control systems. Using pneumatic cylinder can be easily programmed for automatic control, in the process of working, it does not emit as loud noise as other mechanisms. Some of these can offer the important advantages of easy and readily available air availability, very simple, clean, and low-cost application [3, 4]. In addition, the special pneumatic system is easy to maintain and provides high-speed control action with a high power-to-weight ratio [5]. It can be noted from the characteristics of the pneumatic system that the response of the speed control stabilization system can be achieved using the appropriate electro-pneumatic component [6, 7]. Besides, pneumatic technology is also used very effectively in areas where there is a risk of fire and explosion in paint spraying, in the plastic industry or in the production of electronic components [8]. Considering the requirement that the lifting and lowering of household products are usually compact, the volume is not large, so the model uses is a cylinder-piston mechanism. The working system used is a pneumatic system controlled by a PLC programmable controller. For the use of electro-pneumatic systems in machine manufacturing, there are many issues that need to pay attention to as well as carefully and accurately calculate to bring the best effect to the machine, which includes: (1) Designing a mechanical structure system, analyzing a basic way to build an overview of the mechanical design; (2) Calculation of the cylinder included in the machine, how to properly meet the specific requirements output, and at the same time save on input material costs; (3) Designing the electrical system used in the control pneumatics, calculate and use PLC programmable controller instead of industrial electrical circuit. Dimensions of the main and auxiliary cylinders together with accompanying components must be suitable for working conditions including the capacity of the air compressor, the stroke of the cylinder, the weight of the object to be checked, etc. object, travel speed determines the basic parameters of the air compressor. Therefore, calculating the impact force, pressure acting on the cylinder rod, speed and compressed air flow is the first thing to do when designing the system. The application of aluminum profiles is an important element of this model, the cross section of the aluminum bar and the working load determine the durability factor for the entire system. Controlling the entire system is also extremely easy with a combination of electrical circuits and electronic components to help replace humans with extremely accurate counting of lifting and lowering times, allowing users to operate quickly and conveniently.
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2 Design of Inspection System The system is designed with the basic frame size a × b × h = 500 × 500 × 1500 in mm and the material used for this frame is 6063 T5 aluminum profile. The testing samples designed for the inspection are rice cookers according to the SunHouse Company Catalog (as show in Fig. 1).
Fig. 1. Model of the inspection system: 1 - frame system, 2 - pneumatic cylinder, 3 - control system, 4 - light sensor, 5 - rice cooker.
Accordingly, the pneumatic movement system including: a 2-way cylinder, accompanying equipment such as an 5/3 distribution valve, a safety valve, a pressure regulator valve, an air compressor. The control system used an electrical circuit includes: a motion sensor, a 24 V honeycomb source, an HMI monitor, an optical cable. The operating principal diagram of the machine is shown in Fig. 2:
Fig. 2. Diagram of working principle of the machine
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The force used to lift and lower the details is generated directly from the compressed air system with a compressor as the air power supply. When the sample is fixed to the system, there are two options for user, including (1) infinite running mode and (2) finite running mode. In the infinite running mode, the program is already set, the user only needs to press the button, the machine will make consecutive journeys until the photoelectric sensor is affected by the faulty part, or the user direct application stops the program at the emergency stop button. In finite run mode, the user needs to set the initial parameters from the keyboard such as: cylinder travel speed, rest time at the end of each stroke, number of strokes in one test. When the user presses the operation button, the machine will follow the set parameters, the machine will stop working when the photoelectric sensor has the impact of defective parts, or the machine has run all the journeys as has been set or the user directly stops the program with the emergency stop button. Since the system is controlled by a PLC programmable controller combined with a display system, it is easy to set various input parameters. Depending on the company that has different requirements for product quality control, the system meet the strictest requirements of most companies in the market. In the above model, a cylinder-pneumatic system is used with the following required parameters: – Load response: F = 98 (N) – Cylinder stroke: Lxl = 350 (mm) – Drive time: T = 0.5 (s) a) Pneumatic cylinder system Based on the pressure of common compressors on the market, the cylinder diameter can be estimated according to the formula: 4F (1) D= πp where: D- cylinder diameter (mm); F- force acting on the cylinder (N); p - pressure in cylinder (N/mm2 ). From the above preliminary parameters, the type of cylinder is chosen according to the manufacturer’s catalog, then the cylinder force is calculated and tested for the selected cylinder type. The force acting when the piston rod comes out is estimated as: Fa = A1 · p · η
(2)
where: A1 : piston bottom area (cm2 ); D: piston bottom face diameter (cm); p: compressed air pressure in the cylinder (bar); η: cylinder efficiency; Fa : force acting when the piston rod comes out (daN). The force acting when the piston rod comes in is calculated by an expression as follows: Fe = A2 · p · η
(3)
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where: A2 : piston ring area (cm2 ); D: piston bottom face diameter (cm); d: piston rod diameter (cm); p: compressed air pressure in the cylinder (bar); η: cylinder efficiency; Fe : force acting when the piston rod enters (daN). – Piston loading:
Fig. 3. Loading diagram
Figure 3 depicts a diagram of the load acting at the piston rod in a pneumatic cylinder system. The allowable load at the piston rod can be calculated using the following expression: Fk =
π2 · E · J Fk and F ≤ 4L2 S
(4)
where: Fk : bending force (N); E: elastic modulus of the material (N/mm2 ); F: allowable 4 bending force (N); J: moment of inertia(mm4 ), J = π.d 64 ; S: factor of safety; L: length of piston rod (mm). b) Design of system frame After the frame of the system is designed, it is necessary to test the load capacity of the system. Accordingly, it is necessary to conduct stress analysis, factor of safety and stiffness throughout the system. This is done with the help of design and analysis software such as Solidworks. The set working parameters are similar to the actual working conditions of the test system. Thus, the simulation results will give reasonable or inappropriate points in the system design. On that basis, adjust the design to suit the requirements. The simulation results are shown in Fig. 4. Shown in Fig. 4(a), with the results obtained, it is seen that the red positions are the locations where the most stress is concentrated, the blue positions are the locations with the smallest stress, the stress is unevenly distributed at positions on the model. The allowable stress of 6063 T5 aluminum material is 1,45 × 102 (N/mm2 ). From the design model, the location where the most stress is concentrated is the position where
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Fig. 4. (a) Stress diagram (a); (b) deflection chart; (c) diagram of factor of safety
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the cylinder is installed and is directly subjected to the load from the outside. At that position, the resulting stress is the maximum = 8,986 (N/mm2 ). Compare with the allowable stress of the material: 8,986 < 1,45 × 102 => the model is safe at the most dangerous position. The safest position is the position of the 4 bottom corners of the model, in these 4 positions, the model is fixed and completely safe. Shown in Fig. 4(b), it can be seen that the minimum safety factor of the model minFOS = 16 and increasing to the largest is maxFOS = 1016 , corresponding to the shades from red increasing to blue. The required safety factor is 3 ÷ 5, so the system is completely guaranteed in terms of durability according to the safety factor. In addition, Fig. 4(c) shows the largest deflection of the model is the position associated with the red cylinder with the maximum deflection = 0.6113 (mm), the smallest deflection at the position of the 4 bottom corners of the model. min = 10–30 for blue. The red spots have a high probability of being destroyed and damaged first during longterm use. Realizing that the maximum deflection = 0.6 (mm) per 500 (mm) in length is asymptotic to the allowable level of profiled aluminum material, to ensure safety during long-term use, we proceed to increase the durability of the model by using a two-bar model. Rebuild Model and Test for Deflection To increase the durability of the model at the position with the most deflection, we add a load-bearing bar to the model. After redesigning the model, the deflection for the new model on Solidworks software is necessary to check again to ensure the feasible system design is made. Figure 5 shows the new model and deflection chart of the system.
Fig. 5. (a) Frame model diagram and (b) Deflection chart
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From the above calculation results, we see that the deflection of the model has decreased compared to before the redesign, from max = 0.611 (mm) to max = 0.4245 (mm). The position with the most deflection is still the red position and the place where the bearing cylinder mechanism is mounted. This deflection is not asymptotic to the allowable deflection of the material. In fact, the concentrated stress of the new system is reduced compared to the old one. The largest stress about 8.98 (N/mm2 ) is reduced to 7.13 (N/mm2 ) (shown in Fig. 6). But the stress concentration positions changed compared to the old position, the dangerous positions are now red and concentrated in the two bolt-nut joints at the ends of the two bearing bars, so we proceed test operation for the bolt-nut joint in this position.
Fig. 6. Stress diagram of new system
To make the bolt to be safe, the tensile stress must be less than the allowable tensile stress: δk =
1, 3 V
·d12 4
≤ [δk]
Form Eq. (5) ⇒ Bolt tightening force to prevent the joints from slipping: δk · ·d12 V ≤ 4.1, 3
(5)
(6)
In addition: V=
F ·k Vmax · j · f ⇒ Fmax = j·f k
(7)
where i: number of contact surfaces between panels; f: coefficient of friction; k: factor of safety; Fmax : force acting on bolt (N); V: tightening force (N); δk : tensile stress (N/mm2 ); d1 : thread foot diameter (mm).
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c) Control system The control system of the machine is designed to control the actuator and display the necessary information. The system includes a 24 V power supply, an HMI display, a S7-1200 PLC control circuit, an optical sensor, a limit switch, and a solenoid valve. The system uses the ethernet standard to transmit signals between the PLC and the HMI display. When operating, parameters such as stopping time, number of moves and moving speed will be set by the user on the keyboard, from which the actuator will operate according to the parameters desired by the user. Table 1. The operating order of the system No Operation
Handle
1
Initial state Bright red light
2
Press Start
Infinite run mode
Run indicator green light is on, cylinder goes down, then goes up. At the end of each stroke, the cylinder will stop according to the set amount of time and then continue to the next stroke until it is turned off or the part to be inspected is damaged
Finite running mode Setting by pressing buttons on the keyboard, when counting to the correct number of times set, the system stops 3
Press Stop
Stop the system, the red light is on
4
Press Reset Clear the set input parameters
The working principle and operation sequence of control system are described in detail in Table 1. The control diagram of the system using a PLC S7-1200 is shown in Fig. 7.
Fig. 7. Control diagram
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3 Results and Discussion Currently, the problem of testing the durability of the straps is still done manually by the company by allowing workers to lift and lower it with the specified number of times. Our team tested the system and evaluated the results with the test sample being a rice cooker. Shown in Table 2 is the statistics of the rice cooker when using the manual method of SUNHOSE company (statistical data 10/5/2021). Table 2. Sunhouse company statistics Content
Quantity Unit
Average number of products to 10 test in a month
–
Number of products tested in one test
1
–
Number of inspections in a month
10
Times
Time of 1 test cycle
20
s
Number of cycles in a test run
2000
Cycle
Time to perform a test
40000
s
Amount of work for one test
1.388
Workday
Number of inspections in a month
13.88
Workday
Number of inspections in a year
166.56
Workday
With the new strict requirements on the quality of the company’s products, we have tested the machine with the input requirement: rice cooker weight m = 10 kg. After fixing the rice cooker on the clamping arm at the top of the cylinder, turn on the machine and set the input parameters according to the company’s new requirements: cylinder stroke l = 350 mm, one cycle time t = 1 s, rest time at the end of each stroke T = 0.5 s, number of cycles in one test n = 200 cycles. Then, press the button for the system to operate, the system will perform consecutive cycles and rest every 0.5 s until a product error is detected or 200 cycles are exhausted. When the machine runs out of 200 preset cycles, the system will stop automatically. This product is rated as meeting the requirements for the durability of the handle. This is also the final inspection stage of the product at the company. Then workers just need to bring the rice cooker to the packing line and ready to sell to the market.
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If the rice cooker does not guarantee the durability of the handle, will be loose, wobbly, even the body of the rice cooker will come off the handle. At this time, the photoelectric sensor of the system attached to the bottom of the stroke will be affected, the sensor sends a signal to the PLC processor and the machine will stop working. At this time, workers will come and bring the defective parts back to the assembly and replacement department, this part is responsible for replacing the entire shell of the rice cooker and putting it on the inspection lines from the beginning before this product is putted on the market. Most of the company’s products are of good quality and are ready to be sent to the packaging line. A small number of products have broken handles due to possible manufacturing defects; we go to detailed analysis to find the location with the highest failure rate so that the company can fix the product (Fig. 8).
Fig. 8. Stress diagram of the handle of rice cooker
In the green two-position handle there is a possibility of failure due to more stress concentration than other locations. In general, the details are completely durable during use, damage may occur due to some error from the manufacturing process. After testing, the machine meets about 95% of the requirements set by most of today’s home appliance manufacturers. Not only stopping at home appliances, but the system can also be applied to check many other details. The design machine also has the potential to develop more automatic and semi-automatic mechanisms such as: automatic bar feeding mechanism, automatic clamping mechanism to reduce labor even more. The working efficiency between manual testing and the use of the designed system can be described in Table 3.
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Tasks
Manual testing
Test by machine
Before checking
Each inspection needs time to arrange and borrow factory workers
The equipment is pre-installed in the machine, the components to be checked are also arranged in place on the machine
Checking process
Need 1 person to lift the product and count the number of times done. The implementation process is somewhat sporadic due to the limited human endurance
The machine automatically counts, works continuously and determines the moving speed of the product
Check the condition of components
A supervisor is required to detect if the component is still working
The machine automatically stops counting if the component is damaged
After test
At the end of the process, it is necessary to neatly arrange the equipment
The machine is fixed in one place. No need to move
Cost
Depends on product quantity
Fixed costs
4 Conclusion Based on the study of cylinder design theory, the system durability simulation program is used to find out the parameters suitable for the proposed system requirements. The model is formed from theoretical formulas and catalogs of popular manufacturers in the market. The simulation results show that the theoretical formula is reliable, although there are still certain errors because the program boundary conditions are not really close to reality. The system used has shown working efficiency when compared to manual inspection work. This is evident in the comparison table as described above (shown in Table 3).
References 1. Vietnam Standard TCVN 5394:1991 on Automatic rice cookers 2. Ilchmann, A., Sawodny, O., Trenn, S.: Pneumatic cylinders: modelling and feedback forcecontrol. Int. J. Control 79(6), 650–661 (2007) 3. Renn, J.C., Liao, C.M.: A study on the speed control performance of a servo-pneumatic motor and the application to pneumatic tools. Int. J. Adv. Manuf. Technol. 23, 572–576 (2004). https:// doi.org/10.1007/s00170-003-1757-0 4. Badr, M.F.: Modelling and simulation of a controlled solenoid. IOP Conf. Ser. Mater. Sci. Eng. 433, 012082 (2018) 5. Lai, W.K., Rahmat, M.F., Abdul Wahab, N.: Modeling and controller design of pneumatic actuator system with control valve. Int. J. Smart Sens. Intell. Syst. 5(3), 624–644 (2012)
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6. Chen, S., Youn, C., Kagawa, T., Cai, M.: Transmission and consumption of air power in pneumatic system. Energy Power Eng. 06(13), 487–495 (2014). https://doi.org/10.4236/epe. 2014.613042 7. Badr, M.F.: A simplified method for energizing the solenoid coil based on electromagnetic relays. ARPN J. Eng. Appl. Sci. 13(22), 8750–8754 (2018) 8. Bui, H.T., Nguyen, N.Q., Do, H.Q., Nguyen, V.H.: Textbook of hydraulic and pneumatic movement, p. 237. Hanoi University of Industry (2006) 9. Shih, R.H.: Introduction to Finite Element Analysis Using SOLIDWORKS Simulation (2018)
Parameter Auto-tuning for Improving Scale Factor and Washout Effect of Classical Motion Cueing Algorithm with Cylindrical Coordinates Duc An Pham(B) and Quang Nguyen Huu School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam [email protected]
Abstract. Driving simulators are developing for a more realistic driving experience for drivers in a virtual environment. A motion cueing algorithm (MCA) plays a vital role in recreating drivers’ motion cues on the real car and preserves the driving motion platform within its physical boundaries. The comfortable motion perception for simulation cases with a specific input signal, however, depends on the parameters of the MCA, often tuned with experts in the loop by trial and error. This manual tuning technique is so time-consuming that the paper improves a novel generic optimization method, called mean-variance mapping optimization, to look for the suitable parameters general MCAs with novel penalty function of scale factor and washout effect. In the work, the novel auto-tuning method was first used to find the parameters of classical MCA with cylindrical coordinates. Then, the comparison of the simulated quantities of three test cases with different parameter sets achieved from trial and error, auto-tuning methods with old and novel penalty function was implemented to investigate the advantage of the new auto-tuned parameter set. As a result, the classical MCAs responses with new autotuned parameters eliminate the false cues of angular velocity while improving the washout effect and product higher scale factor. Keywords: Motion cueing algorithm · Driving simulator · Auto-tuning parameter · MVMO
1 Introduction The goal of motion cueing algorithms is to maintain the perceptual realism of simulation by using tilting coordination to mimic sustained translational acceleration. The classic MCA was first developed by Conrad and Schmidt [1] and then other MCAs were created with a different optimal technique [2–9]. The algorithm filtered the benefit linear motion cues by a high-pass filter and compute the compensate washout cues by a suitable tilted angles. The simulated motion of the motion platform provide the drivers the motion cues as they in the real vehicle (Fig. 1). Due to the untransparent parameters concerning the output motion [11–14] the parameter tuning process is complicated. In addition, the various parameters of the MCAs and the required features of the human motion © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1292–1298, 2022. https://doi.org/10.1007/978-981-19-1968-8_109
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perception are often difficult tuning tasks. Therefore, the ultimate purpose of the tuning is to increase the perceived motion within the simulator and prevent simulator disease at the same time.
Fig. 1. Driving simulation flow process [16]
However, the driver-or pilot-in-the-loop tuning suggested by Grant and Reid is very time-consuming. Besides, the parameters of the MCAs have significant effects on the simulated motion, which can lead to motion sickness if the motion and visual signals are not reliable. Thus, for particular motion simulation, tuning the best parameters of an MCA remains a challenging task [10]. In the literature, the first tuning is to find the appropriate parameters with which the MCAs generate the simulated quantities in the defined ranges. For a specific drive task, the tuning processes were fulfilled mostly by trial and error; thus, it is very timeconsuming and requires drivers’ experience in the simulation field [14, 15]. The goals of this work are 1) to design automated tuning procedures for any motion task and motion platform forms, and 2) to introduce strategies for the classical motion cueing algorithm with cylindrical coordinates (ClCy) [8] to find the parameters with which the the simulation platform provide stronger simulated perception (higher scale factor) and come back to initial position at the end of the simulation (washout effect). In the next section, the ClCy algorithm is first introduced. Secondly, the auto-tuning method (AT) and its cost functions regarding to improving scale factor and washout effect is explained in detail. Thirdly, an example of the use of the AT approach for the classical algorithm with various amplitudes of input signals is described. Lastly, the results are discussed in the conclusion section.
2 Classical Motion Cueing Algorithm with Cylindrical Coordinate This algorithm (Fig. 2), first introduced by Giordano et al. [8] exploit the capability of the KUKA Robocoaseter that can implement a circular motion of the frame base z-axis to simulate the target lateral acceleration instead of using a linear motion. The transformation coordinate block is used to transform the simulated position of the motion platform form casterian coordinates to the cylindrical ones. Then, as the traditional classical algorithm, the coordinates are filtered by linear high- and low-pass filters. Besides, the tilt-coordination has a important role in replacing the sustained acceleration by proper tilted angle. The centrifugal acceleration compensator block is used to reject the disturbed elements due to the inertial acceleration in moving washout frame.
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Fig. 2. Classical motion cueing algorithm with cylindrical coordinate (ClCy)
3 The Auto-tuning Method with the Improment of Scale Factor and the Washout Effect Mean-variance mapping optimization (MVMO), a population-based stochastic optimization technique developed by Erlich et al., mixes the good performance of a specific number of best individuals to achieve an expected better generation for the following optimizing stages [18]. This method uses its unique transformation strategy for mutated
Fig. 3. The shape error (sh) and scale error (sc) of the simulated signal with regard to target signals
Fig. 4. Principle of auto-tuning method a) numerical index proposed by Pouliot [17] with λ_(m, n) with m = {1f, 1ω}; n = {sh, sc} represent the numerical index related to specific force error (f) and angular velocity errors (ω) regarding to shape and scale error (sh, sc), respectively (Fig. 3). b) The exponential penalty functions for angular velocity with its threshold.
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genes of the offsprings based on the mean-variance of the n-best population by employing the concepts of selection, mutation, and crossover from evolutionary computation algorithms. Table 1. Penalty functions of cost functions for the auto-tuning procedure Penalty function Position
Formulation ⎧ ⎨ 0 i) JP = (Min(P)−L)2 2 (Max(P)−U ) ⎩ (e − 1) + (e − 1) ii)
Shape error
Angular velocity
Jesh =
Jω =
0
e
(maxefsh −δO )2 −1
if maxefsh < δO if maxefsh ≥ δO
⎧ ⎨
0 if |ωmax | < δS 2 ⎩ e(ωmax −ωS) −1 if |ωmax | ≥ δ S ⎧ ⎨
0 if |ω˙ max | < ω˙ S 2 ⎩ e(ω˙ max −ω˙ S) −1 if |ω˙ max | ≥ ω˙ S
Angular acceleration
Jω˙ = Jω =
Scale error
Jsc = (e(kS −kmax ) − 1) if kS ≤ kmax ⎧ ⎨ 0 if ka ≤ kamin 2 Jtr = min ⎩ e ka −ka if k > k min
Linear motion
2
a
(
Washout
JWo = e
i∈ 1...Nf
a
g|ϕS tf ,i |
−1
Note: – i) and ii) are corresponding to No Violation and Violation cases. – Min(P), Max(P) represent the minimum/maximum position of the motion platform, and L/U represents the lower/upper physical limit of the motion platform. – δO the otolith threshold value. – |ωmax | is the maximum of the absolute simulated angular velocity, and δS is a threshold value of the angular velocity perceived by the sesemicircular organ. – ω˙ max is the maximum of the absolute simulated angular acceleration, and ω˙ S is a threshold value of the angular acceleration perceived by the semicircular organ. – kmax represents the maximum scale factor desired. – ϕS tf denotes the final tilt angle, and g is the gravity acceleration. – kS : The ratio of simulated signal to the target signal. – ka : The ratio of translational motion to target motion. – kamin denotes the minimum desired ratio of translational movement to reproduce the translational specific force.
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The cost function of the auto-tuning process with the MVMO method represents the combination of the penalty functions J k (see Table 1 for a full explanation) defined as: FMVMO = wk Jk (1) i∈{P,esh,ω,ω,sc,tr,Wo} ˙
The selected cost function is to solve the tuning problem (Fig. 4) related to 1) limited workspace; 2) constraining scale factor; 3) type of false cues; 4) defined good levels; 5) amount of motion errors. The cost functions not only reduce the cues errors, the advantage of the numerical index [17], but also pull the simulated signals under threshold values of motion perception, such as acceleration and angular velocity. Besides with the novel defined penalty function of scale error and washout effect, the larger scale factor can generate while the position of the motion platform can be pull to initial postion for a period before finishing simualtion task. In the next section, the auto-tuning process is applied to the ClCy algorithm.
Fig. 5. Responses of ClCy algorithm that a) Trial and errors b) Auto-tuning with the previous penalty function of washout effect [19] c) Auto-tuing with novel penalty function of washout effect
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4 Auto-tuning Parameters of the ClCy Algorithm The running task input for auto-tuning parameters is the lateral accelerations of a ride of a virtual roller coaster along the planar S-curve rail with constant velocity here v = 3.6(m/s). The auto-tuning procedures are used to the tuned parameter of the ClCy algorithm with the given lateral acceleration (the red line in Fig. 5). In this works, three parameters set are used, the first set achieved from trial and errors, the second set generated by the auto-tuning process with the penalty functions of the scale factor and washout effect introduced by Pham [19], and the last parameter set found by the proposed novel penalty functions in this research. Figure 5 shows the simulated translational/linear perception quantities produced by ClCy with three kinds of parameter sets. The simulated angular velocities of three cases are under the rotational perception threshold (0.1 rad/s) for all three test cases. It can be seen that the scale factor for the second and third cases are larger than that for the first case, because these cases exploit the workspace of the motion platform to generate the simulated acceleration. The second case concerns to use more simulated acceleration, thus the washout effect is worse than the first and third case – the motion platform comes back to its initial position before the end of the simulation and maintains its status. The third case is the optimal case for scale factor and washout effect compared to the first and second cases. The reason for the optimal response is thanks to the penalty functions of washout effect and scale factor impacting on general cost function of the auto-tuning method. Note that, at the end of the simulating period, the specific force and tilted angle are pulled to zero due to the effect of the washout penalty function J Wo . The washout effect is not considered in the algorithm, but it can be fulfilled with the cost function of the auto-tuning process.
5 Conclusion In summary, the process of auto-tuning parameters for ClCy, with the novel penalty function of scale factor and washout effect, can generate the optimal parameters that improve the washout effect and exploit the workspace of motion platform to increase the scale factor of simulated quantities for the three different parameter set of input signals focus on reducing false cues and reproducing scaled specific force. Compared to manual tuning methods, and even with the prior auto-tuning method [20] the novel the auto-tuning protocol has many benefits. Firstly, the approach is flexible and saves time-consuming for tuning suitable parameters for ClCy to remove the false cues, particularly for the large amplitude of input signals. A designer can easily adjust the weighting parameters for tuning purposes and transparently evaluate the maximum amplitude of input signals for a particular driving simulator. Secondly, the tuning method considers two main quantities: the simulated position that can produce the false cues at the end of simulation if the motion platform has no zero position, and the scale factor that affects the sufficient motion perception. Finally, in the future, the auto-tuning process can apply to find the parameters for online MCAs with different drive-tracks in offline mode. Acknowledgments. This research is funded by the Hanoi University of Science and Technology (HUST) under project number T2020-SAHEP-013.
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References 1. Conrad, B., Schmidt, S.F.: Motion drive signals for piloted flight simulators, vol. 1601, National Aeronautics and Space Administration (1970) 2. Parrish, R.V., Dieudonne, J.E., Martin, D.J., Jr.: Coordinated adaptive washout for motion simulators. J. Aircr. 12, 44–50 (1975) 3. Sivan, R., Ish-Shalom, J., Huang, J.K.: An optimal control approach to the design of moving flight simulators. Syst. Man Cybernet. IEEE Trans. 12, 818–827 (1982) 4. Reid, L.D., Nahon, M.A.: Flight Simulation Motion-base Drive Algorithms. Part 1: Developing and Testing the Equations, Institute for Aerospace Studies, Toronto University (1985) 5. Houck, J.A., et al.: Motion Cueing Algorithm Development: New Motion Cueing Program Implementation and Tuning (2005) 6. Romano, R.: Non-linear optimal tilt coordination for washout algorithms. In: AIAA Modeling and Simulation Technologies Conference and Exhibition (2003) 7. Dagdelen, M., Reymond, G., Kemeny, A., Bordier, M., Maïki, N.: MPC based motion cueing algorithm: development and application to the ULTIMATE driving simulator. In: DSC 2004 Europe (Driving Simulation Conference), pp. 221–233 (2004) 8. Zywiol, H.J., Jr., Romano, R.: Motion drive algorithms and simulator design to study motion effects on infantry soldiers, army tank automotive research development and engineering center warren MI (2003) 9. Giordano, P.R., Masone, C., Tesch, J., Breidt, M., Pollini, L., Bülthoff, H.H.: A novel framework for closed-loop robotic motion simulation. Part II: motion cueing design and experimental validation. In: 2010 IEEE International Conference on Robotics and Automation (ICRA) (2010) 10. Grant, P.R., Reid, L.D.: Motion washout filter tuning: rules and requirements. J. Aircr. 34(2), 145–151 (1997) 11. Song, J.B., Jung, U.J., Ko, H.D.: Washout algorithm with fuzzy-based tuning for a motion simulator. KSME Int. J. 17(2), 221–229 (2003) 12. Hwang, T.S., Yeh, S.K., Lin, J.R., Su, W.P.: Adaptive motion washout filter design by using self-tuning fuzzy control. In: 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 811–815. IEEE (2009) 13. Chen, S.H., Fu, L.C.: An optimal washout filter design with fuzzy compensation for a motion platform. IFAC Proc. Vol. 44(1), 8433–8438 (2011) 14. Reid, L.D., Nahon, M.A.: Flight simulation motion-base drive algorithms: part 2, selecting the system parameters. UTIAS report (N307) (1986) 15. Hess, R.A., Marchesi, F.: Analytical assessment of flight simulator fidelity using pilot models. J. Guid. Control. Dyn. 32(3), 760–770 (2009) 16. Pham, D.A.: A Study on State-of-the-Art Motion Cueing Algorithms applied to Planar Motion with Pure Lateral Acceleration—Comparison, Auto-Tuning and Subjective Evaluation on a KUKA Robocoaster Serial Ride Simulator (2017) 17. Pouliot, N.A., Gosselin, C.M., Nahon, M.A.: Motion simulation capabilities of three-degreeof-freedom flight simulators. J. Aircr. 35(1), 9–17 (1998) 18. Erlich, I., Venayagamoorthy, G.K., Worawat, N.: A mean-variance optimization algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1–6. IEEE (2010) 19. Pham, D.-A.: Mean-variance mapping optimization for auto-tuning parameters of classical motion cueing algorithm. In: Long, B.T., Kim, Y.-H., Ishizaki, K., Toan, N.D., Parinov, I.A., Vu, N.P. (eds.) MMMS 2020. LNME, pp. 952–957. Springer, Cham (2021). https://doi.org/ 10.1007/978-3-030-69610-8_126
Effect of the Pressure Ratio on the Heat Transfer Phenomena of the Evaporator in CO2 Air Conditioning System Thanhtrung Dang(B) and Tronghieu Nguyen HCMC University of Technology and Education, Ho Chi Minh City, Vietnam [email protected]
Abstract. The effects of the pressure ratio on the heat transfer phenomena of the evaporator in CO2 air conditioning system were investigated by the experimental method. Two cases were experimented: C1-using one compressor and C2-using two parallel compressors. In this study, the mass flow rate is doubled for the case of two parallel compressors. The mass flow rate for case 2 decreases from 71 to 61 kg/h as the pressure ratio increases 1.7 to 2.2. When the pressure ratio increases, the air temperature difference increases from 5.2 to 9.1 °C for case 1. For case 2, the air temperature difference increases from 5.0 to 8.8 °C. For case 1, the outlet air temperature decreases from 24.8 to 20 °C as increasing the pressure ratio from 1.7 to 2.2; the outlet air temperature decreases from 20.0 to 16.2 °C for case 2. The superheating of the evaporator obtained from the case 2 (from 6.2 to 7.7 °C) is lower than that obtained from the case 1 (from 9.9 to 13 °C). Keywords: Air conditioning · Heat transfer · Pressure ratio · Experiment · CO2 refrigerant · Evaporator
1 Introduction The natural refrigerant CO2 is safety, non-toxic and non-flammable and abundant. But it requires high pressures to operate in a transcritical cycle and the compressor outlet temperature is high. Compared to the traditional cooling system using HFC refrigerants under the same conditions, the fundamental transcritical CO2 refrigeration system is less effective [1] because the much higher average CO2 temperature is, the greater heat rejection loss was during the gas cooling process. And due to the throttling loss, the entropy was increased during the throttling phase. The more the expansive valve inlet temperature is reduced, the better the cycle efficiency will be. The internal heat exchanger (IHX) can effectively optimize the discharge pressure and provide the opportunity for the use of simpler control systems. Compared to a CO2 system without an IHX, a CO2 system with an IHX increases efficiency up to 25% under high ambient temperature [2, 3]. Kwon et al. [4] studied the influence of IHX through numerical simulation and experimental validation on a CO2 transcritical system. The numerical simulation results were similar to the experiment data. COP © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1299–1305, 2022. https://doi.org/10.1007/978-981-19-1968-8_110
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increases when IHX was added to the CO2 system. Kwon et al. [4] investigated the heat transfer characteristics of the IHX for CO2 system. When the length of IHX increases, the capacity, the effectiveness and the pressure drop increase. The micro channel IHX was 1.4 to 2.4 times better than the co-axial IHX in terms of capacity and effectiveness. The outlet temperature of the gas cooler in the CO2 transcritical system is high due to the high ambient temperature. In this case, the technique of pre-cooling the air before entering the gas cooler is evaporative cooling method. Under the ambient temperature of 40 °C, Fornasieri el al. [5] and Girotto et al. [6] have experimented with precooling 30% and 100% of the ambient volume before entering the gas cooler. The results were that the COP rose 17% with 30% precooling air and 70% with 100% precooling air. In addition, subcooling method could lower the refrigerant temperature before entering the expansion valve. Then, the evaporator inlet quality also reduced further. Sarkar [7] simulated an thermoelectric subcooler (TES) on a CO2 transcritical system. The results shown that the TES not only increased COP but also decreased the discharge pressure and the compressor discharge temperature. Sanchez et al. [8] installed a TES at the exit of the gas cooler. When the saturation temperature is maintained at 10 °C, the cooling capacity increases by 10% and 15% at ambient temperature of 25 °C and 30 °C, respectively. Llopis et al. [9] analyzed a CO2 system using a dedicated mechanical subcooling (DMS) with propane as the refrigerant. At evaporator temperature of 5 °C, the COP reached the maximum of 20% and the cooling capacity up to maximum of 28.8%. From the literature review above, the results regarding to CO2 air conditioning systems were mentioned such as the cooling methods, internal heat exchangers, etc. However, the experimental data for heat transfer behaviors of the evaporator versus the pressure ratio in these systems are still quite limited. So, it is essential to experiment the pressure ratio on the heat transfer phenomena of the evaporator in CO2 air conditioning system. The heat transfer behaviors will be mentioned in this study are the evaporative temperature, the air temperature difference, and the superheating of evaporator.
2 Experiment Setup The experimental system is composed of a CO2 compressor, a gas cooler, an evaporator, and a throttle valve. The CO2 vapor enters the compressor and is compressed to high pressure and temperature, the superheated vapor enters the cooler and then it is cooled. The vapor continues to enter the geothermal cooling unit to reduce its temperature. The geothermal cooling unit and its results were investigated in [10]. After being subcooling by geothermal, the refrigerant continues to pass the throttle valve, reduces the pressure to decrease the temperature and goes into the evaporator to exchange heat with the air in room. The CO2 refrigerant is finally returned to the compressor. Figure 1 shows the test loop of the CO2 air conditioning system.
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Fig. 1. The experimental test loop for CO2 air conditioning cycle
To change the flow rate in the system, two cases were experimented: C1-Using one compressor (case 1) and C2-using two parallel compressors (case 2). In this study, the prototypes of the gas cooler and the evaporator are the fin tube heat exchanger. The evaporator has the heat transfer area of 2.6 m2 . The SANDEN compressor with the electrical power input of 450 W was used. At the inlet and outlet of each component, both pressure sensor and thermocouple were installed for measuring. Uncertainties and ranges of the experimental apparatuses measured data are listed in Table 1. To calculate the heat transfer behaviors of the evaporator, the equations were used. The mass flow rate was calculated as: m=
V v
(1)
The air temperature difference was determined as t = ta,i − ta,o
(2)
The pressure ratio is defined as: ε=
pc pe
(3)
where m is the mass flow rate (kg/h), p is pressure (bar), t is temperature (o C), V is the volumetric flow rate (m3 /h), v is specific volume (m3 /kg), ε is the pressure ratio. Subscripts a stand for air; c and e stand for cooler and evaporator; and i and o stand for the inlet and outlet.
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Testing apparatus
Accuracy
Range
Thermometer
±0.1 °C
−270–400 °C
Anemometer
±3%FS
0.3−45 m/s
Humidity meter
±3% FS
1.0–99.9%
Pressure sensor
±0.05FS
0−100 bar
Digital mass flow rate sensor
±0.5%
400−5000 l/h
3 Results and Discussion For experiments carried out in this study, the data were obtained under the ambient temperature from 30 to 32 °C. The evaporator pressures were varying from 39 to 45 bar; the cooler pressures were varying from 76 to 86 bar. When the cooler pressure increases the evaporator pressure decreases, leading to the pressure ratios were increasing from 1.7 to 2.2. The relationship between the mass flow rate and pressure ratio is shown in Fig. 2. The mass flow rate of the compressor decreases as increasing the pressure ratio (It is due to the throttling area decreases). It is observed that the mass flow rate is doubled for the case of two parallel compressors. The mass flow rate for case 2 (C2) obtained in this study from 61 to 71 kg/h. 80
Mass flow rate, kg/h
70 60
C2
50
C1
40 30 20 10 0 1.7
1.8
1.9
2
2.1
2.2
Pressure ratio
Fig. 2. Mass flow rate vs. pressure ratio
Figure 3 shows the relationship between the evaporative temperature, the air temperature difference, and the pressure ratio for case 1 (C1). The pressure ratio increases at the same time as the evaporative decreases, resulting to the evaporative temperature decreases. However, within the scope of the study, when the pressure ratio increases, the air temperature difference increases from 5.2 to 9.1 °C. The obtained results also have the same rule as the case 2. The results show that the air temperature difference obtained from the case 2 is smaller than that obtained from the case 1, as shown in Fig. 4. However, the difference is negligibly small. For case 2, the air temperature difference increases from 5.0 to 8.8 °C.
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Fig. 3. Temperatures vs. pressure ratio for case 1 12 Temperature, oC
10 8 6 4
C1-Evaporative temperature
2
C1-Air temperature difference
C2-Evaporative temperature C2-Air temperature difference
0 1.7
1.8
1.9
2
2.1
2.2
Pressure ratio
Fig. 4. Temperatures vs. pressure ratio for two cases
The relationships between the outlet air temperature of the evaporator and the pressure ratio for two cases are shown in Fig. 5. Within the scope of the study, when the pressure ratio increases, the outlet air temperature decreases. It is observed that the outlet air temperature obtained from the case 2 is lower than that obtained from the case 1, as shown in Fig. 5. For case 1, the outlet air temperature decreases from 24.8 to 20.9 °C as increasing the pressure ratio from 1.7 to 2.2; the outlet air temperature decreases from 20.0 to 16.2 °C for case 2. When the mass flow rate is double, the outlet air temperature reduces around 5 °C. The superheating of the evaporator and the pressure ratio for two cases are shown in Fig. 6. The superheating is determined by the outlet CO2 temperature of the evaporator minuses the evaporative temperature. The figure shows that the superheating of the evaporator obtained from the case 2 (from 6.2 to 7.7 °C) is lower than that obtained from the case 1 (from 9.9 to 13.0 °C). It is due to the mass flow rate of refrigerant in case 2 is higher than the case 1.
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o
Outlet air temperature, C
30 25 20 15 C1
10
C2
5 0 1.7
1.8
1.9
2
2.1
2.2
Pressure ratio
Fig. 5. Outlet air temperatures vs. pressure ratio 16
Superheating, oC
14 12
C1
10
C2
8 6 4 2 0
1.7
1.8
1.9
2
2.1
2.2
Pressure ratio
Fig. 6. Superheating vs. pressure ratio
4 Conclusion The effect of the pressure ratio on the heat transfer phenomena of the evaporator in CO2 air conditioning system has been experimented. Two cases were experimented: C1-Using one compressor and C2-using two parallel compressors. This study achieved with expected results: The mass flow rate is doubled for the case of two parallel compressors. The mass flow rate for case 2 decreases from 71 to 61 kg/h as the pressure ratio increases 1.7 to 2.2. When the pressure ratio increases, the air temperature difference increases from 5.2 to 9.1 °C for case 1. For case 2, the air temperature difference increases from 5.0 to 8.8 °C. However, the difference between both cases is negligibly small. For case 1, the outlet air temperature decreases from 24.8 to 20.9 °C as increasing the pressure ratio from 1.7 to 2.2; the outlet air temperature decreases from 20.0 to 16.2 °C for case 2. The superheating of the evaporator obtained from the case 2 (from 6.2 to 7.7 °C) is lower than that obtained from the case 1 (from 9.9 to 13.0 °C). Acknowledgments. The supports of this work by the projects (No. T2021-01TÐ sponsored by the specific research fields of HCMUTE and No. B2020-SPK-04 sponsored by the Vietnam Ministry of Education and Training) are deeply appreciated.
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References 1. Kim, M.H., Pettersen, J., Bullard, C.W.: Fundamental process and system design issues in CO2 vapor compression systems. Prog. Energy Combust. Sci. 30(2), 119–174 (2004). https:// doi.org/10.1016/j.pecs.2003.09.002 2. Boewe, D., Yin, J., Park, Y.C., Bullard, C.W., Hrnjak, P.S.: The role of suction line heat exchanger in transcritical R744 Mobile A/C systems. SAE Technical Paper, no. 724 (1999). https://doi.org/10.4271/1999-01-0583 3. Boewe, D.E., Bullard, C.W., Yin, J.M., Hrnjak, P.S.: Contribution of internal heat exchanger to transcritical R-744 cycle performance. HVAC R Res. 7(2), 155–168 (2001). https://doi. org/10.1080/10789669.2001.10391268 4. Kwon, Y.C., Kim, D.H., Lee, J.H., Choi, J.Y., Lee, S.J.: Experimental study on heat transfer characteristics of internal heat exchangers for CO2 system under cooling condition. J. Mech. Sci. Technol. 23(3), 698–706 (2009). https://doi.org/10.1007/s12206-009-0202-1 5. Fornasieri, S.M.E., Girotto, S.: Refrigeration systems for hot climates using CO2 as the working fluid (2008) 6. Girotto, S.M.S.: Refrigeration systems for warm climates using only CO2 as a working fluid. In: Natural Refrigerants - Sustainable Ozone- and Climate-Friendly Alternatives to HCFCs. GTZ-Proklima International, pp. 287–301 (2008) 7. Sarkar, J.: Performance optimization of transcritical CO2 refrigeration cycle with thermoelectric subcooler. Int. J. Energy Res. 37, 121–128 (2011) 8. Sánchez, D., Aranguren, P., Casi, A., Llopis, R., Cabello, R., Astrain, D.: Experimental enhancement of a CO2 transcritical refrigerating plant including thermoelectric subcooling. Int. J. Refrig. 120, 178–187 (2020). https://doi.org/10.1016/j.ijrefrig.2020.08.031 9. Llopis, R., Cabello, R., Sánchez, D., Torrella, E.: Energy improvements of CO2 transcritical refrigeration cycles using dedicated mechanical subcooling. Int. J. Refrig. 55, 129–141 (2015). https://doi.org/10.1016/j.ijrefrig.2015.03.016 10. Dang, T., Nguyen, V., Dang, G., Nguyen, H., Lu, J.H.: An experimental on subcooling potential by geothermal in CO2 air conditioning system. In: The Proceedings of IEEE International Conference on System Science and Engineering 2021 (ICSSE2021), Hochiminh City, Vietnam, 26–28 August 2021
Aerodynamic Performance of a Multi-stage Axial Compressor with Tip Clearance Coupled with Hub Fillet Hoang-Quan Chu1 , Quang-Hai Nguyen2 , Quang-Huy Nguyen3 , Quoc-Viet Nguyen3 , Van-Hoang Nguyen3 , Kim-Dung Thi Hoang3 , Xuan-Truong Le3 , Cong-Truong Dinh3(B) , and Thanh-Tung Tran3 1 Le Quy Don Technical University, 11917 Hanoi, Vietnam 2 Viettel Aerospace Institute, Viettel Building-Hoa Lac Hi-Tech Park, Thach That, 13112
Hanoi, Vietnam 3 School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi
11615, Vietnam [email protected]
Abstract. Turbo jet is one of the most important components of jet plane. Improving the performances of the engine has always been a prime concern of engineers. It is clearly essential to identify the structure and aerodynamic characteristics of engine to maximize the performances of it. This paper presents an investigation of tip clearance and fillet radius parameter on aerodynamic performances of a multi-stage axial compressor using three-dimensional (3-D) Reynolds-averaged Navier-Stokes equations with the k-ω SST model. A numerical validation was tested to. Two geometric parameters of a multi-stage axial compressor with tip clearance for only rotor blades and fillet radius for both of rotor and stator blades were investigated in this research to demonstrate the effect on total pressure ratio, adiabatic efficiency, stall margin. The numerical results showed that the adiabatic efficiency increases with a decrease of tip clearance, whereas radius fillet doesn’t affect the aerodynamic characteristic. Keywords: Multi-stage axial compressor · Tip clearance · Reynolds-averaged Navier-Stokes analysis SST model · Pressure ratio · Adiabatic efficiency · Stall margin
Nomenclature γ η Lref m ˙ EFF RANS PR Pt
Specific heat ratio Adiabatic efficiency Reference length [mm] Mass flow rate [kg/s] Efficiency [%] Reynolds-averaged Navier-Stokese Total pressure ratio Total pressure [Pa]
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1306–1323, 2022. https://doi.org/10.1007/978-981-19-1968-8_111
Aerodynamic Performance of a Multi-stage Axial Compressor
Tt SM ywall Vref
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Total temperature [K] Stall margin The first wall cell width [mm] Reference velocity [m/s]
1 Introduction It is well known that the tip clearance and the blade fillet play an important role in axial compressor. When a compressor operation, the blade root is working under high stress so that sharp edge needs to be avoid. As the results presented by Vu et al. [1], Dinh et al. [2], Chu and Dinh [3], it is necessary to have fillet in the hub of the blade to increase the aerodynamic performance and reduce the displacement at the rotor blades tip and bending stress near the blades root. After the long period time service of an engine, the tip clearance becomes large since both the efficiency and the stall margin deteriorate. The clearance gaps between the rotating blades and the stationary casing are required to prevent physical rubbing between them. The tip clearance height (about 1–5% of span) is not constant during operation for the reason that the casing wall is not completely circumferencial. In addition, the tip gap height is influenced by internal temperature changes, rotor speed changes, Baghdadi [4]. The presence of a fillet at the connection between the blade surface and the hub is removed high bending stress region near the joint and improved the aerodynamic performance. The impact of blade fillets in a 15-stage gas turbine compressor was investigated by Kugeler et al. [5]. This effect has a growing over each stage of the multi-stage compressor by lowering corner stall at the rotor hub and stator tip. The result presented that a throttling range was higher for the fillet case compared with the clean case. Mank et al. [6] was demonstrated secondary flows and fillet radii in a low-speed cascade. In their study, the effect of fillet radii on the generation of secondary flows was examined. The results of the test case illustrated that the location of a fillet radii at 16% of axial chord increase the loss by about 10% at the exit plane. Oh [7] performed a numerical investigation for the influences of blade fillets on aerodynamic performance in a centrifugal compressor. The fillet, around the impeller blade hub, was applied with the uniform radius of 3.7 mm. The result showed that a small scraping vortex, developed in the case of clean blades at the corner of the hub pressure surface, disappears in the case with blade fillets due to a local flow acceleration produced by the fillet. Vasudevan [8] presented impact of fillet radius with experimental study of endwall flow in an axial compressor. Their study performed at a relatively low Reynolds numbers; as a result, the vortex is significantly weaker when the fillets are applied. In general, losses are lower for all sizes of fillet tested compared without fillet case. Curlett [9] presented experiment to clarify the influence of fillet size on aerodynamic performance in a compressor cascade. Three fillet size, 0, 7.5 and 15% chord, were examined in their study. The experiment data showed that the addition of a fillet increase secondary flow and losses for the blade tested. It is generally accepted that the tip clearance has significantly influence on aerodynamic performance characteristic of an axial compressor. Aerodynamic stability is
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considerably affected by the tip vortex flow near the tip of the blade where the blockage region performs. Berdanier et al. [10] investigated effects of rotor tip clearance heigh on stall inception in a multi-stage compressor. Their study examined three rotor tip clearances with ranging from 1.5 to 5% span (1 to 3% chord). The result shown, the clearance effects adjust stage matching with speed and alter the stall inception mechanism. An experimental and computational study was performed to consider the tip clearance flow and its effect on stall inception, Bennington et al. [11]. Their investigation provided direct evidence that the performing of spike disturbances is affected by the adjustment of the interface upstream of the blade leading edge. Kumar et al. [12] presented the aerodynamic characteristic of an axial compressor with non-uniform/asymmetric tip clearance influences. In their research, higher flow variations near the tip area reveal the occurrence of dominant secondary flow impacts, and the stall inception is expected to appear near the tip blade. Wang et al. [13] had an experimental and URANS study about evolution of the flow instability in an axial compressor with considerable size of tip clearance. Their result shown that two critical behaviors of tip leakage vortex are significantly related to the transition of instability pattern. At near stall condition, the forward leakage appearing near the blade leading edge causes in the formation of leading-edge vortex. In the region with occasional leading-edge vortex, the neighboring some blades suffer from the uploading by leading-edge vortex, which affects greater clearance reversed flow, and therefore leads to the creation of stall cells with regional greater blockage and upper entropy. Based on these reasons, a study of tip clearance and fillet radius modifications were conducted to investigate the effect of the gap and fillet radius on the multi-stage (threestage) axial compressor. This paper carried out by changing of the gap height to know how it affected on performance parameters such as pressure ratio, adiabatic efficiency, stall margin.
2 Numerical Analysis 2.1 Design Description The multi-stage transonic axial compressor investigated by Viettel Aerospace Institute. The multi-stage compressor includes 3 stages with 3 rotor stages and 3 stator stages) as shown in Fig. 1. Running 4 cases of changing tip clearance for the rotor blades is 0.5 mm, 0.4 mm, 0.3 mm, and 0.2 mm, respectively. In each case, the tip gap on all three rotor blades is the same, and stator blades don’t have gap. In all four the tip clearance changed; the fillet radius of each blade is 2.0 mm. The reference design was presented in Table 1 and the variation of tip clearance was illustrated in Table 2. To test the effect of fillet on aerodynamics (Fig. 2), in this work, studied 3 cases with radius fillet is 1 mm, 1.5 mm, and 2.0 mm. It is not possible for the larger radius fillet because the distance between the rotor and the stator in the row is not enough. All the changed radius cases run with the tip clearance is 0.4 mm, making it easy to compare difference in cases.
Aerodynamic Performance of a Multi-stage Axial Compressor
(a) Meridional view.
(b) 3D view. Fig. 1. Multi-stage compressor. Table 1. Parameters dimension of reference design. Parameter
Reference
Tip clearance (mm)
0.4
Fillet radius (mm)
2.0
RPM
37672
Table 2. Specific parameters for case studies. Parameter
Values
Tip clearance (mm)
0.2
0.3
Fillet radius (mm)
1
1.5
Fig. 2. Blade Hub Fillet.
0.4
0.5 2.0
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2.2 Numerical Method NUMECA AutoGrid5 [14] were used generate the computational domain mesh. Hexahedral were used to mesh the computational domain with O, H-type for active blade (Fig. 3). The default topology is composed by 5 blocks. 1. 2. 3. 4. 5.
An O block around the blade named skin block. A H block upstream the leading edge of the blade named inlet block. A H block downstream the trailing edge named outlet block. A H block up to the blade section named up block. A H block down to the blade section name down block.
AutoGrid5 allows to control the geometry and meshing parameter of the gap. O, H-type grids are used in the gap (Fig. 4). −7 1 Vref 8 Lref 8 + y ywall = 6 υ 2
(1)
The first wall cell width ranges from 1.5e−6 mm to 1e−5 mm, which suit with y plus results when modeling of turbulent was Shear Stress Transport (SST). The value of the first boundary layer distance of the grid base on the above formula (Eq. 1). To match of the SST with the y+ results as above, we pre-selected y+ by 1. Vref can be taken from an average at the inlet. For instance, relative Mach number at the inlet is 1.15, equivalent to 390 m/s. The reference length used the distance between hub and shroud curves as in the help guide of the software [14, 15]. This is approximate, of course, as the thickness of the boundary layers will vary widely within the computational domain. Fortunately, it is only necessary to place y+ within a range and not at a specific value. The other conditions in the Eq. 1 like the kinematic viscosity of the fluid (m2 /s) was kept. Table 3 summarizes the mesh type and number of nodes used to model the geometry of the different parts that compose the computational domain of the multi-stage compressor. This grid is used during the steady state computations, in which rotor rotational speed is 37672 rpm, tip clearance was 0.4 mm and fillet radius is 2.0 mm (Fig. 5).
Fig. 3. Grid points distribution.
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Fig. 4. Overview of heat transfer channel.
Table 3. Grid summary for the multi-stage compressor model. Domain part
Mesh type
Rotor 1
3D structured
520509
Stator 1
3D structured
542355
Rotor 2
3D Structured
494777
Stator 2
3D structured
542355
Rotor 3
3D structured
494777
Stator 3
3D structured
Summary
Number of grid points
495843 3090616
2.2.1 Boundary Conditions Due to some of reason: first, pressure and temperature have slight change; second, working point is far from gas critical point; and last, calculation resources are not enough, the work use perfect gas model. Mathematical model is Turbulent Navier-Stokes with Modelling of turbulent is k-ω Shear Stress Transport (SST). Reynolds number is 7517045 with characteristic velocity is 390 m/s, characteristic density is 1.225. Then, selected the rotation speed again of the rotor stages. We could change the rotation speed here without setting from the meshing step, which reduced trouble and repeats. It is suitable for simulation with many different rotation speeds to find the best operating point. In the work, rotation speed of 3 rotor stages is 37672 rpm. Absolute total pressure boundary condition is set the domain inlet, relative static pressure boundary condition is set the domain outlet. Total temperature at inlet is 303 K. The value at the inlet is kept at 101325 Pa while the value at the outlet is changed from 101325 Pa to the value where maximum pressure ratio achieved 1000 Pa and 100 Pa to find the maximum adiabatic efficiency point and maximum pressure ratio point, respectively. Flow originating from a fluid that stands still, like external flow across aircraft, typically the turbulence intensity is very low, well below 1%, but before air flow enters the compressor, it must pass through intake. Therefore, a turbulence intensity of 5% is specified at the rotor inlet [16]. The adiabatic smooth wall condition is applied at all the
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(a) Mesh on rotor and stator blade surface
(b) Mesh near rotor hub blade surface
(c) Mesh on rotor tip blade surface Fig. 5. Multi-stage compressor’s mesh.
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walls in the domain such as the blade surface. The periodic surfaces of the domain were connected by rotational periodic condition. Lastly, the boundary conditions used in this multi-stage compressor investigation are shown in Table 4 and Fig. 6. Table 4. Multi-stage compressor boundary conditions. Conditions
Description
Inlet total pressure
1 atm
Inlet total temperature
303 K
Inlet turbulence intensity
5%
Outlet static pressure
Variable
Interface (rotor vs stator) Full nonmatching mixing plane Wall-type
Smooth
Fig. 6. Geometry and computational domain of compressor.
2.2.2 Performance Parameters The compressor performance parameters used in this work are the total pressure ratio, adiabatic efficiency, stall margin, and static pressure coefficient (isentropic flow). These performance parameters are defined as follows [17–23]: PR =
Pt,out Pt,in
P
γ −1 γ
η=
( Pt,out ) t,in
Tt,out Tt,in
(2) −1
−1
(3)
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SM =
m ˙ peak PRstall × − 1 × 100% m ˙ stall PRpeak
(4)
˙ stall are the mass flow rates at peak efficiency, the stall point, respecwhere m ˙ peak , m tively. PRpeak and PRstall are the total pressure ratios at peak efficiency and the stall point, respectively. γ, Pt , and Tt indicate the specific heat ratio, total pressure, and total temperature, respectively.
3 Result and Discussion 3.1 Grid Independence Test Three different meshes were created to exam of the grid number on results. Figure 7 shows a clear rise between the results of total pressure ratio and adiabatic efficiency of Mesh 1, Mesh 2 and Mesh 3. When the number of the grids were kept increasing from Mesh 2 to Mesh 3, there were very small differences in the results; however, the average computation time for each pressure point increased a lot of hours one mesh to the other (Table 5). In order to accurately predict the performance of the multi-stage compressor it is important to deliver the right boundary conditions to this one. Therefore, as a first step it is necessary to validate the multi-stage compressor itself against available experimental data. Unfortunately, the performance map could not be verified. One of reasons is because testing is very expensive, only results at the compressor working point are available. With the boundary conditions in Table 2, when doing CFD simulations on multi-stage compressor, we calculated one speed line of the compressor performance map (Fig. 8).
Fig. 7. Grid independency test.
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Table 5. Meshing and computational time. Mesh
Mesh 1
Mesh 2
Mesh 3
Grid number
1746596
3090616
4282920
Average computational time
12
20
27
Fig. 8. Total pressure ratio performance curve.
Also, when doing CFD simulations on turbomachinery, it is very important to know beforehand the shape of their operational map so to apply the right boundary conditions. The inlet conditions are always provided by the total pressure and temperature. But for the outlet conditions either mass flow or static pressure are most commonly used. It is important therefore to know at which point one should use one or the other. However, the test was conducted outdoors, in which temperature was changes. Determining the exact measuring conditions as carried out in the laboratory was not possible. Therefore, the uncertainty between CFD simulations (NUMECA FineTurbo [15]) and test was inevitable. A point color yellow has adiabatic efficiency maximum, which compared measured operating point. Uncertainly between point color yellow and point color orange, one of the reasons why the temperature at the inlet wasn’t determined exactly. As shown in Fig. 8, the total pressure ratio of the simulation was 3.27, the total pressure ratio of the test was 3.2864, the difference between them was approximate 0,5%. Mass flow at the efficiency of the simulation was 3.295 and the measured was 3.2824, the difference them was 0.3838%. 3.2 Effect of Fillet Radius After the simulation CFD of this compressor was done, then the performance characteristics of the multi-stage compressor at 1 mm, 1.5 mm, and 2.0 mm of fillet radius were plotted in term of corrected mass flow versus the total pressure and adiabatic efficiency in Fig. 9. The difference between three efficiency lines were very small. The peak adiabatic efficiency at radius fillet 2.0 mm, 2 mm, 1 mm are 78.05%, 78.11%, and 78.24%. The
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biggest different between efficiency at radius fillet 2.0 mm and efficiency at radius fillet 1 mm was 0.19%. The same trend was seen in three total pressure ratio (PR) lines. The biggest different between three PR lines was 0.006 at peak adiabatic efficiency. The detailed results of fillet change cases at some important points are summarized in Table 6. In the chocking points of three case changing radius fillet (2.0 mm 1.5 mm 1 mm), mass flow is 3.414, 3.432, 3.440, respectively. Pressure ratio decreases with increases radius fillet of blades. (2.563 > 2.556 > 2.514, radius fillet respectively is 1 mm, 2 mm, 2.0 mm). The other points (peak, near stall) give similar results when the fillet radius decreases, the adiabatic efficiency and total pressure ratio increases. Stall margin results are shown in Fig. 10, the difference is not too much in all 3 cases, corresponds to 9.5436, 10.21, 9.374, respectively. In Fig. 11(a, b and c), the absolute total pressure at the outlet plane of the rotor 1 were shown when the radius fillet was 1 mm 1.5 mm 2 mm, respectively. The different between three figures a, b, c were in position near the rotor hub. In the circle red described the difference with different radius fillets, very small. The other locations at exit plane of the rotor 1 were not different in total pressure. The other stages were similar results. Results on Mach number, temperature was shown in, Fig. 12, there was no difference between the results.
Fig. 9. Comparison of performance curves at different fillet.
Table 6. Effect of fillet radius on aerodynamic performances of multi-stage axial compressor. Fillet radius
Chocking
Peak efficiency
Near stall
PR
EFF
PR
EFF
PR
EFF
2.0 mm
2.541
67.05
3.270
78.05
3.366
75.78
1.5 mm
2.556
67.43
3.273
78.11
3.384
76.25
1.0 mm
2.563
67.52
3.276
78.24
3.390
76.38
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Fig. 10. Stall margin of compressor model with different fillet radius.
(a) Fillet radius of 1.0 mm (b) Fillet radius of 1.5 mm
(c) Fillet radius of 2.0 mm
Fig. 11. Absolute total pressure on the first rotor exit plane at peak efficiency condition (Units: Pa).
From the above results, we saw that when changing fillet radius almost do not affect the aerodynamic performance of the multi-stage axial compressor at choking and peak efficiency conditions. At the near stall condition, the maximal efficiency of fillet 1.0 mm increase of 0.6% as compared to the fillet 2.0 mm. The highest stall margin reaches at 10.21 for the case of fillet 1.5 mm, an increase of 8.9% as compared to the case fillet of 2.0 mm. Effect of the radius fillet was the most important on the structure, the blade
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roof was under higher stress, so that sharp as edge were needed to be avoid, therefore it was necessary to have fillet in this position.
(a) Fillet radius of 1.0 mm
(b) Fillet radius of 1.5 mm
(c) Fillet radius of 2.0 mm
Fig. 12. Contours of relative Mach number at peak efficiency condition (Span: 50%).
3.3 Effect of Tip Clearance Tip clearance was changed and researched base on radius fillet at 2.0 mm. The results were summarized as shown in Fig. 13. The peak adiabatic efficiency at tip gap from 0.5 to 0.2 were 76.96, 78.05, 79.57 and 80.33, respectively. It was clear that mass flow rate and adiabatic efficiency decreased while tip clearance increased. The same trend was seen in total pressure ratio decreased while gap increased. The maximum value of stall margin reaches 17.07 at the tip gap of 0.3 mm. Figures 13 and 14 illustrated that the adiabatic efficiency and total pressure ratio increased when tip clearance decreased. If the tip clearance was small, the stall margin would be small. Therefore, it affected to the compressor structure, stall, and surge. For example, expansion length due to heat of blades during operation may exceed the permissible limit for impacting the casing. The effect of tip clearance vortex is high when the gap increases (Fig. 15). As the results, in case that Gap 0.5 mm has low velocity region as shown clearly in this figure. In contract, in case that Gap 0.2, at the between two blade velocities are quite high (approximate 0.5 Mach), it shows that the effect of tip clearance vortex is not robust. This is one of the reasons cause low the adiabatic efficiency.
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Fig. 13. Comparison of performance curves with different tip gaps.
Fig. 14. Stall margin of compressor model with different tip gaps.
Figure 16 shows absolute total pressure contour for 4 cases changing gap. As the results, the difference between 4 cases is tip clearance position. It is clear that total pressure decreases while tip clearance increases at the tip gap. The other locations on the exit plane of the rotor 1 are not different in total pressure. This is confirmed by results shown in Fig. 17(a) where the total pressure of case gap 0.2 is biggest at gap position in, from span 0.5 to span 0, all lines have the same distribution and there is no difference. The later stage is affected from the previous stage. Total pressure in rotor stage 2 of four cases has difference in full span, because it is affected not only by the gap in that blade itself but also by the results of the first stage. The total pressure in outlet plane of rotor 2 stage in the case of gap = 0.2 is maximum, it decreases with increases of gap, this is confirmed by results shown in Fig. 17(b). The results are similar at rotor stage 3, total pressure in outlet plane increases with tip clearance decreasing from 0.5 to 0.2 (Fig. 17(c)). Stall margin (SM) increases with decreasing gap [24]. However, it would start to decline while tip gap reaches to critical value [25, 26]. Stall margin results of this multistage axial compressor in the Fig. 12. The SM highest value was at a gap 0.3 mm, the smallest SM value was at a gap 0.5 mm. Stall margin increases as gap decreases from 0.5 mm to 0.3 mm. however, when the tip clearance decreases from 0.3 mm to 0.2 mm, the stall margin decreases. From the above results when changing the gap, we
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(a) Tip gap of 0.2 mm
(b) Tip gap of 0.3 mm
(c) Tip gap of 0.4 mm
(d) Tip gap of 0.5 mm
Fig. 15. Tip clearance vortex with different tip gaps.
(a) Tip gap of 0.2 mm
(b) Tip gap of 0.3 mm
(c) Tip gap of 0.4 mm
(d) Tip gap of 0.5 mm
Fig. 16. Absolute total pressure contour of the first rotor.
Aerodynamic Performance of a Multi-stage Axial Compressor
(a) First rotor
(b) Second rotor
(c) Third rotor Fig. 17. Total pressure on the outlet plane.
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could comment that the optimal point was when the gap was equal to 0.3 mm. However, this was the conclusion about aerodynamics, not considering in terms of structure and manufacture.
4 Conclusion In this study, the aerodynamic characteristic of a multi-stage compressor has been studied by CFD simulation in NUMECA software. The radius fillet and tip clearance have been changed for an impact study. As the results, changing radius fillet slightly affect the aerodynamic characteristic on total pressure ratio and adiabatic efficiency, the most influence is the stall margin with a highest. Whereas the adiabatic efficiency increases when tip clearance decreases. Among different scenarios, the efficiency and pressure ratio in the case of gap = 0.2 mm is the highest, but stall margin in this case is lower than that at the case with gap = 0.3 mm. Within the limited time of this study, NUMECA has been set up and deployed in a purely practical approach to rapidly answer simulation requirements, not all of its modules have been used and considered for the study. This work was only focusing on aerodynamic characteristic of the compressor, other aspects in structure and manufacturing yet to be considered. Based on the results of this study, further work is needed to optimize the geometry and operating conditions of a multi-stage compressor with tip clearance and radius fillet to maximize the performance of the compressor. Acknowledgment. This study is funded by Hanoi University of Science and Technology (HUST) under grant numbers T2021-TT-009 and T2021-PC-039.
References 1. Vu, H.T., Vu, D.Q., Dinh, C.T.: Aerodynamic performances of a transonic axial compressor with rotor hub fillet. In: International Conference of Fluid Machinery and Automation Systems – ICFMAS001, Hanoi, Vietnam, October 2018 2. Dinh, C.T., Vu, D.Q.: Effect of stator fillet on aerodynamic performances of a single-stage transonic axial compressor. In: 21st Vietnam Congress of Fluid Mechanics, Quy Nhon, Binh Dinh, Vietnam, July 2018 3. Chu, H.Q., Dinh, C.T.: Aerodynamic and structural performances of a single-stage transonic axial compressor with blade fillet radius. In: The 17th International Conference on Intelligent Unmanned Systems (ICIUS), HCMC, Viet Nam, 25–27 August 2021 4. Baghdadi, S.: Modeling tip clearance effects in multistage axial compressors. J. Turbomach. 118(4), 697–705 (1996). https://doi.org/10.1115/1.2840925 5. Kügeler, E., Nürnberger, D., Weber, A., Engel, K.: Influence of blade fillets on the performance of a 15 stage gas turbine compressor. In: Volume 6: Turbomachinery, Parts A, B, and C (2008). https://doi.org/10.1115/gt2008-50748 6. Mank, S., Duerrwaechter, L., Hilfer, M., Williams, R., Hogg, S., Ingram, G.: Secondary flows and fillet radii in a linear turbine cascade. In: Volume 2C: Turbomachinery (2014). https:// doi.org/10.1115/gt2014-25458
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7. Oh, J.J.: The effects of blade fillets on aerodynamic performance of a high-pressure ratio centrifugal compressor. In: 23rd International Compressor Engineering Conference at Purdue, Paper No. 2396, pp. 1–9 (2016) 8. Vasudevan, K.: Experimental study of endwall flow in a low-speed linear compressor cascade: effect of fillet radius. Int. J. Turbines Sustain. Energy 1(1), 1–7 (2017) 9. Curlett, B.P.: The aerodynamic effect of fillet radius in a low-speed compressor cascade. In: National Aeronautics and Space Administration, Lewis Research Center, USE, NASA-TM105347 (1991) 10. Berdanier, R.A., Smith, N.R., Young, A.M., Key, N.L.: Effects of tip clearance on stall inception in a multistage compressor. J. Propul. Power 34(2), 308–317 (2018). https://doi.org/10. 2514/1.b36364 11. Bennington, M.A., et al.: An experimental and computational investigation of tip clearance flow and its impact on stall inception. In: Volume 7: Turbomachinery, Parts A, B, and C (2010). https://doi.org/10.1115/gt2010-23516 12. Kumar, S.S., et al.: Aerodynamic characterization of a transonic axial flow compressor stage – with asymmetric tip clearance effects. Aerosp. Sci. Technol. 82–83, 272–283 (2018). https:// doi.org/10.1016/j.ast.2018.09.001 13. Wang, H., Wu, Y., Wang, Y., Deng, S.: Evolution of the flow instabilities in an axial compressor rotor with large tip clearance: an experimental and URANS study. Aerosp. Sci. Technol. 96 (2020). https://doi.org/10.1016/j.ast.2019.105557 14. Numeca AutoGrid5 User Guide, Numeca Fine Turbo 13.2 15. Numeca FineTurbo Theory Guide, Numeca Fine Turbo 13.2 16. Denton, J.D.: Some limitations of turbomachinery CFD. In: Proceedings of the ASME Turbo Expo, vol. 7, pp. 735–745 (2010). https://doi.org/10.1115/GT2010-22540 17. Dinh, C.T., Heo, M.W., Kim, K.Y.: Aerodynamic performance of transonic axial compressor with a casing groove combined with blade tip injection and ejection. Aerosp. Sci. Technol. 46, 176–187 (2015) 18. Dinh, C.T., Kim, K.Y.: Effects of non-axisymmetric casing grooves combined with airflow injection on stability enhancement of an axial compressor. Int. J. Turbo Jet-Engines 36(3), 283–296 (2017). https://doi.org/10.1515/tjj-2016-0077 19. Dinh, C.T., Ma, S.B., Kim, K.Y.: Effects of a circumferential feed back channel on aerodynamic performance of a single-stage transonic axial compressor. In: Proceedings of ASME Turbo Expo 2017, Charlotte, North Carolina, USA (2017) 20. Dinh, C.-T., Vu, D.-Q., Kim, K.-Y.: Effects of rotor-bleeding airflow on aerodynamic and structural performances of a single-stage transonic axial compressor. Int. J. Aeronaut. Space Sci. 21(3), 599–611 (2019). https://doi.org/10.1007/s42405-019-00239-5 21. Pham, K.Q., Nguyen, Q.H., Vuong, T.D., Dinh, C.T.: Parametric study on aerodynamic performance of a single-stage transonic axial compressor with recirculation-bleeding channels. Int. J. Fluid Mach. Syst. 13(2), 114–136 (2020). https://doi.org/10.5293/IJFMS.2020.13.2.348 22. Dinh, C.T., Vuong, T.D., Le, X.T., Nguyen, T.M., Nguyen, Q.H.: Aeromechanic performance of a single-stage transonic axial compressor with recirculation-bleeding channels. Aust. J. Mech. Eng. (2020) https://doi.org/10.1080/14484846.2020.1832727 23. Vuong, T.D., Kim, K.Y., Dinh, C.T.: Recirculation-groove coupled casing treatment for a transonic axial compressor. Aerosp. Sci. Technol. 111 (2021). https://doi.org/10.1016/j.ast. 2021.106556 24. Stenning, A.H.: Rotating stall and surge. J. Fluids Eng. Trans. ASME 102(1), 14–20 (1980). https://doi.org/10.1115/1.3240618 25. Farokhi, S.: Aircraft Propulsion, 2nd edn. Wiley, Hoboken (2014) 26. Wilson, A.G., Freeman, C.: Stall inception and development in an axial flow aeroengine. J. Turbomach. 116(2), 216–225 (1994). https://doi.org/10.1115/1.2928356
Theoretical and Experimental Study of the Effective Operation Mode of Absorption Refrigeration Chiller for Ice Production Nghia-Hieu Nguyen1(B) , Hiep-Chi Le2 , and Quoc-An Hoang3 1 Faculty of Heat and Refrigeration Engineering, Industrial University of Ho Chi Minh City,
Ho Chi Minh City, Vietnam [email protected] 2 Department of Heat and Refrigeration Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam 3 Science Technology Office, Ho Chi Minh City University of Technology and Education, Ho Chi Minh City, Vietnam
Abstract. Facing the situation that traditional energy sources are running out, the living environment is being severely polluted, and the ozone hole is getting bigger. Currently, clean energy is being developed and applied many times in life and in industry. Absorption chillers appear as a good candidate because operating from a direct source of heat energy. The use of new energy sources to provide heat for absorption chiller for air conditioning has been widely studied and applied. However, absorption chiller for ice production or deep cooling is still limited in application. Therefore, the experimental research combined with directional simulation study to determine the effective operation mode of absorption refrigeration for ice production in Vietnam will create a scientific basis for fabrication, installation, and operation. This study has brought scientific contributions on the suitable intake NH3 -H2 O solution concentration for absorption refrigeration to produce ice which is 31% mass of NH3 and established the correlation of generation temperature in the ranges of evaporation temperature from −20 °C to − 5 °C, condensation temperature from 28 °C to 36 °C, absorption temperature from 28 °C to 36 °C. Keywords: Effective operation mode · Absorption refrigeration chiller · Absorption refrigeration chiller for ice production
1 Introduction In commercial absorption refrigeration products, generally two types of working fluid pairs are used, lithium bromide-water (LiBr-H2O) [1, 2] and ammonia-water (NH3H2O) [3, 4]. In addition, two new working fluid pairs such as NH3 -LiNO3 and NH3NaSCN are also promising to replace NH3 -H2 O [5]. The original version of this chapter was revised: Affiliation of Prof. Nghia-Hieu Nguyen has been updated. The correction to this chapter is available at https://doi.org/10.1007/978-981-19-1968-8_116 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022, corrected publication 2023 A.-T. Le et al. (Eds.): RCTEMME 2021, LNME, pp. 1324–1346, 2022. https://doi.org/10.1007/978-981-19-1968-8_112
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Ammonia/lithium nitrate (NH3 -LiNO3 ) which is very popular because of its many advantages, overcoming the shortcomings that the NH3 -H2 O pair is facing, without being dragged by NH3 when evaporating, not corrosive to metals [5–7]. But the downside is the risk of crystallization, Heard et al. [8]. However, the researchers evaluated the NH3 -H2 O working fluid pair to have very good physical and thermodynamic properties [9, 10]. Moreover, this pair is very cheap and easy to find in the country, so the absorption chiller using the NH3 -H2 O fluid pair to produce ice is still the preferred choice in this study. When studying absorption chiller for the purpose of application and operation, the evaluation studies are divided according to the following aspects: 1. Properties of working fluid pairs, 2. Heat and mass transfer process, 3. Absorption chiller powered by solar energy, 4. Absorption chiller prototype, 5. Cost of absorption chiller. 1. Properties of working fluid pairs Traditional working fluid pairs used in absorption chiller are H2 O-LiBr, NH3 -H2 O or NH3 -H2 O-H2 . The H2 O-LiBr was applied for absorption cycle greater than 0 °C cooling demands and the NH3 -H2 O working fluid pair is used in continuous absorption chiller for refrigeration. The absorption chiller NH3 -H2 O-H2 uses NH3 as the refrigerant, H2 creates a partial vapor pressure for the refrigerant, so there is no need fhe solution pump. Alternative working fluid pairs must overcome the barriers that trational fluid pairs face. Evaluations of thermodynamic, physical and chemical properties have been thoroughly analyzed over the years. Simplify complex relationships for the properties of NH3 -H2 O solution based on Helmholtz’s free energy, up to the pressure of 400 bars [11]. The entropy of the solution is determined as a function of temperature and pressure from the mathematical model of the absorption chiller [11]. The thermodynamic properties include pressure, enthalpy, vapor specific volume, and equilibrium constant of the solution by corresponding state parameters over different ranges of pressure and temperature: The temperature and pressure ranges above 226.85 °C and 50 bar of Schulz’s state equations [12], temperature and pressure ranges above 377 °C and 200 bar respectively, based on the corresponding state parameters [13], temperature and pressure ranges above 327 °C and 110 bar respectively [14]. The NH3 -LiNO3 fluid pair has the best absorption performance and LiNO3 does not corrode steel [15]. However, some publications show that NH3 -(LiNO3 + H2 O) solution has better heat-transfer process [5–7]. 2. Heat and mass transfer process The root of the efficiency improvement of absorption chillers lies in the structure of the absorber because it determines the efficiency of the absorption process [16, 17]. Therefore, there are many studies on the absorber and generator of absorption chiller. The groups of working fluid pairs including binary mixture (NH3 -LiNO3 ) and ternary mixture (NH3 -LiNO3 + H2 O) were studied experimentally the boiling and absorption processes of an absorption chiller was operated by different low temperature heat sources Oronel et al. [18]. The gold of this was to characterize the absorption and generation process using flat plate heat exchangers, seeking to reduce the investment cost and size
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of absorption chillers. Research has found that the ternary mixture has more similarity than the binary mixture, meaning the absorption and generation processes are better. Taboas et al. [19] also presents an experimental study to determine the heat transfer coefficient by boiling flow and pressure loss by friction of plate heat exchanger with a ternary mixture adding 20% water composition in the absorbent. Jiang et al. [20] through experimental study to analyze the influence of parameters such as heat flow, fluid flow rate, diameter and vapor volume on the heat transfer coefficient by boiling NH3 -LiNO3 fluid pair in a horizontal tube. Heat transfer coefficient increases when observing heat flow, fluid flow rate increases, then fluid flow strongly affects with high heat flow. From heat and mass transfer of falling film type, an analysis of efficiency factor, cooling capacity, and different operating conditions of solution circulation in the absorber and generator is performed. Cooling capacity is 4.5 kW, evaporation temperature 4 °C, COP between 0.2 and 0.62 were determined. Zacarías et al. [21] cited an experimental study of the process of absorbing NH3 vapor into NH3 -LiNO3 solution using a nozzle and supercooled liquid flow to analyze the effects of absorption rate, supercooling flow, heat transfer coefficient, and the equilibrium ratio is achieved. The results show that the nozzles in the spray absorber have high mass transfer value, high application applicability, low cost, uncomplicated structure, compact size, and large absorbent vessel diameter. The absorber with integrated real absorption system used in NH3 -H2 O absorption refrigeration system and added nanoparticles with concentration of (0.1%, 0.3%, and 0.5%) was designed and tested. The absorption efficiency was determined from the strong solution concentration, evaporation conditions, generation, and cooling water inlet temperature. The results show that adding of TiO2 nanoparticles will affect the absorption process because this lowers the strong solution temperature according to Jiang et al. [22]. In the second part of this study, Jiang et al. [23] continue to study the influence of the amount of TiO2 nanoparticles on the efficiency coefficient of the absorption refrigeration chiller. In this experiment, the changes in the evaporator, generation, and cooling water temperature ranges were (−18–0 °C), (105–150 °C), and (22–33 °C), respectively. Using a plate heat exchanger has the advantages of size, ease of installation, low cost, and ease of inspection. Falling film and nozzle absorption type can increase heat and mass transfer efficiency [21, 24]. Therefore, the thermal efficiency of the absorption chiller is higher [25]. Another study of the effect on heat and mass transfer efficiency when using ionic nanoparticles in absorption refrigeration chiller increases COP of the chiller [22, 23]. 3. Absorption chiller powered by solar energy In the current context, the application of absorption refrigeration will explode if it is operated by green energy sources, especially solar energy because of the synchronicity of operation between solar energy and air conditioning. Absorption chillers for air con are capable operation by solar energy through collectors. The important thing here is to design the collector combined with the generator of the absorption chiller so that it is compact and economical. Cerezo et al. [26] based on two platforms of TRANsient System Simulation (TRNSYS) and Equation Engineering Solver (EES), to develop a kinetic model of a singleeffect absorption chiller using five working fluids (NH3 -H2 O, H2 O-LiBr, NH3 -NaSCN,
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H2 O-LiCl, and NH3 -LiNO3 ) driven by using 89 and 100 m2 of solar collector area. The results show that the best COP achieved between working fluids depends on the crystallization of the solution. The H2 O-LiCl solution achieves a maximal solar ratio of 0.67 and a minimal heating ratio of 0.33 when the maximal heat loss ratio is 0.12. The NH3 -LiNO3 and NH3 -H2 O solutions achieve an energy index of 6, a maximal solar ratio of 0.91, and a minimal heating ratio of 0.09. Rivera [27] studied the theory of intermittent cold cycle using working fluid NH3 LiNO3 , powered by solar energy. The parabolic concentrator collector located in Temmico, Mexico throughout the year with values between 0.33 and 0.78. The system can produce 12 kg of ice per day with generation and condensation temperatures of 120 and 40 °C respectively. Luna et al. [28] operating NH3 -LiNO3 absorption chiller with a 5 kW nominal capacity equal to 6.5 kW thermal parabolic collector system. The temperature generated to 105 °C is enough to drive a single absorption chiller. The cooling capacity is about 2 kW when the heat input is about 3.5 kW, the COP is 0.56 which is good for air conditioning. Llamas-Guillen et al. [29] implemented a prototype absorption chiller using the NH3 -LiNO3 solution. Ambient temperature is 25–35 °C, system temperature is achieved below 10 °C in the evaporator and 110 °C in the generator. The system is heated from a vacuum tube collector with a COP between 0.3 and 0.4. 4. Absorption chiller prototype In addition to the new working fluids, new models of absorption chillers have also been developed that bring suitability for the absorption process and are more convenient for the actual of heat source used. Du et al. [30] organized an NH3 -H2 O absorption cooling prototype to recover waste exhaust heat from diesel engines. The experimental results showed that better system performance as exhaust gas increase. The cooling capacity was 33.8 kW and a thermal coefficient of performance was 0.53, the temperature of the supplied water, evaporated refrigerant, and exhausted gas inlet were 26.1 °C, −15.2 °C, and 567 °C, respectively. Du and Wang [3] proposed a single effect NH3 -H2 O absorption chiller for freezing, air conditioning and heating applications to seek better thermal effects. After studying with a single-effect, Du et al. [31] continued to work with a doubleeffect NH3 -H2 O chiller using the pinch technology at the internal heat recovery. The COP values increase between 14% and 34% under the tested conditions. Neyer et al. [32] Energy-economic analysis of the effects of solar and cogeneration heat sources in a half-effect and single-effect absorption chiller. A absorption chiller was built based on flat plate heat exchangers. Models developed were used to simulate operating conditions through the TRNSYS computational platform. Savings of nonrenewable energy up to 70% were verified on this prototype. Quintanar et al. [33] developed an experimental comparison of the solar-driven intermittent absorption cooling system to consider the two solutions NH3 -LiNO3 and NH3 (LiNO3 + H2 O), which is proposed by Rivera et al. [34]. The ternary solution system obtained a coefficient of performance 25% higher than the binary solution system when the evaporator temperature reaches 8 °C for 8 h solar operation. This increase may be
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related to the fact that the generator temperature was 5 °C lower than the binary solution and the pressure reduces with the increase of water fraction in the ternary solution. Zacarías et al. [35] organized an experimental adiabatic NH3 -LiNO3 absorption using the flat plate absorber with a nozzle, acquiring a heat transfer coefficient twice higher than the tubular vertical absorber. Through a mist injector [36], the adiabatic equilibrium factor was 3.7% higher, and the mass transfer coefficient was half of the value obtained in Zacarías et al. [35], respectively. Hernandez-Magallanes et al. [37] performed a NH3 -LiNO3 single-effect system with 3 kW cooling capacity. This one was built for air conditioning and food conservation. With the aim of reducing cost and time, there is a lot of theoretical studies on 2-stage, 3-stage absorption refrigeration systems, complex cycles, and cycles using working fluid pairs for replacing. Ayala et al. [38, 39] performed a theoretical/experimental study of a hybrid cycle (compression and absorption), with NH3 -LiNO3 binary solution. The simulation results determine an increase efficiency of a hybrid cycle compared to an absorption chiller. Farshi and Asadi [40] compare three configurations of double-effect absorption chiller: in Series connection Systems series connection, parallel connection system, and reverse parallel connection system using the pair working fluids of NH3 -NaSCN NH3 LiNO3 with a single-effect system. Between −10 to −5 °C evaporation temperatures, the results provided that the series system has the worst performance due to its higher probability of crystallization and that the NH3 -NaSCN solution has a higher probability of crystallization than the NH3 -LiNO3 solution. Liang et al. [41] proposed two ejectors instead of mechanical pumps for absorption chiller. A numerical chiller model was presented using the solutions of NH3 -NaSCN and NH3 -LiNO3 for an air-cooled absorber, based on the First Law of thermo-dynamics. The COP reach to 0.64, being sufficient for practical study with exhausted heat source. 5. Cost of absorption chiller The cost of the absorption chiller is quite high because of its large size, complex structure of the absorber and generator, the working fluids must be carefully selected, and the high operating cost because the liquid pump consumes a lot of initial cost, power supplied, and does not guarantee for the efficiency stability when there is a change in operating conditions. These disadvantages leading to unrealistic results. So, to ensure the efficiency and stability of the system makes the cost increase. NH3 -H2 O solution is commonly used in commercial type absorption chillers for refrigeration and air conditioning applications [5, 6]. When NH3 evaporate from this solution from the generator, it is necessary to add a refiner to get pure NH3 . This makes the system more complex make an increase in temperature is generated, increased initial investment costs. The operating temperature of single-stage absorption chiller for freezing is also quite high, around 120 °C, which cannot be operated entirely by solar energy or the collectors will occupy a large space. Alternative working fluids have been developed as NH3 -LiNO3 and NH3 -(LiNO3 + H2 O) to minimize the complexity of absorption chillers using NH3 -H2 O solution [16, 42], and also instead of LiBr-H2 O solution suffer from defects in vacuum pressure, corrosion, and solution crystallization [16, 43].
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When using NH3 -LiNO3 solution, the absorption chiller does not need a refiner. The absorber also becomes more compact when using plate heat exchangers [44–46], increases the efficiency factor of the chiller [45, 47], and is able to use entirely the solar energy to supply heat for the generator [6, 7, 48]. As for the heat and mass transfer, NH3 -LiNO3 solution has problems with high viscosity which limits energy efficiency. NH3 -(LiNO3 + H2 O) solution reduces pressure loss by reducing viscosity compared to NH3 -LiNO3 solution [19]. Adding 20% water to the NH3 -LiNO3 solution increases the heat transfer coefficient in the flat plate heat exchanger gave the best energy efficiency [19, 49]. In order for absorption air conditioners to be widely equipped for households, the complete use of solar energy to heat the chiller is the throughout purpose [50]. To meet the thermal demand, various types of collectors have been applied, such as flat plates [51], evacuated tubes [29] and parabolic collector [28]. For a highly efficient absorption refrigeration system, the models are complicated and expensive. Examples include connecting multiple generators [52, 53], connecting multiple re-absorbers [54], using multiple compact heat exchangers [44, 55, 56], integrating with vapor compression machines [80, 89], integration of ejectors into the system [41], integration of electricity and cold cycles [59], etc. These new models are often grouped to analyze suitable heat sources. Although there are many studies on working fluid pairs, there is still no absorbent that replaces water that is widely used for absorption refrigeration on the market today because of its cheapness, availability and no need high-tech. Single-stage NH3 -H2 O absorption chiller has a coefficient of performance from 0.6 to 0.7. The temperature generated is very wide from 50–200 °C. So, it is possible to entirely use solar energy as a heat source. However, when the temperature arises from 100 C or more, many limitations appear. The higher of temperature, the lower of COP.
2 Absorption Chiller Cycle
Fig. 1. Design cycle of NH3 -H2 O single absorption chiller
The strong solution, after leaving the absorber 2, is pumped through the solution heat exchanger and then into the generator at point 4. At the generator, the heated solution
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evaporates at point 5 (the remaining solution is weak solution) out of the evaporator at point 6. Boiling at 5 pulls a lot of water through the rectifier to become almost pure NH3 vapor 10 (the water is separated and returns to the generator at point 9). NH3 vapor in state 10 through the condenser. The cooling water condenses NH3 vapor into NH3 liquid. 11. Liquid NH3 through the expansion valve reduces pressure to state 12 and evaporates in the evaporator in state 13. NH3 vapor is in state 13 go to the absorber in state 1 is absorbed by weak solution 8 and becomes a strong solution in state 2. The strong solution in state 2 is pumped again to state 3 to continue the cycle. 2.1 Thermodynamic Parameters Refrigerant NH 3 [60] The equilibrium of pressure and temperature in the saturated state (2 phases) is related according to the equation: P(T ) = 103
6
ai (T − 273.15)i
(1)
i=0
The enthalpy of NH3 in saturated liquid and vapor saturated states: il (T ) =
6
bi (T − 273.15)i
(2)
ci (T − 273.15)i
(3)
i=0
iv (T ) =
6 i=0
Solution NH 3 -H 2 O [61] Boiling Temperature Equation (4) below presents ways to determine the boiling point T (K) of NH3 -H2 O solution at concentration c and pressure p (bar): T=
a log p + 0, 00847711 − b
(4)
a = −2103.5 + 4669.96.c − 20228.3.c2 + 56507.c3 − 80989.9c4 + 55286.5.c5 − 14361.4.c6 b = 5.65208 − 7.317.c + 37.9018.c2 − 102.912.c3 + 135.789.c4 − 82.7106.c5 + 18.4113.c6 Enthalpy of Solution and Vapor In the formulas below, the units of enthalpy, temperature and pressure are kJ/kg, K and bar, respectively.
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i , i – the enthalpy of a liquid solution (at concentration c, temperature T, and pressure p) and the enthalpy of the vapor released from that solution when boiling. iNH3, iVNH3 – enthalpy of liquid NH3 and vapor NH3 for temperature T and pressure p. iH2O, iVH2O – enthalpy of water and steam for temperature T and pressure p. qt – mixing heat, kJ/kg. cv – concentration of the vapor released from the boiling solution of NH3 -H2 O
i = c.iNH3 + (1 − c).iH2 O + qt
(5)
i = cv .iNH3 + (1 − cv ).iH2 O
(6)
iNH3 = −1058, 43 + 3, 02148.T + 0, 312072.10−2 .T −2 + 0, 155360.p − 0, 123079.10−4 .p2 − 0, 129460.10−5 .p.T 2 iH2 O = −1039, 5 + 3, 56393.T + 0, 919594.10−3 .T −2 + 0, 124376.p − 0, 222797.10−5 .p2 − 0, 356960.10−6 .p.T 2 qt = −839, 87.(1 − 0, 071.c).[(1 − c).c + (1 − c2 ).c2 ] iVNH3 = 770, 761 + 1, 86497.T + 0, 578293.10−4 .T 2 + 0, 731509.10−6 .T 3 + 8, 98074.p − 4580, 15.p/T iVH2 O = 1993, 19 + 1, 88878.T + 0, 205512.10−3 .T 2 + 0, 367295.10−6 .T 3 + 10, 6342.p − 7648, 34.p/T NH3 Vapor Concentration Formula for determining the concentration of vapors emitted from a boiling liquid solution at concentration c, pressure p (bar) and temperature T (K): b+d (7) cv = 1 − (1 − c).e T where: b = −6571,06 + 39,9544.T − 0,23781.10−2 .T 2 + 0,39792.10−6 .T 3 − 22,7722.p − 0,4979.10−7 .p2 + 8286,02.p/T + 0,172363.10−1 .p.T + 0.77344.10−6 .P.T 2 − 3,62962.T . ln T − T . ln p
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d = −1467.c2 (1,25.c3 − 5,58.c2 + 5,96.c − 0,42) + (17,3.c3 − 13,8.c4 ).T Specific Heat Capacity of the Solution The specific heat cp [kJ/(kg.K)] of the solution at concentration c, temperature T (K) and pressure p (bar) is calculated as follows: cp = 3,56393 + 1,83918.10−3 .T + 7,13920.10−7 .p.T − (0,54245 − 4,4232.10−3 .T + 1,875280.10−6 .p.T ).c
(8)
The Thermal Conductivity of the Solution The thermal conductivity coefficient λ [W/(m.K)] of the solution is calculated on the basis of the thermal conductivity coefficients λNH3 and λH2O . Let c be the concentration and t (°C) the temperature of the solution we have: λ = c.λNH3 + (1 − c)λH2 O
(9)
where: λNH3 = 0,528 − 1,669.10−3 .t − 6,2.10−6 .t 2 λH2 O = 0,562 + 1,893.10−3 .t − 7,11.10−6 .t 2
2.2 Mathematical Model of the Absorption Chiller A mathematical model is developed to analyze the performance of a typical computational test system. The temperature and pressure of the working fluid are based on design values. Thermodynamic models of the parts ensure energy and mass balance and a simulation program is developed for cycle analysis. Analysis of the test volume of each component (generator, rectifier, condenser, evaporator, absorber, solution heat exchanger, solution pump, diluent throttle for weak solution, and refrigerant expansion valve). Heat Transfer Rate (Qi ) [62–64] Qi =
j (mα .i)in + (mα .iα )out
(10)
α=1 i α=1
(mα )in =
j
(mα )out
α=1
In which, i Specific enthalpy (kJ/kg); m Mass flow rate (kg/s).
(11)
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The equations of energy balance, mass flow balance between refrigerant lines, log mean temperature difference are used to calculate the heat exchange area of each component in the system. Absorber m8 .i8 + m1 .i1 − m2 .i2 = m22 .(i23 − i22 )
(12)
m2 .C 2 = m8 .C8 + m1 .C 1
(13)
LMTDa =
(T8 − T23 ) − (T2 − T22 ) 23 ln( TT28 −T −T22 )
(14)
Generator m4 .C 4 + m9 .C 9 = m5 .C5 − m6 C 6
(15)
m5 .i5 + m6 .i6 − m4 .i4 − m9 .i9 = m20 .(i20 − i21 )
(16)
LMTDd =
(T20 − T6 ) − (T21 − T4 ) −T6 ln( TT20 ) 21 −T4
(17)
Condenser m10 .(i10 − i11 ) = m24 .(i25 − i24 ) LMTDc =
(T10 − T25 ) − (T11 − T24 ) −T25 ln( TT10 ) 11 −T24
(18) (19)
Evaporator m12 .(i13 − i12 ) = m26 .(i26 − i27 ) LMTDe =
(T26 − T13 ) − (T27 − T12 ) 26 −T13 ln( TT27 −T12 )
(20) (21)
Expansion Valve The expansion valve has the effect of reducing pressure and dividing it into two different pressure levels. There is no heat exchange of the working fluid at the valve. The enthalpy of the working fluid before and after the valve is constant. The process of changing pressure between two points has no change in mass flow rate and the process is considered adiabatic, the volume can change when the fluid has a small amount of vaporization (flashing). Solution Heat Exchanger T7 = T3 .ηshx + T6 .(1 − ηshx )
(22)
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m3 .(i4 − i3 ) = m6 .(i6 − i7 ) LMTDshx =
(T6 − T4 ) − (T7 − T3 ) 4 ln( TT67 −T −T3 )
(23) (24)
Solution Pump The power required to transfer the mass flow rate of solution m2 from pressure P2 to pressure P3 , the pump efficiency is ηp : Wp =
m2 .v2 .(P3 − P2 ) ηp
(25)
The pumping process is entropy, the enthalpy of the solution will increase slightly at the ejector. Therefore, the energy balance across the solution pump can be calculated as: m2 .i2 + Wp = m3 .i3
(26)
m5 .i5 − m9 .i9 − m10 .i10 = m28 .(i29 − i28 )
(27)
Rectifier
LMTDrec =
(T5 − T29 ) − (T10 − T28 ) 29 ln( TT105 −T −T28 )
(28)
The performance is evaluated according to the enthalpy coefficient equation as follows [13]: (29) χ = (i − il ) (iv − il ) where, i is the enthalpy of the fluid at a given pressure; il and iv are the enthalpy of saturated liquid and saturated vapor at the same pressure, respectively. From the definition of χ, the state of the fluid can be known as follows: χ < 0 is too cold, χ = 0 is saturated liquid, 0 < χ < 1 is two-phase, χ = 1 is saturated vapor, and > 1 is overheating. Coefficient of Performance [60, 62–66] Thermal efficiency coefficient of air conditioner COP is the ratio between the heat capacity obtained from the environment to be cooled through the evaporator to the heat capacity supplied to the generator to operate the cycle. COP =
Qe Qg
(30)
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where: Qe Cooling capacity (kW). Qg Heat capacity supplied to the generator (kW). Brine Solution Cooling Efficiency The brine cooling efficiency coefficient of COPu air conditioner is the ratio of the evaporative heat of NH3 to cool the brine to the heat supplied to the generator to operate the cycle. COP u =
QNaCl QgJ
(31)
where: QNaCl Heat of evaporation of NH3 to cool brine (kJ). Qg_J Heat capacity supplied to the generator (kJ). When designing the system, the condenser and absorber should be designed to operate at 3 ÷ 5 °C above ambient (coolant temperature) [67].
3 Experimental Analysis 3.1 Experimental Model With the arrangement of 14 temperature measuring instruments, 4 pressure measuring instruments, 5 liquid flow measuring instruments and 01 steam flow measuring instrument at 12 state points as shown in Fig. 1, all 12 state points. The states found according to the theoretical calculation of the absorption chiller has been verified from the actual measurement (Fig. 2).
Fig. 2. Experimental model
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3.2 Experimental Results In order for the absorption chiller to operate at maximum efficiency according to the operating conditions, the concentration of the intake solution must be appropriate. Experimental analysis of the continuous type NH3 -H2 O absorption chiller with 3.76 kW heating capacity cooling a 23% brine solution from an ambient temperature of 30 °C to − 18 °C. The air conditioner is controlled to operate according to the different flows of the dilute NH3 -H2 O solution stream and the NH3 vapor flow into the absorber to find the maximum COP when the operation is stable. Mass concentration of loading solution in the range from 29.5 ÷ 32.5% was determined from optimal theoretical analysis. The results data of the experiments are calculated when the absorption chiller has finished starting and operating stably until the temperature of 93 kg of brine solution drops to the lowest about −20 °C. In these 14 experiments, the total cooling water flow, through the absorber, condenser, and extraction column was V20 = 25 l/m, respectively; V21 = 16.5 l/m; V22 = 8 l/m; Vct = 0.8 l/m are fixed (Table 1). Table 1. Table type styles experimental mean point parameters Test
t3 oC
t7 oC
1 2 3 4 5 6 7 8 9 10 11 12 13 14
41 42,2 43,6 41,3 41,5 41,8 42,85 42,31 41,04 36,49 40,67 42,8 39,7 39,9
48,5 47,9 50,5 47,1 48,1 48,3 50,13 48,35 49,53 41,98 48,56 48,9 47,6 46,5
t11 oC 30,2 35,9 31 30,9 31,0 31,7 31,99 35,81 33,61 30,73 34,42 31,2 31,1 31,5
t2 oC 37 38,2 38,1 36,1 38,3 38,1 38,38 38,46 38,61 33,32 38,34 37,8 36,7 36,4
t20 oC 30,6 32,1 31,2 30,6 30,9 31,4 31,99 32,57 32,42 28,29 31,05 31,5 31,3 31,3
t21 oC 34,2 35,9 34,8 34,1 34,6 34,4 35,07 35,86 35,79 31,13 34,76 34,9 35,1 34,6
t22 oC 32,3 34,6 33,3 32,9 33,2 33,4 33,92 34,68 34,21 29,92 33,56 33,7 33,2 33,1
V20 l/m 19,3 19 19,2 19,9 19,8 24,2 252 25,04 24,61 21,72 21,09 19,9 19,9 24,9
V21 l/m V22 l/m 12,1 11,8 12,1 12,1 11,9 16,5 16,58 16,54 16,45 14,64 15,56 12,1 12,0 16,1
5,8 5,7 6 6 5,5 7,7 8,23 8,21 7,96 6,79 5,67 5,6 5,5 7,8
The operating parameters of the absorption chiller in 14 experiments are summarized in Table 1 as the average values at the nodes calculated according to the brine temperature from 20 °C down to the limit temperature tlim of each experiment. The results of 14 experiments are summarized in Table 2. The COP/COPu of the experiments was calculated for 6 periods from when the brine temperature at ambient temperature drop to 20 °C (tNaCl ≥ 20 °C); from 20 °C > tNaCl ≥ 10 °C; 10 °C > tNaCl ≥ 0 °C; 0 °C > tNaCl ≥ −10 °C; −10 °C > tNaCl ≥ tlim °C; tlim > tNaCl . In the tNaCl stage ≥ 20 °C, the absorption chiller is being adjusted to operation mode and has not yet operated stably, so this stage is not included in the COP and COPu . Limit temperature of brine solution tlim is the lowest temperature of brine solution that can be reduced without destabilization. If the absorption chiller continues to operate, the brine solution temperature may drop lower, but it is very slow and unstable process, so this period (tlim > tNaCl ) is also not included in the COP and COPu . So, the efficiency coefficients COP
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and COPu are calculated from the time when the brine temperature reaches 20 °C down to the tlim of the experiments (20 °C > tNaCl ≥ tlim °C). Table 2. COP/COPu of the experiments Test
Ci, (%)
V8 / V13/ V2, 20 >tNaCl≥ (l/m) 10, (oC)
10 >tNaCl≥ 0, (oC)
0 >tNaCl≥ 10 (oC)
-10 >tNaCl≥ tlim ( oC)
20 >tNaCl≥ tlim, (oC)
1
29,5
1,39/ 81,72/ 1,36
0,393/ 0,169
0,406/ 0,134
0,406/ 0,17
0,405/ 0,237
0,403/ 0,178
2
29,5
0,65/ 92,21/ 1,11
0,453/ 0,26
0,447/ 0,216
0,432/ 0,13
x
0,444/ 0,202
3
29,5
0,53/ 86,43/ 0,08
0,431/ 0,22
0,427/ 0,195
0,423/ 0,162
0,422/ 0,088
0,425/ 0,166
4
30
0,54/ 83,28/ 11,9
0,45/ 0,228
0,449/ 0,179
0,438/ 0,144
0,425/ 0,113
0,441/ 0,167
5
30
1,12/ 73,63/ 1,37
0,43/ 0,263
0,43/ 0,248
0,419/ 0,18
0,408/ 0,126
0,421/ 0,204
6
30
0,94/ 84,47/ 1,35
0,445/ 0,246
0,434/ 0,204
0,421/ 0,202
0,416/ 0,193
0,429/ 0,21
7
31
1,1/ 79,3/ 1,31
0,432/ 0,263
0,421/ 0,21
0,415/ 0,201
0,404/ 0,228
0,418/ 0,21
8
31
0,81/ 97,32/ 1,33
0,452/ 0,298
0,445/ 0,26
0,428/ 0,12
x
0,442/ 0,226
9
31
2,27/ 72,9/ 2,65
0,412/ 0,308
0,399/ 0,238
0,393/ 0,212
0,389/ 0,102
0,398/ 0,215
10
31
0,78/ 77,75/ 1,21
0,438/ 0,34
0,444/ 0,321
0,437/ 0,289
0,425/ 0,097
0,436/ 0,262
11
31
1,83 / 96,8 / 2,1
0,434/ 0,307
0,426/ 0,272
0,424/ 0,204
0,412/ 0,149
0,424/ 0,233
12
32,5
0,99/ 66/ 1,2
0,429/ 0,173
0,426/ 0,131
0,413/ 0,102
x
0,423/ 0,135
13
32,5
2,13/ 61,86/ 2,3
0,329/ 0,156
0,314/ 0,106
0,318/ 0,091
x
0,32/ 0,119
14
32
1,81/65,3/ 1,91
0,369/ 0,22
0,368/ 0,161
0,364/ 0,155
0,358/ 0,071
0,365/ 0,154
In Table 2, the mass concentration of NH3 -H2 O solution is in the range from 29.5% to 32.5%. The experiments were carried out on a complete absorption chiller with stable operation. The graphs show the relationship of COP and COPu with temperature, concentration over time of 14 tests (represent test 10) to find the most suitable loading concentration of NH3 -H2 O solution according to cooling water temperature tc (°C), ta (°C); and according to the required evaporation temperature te (°C). According to tests 7, 8, 9, 10, and 11, the solution concentration intake were 31%. The average flow of dilute solution from the generator to the absorber V8 = 0.78 ÷ 2.27 l/m. The NH3 vapor flow from the evaporator to the absorber was adjusted V13 = 72.9 ÷ 97.32 l/m. According to test 8, the flow of NH3 vapor from the evaporator into the absorber V13 = 97.32 l/m is very high compared to the average weak solution flow from the generator to V8 = 0.81 l/m. Therefore, the NH3 vapor is not fully absorbed, increasing the absorption pressure, leading to an increase in the evaporator pressure, COP = 0.442 and COPu = 0.226, limit brine temperature tlim = −9 °C