Recent Advances in Mechanical Engineering: Select Proceedings of ICRAMERD 2022 9811994927, 9789811994920
This book presents select proceedings of the International Conference on Recent Advances in Mechanical Engineering Resea
398 44 19MB
English Pages 603 [604] Year 2023
Foreword
Preface
Contents
About the Editors
Heat Transfer and Fluid Flow
Performance of a Single-Turn Pulsating Heat Pipe at Varying Gravity
1 Introduction
2 Physical Model and Governing Equations
3 Results and Discussions
3.1 Flow Velocity
3.2 Heat Flow Rate
3.3 Heat Transfer Coefficient
4 Conclusion
References
Free Convection from Isothermally Heated Hollow Tube with Annular Fins
1 Introduction
2 Problem Description
3 Numerical Methodology
4 Results and Discussion
4.1 Effect of L/D on Thermal Plume
4.2 Effect of S/L on Thermal Plume
4.3 Effect of Ra on Thermal Plume
4.4 Variation of Qnet
4.5 Variation of Nunet
4.6 Behaviour of Fluid Flow Plume
5 Conclusion
References
Chaotic Dynamics of Fluid Conveying Pipe
1 Introduction
2 Analytical Model
3 Galerkin Discretization
4 Result and Discussion
5 Conclusion
References
Study of Flow Distribution in the Falling Film Evaporator Manifold
1 Introduction
2 Limitations of the Theory Proposed
2.1 Uniform Flow Distribution Aspects
2.2 Impact of E (Length of Header to Diameter of the Header)
2.3 Impact of Opening Ratio M (Summation of All Outlet Area to the Area of Manifold)
2.4 Impact of Friction
3 Conclusion
References
Effect of Core Geometrical Characteristics on Performance and Pressure Drop of Rotary Regenerator
1 Introduction
2 Methodology
3 Results and Discussions
3.1 Effect of Length of Regenerator Core
3.2 Effect of Diameter of Regenerator Core
3.3 Effect of Separator Gap
4 Conclusions
References
CFD Analysis of Heat Transfer Coefficient and Pressure Drop in a Shell and Tube Heat Exchanger for Various Baffle Angles
1 Introduction
2 Design and Analysis
3 Results and Discussions
4 Conclusion
References
A Study on Indoor Air Quality of Air-Conditioning System Using Duct Insulation and Filters for Industrial Applications
1 Introduction
2 Methodology and Modelling
2.1 Numerical Values Obtained for Heat Gain Through Ducts Using ANSYS-15
3 Results and Discussions
3.1 Solution Obtained for Steady-State Thermal Condition Using ANSYS-15
3.2 Fluid Fluent Results
3.3 Steady State Thermal Results with Filter
3.4 Transient Thermal Results
4 Conclusion
References
Optimization Applications in Engine
Maximization of Compressive Strength of Fused Deposition Modeled Antibacterial Polylactic Acid by Taguchi Method
1 Introduction
2 Materials and Methods
3 Result and Discussions
3.1 Maximization of Compressive Strength
4 Conclusions
References
Analysis of Drivers and Barriers in Green Supply Chain Management Using Fuzzy AHP and Fuzzy TOPSIS Approach
1 Introduction
2 Experimental Program
2.1 Consideration of the Manufacturing Industry
2.2 Focused Group Discussion
2.3 Application of F_AHP Approach
2.4 Application of F_TOPSIS Approach
3 Result and Discussion
3.1 Results
3.2 Sensitivity Analysis
4 Conclusions
References
Parametric Optimization of LPG Refrigeration System Using Artificial Bee Colony Algorithm
1 Introduction
2 Experimental Method
2.1 Experimental Setup
2.2 Mathematical Modeling
3 Implementation of ABC Algorithm and Result Analysis
4 Conclusions
References
A Generalized Oppositional Differential Evolution Algorithm for Parameter Extraction of Different Photovoltaic Models
1 Introduction
2 Description of PV Models
2.1 PV Cell Model with a Single Diode (PVCSD)
2.2 PV Cell Model with Double Diodes(PVCDD)
2.3 PV Module Model(PVM)
3 Parameter Extraction Strategy
4 Generalized Oppositional Differential Evolution Algorithm (GODEA)
5 Results and Discussion
5.1 Results for PVCSD
5.2 Results for the Model of PVCDD
5.3 Results for PVM
6 Conclusion
References
Analysis of Community Engagement on Social Networking Sites During the Kerala Floods in 2018
1 Introduction
1.1 Motivation
1.2 Problem Definition
1.3 Research Questions
1.4 Method of Investigation
2 Relevant Works
3 Material and Methods
3.1 Case Description—Kerala Flood 2018
3.2 Proposed Framework
4 Results and Discussion
4.1 Data Visualization
4.2 Word Frequency
5 Conclusions
References
Application of Evolutionary Technique for Mapping onto Network on Chip
1 Introduction
2 Problem Analysis
3 Implementation of Genetic Algorithm on 2D Mesh Topology
4 Simulation and Result
5 Conclusion
References
Parametric Optimization of Resistance Spot Welded Dissimilar Metals Utilizing Advanced Hybrid Taguchi-MARCOS Method
1 Introduction
2 Experimental Analysis and Methodology
3 MARCOS Method
4 Results and Discussion
5 Conclusions
References
Prioritizing the Factors Affect in Covid-19 Vaccines, a Fuzzy-AHP Approach
1 Introduction
2 Literature Review
3 Framework
4 Methodology
4.1 Fuzzy Set
4.2 Proposed Approach
4.3 Calculation
4.4 Overall Weightage Calculation
5 Conclusion
References
Apriori Versus FP-Growth for Recommendation System
1 Introduction
2 Literature Review
3 Methodology
3.1 Apriori Algorithm
3.2 FP Growth Algorithm
4 Dataset
5 Results
6 Conclusion
References
Optimization of EDM Process Parameters in Machining of Titanium Alloy Used for Aerospace Applications
1 Introduction
2 Materials and Experimental Techniques
2.1 Set-Up
2.2 Electrode and Work Piece Material
2.3 Design of Experiments
2.4 Single Objective Optimization Using Taguchi Technique
2.5 Multi-Objective Optimization Using Grey Relational Analysis
3 Results and Discussions
3.1 ANOVA for MRR
3.2 ANOVA for Tool Wear Rate
3.3 ANOVA for Surface Roughness
3.4 Grey Relational Technique
4 Conclusion
References
Design and Dynamics
Ergonomic Risk Assessment of Rubber Tappers Using Rapid Upper Limb Assessment (RULA)
1 Introduction
2 Materials and Methods
2.1 Sampling Frame
2.2 Data Collection Method
2.3 Rapid Upper Limb Assessment (RULA)
3 Results and Discussion
4 Conclusions
References
Finite Element Analysis of Cranked Beam with Reinforcement Detailing of SP 34 (1987)
1 Introduction
2 Model Geometry and Validation
2.1 Methodology
2.2 The Finite Element Mesh
3 Results and Discussions
4 Conclusions
References
Design and Analysis of Coir Fibre Reinforced Polypropylene Based Internal Car Door Panel
1 Introduction
2 Literature Review
3 Design of Internal Car Door Panel
3.1 Properties and Dimensions of Internal Car Door Panel
3.2 Human Safety Criterion
4 Result Analysis
4.1 Effect of Impact Force in Side Collision
4.2 Load on the Armrest
5 Conclusion
References
Pedobarography-Based Prosthetic Foot Design and Optimization Methodology
1 Introduction
2 Methodology
2.1 Plantar Pressure Distribution Data
2.2 Foot Modeling, Meshing Optimization Process
3 Topology Optimization Results
3.1 Discussion
4 Conclusion and Scope
References
Atlas Generation of Leg Mechanisms for Walking Platforms Using Creative Synthesis
1 Introduction
2 Living Creatures Coupler Curves
3 Creative Design Procedure
3.1 Analysis of Existing Mechanisms
3.2 Design Specifications and Constraints
3.3 Kinematic Number Synthesis
3.4 Specialized Kinematic Chains
3.5 Particularization
4 Coupler Curves from Novel Mechanisms
5 Conclusion
References
Effect of Curve Angle on Prestressed Box-Girder Bridges
1 Introduction
2 Validation
3 Methodology
4 Result and Disscusion
4.1 Effect of Curve Angle
4.2 Proposed Equation
5 Conclusions
References
Design, Analysis and Optimization of Chassis for a Utility Vehicle
1 Introduction
2 Design Constraints
3 Design Methodology
3.1 Chassis Design
3.2 Design Procedure
3.3 Use of Different Cross Section
3.4 Track Width and Wheel Base
4 Material Selection
5 Structural Analysis
5.1 Front Impact Analysis
5.2 Side Impact Analysis
5.3 Roll Over Analysis
6 Conclusion
References
Design and Development of Semi-automated Manual Transmission
1 Introduction
2 Literature Review
3 Requirements for Conversion of Manual to Semi-automatic
4 Technical Specifications of Gearbox
4.1 Transaxle
4.2 Clutch
5 Design of Gear Shifter
5.1 The Shifter
5.2 The Selector
6 Working
6.1 For Gear Shifts not Involving Change of Shift Gates
6.2 For Gear Shifts Involving Change of Shift Gates
6.3 For Engaging Reverse Gear
7 Conclusion
References
Analysis of Interdependence of Structural Irregularity in Connected Buildings
1 Introduction
2 Modeling in ETABS
3 Results and Discussions
4 Conclusion
References
Numerical Modeling and Analysis
Numerical Investigation on the Effect of Inclination Angle of the Wall Fin on the Hydrogen–Air Micro-combustion
1 Introduction
2 Geometry and Numerical Scheme
2.1 Geometry
2.2 Governing Equations
2.3 Numerical Scheme
3 Results and Discussion
3.1 Grid Independence and Validation
3.2 Influence of Wall-Fin Inclination Angle on the Combustion Efficiency
3.3 Effect of Wall-Fin Inclination Angle on the Outer-Wall Temperature
4 Conclusions
References
Modeling of a Severe Accident in a VVR-Type Research Reactor Using MELCOR Accident Analysis Code
1 Introduction
2 Methodology
2.1 Description of the VVR-Type Reactor
2.2 Modeling of the Accident Scenario
3 Results and Discussion
4 Conclusion
References
Thermal Management of Thermo-Electric Refrigeration System Applying Nanofluids Through a Minichannel Heat Sink
1 Introduction
1.1 Nanofluids and Its Use to Enhance Heat Transfer
1.2 Thermoelectric Refrigeration
2 Governing Equations
2.1 Thermoelectric Refrigerator
2.2 Estimation of Nanofluid Thermophysical Properties Used in Table 1
3 Results and Discussion
3.1 Validation of the Model
3.2 Variation of Nusselt Number
3.3 Cooling Capacity Comparison
3.4 Variation of Input Power
3.5 Variation of COP with Nanoparticle Concentration
3.6 Cooling Capacity Variation with Nanoparticle Concentration
3.7 Temperature Distribution at the TEC
4 Conclusions
References
Mathematical Approach to Find Warpage Deformation of FDM Build Parts
1 Introduction
2 Methods and Analysis
3 ANOVA Analysis
4 Surface Plots for Warpage
5 Conclusion
References
Numerical Study of Pressure Drop Calculation for Newtonian Slurry Through a Multi-segmented HDPE Pipeline
1 Introduction
2 Literature Review
3 Mathematical Formulas and Relations
3.1 Different Flow Parameters and Relations
3.2 Relationship Between Critical Deposition Velocity and Pipeline Diameter
4 CFD and Flow Simulation
5 Results and Discussions
5.1 Variation of Pressure Drop with Varying Bend Angles of the Pipeline
5.2 Variation of Pressure Drop with Varying Inlet Velocity of the Pipeline
5.3 Variation of Pressure Drop with Varying Concentration of Solids in the Slurry
5.4 Variation of Pressure Drop with Varying Areas of Cross Section of the Pipeline
6 Conclusion and Future Scope
References
Novel Applications of IOT in Industries
Development of Automobile Recommender System Using Machine Learning and AHP Algorithms
1 Introduction
2 Statement of the Problem
2.1 Problem
2.2 Factors
3 Algorithm
3.1 User Specific Purpose
3.2 General Purpose
4 Validation of Algorithm
5 Conclusions
References
Correlation of Structural Irregularity in Circular Buildings Using Vital Signs
1 Introduction
2 Modelling and Analysis of Buildings
3 Results and Discussion
4 Conclusions
References
Deterministic Wind Speed Prediction with VMD Based Kernel Random Vector Functional Link Neural Network
1 Introduction
2 Proposed Methodology
2.1 VMD
2.2 KRVFLN
3 Performance Measurement
4 Result Analysis
5 Conclusion
References:
Convolutional and Artificial Neural Network Collated to Descry Brain Tumor
1 Overview
2 Introduction
2.1 Aim
2.2 Significance
2.3 Motivation
2.4 Contribution
3 Related Work
4 Methodology
4.1 Preparing Data
4.2 Data Preprocessing
4.3 Model Architecture
5 Results
6 Conclusion
7 Future Perspectives
References
Comparative Analysis, Hardware Design and Simulation of Solar Tracker System
1 Introduction
2 Problem Formulation
2.1 Objective
3 Solutions and Methodologies
3.1 Proposed Idea
3.2 Modelling of Solar Tracker System
4 Proposed Hardware Model
4.1 Design of Solar Tracker Using Arduino
4.2 Model Descriptions
5 Results and Discussion
5.1 Simulation of Solar Tracker System
5.2 Experiment on Fixed Plate Solar Collector
5.3 Experiment on Tracking Solar Collector
5.4 Discussion
6 Conclusion
References
Power Generation Improvement in Unsymmetrical PV Arrays During Partial Shading via MR Technique
1 Introduction
2 System Description
3 Proposed Modules Position Relocation (MR) Technique
4 Performance Under Partial Shading Scenario
4.1 Performance During Shading Scenario A
4.2 Performance During Shading Scenario B
4.3 Performance During Shading Scenario C and D
5 Conclusion
References
Arc Faults Detection Using VMD Based DTEO in a PV-Battery Based DC Microgrid
1 Introduction
2 Analysis of Arc Faults in PV-Battery Based DC Microgrid
2.1 Building of PV-Battery Model
2.2 Arc Faults Analysis
3 Decomposition of Signals and Their Selection
3.1 Variational Mode Decomposition
3.2 Sparse-Kurtosis Index
4 VMD Based DTEO Detection Scheme
4.1 TEO
4.2 Discrete Teager Energy Operator (DTEO)
5 Simulation Results and Discussion
5.1 Fault Resistance Variation
5.2 Performance Evaluation
6 Conclusion
References
Stability and Quality Analysis of Solar Energy-Based Electrical Network Using an Improved Artificial Neural Network
1 Introduction
2 Model Analysis
2.1 Solar PV Cell
2.2 Boost Converter
2.3 Modeling of Boost Converter
3 Controllers Design
3.1 PID Controller
3.2 Fuzzy Rule Base
3.3 Proposed NARX-ANN Technique
4 Result Analysis
5 Conclusion
References
Wildfire Prevention—An Image Processing Approach
1 Introduction
2 Related Works
3 Methodology
3.1 Dataset
3.2 Convolutional Neural Network
3.3 Binary Image Classification
4 Experimental Summary and Discussion of Results
4.1 Implementation
4.2 Results
5 Conclusions
References
Mechanical and Control System Design, Stress, Static, Dynamic and Control Analysis of 4-Axes SCARA Robot for Automation in Poultry and Vegetable Industry
1 Introduction
2 Market Analysis
3 Design of 4-Axes SCARA Robot and End Effector
3.1 SCARA Robot
3.2 End Effector Design
4 Static Loading Analysis
4.1 Altair HyperWorks
5 Forward Kinematics of the RRPR SCARA Robot
5.1 Link Coordinate Diagram
5.2 Forward Kinematic and Dynamic Analysis
6 Control System Analysis
7 Results and Discussions
8 Conclusion
References
Frequency Stability Improvement in a Four-Area Power System with FOPID Controller
1 Introduction
2 Proposed Model and Optimisation Technique
2.1 Considered Model
2.2 FOPID Control Structure
2.3 Symbiotic Organisms Search Algorithm (SOSA)
3 Result and Discussion
4 Conclusion
References
Energy Resources: Energy Storage and Renewable Resources
Clean Energy Products, Their Circulation and Earnings Compilation Model
1 Introduction
1.1 Clean Energy Products
2 Clean Energy Product-Services Circulation and Earnings Compilation Model for Rural Area
3 Strategies for Adoption of Clean Energy Products in Rural Areas
4 Conclusion and Policy Implications
References
A Feasibility Study to Obtain Biodiesel from Avocado Waste in the Lambayeque Region, Peru
1 Introduction
2 Methodology
3 Results and Discussions
3.1 Physicochemical Parameters of the Waste
3.2 Method for Obtaining Biodiesel from Avocado Waste
3.3 Factors
3.4 Production of Biodiesel Obtained from Avocado Waste
3.5 Use of Biodiesel Obtained from Avocado Waste
4 Conclusions
References
Experimental Studies on Corrosion Performance of Reheated AZ31 Magnesium Alloy
1 Introduction
2 Experimental Details
2.1 Weight Loss Determination
2.2 Electrochemical Experimentation
2.3 Micrograph Study
3 Results and Discussions
3.1 Weight Loss Determination
3.2 Electrochemical Experimentation
3.3 Micrograph Study
4 Conclusions
References
Performance Study on Fuel Cell Electric Vehicle (FCEV) with Parametric Optimization
1 Introduction
2 Solar-Assisted Electric Vehicle
3 Solar-Assisted Fuel Cell Electric Vehicle
4 Results and Discussion
4.1 Fuel Cell Stack Model Correlation
4.2 Parametric Optimization
5 Conclusions
References
Experimental Study on Bio-corrosion Behaviour of Magnesium Alloy Using Sodium Chloride Solution
1 Introduction
2 Experimental Details
2.1 Weight Loss Measurement
2.2 Electrochemical Test
2.3 Surface Morphology
3 Results and Discussions
3.1 Weight Loss Analysis
3.2 Electrochemical Corrosion and OCP Behaviour
3.3 Surface Morphology
4 Conclusions
References
Parametric Appraisal of Mahua Biodiesel-Powered Compression Ignition Engine to Improve Performance Characteristics by RSM Coupled WASPAS Simulated Annealing and Genetic Algorithm
1 Introduction
2 Preparation of Mahua Biodiesel
3 Experimental Setup
3.1 WASPAS Methodology
3.2 Genetic Algorithm
3.3 Simulated Annealing Algorithm
4 Results and Discussion
5 Conclusion
References
Ammonium Picronitrate: An Organic Crystal for Generation of Higher Harmonics and Photonic Applications
1 Introduction
2 Experimental
2.1 Material Synthesis
2.2 Selection of Suitable Solvent
2.3 Crystal Growth
3 Characterization Techniques
4 Results and Discussions
4.1 Single Crystal X-ray Diffraction
4.2 FT-IR Spectral Analyses
4.3 Surface Analysis by SEM and AFM
4.4 TGA-DTA Analysis
4.5 Chemical Etching and Laser Damage Threshold Studies
4.6 Photoluminescence (PL) Studies
4.7 Vibrating-Sample Magnetometer (VSM) Studies
5 Conclusion
References
A Novel Nonlinear Control for Renewable Energy-Based Micro-Grid
1 Introduction
2 Micro-Grid Structure
2.1 Distribution Generation Modeling (DG)
3 Controllers for Micro-Grid
3.1 PI Controller Model
3.2 Backstepping Sliding Mode Controller (BSMC) for PV Converter
4 Result Analysis
5 Conclusion
References
Performance Analysis of 700 KW Solar PV Grid-Connected System: An Empirical Study
1 Introduction
2 Problem Identification
2.1 Specification of Solar Panel
3 Performance Analysis Using SCADA System
3.1 Performance Ratio (PR)
3.2 Capacity Utilization Factor (CUF)
3.3 Inverter Losses (IL)
3.4 PV Loss Due to Temperature
4 Experimental Calculation
4.1 Irradiance
4.2 Performance Ratio
4.3 Total Generation
5 Conclusion and Future Scope
5.1 Future Scope
References
Power Quality Improvement of a Hybrid Renewable Energy Systems Using Dynamic Voltage Restorer with PI Controller
1 Introduction
2 Modelling of the System
2.1 PV Modelling
2.2 Wind Turbine Modelling
2.3 Fuel Cell Modelling
3 Controller Design
4 Results and Discussions
4.1 Simulink Results
4.2 Simulink Results PI Controller Controls the DVR
5 Conclusion
References
Hydro Governor Damping Controller to Improve Dynamic Stability of Wind-Integrated Power System
1 Introduction
2 Wind Energy Integrated Power System
3 Objective Function
4 DEWOA Optimization Technique
5 Result and Discussion
6 Conclusions
References
Gear Pair Analysis: Double Circular Arc with Involute Profile
1 Introduction
2 Design of Double Circular Arc with Involute Tooth Profile Gear Pair
3 Mathematical Formulation for the Double Circular Arc with Involute Gear Pair
3.1 Coordinate Equations for the Involute Segment of the Complete Double Circular Arc
3.2 Coordinate Equations for the Upper Convex Circular Arc Segment of the Complete Double Circular
3.3 Coordinate Equations for the Lower Concave Circular Arc Segment of the Complete Double Circular Arc
4 Conclusion
References
Performance Analysis of Thermal Characteristics of Plate Heat Exchanger Using Al/H2O Nanofluid: An Experimental Study
1 Introduction
2 Experimental Investigation
2.1 Nanofluid Preparation
2.2 Experimental Setup and Procedure
2.3 Data Reduction
3 Results and Discussions
3.1 Convective Heat Transfer Coefficient
3.2 Overall Heat Transfer Coefficient
4 Conclusions
References
Wear and Corrosion Behaviour of WS2 Reinforced Al-Based Composites
1 Introduction
2 Experimental Procedure
3 Results and Discussions
4 Conclusions
References
Optimization of Non-traditional Machining Processes: Application of a Simple Optimization Algorithm
1 Introduction
2 Simple Optimization (SOPT) Algorithm
2.1 Handling of Constraints in the SOPT Algorithm
2.2 SOPT Algorithm
2.3 Settings for SOPT
3 Non-traditional Machining Processes
3.1 Ultrasonic Machining Process (USM)
3.2 Abrasive Jet Machining Process (AJM)
3.3 Wire Electric Discharge Machining Process (WEDM)
3.4 Water Jet Machining (WJM)
4 Optimization Models for Non-traditional Processes
4.1 Optimization Model for USM
4.2 Optimization Model for AJM of Brittle Material
4.3 Optimization Model for AJM of Ductile Material
4.4 Optimization Model for WEDM
4.5 Optimization Model for Water Jet Machining
5 Conclusion
References
Storage, Handling, and Disposal of Hazardous Waste
1 Introduction
2 Methodology
3 Hazardous Waste Management in Nuclear Medicine Department
3.1 Storage
3.2 Collection
3.3 Disposal
3.4 Waste Handling Agency
4 Hazardous Waste Management in the Radiation Therapy Department
4.1 Radioactivity Survey
4.2 Record Keeping
4.3 Waste Storage
4.4 Waste Disposal
5 Conclusion
References
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Lecture Notes in Mechanical Engineering
Sasmeeta Tripathy Sikata Samantaray J. Ramkumar S. S. Mahapatra Editors
Recent Advances in Mechanical Engineering Select Proceedings of ICRAMERD 2022
Lecture Notes in Mechanical Engineering Series Editors Fakher Chaari, National School of Engineers, University of Sfax, Sfax, Tunisia Francesco Gherardini , Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy Vitalii Ivanov, Department of Manufacturing Engineering, Machines and Tools, Sumy State University, Sumy, Ukraine Editorial Board Francisco Cavas-Martínez , Departamento de Estructuras, Construcción y Expresión Gráfica Universidad Politécnica de Cartagena, Cartagena, Murcia, Spain Francesca di Mare, Institute of Energy Technology, Ruhr-Universität Bochum, Bochum, Nordrhein-Westfalen, Germany Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia 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 Jinyang Xu, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
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. To submit a proposal or request further information, please contact the Springer Editor of your location: Europe, USA, Africa: Leontina Di Cecco at [email protected] China: Ella Zhang at [email protected] India: Priya Vyas at [email protected] Rest of Asia, Australia, New Zealand: Swati Meherishi at swati.meherishi@ springer.com Topics in the series include: . . . . . . . . . . . . . . . . .
Engineering Design Machinery and Machine Elements Mechanical Structures and Stress Analysis Automotive Engineering Engine Technology Aerospace Technology and Astronautics Nanotechnology and Microengineering Control, Robotics, Mechatronics MEMS Theoretical and Applied Mechanics Dynamical Systems, Control Fluid Mechanics Engineering Thermodynamics, Heat and Mass Transfer Manufacturing Precision Engineering, Instrumentation, Measurement Materials Engineering Tribology and Surface Technology
Indexed by SCOPUS and EI Compendex. All books published in the series are submitted for consideration in Web of Science. To submit a proposal for a monograph, please check our Springer Tracts in Mechanical Engineering at https://link.springer.com/bookseries/11693
Sasmeeta Tripathy · Sikata Samantaray · J. Ramkumar · S. S. Mahapatra Editors
Recent Advances in Mechanical Engineering Select Proceedings of ICRAMERD 2022
Editors Sasmeeta Tripathy Department of Mechanical Engineering Siksha ‘O’ Anusandhan Bhubaneswar, India
Sikata Samantaray Department of Mechanical Engineering Siksha ‘O’ Anusandhan Bhubaneswar, India
J. Ramkumar Department of Mechanical Engineering Indian Institute of Technology Kanpur, India
S. S. Mahapatra Department of Mechanical Engineering National Institute of Technology Rourkela, India
ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-19-9492-0 ISBN 978-981-19-9493-7 (eBook) https://doi.org/10.1007/978-981-19-9493-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 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
Foreword
It is a matter of immense pleasure that the Department of Mechanical Engineering of the Institute of Technical Education and Research (ITER) has organized the 3rd International Conference on Recent Advances in Mechanical Engineering Research and Development (ICRAMERD-2022) on 12–13 August 2022 in collaboration with Odisha Chapter of IIIE, a premier professional institution having long years of professional association with our university. I am aware that many distinguished academicians and practising professionals from different countries have deliberated during this conference. I sincerely believe that the eminent scientists, academicians, research scholars and students have been benefitted from this event and will be a part of the competent workforce for future development and research innovation through best global practices. The publication of the conference proceedings in Lecture Notes in Mechanical Engineering, Springer, will add much more value towards the furtherance of the knowledge base and confluence of multidisciplinary research by creating a new benchmark. At the outset, I congratulate the organizers and participants for their sincere efforts and support to organize this conference. I wish the conference all success. Prof. (Dr.) Manoj Ranjan Nayak Founder President Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar, Odisha, India
v
Preface
It is our great pleasure to present the proceedings of the 3rd International Conference on ‘Recent Advances in Mechanical Engineering Research and Development’ held during 12–13 August 2022 at the Department of Mechanical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha-751030 (India). This conference is in a series of earlier international conferences ICRAMERD-2020 and ICRAMERD-2021 organized by Siksha ‘O’ Anusandhan (Deemed to be University). The 3rd ICRAMERD was organized in collaboration with the Indian Institution of Industrial Engineering, Odisha Chapter. Indian Institution of Industrial Engineering (IIE), established in 1957, is dedicated to the advancement of the discipline of Industrial Engineering and professional development. With its headquarters in Mumbai and 35 chapters across the country, IIE is engaged in espousing and contributing to productivity and quality movement. The scope of ICRAMERD is to provide a forum for researchers, scientists, academicians, scholars and industrial practitioners from across the world to present papers and exchange their ideas on topics of great importance and recent developments in broad fields of sustainable science, material science and engineering. With the increasing importance of automation, smart manufacturing, their applications in industrial robotics, energy storage and conversion, power system engineering, the conference targets to present the latest research on novel materials, new strategies and innovations for overcoming the technical challenges of machine learning, IoT, mechatronics & robotics, artificial intelligence in manufacturing process, environmental science, renewable energy and automation in healthcare systems. The proceeding consists of 58 papers by leading researchers, academicians and industry experts reflecting the issues related to engineering and technology. The main topics of the proceedings are classified into:
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viii
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Preface
Heat transfer and fluid flow; Optimization Applications in Engineering; Design and Dynamics; Numerical Modeling and analysis; Novel Applications of IOT in industries; Energy Resources: Energy storage and renewable resources.
The committee would like to express a deep sense of gratitude to the reviewers for their persistent efforts in bringing the proceedings to its present form. We are extremely grateful to all the authors and technical session chairs for their contribution. It is our immense pleasure to express our heartfelt appreciation to Prof. Ranjit Pati, Graduate Director of Physics, Michigan Tech; Dr. Sachin Mangla, Knowledge Management and Decision Making, Plymouth Business School, UK; Dr. D. P. Dube, Global CISO and Senior Vice President, Reliance Industries Limited; Mr. Satbir Singh, Sr. General Manager (Procurement and Expediting), Reliance Industries Limited; Mr. Asish Mohapatra, Co-Founder and CEO, Of Business and Oxyzo; Dr. AVV Prasada Raju, Vice-President, IIIE NHQ and other colleagues in India and abroad. Special acknowledgement is made to the support from our students and alumni, especially Mr. Anshuman Rath, Officer on Special Duty, Department of Tourism, Government of Odisha; Mr. Subhamshree Avishek and Mr. Smrutiranjan Biswal. Our sincere gratefulness to the excellent cooperation received from all our colleagues for their efforts in reviewing the manuscripts. We express our thankfulness to Springer for accepting our proposal under the series title ‘Lecture Notes in Mechanical Engineering’. We thank Prof. D. P. Ray, Chancellor, Siksha ‘O’ Anusandhan (Deemed to University), Dr. Manoj Ranjan Nayak, President, Siksha ‘O’ Anusandhan (Deemed to University) and all other faculty members of Siksha ‘O’ Anusandhan (Deemed to University) particularly the Distinguished Professors of Institution of Technical Education and Research for their valuable contributions in publishing this book. It is a contemporary book for developing the understanding of readers in the emerging era of digital technology. We keenly look forward to receiving your impressions and response towards ‘Recent advancements in Mechanical Engineering: Select Proceedings of ICRAMERD-2022’. Bhubaneswar, India Bhubaneswar, India Rourkela, India Kanpur, India
Sasmeeta Tripathy Sikata Samantaray S. S. Mahapatra J. Ramkumar
Contents
Heat Transfer and Fluid Flow Performance of a Single-Turn Pulsating Heat Pipe at Varying Gravity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Satyanarayana, N. V. S. M. Reddy, and P. Rosang Free Convection from Isothermally Heated Hollow Tube with Annular Fins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vakacharla Bharat Kumar, Basanta Kumar Rana, and Swarup Kumar Nayak
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Chaotic Dynamics of Fluid Conveying Pipe . . . . . . . . . . . . . . . . . . . . . . . . . . Shashendra Kumar Sahoo, Bamadev Sahoo, Lokanath Panda, and Dhirendra NathThatoi
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Study of Flow Distribution in the Falling Film Evaporator Manifold . . . Pankaj G. Anjankar, Sanjay S. Lakade, Atul Padalkar, and Sandeep Nichal
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Effect of Core Geometrical Characteristics on Performance and Pressure Drop of Rotary Regenerator . . . . . . . . . . . . . . . . . . . . . . . . . . . Manas Ranjan Padhi, Prakash Ghose, Achinta Sarkar, Basanta Kumar Rana, Manoj Ukamanal, Jitendra Kumar Patel, and Swarup Kumar Nayak
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CFD Analysis of Heat Transfer Coefficient and Pressure Drop in a Shell and Tube Heat Exchanger for Various Baffle Angles . . . . . . . . . R. Anandan, G. Sivaraman, M. Rajasankar, and R. Girimurugan
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A Study on Indoor Air Quality of Air-Conditioning System Using Duct Insulation and Filters for Industrial Applications . . . . . . . . . . . . . . . . Amit Kumar Behera and Debasmita Mishra
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Optimization Applications in Engine Maximization of Compressive Strength of Fused Deposition Modeled Antibacterial Polylactic Acid by Taguchi Method . . . . . . . . . . . . M. Ajay kumar, Dipabrata Banerjee, Swayam Bikash Mishra, Basanta Kumar Nanda, and Santhosh Kumar Nayak
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Analysis of Drivers and Barriers in Green Supply Chain Management Using Fuzzy AHP and Fuzzy TOPSIS Approach . . . . . . . . . Soumya Ranjan Pradhan and Siba Sankar Mahapatra
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Parametric Optimization of LPG Refrigeration System Using Artificial Bee Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sampath Boopathi, M. Sureshkumar, and S. Sathiskumar
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A Generalized Oppositional Differential Evolution Algorithm for Parameter Extraction of Different Photovoltaic Models . . . . . . . . . . . . 107 Shubhranshu Mohan Parida and Pravat Kumar Rout Analysis of Community Engagement on Social Networking Sites During the Kerala Floods in 2018 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Sumit Kumar, Arun Thomas, and Vinay V. Panicker Application of Evolutionary Technique for Mapping onto Network on Chip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Subhashree Choudhury, A. S. Das, Sarita Misra, Ismail Hossain, Taraprasanna Dash, and Kaliprasanna Swain Parametric Optimization of Resistance Spot Welded Dissimilar Metals Utilizing Advanced Hybrid Taguchi-MARCOS Method . . . . . . . . 135 Bhabani Shankar Kamilla, Bibhuti Bhusan Sahoo, Abhishek Barua, Siddharth Jeet, Kanchan Kumari, Dilip Kumar Bagal, and Bibhu Prasad Panda Prioritizing the Factors Affect in Covid-19 Vaccines, a Fuzzy-AHP Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Anil Kumar Das, Sumit Ray, Amit Kumar Sahoo, Priyabrata Mohapatra, and Bharat Chandra Routara Apriori Versus FP-Growth for Recommendation System . . . . . . . . . . . . . . 155 U. Sarath and Nima S. Nair Optimization of EDM Process Parameters in Machining of Titanium Alloy Used for Aerospace Applications . . . . . . . . . . . . . . . . . . . 163 K. K. Mahapatra, S. Biswal, S. Tripathy, D. N. Thatoi, and P. Satapathy
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Design and Dynamics Ergonomic Risk Assessment of Rubber Tappers Using Rapid Upper Limb Assessment (RULA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Abi Varghese, Vinay V. Panicker, Abhishek Prasad, Ansan P. Sam, Albin Alex Uzhuvathu, and Ananthu Ramesh Finite Element Analysis of Cranked Beam with Reinforcement Detailing of SP 34 (1987) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Bipul Sharma, Neelam Rani, and M. Abdul Akbar Design and Analysis of Coir Fibre Reinforced Polypropylene Based Internal Car Door Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Ronak Swayam, Somya Prasad Sahoo, Manohar Nayak, Aditi Sahoo, and Tanmayee Khuntia Pedobarography-Based Prosthetic Foot Design and Optimization Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Akash Lawand Atlas Generation of Leg Mechanisms for Walking Platforms Using Creative Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Manoj Balasaheb Awaghade and Pankaj Vinayak Dorlikar Effect of Curve Angle on Prestressed Box-Girder Bridges . . . . . . . . . . . . . 233 Preeti Agarwal and Deepak Kumar Singh Design, Analysis and Optimization of Chassis for a Utility Vehicle . . . . . . 243 Bhaskarabhatla Jahnavi Manaswini, Kolakotla Abhishek, Nuthalapati Hemanth, B. Anjaneya Prasad, and A. Somaiah Design and Development of Semi-automated Manual Transmission . . . . 257 Kamlesh Sasane, Aqleem Siddiqui, Suryateja Chadalawada, Shanto Mathai, Quirenius Mendonsa, Aldrin Rego, and Melissa Vazapully Analysis of Interdependence of Structural Irregularity in Connected Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Rakesh Pasunuti and M. Abdul Akbar Numerical Modeling and Analysis Numerical Investigation on the Effect of Inclination Angle of the Wall Fin on the Hydrogen–Air Micro-combustion . . . . . . . . . . . . . . 277 K. A. Srinivasa Raghavan, E. Rajesh, V. R. K. Raju, and S. Srinivasa Rao Modeling of a Severe Accident in a VVR-Type Research Reactor Using MELCOR Accident Analysis Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Salauddin Omar, Abid Hossain Khan, Mikhail Vasilievich Kascheev, and Azmaeen Bin Amir
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Thermal Management of Thermo-Electric Refrigeration System Applying Nanofluids Through a Minichannel Heat Sink . . . . . . . . . . . . . . . 299 Bibhuti Bhusan Nayak, Subham Kumari Thakur, and Upashana Sah Mathematical Approach to Find Warpage Deformation of FDM Build Parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Dipabrata Banerjee, Madala Ajay Kumar, Ankesh Pandey, Swayam Bikash Mishra, and Bharata Chandra Routara Numerical Study of Pressure Drop Calculation for Newtonian Slurry Through a Multi-segmented HDPE Pipeline . . . . . . . . . . . . . . . . . . . 321 S. S. Mishra, S. D. Sahoo, A. S. Khuntia, A. K. Parida, S. Saha, S. K. Mohanty, D. N. Thatoi, and N. D. Rao Novel Applications of IOT in Industries Development of Automobile Recommender System Using Machine Learning and AHP Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 Sanjeev Kumar, Ashirbad Sarangi, and R. P. Mohanty Correlation of Structural Irregularity in Circular Buildings Using Vital Signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Renil Sabhadiya and M Abdul Akbar Deterministic Wind Speed Prediction with VMD Based Kernel Random Vector Functional Link Neural Network . . . . . . . . . . . . . . . . . . . . 355 Snigdha Sarangi, Pradipta Kishore Dash, Ranjeeta Bisoi, and Badri Narayan Sahoo Convolutional and Artificial Neural Network Collated to Descry Brain Tumor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 C. Athira, J. R. Dhanya, and M. Soumya Krishnan Comparative Analysis, Hardware Design and Simulation of Solar Tracker System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Sayak Roy, Chitralekha Jena, Arjyadhara Pradhan, Lipika Nanda, Babita Panda, Sarita Samal, and Tarakanta Jena Power Generation Improvement in Unsymmetrical PV Arrays During Partial Shading via MR Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 Priya Ranjan Satpathy, Sudhakar Babu Thanikanti, Belqasem Aljafari, Siva Rama Krishna Madeti, and Renu Sharma Arc Faults Detection Using VMD Based DTEO in a PV-Battery Based DC Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Kanche Anjaiah, P. K. Dash, Ranjeeta Bisoi, and Jonnalagadda Divya
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Stability and Quality Analysis of Solar Energy-Based Electrical Network Using an Improved Artificial Neural Network . . . . . . . . . . . . . . . 409 Niranjan Nayak, Anshuman Satpathy, Naeem M. S. Hanoon, and J. R. Das Wildfire Prevention—An Image Processing Approach . . . . . . . . . . . . . . . . . 419 K Rekha, Sandra Luiz, and R. Nandakumar Mechanical and Control System Design, Stress, Static, Dynamic and Control Analysis of 4-Axes SCARA Robot for Automation in Poultry and Vegetable Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Lakshmi Girish and Pramod Sreedharan Frequency Stability Improvement in a Four-Area Power System with FOPID Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 N. K. Jena, S. Sahoo, A. K. Naik, and B. K. Sahu Energy Resources: Energy Storage and Renewable Resources Clean Energy Products, Their Circulation and Earnings Compilation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 Shiv Sankar Das, Bijaya Bijeta Nayak, Debashree Debadatta Behera, Soumya Ranjan Giri, and Manoranjan Dash A Feasibility Study to Obtain Biodiesel from Avocado Waste in the Lambayeque Region, Peru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457 Flavio Ronald Yovera-León, Zaida Brenilda Chavez-Romero, Carlos Alexis Alvarado-Silva, and Geraldo Cesar Rosario de Oliveira Experimental Studies on Corrosion Performance of Reheated AZ31 Magnesium Alloy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Pabak Mohapatra, N. K. Kund, and S. K. Sahu Performance Study on Fuel Cell Electric Vehicle (FCEV) with Parametric Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 R. Sharath Prasanna, Vatambedu Rupesh Kumar, R. Shanthi, and B. Mullai Sudaroli Experimental Study on Bio-corrosion Behaviour of Magnesium Alloy Using Sodium Chloride Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 S. K. Sahu, N. K. Kund, and Pabak Mohapatra Parametric Appraisal of Mahua Biodiesel-Powered Compression Ignition Engine to Improve Performance Characteristics by RSM Coupled WASPAS Simulated Annealing and Genetic Algorithm . . . . . . . 505 Bibhuti Bhusan Sahoo, Shakti Prakash Jena, Abhishek Barua, Siddharth Jeet, Kanchan Kumari, Dilip Kumar Bagal, and Madhumanjari Saran
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Ammonium Picronitrate: An Organic Crystal for Generation of Higher Harmonics and Photonic Applications . . . . . . . . . . . . . . . . . . . . . 515 Redrothu Hanumantha Rao, CH. V. V. Ramana, and Pakki Suresh Patnaik A Novel Nonlinear Control for Renewable Energy-Based Micro-Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Anshuman Sathpathy, Satis Choudhury, and Niranjan Nayak Performance Analysis of 700 KW Solar PV Grid-Connected System: An Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Gnanasekaran Sasikumar, Sivasangari Ayyappan, and U. Sudhakar Power Quality Improvement of a Hybrid Renewable Energy Systems Using Dynamic Voltage Restorer with PI Controller . . . . . . . . . . 549 Hitesh Tata, Siva Rama Krishna Madeti, and M. Sai Veerraju Hydro Governor Damping Controller to Improve Dynamic Stability of Wind-Integrated Power System . . . . . . . . . . . . . . . . . . . . . . . . . . 561 Narayan Nahak, Samarjeet Satapathy, Santosh Kumar Swain, and Jyotiswarup Samal Gear Pair Analysis: Double Circular Arc with Involute Profile . . . . . . . . . 571 Vineet Sahoo, B. Srikant, and Anshuman Rath Performance Analysis of Thermal Characteristics of Plate Heat Exchanger Using Al/H2 O Nanofluid: An Experimental Study . . . . . . . . . . 583 Shubhamshree Avishek and Pankaj Kumar Wear and Corrosion Behaviour of WS2 Reinforced Al-Based Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Sweta Rani Biswal and Seshadev Sahoo Optimization of Non-traditional Machining Processes: Application of a Simple Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601 Joji Thomas, Vivek Kumar Chouhan, Anshuman Kumar Sahu, and Siba Sankar Mahapatra Storage, Handling, and Disposal of Hazardous Waste . . . . . . . . . . . . . . . . . 619 Sonali Goel and Renu Sharma
About the Editors
Dr. Sasmeeta Tripathy is currently an associate professor in the Department of Mechanical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, India. She obtained her B.Tech. (Mechanical) from Utkal University and M.Tech. (Production) from National Institute of Technology (NIT), Rourkela. She had her Ph.D. from Siksha “O” Anusandhan, Deemed to be University. Her major areas of research interests include manufacturing, nontraditional machining, optimization and soft computing. She has published more than 18 papers in scholarly, peer-reviewed indexed international journals & conferences and authored a number of chapters in edited books besides an edited book to her credit. She has been a reviewer in peer-reviewed journals and international conference proceedings. She is a life member of Institution of Engineers (India) and Indian Institution of Industrial Engineering. Currently, she is the honorary secretary of Indian Institution of Industrial Engineering, Odisha Chapter. She is the recipient of Brundaban Sahu Memorial Award and Bipin Behari Mohanty Memorial Award from the Institution of Engineers, India, and conferred with Fellowship Award from Indian Institution of Industrial Engineering (IIIE). Dr. Sikata Samantaray is currently an associate professor in Department of Mechanical Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, India. She obtained her B.E. from Utkal University (Mechanical), M.Tech. from Indian Institute of Technology (IIT), Kharagpur (Thermal) and Ph.D from Jadavpur University. Her research interests include computational methods in thermal Engineering, numerical analysis of bioheat transfer, experimental analysis of thermal systems using nanofluids, numerical and experimental analysis of non-conventional machining process of ceramic matrix composite. She has published more than 18 research papers in various national and international journals and conferences. She has filed more than 3 patents. She is a member of Institution of Engineers (India) and ASME. Dr. J. Ramkumar is currently professor at the Department of Mechanical Engineering and Design Program, Indian Institute of Technology (IIT), Kanpur. He xv
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About the Editors
obtained his B. Tech. (Production) from National Institute of Technology (NIT) Trichy followed by M.Tech. and doctorate in Mechanical Engineering from IIT Madras, Chennai. He pursued his post-doctoral research in the area of intermetallic composites machining and fabrication at Osaka University, Japan. Areas of his research interests include micro and nanomachining and finishing, tribology, coatings and corrosion for engineering and biomedical applications. He has published over 200 articles in the peer-reviewed international journals and has delivered over 80 lectures in the international conferences. His h-index of 23 endorses his high research productivity. Out of 69 patents to his credit, five have been commercialized. He received research funding of over $4 million during his career at IIT Kanpur. He is the project investigator of MedTech facility being developed in IIT Kanpur, funded by BIRAC, Department of Biotechnology, Government of India. Dr. Ramkumar is the recipient of several awards, fellowships and recognitions such as Young Engineer award of ISAC, India, Gopal Das Bhandari Best teacher Award, by IIT Kanpur, recently honored with “Eminent Engineer award” from IEI, India, Young Alumni Achiever Award from NIT Trichy and many more. Currently, he is a reviewer of above 25 technical journals from leading global publishers. He has organized several workshops, short-term courses and training programmes on product design and manufacturing, design thinking, rapid prototyping, medical devices prototyping, manufacturing process pedagogy, manufacturing of composites, micro and nanofabrication, among others. Dr. Siba Sankar Mahapatra is currently professor at the Department of Mechanical Engineering, National Institute of Technology (NIT), Rourkela. He obtained his B.Tech.(Mechanical) and M.Tech.(Production) from Veer Surendra Sai University of Technology, India. He pursued his Ph.D. (Industrial Engineering) from Indian Institute of Technology (IIT), Kharagpur. His major research areas include manufacturing, soft computing, neural computing, prediction tools, non-traditional optimization, modelling and simulation. He has guided more than 27 doctoral and over 57 M.Tech. students. He has published more than 320 papers in various national and international journals and conferences. He was a visiting faculty to Industrial Systems Engineering, School of Engineering and Technology, Asian Institute of Technology, Bangkok, as a secondment faculty from India during 2009 and 2012. He is the recipient of many prestigious awards like Professor B.G.Raghavendra Memorial Award, Brundaban Sahu Memorial Award, Outstanding Research Faculty Award in Business, Management and Accounting discipline by Career 360, Conferred Fellowship Award from Indian Institution of Industrial Engineering and many more. He is a reviewer of various technical journals of leading global publishers.
Heat Transfer and Fluid Flow
Performance of a Single-Turn Pulsating Heat Pipe at Varying Gravity K. Satyanarayana, N. V. S. M. Reddy, and P. Rosang
Abstract A two-dimensional numerical simulation was performed on a 3 mm internal diameter, one-turn pulsating heat pipe (PHP) to perceive the flow velocity and thermal performance of PHP at varying gravity conditions. All the simulations are carried at the stable wall temperature of 300 K at condenser and 373 K at evaporator sections. Obtained numerical results at microgravity PHP is compared with the PHP at earth’s gravity. Based on the average flow velocity, power spectrum density analysis results found that flow velocity during the microgravity PHP is low in comparison with PHP operating at g = 9.8 m/s. The mean heat transfer coefficient of microgravity PHP is 4.2 times lower than that of normal PHP, heat transfer in microgravity PHP is mainly due to the effective thermal conductive nature. Keywords Pulsating heat pipe · Flow velocity · Numerical analysis · Heat transfer coefficient
1 Introduction Heat pipes and pulsating heat pipes, which function on the basis of evaporation and condensation are the best passive heat transfer devices available for cooling electronics in both terrestrial and space settings. The standard pulsing heat pipe (PHP) is wickless and has long-distance transfer capabilities, but it can manage multiple heat sources or a substantial heat source unlike traditional heat pipes. PHP is made up of a lengthy capillary tube twisted into a meandering shape and filled with working fluid. PHP mainly consists of three sections namely condenser, adiabatic and evaporator sections. Depending on the tube diameter, properties of the working fluid (WF), the fluid inside the PHP is distributed as liquid slugs and vapour plugs. The evaporator section receives the heat to initiate the evaporation process and the condenser section delivers the heat to the surrounding media and undergoes the condensation process inside the PHP. The pressure difference obtained during the K. Satyanarayana (B) · N. V. S. M. Reddy · P. Rosang National Institute of Technology, Nagaland, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_1
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condensation and evaporation process allows the WF to oscillate and circulate in the PHP. To achieve a consistent and superior thermal performance of PHP, researchers did experimental and numerical studies, like filling ratio (FR) [1]. Using different working fluids (WF) such as acetone, water, ethanol and methanol [2], Nanofluids like SiO2 –CuO [3]. Operating the PHP at different tilt angles and heat loads [4]. Varying the number of meandering turns [5]. Looking at the implementation of heat pipes in space and satellite applications. The study of thermo-hydrodynamics in different regimes and in microgravity is at the heart of a significant advancement in modelling and computational approaches. Reference [6–8] investigated PHP’s performance in different gravity levels and found that it performed well even in microgravity. Mangini et al. [9] revealed that the flow pattern changes from a stratified flow in ordinary gravity condition to a slug flow in microgravity condition. Improving the viability of PHPs for space applications, Ayel et al. [10] carried out an experiment on a single-turn flat plate PHP machined on a metallic plate at various gravity conditions and noticed high heat transfer distribution and better thermal performance in Flat Plate PHP even at microgravity conditions. The intricate hydrodynamics, movement and heat fluctuations associated with the operation of PHP are difficult to explain through the experiment procedure due to the PHP’s closed-loop and non-transparent character. One of the approaches for revealing complicated hydrodynamics, movement and thermal features of PHP’s operation is to do a numerical analysis. Pouryoussefi et al. [11] simulated a PHP to study the chaotic behaviour of working fluid by analysing the time series wall temperature. Nonexistence of predominant peaks in Power Spectrum Density(PSD), non-negative value of Lyapunov exponent (LE) during the 30% filling ratio represents the chaotic nature of PHP. To understand the multisource heating on the performance of PHP [12] simulated a single-turn PHP with different geometry. Their results revealed that the use of right-angled shape evaporator enhances the thermal performance and startup time. Satyanarayana et al. [13] conducted a simulation by providing an additional branch (AB) of different diameters (2, 3 and 4 mm) at the PHPs evaporator section. Their research revealed that, by the use of 3 mm AB, the thermal performance of PHP is improved by 8.2% with a mean HTC of 960 W/m2 .
2 Physical Model and Governing Equations The geometric specifications of the PHP are shown in Fig. 1a. Based on the Bond number and current working conditions, the diameter of the PHP is considered as 3 mm. the total height of the model is 150 mm, whereas the condenser, adiabatic and evaporator length is considered as 50 mm. Simulations were performed at a wall temperature of 300 at condenser and 375 at evaporator wall temperature. Figure 1b depicts the quadrilateral mesh used for simulation. The initial flow velocity is considered as 0. The whole computational domain is patched with 50% working fluid. In order to capture the small movement of two-phase fluid, 0.0001 is considered as the
Performance of a Single-Turn Pulsating Heat Pipe …
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time step, and 10−6 is set as residual for all parameters. Simulations are performed in ANSYS FLUENT software package using the CFD technique, the VOF model [13], was used to capture the interaction between the fluid and liquid phase, flow structures and velocity profiles during condensation inside a smooth tube. Equations 1, 2 and 3 are the conservation, momentum and energy equations respectively used in solving the fluid flow and heat transfer are given below: Sm, ∂α + ∇.(uα) = ∂t ρ
(1)
→ d(ρ − v) − → → → → → + ∇.(ρ − v− v ) = ρ− g + ∇.[μ(∇ − v )T ] − ∇ p + F σ dt
(2)
d(ρ E) → + ∇.[− v (ρ E + p)] = ∇.(K e f f ∇T ) + Se dt
(3)
Fig. 1 a Physical model, b Quadrilateral mesh
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Fig. 2 Flow velocity
where u, ρ and S are the velocity, density and mass source terms in conservation − → equation, whereas α, g, p, v and F σ are the volume fraction, gravity, pressure, velocity and surface tension forces in momentum equation, respectively. K and Se are the and thermal conductivity and heat source term in energy equation.
3 Results and Discussions 3.1 Flow Velocity Figure 2 depicts the fluid flow velocity inside the PHP with gravity and microgravity. The average flow velocity inside the PHP is 0.12 m/s, whereas, the average fluid flow velocity in PHP with microgravity is 0.04 m/s. In order to understand the fluid flow inside PHP the time series analysis of average fluid flow is analysed by Power Spectrum Density (PSD). Figure 3 shows the PSD analysis of average fluid flow velocity with respect to the frequency. Spikes in Fig. 3a represent that flow is periodic with the highest frequency at 0.0038 Hz. In the case of PHP with microgravity as shown in Fig. 3b, no spikes of PSD with respect to the frequency represents that there is no flow inside the PHP.
3.2 Heat Flow Rate Figure 4a, b depict the average heat flow rates (HFR) in PHPs condenser and evaporator walls. In the case of PHP with gravity g, oscillations of HFR are observed in both condenser and evaporator with a mean heat flow rate of 10W and 13W, respectively. Whereas, the mean HFR in evaporator and condenser in microgravity condition is 4W
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Fig. 3 a PSD of PHP with g, b PSD of PHP with microgravity
and 3W, respectively. Oscillations in HFR of condenser and evaporator are mainly due to the slug-plug flow inside PHP. However, in the case of microgravity condition, stagnation of working fluid in vapour phase does not allow the working fluid to move and restricts the oscillation of heat flow rates.
3.3 Heat Transfer Coefficient To comprehend PHPs thermal performance, heat transfer coefficient (HTC) at evaporator section is evaluated. Equation 4 is used to calculate the (HTC) of PHP at evaporator section, in which h E is the HTC, q E is the wall heat flux and T is the temperature difference between the condenser and evaporator section. Figure 5 represents the comparison of HTC between the PHP with gravity and microgravity. The oscillations in HTC with respect to the flow time represent the heat transfer in
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Fig. 4 a HFR in PHP with g, b HFR in PHP with microgravity
PHP. hE =
qE T
(4)
The mean HTC for the PHP with gravity is 410 (W/m2 K), whereas the mean HTC for microgravity condition is noticed as 92 (W/m2 K). Low mean HTC in the case of microgravity is mainly due to the low wall heat flux(qE ) at evaporator and condenser sections. The low mean HTC in microgravity PHP represents the heat transfer in microgravity condition due to the heat conduction from the evaporator walls to the condenser walls.
4 Conclusion 2D numerical simulations are carried out using the CFD technique to understand the heat and fluid flow in PHP at gravity and microgravity conditions.
Performance of a Single-Turn Pulsating Heat Pipe …
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Fig. 5 HTC of PHP at g and microgravity
• The average fluid flow velocity in PHP with gravity is 0.12 m/s. whereas the considerable flow velocity is not observed in the case of PHP with microgravity. • In PSD analysis of PHP, spectral lines are not observed in the case of PHP with microgravity, whereas a periodic fluid flow is noticed at 0.0038 Hz in the case of PHP with gravity. • Oscillations in heat flow rates of evaporator and condenser are noticed only in PHP with gravity. • The HTC of PHP at gravity (g) is 4.5 times that of PHP with microgravity.
References 1. Chao CI, Lin WK, Hsiung TY, Liaw K, Wang M, Yeh YC, Sheng HC, Chen SI, Chen SW (2013) Performance tests of defrosting plates designed with a pulsating heat pipe (php) as the heat carrier. J Enhanced Heat Transfer 20(6) 2. Han H, Cui X, Zhu Y, Sun S (2014) A comparative study of the behavior of working fluids and their properties on the performance of pulsating heat pipes (php). Int J Therm Sci 82:138–147 3. Nazari MA, Ahmadi MH, Sadeghzadeh M, Shafii MB, Goodarzi M (2019) A review on application of nanofluid in various types of heat pipes. J Central South Univ 26(5):1021–1041 4. Liang Q, Li Y, Wang Q (2018) Numerical investigation of thermal performance of a cryogenic oscillating heat pipe. Heat Transfer Res 49(12) 5. Karthikeyan V, Ramachandran K, Pillai B, Solomon AB (2013) Effect of number of turns on the temperature pulsations and corresponding thermal performance of pulsating heat pipe. J Enhanced Heat Transfer 20(5) 6. Gu J, Kawaji M, Futamata R (2005) Microgravity performances of micro pulsating heat pipe. Micrograv Sci Tech 16:181–185 7. Mameli M, Araneo L, Filippeschi S, Marelli L, Testa R, Marengo M (2014) Thermal response of a closed loop pulsating heat pipe under a varying gravity force. Int J Therm Sc 80:11–22 8. Iwata N, Ogawa H, Miyazaki Y, Kawai H, Fukuda S (2016) Innovative thermal design satellite with networked variable conductance oscillating heat pipes. Proc of Joint 18th IHPC and 12th IHPS, Jeju, Korea, 8 p 9. Mangini D, Mameli M, Georgoulas A, Araneo L, Filippeschi S, Marengo M (2015) A pulsating heat pipe for space applications: ground and microgravity experiments. Int J Therm Sc 95:53–63
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10. Ayel V, Araneo L, Scalambra A, Mameli M, Romestant C, Piteau A, Marengo M, Filippeschi S, Bertin Y (2015) Experimental study of a closed loop flat plate pulsating heat pipe under a varying gravity force. Int J Therm Sc 96:23–34 11. Pouryoussefi S, Zhang Y (2016) Numerical investigation of chaotic flow in a 2D closed-loop pulsating heat pipe. Appl Therm Eng 98:617–627 12. Xie F, Li X, Qian P, Huang Z, Liu M (2020) Effects of geometry and multisource heat input on flow and heat transfer in single closed-loop pulsating heat pipe. Appl Therm Eng 168:114856 13. Satyanarayana K, Reddy N, Venugopal S (2021) Numerical investigation of single turn pulsating heat pipe with additional branch for the enhancement of heat transfer coefficient and flow velocity. Heat Transfer Res 52(4)
Free Convection from Isothermally Heated Hollow Tube with Annular Fins Vakacharla Bharat Kumar, Basanta Kumar Rana, and Swarup Kumar Nayak
Abstract A computational study is performed on natural convection heat transfer from a hollow vertical tube with annular fins. The tube thickness is negligible, and it is suspended in stagnant air. Thisstudy has been carried out within the laminar regime by varying Rayleigh number 104 ≤ Ra ≤ 107 . In addition, various influencing input parameters, namely, aspect ratio (L/D), and spacing between fins (S/L) are considered to delineate the thermofluidic behaviour around the heated finned tube. The net heat loss rate is estimated to be greater at the lower value of S/L for particular Ra and L/D. However, the Nusselt number is predicted to be lower in the same condition. Lastly, velocity vectors are employed to understand the fluidic behaviour and stronger fluid circulations are found at the greater value of S/L. Keywords Free convection · Annular fin · Thermal plume · Rayleigh number · Heat transfer
1 Introduction Free/natural convection heat transfer is a quite common and popular mode of cooling method because this mode requires no extra components. Thus, the system becomes simple, noise-free, inexpensive and low maintenance. Therefore, free convection is abundantly employed in various practical and industrial operations. However, fins are embedded to several devices to enhance the heat transfer in this mode. Free convection from the finned device encounters various applications such as electrical and electronic components [1, 2], heat exchangers [3, 4], internal and external combustion engines [5], air conditioning and refrigeration [6, 7], etc. Furthermore, the annular fin on the hollow cylinder is commonly observed in various engineering operations. A series of literature exist on natural convection with fins. Sparrow and V. B. Kumar · B. K. Rana (B) · S. K. Nayak School of Mechanical Engineering, KIIT Deemed to Be University, Bhubaneswar 751024, Odisha, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_2
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Bahrami [8] carried out experiments on free convection from finned horizontal tubes and observed the maximum and minimum heat transfers nearby periphery and inner portion of fin, respectively. Hahne and Zhu [9] investigated annular finned horizontal cylinders and reported the dependency of fin height. Chen and Chou [10] analysed the free convection flow from vertical squared fins on tube heat exchangers and developed a correlation to predict the averaged heat transfer coefficient. Yildiz and Yüncü [11] reported the performance of annular fins on horizontally oriented cylinder and determined the optimum fin spacing. An et al. [12] developed an empirical correlation for predicting the Nusselt number for free convection from cylinders with plate fins. Park et al. [13] experimented to study the free convection flows from vertical cylinders with branched plate fins and explored the effect of several input parameters like branch angles, fin numbers and base temperature. Senapati et al. [14] reported natural convection flows around a horizontal cylinder with annular fins and proposed a correlation to estimate the optimum fin spacing. Again, in their next attempt [15] considered the vertical cylinder with annular fins and predicted the optimum gap between the fins to attain maximum heat transfer in turbulent flows. Recently, Kim and Kim [16] conducted experiments on free convection from vertical cylinders with branched pin fins and proposed a correlation for the Nusselt number. Again, a very few investigations were carried out on natural convection from tubes [17–23]; however, researchers have not paid attention on investigating natural convection from hollow tube with fins. This study reports the hollow vertical tube with annular fins, which is still pending in the existing literature. Efforts are made to describe the role of Rayleigh number (Ra), length-to-diameter ratio (L/D) and spacing between the fins (S/L) on rate of heat transfer.
2 Problem Description Figure 1a illustrates the hollow vertical cylinder with annular fins with negligible wall thickness. We considered L and D as the length and diameter of the hollow cylinder, respectively. Again, l and S are the length and spacing of the fins, respectively. The annular finned tube is exposed to the ambient air and all the surfaces are heated isothermally. Figure 1b describes the axisymmetric computational domain for the simulations. In this study, flow is considered to be a two-dimensional axisymmetric model. The cylinder wall and fin surface are imposed with isothermal condition, i.e. Tw = 326 K and the temperature of ambient air is 300 K. The right-side edge of working domain is treated as the axis, whereas rest three edges are imposed with pressure outlet condition, as depicted in Fig. 1b.
Free Convection from Isothermally Heated Hollow Tube …
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Fig. 1 a Schematic representation of the isometric view of the hollow tube with annular fins and b Axisymmetric computational domain
3 Numerical Methodology Fluid flow is treated as steady, laminar, two-dimensional axisymmetric and incompressible. Governing differential equations specified below are to be solved by using the finite volume method. Continuity equation: ∇ · V = 0
(1)
ρ V · ∇ V = −∇ p + μ∇ 2 V + ρ g
(2)
Momentum equation:
Energy equation:
V · ∇ T = α∇ 2 V
Energy equation in solid slab: where V = eˆr vr + eˆz vz is the fluid velocity.
(3)
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Fig. 2 Validation of present work with Churchill [24]
Boussinesq approximation is implemented to predict density as a function of temperature to analyse the present natural convection study. The specified boundary conditions are written as follows: At the cylinder walls and fin surfaces: T = Tw , vr = vz = 0 On the axis: ∂()/∂θ = 0 On boundaries of the computational domain (excluding axis): P = Patm , T = Tatm Fluid flow and temperature fields around the finned tube are governed by the following relevant non-dimensional numbers: Rayleigh number, Ra = gβ L 3 (Tw − T∞ ) /αν Average Nusselt number: N u = h L/k and h can be defined as h = Q/( As × T ), where As = exposed wall area of the finned cylinder. All the simulations are carried out by using the commercial software ANSYS FLUENT 19.0. The governing differential equations are integrated over each control volume and subsequently discretized by using the finite volume technique. In this study, we compared our numerical results with the experimental work of Churchill [24] before explaining the detail of new results of present investigations. We found an excellent match with Churchill [24], illustrated in Fig. 2.
4 Results and Discussion We have herein elucidated thermofluidic characteristics around a hollow cylinder with annular fins. Analysis has been carried out by observing the influence of various important relevant input parameters, such as Ra, L/D and S/L. Fin length (l/L = 0.2) remained constant throughout the study.
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Fig. 3 Structure of thermal plume at different L/D with constant Ra = 104 and S/L = 0.031
4.1 Effect of L/D on Thermal Plume Temperature contours are commonly represented to describe the fluidic behaviour in such kinds of studies. Figure 3 illustrates the impact of aspect ratio (L/D) on the pattern of thermal plume at a fixed Ra = 104 , and S/L = 0.031. Here, to achieve the change in L/D, only the D varies, whereas L remains constant. Fin surface rises as L/D falls and the heat removal rate gets enhanced at lower L/D compared to higher one. Concomitantly, the inner surface shows a drastic increase in heat transfer rate at the lower length-to-diameter ratio, because the heated plume escapes easily through the cylinder when the tube diameter is higher, i.e. at a lower length-to-diameter ratio. Again, it is found that the plume experiences a greater resistance for narrow tube (higher length-to-diameter ratio) and thus, heat removal rate drastically drops at a greater value of L/D.
4.2 Effect of S/L on Thermal Plume Efforts are also made to demonstrate the influence of number of fins (S/L) on the structure of temperature contours for a particular value with constant Ra = 104 and L/D = 2, depicted in Fig. 4. A wide range of S/L is chosen for this study, i.e. 0.485 ≤ S/L ≤ 0.00421, where S is the gap between two consecutive fins. The value of S/L reduces drastically with the increase in the number of fins. It is obvious that the surface area of the cylinder excluding the inner wall increases with the reduction in S/L, however, heat transfer is not increased significantly, because the heated plumes are stuck in the gap between the annular fins, which is clearly shown at S/L = 0.00421 in Fig. 4. However, air circulations are found at a greater value of S/L (the gap between the fins is higher), which is evident at S/L = 0.485 in Fig. 4. Whereas the heated surface area of the finned tube is lower. Again, at a higher value of S/L, the circulations of plumes make the plume flow rate slower through the tube,
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Fig. 4 Variation in pattern of thermal plume at different S/L with constant Ra = 104 and L/D = 2
and thus, one can clearly find entrainment of fresh ambient air is appreciably lower at a higher S/L than a lower one. Moreover, at lowest S/L, it can be assumed that heat transfers from thick pipe because the entrapped heated plumes are stuck within the gap between the annular fins. Hence, fluid flow does not face any hindrance and, subsequently, the entrainment of fresh air becomes stronger at lower S/L.
4.3 Effect of Ra on Thermal Plume We have also attempted to describe the impact of Ra on the pattern of temperature contours within laminar regime. Figure 5 illustrates the influence of Ra (=104 –107 ) on thermal plumes at a fixed value of S/L = 0.155 and L/D = 2. Firstly, one can observe the bulky plume around the finned tube at a lower Rayleigh number than higher since the buoyant force around the tube dramatically rises with an increase in Ra. Hence, the flow becomes faster, and a thinner boundary layer develops adjacent to the heated surfaces. Therefore, the temperature gradient adjacent to solid surface is significantly higher at a greater value of Ra, and thus, the heat loss rate is enhanced remarkably. Again, the circulations or the mixing within the gap between the fins are found to be stronger and thus, the heat transfer gets enhanced appreciably higher at higher Ra.
4.4 Variation of Q net Estimation of heat removal rate from surfaces is the most important and noteworthy finding in such investigations. Attempts are made to elucidate the influence of S/L
Free Convection from Isothermally Heated Hollow Tube …
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Fig. 5 Variation in pattern of thermal plume at different Ra with constant S/L = 0.155 and L/D = 2
on net rate of heat loss (Q net ) from the finned tube for different Ra by keeping L/D constant. We have presented herein two extreme values of L/D (=1 and 20), depicted in Fig. 6. It is expected that a decreasing trend of Q net is observed with the increase of S/L for a particular value of Ra and L/D. This is just a result of the finned tube’s exposed surface area growing. Again, it is also obvious that the Q net dramatically rises with Ra for all S/L and L/D due to the growth of strength of buoyancy field. The percentage rise of Q net from maximum to minimum value of S/L is relatively greater in the case of L/D = 1 than L/D = 20 for a particular Ra. Values are approximately 108% and 65% for Ra = 104 at L/D = 1 and 20, respectively. Concomitantly, the growth gradient of Q net in reference to S/L is significantly higher at higher Ra than lower Ra for a particular L/D. These values are approximately 38 and 10.8 for Ra = 104 and 108 , respectively, for L/D = 1.
Fig. 6 Influence of S/L on Q net for different Ra, a L/D = 1 and b L/D = 20
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Fig. 7 Influence of S/L on N u net for different Ra, a L/D = 1 and b L/D = 20
4.5 Variation of N unet Nusselt number is a well-known dimensionless number, which measures the rate of heat transfer in the non-dimensional form. Figure 7 illustrates the change of net surface averaged Nusselt number (N u net ) for heated surface area of finned tube with S/L. An opposite behaviour has been observed in the case of N u net compared to Q net . It has been noticed that the N u net grows continuously with the rise of S/L for a fixed value of Ra and L/D. Because surface area of finned tube is higher at lower value of S/L, whereas the heat transfer rate does not rise appreciably when the fins are very close to each other. However, the growth rate of N u net is remarkably greater at the greater Ra than lower Ra. Again, the percentage rise of N u net from lower to higher value of Ra is significantly greater at the highest value of S/L for a fixed length-to-diameter ratio. Because the circulations inside the spacing between the fins become stronger due to which enhancement of heat extraction rate is observed to be very abrupt. Moreover, N u net is relatively higher for a greater value of L/D because of the relative growth of heat transfer is quite significant compared to the exposed surface area of the finned tube.
4.6 Behaviour of Fluid Flow Plume Velocity vectors are the important and interesting tool to elucidate the behaviour of fluid flow around the finned pipe. Figure 8 illustrates the pattern of flow plumes for different S/L at a fixed Ra and L/D. Strength of circulation of fluid is significantly stronger at higher S/L compared to lower. Hence, N u net increases drastically at a greater value of S/L. However, fluid velocities are almost zero within the fin spacing at lower value of S/L, and thus heat removal rate from that portion decreases appreciably.
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Fig. 8 Pattern of velocity vector for different S/L at fixed value of Ra = 104 and L/D = 2
5 Conclusion A study is reported on free convection from an annular finned hollow vertical tube exposed to air with negligible wall thickness. A rigorous analysis is performed with the influence of various pertinent parameters, such as Ra, L/D and S/L within the laminar regime. The following important underlined observations could be drawn: I.
Thermal plumes are presented to describe the behaviour of flow and thermal fields. Heated plumes are trapped inside the spacing between the fins at a lower value of S/L. Inner surface heat transfer is decreased at a greater value of S/L than that of athe lower one at constant Ra and L/D. II. Q net decays as S/L grows at a fixed Ra and L/D. In addition, it is expected that the Q net increases abruptly with the increase of Ra for a fixed value of S/L and L/D. III. However, N u net grows continuously with the rise of S/L for a constant Ra and L/D Again, N u net is observed to be higher at higher Ra for a fixed value of S/L and L/D. IV. Lastly, velocity vectors are illustrated to describe the flow field behaviour nearby finned tube. Stronger circulation of fluid is observed within fin spacings at a greater value of S/L.
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References 1. Singh RJ, Chandy AJ (2020) Numerical investigations of the development and suppression of the natural convection flow and heat transfer in the presence of electromagnetic force. Int J Heat Mass Transf 157:119823 2. Zhang Q, Jackson TH, Ungan A (2000) Numerical modeling of microwave induced natural convection. Int J Heat Mass Transf 43(12):2141–2154 3. Spitler JD, Javed S, Ramstad RK (2016) Natural convection in groundwater-filled boreholes used as ground heat exchangers. Appl Energy 164:352–365 4. Chen HT, Hsu WL (2007) Estimation of heat transfer coefficient on the fin of annular-finned tube heat exchangers in natural convection for various fin spacings. Int J Heat Mass Transf 50(9–10):1750–1761 5. Woschni G (1968) A universally applicable equation for the instantaneous heat transfer coefficient in the internal combustion engine. SAE Transactions, 3065–3083 6. Fazilati MA, Alemrajabi AA, Sedaghat A (2017) Liquid desiccant air conditioning system with natural convection. Appl Therm Eng 115:305–314 7. Ding G, Zhang C, Lu Z (2004) Dynamic simulation of natural convection bypass two-circuit cycle refrigerator–freezer and its application: Part I: Component models. Appl Therm Eng 24(10):1513–1524 8. Sparrow EM, Bahrami PA (1980) Experiments on natural convection heat transfer on the fins of a finned horizontal tube. Int J Heat Mass Transf 23(11):1555–1560 9. Hahne E, Zhu D (1994) Natural convection heat transfer on finned tubes in air. Int J Heat Mass Transf 37:59–63 10. Chen HT, Chou JC (2006) Investigation of natural-convection heat transfer coefficient on a vertical square fin of finned-tube heat exchangers. Int J Heat Mass Transf 49(17–18):3034–3044 11. Yildiz S, ¸ Yüncü H (2004) An experimental investigation on performance of annular fins on a horizontal cylinder in free convection heat transfer. Heat Mass Transf 40(3):239–251 12. An BH, Kim HJ, Kim DK (2012) Nusselt number correlation for natural convection from vertical cylinders with vertically oriented plate fins. Exp Thermal Fluid Sci 41:59–66 13. Park KT, Kim HJ, Kim DK (2014) Experimental study of natural convection from vertical cylinders with branched fins. Exp Thermal Fluid Sci 54:29–37 14. Senapati JR, Dash SK, Roy S (2016) Numerical investigation of natural convection heat transfer over annular finned horizontal cylinder. Int J Heat Mass Transf 96:330–345 15. Senapati JR, Dash SK, Roy S (2017) Numerical investigation of natural convection heat transfer from vertical cylinder with annular fins. Int J Therm Sci 111:146–159 16. Kim D, Kim DK (2021) Experimental study of natural convection from vertical cylinders with branched pin fins. Int J Heat Mass Transf 177:121545 17. Rana BK (2022) Numerical investigation on free convection from an isothermally heated hollow inclined cylinder suspended in air. Numer Heat Transfer Part A Appl, 1–25 18. Rana BK, Singh B, Senapati JR (2021) Thermofluid characteristics on natural and mixed convection heat transfer from a vertical rotating hollow cylinder immersed in air: a numerical exercise. J Heat Transfer 143(2):022601 19. Acharya S, Dash SK (2020) Turbulent natural convection heat transfer from a vertical hollow cylinder suspended in air: a numerical approach. Thermal Sci Eng Progress 15:100449 20. Rana BK (2022) Conjugate steady natural convection analysis around thick tapered vertical pipe suspended in the air. S¯adhan¯a 47(1):1–16 21. Rana BK, Senapati JR (2021) Laminar mixed convection over a rotating vertical hollow cylinder exposed in the air medium. In: Proceedings of international conference on thermofluids, pp. 375–385. Springer, Singapore
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22. Rana BK, Senapati JR (2021) Entropy generation analysis and cooling time estimation for a rotating vertical hollow tube in the air medium. J Heat Transfer 143(4):042101 23. Vakacharla BK, Rana BK (2022) Free convection heat transfer from a spherical shaped open cavity. J Heat Transfer 144(9):092601 24. Churchill SW (1977) A comprehensive correlating equation for laminar, assisting, forced and free convection. AIChE J 23(1):10–16
Chaotic Dynamics of Fluid Conveying Pipe Shashendra Kumar Sahoo, Bamadev Sahoo, Lokanath Panda, and Dhirendra NathThatoi
Abstract Fluid elastic instabilities are common phenomena in fluid-conveying pipes. The current paper investigates the chaotic behavior of fluid conveying pipe simply supported at both the ends. The pipe is considered as an Euler–Bernoulli beam and accounts for the geometric nonlinearity due to the midplane stretching of pipe. The nonlinear integro-partial differential equation of motion is nondimensionalized and then truncated through the Galerkin approach. The fluid velocity has a periodically varying component over a steady mean velocity. The velocity amplitude and fluctuation frequency are the control parameters. The dynamic behavior is investigated numerically through phase plane trajectory and time history via fourth-order Runge–Kutta integration technique. The evolution of chaotic oscillations in a double well potential is observed for higher values of amplitude and frequency of fluctuation of velocity. Keywords Pipe conveying fluid · Nonlinearity · Galerkin approach · Runge–Kutta integration method · Chaos
1 Introduction Dynamic instabilities with fluid elastic interaction are common in physical systems such as rotor blades in helicopters, pipes conveying fluid, aircraft wings, printing presses, and steam turbines. Study of the dynamic instabilities is of paramount S. K. Sahoo (B) · D. NathThatoi Department of Mechanical Engineering, I.T.E.R, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, India e-mail: [email protected] B. Sahoo Department of Mechanical Engineering, IIIT, Bhubaneswar, India L. Panda Department of Mechanical Engineering, Odisha University of Technology and Research, Bhubaneswar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_3
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importance for safe operation of the physical systems. Holmes [1, 2] studied the nonlinear dynamic behavior of fluid conveying pipe using finite dimensional analysis. Jayaraman et al. [3] studied the chaotic dynamics of fluid conveying using multi-time scales analysis. Jin and Song [4] examined the parametric instability of pipe conveying fluid applying method of averaging. Panda and Kar [5, 6] investigated the parametric instability of fluid conveying pipe with 3:1 internal resonance by applying multi-time scale analysis. Sahoo et al. [7–12] investigated the stability, bifurcation, and chaotic dynamics of traveling viscoelastic beam under different parametric excitation with emphasis on internal resonances with different boundary conditions. The current paper examines the chaotic behavior of a fluid conveying pipe supported at both the ends. The axial fluid velocity is assumed to be periodically varying over a steady mean velocity. The dimensionless equation of transverse motion is truncated via the Galerkin approach. The velocity amplitude and frequency of fluctuation of velocity are control parameters. The dynamical behavior is examined numerically through phase plane trajectory and time history via the Runge–Kutta integration method.
2 Analytical Model The analytical model of a pipe conveying fluid supported at either end is illustrated in Fig. 1. The motion is assumed to be planar and the pipe is taken as an Euler–Bernoulli beam. An incompressible fluid having a plug flow condition is considered. The equation of motion governing transverse deflection of fluid-conveying pipe with geometric nonlinearity may be written as Holmes [1, 2] 5 ∂2W ∂2W ∂4W ∗ ∂ W + 2MU + + m) + E I (M ∂x4 ∂ x 4 ∂t ∂ x∂t ∂t 2 2 L W ∂W E A ∂ 2 + MU 2 − T − +c =0 W dx ∂t 2L 0 ∂x2
EI
having boundary conditions
Fig. 1 Analytical model of Simply supported pipe conveying fluid
(1)
Chaotic Dynamics of Fluid Conveying Pipe
25
∂2W ∂2W t) = (0, (L , t) = 0, ∂ x2 ∂ x2
W (0, t) = W (L , t) =
(2)
where x and t are the axial coordinate and time, respectively, W (x, t) is the transverse deflection, T is the externally imposed axial tension, EI is the flexural stiffness, m and M are the mass of pipe and fluid per unit length, respectively, E* is the coefficient of Kelvin–Voigt internal dissipation, and c is the coefficient of external damping. Considering the following dimensionless quantities as w=
x t W ,ξ = ,τ = 2 L L L
EI M +m
1/2
,u=
M EI
1/2 LU,
M cL 2 T L2 ,β= ,σ = √ , EI m + M E I (M + m)
1/2 I AL 2 E∗ , ,γ = α= 2 L (M + m)E 2I
=
(3)
And inserting Eq. (3) into Eq. (1), the dimensionless equation of motion becomes α w˙ + w +
⎧ ⎨ ⎩
1 u2 − − γ
⎫ ⎬
w 2dξ w + 2β 1/2 u w˙ + σ w˙ + w¨ = 0 ⎭
(4)
0
with nondimensional boundary conditions w(0, τ ) = w(1, τ ) = 0, w,ξ ξ (0, τ ) = w,ξ ξ (1, τ ) = 0.
(5)
Setting α = = 0, we get the dimensionless equation of motion as ⎧ ⎫ 1 ⎨ ⎬ w + u 2 − γ w 2dξ w + 2β 1/2 u w˙ + σ w˙ + w¨ = 0. ⎩ ⎭
(6)
0
3 Galerkin Discretization The solution to Eq. (6) according to the Galerkin approach can be assumed as w(ξ, τ ) =
∞ m=1
qm (τ )φm (ξ ),
(7)
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where φm (ξ ) are the eigen functions of the√simply supported beam satisfying boundary conditions (5), namely φm (ξ ) = 2 sin(mπ ξ ) and qm (τ ) is the mth generalized co-ordinate. Substituting Eq. (7) into Eq. (6) and using the Galerkin approach, we get for two-mode truncation (m = 2), a set of gyroscopically coupled equations as .
..
q1 +π 2 (π 2 − u 2 )q1 + σ q − 1
.
..
16 1/2 . 1 2 2 β u q2 + γ π (q1 + 4q22 )q1 = 0, 3 2
q2 +4π 2 (4π 2 − u 2 )q2 + σ q + 2
16 1/2 . β u q1 +2γ π 2 (q12 + 4q22 )q2 = 0. 3
(8) (9)
The fluid axial velocity is assumed to vary periodically about a steady mean velocity and can be written in a dimensionless form as u = u 0 (1 + μ sin ωτ ),
(10)
where u 0 is the steady mean flow velocity, μ and ω are the amplitude and fluctuation frequency in velocity, respectively.
4 Result and Discussion Computational analysis is performed applying Runge–Kutta integration method. The nondimensional parameters taken are [3] σ = 0.883, β = 0.78, γ = 9050, for a typical pipe made of rubber with the following parameters length L = 50cm, diameter D = 1cm, thickness h = 0.05cm, c = 0.981 Ns/m2 , M = 0.0785 Kg/m, m = 0.0221 kg/m and E = 3433 × 109 N/m 2 . The initial conditions taken are . . q1 = q2 = 0 and q1 = q2 = 0.01. For control parameters μ = 0.001, u 0 = 5, ω = 10, both the first and second modes show stable limit cycle oscillations. For control parameters μ = 0.025, u 0 = 5, ω = 10, the first mode exhibits a cascade of perioddoubling bifurcations in a double well potential leading to chaos as observed from the phase plane trajectory. The second mode exhibits chaotic oscillations between two coexisting fixed points (−0.0075, 0.0) and (0.0075, 0.0) in the phase plane trajectory. For control parameters μ = 0.1, u 0 = 5, ω = 10, the first mode exhibits chaotic oscillations between three equilibrium points (−0.06, 0.0), (0, 0), (0.06, 0.0) in phase plane trajectory. The origin (0, 0) is a saddle point which is dynamically unstable, while the other two equilibrium points are dynamically stable. The system exhibits chaotic oscillations between two stable equilibrium points (−0.06, 0.0) and (0.06, 0.0). The second mode exhibits strong chaotic oscillations as observed from complex phase plane trajectory (Figs. 2, 3, 4, 5, 6 and 7).
Chaotic Dynamics of Fluid Conveying Pipe
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Fig. 2 Phase plane trajectory and time history illustrating stable limit cycle oscillations at u 0 = 5, ω = 10, μ = 0.001 for the first mode
Fig. 3 Phase plane trajectory and time history illustrating stable limit cycle oscillations at u 0 = 5, ω = 10, μ = 0.001 for the second mode
Fig. 4 Phase plane trajectory and time history representing cascade of period-doubling bifurcations in a double well potential at u 0 = 5, ω = 10, μ = 0.025 for first mode
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Fig. 5 Phase plane trajectory and time history representing chaotic oscillations between two coexisting fixed chaotic attractors at u 0 = 5, ω = 10, μ = 0.025 for the second mode
Fig. 6 Phase plane trajectory and time history representing chaotic oscillations in a double well potential at u 0 = 5, ω = 10, μ = 0.1 for the first mode
Fig. 7 Phase plane trajectory and time history representing strong chaotic oscillations at u 0 = 5, ω = 10, μ = 0.1 for the second mode
Chaotic Dynamics of Fluid Conveying Pipe
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5 Conclusion The chaotic behavior is examined numerically for simply supported pipe with periodically varying velocity. The dimensionless equation of transverse motion is truncated via the Galerkin technique. Computational simulation is carried out utilizing the Runge–Kutta integration method. Phase portrait and time history are illustrated to study the nature of motion varying the amplitude and frequency of velocity fluctuation. The numerical results give the following conclusions: (1) The nonlinear dynamic behavior is dependent on initial conditions. (2) For amplitude of velocity fluctuation μ = 0.001, the first and second modes show stable limit cycle oscillations. (3) For amplitude of velocity fluctuation μ = 0.025, the first mode undergoes a series of period-doubling bifurcations, whereas the second mode exhibits chaotic oscillations between two fixed equilibrium points. (4) For amplitude of velocity fluctuation μ = 0.1, the first mode exhibits chaotic oscillations in a double well potential between two stable equilibrium points, whereas the second mode exhibits strong chaotic oscillations.
References 1. Holmes PJ (1977) Bifurcations to divergence and flutter in flow-induced oscillations: a finitedimensional analysis. J Sound Vib 53(4):471–503 2. Holmes PJ (1978) Pipes supported at both ends cannot flutter. J Appl Mech 45:619–622 3. Jayaraman K, Narayanan S (1966) Chaotic oscillations in pipes conveying pulsating fluid. Nonlinear Dyn 10:333–357 4. Jin JD, Song ZY (2005) Parametric resonances of supported pipes conveying pulsating fluid. J Fluids Struct 20:763–783 5. Panda LN, Kar RC (2007) Nonlinear dynamics of a pipe conveying pulsating fluid with parametric and internal resonances. Nonlinear Dyn 49:9–30 6. Panda LN, Kar RC (2008) Nonlinear dynamics of a pipe conveying pulsating fluid with combination, principal parametric and internal resonances. J Sound Vib 309:375–406 7. Sahoo B, Panda LN, Pohit G (2013) Non linear dynamics of an euler-bernouilli beam with parametric and internal resonances. Procedia Eng 64:727–736 8. Sahoo B, Panda LN, Pohit G (2014) Nonlinear dynamics of a travelling beam subjected to multi-frequency parametric excitation. Appl Mech Mater 592:2076–2080 9. Sahoo B, Panda LN, Pohit G (2015) Combination parametric and internal resonances of an axially moving beam. J Vibr Eng Technol 3(2):137–150 10. Sahoo B (2020) Non linear dynamics of a viscoelastic beam traveling with pulsating speed, variable axial tension under two-frequency parametric excitations and internal resonance. Nonlinear Dyn 99:945–979 11. Sahoo B (2022) nonlinear dynamics and bifurcation analysis of a hinged-clamped beam under parametric and internal resonances. Int J Bifurcation Chaos 32(01):2250005 12. Sahoo B (2022) Multi-scale analysis of a moving beam under parametric and auto-parametric resonances. J Braz Soc Mech Sci Eng 44(1):1–15
Study of Flow Distribution in the Falling Film Evaporator Manifold Pankaj G. Anjankar, Sanjay S. Lakade, Atul Padalkar, and Sandeep Nichal
Abstract In many fields, such as agriculture (irrigation), chemical, pharmaceutical, food processing, and chillers uniform flow from each outlet of the distributor is prime important. The static head in the manifold is changing due to skin friction and momentum changed along the length and at the close end. Therefore, the study of pressure gradient and flow distribution plays a critical part in the prediction of efficiency and performance of manifold. In this paper, various findings on the manifold design are studied. The paper also explains the theoretical design, along with various methods used to arrive for solution, and highlighted the milestone of previous research along with a brief description of parameters affecting the even flow distribution. The governing equation and its solution can be obtained using mass, energy, and momentum theory with discrete and analytical methodology. Different studied generalized models are stated here that can be utilized for the optimization of manifold design. The rise and fall in pressure along the length of the manifold due to friction and momentum are adjusted by the geometry of the manifold. Keywords Manifold · Flow distribution · Low momentum design
P. G. Anjankar (B) Pimpri Chinchwad College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India 411044 e-mail: [email protected] A. Padalkar Flora Institute of Technology, Savitribai Phule Pune University, Pune, Maharashtra, India 412205 S. Nichal Deputy General Manager, Kirloskar Chillers Private Limited, Satara, Maharashtra, India 412801 P. G. Anjankar · S. S. Lakade JSPM’S Rajarshi Shahu College of Engineering, Savitribai Phule Pune University, Pune,Maharashtra, India 411033 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_4
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1 Introduction Generally, flow manifold is developed with a purpose of uniform outflow from each outlet. In many industries such as agriculture, chemical, pharmaceutical, and chillers are required to divide the mainstream of fluid into many sub-streams. Maldistribution is a challenge in manifold design, certain sub-stream outlets may have low flow rates, while the other may have excess, which reduces system performance and efficiency. Hence, the study of flow in the manifold is extremely important to understand the mechanism of fluid flow, fluid spreading, and pressure variation along the axis of the distributor. Manifold design methods can be classified according to the motion of fluid in the system as dividing, combing, u profile-reverse flow manifold, and z profile-parallel flow manifolds as indicated in Fig. 1 [6]. In most of the cases of plate heat exchanger, parallel flow and reverse flow manifolds are mostly used. In parallel flow manifold, the direction of flow in the dividing and combining header is same, whereas, in reverse flow manifolds, it is the opposite [11]. Uneven distribution leads to reduce system performance. Therefore, it is important to predict the functioning and capability of the different manifolds which leads to cost reduction with good efficiency. The following are the way to analyze the pressure drop and discharge of the manifold; CFD [16], discrete models [15], and analytical models [13, 15]. The CFD can provide comprehensive pressure drop and discharge of 3D geometry without any knowledge of the flow coefficient. The limitation of using CFD is that there is still unreliability of solution in a turbulent model and it is difficult to optimize the design on CFD because it is time-consuming. In a discrete model, the governing equation is resolved by an iteration program at a junction of fluid flow. This method is lengthy for the optimization of new design. The analytical model or continuous model is a case of discrete model. Simplicity and flexibility are merits of analytical model for the optimization of manifold design. Independently of the kind of technique used, there has been a great effort to optimize the manifold systems, as shown by the large list of patents and scientific literature available on this topic. Traditionally, most of the design is based on a low momentum method and the performance analysis is done with the Bernoulli equation, or energy equation, or by conservation of momentum theory. The applicability of the Bernoulli equation and energy equation had been questioned when Acrivos et al. [1] experimentally presented a pressure elevation after tri-joint due to flow separation as shown in Fig. 2. Wang [12] explains the phenomenon of a rise in pressure after T junction, Fig. 1 Manifold arrangement for flow distribution [6]
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Fig. 2 Pressure elevation experience near a single outlet [1]
because of the inertia of fluid, fluid chose to move in a straight direction. Thus, the quantity of fluid flow in forward direction in the system (pipe) is more than the lateral (hole or outlet). The physics behind the difficulty of applying the Bernoulli equation is explained by Wang [4–11] as it is difficult to catch the streamline for the conservation of energy and to find out the friction losses. In addition to this, the difficulty of applying the energy equation is due to a higher energy fluid flow at the center of the pipe and this higher energy retains in the pipe and the lower energy fluid flow at the circumference and separate outs through the outlets. Therefore, there is an error in the calculation of energy balance. Hence, a lot of theories and equations are made for manifolds based on energy equations and Bernoulli theorems are complicated and lead to an error. Bajura et al. and Bassiouny et al. [2, 5] applied the momentum equation to avail the advantage of momentum theory, as it is not essential to know comprehensive flow patterns. Acrivos et al. [1] show there is a rise in pressure after the T junction, now to account for the rise in pressure. Acrios et al. changed Bernoulli’s equation by inducing a correction term known as the momentum recovery factor k. The governing equation developed is based on the Blasius law as f = f0U–1/4. Therefore, the equation applied to Blasius flow is applicable for Reynolds numbers ranging from 2000 to 100,000. Hence, the model developed is not generalized. The normalized governing equation is as follows: 2 dU d U dU +U + F0 U 7/4 = 0 (1) dy 2 dy dy where U, p, y, and F0 are normalized axial velocity, pressure, coordinate of manifold, and amalgamation of friction and momentum recovery factor, respectively [1]. Equation (1) is the second-order nonlinear differential equation which combines the effect of friction and momentum in a single parameter F0 which makes it difficult to find out their individual effect. Also, it is difficult to have a clear prediction of fluid flow in the manifold with the Acrivos equation. This constraint in the Acrivos et al. equation leads to the further development of the manifold design. Bajura [2, 3] developed the theoretical model for dividing and combining manifolds based on mass momentum conservation theory and represented the first general theoretical
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model for flow distribution and formulated a pressure recovery factor. But author failed to obtain the solution which accounts for variation of friction with varying velocity. Bassiouny and Martin [5] studied the mass momentum equation of Bajura for dividing flow as well as combining flow in the case of plate heat exchanger. Wang et al. [13] extended this concept and used it for the design of the manifold. Bassiouny et al. and Wang [5, 12, 13] extended this study further and developed a governing equation for the design of manifolds, the equation is as follows: 1 dp dV λ 2 + V + 2kV =0 ρ dx 2D dx
(2)
where λ is the friction factor, D is the diameter of the manifold, k is the pressure recovery factor, V is the axial velocity in the distributor, x is the axial coordinate, ρ is the density of fluid, and p is the static pressure in the distributor. Many researchers [4, 12] studied the flow distribution in manifold systems. Wang [4–11] developed the most generalized model, based on mass momentum conservation. The governing equation given for the combining arrangements is as follows: λ 2 1 dp + V + ρ dx 2D
2 − β dV 2 =0 2 dx
(3)
where β is the average velocity ratio in manifold (Vc/V) and Vc is the axial component of outlet velocity. The β term is a momentum factor for the correction of inertial effects. Equation 2 can be solved to Acrivos et al. model after substituting the Blasius equation in it; hence, Acrivos et al. model is a limited case of Eq. 2. Therefore, Kulkarni et al. [7] statement was contradictory as he mentions Acrivos et al.’s model is generalized. The following equation governs dividing flow: λ 2 1 dp − V + ρ dx 2D
2 − β dV 2 =0 2 dx
(4)
Equation (1) and Eq. (4) are nonlinear differential equations of second order for dividing, combining, and flow manifold. The equation combines two forces, friction, and pressure. The middle and last terms accounts for frictional and momentum share, respectively. The correct solution to the analytical equation is given by Wang [4– 13]. The solution provided by Wang is dimensionless; hence, it is independent of geometry. Wang mentions the flow in the manifold is controlled by either friction force or momentum, whichever is dominant. The key part of the solution is where it is independent of fluid properties. Liu H et al. [8] developed a governing equation for dividing manifold. The governing equation contains one additional force of gravity. The governing equation is as follows: 1 dp V dV 2 λ 2 + V + α + 2βln = gI ρ dx 2D V0 d x
(5)
Study of Flow Distribution in the Falling Film …
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where α and β are the pressure recovery coefficients. The solution provided by Liu H et al. [8] is based on velocity variation as a power function. Wang [14, 15] provided procedures to get a uniform outflow. Equations (1)– (5) are the governing equations for either dividing or combining flow.
2 Limitations of the Theory Proposed The complex flow in the manifold system is a real problem in getting a solution to the generalized governing equation even for simpler geometry. It is challenging to combine the variation of friction and momentum with varying velocities along the length of the manifold. However, researchers neglected or assumed a certain variable/constant to simplify the model; this caused a considerable error. Bajura [2] developed the governing equation for the manifold and the governing equation is solved by negating the friction effect, thus cannot be used for a long manifold. Bassiouny and Martin [5] developed the governing equation for their models like Bajura author also neglected friction effect in the solution. In addition to this, the author formulated parameters to determine flow regions. Wang et al. [12] developed the governing equation of the manifold (Eq. 1) and solved it successfully for all laminar to turbulent flow conditions and account for deviation of friction and pressure recovery factor. Here, the author assumed a linear velocity profile. Hence, Wang et al. [16] changed the presumption of linear to power velocity profile to avoid any limitation. Wang et al.’s [12, 13] results showed that there were three different profiles for pressure distribution along the length of the manifold depending on which force is dominant. These regions are momentum, friction, and their interdependent dominant regions. Wang et al. [12, 13] adjusted the variation of pressure due to momentum changed and friction by adjusting geometric parameters like length of the manifold, diameter of channels, and spacing of outlet. Wang [10, 11] provided a general solution of U-type and Z-type manifolds. This is a new milestone in manifold design. His work on the manifold gives new insight into design and flow distribution.
2.1 Uniform Flow Distribution Aspects The entire theoretical model studied by the time is based on a single goal of getting uniform flow from each outlet of a manifold. To get uniform flow from each outlet, it is important to maintain constant header pressure along the length of the manifold. The branching of a fluid stream into small streams through a distributor is the result of fluid pressure variation due to skin friction and changing fluid momentum. Because of friction pressure falls while sharp variation in direction felt by consecutive components of the stream built the pressure rise in a dividing, whereas drop in combining manifold. Hence, it is possible to maintain the head by balancing the drop and rise in pressure along the length of the manifold. This can be done by adjusting
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Fig. 3 Pressure profile of manifold with varying ratio of E/Re0 [12]
the geometry of the manifold. The effect of structural parameters on the performance of manifold is wildly discussed in the literature.
2.2 Impact of E (Length of Header to Diameter of the Header) Wang et al. [12] analyzed the effect of L/D on a manifold for a Reynolds number less than 2200. In Fig. 3, it is observed that, when E (L/D) increases or Re decreases, Eu number decreases; it means that, for a larger value of E, the friction force is dominated in the manifold and, for a small value of E, momentum force is dominated in the manifold. For Reynold’s number less than 2200, we observe either a friction or momentum effect is dominated on the entire length of the manifold. Liu [8] analyzed the effect of E (L/D) for a Reynold’s number ranging from 2200 to 100,000, the calculated result is shown in Fig. 4. It is observed that, for a higher value of E, the first half of the manifold shows friction effect is dominated for a certain length and in other half shows the momentum effect is dominated. Hence, for laminar flow, the flow in the manifold is either momentum control or friction controlled and, for turbulent flow, both effects can be seen. Therefore, adjusting only E for uniform pressure distribution is not sufficient.
2.3 Impact of Opening Ratio M (Summation of All Outlet Area to the Area of Manifold) The opening (area) ratio is also seen as the opening density of the header. It is observed from Fig. 5 that velocity, pressure, and flow distribution are uniform for
Study of Flow Distribution in the Falling Film …
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Fig. 4 Effect of length-to-diameter ratio on the pressure profiles [8]
M = 0.5 for laminar flow. In this case, the drop in pressure due to skin friction is balanced by a rise in pressure due to momentum (outflow opening, i.e., hole) and hence found better performance. When M is high, the friction effect cannot balance the momentum effect and hence flow is momentum-controlled flow.
2.4 Impact of Friction The friction coefficient which is calculated under the assumption that branching of the flows does not affect the friction pressure loss in the distributor in the governing equations is dependent on the selection of a particular surface finish or material. Liu H [8] considered that the friction factor for pipe and for perforated pipe is same. In open literature, many authors consider the friction factor for short pipe is negligible. Wang et al. [12] related the effect of flow branching on friction factor and mentioned friction factor may equal to or less than or greater than the pipe friction depending on the geometry of perforated pipe.
3 Conclusion We studied the development of analytical models for flow manifold, and highlight the milestone from the last 60 years. 1. To understand the physics behind the distribution of the flow as well as pressure drop with various geometrical parameters and flow conditions in falling film evaporator, the analytical model is the key way for that. The governing equation contains two terms that separately show the friction effect as well as the momentum effect on flow distribution and both this friction effect and momentum effect are quantitively described by friction factor and pressure recovery factor.
38 Fig. 5 Effects of area ratio on velocity profile, flow distribution, and pressure drop, respectively [11]
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2. For turbulence flow, the effect of momentum dominates in the first half-length of the manifold, hence momentum controls flow and, in the next half, friction dominates, hence friction controls flow.
References 1. Acrivos A, Babcock B, Pigford L (1959) Flow distributions in manifolds. Chem Eng Sci 10(2):112–124. https://doi.org/10.1016/0009-2509(59)80030-0 2. Bajura A (1971) A model for flow distribution in manifolds. Journal of engineering for power 93:7–12. https://doi.org/10.1115/1.3445410 3. Bajura A, Jones H (1976) Flow distribution manifolds. J Fluids Eng 98(4):654–665. https:// doi.org/10.1115/1.3448441 4. Bassiouny K, Martin H (1984) Flow distribution and pressure drop in plate heat exchanges: Part II-Z-Type arrangement. Chem Eng Sci 39(4):701–704. https://doi.org/10.1016/0009-250 9(84)80177-3 5. Bassiouny K, Martin H (1984) Flow distribution and pressure drop in plate heat exchanges: Part I-U-Type arrangement. Chem Eng Sci 39(4):693–700. https://doi.org/10.1016/0009-250 9(84)80176-1 6. Jafar H, Thamer M, Wahid M, Wissam A (2014) Modeling the uniformity of manifold with various configurations. Hindawi publishing corporation. J Fluids 8, Article ID 325259, https:// doi.org/10.1155/2014/325259. 7. Kulkarni A, Roy S, Joshi J (2007) Pressure and flow distribution in pipe and ring spargers: Experimental measurements and CFD simulation. Chem Eng J 133(3):173–186 8. Liu H, Zong Q, Lv H, Jin J (2017) Analytical equation for outflow along the flow in a perforated fluid distribution pipe. PLoS ONE 12(10). https://doi.org/10.1371/journal.pone.0185842 9. Wang Y (2008) Pressure drop and flow distribution in parallel-channel of configurations of fuel cell stacks: U-type arrangement. Int J Hydrogen Energy 33(21):6339–6350. https://doi.org/10. 1016/j.ijhydene.2008.08.020 10. Wang Y (2010) Pressure drop and flow distribution in parallel-channel of configurations of fuel cell stacks: Z-type arrangement. Int J Hydrogen Energy 35(11):5498–5509. https://doi.org/10. 1016/j.ijhydene.2010.02.131 11. Wang Y (2011) Theory of flow distribution in manifolds. Chem Eng J 168(3):1331–1345. https://doi.org/10.1016/j.cej.2011.02.050 12. Wang Y, Gao L, Gan H, Wu D (2001) Analytical solution of flow coefficients for a uniformly distributed porous channel. Chem Eng J 84(1):1–6. https://doi.org/10.1016/S13858947(00)00263-1 13. Wang J, Ge L, Wu D (1998) Progress of flow in manifolds. Adv Mech Eng 28(3):392–401 14. Wang Y, Wang L (2012) Flow field designs of bipolar plates in PEM fuel cells: theory and applications. Fuel Cells. Fuel cells 12(6):989–1003. https://doi.org/10.1002/fuce.201200074 15. Wang Y, Wang L (2012) Discrete approach for flow-field designs of parallel channel configurations in fuel cells. Int J Hydrogen Energy 37(14):10881–10897. https://doi.org/10.1016/j.ijh ydene.2012.04.034 16. Wang Y, Zhang M, Wu D (1999) The effects of velocity distribution on the flow uniformity in the boiler’s dividing header. Proc Chinese Soc Electr Eng 19(5):9–12
Effect of Core Geometrical Characteristics on Performance and Pressure Drop of Rotary Regenerator Manas Ranjan Padhi, Prakash Ghose, Achinta Sarkar, Basanta Kumar Rana, Manoj Ukamanal, Jitendra Kumar Patel, and Swarup Kumar Nayak Abstract Rotary regenerative heat exchangers or rotary regenerators are used extensively in many industries for heat recovery applications. Many researchers have studied different operating conditions in order to improve the effectiveness as a key performance parameter of rotary regenerators. However, few of them have worked for optimizing the design parameters that affect the performance of this heat exchanger. Moreover, the parameters, like pressure drop, are also need to be analysed in detail. In the current work, the numerical investigation of rotary regenerator is carried out using CFD and the effect of important design parameters like regenerator core dimensions and width of separator gap on effectiveness as well as pressure drop is studied. It is revealed that regenerator core dimensions such as length and diameter have a considerable effect on performance and pressure drop. The width of the separator gap between hot and cold fluid sections also plays a crucial role in predicting the performance of a rotary regenerator. Keywords Rotary regenerator · Effectiveness · Pressure drop · Core
1 Introduction Several energy extraction techniques are used by different researchers to tackle the problem of energy loss in power sector. The common energy extraction devices like recuperators, economizers and condensers which are used in thermal power plants are not sufficient to meet the present energy demand [1, 2]. Therefore, researches are being carried out worldwide for more energy recovery techniques. Rotary regenerative heat exchanger which is also called rotary regenerator is not only used in power plant but also find wide applications in other industries as heat recovery device. They are preferred over other heat exchangers due to their compact size, low cost M. R. Padhi (B) · P. Ghose · A. Sarkar · B. K. Rana · M. Ukamanal · J. K. Patel · S. K. Nayak KIIT Deemed to be University, Bhubaneswar 751024, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_5
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and good heat exchange efficiency [3]. In a rotary regenerator, the heat exchange material or matrix in the form of a wheel rotates between hot and cold fluids. Thus, heat is exchanged between two fluids through the rotating matrix. Al-Kayiem and Mahdi [4] studied the effect of roughness of heat transfer elements on the thermal performance of rotary regenerator. Çiftçi and Sozen [5] predicted the effectiveness of rotary regenerator which was used as a heat wheel by applying computational fluid dynamics. Antonellis et al. [6] carried out their experimental work to optimize the effectiveness and pressure loss for a rotary regenerator in the form of a heat wheel. In most of the numerical investigation, the heat transfer along the radius of rotary regenerator was not considered. Alagic et al. [7] presented a comparison of performance between one-dimensional and three-dimensional computational models. Many researchers did three-dimensional simulations of rotary regenerators rather than one or two dimensions for better computational accuracy. In most of the simulation work, results were presented in terms of temperature and velocity field. The use of porous media in numerical simulation proved the advantage of reducing computational expenses. Kaydan and Hajidavalloo [8] used a porous media approach to investigate the performance parameters such as rotor speed, mass flow rate of fluids, inlet temperature of fluid and matrix material. However, they have not investigated regarding pressure drop. As the fluid is allowed to pass through rotor matrix composed of a large number of narrow channels, pressure drop plays a significant role in evaluating the performance of rotary regenerator [9]. Mioralli and Ganzarolli [10] studied maximum porosity causing heat transfer in rotary regenerator at constant pressure drop. Despite all this research work on rotary regenerator, the effect of different parameters on pressure drop is not properly investigated and also the effects of dimensional parameters on effectiveness need to be studied deeply. In the present paper, the pressure drop across the rotary regenerator is obtained by applying computational fluid dynamics. The influence of geometrical parameters of regenerator core on pressure loss is investigated.
2 Methodology In the present study, the core of the rotary regenerator is modelled with numerous circular channels of very small cross section. For the sake of simplicity and to reduce computational time, porous media approach is used in the process of simulating fluid flow and heat transfer through the regenerator. The mathematical modelling involves the following conservation equations in a porous medium. Conservation of mass: γ
∂ρ f + ∇ ρfv = 0 ∂t
(1)
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Conservation of momentum: ∂v ρ f γ −1 + γ −2 (v.γ )v = −∇ P ∂t
(2)
Considering the thermal equilibrium condition between the fluid and solid medium, the conservation of energy equation for the porous medium can be written as (ρC p )m
∂T + (ρC p ) f v.∇T = ∇.(km ∇Tm ) + γ q m ∂t
(3)
The porosity (γ) is defined by the relation as follows: ϕ=
VS VT − VS =1− VT VT
(4)
where VT is known as the volume of cylindrical core and VS is the total volume of channels placed inside it. The pressure drop across the rotary regenerator matrix is given by ∇P =
1 μ U + C ρ|U |U K 2
(5)
The geometrical model of the rotary regenerator was discretized into structured grid. For the accuracy of the result, four different sizes of grid was chosen as shown in Table 1. It was found that the grid size of 741,260 elements is suitable for numerical simulations as results are independent of grid size. The mass, momentum and energy conservation equations in a porous medium are solved by using FLUENT, a commercial CFD software. In order to incorporate the rotation of the regenerator matrix, mesh motion is considered. Mass flow rates of fluids are chosen as the inlet boundary conditions and pressures are taken as outlet boundary conditions. It is noted that the same inlet mass flow rates are considered for both the fluids. The numerical results obtained in the form of outlet temperatures of high- and low-temperature fluids are used to estimate the thermal performance of rotary regenerator. The thermal performance which is also called effectiveness can be calculated as Table 1 Grid independence test
Number of studies
Number of elements
Tco (K)
Tho (K)
1
176,320
375.38
324.61
2
452,547
373.45
326.54
3
741,260
372.41
327.57
4
1,013,463
372.29
327.68
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Table 2 Validation of numerical result Results
Speed (rpm)
Porosity (γ )
Effectiveness (ε)
Numerical results
5
0.80
0.724
Analytical results by Kays and London
5
0.799
0.736
Deviation (%)
0
0.00125
1.630
Tcoldair,out − Tcoldair,in heat transfered = ε= maximum possible heat transfered (Thotair,in − Thotair,out )
(6)
The present numerical result was validated by comparing it with earlier published analytical work by Kays and London [11]. The effectiveness obtained by them using the NTU method was compared with the effectiveness obtained from the above relation with the same input parameters. It is apparent from Table 2 that the simulation result of effectiveness by using porous media approach is in good agreement with the earlier published result. The deviation obtained is within an acceptable range. This small variation may be due to approximations in both works.
3 Results and Discussions Figure 1 displays the temperature distribution of rotary regenerator along with its height obtained from numerical simulation. When the matrix rotates, it takes up the heat given by the high-temperature fluid and rejects it to the low-temperature fluid. Thus, heat is exchanged between two fluids within the matrix rotor. As a result of heat exchange, the outlet temperature of high-temperature fluid is reducing and that of low-temperature fluid is enhancing, as observed from temperature distribution.
3.1 Effect of Length of Regenerator Core The effectiveness of rotary regenerator is obtained for different lengths of regenerator core as shown in Fig. 2. Length of regenerator core is a key design parameter influencing significantly the performance of rotary regenerator. The increase in the length of regenerator core exposes more heat exchange area of the channels to the fluid flow. Thus, effectiveness increases with an increase in the length of regenerator core. The increase in regenerator core length contributes to more drop in pressure. It is due to the fact that when the channel length increases as a result of increasing core length, the frictional resistance offered to the fluid flow becomes more which causes more drops in pressure.
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Fig. 1 Temperature distribution of rotary regenerator
Fig. 2 Effect of core length on effectiveness and Pressure drop
3.2 Effect of Diameter of Regenerator Core The effect of diameter of rotary regenerator core on effectiveness and pressure drop can be studied in Fig. 3. Diameter of regenerator core is another key design parameter. It can be noticed that there is an increase in the effectiveness of regenerator with an increase in core diameter. When the regenerator core diameter increases, more number of channels can be accommodated within it which causes enhancement in
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Fig. 3 Effect of core diameter on effectiveness and pressure drop
surface area. Thus, more heat can be stored within the core due to which effectiveness increases. But since the mass flow rate of fluid is kept constant, the amount of flow per channel reduces and a subsequent reduction in flow velocity within channels. Therefore, there will be less pressure drop across the rotary regenerator core. However, regenerator core with a very large diameter should not be recommended as this may lead to the increase in motor power for driving the rotary regenerator.
3.3 Effect of Separator Gap The effect of the presence of separator gap on effectiveness and pressure drop is shown in Fig. 4. As the separator gap between hot and cold fluid sections increases, the effectiveness reduces. It is because of the reason that separator gap reduces the flow area of the fluid through the regenerator core. Therefore, the total contact surface area of the channels decreases due to the reduction in the number of channels that causes the reduction in the effectiveness of rotary regenerator. The enhancement in pressure drop as a result of an increase in the separator gap is also not supporting more width of the separator gap. However, the separator gap cannot be completely eliminated to provide the radial seal clearance in order to mitigate the leakage between hot and cold fluids.
4 Conclusions CFD simulation of rotary regenerator is conducted by using a porous media approach. The results are obtained in terms of outlet temperatures of hot and cold fluid at the exit of rotary regenerator and, subsequently, effectiveness is determined. The
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Fig. 4 Effect of separator gap on effectiveness and pressure drop
numerical results are validated with analytical results of Kays and London and the deviation obtained is within acceptable limit. The effect of important core geometrical characteristics such as length, diameter and separator gap on effectiveness and pressure drop is investigated. The study reveals that the length of regenerator core increases both effectiveness and pressure drop, while core diameter enhances effectiveness. The study of the effect of separator gap between hot and cold fluid sections tells that separator gap increases the pressure drop and reduces the effectiveness of the regenerator. However, the separator gap cannot be eliminated in order to provide the space for seal to minimize leakage.
References 1. Christodoulides P, Agathokleous R, Aresti L, Kalogirou SA, Tassou SA, Florides GA (2022) Waste heat recovery technologies revisited with emphasis on new solutions, including heat pipes, and case studies. Energies 15(1):384 2. Jouhara H, Khordehgah N, Almahmoud S, Delpech B, Chauhan A, Tassou SA (2018) Waste heat recovery technologies and applications. Thermal Sci Eng Progress 6:268–289 3. Wang HY, Zhao LL, Xu ZG, Chun WG, Kim HT (2008) The study on heat transfer model of tri-sectional rotary air preheater based on the semi-analytical method. Appl Therm Eng 28(14):1882–1888 4. Al-Kayiem H, Mahdi H (2010) Performance enhancement of rotary air preheater by the use of pin shaped turbulators. WIT Trans Eng Sci 68:35–49 5. Çiftçi E, Sözen A (2017) Numerical investigation of a heat wheel performance used for enthalpy recovery applications. Res Eng Struct Mater 3(3):202–209 6. De Antonellis S, Intini M, Joppolo CM, Leone C (2014) Design optimization of heat wheels for energy recovery in HVAC systems 7(11):7348–7367 7. Alagic S, Stosic N, Kovacevic A, Buljubasic I (2004) Numerical analysis of heat transfer and fluid flow in rotary regenerative air pre-heaters. In: Proceedings of the ASME-ZSIS international thermal science seminar II, pp 489–495. Begel House Inc
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8. Heidari-Kaydan A, Hajidavalloo E (2014) Three-dimensional simulation of rotary air preheater in steam power plant. Appl Therm Eng 73(1):399–407 9. Alhusseny A, Turan A (2010) An effective engineering computational procedure to analyse and design rotary regenerators using a porous media approach. Int J Heat Mass Transf 95:593–605 10. Mioralli P, Ganzarolli MM (2013) Thermal analysis of a rotary regenerator with fixed pressure drop or fixed pumping power. Appl Therm Eng 52:187–197 11. Kays WM, London AL (1984) Compact heat exchangers, 3rd edn. McGraw-Hill, New York
CFD Analysis of Heat Transfer Coefficient and Pressure Drop in a Shell and Tube Heat Exchanger for Various Baffle Angles R. Anandan , G. Sivaraman , M. Rajasankar , and R. Girimurugan
Abstract A shell and tube heat exchanger is analyzed numerically to determine the effect of the baffle angle on the heat transfer coefficient and the pressure drop. Heat exchangers that use shells and tubes benefit greatly from baffles because they allow for more effective heat transfer with less pressure drops. CATIA and ANSYS— Workbench Flow Simulation software is used to construct heat exchangers with baffle angles of 0, 10, 20, 30 and 40°, and fluid dynamic simulations are also performed. The greatest heat transfer occurred at a baffle angle of 40°, assuming a constant heat transfer coefficient between the baffle and shell. It has been shown that a 40° baffle angle produces the least amount of pressure drop. Our heat exchanger features a combination of rotational and helical baffle patterns, which greatly improves its heat transfer coefficient per unit pressure drop. Keywords Baffle angles · Pressure drop · Heat exchanger · Computational fluid dynamics · Heat transfer · Shell and tube
R. Anandan Department of Mechanical Engineering, Vinayaka Mission’s Kirupananda Variyar Engineering College, Salem, Tamilnadu, India e-mail: [email protected] G. Sivaraman Department of Mechanical Engineering, Sona College of Technology, Salem, Tamilnadu, India M. Rajasankar Department of Mechanical Engineering, Rathinam Technical Campus, Coimbatore, Tamilnadu, India e-mail: [email protected] R. Girimurugan (B) Department of Mechanical Engineering, Nandha College of Technology, Perundurai, Tamilnadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_6
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1 Introduction There are many different shell and tube heat exchangers available that can be used for high pressure activities. Shell and tube heat exchangers typically use a number of tubes joined by shells to transfer heat between two fluids. The fluid is contained within the shell itself. Other shapes are required for some purposes, but the most typical shape is a cylinder with a circular cross-section [1–3]. The researchers make use of a single-pass shell called E for the purposes of this investigation. E shells are by far the most frequently used due to their high log-mean temperature difference adjustment factor and low cost/simplicity [4]. As many times as the tubes pass through, additional fluids are heated or cooled by the shell’s internal fluids. A tube sheet separates the tube from the shell. There are some small leaks and bypasses in the heat exchanger model utilized in this study but the mainstream is leak-proof [5]. With the goal of increasing the heat transfer coefficient, baffles are used to deflect fluid flow and provide structural support for the tubes [6]. The distance between the centre lines of two adjacent baffles is referred to as “baffle spacing” [7–9]. The segment height to shell inner diameter ratio is expressed as a percentage on the baffle. The diameter of the inside of the shell can be reduced by 15–45% to accommodate the baffle. Baffles are advantageous for shell and tube heat exchangers because they improve heat transfer and lessen pressure drop [10]. SOLIDWORKS Flow Simulation is used to create heat exchanger shell and tube designs using fluid dynamic baffles like helical and floral baffles [11]. Helical baffles are preferred over segmental baffles when remodelling a shell and tube heat exchanger because they minimize pressure loss and remove dead zones [12]. Three-zone baffles are proposed in this study to dramatically decrease pressure on shell side loss without affecting thermal efficiency. According to the research, shell and tube heat exchangers benefit from three-zone baffles because they speed up heat transmission and decrease pressure loss. Current research involves validating CFD and numerical models of shell side streams. In shell and tube heat exchangers, the performance of the heat exchange can be improved by adjusting the baffles [13]. Each of the alternate baffles was given a different set of angles (0, 45, and 90°). The 45° baffle configuration has a higher heat transfer coefficient than other examples [14]. Researchers in this study compared three STHEs with different percentages of baffle cutouts (between 20 and 40%). The baffle cut has a considerable impact on heat transport and pressure loss, as shown by the CFD analysis. When the baffle was reduced by 40%, heat transfer per pressure loss was greatest, whereas it was lowest when the baffle was reduced by just 20% [15]. In our project, a small heat exchanger is used for CFD simulations. In order to build CFD meshes, the ANSYS Fluent Version and the Gambit mesh generating program are utilized. Model options have a direct effect on the results of the simulation. For five different baffle inclinations, simulations are done using appropriate meshes, discretization methods and turbulence models. The side wall heat flux and pressure loss can be estimated using the simulation findings. Each model is tested against others to see which is the most accurate.
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2 Design and Analysis Using a tiny heat exchanger in this study allows for more accurate modelling of the flow inside the shell, as well as more accurate observations of the flow. There are other design and geometry parameters that have previously been established, as shown in Table 1. Figure 1a–d show the heat exchanger’s geometric models with five various baffle inclination angles, ranging from 40 to 10°. The baffle cut value can be set to either below or above the centre row of the tube, depending on the inclination angle values specified. On the outside of the shell, water was used as a working fluid. To improve the fluent database’s accuracy, the “Thermo-Physical Properties for Saturated Water” tables are used to recalculate the water’s properties. In this research work, Ansys Fluent is selected as the solver and the solver type is a pressure-based solver. Flow and temperature values have been allotted to the heat exchanger’s inlet nozzle to ensure that the heat exchanger performs as expected. The intake of the shell is preheated to 300 K. The pressure differential between the inlet and outlet is measured by applying zero-gauge pressure to the output nozzle. It is assumed that the fluid flow’s input velocity will never change from 5 m/s. Nonslip status is given to all surfaces. If the shell is suitably insulated, the boundary condition for the outer wall is set to zero heat flux. Because addressing the tube side flow is more complicated, our inquiry is concentrated on the flow of the shell side. After being modelled as solid cylinders, tube walls are heated to a constant 600 K. Semi implicit pressure linked equation is selected as the solver equation. In order to get better results, the CFD output results iterations count is maintained as 501 times. Ansys Workbench’s mesh tool is used to generate meshes. Tetrahedral elements are used to mesh the model’s surfaces. Unstructured mesh elements are used to mesh the shell volume; a coarse mesh with around 430,332 elements, and an even finer mesh with about 2,311,900 elements, are both employed in the five-baffle case. The use of turbulence mode is necessary since the flow in this investigation is turbulent. In CFD simulations, the turbulence model used is of paramount importance. A turbulence model can’t be chosen based on a single factor. The turbulence model used in one research may not be applicable to another. It’s a good idea to experiment with a variety of turbulence models. A comparison is made between the two K-Epsilon turbulence Table 1 Design factors and fixed geometric factors
Description
Notation
Value
Length of heat exchanger
L
570 mm
Inner dia of shell
Di
135 mm
Outer dia of tube
DO
20 mm
Pitch
P
30 mm
No of tubes
Nt
7
No. of baffles
Nb
5
Space of Central baffle
B
110 mm
Inclined angle of Baffle
–
0 to 40°
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Fig. 1 3D image of shell and tube heat exchanger with a twist in baffles at a 40°, b 30°, c 20° and d 10°
models used in this research. Because there is only one turbulence equation to solve in the K-Epsilon turbulence models, they are the most computationally efficient in ANSYS Fluent. The standard and realizable k–e turbulence models are tested out. The transport computations, viscosity calculation method, and model variables are the main areas where these two models diverge.
3 Results and Discussions Analysis of heat exchanger baffle inclination angles (0 to 40°) was used to provide the following results and debates, which are summarized below. This study looked at how heat flow and pressure changed inside the heat exchanger when the baffles are angled in different directions. Previous studies have shown that a comprehensive CFD model can accurately simulate turbulent flow heat exchangers by comparing their results to data from an educational demonstration unit with four different baffle inclination angles, which is consistent with our general approach. Turbulence model and discretization order are examined by four dissimilar baffle inclination angles in the first part of this study, which deals with a bigger heat exchanger with turbulent flow. A turbulence model and discretization approach were utilized to assess how the rate flow and baffle angle affected the shell side heat flux causing a decrease in pressure. Now let’s look at how various inclination angles impact wall heat flow and
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cause pressure drop. Figures 2, 3, 4, 5 and 6 demonstrate the changes in wall heat flux within the shell and tube heat exchanger when baffle twist angles range from 0 to 40°. Under the presumption of a vertical baffle, the impacts of baffle inclination on wall heat flow and pressure reduction were investigated. CFD simulations are used to determine the shell’s output temperature, pressure drop and overall heat transfer rate. Minimum and maximum wall heat flux values of 9.49 × 105 and 2.53 × 106 W/m2 K are obtained at 20 and 40° baffle inclination angles. The percent differences between the minimum and maximum wall heat flux values from the CFD analysis results are found 62.44. The percent differences between the 0 and 40° baffle inclination angles for wall heat flux values from the CFD analysis results are found 39.31. The wall heat flux numbers of 0° baffle inclination angle are considered as reference values for percent variance computations for the other four models (10, 20, 30 and 40°). The CFD results on wall heat flow in tube and shell heat exchangers with a 40° baffle inclination angle are more trustworthy because the geometry is changed and the working fluids contact more strongly with the tube’s side surfaces. The agreement still can be considered acceptable only for a 40° baffle inclination angle. In all cases, the agreements in wall heat flux are better when the baffle inclination angle is 40° as compared to the findings obtained using a baffle inclination angle of 0° as the reference. Baffle inclination data produced from heat exchangers with a 0° baffle inclination angle is the most prevalent and hence accounts for the majority of the data set. Therefore, it should be expected for 40° baffle inclination angle to give more accurate results at that angle than other values. It is hard to compare the wall heat flux result for the 40° baffle inclination angle with the other three models (10, 20 and
Fig. 2 Variations on wall heat flux for 0° twist angle in baffle
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Fig. 3 Variations on wall heat flux for 10° twist angle in baffle
Fig. 4 Variations on wall heat flux for 20° twist angle in baffle
30) because the percent differences are in lesser values. There is a small improvement in overall agreement with an inclination angle of 40° when equated to the base values (0 baffle inclination angle). In Fig. 6, the heat flux values for a 40° baffle inclination angle are shown to demonstrate where the principal heat flux streams are located. In the remaining representations (Figs. 2, 3, 4 and 5), recirculation zones may be seen where there are no or few heat flux channels, but the heat transfer area is underutilized. Figures 2, 3, 4, 5 and 6 illustrate that the baffle inclination angle has the greatest effect on the wall heat flux discoveries and that this effect demonstrates exceptionally well-
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Fig. 5 Variations on wall heat flux for 30° twist angle in baffle
Fig. 6 Variations on wall heat flux for 40° twist angle in baffle
covered cross flow with sufficient heat flux streams. According to Fig. 2, when the baffle’s inclination angle is adjusted to 0°, reflections from the baffle that follows it significantly lower the level of agreement. Simulations are carried out with the baffles inclined at 0 and 40°, and the impact of the inclination angle on pressure drop is examined. The technique is the same as in the previous section. CFD runs are once again used to determine shell output temperature and shell pressure drop data, as in the preceding sections. Figures 7, 8, 9, 10 and 11 depict the equivalent changes in pressure loss inside the tube and shell heat exchanger from 0 to 40° of
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Fig. 7 Variations on pressure drop for 0° twist angle in baffle
twist angle in the baffle. The maximum and minimum pressured drop values of 1.86 × 105 and 1.15 × 105 Pa are obtained at 0 and 40° baffle inclination angles. 1.24, 14.07, 25.59 and 38.06 are pressure drop percentages on the shell side through a baffle inclination angle of 0, 10, 20 and 30°, respectively. The percent differences between the pressure drop at 40° baffle inclination angles from the CFD analysis results are found in the range of 12.47, 23.99 and 22.75 for 10, 20 and 30° baffle inclination angles correspondingly. These data serve as a basis for pressure drop calculations for baffles with an inclination angle of 40°. It is advisable to compare data from the 400-baffle inclination angle with data from the 40° baffle inclination angle for the other three baffle inclination angles (10, 20 and 30°), as the continuous helical baffles’ geometry raises the heat transfer coefficient. Only a baffle inclination angle of 40° can be regarded as appropriate for the agreement. When the findings of a 0° baffle inclination angle are used as a reference, it is found that the pressure drop agreements are better when a 40° baffle inclination angle is used. 0° baffle inclination angle is the most common baffle angle, hence most of the reference data comes from heat exchangers with a 0° baffle inclination angle. For this particular baffle angle, it should be predicted that an inclination angle of zero will yield a more accurate result. Comparing findings for baffle inclination degrees of 10, 20 and 30° is easier because the percent differences are smaller than for baffle inclination angles of 0 or 40°. The maximum pressure drops at shell side which is obtained as contour plot for 0° baffle inclination angle is exposed in Fig. 7. Maximum pressure loss occurs at an angle of inclination of 0° for the baffle due to the direct laminar flow of the fluid at the shell side. Figures 8 through 10 depict the pressure drop plots for the working fluid at constant velocity and flow rate for 10, 20 and 30° baffle inclination angles, respectively. Figures 7, 8, 9, 10 and 11 show
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Fig. 8 Variations on pressure drop for 10° twist angle in baffle
Fig. 9 Variations on pressure drop for 20° twist angle in baffle
where the mainstream flows have pressure drops on the shell side. Recirculation zones, depicted in these diagrams as areas with few or no particle routes, are places where available heat transfer area is underutilized. The pressure loss values for tube and shell heat exchangers with 0 and 40° of baffle inclination are shown in Figs. 7 and 11, respectively. At baffle inclination angles of 10, 20, 30, or 40°, the lowest pressure drop is noticed, as it is a well-covered turbulent flow of working fluid. Figure 7 illustrates that the following baffle’s reflections reduce the agreement in
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Fig. 10 Variations on pressure drop for 30° twist angle in baffle
Fig. 11 Variations on pressure drop for 40° twist angle in baffle
pressure drop readings, especially for an inclination angle of 0°. The tube and shell heat exchanger with a 40° baffle tilt has the lowest pressure drop. This results from both the geometrical changes on the baffles and the turbulence of the working fluid flow.
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4 Conclusion Through modelling and analysis of a standard shell and tube heat exchanger at 0, 10, 20, 30 and 40° of inclination, researchers can ascertain which angles are best for striking this balance between high wall heat flow and little pressure drop. It is possible to make an approximation of the heat flow and pressure loss along the side wall of the shell using the results of the CFD simulation. With the aid of turbulence modelling, the wall heat transfer and pressure loss due to the working fluid’s shell side flow may be determined. Baffles with 40° inclination angles produce the best wall heat flux and pressure drop outcomes in comparison to those with 0°. The wall heat flux values obtained from CFD results are in the range of 1.53 × 106 , 1.17 × 106 , 9.49 × 105 , 1.18 × 106 and 2.53 × 106 W/m2 K for 0, 10, 20, 30 and 40° baffle inclination angles correspondingly. The minimum and maximum wall heat flux values of 9.49 × 105 and 2.53 × 106 W/m2 K are achieved at 20 and 40° baffle inclination angles, respectively. The wall heat flux values of the 40° baffle inclination angle model are 39.31% higher than that of the 0º baffle inclination angle model. The pressure drop values obtained from CFD results are in the range of 1.86 × 105 , 1.83 × 105 , 1.59 × 105 , 1.48 × 105 and 1.15 × 105 Pa for 0, 10, 20, 30 and 40º baffle inclination angles correspondingly. The minimum and maximum wall pressure drop values of 1.15 × 105 and 1.86 × 105 Pa are achieved at 40º and 0º baffle inclination angles, respectively. The 40° baffle inclination angle model’s pressure drop values are 38.06% lower than those of the 0° baffle inclination angle model. The baffle angle is important when mixing shell and tube heat exchangers.
References 1. Gaddis D (2007) Standards of the tubular exchanger manufacturers association, 9th edn. Tarrytown, New York 2. Schlunder EV (1983) Heat exchanger design handbook. Hemisphere Publishing Corp, New York 3. Mukherjee R (2004) Practical thermal design of shell-and-tube heat exchangers. Begell House, New York 4. Ozden E, Tari I (2010) Shell side CFD analysis of a small shell-and-tube heat exchanger. Energy Convers Manage 51(5):1004–1014 5. Kapale UC, Chand S (2006) Modeling for shell-side pressure drop for liquid flow in shell-andtube heat exchanger. Int J Heat Mass Transf 49(3–4):601–610 6. Thirumarimurugan M, Kannadasan T, Ramasamy E (2008) Performance analysis of shell and tube heat exchanger using miscible system. Am J Appl Sci 5(5):548–552 7. Sparrows EM, Reifschneider LG (1986) Effect of inter baffle spacing on heat transfer and pressure drop in a shell-and-tube heat exchanger. Int J Heat Mass Transf 29(11):1617–1628 8. Li H, Kottke V (1998) Effect of baffle spacing on pressure drop and local heat transfer in shelland-tube heat exchangers for staggered tube arrangement. Int J Heat Mass Transf 41(10):1303– 1311 9. Than STM, Lin KA, Mon MS (2008) Heat exchanger design. World Acad Sci Eng Technol 46(22):604–611
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10. Kakac S, Liu H (2002) Heat exchangers selection, rating and thermal design, 2nd edn. CRC Press, Washington D C 11. Ambekar AS, Sivakumar R, Anantharaman N, Vivekenandan M (2016) CFD simulation study of shell and tube heat exchangers with different baffle segment configurations. Appl Therm Eng 108:999–1007 12. Zhang M, Meng F, Geng Z (2015) CFD simulation on shell-and-tube heat exchangers with small-Angle Helical Baffles. Front Chem Sci Eng 9(2):183–193 13. Biçer N, Engin T, Ya¸sar H, Büyükkaya E, Aydın A, Topuz A (2020) Design optimization of a shell and tube heat exchanger with Novel Three-Zonal Baffle by using CFD and Taguchi method. Int J Therm Sci 155:106417 14. Mukilarasan N, Karthikeyan R, Ramalingam S, Dillikannan D, Ravikumar J, Sampath S, Kaliyaperumal G (2022) Influence of baffles in heat transfer fluid characteristics using CFD evaluation. Int J Ambient Energy 1–13 15. I¸sık E, Tugan V (2021) Investigation of the effect of baffle cut on heat transfer and pressure drop in shell-and-tube heat exchangers using CFD. Heat Transfer Res 52(10)
A Study on Indoor Air Quality of Air-Conditioning System Using Duct Insulation and Filters for Industrial Applications Amit Kumar Behera and Debasmita Mishra
Abstract Heating Ventilation and Air-Conditioning System (HVAC) is very much essential for proper human health and well-being of domestic environment as well as office buildings and industries. Air-conditioning system for comfort conditions used to be very expensive and extensively used only by a few people, but these days, it has become a necessity and not a luxury anymore. Air-conditioning in small spaces like domestic homes and small office buildings, etc. use split or window air conditioners, while large multiplexes and buildings use Central air-conditioning system because there the cooling load required is very high. In Central Air-conditioning, the central plant is installed away from the space to be conditioned where water and air are to be cooled. The air which is cooled through the central plant is not directly supplied to the spaces or areas to be conditioned rather they are carried away through ducts. The cool air is carried by the ducts from the air-conditioning system to the rooms and again air returning from the rooms is directed back to the conditioning equipment to be conditioned and circulated again. Proper design of a duct is essential to avoid frictional losses, noise, uneven distribution of cool air inside the cooling space, higher power consumption and more capital. In this research, a numerical study with comparative analysis is carried out about duct insulation. It also showcases the cumulative comparison of different filters and different types of insulation provided inside the duct which analyses their efficiency and workability for indoor air quality. Keywords Air-conditioning system · MERV rating · Air filters · Insulation material · Heat gain
A. K. Behera · D. Mishra (B) Department of Mechanical Engineering, Veer Surendra Sai University of Technology, Burla, Odisha 768018, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_7
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1 Introduction As the world’s population growth increases, the demand for comfort increases at a rapid rate. Because of the inferior state of the environment, humans require more comfort (like light, sound, and machine which produces heat). Human comfort is greatly influenced by sound, light, and heat. According to researchers, the human body is accustomed to 22–25 °C of temperature. The human body feels unpleasant when the room temperature becomes higher than or even when it is lower than this range. This is because it is designed to be exposed to a specific amount of light, heat, and humidity. Failure to do so can result in sunburns, heatstroke, and other skin disorders. There are numerous kinds of window air conditioners, split air conditioners, and other air conditioners (central air conditioning). These conditioning systems are utilized in small rooms or offices when the cooling load is modest but central air conditioning systems are utilized when it is particularly high, such as in a multiplex building or a hospital. Air that is cooled is not sent directly into the spaces in a central air conditioning system. To offer a comfortable environment, this cool air should be dispersed appropriately in the rooms or places to make them chilly. Ducts are placed when air that is cooled could not be distributed to various spaces, especially larger spaces directly. A series of ducts connected to the supply duct transport cooled air through the air-conditioning unit to different points as well as carry the returning air from the spaces for re-conditioning and recirculation. Since ducts for air transport and distribution cost about 20–30% of the overall cost, it becomes vital to consider designing the duct system in such a way that the initial cost as well as the running cost of ducts are reduced. Azimi [1] discovered that filtering of recirculation air might be employed to minimize airborne infectious illnesses, particularly in the indoor environment but by employing certain standard devices, no direct link has been established between the use of mechanical filters in buildings and reduced asthma symptoms. According to Chatoutsidou [2], mechanical ventilation and filtration can remove more than 50% of outdoor particles; also, the mass concentration of outdoor PM 10 was higher than that of inside PM 10, and the efficient removal of particulate matter was substantial outside. Outdoor air is a key source of particle matter and added total particles to indoor air during the absence of occupants’ activities. Using the Taguchi approach, Isak Kotcioglu et al. [3] discovered what was the optimal design value in the case of a rectangular duct. Their investigation used an optimization technique to get the lowest pressure drop and highest heat transfer rates. They arrived at an appropriate determined value that satisfied the criterion, i.e., less friction drop and maximum heat transmission, after ten experiments. Aryal et al. [4] investigated the effects of allotments in a cooled building on warm comfort and indoor air quality. CFD experiments were used to replicate elements in interior air before or after segment establishment or expulsion. The recommended philosophy will aid in obtaining solid evidence in the segment’s set-up for various needs in indoor areas. Pillai et al. [5] reviewed the studies regarding the flow of air and temperature in the system with variation in cooling loads by CFD analysis. As a result, CFD simulation is utilized to analyse various systems listed above in order to obtain appropriate
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findings, which are then compared to numerical values acquired. By properly positioning the air inlet vents and exhaust ports, comfort conditions could be achieved. The CFD tool can hence be used to design and obtain the desired optimum results. An air-conditioning system should deliver specified rates of flow to prescribed locations. Also, cost-effectiveness in terms of the original investment, fan operation, and building space should be considered. While designing an air conditioning duct system, it should be taken care that it should not transmit or emit unpleasant noise in general. Fans and exhaust of air positions should be fixed early on and then the duct layout is created, taking into account the available space as well as the convenience of installation. A variety of combinations can be used to transport the appropriate amount of air via the air conditioning ducts. However, only one set of findings is the best design for a specific system. The objective of this research work is to analyze the impact of insulation of square duct on the indoor air quality of air conditioning system, and also analyze how efficiently the insulation of duct can improve indoor air quality during summer air conditioning by additional use of filter.
2 Methodology and Modelling The gain in heat is dependent on the temperature of the air inside the duct as well as the temperature of the space surrounding the duct. The gain of heat (Qd ) can be calculated as Q d = U Ad (Ta − Ts)
(1)
where U= Overall heat- transfer coefficient Ad = Surface area of the duct Ta = temperature of ambient Ts = temperature of supply air Heat gain through the duct (Qd ) for steady-state thermal condition is calculated as Q d = U Ad (Ta − Ts)
(2)
where A = 0.9*0.9 = 0.81 m2 Ta = 37 °C = 310 K Ts = 27 °C = 300 K Q d = 40.5 W
2.1 Numerical Values Obtained for Heat Gain Through Ducts Using ANSYS-15 The framework was to optimize the impact of insulation of duct in indoor air quality by using steady-state thermal analysis and fluid fluent by using ANSYS (R-15).
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Analysis was carried out by the following two methods: Steady State Thermal Analysis and Fluid Fluent. Transient thermal was also used to represent temperature over time. ANSYS CFX is a fluid flow software that works on a workbench environment that serves to be the foundation of simulation and analysis. Its adaptable framework enables users to quickly install anything from general fluid glide analyses to complex interacting systems using simple drag-and-drop procedures.
2.1.1
Simulation and Analysis of the Duct by Using ANSYS-15 (Steady State Thermal)
The duct material selected was aluminium alloy whose thermal conductivity is 202 W/mk. The insulative material taken was Polyurethane foam (PUF) with thermal conductivity of 0.023 W/mk. The reason behind opting PUF as the insulation material is because of its high value of R, which is a measure of the type of insulation. A higher value of R signifies better insulation as well as good dimensional stability. The dimension of the outer section is 5 × 2 × 2 m and the inner section is 5 × 1 × 1 m. The temperature applied in the outer part is 37 °C and the temperature applied at the inner part is 27 °C. The heat flux provided from the outside wall to inside insulation is 5 W/m2 and the heat transfer coefficient provided from inner insulation to air is 5 W/m2 °C.
2.1.2
Simulation and Analysis of the Duct by Using ANSYS-15 (Fluid Fluent)
Air fluid which is prominent or standardized for analyzing indoor air quality was used inside the duct. Different parameters have been set for finding out various solutions using fluid fluent technique. The inlet pressure was maintained at 1 bar and outlet pressure at 2 bar with a velocity of 2 m/s. The temperature inside the duct is 290 K, the temperature of the insulated region outside the duct is in the range of 1–300 K, and the temperature of the insulated region inside the duct is in the range of 2–300 K. While considering the pressure velocity coupling, Simplec scheme was provided with a least square cell-based gradient to get the solution.
2.1.3
Simulation and Analysis of the Duct by Using ANSYS-15 with Filter
A pleated filter material made of polyester was provided in order to prevent the passage of air pollutants such as pollen, pet dander, and mould spores with a thermal conductivity of 0.05 W/mk. It also captures more particles without restricting airflow which results in greater efficiency. The dimensions taken for the filter are 0.9 × 0.9x0.2 m. The mesh size provided was 0.001 mm. All the parameters set for the square duct with a filter were the same as the square duct having no filter.
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2.1.4
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Simulation and Analysis of the Duct by Using ANSYS-15 (Transient Thermal with Filter)
The same procedure was followed as per the steady state with filter and all the material selection is the same used in steady state thermal. After meshing, all parameters set for this square duct with a filter were the same as before, i.e., for the square duct having no filter.
3 Results and Discussions 3.1 Solution Obtained for Steady-State Thermal Condition Using ANSYS-15 3.1.1
Temperature Profile
Figure 1 depicts the temperature flow inside the duct with an additional layer of outside insulation. It was assumed that the flow of air inside the duct is steady. The maximum temperature observed was at the outer region of the duct, i.e., about 37.44 °C which reduces to 33.743 °C at the outer section of insulation and gradually drops to 29.248 °C at the inner section of insulation. The minimum temperature obtained across the cross section of the duct was about 27 °C, which is the supply of air through the duct to the Air-conditioning equipment. Ducts are insulated so that when the air through the ducts is carried back to the air-conditioning equipment, it does not leak outside the ducts. In this problem statement, we have assumed that the leakage is minimal as another insulation region was taken outside the duct which keeps the airflow inside the duct to be steady and at a constant temperature.
3.1.2
Variation of Heat Flux
As per the simulated values, it has been observed in the temperature profiles that the maximum heat flux was obtained at the innermost edges of the duct which was about 56.909 W/m2 and minimum heat flux was obtained at the outermost edges of the duct or outermost edge of the insulation part which was about 0.48151 W/m2 . There was no loss of heat flux through the insulation part of the duct (Fig. 2).
3.1.3
Directional Heat Flux
As per the simulated values, various values of directional heat flux have been observed in the temperature profiles. Maximum directional heat flux was obtained across outside and inner insulation of some part having value 44.722 W/m2 . Across the
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Fig. 1 Temperature profile for steady state of square duct
Fig. 2 Heat flux profile for steady state of square duct
inner part of the duct entrance, it has a moderate value of 4.969 W/m2 and the least value occurs in the down part of the duct entrance towards the outer part (Fig. 3).
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Fig. 3 Directional heat flux profile steady state of square duct
3.2 Fluid Fluent Results 3.2.1
Temperature Variance Contour
After applying all boundary conditions, the temperature variance contour has been depicted across the duct which is shown by selecting one plane. Figure 4 depicts the temperature flow inside the duct with an additional layer of outside insulation. It was assumed that the flow of air inside the duct is steady and the maximum temperature observed was about 300 K at the outer region of the duct which reduces to 298 K at the outer section of insulation and gradually drops to 293 K at the inner section of insulation. The minimum temperature obtained across the cross section of the duct is about 290 K which is the supply of air through the duct to the Air-conditioning equipment. Insulation of ducts is done so that air moving inside the duct system stays at the same temperature such that the heat does not leak outside.
3.2.2
Pressure Variance Contour
Various pressure values have been depicted in the plot which shows that nearly negligible pressure drops have occurred. As per the boundary conditions at the inlet, we have taken the pressure value of 1 bar and 2 bar at the outlet. The pressure contours have been shown in the figure below. It shows that a constant value of 1.004 bar or 10,040 Pa pressure has been maintained through the duct which ensures a negligible pressure drop inside the duct flow (Fig. 5).
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Fig. 4 Temperature variance contour for square duct
Fig. 5 Pressure variance contour for square duct
3.2.3
Wall Shear X
Figure 6 shows the wall shear in the x direction by selecting one plane. As per the simulation, different values have been plotted across the plane and ensure about shear factor across the plane.
Fig. 6 Wall shear x-contour for square duct
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Fig. 7 Velocity contour for square duct
3.2.4
Velocity Contour
Figure 7 depicts about velocity contour inside the duct. As per the boundary condition applied for the square duct, the minimum velocity obtained by using fluid fluent is 2 m/s which is the supply velocity during boundary condition and ensures a steady flow inside the duct. It shows the detailed thermal analysis of the square duct and its temperature behaviour across insulation. It also shows fluent characteristics about pressure wall pressure and velocity contour inside airflow.
3.3 Steady State Thermal Results with Filter 3.3.1
Temperature
Figure 8 depicts the temperature flow inside the duct with an additional layer of outside insulation along with the filter. It was assumed that the flow of air inside the duct is steady and the maximum temperature observed was at the outer region of the duct outside insulation of about 37 °C. The minimum temperature obtained across the cross section of the duct is about 27 °C which is the supply of air through the duct to the air-conditioning equipment. Since we are using filter while performing the analysis only we could only visualize away from the filer which is shown in blue colour.
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Fig. 8 Temperature profile for steady state of square duct using filter
3.4 Transient Thermal Results 3.4.1
Temperature Global Minimum and Temperature Global Maximum
Figure 9 depicts the variation of temperature with respect to time from 0.017 up to 1 s. The figure clearly shows a negligible variance of change in temperature with respect to time. For 0.017 s, the temperature value is found to be 26.999 °C. After that, from 0.1203 to 1 s, the temperature value was found to be steady at a value of 27 °C. Figure 10 depicts the variation of temperature with respect to time starting from 0.0.17 up to 1 s. The figure clearly shows no change in temperature with respect to time. From 0.017 to 1 s, the temperature value was found to be steady at 37 °C.
3.4.2
Temperature Global Minimum and Temperature Global Maximum for Transient Thermal Condition
For global minimum, the value of temperature was found to be 26.99 °C from 0.017 to 0.08 s and from 0.1203 to 1 s, the temperature was found to be 27 °C. For global maximum, it was found to be steady at a temperature of 37 °C.
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Fig. 9 Temperature global minimum
4 Conclusion The pressure variance has a negligible effect on the change across the insulation foam. By reducing heat transfer and leaks, we can see a significant gain in performance. The reduction of energy consumption is just one benefit of adding insulation to the ductwork. When the temperature falls below enough, the moisture-laden air from within the space will begin to lose its moisture and condensation starts. The use of a pleated filter at the entrance of the square duct increases the surface area, creating more density and improving airflow because these filters collect more number of particles without causing an increase in energy consumption. The use of a filter helps to easily catch dust, pollen, and mould, and creates a low chance of bacterial growth on pleated filters which results in better performance and greater efficiency. In addition to that, insulation of the duct reduces overall utility expenses. Insulation of the duct prevents sudden temperature changes, thereby preventing thermal shocks. In some aspects, moisture plays a major role inside the duct which may cause mould issues across the duct. Therefore, a duct with proper insulation and filter protects it from mould getting stuck to it. Lastly, it acts as a sound barrier which helps to allow flow smoothly and efficiently.
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Fig. 10 Temperature global maximum
References 1. Azimi P, Stephens B (2013) HVAC filtration for controlling infectious airborne disease transmission in indoor environments: predicting risk reductions and operational cost at. Build Environ 70:150–160 2. Chatoutsidou SE, Ondracek J, Tesar O, Torseth K, Zdimal V, Lazaridis M (2015) Indoor/outdoor particulate matter number and mass concentration in modern offices. Build Environ 92:462–474 3. Kotcioglu I, Cansiz A, Khalaji MN (2013) Experimental investigation for optimization of design parameters in a rectangular duct with plate-fins heat exchanger by Taguchi method. Appl Therm Eng 50:604–613 4. Aryal L (2016) CFD analysis on thermal comfort and indoor air quality affected by partitions in air-conditioning building. Appl Mech Mater 836:121–126 5. Pillai S, Bhand S, Shinde V (2016) A Review on CFD analysis in air-conditioning system. Int J Curr Eng Technol E-ISSN-2277–4106, P-ISSN 2347–5161
Optimization Applications in Engine
Maximization of Compressive Strength of Fused Deposition Modeled Antibacterial Polylactic Acid by Taguchi Method M. Ajay kumar , Dipabrata Banerjee , Swayam Bikash Mishra , Basanta Kumar Nanda, and Santhosh Kumar Nayak Abstract Because it can create 3D physical prototypes without being constrained by geometric complexity, fused deposition modelling is acquiring unique advantages. However, when it comes to accuracy and efficiency, these advantages are not as obvious, therefore, it is important to investigate how to increase them. The antibacterial polylactic acid is chosen as the material for developing the specimens by fused deposition modeling. The layer thickness, printing speed, and infill density are chosen as input parameters and compression is designated as the output constraint. The L9 orthogonal array is utilized to find the combination of input constraints to print the specimens and the Taguchi approach is adopted to exploit the compression of printed specimens. The optimized process constraints for maximizing the compressive strength are a printing speed of 80 mm/sec, infill density of 60%, and layer thickness of 0.12 mm. Keywords Silver infused polylactic acid · Fused deposition modeling · Compressive strength · Taguchi approach
1 Introduction Additive manufacturing is gaining traction in a variety of solicitations, and the recent engineering sector is eager to use it to substitute traditional methods where possible [1, 2]. Additive manufacturing (AM) and fast prototyping are other terms for 3D printing (RP). FDM is a complicated process with many variables that influence the quality of products and the qualities of the material, and the interaction of selected variables is often difficult to comprehend. Some of the printing parameters are the thickness of the layer, orientation of build products, angle of the raster, air gap, width of the raster, feed rate, infill pattern, infill density, and printing speed. These M. A. kumar · D. Banerjee · S. B. Mishra (B) · B. K. Nanda · S. K. Nayak KIIT DU, Bhubaneswar 751024, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_8
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parameters showed more effect on tensile strength, flexural strength, compressive strength, and hardness [3, 4]. Because mechanical qualities are imperative for practically used parts, it’s perilous to expression into the effect of process constraints over mechanical performance. The most important parameter is layer thickness; the thicker the layer, the greater the compressive strength of the FDM-produced object [2, 5, 6]. Similar to the tensile property and compressive property of an FDM, the printed object increases with increasing infill fraction, detailed infill pattern, and amount of solid silhouettes or external layers. To comprehend their effect on compressive strength, more factors need to be explored, including printing temperature, the width of the raster, and air gap, among others [7, 8]. Investigations are being done on the mechanical characteristics of PLA parts materials and the hexagonal design was the strongest when the samples were printed at a density of 15%, while the 100% infill pattern was the weediest when printed at a density of 100%, producing results that behaved like brittle materials. The results showed that printed goods’ mechanical characteristics are expressively affected by the infill pattern [9]. Researchers discovered that when the thickness of layer escalations and reductions as rate of feed rises, the compressive property and hardness of FDM prints in the upright orientation both improve. For the on-edge orientation, they found very negligible changes in mechanical characteristics with variations in layer thickness and feed rate, with the exception of low layer thickness [10, 11]. Design of the experiments employed on the nonfiction for FDM to show that production constraints such as the angle of the raster, air gap, thickness of the layers, width of the raster, and elevation of slice have a substantial influence over the mechanical characteristics of FDM printed prototypes. The speed of printing and thickness of the layer of the FDM machine affect the tensile and compressive strengths. Using reaction surface plots, the consequence of the fused deposition modeling procedure as factors such as the orientation of the build, the thickness of the layer, and so forth on the power of the quantities was explained using central composite design (CCD). It has been proposed that when layer thickness increases, strength increases because the distortion effect is reduced. Similarly, the associated strength reduced as the orientation of the build rate improved from a perfect usual value [12–14]. In this research, silver-infused polylactic acid is selected as the input filament material for developing the specimens with a fused deposition modeling machine. The L9 orthogonal array is adopted to find the combinations of printing constraints and the Taguchi approach is adopted to maximize the compressive strength.
2 Materials and Methods Silver-infused polylactic acid is selected as a material for developing the specimens for checking the compressive strength of the materials. The Taguchi approach is adopted to improve the compressive strength and the L9 orthogonal selection is employed to find the nine combinations of process constraints. The printing speed,
Maximization of Compressive Strength of Fused Deposition Modeled … Table 1 Combinations of process parameters
77
Printing speed
Infill density
Layer thickness
40
40
0.12
40
60
0.18
40
80
0.24
60
40
0.18
60
60
0.24
60
80
0.12
80
40
0.24
80
60
0.12
80
80
0.18
infill density, and layer thickness are nominated as input process constraints. The output constraints are compressive strength. The maximum and minimum ranges of process parameters are 40 to 80 mm/sec of printing speed, 40 to 80% of infill density, and 0.12 to 0.18 mm of layer thickness. The combination of process parameters is displayed in Table 1. The fit to a linear model-based Taguchi approach is employed to maximize the compressive strength and the ANOVA is adopted to check the effect of the printing constraints on the output constraints. The specimens are printed by a fused deposition modeling machine by following the ASTM standard. The CAD software is used to create the three-dimensional specimens by using the ASTM standard and slicing software is employed to prepare the code for printing the specimens for testing. The nine combinations of process parameters are used as input in slicing software for preparing the code to give the input for the fused deposition modeling machine.
3 Result and Discussions The nine specimens are printed using a universal testing machine (UTM) employed to test the compressive property of the printed samples, and the results are displayed in Table 2. The input process parameters are printing speed, infill density, layer thickness, compressive strength is selected as output parameters and a linear modelbased Taguchi approach is adopted to maximize the output. A means and signalto-noise ratio-based response table is utilized to find the most significant process constraint on the compressive strength. The ANOVA examination is adopted to check the influence of process constraints on compressive strength. The means and signalto-noise ratio-based main effect diagram are used to check the range of parameters influencing the compressive strength. The combination of input printing constraints to maximize the compressive strength is predicted by the Taguchi approach.
78 Table 2 Combinations of process parameters with compressive strength
M. A. kumar et al. Printing speed
Infill density
Layer thickness
Compressive strength
40
40
0.12
32.58
40
60
0.18
35.28
40
80
0.24
37.04
60
40
0.18
35.87
60
60
0.24
41.56
60
80
0.12
39.82
80
40
0.24
41.18
80
60
0.12
43.48
80
80
0.18
41.68
3.1 Maximization of Compressive Strength The means and S/N-based reaction table are exposed in Table 3. The individual effect of input process constraints over the compressive property is checked by the response table by S/N ratio and means. The larger is better is chosen as the option for exploiting the compressive property. Based on the three levels of the input parameters and their output values, the delta values are calculated. The printing speed, infill density, and layer thickness are ranked based 3.1on the delta values. The delta values from the response table for the S/N ratio are 1.63, 0.81, and 0.53 for printing speed, infill density, and layer thickness, respectively. The delta values from the response table for means are 7.15, 3.56, and 2.32 for printing speed, infill density, and layer thickness. The delta values for S/N ratios and means clearly display that the printing speed is ranked first and the infill density and the layer thickness are ranked as second and third. It clearly displayed that the printing speed is the most influential process constraint than the infill density and thickness of the layer of samples and the density of the infill of samples is more substantial than the layer thickness. It means that the printing speed is showed most influence on the compressive strength than the other two process parameters. Both S/N ratio and means constructed on the response table show that the printing speed is the most influential parameter on the compressive strength. The main effect plots based on S/N ratio and means are displayed in Fig. 1. The main effect plot is utilized to show the influence of individual process constraints on compressive strength and the main effect plot for means is displayed in Fig. 1a. The means of means for the plot is 38.86 N/mm2 and when the printing speed is low, the compressive strength is very low, the speed is increased to 60 mm/s and the compressive strength is increased and the strength is high at maximum speed. The compressive property is low at the low density of the infill, and it is high at 60% of the infill density. The compressive strength is medium at low layer thickness and it is low at medium layer thickness and it is maximum at high layer thickness.
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Table 3 Means and signal-to-noise ratio-based response table Response table for means
Response table for S/N ratio Level
Printing speed
Infill density
Layer thickness
Printing speed
Infill density
Layer thickness
1
30.86
31.22
31.68
34.97
36.54
38.63
2
31.82
32.03
31.48
39.08
40.11
37.61
3
32.49
31.92
32.01
42.11
39.51
39.93
Delta
1.63
0.81
0.53
7.15
3.56
2.32
Rank
1
2
3
1
2
3
Fig. 1 Main effect plot for means and signal-to-noise ratio
The main effects plot for the S/N ratio is displayed in Fig. 1b and the means of the S/N ratio is 31.75 N/mm2 . The compressive property is low at low printing speed and it is creased with increasing the printing speed at it is maximum at high printing speed. The compressive property is low at the low density of infill, and it is maximum at 60% of infill density. The compressive strength is medium at low layer thickness, low at medium layer thickness, maximum at high layer thickness. The outcome of individual input process constraints on compressive strength is analyzed by ANOVA and is revealed in Table 4. The sum of squares and the adjusted sum of squares are also displayed in Table 4. The adjusted mean squares for printing speed, infill density, and layer thickness are 38.61, 10.94, and 4.05, respectively. It displayed that the speed of printing is the greatest individual influential process constraint on the compressive strength than the remaining two process constraints. The P-value is most important to check the consequence of the input process constraints on the compressive property and the value must be 0.05. The P-value for printing speed, infill density, and layer thickness are 0.001, 0.003, and 0.007. So, the value is less than 0.05 and all three process parameters are showed the individual and combined effect on the compressive strength. The R-square and adjusted R-square are 99.94 and 99.77% and the between them must be less than 2%.
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Table 4 ANOVA table for compressive property Source
DF
Seq SS
Adj SS
Adj MS
F
P
Printing speed
2
77.203
77.2027
38.6013
1266.54
0.001
Infill density
2
21.870
21.8703
10.9351
358.79
0.003
Layer thickness
2
8.091
8.0906
4.0453
132.73
0.007
Residual Error
2
0.061
0.0610
0.0305
Total
8
107.224
S
R-Sq
R-Sq(adj)
0.1746
99.94%
99.77%
Model summary
The difference between them is 0.17 and it clearly displays that the selected three process constraints showed the combined effect on the compressive strength.
4 Conclusions The silver-infused polylactic acid is selected as the input filament material for developing the specimens by a fused deposition modeling machine. The L9 orthogonal array is adopted to find the combinations of process constraints and the Taguchi approach is adopted to maximize the compressive strength. The output of the optimization is as follows: . The signal-to-noise and means-based response tables clearly show that the printing speed is the most influential parameter of the other two process parameters on compressive strength. . The main effect plots clearly displayed that the medium and high ranges of input process parameters are shown the effect on the output. . The ANOVA table showed that the three process parameters’ individual and combined influence the compressive strength. . The optimized process parameters for maximizing the compressive strength are a printing speed of 80 mm/sec, infill density of 60%, and layer thickness of 0.12 mm.
References 1. UzZaman U, Boesch E, Siadat A, Rivette M, Baqai A (2016) Impact of fused deposition modeling (FDM) process parameters on strength of built parts using Taguchi’s design of experiments. Int J Adv Manuf Technol 101:1215–1226 2. Srivastava RS (2018) Optimization of FDM process parameters by Taguchi method for imparting customized properties to components. Virtual Phys Prototyp 13:203–210
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3. Chacon JM, Caminero MA, Nunez PJ, Garcia-Plaza E, Becar JP (2021) Effect of nozzle diameter on mechanical and geometric performance of 3D printed carbon fibre-reinforced composites manufactured by fused filament fabrication. Rapid Prototyp J 27:769–784 4. Enemuoh E, Menta V, Abutunis A, O’Brien S, Kaya L, Rapinac J (2021) Energy and eco-impact evaluation of fused deposition modeling and injection molding of polylactic acid. Sustainability 13:1875 5. Mohamed S, Masood H, Bhowmik JL (2015) Optimization of fused deposition modeling process parameters: a review of current research and future prospects. Adv Manuf 3:42–53 6. Popescua D, Zapciua A, Amzab C, Baciuc F, Marinescud R (2018) FDM process parameters influence over the mechanical properties of polymer specimens: a review. Polym Test 69:157– 166 7. Dey A, Yodo N (2019) A systematic survey of FDM process parameter optimization and their influence on part characteristics. J Manuf Mater Proc 3:64 8. Peng T, Yan F (2018) Dual-objective analysis for desktop FDM printers: energy consumption and surface roughness. Procedia CIRP 69:106–111 9. Griffiths C, Howarth J, De Almeida-Rowbotham G, Rees A, Kerton R (2016) A design of experiments approach for the optimisation of energy and waste during the production of parts manufactured by 3D printing. J Clean Prod 139:74–85 10. Dakshinamurthy D, Gupta S (2016) A study on the influence of process parameters on the viscoelastic properties of ABS components manufactured by FDMprocess. J Instit Eng (India)– Series C 99:133–138 11. Mohan N, Senthil P, Vinodh S, Jayanth N (2017) A review on composite materials and process parameters optimisation for the used deposition modelling process. J Virtual Physic Prototyping 12(1):47–59 12. Dong G, Wijaya G, Tang Y (2018) Zhao YFOptimizing process parameters of fused deposition modeling by Taguchi method for the fabrication of lattice structures. Addit Manuf 19:62–72 13. Lanzotti A, Grasso M, Staiano G, Martorelli M (2015) The impact of process parameters on mechanical properties of parts fabricated in PLA with an open-source 3-D printer. Rapid Prototyp J 21(5):618–627 14. Zhang JW, Peng AH (2012) Process-parameter optimization for fused deposition modelling based on Taguchi method. Adv Mater Res 538:444–447
Analysis of Drivers and Barriers in Green Supply Chain Management Using Fuzzy AHP and Fuzzy TOPSIS Approach Soumya Ranjan Pradhan and Siba Sankar Mahapatra
Abstract Environmental concerns around the world necessitate manufacturing firms to embrace new practices that may lessen the negative environmental effect. These days, green supply chain management (GSCM) is gaining more popularity in the manufacturing sector from material acquisition to product delivery to the customers. Adoption of GSCM by Indian manufacturing firms is comparatively slow due to a lack of identification of different barriers and drivers of GSCM. To this end, the present study aims at the identification of barriers and drivers of GSCM, typical to the Indian scenario. Individual groups of six barriers and eight drivers have been identified for this research by industry experts. In addition, the study prioritizes these enabling factors using multi-criteria decision-making (MCDM) processes such as the analytic hierarchy process (AHP) approach and the technique for order of preference by similarity to ideal solution (TOPSIS) approach. In order to handle the ambiguity in decision-making, the data have been extracted from the experts using linguistic terms. A case of an Indian paper manufacturing firm is considered for this study. The fuzzy AHP (F_AHP) model indicates financial implication and lack of awareness/participation in GSCM as first and last ranked barriers, respectively, whereas the fuzzy TOPSIS (F_TOPSIS) model indicates economic consideration and customer, market and societal pressure as first and last ranked drivers, respectively. Finally, to encapsulate the robustness of the final result, sensitivity analysis has been carried out. This research will undoubtedly assist policymakers in developing policies that will facilitate GSCM implementation. Keywords GSCM · MCDM · F_AHP · F_TOPSIS · Sensitivity analysis
S. R. Pradhan (B) · S. S. Mahapatra Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha 769008, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_9
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1 Introduction Rapid industrialization causes a negative impact on the environment by generating solid waste as well as chemical emissions. If the rate of emissions persists, it will undoubtedly pose serious concerns in the long run for both the current and future generations [1, 2]. Therefore, the concept of sustainability must be adopted. Now, the demand for green products is increasing at a rapid pace. It is felt that GSCM is vital for the sustenance of industries. In the twenty-first century, industries are becoming more concerned with environmental stability and constantly looking for new ways to meet long-term sustainability standards [3]. Manufacturing industries intend to reduce pollution levels by implementing GSCM practices because GSCM considers environmental effects at each phase of the supply chain (SC) and tries to minimize adverse environmental effects [4–7]. Manufacturing firms encounter numerous barriers to GSCM implementation that they wish to overcome. The most common barriers are lack of awareness, inadequate commitment from top management, economic hindrances, and so on. However, these barriers may differ from one firm to the other [8]. Therefore, it is vital to identify relevant barriers specific to industry type and geographical location and recognize their interdependence. Though there are many opposing factors in the implementation of GSCM, there are also many positive factors that influence the process and motivate firms to adopt sustainable practices in their SC activities. These elements are commonly referred to as GSCM drivers. Govindan and Bouzan [9] have investigated reverse logistics and identified thirty-six barriers and thirty-seven drivers in the coal sector. Singh and Trivedi [10] have considered various drivers such as top management commitment, green packaging, vendor development, and so on that enhance the industries’ SC performance. Strict government regulations motivate industries to GSCM implementation [11]. To determine the interdependence of those enabling factors, MCDM approaches have been adopted to prioritize the factors influencing GSCM. In order to solve MCDM problems, input data (expert opinion) must be provided. In some cases, a qualitative opinion is required to define certain criteria rather than a quantitative one. Therefore, the fuzzy concept can be adopted to aid in reducing the uncertainty and ambiguity that exists in the decision-making processes. The two MCDM techniques widely used in decision-making are the F_AHP method and the F_TOPSIS method. Patil and Kant [12] have successfully implemented the fuzzy AHP-TOPSIS method to distinguish knowledge management solutions and prioritize those solutions that can assist organizations in implementing them in their business. Sirisawat and Kiatcharoenpol [13] have proposed a model to prioritize reverse logistics solutions with the help of F_AHP and F_TOPSIS. Ertu˘grul and Karaka¸so˘glu [14] have carried out a comparison between the F_AHP approach and the F_TOPSIS approach for selecting the facility location of a Turkish textile company. This study focuses on identifying and categorizing relevant GSCM barriers and drivers, as well as ranking those enabling factors to assist industries in effectively
Analysis of Drivers and Barriers in Green Supply Chain Management …
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implementing GSCM. Because this study focuses on Indian industries, a paper manufacturing company located in the Eastern part of the country is considered. At the outset, barriers and drivers are classified considering the opinions collected from industry experts. Then, in this study, fuzzy concept incorporated MCDM approaches such as F_AHP and F_TOPSIS are used. The main reasons behind the adoption of this integrated model are the level of simplicity and ease of converting the procedures into programmable ones [15]. Next, the integrated model is capable of determining the weights of criteria and using them further in deciding the rank of alternatives according to individual geometric distance from the ideal solution. The AHP is capable of minimizing the complexity of decision-making by transforming the complex problem into a simpler hierarchical structure [15]. The TOPSIS is capable of prioritizing the alternatives by evaluating the optimal solution which is nearest to the positive ideal solution and furthest from the negative ideal solution [16]. To handle the uncertainty with simpler calculations, triangular fuzzy numbers (TFNs, i.e. (a, b, c)) are used. Particularly, the F_AHP method is employed to compute the fuzzy and crisp weights of barriers and rank them based on their crisp weights. Then, to rank the drivers, the F_TOPSIS method is applied, taking into account the fuzzy weights of the barriers. Finally, the volatility of the implemented model has been encapsulated by performing a sensitivity analysis.
2 Experimental Program 2.1 Consideration of the Manufacturing Industry ABC Ltd., a paper manufacturing company located in eastern India, is considered for this study. It has a revenue of INR 356 Cr. The company employs approximately 1400 skilled and unskilled workers. It has ISO 14001 certification. This company primarily manufactures writing and printing paper, industrial paper, paper products, and boards, and it is also a major producer of tissue paper. Despite being an ISO 14001 certified company, it still deals with a variety of environmental issues such as deforestation, air pollution, water pollution, paper waste, chemical emissions from the de-inking process, and so on. All of these have negative consequences on the environment. Greening the SC is required for this company to meet environmental requirements.
2.2 Focused Group Discussion This group discussion included three members (k = 3) from the company. They are all mid-level employees who work in SC operations. The experts were given detailed information about this study. The experts were given a list of 45 barriers [17–20] and
86 Table 1 List of barriers and drivers of GSCM
Table 2 Linguistic scale for the collection of judgments for F_AHP approach
S. R. Pradhan and S. S. Mahapatra Barriers of GSCM
Drivers of GSCM
1. Resistance to change (BA1) 2. Financial implications (BA2) 3. System complexity (BA3) 4. Lack of awareness/participation in GSCM (BA4) 5. Organizational issues (BA5) 6. Unhealthy legislation & regulations (BA6)
1. Environmental certification & Govt. regulation (DR1) 2. Green technology (DR2) 3. Green supply chain (DR3) 4. Customer, market & societal pressure (DR4) 5. Economic consideration (DR5) 6. Organizational factors (DR6) 7. Green awareness (DR7) 8. Image building (DR8)
Linguistic variables
TFNs
Approx. similar
(1/2, 1, 2)
Approx. ‘u’ times more vital
(u − 1, u, u + 1)
Approx. ‘u’ times less vital
(1/(u + 1), 1/u, 1/(u − 1))
Between ‘v’ and ‘w’ times more vital
(v, (v + w)/2, w)
Between ‘v’ and ‘w’ times less vital
(1/w, 2/(v + w), 1/v)
33 drivers [1, 21–24] in GSCM implementation found in the literature. Following a review of the enabling factors, the experts proposed some broad groupings for both barriers and drivers. After grouping, the total number of barriers (n = 6, BA1 to BA6) and drivers (m = 8, DR1 to DR8) was calculated. Table 1 shows the complete categorization. The linguistic scale for F_AHP [25] is shown in Table 2. The linguistic scale for F_TOPSIS [13] represented as Very High (VH), High (H), Medium (M), Low (L), and Very Low (VL) are expressed in triangular fuzzy numbers (5,6,7), (4,5,6), (3,4,5), (2,3,4), and (1,2,3), respectively. Both scales were then shared with the experts for judgment collection.
2.3 Application of F_AHP Approach The method proposed by Buckley [26] is used in this study for the determination of fuzzy weights of the barriers. Concurrently, the crisp weights are determined using a formula proposed by the same author. There are certain standard operational laws for TFNs [27] that will aid in TFN calculation. Consider two TFNs F1 = ( p1 , p2 , p3 ) and F2 = (q1 , q2 , q3 ). According to the laws
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( p1 , p2 , p3 ) ⊕ (q1 , q2 , q3 ) = ( p1 + q1 , p2 + q2 , p3 + q3 )
(1)
( p1 , p2 , p3 ) ⊗ (q1 , q2 , q3 ) = ( p1 q1 , p2 q2 , p3 q3 )
(2)
( p1 , p2 , p3 )−1 = (1/ p3 , 1/ p2 , 1/ p1 )
(3)
F_AHP also relies on these operational laws to determine weights. The following are the steps involved in F_AHP: Step 1 Formation of pairwise comparison matrix (PCM): Every single expert gave his opinion in the linguistic scale as represented in Table 2. Three PCMs are generated using those ratings by defining how many times one is more/less important than the other. The matrix will be a n×n matrix (n = the number of criteria (barriers)). In this case, n = 6. The representation of the matrix is ⎡
1 ⎢ b˜21 ⎢ B˜ = ⎢ . ⎣ .. b˜n1
b˜12 1 .. . b˜n2
⎤ ⎡ 1 b˜12 b˜1n ⎢ 1/b˜12 1 b˜2n ⎥ ⎥ ⎢ .. ⎥ = ⎢ .. .. . ⎦ ⎣ . . ··· 1 1/b˜1n 1/b˜2n ··· ··· .. .
⎤ b˜1n b˜2n ⎥ ⎥ .. ⎥ . ⎦ ··· 1 ··· ··· .. .
(4)
where b˜i j represents the comparison between i and j criterion. Step 2 Creation of fuzzy PCM: The linguistic variables are transformed into TFNs using the linguistic scale. The experts provide a total of three matrices. Table 3 contains the fuzzy PCM obtained from expert 1 for reference purposes. Step 3 Generation of synthetic PCM: Buckley’s [26] geometric mean technique is implemented to compute the synthetic PCM (Table 4). The formula used in this calculation is as follows: )1/k ( b˜i j = b˜i1j ⊗ b˜i2j ⊗ b˜i3j ⊗ . . . ⊗ b˜ikj
(5)
where k = no. of experts. The final synthetic PCM is shown in Table 4. Table 3 Fuzzy pairwise comparison matrix BA1
BA2
BA3
BA4
BA5
BA6
BA1
(1.00,1.00,1.00)
(0.33,0.50,1.00)
…
…
(1.00,2.00,3.03)
(0.33,0.50,1.00)
BA2
(1.00,2.00,3.03)
(1.00,1.00,1.00)
…
…
(0.20,0.25,0.33)
(2.00,3.00,4.00)
BA3
(0.25,0.29,0.33)
(0.33,0.40,0.50)
…
…
(0.20,0.25,0.33)
(2.00,2.50,3.00)
BA4
(0.25,0.33,0.50)
(0.25,0.33,0.50)
…
…
(2.00,3.00,4.00)
(1.00,2.00,3.03)
BA5
(0.33,0.50,1.00)
(3.03,4.00,5.00)
…
…
(1.00,1.00,1.00)
(0.33,0.50,1.00)
BA6
(1.00,2.00,3.03)
(0.25,0.33,0.50)
…
…
(1.00,2.00,3.03)
(1.00,1.00,1.00)
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Table 4 Synthetic pairwise comparison matrix BA1
BA2
BA3
BA4
BA5
BA6
BA1
(1.00,1.00,1.00)
(0.33,0.43,0.63)
…
…
(1.26,2.15,3.01)
(0.30,0.44,0.79)
BA2
(1.59,2.32,3.03)
(1.00,1.00,1.00)
…
…
(0.22,0.27,0.38)
(2.00,2.82,3.63)
BA3
(0.28,0.32,0.38)
(0.30,0.38,0.50)
…
…
(0.22,0.27,0.38)
(2.62,3.27,3.91)
BA4
(0.25,0.32,0.44)
(0.28,0.34,0.44)
…
…
(2.00,2.82,3.63)
(1.26,2.29,3.32)
BA5
(0.33,0.46,0.79)
(2.64,3.65,4.64)
…
…
(1.00,1.00,1.00)
(0.30,0.44,0.79)
BA6
(1.26,2.30,3.32)
(0.28,0.35,0.50)
…
…
(1.26,2.30,3.32)
(1.00,1.00,1.00)
Step 4 Calculation of weights: The geometric mean technique suggests following formulas to find |the geometric mean of fuzzy ( )| comparison value g˜ j of each criterion and the weights w˜ j = Pw j , Qw j , Rw j of the criteria. )1/n ( g˜ j = b˜ j1 ⊗ b˜ j2 ⊗ b˜ j3 ⊗ . . . ⊗ b˜ jn
(6)
w˜ j = g˜ j ⊗ (g˜ 1 ⊕ g˜ 2 ⊕ g˜ 3 ⊕ . . . ⊕ g˜ n )−1
(7)
where j = 1, 2, 3 . . . n. After calculation, all the values are tabulated and shown in Table 5. Step 5 Assignment of ranking: After obtaining the fuzzy weights, we must determine the defuzzified value of each weight to obtain the best nonfuzzy performance (BNP). To find the weights in crisp values, the center of average (COA) technique is used. Following that, normalized weights are computed. The ranking has been completed and is shown in Table 5 based on the normalized crisp weight (BNP) value. The formula for calculating the BNP value is B N Pw j =
|(
) ( )| Rw j − Pw j + Qw j − Pw j /3 + Pw j
(8)
Table 5 Ranking of barriers Barriers BA1
∼
∼
rj
wj
BNP
Normalized BNP
Rank
(0.953,1.260,1.672)
(0.118,0.203,0.349)
0.223
0.204
2
BA2
(1.210,1.556,1.920)
(0.150,0.250,0.401)
0.267
0.244
1
BA3
(0.722,0.863,1.047)
(0.090,0.139,0.219)
0.149
0.136
4
BA4
(0.578,0.751,0.957)
(0.072,0.121,0.200)
0.131
0.120
6
BA5
(0.759,0.992,1.376)
(0.094,0.160,0.287)
0.180
0.165
3
BA6
(0.568,0.793,1.090)
(0.070,0.128,0.227)
0.142
0.130
5
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2.4 Application of F_TOPSIS Approach The F_TOPSIS method assists in determining the best alternative (i.e. “closest to fuzzy positive ideal solution (FPIS) and farthest from fuzzy negative ideal solution (FNIS)”). Barriers are used as cost criteria in this study, while drivers are used as alternatives. For calculation purposes, the steps proposed by Chen [28], Kannan et al. [29], Sun [30], Chen et al. [31] are considered. The steps are as follows: Step 1 Collection of ratings: Expert ratings are gathered using linguistic variables taking into account how each barrier affects the driver. Step 2 Construction of fuzzy performance matrix: The accumulated decisions are represented as a matrix (performance matrix). Table 6 contains expert 1’s decision as a reference. Similarly, all three performance matrices are gathered from experts. Then, each element of the performance matrix is replaced with fuzzy numbers as considered on the linguistic scale. Finally, a fuzzy performance matrix of size q × n is obtained (q = no. of alternatives (drivers) = 8). Step 3 Creation of aggregate fuzzy performance matrix: After obtaining three performance matrices, we must create a single matrix that incorporates all of the judgments. The elements of the aggregate fuzzy performance matrix are calculated by taking into account all of the expert judgments for that element. The aggregate rating is symbolized as (l, m, u), the value of which is determined by the formula 1E qabk , u = max{rabk } k k k=1 k
l = min{ pabk }, m = k
(9)
where X˜ abk = ( pabk , qabk , rabk ) is the fuzzy rating given by the Kth expert in which a1, 2, 3 . . . q and b = 1, 2, 3 . . . n. The required aggregate fuzzy performance matrix is represented in Table 7. Step 4 Formation of normalized fuzzy decision matrix: The representation of normalized fuzzy decision matrix is given by Table 6 Performance matrix in terms of linguistic variables BA1
BA2
BA3
BA4
BA5
BA6
DR1
M
VH
…
…
M
M
DR2
H
VH
…
…
H
H
DR3
M
H
…
…
VH
L
DR4
L
M
…
…
M
M
DR5
M
VH
…
…
L
L
DR6
L
H
…
…
VH
M
DR7
M
M
…
…
H
VH
DR8
M
H
…
…
M
H
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S. R. Pradhan and S. S. Mahapatra
Table 7 Aggregate fuzzy performance matrix DR1
BA1
BA2
BA3
BA4
BA5
BA6
(2.00,3.67,5.00)
(4.00,5.67,7.00)
…
…
(3.00,4.67,6.00)
(3.00,4.00,5.00)
DR2
(3.00,4.67,6.00)
(5.00,6.00,7.00)
…
…
(3.00,4.67,6.00)
(4.00,5.33,7.00)
DR3
(2.00,3.67,5.000
(3.00,4.67,6.00)
…
…
(5.00,6.00,7.00)
(2.00,4.00,6.00)
DR4
(2.00,3.33,5.000
(3.00,4.33,6.00)
…
…
(2.00,3.67,5.00)
(3.00,4.00,5.00)
DR5
(2.00,3.67,5.00)
(5.00,6.00,7.00)
…
…
(2.00,3.33,5.00)
(2.00,3.33,5.00)
DR6
(2.00,3.33,5.00)
(4.00,5.00,6.00)
…
…
(5.00,6.00,7.00)
(2.00,3.67,5.00)
DR7
(2.00,3.67,5.00)
(3.00,4.33,6.00)
…
…
(4.00,5.33,7.00)
(4.00,5.33,7.00)
DR8
(3.00,4.67,6.00)
(4.00,5.67,7.00)
…
…
(3.00,4.00,5.00)
(4.00,5.00,6.00)
| | N˜ = si j q×n
(10)
where i = 1, 2, 3 . . . q and j = 1, 2, 3 . . . n. ( s˜ =
l −j
,
l −j
,
l −j
)
u i j m i j li j
( ) and l −j = min li j
(11)
where li j is the cost criteria. Since barriers are opposing the implementation of GSCM, those are regarded as cost criteria. So using Eq. 11, the normalized fuzzy decision matrix is evaluated and represented in Table 8. Step 5 Calculation of weighted fuzzy normalized decision matrix: The required formula for this calculation is | | V˜ = v˜ i j m×n
(12)
where v˜ i j = s˜i j × w˜ j . Table 8 Normalized fuzzy decision matrix DR1
BA1
BA2
BA3
BA4
BA5
BA6
(0.40,0.55,1.00)
(0.43,0.53,0.75)
…
…
(0.33,0.43,0.67)
(0.40,0.50,0.67)
DR2
(0.33,0.43,0.67)
(0.43,0.50,0.60)
…
…
(0.33,0.43,0.67)
(0.29,0.38,0.50)
DR3
(0.40,0.55,1.00)
(0.50,0.64,1.00)
…
…
(0.29,0.33,0.40)
(0.33,0.50,1.00)
DR4
(0.40,0.60,1.00)
(0.50,0.69,1.00)
…
…
(0.40,0.55,1.00)
(0.40,0.50,0.67)
DR5
(0.40,0.55,1.00)
(0.43,0.50,0.60)
…
…
(0.40,0.60,1.00)
(0.40,0.60,1.00)
DR6
(0.40,0.60,1.00)
(0.50,0.60,0.75)
…
…
(0.29,0.33,0.40)
(0.40,0.55,1.00)
DR7
(0.40,0.55,1.00)
(0.50,0.69,1.00)
…
…
(0.29,0.38,0.50)
(0.29,0.38,0.50)
DR8
(0.33,0.43,0.67)
(0.43,0.53,0.75)
…
…
(0.40,0.50,0.67)
(0.33,0.40,0.50)
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Step 6 Calculation of FPIS and FNIS: The formulae used in this step are as follows: ( ) A+ = v1+ , v2+ . . . vn+ , where v+j = max vi j
(13)
( ) A− = v1− , v2− . . . vn− , where v−j = min vi j
(14)
Step 7 Estimation of the distance of each alternative: For each alternative, the distance is calculated from FPIS and FNIS and represented as d˜i+ and d˜i− , respectively. The required formulae are d˜i+ =
n E
dv (˜vi j , v˜ +j )
(15)
dv (˜vi j , v˜ −j )
(16)
j=1
d˜i− =
n E j=1
/ | | 1 p − q1 )2 + ( p2 − q2 )2 + ( p3 − q3 )2 represents the distance where dv = 3 ( 1 between two fuzzy numbers F1 = ( p1 , p2 , p3 ) and F2 = (q1 , q2 , q3 ). Step 8 Determination of closeness coefficient (C C i ): The CC i value is evaluated so that the rank of the alternatives can be determined. The CC i value actually represents distances from both FPIS and FNIS concurrently. The formula for CC i is given as follows: CCi =
di− di+ + di−
(17)
The calculated values of di+ , di− , CC i and the ranking for the drivers are represented in Table 9. Table 9 Closeness coefficient (CC i ) and ranking of drivers Drivers
d+ i
d− i
CCi
Rank
DR1
0. 275
0. 197
0. 417
5
DR2
0. 396
0. 069
0. 149
8
DR3
0. 228
0. 255
0. 528
3
DR4
0. 152
0. 321
0. 679
2
DR5
0. 103
0. 366
0. 780
1
DR6
0. 250
0. 218
0. 467
4
DR7
0. 286
0. 183
0. 389
6
DR8
0. 317
0. 151
0. 323
7
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3 Result and Discussion 3.1 Results Following the application of F_AHP, both fuzzy and crisp weights of barriers were obtained. The final ranking of barriers was determined using crisp weights. From Table 6, it is found that the barriers are following the sequence of BA2 > BA1 > BA5 > BA3 > BA6 > BA4. Financial implication (BA2) was ranked first with the highest BNP value, while lack of awareness/participation in GSCM (BA4) was ranked last with the lowest BNP value. Hence, BA2 has the highest weightage among all of the GSCM barriers considered for this study. Because this study focuses on reducing uncertainty in decision-making, only fuzzy weights are used to generate a weighted fuzzy normalized decision matrix rather than a crisp one. After successfully calculating the value of closeness coefficient (CC i ), the final ranking of the drivers is given by DR5 > DR4 > DR3 > DR6 > DR1 > DR7 > DR8 > DR2 (Table 9). It actually represents that the economic consideration (DR5) was given the highest priority, with the highest CC i score, implying that DR5 is the farthest away from the FNIS and the nearest to the FPIS. It also shows that green technology (DR2) received the lowest priority, with the lowest CC i score, i.e., closest one from FNIS.
3.2 Sensitivity Analysis Sensitivity analysis is commonly employed to assess the robustness of any result. That is, the volatility of the result can be tested using sensitivity analysis when the input data is changed [32]. Librantz et al. [33] and Syamsuddin [34] proposed a method for performing sensitivity analysis. In this approach, we can perform the analysis by reducing the weight of each criterion to zero without changing the data for the remaining criteria. The modified values are tabulated after each iteration (Table 10), and the corresponding graph is plotted (Fig. 1). Table 10 Sensitivity analysis results Criteria having zero weight
C C i score of alternatives DR1
DR2
DR3
DR4
DR5
DR6
DR7
DR8
BA1
0.323
0.175
0.457
0.623
0.754
0.372
0.289
0.380
BA2
0.433
0.188
0.420
0.596
0.983
0.487
0.230
0.313
BA3
0.430
0.113
0.607
0.732
0.744
0.488
0.453
0.211
BA4
0.476
0.171
0.552
0.775
0.747
0.485
0.447
0.371
BA5
0.411
0.066
0.670
0.599
0.718
0.598
0.452
0.285
BA6
0.428
0.174
0.462
0.734
0.743
0.382
0.455
0.373
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BA1 = 0 1.0
Fig. 1 Radar diagram for sensitivity analysis
0.8 0.6
BA6 = 0
BA2= 0
0.4 0.2 0.0
BA5 = 0
BA3 = 0
BA4= 0 DR1
DR2
DR3
DR4
DR5
DR6
DR7
DR8
Both the table and the figure show that in five of the six cases (BA1, BA2, BA3, BA5, and BA6), the DR5 driver has the highest score, and only when BA4 is assigned zero weight, the DR4 driver has acquired the first rank. It can also be seen that the DR2 driver received the lowest score in all six cases. As a result, the calculated ranking obtained from F_TOPSIS is correct, i.e., DR4 is first and DR2 is eighth. Therefore, the suggested model is robust and stable.
4 Conclusions Environmental concerns have grown in importance around the world in recent years. All companies and business units are eager to implement GSCM practices. The barriers make it more difficult for business units to implement GSCM, whereas the drivers make it easier. As a result, we must analyze these enabling factors by categorizing and prioritizing them using various MCDM methods. F_AHP and F_TOPSIS techniques are implemented in this study to prioritize the barriers and the drivers, respectively. The industry experts have identified and classified six barriers and eight drivers. The F_AHP method ranked the financial implication (BA2) as the highest-ranked barrier among all its allies, whereas the F_TOPSIS method ranked the economic consideration (DR5) as the highest-ranked driver among all drivers. In the end, the robustness of the model has been verified from the result of the sensitivity analysis. When the Indian scenario is considered, these results appear more realistic because financial stability is a major factor for any developing country. As a result, this study can undoubtedly serve as a resource for decision makers considering the implementation of GSCM in their organization. They can easily develop policies and regulations that take into account the drivers in order to facilitate GSCM by reducing
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S. R. Pradhan and S. S. Mahapatra
the impact of barriers. This study can be made more relevant for future research in this area by employing various fuzzy-integrated hybrid MCDM techniques such as the fuzzy-assisted TOPSIS-VIKOR, fuzzy-assisted ANP-TOPSIS, fuzzy-assisted ELECTRE, and so on.
References 1. Dhull S, Narwal MS (2018) Prioritizing the drivers of green supply chain management in Indian manufacturing industries using fuzzy TOPSIS method: government, industry, environment, and public perspectives. Proc Integ Optimiz Sustain 2:47–60 2. Ghorbanpour A, Pooya A, Azimi ZN (2022) Application of green supply chain management in the oil Industries: Modeling and performance analysis. Mater Today Proc 49(3):542–553 3. Eltayeb TK, Zailani S (2014) Going green through green supply chain initiatives toward environmental sustainability. Operat Supply Chain Manag 2(2):93–110 4. Gunasekaran A, Spalanzani A (2012) Sustainability of manufacturing and services: investigations for research and applications. Int J Prod Econ 140(1):35–47 5. Kumar R, Singh K, Shankar R (2014) Strategy development by Indian SMEs for improving coordination in supply chain. Compet Rev 24(5):414–432 6. Luthra S, Garg D, Haleem A (2014) Empirical analysis of green supply chain management practices in Indian automobile industry. J Instit Eng (India) Ser C 95(2):119–126 7. Sarkis J (2012) A boundaries and flows perspective of green supply chain management. Supply Chain Manag Int J 17(2):202–216 8. Diabat A, Khreishah A, Kannan G, Panikar V, Gunasekaran A (2013) Benchmarking the interactions among barriers in third-party logistics implementation. Benchmark Int J 20(6):805– 824 9. Govindan K, Bouzon M (2018) From a literature review to a multi-perspective framework for reverse logistics barriers and drivers. J Clean Prod 187:318–337 10. Singh A, Trivedi A (2016) Sustainable green supply chain management: trends and current practices. Compet Rev 26(3):265–288 11. Mathiyazhagan K, Haq AN (2013) Analysis of the influential pressures for green supply chain management adoption–an Indian perspective using interpretive structural modeling. Int J Adv Manuf Technol 68:817–833 12. Patil SK, Kant R (2014) A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge management adoption in Supply Chain to overcome its barriers. Expert Syst Appl 41:679–693 13. Sirisawat P, Kiatcharoenpol T (2018) Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Comput Ind Eng 117:303–318 14. Ertu˘grul I, Karaka¸so˘glu N (2008) Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. Int J Adv Manuf Technol 39:783–795 15. Barrio MAO, De Felice F, Negrete KP, Romero BA, Arenas AY, Petrillo A (2016) An AHPTOPSIS integrated model for selecting the most appropriate tomography equipment. Int J Inform Technol Decis Making 15 16. Saksrisathaporn K, Bouras A, Reeveerakul N, Charles A (2015) Application of a decision model by using an integration of AHP and TOPSIS approaches within humanitarian operation life cycle. Int J Inform Technol Decis Making 14 17. Barve A, Muduli K (2013) Modelling the challenges of green supply chain management practices in Indian mining industries. J Manuf Technol Manag 24(8):1102–1122 18. Govindan K, Kaliyan M, Kannan D, Haq AN (2014) Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. Int J Prod Econ 147:555–568
Analysis of Drivers and Barriers in Green Supply Chain Management …
95
19. Jayant A, Azhar M (2014) Analysis of the barriers for implementing green supply chain management (GSCM) practices: an interpretive structural modeling (ISM) approach. Procedia Eng 97:2157–2166 20. Soda S, Sachdeva A, Garg RK (2017) Barriers analysis for green supply chain management implementation in power industry using ISM. Int J Logist Syst Manag 27(2):225–259 21. Dashore K, Sohani N (2013) Green supply chain management–barriers & drivers: a review. Int J Eng Res Technol 2(4):2021–2030 22. Diabat A, Govindan K (2011) An analysis of the drivers affecting the implementation of green supply chain management. Resour Conserv Recycl 55:659–667 23. Govindan K, Muduli K, Devika K, Barve A (2016) Investigation of the influential strength of factors on adoption of green supply chain management practices: an Indian mining scenario. Resour Conserv Recycl 107:185–194 24. Taghavi E, Fallahpour A, Wong KY, Hoseini SA (2021) Identifying and prioritizing the effective factors in the implementation of green supply chain management in the construction industry. Sustain Operat Comp 2:97–106 25. Wang L, Chu J, Wu J (2007) Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. Int J Prod Econ 107:151–163 26. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets. System 17(1):233–247 27. Chang D-Y (1996) Application of the extent analysis method on fuzzy AHP. Eur J Oper Res 95:649–655 28. Chen CT (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets System 114:1–9 29. Kannan D, Jabbour ABLDS, Jabbour CJC (2014) Selecting green suppliers based on GSCM practices: using fuzzy TOPSIS applied to a Brazilian electronics company. Eur J Oper Res 233:432–447 30. Sun CC (2010) A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst Appl 37:7745–7754 31. Chen CT, Lin CT, Huang SF (2006) A fuzzy approach for supplier evaluation and selection in supply chain management. Int J Prod Econ 102:289–301 32. Kumar S, Kumar S, Barman AG (2018) Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit. Procedia Comp Sci 133:905–912 33. Librantz A, Santos F, Dias C, Cunha A, Costa I, Spinola MM (2016) AHP modelling and sensitivity analysis for evaluating the criticality of software programs. In: IFIP Advances in Information and Communication Technology. pp 248–55 34. Syamsuddin I (2013) Multicriteria evaluation and sensitivity analysis on information security. Int J Comp Appl 69:22–25 35. Mangla SK, Kumar P, Barua MK (2007) Risk analysis in green supply chain using fuzzy AHP approach: a case study. Resour Conserv Recycl 104:375–390
Parametric Optimization of LPG Refrigeration System Using Artificial Bee Colony Algorithm Sampath Boopathi , M. Sureshkumar, and S. Sathiskumar
Abstract The artificial bee colony (ABC) algorithm is first used to forecast the best operating parameters for a liquefied petroleum gas (LPG) refrigeration system. LPG has been employed as a refrigerant to minimize the global warming problem. An experimental setup has been made up of an evaporator (cooling box), gas burner, LPG supply unit, capillary tube length, and a pressure regulator. The running time (top), coil diameter (D), and inner diameter (di) of the capillary tube are considered as operating parameters to examine their influences on the evaporated temperature (TE) by full factorial design. The best mathematical equation for TE has been developed by regression analysis. The best values of process parameters (top = 50 min, D = 190 mm, and di = 0.8 mm) for the maximum evaporating temperature (TE = 11.9 °C) have been estimated by ABC MATLAB code. Keywords Evaporator temperature · Full factorial design · Regression model · Coil diameter · Running time · Inner diameter
S. Boopathi (B) Department of Mechanical Engineering, Muthayammal Engineering College (Autonomous), Rasipuram, Namakkal (Dt)., Tamilnadu 637408, India e-mail: [email protected] M. Sureshkumar Department of Mechanical Engineering, Bannariamman Institute of Technology, Erode, Tamil Nadu 638401, India e-mail: [email protected] S. Sathiskumar Department of Mechanical Engineering, Kongu Engineering College, Erode, Tamil Nadu 638060, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_10
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1 Introduction Refrigeration is the heat removal process by employing additional or external work to remove the heat. The temperature drop is caused by the application of magnetic, thermo-electrical, electrical, and laser energy instead of mechanical effort. It was revealed from many kinds of literature that the first and second-generation refrigerants had not been promoting eco-friendly cooling systems [1, 2]. The COP of the LPG refrigerator is greater than the existing system [3]. While passing LPG in the small tubes, the pressure was decreased by phase changes to the liquid state, and LGP also performed better compared to the commercial system [4]. It was found from the experiments that the COP, energy consumption, and energetic efficiency of LPG are better than R600a refrigerant systems [5]. It has been attempted to reduce energy consumption and operating costs by combining LPG with domestic refrigerants [6]. The silicon dioxide, nanoparticle mineral oil with LPG refrigerant has been performed with using R134a to expand the work done and minimize power consumption [7]. An innovative approach combining a solar-heat pump with an LPG system has improved the COP and overall efficiency of refrigeration [8]. The Al2 O3 nanoparticle mixed with LPG also reduced the power consumption rate [9]. The influence of working temperature on LPG gas refrigerators has been investigated to reduce the pressure ratio, and the energy consumption [10]. Effect of refrigerant quantity and length of tube on domestic refrigerator to minimize the pull-down time, mass flow rate, and output temperature using LPG and R134a [11]. The nanolubricant was used to lubricate LPG refrigerator compressors to minimize energy usage [12]. Hence, LPG is the best substitution for saving the environment with good performance. Very recently, the operating parameters of the LPG refrigerator were optimized to improve the system temperature using the Taguchi and simulated annealing optimization techniques [13, 14]. According to previous research activities, only a few research activities were performed to replace the harmful HFC134a refrigerator. In this chapter, the ABC algorithm has been applied to estimate the best operating parameters of the LPG system to improve the evaporator temperature.
2 Experimental Method 2.1 Experimental Setup The investigational arrangements consist of capillary tubes, evaporator, pressure controller, gas supply system, burner, inlet, and outlet pressure dials are displayed in Fig. 1. LPG comprises 18.2% isobutene, 25.4% propane, and 56.4% butane. It is provided to the capillary tube through a pressure controller in which the gas pressure
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is dropped from 21 to 50 psi. High-pressure LPG is transformed into the tube at low pressure without the change in enthalpy. Then the evaporator is filled with lowpressure LPG. When a low-temperature and low-pressure gas was pumped into the evaporator, the heated gas was drawn out of the chamber. By using an outlet pressure gauge, the evaporator’s outlet pressure can be estimated. The low-pressure and lowtemperature LPG gas is then introduced into the evaporator. The burner received the gas. The tests were carried out using capillary tubes with inner diameters of 0.6, 0.8, and 1.0 mm and coil diameters of 70, 130, and 190 mm. The system is set up to take observations every 10, 20, and 30 min. Every time the experiment is repeated, the evaporator’s original temperature of 34 °C is maintained. Full factorial designs have been used to conduct the testing (27 trials). Twentyseven experiments were chosen for the three-level, three-factor problems based on the literature [15]. The values of each level of factor are illustrated in Table 1. The two-time tests were randomly replicated and the average values of experimental observations are listed in Table 2. A thermometer and a stopwatch were used to measure the temperature and operating time, respectively.
Fig. 1 LPG refrigeration setup
Table 1 Operating parameter’s levels
Operating parameter
Level 1
Level 2
Level 3
Running time (t op )
10
20
30
Coil diameter (D)
70
130
190
Inner diameter (d i )
0.6
0.8
1.0
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Table 2 Full factorial design of experiment and results Exp. no
t op (min)
1.
10
D (mm) 70
d i (mm)
T E (°C)
0.6
22.98
2.
10
70
0.8
19.43
3.
10
70
1.0
25.13
4.
10
130
0.6
20.87
5.
10
130
0.8
17.64
6.
10
130
1.0
22.82
7.
10
190
0.6
18.75
8.
10
190
0.8
15.86
9.
10
190
1.0
20.51
10.
30
70
0.6
20.23
11.
30
70
0.8
17.11
12.
30
70
1.0
22.13
13.
30
130
0.6
18.37
14.
30
130
0.8
15.53
15.
30
130
1.0
20.09
16.
30
190
0.6
16.51
17.
30
190
0.8
13.96
18.
30
190
1.0
18.06
19.
50
70
0.6
18.33
20.
50
70
0.8
15.50
21.
50
70
1.0
20.05
22.
50
130
0.6
16.65
23.
50
130
0.8
14.07
24.
50
130
1.0
18.21
25.
50
190
0.6
14.96
26.
50
190
0.8
12.65
27.
50
190
1.0
16.45
2.2 Mathematical Modeling A mathematical model is used to estimate the relationship between the operating and response elements [16, 17]. In this article, factors are independent quantities, whereas evaporator temperature is a dependent factor. The unknown constants of predicted models are estimated by regression analysis. Each parameter’s square terms and all interactions have been developed into a quadratic model. The constants of the mathematical model are formed using regression analysis. Equation (1) illustrates the quadratic model. The R2 , adjusted R2 , and predicted R2 values are 99.39, 99.21, and 98.79%, respectively.
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Table 3 ANOVA for quadratic model Term
DF
Model
6
SS
MS
237.645
39.6076
F-value 545.34
Contribution (%) 100
t
1
6.950
6.9501
95.69
3.51
D
1
19.344
19.3440
266.34
9.76
d
1
79.958
79.9578
1100.91
40.35
t×D
1
0.563
0.5633
7.76
0.28
t2
1
0.877
0.8766
12.07
0.44
d2
1
84.625
84.6252
1165.18
42.71
Error
20
1.453
Total
26
198.139
0.0726
–
–
–
R2
Adjusted R2
Predicted R2
99.39%
99.21%
98.79%
– 97.79
TE = 96.25 − 0.1839t − 0.03614D − 164.61d + 0.000956t 2 + 93.89d 2 + 0.000181t × D
(1)
The models provide a significant relationship between input and response factors. When a model’s error is less than 5%, it is statistically significant. However, the main parameters ‘D’ and ‘di ’ Pure error numbers exceed the confidence level and cannot be discounted. The quadratic terms ‘D × di ’ and ‘top × di ’ have been pooled from the actual model due to insignificant variance in the model [18–20]. ANOVA the second order model is listed in Table 3. Thus, this model has been utilized to forecast the ideal parameters using the ABC algorithm.
3 Implementation of ABC Algorithm and Result Analysis ABC is a population-based random search nontraditional optimization technique. The natural behavior of honey bee crowds was inspired to develop this algorithm. In this algorithm, three types of honey bees were employed: onlooker bees, employed bees, and scout bees. Employed bees are directly connected to gathering nectar from the specific food source and conveying that information to all the bees in the hive. Onlooker bees initially work to collect primary food source data from employed bees, and they select the optimum food source for further searching processes. The scout bee is finally searching for food locations to simulate the activities of employed bees. This colony is divided into two sections. The primary half portion of the bee colony is employed bees, and the next half has onlooker bees. Every food source has been occupied by employed bees [21]. The location of food availability is analogous to the problem’s output results. The quantity of nectar in the food location produced the best results. Initially, the onlooker bees initiate the searching process and appropriate food
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location for the random search processes. Then, the nectar quantities of food points are estimated. Each employed bee computes a novel food point from the existing location of bees. This quality food source is calculated by the objective functions of the optimization problem. The key procedures of the ABC algorithm are illustrated in Fig. 2. The objective function and boundary conditions for the LPG refrigerator are designated by Eqs. (2), (3), and (4). Minimize function f (x) = [TE] Upper Bound : [10 70
(2)
0.6]
(3)
Lower Bound : [50 190 1.0]
(4)
In this paper, an objective function of minimization of TE has been solved by ABC. The MATLAB coding of ABC has been employed to obtain the optimum input parameters. Table 4 displays the predicted optimum input and output parameters. Figure 3 depicts the iteration process for obtaining an optimal solution using the ABC MATLAB program. The flow rate is low due to refrigerant flow restriction caused by the effective pressure drop in the small tube diameter. The temperature drops in the evaporators have been amplified by the large diameter of the cooling coil due to the maximum contact area with soundings. While increasing the system’s working time, the temperature drop could be improved due to an increase in the amount of work done. Hence, the 190 mm coil diameter, the 0.8 mm diameter of the capillary tube, and an operating time of 50 min provide good refrigeration efficiency. The set of validation tests was performed or confirmed to the best results obtained by ABC as listed in Table 4.
4 Conclusions The LPG refrigeration system has been fabricated and performed systematic experiments using the full factorial design. The quadratic mathematical model for TE has been established as the source of the ABC algorithm. The effects of evaporator temperature on operating time, length, inner diameter, and coil diameter have been studied. A small inner diameter (di = 0.8 mm), a large capillary coil diameter (190 mm), and a maximum running time resulted in the highest evaporator temperature (11.9 °C) (50 min).
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Fig. 2 Working mechanism for ABC Algorithm
Table 4 Experiments for checking predicted optimum result Operating parameters
Result
Description
top (Min)
D(mm)
d i (mm)
T E (°C)
49.990
189.99
0.8766
12.15
ABC algorithm
50.00
190
0.8
11.9
Experiments
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Fig. 3 Optimum solutions versus iteration—ABC algorithm by MATLAB
References 1. Akash BA, Said SA (2003) Assessment of LPG as a possible alternative to R-12 in domestic refrigerators. Energy Conv Manag 44(3):381–388. https://doi.org/10.1016/S01968904(02)00065-1 2. Kumar V, Karimi MN, Kamboj SK (2020) Comparative analysis of cascade refrigeration system based on energy and exergy using different refrigerant pairs. J Thermal Eng 6(1):106–116. https://doi.org/10.18186/THERMAL.671652 3. Dube AS (2014) Performance evaluation of vapour compression cycle operating On LPG as a refrigerant. Int J Comp Appl 2016 4. Mhaske MM, Deshmukh TS, Ankush SM, et al (2016) Performance evolution of domestic refrigerator using LPG cylinder. Int Res J Eng Technol 5. Babarinde TO, Akinlabi SA, Madyira DM (2018) Exergetic performance of household refrigerator using R600a and LPG. IOP Conf Ser Mater Sci Eng 413(1). https://doi.org/10.1088/ 1757-899X/413/1/012069 6. Rai R, De A, Bhadeshia HKDH, et al (2011) Review: friction stir welding tools. Sci Technol Weld J 16(4):325–42. https://doi.org/10.1179/1362171811Y.0000000023 7. Ohunakin OS, Adelekan DS, Gill J, et al (2018) Performance of a hydrocarbon driven domestic refrigerator based on varying concentration of SiO2 nano-lubricant. Int J Refrig Elsevier Ltd 94:59–70. https://doi.org/10.1016/j.ijrefrig.2018.07.022 8. Shi GH, Aye L, Dai R, et al Dynamic modelling and performance evaluation of a directexpansion solar-assisted heat pump for LPG vaporisation applications. Appl Therm Eng Elsevier 149:757–71. https://doi.org/10.1016/j.applthermaleng.2018.12.101 9. Onakade MA, Adelekan DS, Ohunakin OS, et al (2019) Experimental performance of the energetic characteristics of a domestic refrigerator with Al2O3 nanolubricant and LPG refrigerant. J Phys Conf Ser 1378(4). https://doi.org/10.1088/1742-6596/1378/4/042083 10. Olatunji OR, Ohunakin OS, Adelekan DS (2019) Effect of ambient temperatures on an R134a domestic refrigerator retrofitted with R600a and LPG refrigerants. J Phys Conf Ser 1378(2). https://doi.org/10.1088/1742-6596/1378/2/022100 11. Ahmad RH, Bhuiyan AA, Xu F, et al (2020) Comparative analysis of refrigerant performance between LPG and R134a under subtropical climate. J Therm Anal Calorimetry Springer International Publishing 139(4):2925–35. https://doi.org/10.1007/s10973-019-09126-3
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12. Kumar R, Singh DK, Chander S (2020) An experimental approach to study thermal and tribology behavior of LPG refrigerant and MO lubricant appended with ZnO nanoparticles in domestic refrigeration cycle. Heat Mass Transfer/Waerme Stoffuebertragung 56(7):2303–11. https://doi.org/10.1007/s00231-020-02860-7 13. Boopathi S (2019) Experimental investigation and parameter analysis of LPG refrigeration system using Taguchi method. SN Appl Sci 1: 892. https://doi.org/10.1007/s42452-019-0925-2 14. Sampath B, Pandian M, Deepa D, et al (2022) Operating parameters prediction of liquefied petroleum gas refrigerator using simulated annealing algorithm. AIP Conference Proceedings 2460: 070003. https://doi.org/10.1063/5.0095601 15. Boopathi S (2021) Improving of green sand-mould quality using Taguchi technique. J Eng Res (in Press): 1–12. https://doi.org/10.36909/jer.14079 16. Boopathi S (2022) An investigation on gas emission concentration and relative emission rate of the near-dry wire-cut electrical discharge machining process. Environ Sci Pollut Res 29: 86237–86246. https://doi.org/10.1007/s11356-021-17658-1 17. Haribalaji V, Boopathi S, Asif MM (2022) Optimization of friction stir welding process to join dissimilar AA2014 and AA7075 aluminum alloys. Mater Today Proc 50: 2227–2234. https:// doi.org/10.1016/j.matpr.2021.09.499 18. Sampath B, Naveenkumar N, Sampathkumar P, et al (2022) Experimental comparative study of banana fiber composite with glass fiber composite material using Taguchi method. Mater Today Proc 49(5): 1475–1480. https://doi.org/10.1016/j.matpr.2021.07.232 19. Boopathi S, Myilsamy S (2021) Material removal rate and surface roughness study on near-dry wire electrical discharge machining process. Mater Today Proc 45(9):8149–81456. https://doi. org/10.1016/j.matpr.2021.02.267 20. Elango K, Trabelsi Y, Boopathi S, et al (2022) Influences of cryogenically treated work material on near-dry wire-cut electrical discharge machining process. Surf Topogr Metrol Prop 10(1): 015027. https://doi.org/10.1088/2051-672X/ac53e1 21. Boopathi S, Sivakumar K (2016) Optimal parameter prediction of oxygen-mist near-dry Wirecut EDM. Int J Manuf Technol Manag 30(3–4):164–78. https://doi.org/10.1504/IJMTM.2016. 077812
A Generalized Oppositional Differential Evolution Algorithm for Parameter Extraction of Different Photovoltaic Models Shubhranshu Mohan Parida and Pravat Kumar Rout
Abstract For improving the overall efficiencies of the photovoltaic energy systems, it is very important to model the photovoltaic cells and modules accurately. This necessitates the parameters of the photovoltaic systems to be extracted correctly. Then only it can serve the aforementioned purpose. Different methodologies and techniques are proposed by different authors in the past. But to overcome slow convergence in some of the techniques, a Generalized Oppositional Differential Evolution Algorithm is proposed for cell and module parameters extraction. It has a proper equilibrium between the intensification and diversification of candidate solutions within the search space. Basically, the root-mean-square error of experimental and simulated current for a model is considered as an objective function that undergoes minimization. For evaluating the performance of the projected approach, the parameters of a photovoltaic cellcand-a-module are extracted and compared with those extracted with other techniques proposed earlier. The simulation results are obtained in Matlab. The experimental and simulated I-V graphs of each model are superimposed to test the accuracy further. Keywords Photovoltaic systems · Cells · Modules · Parameter extraction · Differential evolution
1 Introduction The output power in a photovoltaic (PV) energy conversion system can be correctly forecasted only if the PV system model is accurate. An accurate PV system model can be realized only if its parameters are precise. Therefore, one must extract the PV system parameters precisely before evaluating its performance. The PV system S. M. Parida (B) Department of Electrical Engineering, Siksha O Anusandhan, Bhubaneswar, India e-mail: [email protected] P. K. Rout Department of Electrical & Electronics Engineering, Siksha O Anusandhan, Bhubaneswar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_11
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parameters can be extracted either by referring to the information given in the manufacturer’s datasheet or by using the current–voltage data of a cell/module at a particular irradiance and temperature. Mainly, the PV parameters can be extracted by 3 different approaches: (1) Analytical, (2) Numerical, and (3) Metaheuristic. Many authors have proposed their own methods in the past that belongs to one or a combination of the above categories. It is observed that the analytical approach deals with the solution of transcendental equations subject to a certain objective to arrive at unknown parameters. Initially, a method that used data from voltage intensity curves was proposed [1]. Subsequently, another method using Lambert’s W-function was also suggested [2]. These methods are easier to implement, take lesser computational time, and yield reasonably accurate solutions. But they involve errors due to initial approximation and errors in extracting information from characteristic curves of cells/modules. To improve the accuracy of the obtained solutions, numerical approaches are proposed. In this regard, numerical approaches [3, 4] were offered for PV models’ parameter extraction. Subsequently, the Newton–Raphson technique was also developed for extracting PV model parameters. The numerical approaches have drawbacks such as the possibility of the solution becoming locked at local minima locations and taking longer to compute. To lessen the impact of inherent demerits of the analytical and numerical approaches, various authors have proposed metaheuristic approaches. In addition, a graphical approach was projected for parameter extraction of diverse PV models [5]. These metaheuristic approaches offer several advantages such as simpler techniques and freedom from derivatives as well as complex expressions of the objectives. Also, many population/swarm-based metaheuristic approaches like Artificial BeeColony (ABC) were proposed for the PV parameter extraction. It is seen that each of these approaches has its own merits and demerits. Therefore, several hybrid algorithms are proposed for parameter extraction by overcoming individual shortcomings. Some of the effective ones are, Enhanced leader particle swarm optimization (ELPSO) [6], Hybridized interior search algorithm (HISA) [7], Genetic algorithm with non-uniform mutation (GAMNU) [8], Supply–demand based optimizer (SDO) [9].
2 Description of PV Models The different PV Models considered for the study are described next as follows.
2.1 PV Cell Model with a Single Diode (PVCSD) Presented in Fig. 1a is the electrical equivalent circuit for the PV cell model with a single diode. It entails a photon-generated current source I ph , an antiparallel diode D, a shunt resistor with resistance Rsh , and a series resistor with resistance Rs .
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Fig. 1 Electrical equivalent circuits of a PVCSD, b PVCDD
From the above figure, Kirchhoff’s current law gives the resulting equation: I ph = Idc + Ish + I
(1)
where I stands for photovoltaic cell current, V for cell voltage, I sh for current flowing via a shunt resistance, and I dc for diode current is given as Idc
q(V + I.Rs ) −1 = Isat ex p m.K .T
(2)
where m, K, T, and q denote the diode ideality factor, the Boltzmann constant, the cell temperature, and electron charge, respectively, and I sat signifies the saturation current of the diode. I sh is given as Ish =
V + I.Rs Rsh
(3)
Using Eqs. (1), (2), and (3), we get I ph
q(V + I.Rs ) V + I.Rs +I −1 + = Isat ex p m.K .T Rsh
(4)
2.2 PV Cell Model with Double Diodes(PVCDD) The PV cell model with double diodes (PVCDD) has an electrical equivalent circuit wherein most of the components are similar to that of PVCSD with the exception that it has 2 diodes D1 and D2 . The equivalent circuit of PVCDD is depicted in Fig. 1b. Kirchhoff’s current law gives the resulting equation for PVCDD I ph = Idc1 + Idc2 + Ish + I
(5)
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where I dc1 and I dc2 refer to the currents flowing through D1 and D2 , respectively, while others are defined earlier. The currents I dc1 and I dc2 are given as follows: Idc1 Idc2
q(V + I.Rs ) −1 = Isat1 ex p m 1 .K .T q(V + I.Rs ) −1 = Isat2 ex p m 2 .K .T
(6) (7)
where m1 and m2 denote the ideality factors, while I sat1 and I sat2 denote the saturation currents of D1 and D2, respectively. Using eqs. (3), (6), and (7) in Eq. (5) gives I ph
q(V + I.Rs ) q(V + I.Rs ) − 1 + Isat2 ex p −1 = Isat1 ex p m 1 .K .T m 2 .K .T V + I.Rs +I (8) + Rsh
2.3 PV Module Model(PVM) Assuming the PV module to be based on the PVCSD, the expression for the equivalent photon-generated source current I peq is obtained as q(V + I.Rseq ) V + I.Rsm +I −1 + I peq = Iseq ex p M.K .T Rshm
(9)
where I denotes the current of the module, V denotes the voltage of the module, N cs denotes the number of cells connected in series within the module, M signifies the effective ideality factor of the module (M = m.N cs ), I seq signifies the equivalent module saturation current, Rsm signifies series resistance, while Rshm signifies the shunt resistance of the module.
3 Parameter Extraction Strategy Basically, the experimental I-V data of the cell and module is utilized for the extraction of parameters for the above models. Here the parameters are extracted by minimizing an objective function (OF), i.e., root-mean-square error (RMSE) formulated from difference of the current from experiment and the current from simulation for a particular photovoltaic model subject to the parameter bounds. Therefore, it becomes an optimization problem that can be expressed as follows:
A Generalized Oppositional Differential Evolution Algorithm …
N 1 MinR M S E(λ) = MinO F(λ) = Min (Ik − Isk )2 N k=1
111
(10)
where λ is the parameter set to be extracted for a PV model, k refers to the index, while N denotes the number of experimental I-V data, and (I k ,V k ) denotes the experimental current–voltage data pair for a PV model. Here, Isk refers to the simulated current using the extracted parameter data. The simulated current is obtained by solving the equation H(I s , V, λ) = 0 for each PV model by Newton–Raphson approach. For the PVCSD, λ = {I ph , I sat , Rsh , Rs , m} and q(V + Is .Rs ) V + Is .Rs + Is − I ph (11) −1 + H (Is , V , λ) = Isat ex p m.K .T Rsh Considering the PVCDD, λ = {I ph , I sat1 , I sat2 , Rsh , Rs , m1 , m2 } q(V + Is .Rs ) q(V + Is .Rs ) − 1 + Isat2 ex p −1 H (Is , V , λ) = Isat1 ex p m 1 .K .T m 2 .K .T V + Is .Rs + + Is − I ph Rsh
(12) For the PVM, λ = {I peq , I seq , Rshm , Rsm , M} and q(V + Is .Rsm ) V + Is .Rsm + Is − I peq −1 + H (Is , V , λ) = Iseq ex p M.K .T Rshm (13)
4 Generalized Oppositional Differential Evolution Algorithm (GODEA) In this study, a Generalized Oppositional Differential Evolution is suggested for the extraction of optimal parameters for a given PV Model by minimizing the objective function in Eq. (10). Storn and Price [10] proposed the original version of Differential Evolution (DE). DE is an efficient and robust search technique. But later, the opposition-based DE was proposed in [11] for speeding up the converging process of the DE by simultaneously considering the present population alongside the opposite population. The steps of the proposed DE are described next. Similar to DE, the initial population is randomly generated as
g g g g X i, j = xi1 , xi2 , xi3 , ............., xi Dm
(14)
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Table 1 Parameter limit for extraction PV model parameters
PVCSD/PVCDD
PVM
Maximum value
Minimum value
Maximum value
Minimum value
I ph , I peq (A)
1
0
10
0
I sat , I sat1 , I sat2 ,I seq (µA)
1
0
10–3
10–15
Rsh , Rshm ()
1000
0
1500
0.001
Rs , Rsm ()
1
0
2
0
m, m1 , m2 , M
2
1
100
1
where i = 1,2,3,….,Ps and j = 1,2,3,….,Dm . Dm and Ps , respectively, refer to the dimensionality of decision variables and population size. In the same manner, an opposite population is generated as O X i, j = L B j + U B j − X i, j
(15)
where UBj and LBj refer to the predefined maximum and minimum values of the respective variables. Thereafter, the fitness calculation of original and opposite population members is done. Finally, Ps no of fittest members are selected as current population from the union of initial and opposite population to start the evolutionary process that involves mutation, cross-over and selection phases (similar to that as in DE[10]). But here an additional step is involved whose details are as follows. Generation Jumping: In this additional step, oppositional generation jump occurs if certain conditions are satisfied, else the termination criterion is checked. The opposite population is obtained for generation jump as g+1 O X i, j
=
g+1
M I N j + M AX j − X i, j , i f rand (0, 1) < Jr check f or stopping criterion, other wise
(16)
where [MIN j , MAX j ] refers to the variables’ current interval in the population. Again the fitness of the current and the opposite members are evaluated to select Ps fittest members from {X, OX}. These Ps selected members are considered as the current population in generation (g + 1). Finally, if the termination criterion is satisfied, then the search process terminates, else continues following the steps described above. Here, Ps = 10*Dm , G = 1000. The maximum and minimum values of considered model parameters (decision variables) are showcased in Table 1.
5 Results and Discussion The results of extraction are as follows.
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Table 2 Parameters of PVCSD model extracted by various techniques PVCSD parameters
ELPSO
ABC
I ph (A)
0.115016
0.1003312
SDO
I sat (µA)
0
3.23E-5
Rsh ()
14.429507
100
668.5946
661.263259
Rs ()
0.159052
0.5
0.6499
0.641622
m
1.768590
1.774159
1.6467
1.661946
RMSE
2.5400E-2
2.0412E-3
2.3487E-4
1.5925E-4
0.1000 5.944 E-6
GODEA 0.100038 7.44343E-6
Fig. 2 Experimental and Simulated I-V graphs of a PVCSD, b PVCDD
5.1 Results for PVCSD Here, all the PVCSD model parameters belonging to PVM-752aGa-As thin film cell are extracted. The best parameters set is chosen from a trial of 30 runs. The extracted parameters and corresponding RMSE obtained with previously proposed techniques and the GODEA technique are showcased in Table 2. The comparative analysis reveals that the GODEA technique results in an RMSE of 1.5925E-4 for the equivalent ckt parameters of the PVCSD which is much lesser than that obtained in other techniques. This showcases the accuracy of the parameters extracted by the GODEA technique. The experimental I-V graph is superimposed with the simulated I-V graph (using extracted parameters) as shown in Fig. 2a.
5.2 Results for the Model of PVCDD Here, all the PVCDD model parameters belonging to PVM-752aGa-As thin film cell are extracted by considering the same experimental data as in PVCSD. An experiment of 30 runs is conducted for the selection of the best parameter set. For the PVCDD model, the 7 extracted parameters and corresponding RMSE attained with previously
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Table 3 Parameters of PVCDD model extracted by various techniques PVCDD parameters
ELPSO
ABC
I ph (A)
0.103192
0.103252
SDO 0.1001
0.100036
I sat1 (µA)
1.775E-4
4E-5
7.2313E-4
2.6904E-5
I sat2 (µA)
1E-6
1E-6
6.2352E-5
Rsh ()
100.00
100.00
Rs ()
0.5
0.5
0.6684
0.6683
m1
2.000000
1.792987
2.0000
1.795476
m2
1.571052
2.000000
1.5152
1.294805
RMSE
2.075E-3
2.044E-3
2.131E-4
1.408E-4
637.302
GODEA
3.6244E-9 678.903
proposed techniques and the proposed GODEA technique are presented in Table 3. A Qualitative analysis reveals that the GODEA technique results in an RMSE of 1.408E-4 for the equivalent ckt parameters of the PVCDD which is much lesser than that obtained in other techniques. This highlights the accuracy of the GODEA technique. Moreover, the simulated I-V characteristic is superimposed on the experimental I-V characteristic of the PVM-752aGa-As thin film cell as shown in Fig. 2b. It is observed that all the points on the simulated I-V characteristic and those on the experimental I-V characteristic are in good agreement with each other. This reveals the accuracy of the proposed GODEA.
5.3 Results for PVM In this case, five parameters of the PVM model of STP6-120/36 are extracted at required experimental conditions using its I-V data points. An experiment of 30 runs is conducted to select the best parameters set. For the PVM model, the 5 parameters extracted and corresponding RMSE attained with previously proposed techniques and the proposed GODEA technique are presented in Table 4. The comparative analysis shows that the GODEA technique results in an RMSE of 1.39631E-2 for the parameters of the PVM which is much lesser than that obtained in other techniques that reflect the accuracy of parameters extracted by the GODEA technique. Here, the simulated I-V characteristics of the PVM using extracted parameters is overlapped with the experimental I-V characteristics as demonstrated in Fig. 3. It is observed that all the data belonging to the simulated characteristics are almost identical to all the data belonging to the experimental characteristics. It reflects higher accuracy of parameters extracted by GODEA.
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Table 4 Parameters of PVM model extracted by various techniques PVM parameters
GAMNU 7.4690
I peq (A) I seq (µA) Rshm ()
2.7390 1468.618
Rsm () M RMSE
0.16269 45.84837 1.6735E-2
Graphical 7.48391 1.151279 338.91116 0.1772 43.326 1.6514E-2
HISA 7.475284
GODEA 7.475284
1.930888
1.930888
570.197656
570.197460
0.168918
0.168918
44.800423
44.784145
1.42510E-2
1.39631E-2
Fig. 3 Experimental and simulated I-V graphs of PVM
6 Conclusion The proposed Generalized Oppositional DE algorithm is able to outperform previously suggested techniques in terms of the least RMSE in all of the tested PV models, which is revealed by the comparative results. Moreover, the proposed GODEA is able to reproduce the experimental I-V characteristics precisely from the extracted parameters, which is evident from the graphs. Since the RMSE is a measure of accuracy, the comparative results demonstrate the effectiveness of GODEA in terms of accuracy as compared to other approaches.
References 1. De Blas MA, Torres JL, Prieto E, Garcıa A (2002) Selecting a suitable model for characterizing photovoltaic devices. Renew Energy 25(3):371–380 2. Jain A, Kapoor A (2004) Exact analytical solutions of the parameters of real solar cells using Lambert W-function. Sol Energy Mater Sol Cells 81(2):269–277
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3. Mokhliss H, El-Amiri A, Rais K (2019) Estimation of five parameters of photovoltaic modules using a synergetic control theory approach. J Comput Electron 18(1):241–250 4. Muhammadsharif FF et al (2019) Brent’s algorithm based new computational approach for accurate determination of single-diode model parameters to simulate solar cells and modules. Sol Energy 193:782–798 5. Bencherif M, Benouaz T (2020) Parameter extraction of solar panels using the graphical method. Int J Ambient Energy 41(8):927–944 6. Jordehi AR (2018) Enhanced leader particle swarm optimisation (ELPSO): an efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Sol Energy 159:78–87 7. Kler D, Goswami Y, Rana KPS, Kumar V (2019) A novel approach to parameter estimation of photovoltaic systems using hybridized optimizer. Energy Convers Manage 187:486–511 8. Saadaoui D, Elyaqouti M, Assalaou K, Lidaighbi S (2021) Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation. Energy Convers Manag X 12:100129 9. Xiong G, Zhang J, Shi D, Yuan X (2019) Application of supply-demand-based optimization for parameter extraction of solar photovoltaic models. Complexity 10. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359 11. Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79
Analysis of Community Engagement on Social Networking Sites During the Kerala Floods in 2018 Sumit Kumar, Arun Thomas, and Vinay V. Panicker
Abstract Social Networking site data have emerged as an important source for identifying and tracking disasters. According to a number of recent research, the utilization of social networking data streams to extract meaningful data for emergency workers so that rescue operations are possible. If the information gathered is successfully utilized, it can reduce casualties and aid in the provision of basic requirements and medical care. The data gathering and evaluation procedure for the intended building of real-time catastrophes-related communication. The features of tweets during the Kerala Floods of 2018 were examined for this purpose. About 154,524 tweets were collected using the API during the period between July 2018 and January 2019. They were categorized on the basis of location, Kerala, with the help of MS Excel, and finally obtained 10,704 tweets. The data being posted was analyzed. Data visualization has been done with the help of Tableau and the most commonly used words were identified using a data set of 10,704 tweets in this study. Keywords Twitter’s API · MS excel · Machine learning · Data analysis · Tableau · Tweets
1 Introduction 1.1 Motivation During catastrophes such as earthquakes, floods, and forest fires, gathering information on the scope and severity of a threat in the early hours after its occurrence is S. Kumar (B) · A. Thomas · V. V. Panicker Supply Chain and Systems Simulation Laboratory, Department of Mechanical Engineering, National Institute of Technology Calicut, Kozhikode, Kerala 673 601, India e-mail: [email protected] V. V. Panicker e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_12
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challenging. One of the main obstacles to speedy catastrophe mapping is the lack of information available during or soon after a disaster. Social networking has been increasingly popular in recent years for disaster response. Two-way communication is at the heart of social media. Rescue workers and social networking sites’ content analyzers may be able to extract useful data from social networking sites data to help identify and rank serious questions or provide further insight into problems that have been detected. When catastrophe victims or eyewitnesses use social media to exchange information, seek or offer assistance, or inform the event, they contribute to the information streams that communities and rescue workers depend on during a catastrophe. In times of big crises, Twitter, a kind of social networking site, is a rapidly growing medium for having opinions, disseminating news, and allowing people and organizations to communicate with one another.
1.2 Problem Definition Natural and man-made catastrophes have increased in frequency, severity, and impact in nations all over the world during the previous several decades. In order to locate and offer a safe environment for those who have been affected, appropriate information is required. As smartphones, communications, and the Internet become more generally available, social media is becoming a more important platform for social involvement. However, when it comes to catastrophe information, social media platforms have significant problems, such as information overload and the existence of unnecessary material. As a result, it is necessary to investigate the influence of community interaction on social media platforms during catastrophes.
1.3 Research Questions The primary research questions are as follows: 1. Which zones are mostly affected or moderately affected or less affected? 2. What are the keywords most frequently used during the Kerala floods in 2018?
1.4 Method of Investigation In this study, firstly, data cleaning is conducted with the data obtained from social media platforms such as Twitter. They were categorized on the basis of location, Kerala, with the help of MS Excel, and finally obtained 10,704 tweets. The data being posted was analyzed. Data visualization has been done with the help of Tableau and
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the most commonly used words were identified using a data set of 10,704 tweets in this study. An unsupervised learning approach is adopted for topic modeling in order to extract information valuable for emergency preparedness teams from tweets. Topic modeling may assist discover themes from clusters of flood tweets.
2 Relevant Works The goal of Alshehri [1], was to learn more about how social media plays a role in catastrophes and how to assess and anticipate the forms of online community interaction on Twitter during catastrophes. In this research, the author has investigated how to construct a model that would help emergency responders prioritize Twitter information requests. Bhavaraju et al. [2] gathered and analyzed Twitter feed data for five natural disasters: five tornadoes, five forest fires, five wildfires, and five floods. There is a possibility of crowd-sourcing useful catastrophe information for disaster occurrences in which the tweet frequency rises from stable to disturbed states. Wildfires are less likely than the other three danger kinds to change tweet frequency or affect negative sentiment. Chair et al. [3] propose a framework for a platform based on social networking sites that aid victims by giving them better situational awareness and resources to use during a disaster to help them navigate and preserve their lives. Hao and Wang [4] considered the two recent hurricane occurrences to study the efficacy of a data-driven technique outlined to collect rapid, fine-grained, and full damage details from multimodal online data. Rescue managers and first rescuers are often engaged in critical decision-making and activities during catastrophes. Huang and Xiao [5] studied Hurricane Sandy in 2012 and reported that a variety of common text mining algorithms were utilized in an attempt to categorize the gathered tweets. Karam et al. [7] deciphered the content of data posted in natural language on social networking sites. According to Karami et al. [8], information exchange and attitudes of Geotagged users and Non-Geotagged are largely different. Kemavuthanon and Uchida [9] analyzed the tweets. Before and after Japan’s 2018 Osaka North earthquake, the tweet frequencies were determined. The results will serve as the foundation for the creation of a system that can gather and evaluate disaster-related data twitted on Twitter in order to deliver relevant real-time information to international visitors visiting Japan. Moumtzidou et al. [10] proposed a framework to be implemented for crisis management and management information systems. This may contribute to crisis management processes before, during, and after a crisis. Palshikar et al. [11] offer a weakly supervised technique for learning a bag of words model from a news corpus for different catastrophes, followed by a simple model transfer algorithm for augmenting the news-based model with a corpus of unlabeled tweets. Ragini et al. [12] provide a huge data-driven approach that investigates the many processes underlying sentiment categorization. Ragini et al. [13] applied actual time for action or event text classification and analysis and proposed a hybrid approach for
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spotting people at risk during and after a catastrophe. The suggested model results can be utilized to prevent casualties during disasters by monitoring social media. Sattaru et al. [14] offered a method for automatic tweet mining and processing in the case of a crisis to help locate catastrophe events and enhance flood monitoring. The proposed paradigm is applied in the Python space, which offers a scripting framework for automatically chaining together diverse modules. Singh et al. [15] developed a Twitter classification method for identifying tweets from catastrophe victims requesting help. The system creates a Markov model from the users’ temporal location data, which is then utilized for location inference.
3 Material and Methods 3.1 Case Description—Kerala Flood 2018 For the state of Kerala, 2018 was considered to be the wettest monsoon season in decades. Rainfall was 2346.3 mm, more than the average of 1649.55 mm, according to the India Meteorological Department (IMD). Flood waters reached depths of up to 4.5 m in certain spots. Local officials report that over 410 people have died as a result of floods in Kerala, the southernmost state of India, since June 2018, making it one of the deadliest disasters in a century. Numerous fatalities were crushed by landslide debris. In the 3,200 emergency relief camps that were set up in the impacted area, one million people were left homeless. All 14 districts of Kerala have closed their schools, and many places have banned tourists because of safety reasons. According to officials, more than 83,000 km of roads would need to be repaired, costing between £2.2 billion and $2.7 billion in total. More than 300 boats were needed for the rescue operation. The state government said that each boat would get 3000 rupees (£34) per day’s work and would be responsible for any damage to the boats [6].
3.2 Proposed Framework The proposed platform’s goal is to deliver individualized advice to persons trapped during catastrophes in real time. Based on data and information gathered from social networking sites, this platform has been developed, specifically Twitter. Data is gathered directly from the source, which is essential for detecting impending crises and developing situational awareness. This platform is primarily intended to increase citizen communication before, during, and after a disaster. Framework consists of the following components: (i) Data Collection, (ii) Data preprocessing, (iii) Data Visualization, and (iv) Word Frequency Analysis. Data Collection: The data collection is responsible for acquiring information on the disaster’s current state. The module combines data from a number of different
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sources, including social networking sites, virtual communities, and GPS data. Data from social networking sites may be collected via Application Programming Interfaces in a variety of formats (including images, videos, and text). There are several data gathering methods available, each of which is depending on the properties of sources of data and the analytical aims. In this paper, APIs are used to gather data in real time. APIs allow data to be collected based on a time period, geographic location, or a list of keywords in the tweet. “Kerala Floods 2018” is the keyword that was used to collect this information from Twitter. The data is obtained between the dates of the tragedy, which are July 19, 2018 and January 31, 2019. Data Processing: Data processing is a critical step in presenting data in a readily evaluable way. Data processing includes operations such as data cleaning, formatting, reduction, and transformation. A variety of approaches for processing social media data have been proposed in the literature, including data visualization, and word frequency analysis. Data cleansing, data integration, and data analysis are all phases in the data processing module. The first phase allows for the detection of erroneous, incomplete, or inappropriate data, which may subsequently be modified or deleted to enhance data quality. Numerous criteria, including data formats, completeness, logic, and restrictions, should be examined throughout the cleaning phase. The second phase, data integration, tries to combine data from many sources to create a unified perspective of data. The goal of the data analysis procedure is to collect accurate data about the crises’ progression (disaster regions, blocked highways, shelter locations, etc.) that residents, volunteers, and rescuers may utilize. In their tweets, Twitter users frequently employ colloquialisms, slang, acronyms, and spelling and grammatical mistakes. Using the Natural Language Toolkit library in the Python programming language, text processing techniques such as punctuation, stop-word removal (“an”, “a”, “in”, “the”, etc.), Lemmatization (working, converting work, worked, and workers are to the root of the word), eliminated unnecessary white spaces that were used, and lowercased all characters. Data Visualization: After data preprocessing, data visualization is performed. Data visualization is the process of displaying information in a graphical format. It lets decision-makers examine statistics in a visual manner, making it simpler for them to grasp complex issues or detect new trends. With the help of Tableau, different plots are constructed. Word Frequency Analysis: In order to evaluate disaster-related information posted on Twitter a word frequency analysis is conducted. About 154,524 tweets were collected using the API during the period between July 2018 and January 2019. The most commonly used words were identified using a data set of 10,704 tweets in this study.
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4 Results and Discussion 4.1 Data Visualization From Fig. 1, it can be concluded that the highest number of tweets during the flood came from the district of Ernakulam, Kerala, India. Considering the assumption that every person in Kerala has a mobile phone and uses Twitter during a disaster, the number of tweets indicates the severity of floods. It can be inferred that the district of Ernakulam is worst affected, whereas the districts of Kottayam and Kannur are moderately affected and the district of Wayanad is least affected. From Fig. 2, it can be concluded that a number of tweets drastically increased in the month of August 2018. This is the period when the Kerala floods of 2018 were experienced. Hence, the increase in the number of tweets may be accounted for due to the Kerala floods in 2018.
Fig. 1 Count of tweets from different locations
Fig. 2 Numbers of tweets during months
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Fig. 3 Number of tweets on different dates in August
Fig. 4 Number of tweets during august from different locations
From Fig. 3, it can be concluded that August 18, 2018, has experienced the maximum number of tweets. From the 12th it’s increasing till the 18th then it starts decreasing. Figure 4 shows the number of tweets coming from different districts of Kerala on different dates in August. It can be concluded that a maximum number of tweets are posted from Ernakulam between August 17 and 19, 2018.
4.2 Word Frequency To extract meaningful information, the frequency of terms in the tweets was evaluated. A graph is plotted to find the top 20-word frequency used in the Kerala floods in 2018. In this work, a data collection consisting of 10,704 tweets was utilized to identify the most frequently used unigram, bigram, and trigram terms. N-grams are probably the easiest machine learning concept to understand [16].
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Fig. 5 Top 20 Frequencies of the unigram in the tweets
Most of the words used during Kerala floods are “Kerala” and “Floods”, the moderate frequencies of the words are “help”, “relief”, and “rescue”, and the least frequencies of words are “death” and “express” as shown in Fig. 5. The tweets on the Kerala floods showed up in Fig. 6 as using the most frequent bigram terms. The terms Kerala floods, United Nations, and live update are used the most in tweets on the Kerala floods. A bigram has more significance than a unigram since it contains two words. The most frequent trigram terms are used in tweets on the Kerala floods, as seen in Fig. 7. The most common words in tweets concerning the Kerala floods were “aid Kerala floods,” “Kerala floods live,” and “Kerala floods untied”.
5 Conclusions Social media communications are densely packed with information, recording, and reflecting many facets of people’s lives, experiences, behaviors, and emotions to a particular topic or event. Analyzing data collected on social media platforms may help support and enhance decision-making. In this research, data analysis is done in which the most affected, moderately affected, and least affected districts are identified. It is observed that the most of the tweets happened in the month of August in 2018 and the highest number of tweets were tweeted on August 18th. This answers the first research question. The answers for the second question is obtained by getting the top 20 unigram, bigram, trigram words with the highest frequency, present in 10,704 tweets, are determined. Although every word has a distinct meaning, it will be better to understand when we combine words to create a group of words. So, trigram gives more meaning than bigram and unigram.
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Fig. 6 Most frequently used bigrams in tweets without a stop word
Fig. 7 Most frequently used trigrams in tweets without a stop word
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As a scope for future work, Topic Modeling and Sentiment analysis are proposed. It will be interesting to categorize different topics into different sub groups in order to prioritize the tweets and rank them.
References 1. Alshehri A (2019) A machine learning approach to predicting community engagement on social media during disasters. University of South Florida 2. Bhavaraju SKT, Beyney C, Nicholson C (2019) Quantitative analysis of social media sensitivity to natural disasters. Int J Disast Risk Reduct 39:101251 3. Chair S, Charrad M, Saoud NBB (2019) Towards a social media-based framework for disaster communication. Procedia Comp Sci 164:271–278 4. Hao H, Wang Y (2020) Leveraging multimodal social media data for rapid disaster damage assessment. Int J Disaster Risk Reduct 51:101760 5. Huang Q, Xiao Y (2015) Geographic situational awareness: mining tweets for disaster preparedness, emergency response, impact, and recovery. ISPRS Int J Geo Inf 4:1549–1568 6. Internet Geography (2022) https://www.internetgeography.net/topics/kerala-flood-case-study/ Accessed on 22 June 2022 7. Karam E, Hussein W, Gharib TF (2021) Integrating location and textual information for detecting affected people in a crisis. Social Network Analysis and Mining 8. Karami A, Kadari RR, Panati L (2021) Analysis of geotagging behavior: do geotagged users represent the Twitter population? ISPRS Int J Geo Inf 10:373 9. Kemavuthanon K, Uchida O (2019) Social media messages during disasters in Japan: an empirical study of 2018 Osaka North Earthquake in Japan. In: 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT). IEEE, pp 199–203 10. Moumtzidou A, Andreadis S, Gialampoukidis I (2018) Flood relevance estimation from visual and textual content in social media streams. In: Companion of the The Web Conference 2018 on The Web Conference 2018–WWW ‘18 11. Palshikar GK, Apte M, Pandita D (2018) Weakly supervised and online learning of word models for classification to detect disaster reporting tweets. Inf Syst Front 20:949–959 12. Ragini JR, Anand PMR, Bhaskar V (2018a) Big data analytics for disaster response and recovery through sentiment analysis. Int J Inf Manage 42:13–24 13. Ragini JR, Anand PMR, Bhaskar V (2018b) Mining crisis information: a strategic approach for detection of people at risk through social media analysis. Int J Disaster Risk Reduct 27:556–566 14. Sattaru JS, Bhatt CM, Saran S (2021) Utilizing geo-social media as a proxy data for enhanced flood monitoring. J Indian Soc Remote Sens 49:2173–2186 15. Singh JP, Dwivedi YK, Rana NP, Kumar A, Kapoor KK (2019) Event classification and location prediction from tweets during disasters. Ann Oper Res 283(1):737–757 16. Toward Data Science Homepage https://towardsdatascience.com/understanding-word-ngrams-and-n-gram-probability-in-natural-language-processing-9d9eef0fa058 Accessed 27 Nov 2019
Application of Evolutionary Technique for Mapping onto Network on Chip Subhashree Choudhury, A. S. Das, Sarita Misra, Ismail Hossain, Taraprasanna Dash, and Kaliprasanna Swain
Abstract Network on Chip (NoC) is a communication subsystem between various IPs interconnected through an on-chip router inside a single chip. The on-chip interconnection infrastructure connects the different intellectual property (IP) with the help of various industry-standard high-performance (HPC) interconnection topologies. Also, the on-chip interconnection network facilitates the connection of more Processing Elements (PE) to enable deadlock-free scheduling and parallel processing. The present work addresses one important aspect of NoC which is “Optimal Mapping” which allows the participation of cores in an optimal way for achieving low bandwidth requirement, low latency hence greater throughput. To achieve the optical mapping, a Genetic Algorithm is proposed here for the optimization of NoC performance by employing 2D Mesh topology. The genetic algorithm produces consistent and comparable results at par with standard algorithms. A deterministic seeding and successive seeding would yield better results and consume less CPU time. The proposed result is marginally better than NMAP and PSMAP. S. Choudhury (B) Department of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India e-mail: [email protected] A. S. Das J Systems and Control, Cuttack, India S. Misra Department of Electrical Engineering, GITA Autonomous College, Bhubaneswar, India I. Hossain Department of Nuclear Power Plants and Renewable Energy, Ural Federal University, Yekaterinburg, Russia T. Dash Department of Electrical and Communication Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India e-mail: [email protected] K. Swain Department of Electronics and Communication Engineering, GITA Autonomous College, Bhubaneswar, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_13
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Keywords Network on chip · Genetic algorithm · Application mapping · 2D mesh topology
1 Introduction Modern microprocessors and multicore chips are getting more complicated every day. An increase in complications directly affects the communication between cores. Inefficient communication between cores results in an inefficient subsystem; which leads to an inefficient system. To increase the efficiency of multicore chips, the System on Chip (SoC) concept was introduced. SoC uses the bus to communicate between different cores and practices the master–slave concept for bus arbitration. An overloaded bus gives rise to inefficient performance, huge latency, and degraded throughput [1]. NoC (Network on Chip) is the recent trend in the industry. This technique utilizes the concept of a network instead of a bus for communication between different cores of the subsystem. NoC is an on-chip communication infrastructure that interconnects different Intellectual Property (IPs) of the subsystem [2]. Optimal positioning depends on the communication pattern of nodes. Highly communicating nodes should be placed as neighbors for better performance [3]. Among many NoC topologies, 2D Mesh topology is proposed in this work. The choice of task graphs for this research work is based on availability [4]. Most of the previous research work is done using a single task graph, either Video Object Plane Decoder (VOPD) or MPEG4. However, in this research work, eight task graphs are considered, and they are PIP, MWD, MP3 Encoder Decoder, MPEG4 Decoder, 263 Encoder MP3 Decoder, 263 Decoder MP3 Decoder, VOPD, and DVOPD [1, 5] as shown in Fig. 1. Also, one 64 core graph and one 128 core graph also considered for verification. As the mapping problem is NP-hard, evolutionary algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), etc., yield better performance in NP problems [2]. As GA falls in the transformative heuristic category, the current research work proposes a Genetic algorithm (GA) for mapping to optimize the bandwidth. Pertaining to GA with respect to mapping onto NoC, Ozcan Ozturk and Dilek Demirbas [6] described an approach for heterogeneous NoC mapping using Genetic Algorithm and compared it with ILP result. Glenn Leary et al. [7] considered a multiobjective GA to generate a floor plan of NoC for a given architecture. In their research work, Authors considered power consumption and router resources as constraints. Mesidis and Indrusiak in [6] discussed the use of genetic algorithm-based mapping for hard real-time applications. In their work, authors considered “hard real-time applications running over multicore processors based on wormhole Networks on Chip (NoCs)”. The main contribution of this research work is to create a framework for application mapping using GA for 2D Mesh topology, such that the overall bandwidth
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Fig. 1 Benchmark application core graphs with communication bandwidth [1]
requirement for a particular application is minimum. Also, the results obtained are compared with already published results from various authors.
2 Problem Analysis The mapping application is the process of mapping a task graph to a given highperformance topology, such that every node in the task graph is mapped to a unique node in the topology graph. The task graph, topology graph mapping function, and constraint are represented mathematically as follows: Task graph : G (V , E) ∀ vi V
∃
ei, j ∈ E ∀ i /= j
Topology graph : T (U, F) ∀ ui ∈ U and f i, j ∈ F ∀ i /= j
(1) (2)
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Mapping function : map (vi ) = u j∀vi ∈ V , ∃u j ∈ U and |V | ≤ |U |
(3)
Constraint or objective function is min
( |E| E
) bwi, j × distance(u i , vi ) distance(u i , vi )
i=1
={
0, i f i = j V ei, j ∈ /E (xhops + yhops)
(4)
Task graph G(V,E) is a directed graph where V represents a set of all nodes present in the graph and E represents a set of all edges. A topology graph is represented by T (U,F) ∀ ui ∈ U and f i,j ∈ F ∀ i /= j where U is set of all vertices in the graph and F is set of all edged in the topology graph and f i,j is termed as bandwidth bwi,j which is bandwidth needed between node ui and uj [1]. A mapping between G(V,E) and T(U,F) is defined as map (vi) = uj ∀ vi ∈ V, ∃ uj ∈ U and |V|≤ |U|. In other words, a map of a core vi to router uj is possible when the number of cores is less or equal to the number of routers in 2D mesh or the number of leaf connectors in tree-based topology graph. Mapping of task graph G to topology graph T should be such that, the total bandwidth consumption of all the cores is at a minimum. This implies that highly communicating cores should be placed nearer to each other. Here, distance (u i , vi ) is the function which returns the distance between source and sink routers.
3 Implementation of Genetic Algorithm on 2D Mesh Topology The research work [7] used a Genetic algorithm-based approach to solve the NoC optimization problem. The chromosomes in GA are generally implemented by a string of binary digits (0 or 1). But variations are also adopted in real-world problemsolving. Chromosomes can be a string of characters for an array of numbers or any datatype. Depending on the data type used for chromosomes, all operator functions are to be implemented accordingly. For solving the mapping problem (which is QAP, NP-hard), a real value chromosome structure is adopted for the present work. This implementation uses the number of cores as the size of the chromosome and each genome is represented by a router number. Initially, populations of chromosomes are created by randomly assigning router numbers to individual genomes. This is achieved by unique random number generation. Unique means in a single chromosome there should not be duplication in the genome. A set of the genome is created using the above method and termed as the initial population. All the operations are carried out over this initial population. Crossover is performed on a set of random points which are generated for a generation. For each crossover operation, two chromosomes are needed. Chromosome
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selection is performed by the roulette wheel method which is governed by crossover rate (cr). The crossover rate signifies the fraction of chromosomes that will take part in reproduction. A small defect on the individual chromosome is introduced by mutation operation. Here, the small defect indicates a genetic change of a few chromosomes in the population. This helps the evolution to avoid local maxima or local minima depending on the situation, which ultimately avoids premature convergence. The mutation rate decides the fraction of the population which is to be mutated. Here, the mutation exchanges the genomes randomly, and for this genome are selected randomly for exchange between them.
4 Simulation and Result By considering the environment setup (Intel core i3, 2.10 GHz, 8th generation, 2 GB RAM, GNU C compiler), a comparison table is presented where the communication cost is taken into account by implementing 2D Mesh topology. Here, eight benchmark task graphs are selected and the results of all 8 task graphs along with the comparison data with NMAP [1] and PSMAP [7] are presented in Table 1. Table 1 demonstrates the communication costs of our algorithm with NMAP [1] and PSMAP [7]. It is observed that our results are consistent and it is up to 6.4% better than NMAP in the case of the MP3 encoder decoder task graph. Again, Fig. 2 displays the normalized costs for our algorithm, NMAP, and PSMAP where it is inferred that our algorithm yields a similar result like PSMAP but a better solution than NMAP. Again, Table 2 represents the CPU time consumed by the proposed algorithm for the different number of cores along with the crossover rate and mutation rate. Also, a graph is plotted between the number of cores versus CPU time consumed as shown in Fig. 3. In the graph, CPU time is taken on the Y-axis, and the number of cores on the X-axis. The graph is drawn with the environmental setup which is discussed earlier. Table 1 Comparison of communication cost with NMAP [1] and PSMAP [7] Task graph
Communication cost (hop × bandwidth)
Normalized to our result
Proposed result
NMAP
NMAP
PSMAP
PSMAP
640.0
640.0
640.0
1.0
1.0
MWD
1120.0
1184.0
1120.0
1.057
1.0
MPEG4
3567.0
3672.0
3567.0
1.029
1.0
PIP
MP3ENCDEC
17.021
18.171
17.021
263enc-mp3dec
230.427
230.407
230.407
263dec-mp3dec
19.823
20.073
19.823
1.067
1.0
1.0
1.0
1.013
1.0
VOPD
4119.0
4265.0
4119.0
1.036
1.0
DVOPD
9720.0
10,253.0
9752.0
1.055
1.003
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Fig. 2 Normalized cost graph
Here, the trend of the graph is exponential and is expressed as follows: C PU time = (c×cor es) ) , Where a = 6.453 × 102 , b = −10.983 and c = −2.802 × 10−2 a × e(b×e and the RMS error is 5.782 × 10−2 . A graph is plotted between CPU time and population which indicates that CPU time increases with population but in a random manner as shown in Fig. 4. Table 2 CPU time, crossover rate, mutation rate, best result Population
Crossover rate Cr
Mutation rate Mr
8
256
0.1
0.1
640.0
0.150
MWD
12
256
0.1
0.4
1120.0
0.419
MPEG4
12
256
0.1
0.5
3567.0
0.484
263enc-mp3dec
12
256
0.7
0.3
230.407
0.960
MP3ENCDEC
13
256
0.3
0.7
17.021
0.865
263dec-mp3dec
14
256
0.2
0.5
19.823
0.712
VOPD
16
256
0.1
0.3
DVOPD
32
256
0.5
0.4
9720.0
G1
64
256
0.6
0.6
16929.469
103.786
G2
128
256
0.1
0.4
347190.688
476.128
Task graph PIP
Number of cores
Fig. 3 CPU times versus number of cores
Result (hops x Time (s) bandwidth)
4119.0
0.81 7.265
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Fig. 4 CPU time versus population
5 Conclusion Application mapping onto the 2D mesh problem for static mapping has been attempted here. It is well-known fact that application mapping is complex, demanding, and very hard to realize the problem. There is no fixed algorithm that exists for all goals and system parameters. With a slight variation in system parameters and/or goal statement, the solution is very different. The genetic algorithm produced consistent and comparable results at par with standard algorithms. Even if this implementation does not have iterative improvement or deterministic seeding, but produced a good comparable result as produced by iterative improvement algorithms. A deterministic seeding and successive seeding would yield better results and consume less CPU time. A comparison of results in Table 3 reveals that the proposed algorithm contributes consistent results. In the case of DVOPD (32core), the proposed result is marginally better than NMAP and PSMAP. Table 3 CPU time for the best result in a population versus population
Population
CPU time (second)
40
0.156
50
0.233
60
0.225
70
0.17
80
0.341
90
0.537
100
0.376
110
0.414
120
0.337
130
0.36
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References 1. Murali S, De Micheli G (2004) Bandwidth-constrained mapping of cores onto NoC ARchitectures. In: Proceedings of the conference on design, automation and test in Europe, vol 2. Washington, DC, USA, pp 20896–20902 2. Mahanta HJ, Biswas A, Hussain MA (2014) Networks on chip: the new trend of on-chip interconnection. In: 2014 Fourth international conference on communication systems and network technologies. Bhopal, India, pp 1050–1053 3. Sikandar S, Baloch NK, Hussain F, Amin W, Zikria YB, Yu H (2021) An optimized natureinspired metaheuristic algorithm for application mapping in 2D-NoC. Sensors 21:5102 4. Alagarsamy A, Gopalakrishnan L (2016) SAT: a new application mapping method for power optimization in 2D—NoC. In: Proceedings of the 2016 20th international symposium on VLSI design and test (VDAT). Guwahati, India, pp 1–6 5. Ozturk O, Demirbas D (2010) Heterogeneous network-on-chip design through evolutionary computing. Int J Electron 97(10):1139–1161 6. Leary G, Srinivasan K, Mehta K, Chatha KS (2009) Design of network-on-chip architectures with a genetic algorithm-based technique. IEEE Trans Very Large Scale Integr Syst 17(5):674–687 7. Mesidis P, Indrusiak LS (2011) Genetic mapping of hard real-time applications onto NoCbased MPSoCs–a first approach. In: 6th International workshop on reconfigurable communicationcentric systems-on-chip (ReCoSoC). pp 1–6
Parametric Optimization of Resistance Spot Welded Dissimilar Metals Utilizing Advanced Hybrid Taguchi-MARCOS Method Bhabani Shankar Kamilla, Bibhuti Bhusan Sahoo , Abhishek Barua , Siddharth Jeet , Kanchan Kumari , Dilip Kumar Bagal , and Bibhu Prasad Panda Abstract Resistance spot welding is a supreme noteworthy joining practice exploited in various engineering. To circumvent welding failures and the quality of some materials, optimization of welding parameters turns out to be vital for forecasting upright welded joints. This study targets at scrutinizing the impact of spot welding variables on dissimilar lap joints for 50HS Stainless Steel; AA1200 Aluminum Alloy. Experimentation is planned as per Taguchi’s L9 orthogonal array. Expectations of ANOVA are conversed and carefully scrutinized. Measurement Alternatives and Ranking according to Compromise Solution (MARCOS)based Taguchi methods are applied to investigate the output responses of resistance spot welding operation. The results revealed that welding current is the main affecting parameter of nugget diameter and tensile-shear strength. After performing confirmatory tests. Keywords 50HS stainless steel · AA1200 aluminum alloy · MARCOS · Resistance spot welding B. S. Kamilla · B. B. Sahoo (B) Department of Mechanical Engineering, ITER, Siksha ‘O’ Anusandhan (Deemed to Be University), Bhubaneswar, Odisha, India e-mail: [email protected] A. Barua · S. Jeet Department of Mechanical Engineering, Centre for Advanced Post Graduate Studies, BPUT, Rourkela, Odisha, India K. Kumari Department of Mechanical Engineering, Parala Maharaja Engineering College, Berhampur, Odisha, India D. K. Bagal Department of Mechanical Engineering, Government College of Engineering, Kalahandi, Bhawanipatna, Odisha, India B. P. Panda Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_14
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1 Introduction Resistance Spot Welding (RSW) is a quick procedure that takes only a fraction of a second to complete and it is one of the cleanest and most effective welding methods used in sheet metal manufacturing. Its primary focuses are on the pace of the process, the instance of activity, and its adaptableness to computerization in the manufacture of sheet metal collections. The limitations of RSW include the cost and force prerequisites of the hardware, the difficulty of dismantling the joints for support or repair, and the concept of the structure required for the process. There has recently been a surge in concern in high-volume RSW of Al alloys. However, unlike steel RSW, which easily generates high-quality and lasting welds, Al alloy RSW offers significant challenges. As a result, electrode tips degrade quickly and joint quality varies [1–6]. In this research, resistance spot welding factor effects were investigated to realize the nethermost weld time along with the greatest nugget diameter and tensile-shear strength in dissimilar materials. The optimum circumstances of the resistance spot welding parameters for nugget diameters, weld duration, and tensile-shear strength. In this research, three parameters such as welding time, squeeze time, and current were selected and also tuned to distinguish almost the modification of mechanical characteristics surrounding the fused nugget region. Taguchi’s Design of experiment along with MARCOS was employed in this study to produce the best results.
2 Experimental Analysis and Methodology As shown in Figs. 1 and 2, the basic alloys utilized in this research were AA1200 Aluminum alloy sheets and 50HS stainless steel sheets. The sheets were trimmed to the appropriate size using a shear-off machine. The measurements of both AA1200 and 50HS sheets are shown in Fig. 1. Resistance spot welding was used to join the sheets together in a 25.4 mm overlap arrangement. Table 1 shows the chemical compositions of the two materials in comparison to one another. Using a SIP type PPV50 spot welding machine, the specimen is spot welded. For the tensile-shear testing, a tensile test machine was utilized to perform the experiments. Table 2 lists the orthogonal array parameters and level values that were used.
3 MARCOS Method MARCOS method was originally articulated for solving practicable supplier collection in the healthcare sector [7, 8]. AHP method-based weights were used for the computation of weighted matrix [9–14] for the MARCOS method which consists of the subsequent steps (Fig. 3).
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137
Fig. 1 RSW specimen dimensions [8]
Fig. 2 Universal Testing Machine for testing strength along with the 9 welded spots
4 Results and Discussion Taguchi’s design (L9) was employed for specimen fabrication (Table 3). The samples were spot welded in accordance with the specified dimensions by the virtue of RSW process. Nugget Diameter in mm, Tensile Shear Strength in MPa, and Weld time in ms are recorded and optimized. Computational particulars of MARCOS method optimization are depicted in Table 4. From the utility function of experimental value, it was perceived that investigational consequences of experiment no. 2 are the paramount outcome according to the ranking for both the welded specimen. In Fig. 4, the arrangement of A2 B2 C2 depicts the minimum assessment of SN ratio plot for RSW of stainless steel and aluminum alloy which shows the arrangement as the prime constraint combination for RSW. Table 5 give the upshots of ANOVA for RSW. Conferring to Table 5, ‘C’ factor, i.e. Input Current with 49% is the best influential parameter for RSW.
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Fig. 3 MARCOS method algorithm
To affirm the enrichment of output quality in the wake of the definition of the best degree of input bounds, a confirmatory trial is fulfilled. Table 6 illustrates the initial and ideal level enactment.
5 Conclusions This exploration explains the method for probing the upshot of the resistance spot welding constraints on dissimilar spot-welded joints of aluminum alloy and stainless steel in dissimilar spot-welded joints. It was absolute to use the Hybrid Taguchi technique in conjunction with the MARCOS approach in order to anticipate the optimal responses from the input variables as a result of the resistance spot welding operation. Some significant implications may be made from this study, which are detailed below: A close examination of the tentative consequences, as measured by signal-tonoise ratio along with mean responses, revealed that current has a substantial effect
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139
Table 1 Metals’ chemical composition Constituent
50HS (content wt %)
AA1200 (content wt %)
Manganese, Mn
4–6
≤0.050
Iron, Fe
–
≤1
Titanium, Ti
–
≤0.050
Nitrogen, N
0.20–0.40
– –
Chromium, Cr
20.5–23.5
Aluminum, Al
–
≥99
Zinc, Zn
–
≤0.10
Nickel, Ni
11.5–13.5
–
Niobium, Nb
0.10–0.30
–
Silicon, Si
6
14
4–6
20
300
18
Single round
38
Ergonomic Risk Assessment of Rubber Tappers …
181
Fig. 2 Posture of arm, wrist, trunk, and neck during tapping at a lower height
total wrist and arm scores add up to 6. For the neck, trunk, and leg analysis, 2 points are given as neck score as the angle of deviation does not exceed 10°–20°. For trunk score 2 points are given as the angle of deviation is in the range of 0°–20°. As legs and feet are supported 1 is given as the leg score. So, from RULA Table B, score 2 is given as Posture Score B. Similarly, as before Muscle use score is given as 1 and the Force/load score is given as 1. So, it adds up to 4. Therefore, from Table C, the RULA score is evaluated as 3. It can be seen that the tapping height creates a significant change in scores A and B. If the tapping needs to be done at a height above the shoulder level a higher degree of flexion for the wrist and extension of the arm is necessary but a lower deviation for the trunk and neck. At higher heights, the upper arm must also be flexed more. This increases the arm and wrist score (Score A). However, tapping at a height lower
Fig. 3 Posture of arm, wrist, trunk, and neck during tapping at a higher height
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than the shoulder lever poses a lesser degree of flexion for the wrist and extension arm but a higher deviation for the trunk. During tapping at various heights, the lower back or trunk must be flexed more, increasing the postural score of the neck, trunk, and legs. (Score B). The circumference of the tree trunk prevents the tappers to tap at a fixed position. In order to maintain a continuous channel/path to and fro shearing of the tapping is done around the tree. As they move around, they constantly cross one leg over the other making the legs flexion repetitively. This contributes to an increase in score B. The slope of the land, uneven terrain, or hillside forces the leg to raise during tapping. This contributes to an increase in the score of legs. The muscle use and the force/load also contribute to scores A and B. To the best of our knowledge, no posture assessment with the RULA ergonomic tool has been conducted among rubber tappers in India. RULA has been the deal posture risk assessment tool to understand the postures of the rubber tappers vividly, as it evaluates all of the necessary parameters that are required to understand the ergonomics of the working environment (Table 3).
4 Conclusions The study confirms that 95% of the rubber tappers pose a higher risk as they have a RULA score above 5 which signifies that they are at a medium risk level or higher and a change is necessary. Among them, 85% of the tappers have a score higher than 6 (high risk) which demands an immediate solution/change, or the tappers will end up with a permanent musculoskeletal disorder. The most MSD-affected regions among the rubber tappers are the wrists. Its prolonged and repetitive exposure to awkward postures during rubber tapping makes them highly prone to MSDs like Carpal Tunnel Syndrome (CTS). It would benefit the tapper if design improvements are done on the tapping tool to minimize the awkward postures faced during tapping.
Posture Score B
1
1
1
1
1
1
4
5
9
4
3
3
4
4
5
3
6
4
3
3
4
5
4
4
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
1
1
1
1
1
1
1
1
1
1
1
1
1
4
2
1
4
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
6
6
7
6
5
5
6
8
5
7
6
6
5
5
6
11
7
6
6
6
3
3
3
4
4
4
5
2
2
5
5
7
3
4
3
2
5
3
6
7
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Muscle Use score
Wrist and Arm Score
Group B
Force or Load Score
Posture Score A
Muscle Use score
Group A
1
Sl. No
Table 3 Posture analysis results
1
1
1
0
0
0
0
0
0
0
1
1
1
1
0
0
0
1
1
1
Force or Load Score
5
5
5
5
5
5
6
3
3
6
7
9
5
6
4
3
6
5
8
9
Neck, Trunk, Leg Score
6
6
6
6
6
6
6
6
3
7
7
8
5
6
5
7
7
6
8
9
(continued)
RULA Grand Score
Ergonomic Risk Assessment of Rubber Tappers … 183
1
1
1
1
1
1
2
4
4
4
4
5
4
5
6
4
4
4
3
4
5
7
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
1
1
1
1
1
1
1
1
1
1
1
4
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
7
6
5
6
6
6
8
7
6
7
6
6
6
6
4
6
5
5
5
4
4
5
5
3
4
3
2
7
5
5
2
5
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
23
3
22
6
1
4
21
1
Muscle Use score
Posture Score B
Group B Wrist and Arm Score
Posture Score A
Force or Load Score
Muscle Use score
Group A
Sl. No
Table 3 (continued)
0
0
0
0
0
0
0
0
0
1
0
1
1
1
1
1
1
1
Force or Load Score
6
6
6
5
5
6
6
4
5
5
3
9
7
7
4
7
7
5
Neck, Trunk, Leg Score
7
7
7
6
6
7
7
7
7
6
6
7
7
7
5
6
7
6
RULA Grand Score
184 A. Varghese et al.
Ergonomic Risk Assessment of Rubber Tappers …
185
References 1. Association of Natural Rubber Producing Countries (ANRPC). Report on rubber production. www.anrpc.org (2017) 2. Boonphadh P (2008) The perceived effects of work on the health of rubber farmers in southern Thailand. Massey University, New Zealand 3. David G, Woods V, Li G, Buckle P (2008) The development of the Quick Exposure Check (QEC) for assessing exposure to risk factors for work-related musculoskeletal disorders. Appl Ergon 39:57–69 4. Doi MAC, Yusuff RM, Leman Z (2007) A preliminary study of the prevalence of musculoskeletal disorders among Malaysian rubber tappers. Age 45:25–78 5. Hignett S, McAtamney L (2000) Rapid Entire body assessment (REBA). Appl Ergon 31:201– 205 6. Karhu O, Kansi P, Kuorinka I (1977) Correcting working postures in industry: a practical method for analysis. Appl Ergon 1;8(4):199–201 7. McAtamney L, Corlett EN (1993) RULA: a survey method for the investigation of workrelated upper limb disorders. Appl Ergon 24(2):91–99 8. Meksawi S, Tangtrakulwanich B, Chongsuvivatwong V (2012) Musculoskeletal problems and ergonomic risk assessment in rubber tappers: a community-based study in southern Thailand. Int J Ind Ergon 42(1):129–135 9. Pramchoo W, Geater AF, Tangtrakulwanich B (2018) Physical ergonomic risk factors of carpal tunnel syndrome among rubber tappers. Arch Environ Occup Health 75(1):1–9 10. Pramchoo W, Geater AF, Harris-Adamson C, Tangtrakulwanich B (2018) Ergonomic rubber tapping knife relieves symptoms of carpal tunnel syndrome among rubber tappers. Int J Ind Ergon 68:65–72 11. Stankevitz K, Schoenfisch A, de Silva V, Tharindra H, Stroo M, Ostbye T (2016) Prevalence and risk factors of musculoskeletal disorders among Sri Lankan rubber tappers. Int J Occup Environ Health 22(2):91–98 12. Udom C, Janwantanakul P, Kanlayanaphotporn R (2016) The prevalence of low back pain and its associated factors in Thai rubber farmers. J Occup Health 58(6):534–542 13. Udom C, Kanlayanaphotporn R, Janwantanakul P (2019) Predictors for nonspecific low back pain in rubber farmers: a 1-year prospective cohort study. Asia Pacific Journal of Public Health 31(1):7–17 14. Van Beilen JB, Poirier Y (2007) Establishment of new crops for the production of natural rubber. Trends Biotechnol 25(11):522–529 15. Varghese A, Panicker VV (2022) Impact of musculoskeletal disorders on various agricultural operations: a systematic review. S¯adhan¯a 47(1):1–10 16. Varghese A, Panicker VV (2021) Computer-aided ergonomic analysis for rubber tapping workers. In: Advanced manufacturing systems and innovative product design: select proceedings of IPDIMS 2020 (pp 293–302). Springer Singapore 17. Varghese A, Panicker VV, Abraham J, Gimmi J, Tom J, Desini K (2021) Ergonomic evaluation of rubber tapping workers using Ovako working posture Analysing system (OWAS). 19th annual international ergonomics conference ‘Humanizing work and Work Environment’ (HWWE) 2021, at Department of Design, Indian Institute of Technology Guwahati 18. Varghese A, Panicker VV, Abraham J, Gimmi J, Tom J, Desini K (2021) Ergonomic evaluation of rubber tapping workers using rapid entire body assessment (REBA). 2nd international conference on recent advances in manufacturing (RAM-2021), at Department of Mechanical Engineering, S. V. National Institute of Technology, Surat 19. Velásquez S, Valderrama S, Giraldo D (2016) Ergonomic assessment of natural rubber processing in plantations and small enterprises. Ingeniería y competitividad 18(2):233–246
Finite Element Analysis of Cranked Beam with Reinforcement Detailing of SP 34 (1987) Bipul Sharma, Neelam Rani, and M. Abdul Akbar
Abstract The cranked beam is commonly used to support sloping roofs and has the advantage of lower bending moments owing to its shape. The notch reinforcement of the cranked beam requires special detailing to lower the stress concentration. In this study, the goodness of the reinforcement details of the cranked beam given in Indian Code, SP 34 (1987), is put to test through Finite Element Analysis (FEA). The basic proportions of the beam were traced in AutoCAD. A design spreadsheet was developed based on the limit state design philosophy of IS: 456 (2000) for the cranked beam. The finalized dimensions of the cranked beam along with the reinforcements were modelled in ABAQUS. The preliminary model after validation was analysed for the central point load of the base case for M20 concrete, then the model was modified for other grades of concrete and loads. From the analysis results and stress contours, the stress concentrations and deflection of the cracked beam with constant cross-section were determined and the ideal proportion of the cross-section and reinforcement detailing were arrived at. Keywords Cranked beam · Finite element analysis · ABAQUS · Reinforcement detailing · Stress concentration
1 Introduction The cranked beam is widely utilized to support sloping roofs and because of its shape, it has the advantage of lower bending moments. It connects two beam ends B. Sharma (B) · N. Rani · M. A. Akbar Department of Civil Engineering, Dr. B R Ambedkar National Institute of Technology, G.T. Road, Amritsar Bypass, Jalandhar, Punjab 144011, India e-mail: [email protected] N. Rani e-mail: [email protected] M. A. Akbar e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_19
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Fig. 1 Sloping roof
having pinned supports. In a sloping roof, the cranked beam may be provided at the wall location. The roof protects against various weather conditions such as heat, rain, snow, and so on. A sloping roof system has a deck or surface with a gradual slope from the hip of the roof to cover the entire building structure. The roof’s minimum pitching or sloping angle is determined by the minimum slope required to exclude the drain rainwater and roof covering to the eaves or valley gutter. The sloping roof is shown in Fig. 1. A study was conducted in which an ABAQUS Finite Element model was developed to assess the effect of rocket launch-induced blast loading on the three-hinged arch and the tie connection. In that paper the launch pad’s physical characteristics/geometry was used to model the impact of rocket launching, and when using the CONWEP module, a blast load intensity equal to 20,000 pounds of TNT was applied [1]. Cracked Beams with various boundary conditions have been analysed. In the results of past studies, experiments were compared to the results of ABAQUSanalyzed Finite Element Analysis. There is no information in the study about the effects of crack opening size and mesh refinement [2]. A study that examined the modal analysis of a cracked cantilever beam using finite element simulation found that the crack causes the natural frequency to fall. The extent of the decrease depends on the location, depth, and size of the crack. With increasing analysis time, accuracy increases and the effect of a crack varies depending on the mode of vibration [3]. In a simply supported beam analysis, ABAQUS was used to simulate the beam testing by using a concrete damaging model and embedding steel bars in concrete. The results were similar to experimental studies [4]. Taking into consideration the effect of flexure and shear failure on concrete strength, steel percentage, and RC beam stirrups, a Finite Element (FE) modelling that focuses on crack initiation and the pattern was developed and the design load was found to be greater than the serviceability load [5]. Reinforced concrete (RC) beam behaviour was simulated using the ABAQUS software and the concrete damaged plasticity approach is used in the finite element model; this model can be utilised to validate theoretical calculations and enhance experimental behavioural research and it was validated by a model that had previously
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been experimentally tested and reported [6]. Crack branching in brittle materials was numerically represented by finite elements with displacement discontinuities expressing the solution of the following boundary problem. A branching criterion based on tip of the crack velocity had been used to address and analyse the effects of the critical velocity at the branching location. For the benchmark problem, various numerical simulations were presented for a central broken plate that has been given, with other numerical techniques in good agreement [7]. The interval finite element approach was utilised to handle the uncertainty issues with beam structures, using interval parameters representing the beam characteristics and the result of interval analysis [8]. Different countries have developed national building codes to establish principles for the design and construction of structures. The codes were developed over time from the collective wisdom of expert structural engineers. Since the publication of SP 34(S&T) (1987), the development of software to analyse structural analysis problems has improved significantly and advanced softwares are available to model and analyze complex structures. These codes are updated on a regular basis to reflect current research and current trends. In this study, the behaviour of an RC Cranked beam is simulated using the ABAQUS software. This model can help to study the goodness of RCC detailing for cranked beam, given in SP 34(1987) and confirm theoretical calculations while also serving as a valuable addition.
2 Model Geometry and Validation When a beam is loaded with external loads, all the sections of the beam will experience bending moments. The major stresses induced due to bending are normal stresses of compression and tension. To lower the bending moment, cranked beam is provided which is most common in sloping roofs. The pinned supported beam was 4500 mm long and a cross-section of 200 mm width and 400 mm depth with 25 mm clear cover was adopted. Concrete of grade 25 N/mm2 , density 2460 kg/m3 , and Poisson’s ratio 0.18 was used for this analysis. Longitudinal reinforcement and confinement bars with a yield stress of 500 N/mm2 (Fe500) were used. The density of steel 7850 kg/m3 , Poisson’s ratio 0.3, and young’s modulus 210,000 N/mm2 were used. To simulate the bonding between the reinforcement and the concrete, the former was embedded in the latter in ABAQUS. For interpretation of results due to stress concentration, a path was defined at the upper and lower portion of the cross-section at the crown. As part of the objective of this study, the cranked beam was modelled using AutoCAD 2021, reaction validated from manual hand calculation, and detailed Finite Element Analysis was carried out using ABAQUS. The effect of point loads of 105 kN, 100 kN, 95 kN, 90 kN, 85 kN, 80 kN, and 75 kN was analysed for concrete grades M20, M25, M30, M35, M40, M45, and M50 respectively.
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2.1 Methodology The details and geometric model of the Reinforced Cement Concrete (RCC) Cranked Beam was taken from the Indian code SP 34(S&T) (1987)[9], shown in Fig. 2. The beam member was traced in AutoCAD (shown in Fig. 4) to obtain the constant crosssection proportions in relation to other dimensions and derive its equation. Based on Eq. (1), a 4.5-m-long cranked beam and a rise of 0.875 m was finalised and initially subjected to a 100 kN point load on the top of the beam. The cranked beam’s equation was then derived by tracing it. The formula for the equation is as follows:
Y =
2h ×X L
(1)
where L = span of the beam, h = rise of the crown, and (Y,X) = coordinate of any cut-out section. The shear and bending designs were performed using a spreadsheet developed and safety for the beam was checked for axial compression using Limit State Method (LSM) design philosophy based on the Indian codes, IS 456 (2000) [10] and SP 16 (1980) [11]. In Cranked Beam, reinforcement in the crown part is the most difficult while modelling. Hence, the main reinforcing bars and stirrups were drawn in AutoCAD as to get the exact scale, after which it was imported into ABAQUS. All the reinforcement was embedded in concrete. The main reinforcing bars cross at an angle of 137.94°. Reinforcement and Cranked Beam modelling are shown in Fig. 3. The RCC Cranked Beam with a cross-section of 200 mm400 mm, two bars of 16 mm diameter at both the top and bottom faces, and two stirrups of 8 mm diameter with 250 mm c/c spacing were modelled in ABAQUS. The support’s vertical reaction and a manually calculated bending moment at the crown were used to validate the model. The theoretical vertical reaction at each support is 54.82 kN, when the beam is subjected to a point load of 100 kN at the crown, the result of an ABAQUS analysis is 54.45 kN due to the self-weight and reaction of the beam.
Fig. 2 Reinforcement detailing from SP 34 (1987)
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Fig. 3 ABAQUS modelling of a cranked beam with notch, and b reinforcement
Fig. 4 Traced image from AUTOCAD
2.2 The Finite Element Mesh To get accuracy in results, all the elements of the Finite Element model were assigned the same mesh size which is shown in Fig. 5. After assembling and assigning of properties, an input file was created which was then imported in order to create a mesh. The elements used for the study are orphan elements as the reinforcement and stirrups were imported from AutoCAD. In ABAQUS, frictional contact between steel and concrete is achieved through embedded. The type3 of mesh selected in the model is structured.
3 Results and Discussions Detailed Finite Element Analysis was carried out using ABAQUS. The effect of point loads of 105 kN,100 kN, 95 kN, 90 kN, 85 kN, 80 kN, and 75 kN were analysed for concrete grades M20, M25, M30, M35, M40, M45, and M50 respectively and vertical displacement (U2), direct tensile stress (S11), and bending tensile stress (S22) were
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Fig. 5 Concrete meshed model in ABAQUS
obtained for upper and lower edges of the pinned supported cranked beam. The results of an ABAQUS study for a cranked beam with a 50 mm mesh size are shown in Fig. 6. By design calculations, the dimension of the cross-sectional, material characteristics, amount of reinforcement, and other information about the beam were determined. Initially, a point load of 100 kN was applied on the pinned supported cranked beam with M25 concrete. The maximum Mises stress is 86.28 MPa and appears on the crown of the cranked beam for the grade of M25. Stresses near the crown and supports are formed by the stress distribution between two supports. Under the effect of reinforcement, mises stress is small in the bottom of the beam and some part of the length of the beam. From the stress variation graph, by increasing the grade and decreasing the loads in which the effect of point loads of 105 kN,100 kN, 95 kN, 90 kN, 85 kN, 80 kN, and 75 kN were analysed for concrete grades M20, M25, M30, M35, M40, M45, and M50 respectively. For vertical displacement (U2), it was observed that for the upper and lower edge, the variation of displacement is increasing with increasing grades. Stresses (S11) and (S22) were noted to determine the trend of stress concentration at the notch in relation to different loads. It was observed that as the grade increased, the stress concentration at the notch increased for both upper and lower edges (Fig. 7).
Fig. 6 Finite element model of cranked beam
35 30 25 20 15 10 5 0
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100 True distance along path
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10 5 0 -5 0 -10 -15 -20 -25 -30
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The line should be straight but due to the effect of stress concentration, displacement decreases due to lower values than a straight-line relationship and it can be said that it is due to the modulus of elasticity. There is a slight increase in lower edge displacement due to self-weight (Fig. 8).
4 Conclusions The paper presents the results of stress concentration and displacement of cranked beam detail given in SP 34 (1987). The following conclusions were drawn from the research: . Due to modulus of elasticity, as the values are lower than in a straight-line relationship, the displacement reduces. . The maximum von mises stresses for upper and lower edges are 32.17944 MPa and 6.5711 MPa respectively. . The maximum stress value for upper and lower edges of S11 is −2.567 MPa and 7.07 MPa respectively. . The maximum stress value for upper and lower edges of S22 is 4.28 MPa and 1.28 MPa respectively. . The maximum displacement of upper and lower edges of cranked beam is − 1.335 MPa and −1.89 MPa respectively. . By taking 50 MPa stress as a reference for comparison other stresses have been compared and percentage changes have been plotted in the graph.
References 1. Singh L, Ravaliya NR, Akbar MA (2021) Analysis of reinforced concrete structures for accidental blast during launching of a rocket. Incas Bull 13(3):195–204. https://doi.org/10.13111/ 2066-8201.2021.13.3.16
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2. Jagdale PM, Chakrabarti MA (2013) Free vibration analysis of cracked beam. Int J Eng Res Appl 3(6):1172–1176 3. Mia MS, Islam MS, Ghosh U (2017) Modal analysis of cracked cantilever beam by finite element simulation. Procedia Eng 194:509–516. https://doi.org/10.1016/j.proeng.2017.08.178 4. Deng S, Qie Z, Wang L (2015) Nonlinear analysis of reinforced concrete beam bending failure experimentation based on ABAQUS. Proceedings of first international conference inference science machinery mater Energy, vol 126, no Icismme, pp 440–444, 2015, https://doi.org/10. 2991/icismme-15.2015.88 5. Study of crack pattern in RCC beam using ABAQUS, vol 2657, pp 116–123, https://doi.org/ 10.36348/sjce.2021.v05i05.003 6. Sinaei H (2012) Evaluation of reinforced concrete beam behaviour using finite element analysis by ABAQUS. Sci Res Essays 7(21):2002–2009. https://doi.org/10.5897/sre11.1393 7. Linder C, Armero F (2009) Finite elements with embedded branching. Finite Elem Anal Des 45(4):280–293. https://doi.org/10.1016/j.finel.2008.10.012 8. Chen SH, Yang XW (2000) Interval finite element method for beam structures. Finite Elem Anal Des 34(1):75–88. https://doi.org/10.1016/S0168-874X(99)00029-3 9. SP34 (1987) Handbook on reinforcement, p 155 10. IS 456 (2000) Concrete, plain and reinforced. Bur Indian Stand Dehli, pp 1–114 11. IS 456 (1978) sp:16–1980_design aids for reinforced concrete to IS:456–1978. Bur Indian Stand New Delhi, p 232
Design and Analysis of Coir Fibre Reinforced Polypropylene Based Internal Car Door Panel Ronak Swayam, Somya Prasad Sahoo, Manohar Nayak, Aditi Sahoo, and Tanmayee Khuntia
Abstract In today’s world demand for durability and recyclability is among the main factors considered when choosing a material for any product to be made. In the automobile sector apart from exterior parts of the vehicles, the interior parts should also be durable as it faces different types of loads in various situation. The internal door panel is considered one of the most important interior parts of the vehicle as it has to undergo different forces from the user side and also are meant to reduce the impact force on the passenger in an accident. Natural fiber-reinforced polymer composites are considered to be very durable and are lighter than a lot of other materials present in the market. They have higher impact strength and higher tensile strength than a regular material. Young’s modulus of coir fibers usually lies between 4–6 GPa. In this work coir fiber-reinforced polypropylene was used as the material for the internal door panel. It has an elastic modulus of 1.9 GPa and has a tensile strength of 34 MPa. An FEA was done on the door panel to determine whether it is going to be a suitable replacement for the current material which is being used. The deformation seen was around 1.036 mm with the maximum stress being 3.877X 10+07 mN2 , which is an acceptable value for protecting the occupant. When a load of 100N was put on the armrest it showed a deformation but not more than Young’s modulus meaning it can handle a load on the armrest when someone put their hand on it. The maximum deformation in the armrest was only 6.490X 10−4 mm, showing it is suitable as a material for the internal car door panel. Keywords Internal door panel · Natural fibre reinforced polymer · FEA · Human safety criterion · Coir fibre · Polypropylene
R. Swayam (B) · S. P. Sahoo · M. Nayak · A. Sahoo · T. Khuntia Department of Mechanical Engineering, Institute of Technical Education and Research, S‘O’A Deemed To Be University, Bhubaneswar Odisha-751030, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_20
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1 Introduction In recent years, there has been a lot of emphasize on environmentally friendly products and the need of making them more durable and sustainable than the existing products available. Natural fiber-reinforced polymer composites are considered to be on top of the list of environmentally friendly materials with high strength and high modulus to withstand under different scenarios and also have reduced carbon footprint on the environment. Application of natural fiber-reinforced polymer composites has already spread out to different sectors of the industry with the automobile industry slowly stepping into the trend. In the automobile industry not only the exterior but the interior components of the vehicles are meant to be strong as well as lightweight to let the vehicle work at its maximum efficiency. Interior car door panels are one of the important interior components of a car which are required to perform well and withstand impact force during a sidecar collision and also should be able to handle the weight without reaching its failure point. The safety of the passenger and the driver during a car crash is one of the important factors in designing the car and its components. During side car collisions the door trim should minimize or even stop the impact force to reach the passenger and prevent fatal injury. According to global accident statistics around 30% of car collisions are side collisions and also result around 35% of fatal injuries. The interior door panel materials used in today’s industries are only plastics and sometimes polypropylene. Polypropylene which is considered to be one of the best types of thermoplastics are being used in the coming years for different interior components of the car. There are many types of natural fibers that are used for natural fiber-reinforced polymer composites like hemp, sisal, kenaf, wood, and many more. One such natural fiber is the coir fiber. Young’s modulus of coir fibers usually lies between 4–6 GPa. In this study coir fiber was taken as the reinforcing fiber and polypropylene as the polymer matrix as the material for the interior car door panel. In this experiment the main objective to test out whether coir fiber reinforced polypropylene is suitable for the interior car panel to undergo different conditions like the weight of a human body and whether it would be able to protect the human body during a side car collision and some minor weights also. The properties of the coir fiber and polypropylene were taken from different articles and based on that the value of the properties of the materials was set for the finite element analysis (FEA) of the internal car door panel. The elastic modulus and the tensile strength were taken as 1.9 GPa and 34 MPa respectively. As for the internal car door panel, the dimension for the panel was taken reference from Range Rover Evoque. The parameters for the human body injury criterion were set according to NHTSA, and with the appropriate formula they were calculated so that they could be cross-checked with the results that FEA gave for the door panel. This way it can be determined whether the material for the interior car panel is a success or a failure [9–14].
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2 Literature Review Dande S. M. et al. [1] did a CAE analysis of the side door panel during impact force. Hypermesh software was used to design the model and do the analysis, the different parameters like displacement plot, stress plot, and impactor displacement graph. The impactor’s velocity was given around 2.4 m/s. The reaction force was measured using a transducer which forms at the car door trim. The Federal Motor Vehicle Safety Standards were used as the safety norms for testing. It showed that the peak amount of force which can be used for the trim parts was around 19000N, this force acts for only 0.004 s after the crash. The maximum displacement for the door trim was found to be 10.75 mm which is not equal to the safe value but slightly higher than the value. The only limitation was that the material properties were not put for different grades of the material. According to Eskezia E. et al. [2], Finite Element Analysis was done on the internal door panel taking the Bamboo fibre reinforced epoxy composite as its material and then comparing the results to the previously recommended material which have already been used as its material (Ligno-cellulosic composite and Polypropylene panel). The following analysis was done using CATIA V5 R20 modeling software. The car model used for the door panel’s dimensions is that of Toyota DX. When put under the same boundary conditions for testing of BFREC, Polypropylene panel, and lingo-cellulosic composites, the stress was found to be 36.5 MPa for bamboo fibre reinforced epoxy composite, whereas in the case of both polypropylene and lingo cellulosic composite it came 45 MPa and 43 MPa respectively. Thus, showing it yields better results for stress. A displacement curve was also generated to see the difference in the result. The results showed that the displacement of the bamboo fibre reinforced epoxy composite was lesser than the lingo-cellulosic composite and polypropylene composite by around 34% and 50% respectively. The weight difference was also noticeable with a little decrease of 6% and 8% for lingo-cellulosic composite and polypropylene composite respectively. Overall, it showed that it is a better alternative to existing materials for the door panel. According to Terciu O. M. et al. [3], the Mechanical properties of lingo-cellulose material for door trim were determined using the CATIA software and finite element analysis (FEA) was done on it. The following results were then compared with that of Polypropylene material. It showed a decrease of displacement of the lingo-cellulose materials by 43% when there was an impact on it with a constant acceleration of 350 m/s^2. The change in the value of displacement is due to its high rigidity of the material and the smaller mass due to its less thickness. Speaking of lower weight, it showed that it decreased from 1.81 kg to 1.49 kg which is the weight of polypropylene door trim of a car. According to Yashas S. et al. [4], finite element analysis (FEA) was done on car door trim and the displacement and stress plots were made for three different materials for the door trims i.e., steel, aluminium and plastic using the Hypermesh software. They applied a load of 100N in each case and recorded the values and made the graphs. In the case of steel, it showed a displacement of 0.12 mm being the lowest among the three materials. But in the stress plot it showed
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Fig. 1 Comparison between the actual and computer-generated data [5]
that the plastic had the most advantage over the others with a value of 15.745 MPa. On an overall scale it showed that among the three materials i.e., steel, aluminium, and plastic the most effective material would be plastic considering both stress and displacement plots. As per Diwate R. P. et al. [5], here the finite element analysis was done to determine the safety of the occupant during a side crash and see what effect it has on the pelvic region determining the maximum damage it did on the pelvis using Hypermesh 11 software and LS-Dyna solver for computer analysis. For the physical test on the prototype a 20 kg impactor was used to transfer an energy of 640 J when impact took place and the data were measured and compared with the computer simulation data. Below is the given graph of the test in Fig. 1.
3 Design of Internal Car Door Panel 3.1 Properties and Dimensions of Internal Car Door Panel The dimension for the internal car door panel has been referenced from an existing car model i.e., the Range Rover Evoque. The dimensions of the car door panel are given below. It is a prototype so we have made some changes in our model as it does not represent the real life size of the internal car door panel. The dimensions are given in Table 1 and the model in Fig. 2. The coir-PP composite’s properties are given in Table 2.
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Car model name
Length (mm)
Breadth (mm)
Thickness (mm)
Range Rover Evoque
487.5
1018.5
100
Fig. 2 Prototype of internal car door panel
Fig. 3 Meshing of the model
Table 2 Properties of coir-PP composite
Property
Value
Unit
Elastic Modules
1,900,000,000
N/m^2
Poisson’s Ratio
0.43
N/A
Shear Modulus
400,000,000
N/m^2
Mass Density
1152
Kg/m^3
Tensile Strength
34,000,000
N/m^2
Yield Strength
32,900,000
N/m^2
Thermal Conductivity
0.17
W/(m.K)
Specific Heat
1963.5
J/(kg.K)
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3.2 Human Safety Criterion During a car crash, the human body goes under a lot of force which can result in serious or even fatal injuries. So, to reduce this outcome, the National Highway Traffic Safety Administration (NHTSA) has ruled out some safety parameters for the human body to be followed by the vehicle manufacturers and to design their vehicles accordingly. The parameters are meant to be met when different types of crash tests are done on it. The main parts of the body which are affected during a car collision are the head, chest, pelvis, and abdomen and they have their respective criterion for their safety regulation i.e., Head Injury Criterion, Chest Criterion, Pelvic Criterion, and the Abdominal Criterion. Below is given safety criteria for human body to survive a car collision. • NHTSA proposed that the limit for head injury criterion (HIC) for a car crash is 1000 for a time interval of 36 ms as above that range gives a 50 percent risk of head injury. • Chest deflection range to prevent the AIS 3 + injury should not be more than 42 mm. The NPRM whereas on the other hand gave an alternative range of 35 to 44 mm (1.38 to 1.73 in). • The NHTSA proposed a limit of 2500 N or 2.5 kN of force is allowable for the abdominal injury criterion. • For the pelvic injury criterion, a force of 6.5 kN (1,461.26 pounds) was set as a safety limit by the NHTSA. Since it is a sidecar collision, we will be focusing more on the pelvic injury criterion and abdominal injury criteria are the two areas we will be focusing for in the paper. If the panel fails in the impact testing, then we should calculate the remaining force which will be exerted on the passenger after absorption of some amount of force by the panel and then cross-checking with the criteria for the following parts of the body [6–8].
4 Result Analysis 4.1 Effect of Impact Force in Side Collision Impact force is defined as a force that delivers a shock or excessive impact in a very short period of time. When two entities collide, it happens. The result of one object falling into or slamming into another is a collision. As energy from the collision is transferred to the impacted thing, it causes a shock. Vehicle damage is eventually brought on by this energy. During a sidecar collision, it was concluded that 66% of the impact force is absorbed by the car door and remaining force is exerted to the door panel and even human body. If the interior car door panel reaches its failure limit, then it will affect the human body else it will be
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absorbed by the interior door panel. In a car collision 263,795.08N of force is exerted as we can see from the calculation done in Equation (i), so after absorbing 66% of force by car door the remaining 89960N force is exerted on the internal door panel. So that is the force we have taken from our impact force. Below is the calculation of impact force in a sidecar collision [11]. W = K E = Fd = F=
1 mv 2 1 2 mv ⇒ F = 2 2 d
1 (22000)(13.41)2 2
0.75
= 263795.0853N
(1)
As per the requirements all the sides of the panel were fixed as fixed ends and then a force was applied from the back end of the door panel where it faces the car door with a force of 89960N, the maximum displacement was found to be 1.036X 10+00 mm which can be considered as an acceptable value for an internal car door panel and the minimum displacement was found to be 1.000X 10−30 mm. The displacement of the internal door panel is shown in Figs. 4 and 5. As per the requirements, all the sides of the panel were fixed as fixed ends and then a force was applied from the back end of the door panel where it faces the car door with a force of 89960N, the maximum stress was found to be 3.877X 10+07 mN2 which is marginally higher than the ultimate tensile strength and the minimum stress was found to be 2.206X 10+02 mN2 , as shown in Figs. 6 and 7.
Fig. 4 Displacement of car door in isometric view
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Fig. 5 Displacement of car door in side view
Fig. 6 Stress of car door in front view
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Fig. 7 Stress of car door in side view
4.2 Load on the Armrest In this part of the study, it was considered to find the average force an armrest experiences when a human puts his/ her arm on it or leans on the armrest for support to get of the car or any other scenario. Accordingly it was found that a human usually exerts 90–102 N on the car armrest for support. So we took 100 N of force and applied in the armrest. The armrest here is considered a cantilever beam and 100N of force is uniformly distributed on it. After fixing the sides as fixed ends, force was applied on the armrest of 100N, the maximum displacement was found to be 6.490X 10−4 mm and the minimum displacement was found to be 1.000X 10−30 mm, as shown in Figs. 8 and 9. After fixing the sides as fixed ends, the force was applied on armrest of 100N, and the maximum stress was found to be 1.213X 10+04 mN2 as shown in Figs. 10 and 11.
5 Conclusion For the study an internal car door panel was made taking reference of a real-life existing model, the dimensions were taken, and according a miniature prototype was made. The properties of coir fibre and polypropylene were taken from existing data. An FEA was created to complete the study. As it was seen from the FEA, it showed that the panel can work as a replaceable material for internal door panels.
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Fig. 8 Displacement of armrest in isometric view
Fig. 9 Displacement of armrest in side view
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Fig. 10 Stress of armrest in isometric view
Fig. 11 Stress of armrest in side view
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In the case of the side car impact test, the deformation was only 1.036 mm and the maximum stress was around 3.877X 10+07 mN2 which is higher than the yield strength i.e., 3.29 3.877X 10+07 mN2 and is also above the ultimate tensile strength but as for in an accident it shows that it would be able to absorb a lot of impact energy by the impactor. As for the load on the armrest it can be seen that there was a maximum deformation of 6.490X 10−4 mm and a maximum stress of 1.213X 10+04 mN2 . So, when compared with the human safety criterion for pelvic criterion, the load would not be more than the maximum limit for a severe injury. From this study, it can be concluded that a coir fibre-reinforced polypropylene can be used as the material for internal car door panels.
References 1. Dand SM (2021) CAE analysis of side impact on front door trims. Int Res J Eng Technol 08(07):3789–3795 2. Eskezia E, Tilahun D, Abera A (2017) Finite element analysis of internal door panel of a car by cons idering bamboo fiber reinforced epoxy composite. J Appl Mech Eng 6(1):1–6 3. Terciu OM, Curtu I, Cerbu C (2012) Fem modelling of an automotive door trim panel made of lignocelulozic composites in case of a door slam simulation 4. Yashas S (2020) Development and analysis of automotive car door crop by using fea method. Int J Eng Res Technol 09(10) 5. Diwate RP, Deshmukh P (2015) Development of passenger vehicle door trim for occupant safety by using CAE. Int J Sci Technol Res 4(01) (2015) 6. Department of Transportation, National Highway Traffic Safety Administration, 49 Cfr Parts 571 And 585, Side Impact Phase-In Reporting Requirements 7. National Highway Traffic Safety Administration (2004) Injury criteria for side impact dummies 8. Chu CC (2004) Fundamental principles for vehicle/occupant systems analysis 9. Adeniyi AG, Onifade DV, Ighalo JO, Adeoye AS (2019) A review of coir fiber reinforced polymer composites. Compos B Eng 176:107305 10. Rahman MA, Parvin F, Hasan M, Hoque ME (2015) Introduction to manufacturing of natural fibre-reinforced polymer composites. In: Manufacturing of natural fibre reinforced polymer composites, pp 17–43 11. Long CR, Yuen S, Nurick GN (2019) Analysis of a car door subjected to side pole impact. Latin Am J Solids Struct (16) 12. Townsend J, Kaczmar M, El-Sayed M (2001) Modular door system for side impact safety of motor vehicles (No. 2001–06–0167). SAE Technical Paper 13. Sahoo M, Liangfei RJ, Gudula HN, Taruvai R (2017) A study on various structural concepts of automotive door trim (No. 2017–01–1343). SAE Technical Paper 14. Majoo A (2016) Design of the interior door panels in a 1969 Chevrolet Camaro
Pedobarography-Based Prosthetic Foot Design and Optimization Methodology Akash Lawand
Abstract This paper describes the design procedure for a new passive prosthetic foot using topology optimization. The proposed procedure can provide an improved optimized patient-specific foot design. The procedure works by reducing the weight of the prosthetic foot using topology optimization. This process can be utilized for patients with one leg amputation, whose other foot is used for capturing pressure plate data from a gait cycle. As nature has created every human being differently, the pressure pattern of an individual obtained using pedobarography or pressure plate data is also unique patient specific. Six varieties of materials along with seven cases of optimization are used to get optimized foot models. Based on their comparison final foot optimization is used to define the methodology. Finally, 42 different footoptimized models are developed for the combination of different materials and cases of optimization. The developed optimized models are illustrated and their masses are compared with their masses before optimization. Strategy to improve methodology and necessary materials for Additive Manufacturing are discussed. Keywords Prosthetic foot · Topology optimization · Pedobarography · Design methodology · Additive manufacturing · Plantar pressure imaging
1 Introduction There are several prosthetic foots available for lower limb amputees which can provide good mobility. With the advances in additive manufacturing processes, the manufacturing of patient-specific prostheses can be made easier and better in design. Tao et al. [1] described the design process of a passive prosthetic foot which is manufactured using a 3D printing process. They used a simple foot model and the forces are given only to specific points; such as, at heel strike and at toe-off. Polylactic acid (PLA) material properties are used for optimization and 3D printing. Yap and A. Lawand (B) Department of Mechanical Engineering, Army Institute of Technology, Pune 411015, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_21
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Renda [2] developed a 3D printable energy-storing-and-return (ESR) foot which provides a common design of prosthetic foot for all types of amputees. Park et al. [3] developed an optimized foot structure using point loads at the ankle joint of the model and constraints at the bottom of the foot during heel-strike, mid-stance, and toe-off phases of the gait cycle. Fey et al. [4] developed a prosthetic foot prototype manufactured from Nylon-11 using SLS technology, which used a 2D optimization grid with 34 vertical elements by 72 horizontal elements. Body weight in the form of the load was applied at the ankle from above and constraints were given at the toe and heel region. Srikanth and Bharanidaran [5] used a similar design procedure to develop a compliant prosthetic foot. These foot designs are all unique and provide a good direction for our research work. Through the study of these designs, we found that the necessity of patient-specific prosthetic foot design could not be fulfilled. Hence we have proposed a design methodology for the prosthetic foot that can be patient-specific and aid in improved walking gait for the amputee. Thus we are proposing a novel methodology to enhance the quality of foot prostheses through topology optimization. The most important feature of this new methodology is that it can be used to fabricate patient-specific foot prosthesis. For patients with bilateral amputation, this methodology can be utilized by using normalized Plantar Pressure data for normal walking. The method utilized by Tao et al. [1] can be considered good for providing structural rigidity. For a prosthetic foot to perform similarly to a human foot, some amount of compliance behavior is necessary. This compliant behavior is achieved by providing minimum material at the regions with lesser loads in all phases of a normal gait. Topology optimization using Plantar Pressure data will provide the necessary compliance and flexibility to the foot prosthesis. The study of planar pressure distributions is a necessary part of this research work. There are simple ways through which plantar pressure distribution can be distributed for any person. Plantar Pressure Imaging (PPI) devices are available for easy capturing of dynamic plantar pressure distribution. PPI consists of pressuresensitive sensors arranged in an array which can sense pressure up to the accuracy of less than 3 mm in some equipment. PPI are majorly available in three categories; in-sole PPI which is worn inside the shoes, platform-based PPI which is above 0.3 m × 0.5 m in size, and mat-platform-based PPI which can scan a person’s whole gait cycle. Pataky et al. [6] in 2011 explained the working of the PPI devices with the proper illustration of the Plantar Pressure Imaging data through pressure images and graphs. The data illustrated is excellent for understanding the plantar pressure distribution during every stage of the gait. In a similar research work by Wafai et al. [7], plantar pressure data was used to identify foot pathologies by comparing the data with data of normal people and that of different pathologies. We have used the data provided by Linah Wafai for the optimization of the foot and to explain the methodology of patient-specific optimized foot.
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2 Methodology The data on plantar pressure provided by Wafai et al. [7] for healthy people during the stance phase is excellent and clear enough to capture and use for further analysis. The images of Plantar Pressure were used to prepare the base of the model used for the optimization process. The image was first traced on a Solidworks™ software, and the surface file was imported onto Hypermesh™ software. This surface file was then 2D meshed with Quad elements and then dragged to create 3D mesh elements, to match the human foot dimensions. This optimization procedure is intended for producing the prosthetic foot using additive manufacturing and hence the materials included in the study are those which can be used for additive manufacturing. Please refer Fig. 1 for the proposed methodology.
2.1 Plantar Pressure Distribution Data The plantar pressure distribution illustrated by Wafai [7] is captured by using an in-shoe PPI device that shows data for different stance phases of normal person gait. These phases are Heel Strike, Foot Flat, Midstance, and Heel Off. The data is concluded with an averaged stance pressure distribution which indicates the
Fig. 1 Process of foot optimization
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maximum pressure exerted on the plantar surface of the foot. The Plantar Pressure Image data as taken from the research paper [7] is shown below in Table 1.
2.2 Foot Modeling, Meshing Optimization Process The plantar pressure distribution for averaged stances as shown in Fig. 2 is used to model the plantar surface of the foot. Using the image in SolidWorks software, the first model with different surfaces for each pressure range is developed for the average dimensions of a normal human. This surface model is then imported into HyperMesh™ software as an IGES file. The 2D surface model as imported in HyperMesh™ is shown in Fig. 2b. Using 2D-automesh, the surface model is meshed as mixed elements with the majority of quad-elements and very few tria-elements (Fig. 2c). These 2D-meshed elements are then dragged up to the height and angle of the average dimensions of a normal human. This part is the design space for optimization. At the top of the foot, the ankle part as a non-design component is added which will not undergo optimization. The boundary condition as geometrical constraints and loading conditions as pressure is applied on the 3D Meshed geometry as shown in Fig. 3 for topology optimization. The Material properties and optimization case conditions are then applied to the above 3D meshed model. Material properties for six different materials that are used for additive manufacturing are provided in Table 2 that create six different 3D meshed models. All of these materials are already being used for manufacturing prosthetic foots. Seven different optimization conditions for minimum retained volume conditions are applied as 30%, 40%, 50%, 60%, 70%, 80%, and 90%. The optimization process is then run using OptiStruct™ as the solver.
3 Topology Optimization Results The Result of the optimization is then visualized using HyperView™, and the results for each material and optimization case conditions are compared. Please refer to the optimized output for ABS, Nylon-12, Onyx, Polysilicon, Steel, and Titanium material in Figs. 4, 5, 6, 7, 8 and 9 respectively. The final mass of each optimized foot is the sum of the optimized mass of the design part and the un-optimized mass of the non-design part of the foot. The mass of each optimized foot is compared in Table 3. The model geometry for metallic feet can be found to be having too much weight. We can reduce the weight of the models made of heavier materials by reducing the Retained Volume criterion. The foot model made of Nylon 12 material can be observed to be the lightest of all, followed by Onyx material. Hu et al. [12] concluded that Nylon 12, also known as PA12, exhibits good biocompatibility which might be
3D
2D
L
L
R
FF
HS
One stance (Heel strike to heel off) R
L
MSt R
L
HO R
L
R
AveragedStances KPa
Scale
Table 1 The 2D and 3D representation of plantar pressure imaging data in 31 healthy people during the stance phase of the gait cycle [7]
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Fig. 2 a Averaged stance pressure distribution. b Surface model of plantar surface of foot. c Meshed foot model with pressure given to the plantar surface in HyperMesh™ Fig. 3 3D meshed model of foot with constraints (Red) and varying Plantar Pressure (Green)
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Table 2 Material Properties of 3D printable materials as given to models in HyperMesh™ Sr. No
Material
Modulus of Elasticity (MPa)
Poisson’s ratio (unit less)
Density (Tonne/mm3 )
1
ABS [8]
2200
0.35
1.05e-09
2
Nylon 12 [9]
2310
0.408
0.977e-09
3
Onyx [8]
1400
0.33
1.2e-09
4
Polysilicon [10]
160,000
0.22
2.33e-9
5
Steel [as in HyperMesh]
210,000
0.30
7.85e-09
6
Titanium [11]
116,000
0.34
4.5e-09
Fig. 4 Topology optimized models of ABS material
Fig. 5 Topology optimized models of Nylon-12 material
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Fig. 6 Topology optimized models of Onyx material
Fig. 7 Topology optimized models of Polysilicon material
Fig. 8 Topology optimized models of Steel material
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Fig. 9 Topology-optimized models of Titanium material
used for various prosthetic and orthopedic devices. It is also important to note that Nylon 12 material is easily available and can be easily fabricated into different intricate shapes. Hence from the above results, Nylon 12 material can be preferred for foot prosthesis fabrication.
3.1 Discussion The output models of topology optimization clearly justify the maximum material density at the region of the foot where the Plantar Pressure is higher. The regions with lower Plantar Pressure have thin cross sections which will provide flexibility and compliance ability to the foot prosthesis. These results are seen as prominent for the models with lower Retained Volume criterion. More number of iteration can help to improve the value of Retained Volume criterion to improve the load-carrying capacity and compliant behavior of the foot prosthesis to enhance the mobility of the amputee. Different materials used for the models show very less differences in the optimized models for the same Retained Volume criterion, but close observations convey that material density in some regions is different. This is due to differences in the material properties like Poisson’s ratio.
4 Conclusion and Scope Topology optimization with the assistance of Additive Manufacturing has emerged as a new area for designing components exhibiting high efficiency with better strengthto-weight ratio. These technologies are providing better opportunities for the Prosthetists to fabricate better prosthetic components that can improve the mobility of the amputees. This research on the development of methodology for optimized foot
0.3458
1.1652
0.6679
Nylon 12
Onyx
Polysilicon
Steel
Titanium
2
3
4
5
6
0.1781
0.1450
0.1558
ABS
1
Non-design mass (Kg)
Material
Sr. no
9.2908
16.207
4.8106
2.4776
2.0171
2.1679
Initial Design material mass (Kg)
Table 3 Comparison of foot masses before and after optimization
3.455
6.027
1.789
0.921
0.750
0.806
30%
4.384
7.648
2.270
1.169
0.952
1.023
40%
5.313
9.268
2.751
1.416
1.153
1.239
50%
6.242
10.89
3.232
1.664
1.355
1.456
60%
7.171
12.51
3.713
1.912
1.557
1.673
70%
8.100
14.13
4.194
2.160
1.758
1.890
80%
Final model material mass (Optimized Design mass + Non-design mass) (Kg)
9.029
15.75
4.675
2.408
1.960
2.107
90%
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prosthesis has shown the possibility of improving the design using Pedobarographic data. Even though the Plantar Pressure data utilized here is of a random individual, a normalized data set can help this methodology. The developed 42 different optimized foot models are not the up-to the mark of the best optimum design, but further study can help improve the design. The optimized foot models can be observed to have rough surfaces. Further smoothening of these optimized foot models can be done on software and FEA analysis must be carried out prior to producing a physical model using the Additive Manufacturing technique. The materials available for AM process are studied and observed that Nylon 12 (PA12) is an excellent material for foot prosthesis. The foot models after manufacturing using AM technique should be validated by physical testing under different loading conditions of a normal gait cycle. Any of these models cannot be considered the best as further testing on these designs is necessary.
References 1. Tao Z et al (2017) Design and optimization of prosthetic foot by using polylactic acid 3D printing. J Mech Sci Technol 31(5):2393–2398. https://doi.org/10.1007/s12206-017-0436-2 2. Yap J, Renda G (2015) Low-cost 3D-printable prosthetic foot. In: Proceedings of the third European conference on Design4Health 2015, Sheffield, 13–16 July 2015 3. Park K, Ahn H-J, Lee K-H, Lee C-H, Development and performance verification of a motorized prosthetic leg for stair walking. Appl Bionics Biomech 2020 4. Fey NP, South BJ, Seepersad CC, Neptune RR (2009) Topology optimization and freeform fabrication framework for developing prosthetic feet. In: 2009 international solid freeform fabrication symposium, University of Texas at Austin 5. Srikanth SA, Bharanidaran R (2017) Design of a compliant mechanism based prosthetic foot. Int J Mech Product Eng Res Develop (IJMPERD) 7(3):33–42 6. Pataky TC et al (2012) Gait recognition: highly unique dynamic plantar pressure patterns among 104 individuals. J R Soc Interface 9:790–800 7. Wafai L et al (2015) Identification of foot pathologies based on plantar pressure asymmetry. Sensors (Basel) 15:20392–20408. https://doi.org/10.3390/s150820392 8. Um H-J, Kim H-S, Hong W, Kim H-S, Hur P (2021) 3D-printable toe-joint design of prosthetic foot. In: 2021 18th international conference on ubiquitous robots (UR), 2021, pp 9–13. https:// doi.org/10.1109/UR52253.2021.9494658 9. Amado-Becker A, Ramos-Grez J, José Yañez M, Vargas Y, Gaete L (2008) Elastic tensor stiffness coefficients for SLS Nylon 12 under different degrees of densification as measured by ultrasonic technique. Rapid Prototyp J 14(5):260–270. https://doi.org/10.1108/135525408 10907929 10. Hopcroft MA, Nix WD, Kenny TW (2010) What is the Young’s modulus of silicon? J Microelectromech Syst 19:229–238. https://doi.org/10.1109/JMEMS.2009.2039697 11. Ross RB (1992) Metallic materials specification handbook, 4th edn. Chapman & Hall, London 12. Hu G et al (2014) Development of nanofluorapatite polymer-based composite for bioactive orthopedic implants and prostheses. Int J Nanomed 9:3875–3884. https://doi.org/10.2147/IJN. S65682
Atlas Generation of Leg Mechanisms for Walking Platforms Using Creative Synthesis Manoj Balasaheb Awaghade and Pankaj Vinayak Dorlikar
Abstract Mechanical legs are important elements of robotic walking platforms. They give the platforms excellent ability to traverse irregular and uneven terrains. There has been a lot of development in robotics for locomotion and operational purposes in the last few decades. This work is an exploration based on the extensive literature survey of walking machines. A multitude of leg mechanisms was analyzed from patents and their topological structures have been investigated for the generation of novel mechanisms using creative design theory. An Atlas of 22 novel leg mechanisms has been generated. Also, a study of foot point coupler curves of biped human, quadruped mammal, reptiles and insect’s feet have been given here to find out the common aspect of walking mechanisms with generated coupler curves in this research work. The mechanically generated coupler curves are comparable with the animal foot trajectories. The DLTdv7 digitizing tool from MATLAB was used for the extraction of foot trajectories by video graphic analysis. Keywords Walking machines · Leg mechanisms · Creative design theory · Coupler curves
1 Introduction It is well known that animals can traverse over uneven terrain much more efficiently than that with wheeled, tracked or hybrid mode of locomotion. Even a human being by mimicking quadrupeds can move over highly irregular terrains which are impossible in the case of wheeled or tracked vehicles to traverse. It’s thus of immense importance to study how machines can perform land locomotion when their design is inspired by nature itself. Legs are used by animals to move swiftly and reliably over various terrains with efficient locomotion and agility. A study of foot point coupler curves of the human walking gait cycle, Dog walking and amble gait cycles, and hoarse M. B. Awaghade (B) · P. V. Dorlikar Department of Mechanical Engineering, Army Institute of Technology, Pune-411015, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_22
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galloping gait cycle is included to elaborate the coupler curves that can be mimicked by walking machines. In the last few decades mobile robots development has been given much importance cause of their ability to explore in unusual locations, space, search and rescue operations, completing some tasks without human intervention. The advantages of legged locomotion over wheeled or tracked ones depend on their postures, the quantity of legs and their functionality. Even though the wheeled or tracked locomotives can work on plain terrain, but on cluttered irregular terrain or in hazardous environments they lose their functionality. Legged robots can roam almost everywhere on the planet earth same as human beings or animals. A lot of effort has been put in the study and development of walking mechanisms. Desai et al. [4] developed a new eight linked single degree of freedom crank driven Peaucellier-Lipkin type walking leg mechanism. Geonea et al. [5] developed an eightlinked leg mechanism inspired by human legs. Batayneh et al. [6] designed a single DOF linkage based on Stephenson III-type mechanisms. Araidah et al. [7] designed a human-like eight-bar leg mechanism. Ghassei [9] designed and optimized a crankbased eight-link mechanism known as ‘Ghassei’s Linkage’. Prashant et al. [13] have attempted optimizing Theo Jansen mechanism’s foot locus trajectory. Yang et al. [14] have done a comprehensive review on structural synthesis methods of mechanisms and kinematic chains for their various types.
2 Living Creatures Coupler Curves Since the dawn of bionics in 1960, the sophisticated structure, motion mechanism, and motion manner of living organisms have become the goal of robotics imitation. As a byproduct of the effective amalgamation of robotics and bionics, a legged locomotive is a walking machine designed by mimicking the structure and motion of legs and feet of mammals, insects and amphibians. Humans as biped, dogs, horses followed by crocodile, turtle as quadrupeds and finally ant, spider as insects for their foot tip trajectories using DLTdv7 digitizing tool were studied. Figure 1 shows the foot point curves of human being while walking, running and exhausted walking. Then walking and running curves of mammals like dogs, horses, and cheetah are shown. It also shows the curves for reptiles like crocodile, turtles, lizards and those for insects like ants and spiders. All the coupler curves are obtained from ultraslow motion videos available in the public domain. Shigley [1] had proposed an idealized coupler curve as shown in Fig. 2 for walking motion generation. Here the horizontal section ‘AB’ of the above curve can be called the stride. The perpendicular distance of point ‘C’ from segment ‘AB’ is the height of step. The direction of arrow shows the motion direction of the foot. In order to obtain such kind of coupler curves it is quite imperative to study and synthesize mechanisms which will meet the functional requirements and mimic motion efficiently. This will be achieved by creative methodology by Yan [3].
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Fig. 1 Coupler curves of living creatures
Fig. 2 Idealized coupler curve
3 Creative Design Procedure The necessary steps of the Creative Design Theory are as follows: Using the generalization rules, transform any existing design into its matching generalized chain. Step 1 Identifying the existing products, designs with intended design specifications and concluding their topological characteristics. Step 2 Transforming existing design into its generalized chain form using the generalizing rules based on generalizing principles. Step 3 Selecting the sketch of a generalized chain with the same degree of freedom, number of joints and links as the parent design, from the available atlas of generalized chains in the literature. Step 4 Each generalized chain formed in the previous step should have its own set of links and joints. This will aid us in the development of a viable specialized chain atlas based on the specialization that meets the design restrictions.
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Step 5 To create an atlas of mechanisms, particularize each generated specialized chain from the previous step into its appropriate graphic depiction of mechanism. Step 6 Identify and eliminate all the existing mechanisms from the developed atlas, to have a final atlas of novel mechanisms.
3.1 Analysis of Existing Mechanisms Three Link Mechanism with one Higher Pair. Below Fig. 3a shows a schematic of an animated walking toy invented by Gund [11]. The walking mechanism in given invention is containing an actuating crank (L1 ) connected to a coupler which is further attached to the ground link by a higher pair (JT ) as shown in Fig. 3b. The ground link in the above mechanism is denoted as ‘KF ’. Figure 3c shows the Generalized Kinematic chain of the walking mechanism. The blackened circle shows higher pair (JT ). This mechanism contains 3 link elements along with three joints. Out of the three joints two are lower and one is a higher pair joint as shown. The mobility (DOF) of shown mechanism is obtained by Grübler’s criteria as ‘DOF = 1’. The symmetric topology matrix of the mechanism in Fig. 3 is obtained in (1) ⎤ K F J R JT MT = ⎣ K L1 J R ⎦ K L2 ⎡
(1)
Five Link Mechanism with one Higher Pair. The mechanism shown in Fig. 4a is a mechanism with five links and six joints in total. Out of these six five are lower pair revolute joints and one is a higher pair trunnion joint denoted by ‘JT ’. It is a one-DOF planar mechanism. Figure 4b shows the generalized mechanism from Douglas’ patent [12]. The ground link is denoted as ‘KF ’. Figure 4c shows the generalized kinematic chain of the same. In here the higher pair is shown by the blackened circle. The
Fig. 3 a Actual mechanism from patent b Functional schematic c Specialized kinematic chain
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Fig. 4 a Actual mechanism from patent b Functional schematic c Specialized kinematic chain
symmetric topology matrix indicating the links and joints can be given by (2). ⎡
K F JR JR ⎢ K L1 0 ⎢ ⎢ MT = ⎢ K L2 ⎢ ⎣
0 JR JR K L3
⎤ JT 0 ⎥ ⎥ ⎥ 0 ⎥ ⎥ JR ⎦ K L4
(2)
From (1) and (2), topological structure characteristics of the 3-Link mechanism and 5-Link mechanism can be summarized as follows: • They both act as a planar mechanism with one degree of freedom ensuring certain motion. • They both have one ground link indicating trunk of the locomotive. • There is only one higher pair in each of the above mechanism. • The former has a simple structure than the latter having five numbers of elements and more complexity.
3.2 Design Specifications and Constraints After the extensive study of existing state of art leg mechanisms for the creative generation of atlas of novel mechanism the next step is defining the design specifications and design constraints. For the present research the design specifications can be stated as follows: 1. There should be at least one input link for actuation purpose. 2. The input link is attached to the ground link which is also part of the trunk of walking machine. 3. The degree of freedom of mechanism can be taken as ‘1’ for simplicity of actuation. 4. There should be at most two higher pairs in the mechanism.
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5. The number of links can be taken up to as high as ‘5’ for and simplicity of structure. 6. The number of joints should be between ‘3’ and ‘6’.
3.3 Kinematic Number Synthesis Using the design specifications and constraints the number synthesis was carried out for mechanisms with link number ranging between ‘3’ and ‘5’. Also number synthesis was carried out for two degrees of freedom mechanism having six links. The results are shown in Table 1. Link assortment AL indicating the number of binary, ternary, quaternary etc. links in a mechanism can be found by the procedure explained ahead. In number synthesis actually the link assortments are found out for a mechanism having FP degrees of freedom, NL number of links and NJ number of joints. The link assortment AL can be expressed as follows (3). A L = [N L2 /N L3 /N L4 /...]
(3)
According to the graph theory, the quantity of links and joints follows the following condition shown in (4): N L ≤ N J ≤ N L (N L − 1)/2
(4)
A link can have at most m joints as shown (5): m max =
N L ≤ N J ≤ 2N L − 3 N J − NL + 2 NL − 1 2N L − 3 ≤ N J ≤ N L (N L − 1)/2
(5)
According to the graph theoretical approach of mechanism representation, the link assortment for a mechanism having NJ joints with NL linkages is obtained using Eqs. (6) and (7). N L2 + N L3 + N L4 + · · · + N Lm = N L
(6)
2N L2 + 3N L3 + 4N L4 + · · · + m N Lm = 2N J
(7)
Table 1 Link assortments for links ranging 3–5 (FP )
(NL )
(NE )
(NF )
(NJ = NE + NF )
(m)
(AL )
Schematics (Tables 2, 3)
1
3
2
1
3
2
[3/0]
Fig. (a1 )
1
4
3
2
5
3
[2/2]
Fig. (b1 )–(b10 )
1
5
5
1
6
3
[3/2]
Fig. (c1 )–(c11 )
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Table 2 Specialized kinematic chains
(a1)
(b1)
(b2)
(b3)
(b4)
(b5)
(b6)
(b7)
(b8)
(b9)
(b10)
(c1)
(c2)
(c3)
(c4)
(c5)
(c6)
(c7)
(c8)
(c9)
(c10)
(c11)
For the planar mechanism, Grübler’s criteria can be used to calculate the degrees of freedom FP for NJ joints with NL linkages made up of NE lower and NF higher pairs: F p = 3(N L − 1) − 2N E − N F
(8)
Whence the link assortments are obtained by the procedure explained above the next step is sketching the generalized kinematic chains based on obtained link assortments. Most of the sketches can be drawn by referring to already existing literature [3, 10]. Whence the generalized kinematic chain sketches are ready the next step of specialization can be carried out.
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Table 3 Novel mechanisms
(a1)
(b1)
(b2)
(b3)
(b4)
(b5)
(b6)
(b7)
(b8)
(b9)
(b10)
(c1)
(c2)
(c3)
(c4)
(c5)
(c6)
(c7)
(c8)
(c9)
(c10)
(c11)
3.4 Specialized Kinematic Chains Specifying the joints and members for each generic chain that is available is the next phase in the design process, subject to a few design requirements that are produced by the topological structures of current designs. The kinematic chains with specific members obtained are shown in following Table 2. In here the fixed or ground link which is to be attached to the platform or trunk of walking machine is denoted by KF . Other links are denoted by L1 , L2 , L3, etc. The higher pair used in leg mechanisms is a Trunnion comprised of a revolute and a translational degree of freedom. It is denoted by JT .
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3.5 Particularization As now the specialized kinematic chains are constructed their particularized mechanical schematics in skeleton drawings as shown in Table 3 can be determined. This is where the complete idea generation of design concepts is achieved. Further these concepts can be optimized, simulated to meet various performance criteria and validation.
4 Coupler Curves from Novel Mechanisms From Fig. 5 shown below it can be concluded that most of the generated mechanisms provide a satisfactory coupler curve in order to imitate the walking motion. The position and orientation of the leg mechanism on the trunk along with the dimensions of mechanism elements will dictate the output curve hence these can be investigated further for generated mechanism concepts. For the design of the leg mechanism, a concept can be selected from the novel mechanism atlas. The coupler curve output of these mechanisms will be used for that purpose.
Fig. 5 Coupler curves generated using novel mechanisms from Table 3
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5 Conclusion Biomimetic studies of foot point curves for walking of mammals, reptiles and insects were studied for imitation of walking leg mechanism which can be utilized for natural terrain exploration, and space exploration purposes. By doing a survey of existing leg mechanisms the design requirements were concluded for number synthesis, and type synthesis was done for specialization of novel mechanism concepts as explained. The creative design methodology was utilized for generating atlas of 22 novel leg mechanisms. The coupler curves extracted from these mechanisms were plotted. It was observed that most of them followed idealized motions necessary for walking motion generation with alteration in its position and orientation with respect to the walking platform trunk. The curve configurations are usually based on the orientation and placement of mechanism elements i.e. links with respect to platform; further investigation of the mechanisms with respect to dimensional aspect can lead to more precise and fitting curves and hence, more apt design solutions.
References 1. Shigley J (1960) The mechanics of walking vehicles. Land locomotion laboratory research division research and engineering directorate 2. Biswal P, Mohanty P (2021) Development of quadruped walking robots: a review. Ain Shams Eng J 12(2):2017–2031 3. Yan H (1998) Creative design of mechanical devices. Springer, Singapore 4. Desai SG, Annigeri AR, Timmana Gouda A (2019) Analysis of a new single degree-of-freedom eight link leg mechanism for walking machine. In: Mechanism and machine theory, vol 140. Elsevier Ltd, pp 747–764 5. Geonea D, Margine A, Dumitru N, Copilusi C (2014) Design and simulation of a mechanism for human leg motion assistance. In: Advanced materials research, vol 1036. Trans Tech Publications, Switzerland, pp 811–816 6. Batayneh W, Araidah O, Makkawi S (2013) Biomimetic design of a single DOF Stephenson III leg mechanism biomimetic design of a single DOF Stephenson III leg mechanism. In: Mechanical engineering research, vol 3, No 2, ISSN 1927–0607 E-ISSN 1927–0615 Published by Canadian Center of Science and Education (2013) 7. Araidah O, Bataneh W, Darabseh T, Banihani S (2011) Conceptual design of a single DOF human-like eight-bar leg mechanism. Jordan J Mech Indust Eng 5(4):285–289. ISSN 1995– 6665 8. Sheih W (1996) Design and optimization of planar leg mechanisms featuring symmetrical foot-point paths. PhD thesis, University of Maryland 9. Ghassaei A (2011) The design and optimization of a crank-based leg mechanism. PhD thesis, Pomona College, Department of Physics and Astronomy 10. Jensen P (1991) Classical and modern mechanisms for engineers and inventors. Marcel Dekker Inc., New York 11. Gund A (1915) Animated toy. United States Patent, US1146700A 12. Douglas KR (1972) Walking leg linkage and propulsion mechanism. United States Patent, US3680395A 13. Prashant N, Karthik S, Rahul G, Sandarsh T (2020) Design and optimization of foot locus trajectory of Theo Jansen mechanism. In: Advances in structures, systems and materials. Lecture notes on multidisciplinary industrial engineering. Springer, Singapore (2020)
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14. Yang W, Ding H, Kecskeméthy A (2022) Structural synthesis towards intelligent design of plane mechanisms: current status and future research trend. Mech Mach Theory 171:104715. ISSN 0094–114X
Effect of Curve Angle on Prestressed Box-Girder Bridges Preeti Agarwal
and Deepak Kumar Singh
Abstract The analysis of a curved box-girder bridge is quite complicated as it experiences additional torsional moments compared to a straight bridge. Also, the behaviour of such bridges is different for different curve angles. This paper studies the effect of curve angle on simply supported single-cell trapezoidal prestressed concrete box-girder bridges using CSiBridge v.20 software. To identify the appropriate mesh, a convergence study is performed. The present approach is validated with the published results. The variation of forces, stress and deflection with curve angle for a singlecell bridge under dead load and IRC live load is evaluated. Further, the equations are deduced using the statistical approach so that the results may be estimated for different curved bridges. This study may be useful for the designers to analyse curved prestressed bridges. Keywords Prestressed bridge · Indian Road Congress (IRC) load · Finite Element Method (FEM) · Curve angle · Single-cell · CSiBridge
1 Introduction In ancient times, bridges with curved alignment were rare; however, addressing the problem of space limitation, direction of traffic flow, increased traffic, and a variation in the speed limit, the majority of the bridges are made curved. Box-girder bridge has a cellular cross-segment, which resists high torsional moment and becomes more efficient, if the bridge deck is curved. Curved bridges are widely accepted due to their effectiveness, stability, functionality, economy in construction, and aesthetically pleasing appearance. They are often chosen to be circular, if possible, combined with P. Agarwal (B) School of Engineering and Technology, Maharishi University of Information Technology, Luckow, Uttar Pradesh 226013, India e-mail: [email protected] D. K. Singh Amity School of Engineering and Technology, Amity University, Patna 801503, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_23
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other curves (spiral, parabola, etc.) as transition curves. The curved bridges may be analysed as a straight bridge with defined restrictions but it will not simulate the exact behaviour. So, the behaviour of curved bridges may be examined using a numerical method. Some of the studies related to the analysis of box-girder bridges are discussed in the next paragraph to show the importance of analysis. The curved bridges including longitudinal warping and transverse cross-sectional distortion are presented [1]. Box-girder bridges with varied spans are analysed using the finite element difference method [2]. A number of three-span presstressed bridges are analysed [3]. The horizontally curved bridges for dead and truck loads are analysed [4]. The structural response of 240 equal-span box-girder bridges (curved) is investigated by altering the geometries [5]. The analysis of posttensioned bridges is studied [6]. The new girder distribution factors (GDF) for curved bridges are developed [7]. The impact of AASHTO truck load on forces and deflection of curved box-girder bridges is studied [8]. The box-girder bridges subjected static live load of two heavily loaded trucks are evaluated [9]. The distribution of load in straight prestressed concrete (PSC) girder bridges using the finite element method is presented [10]. The PSC curved girder bridges with Multi-tasking formwork having different curvatures are analysed [11]. The response of straight box-girder bridges subjected to IRC loads is studied using ANSYS [12]. The flexural behaviour of box-girder bridges (skew-curved) subjected to IRC, Class-70R load, is predicted, using CSiBridge software [13]. The responses of forces, deflection and fundamental frequency of curved box-girder bridge are analysed using SAP 2000 [14, 15]. The response of different box-girder bridges are investigated, and the equations for these responses are also proposed [16–19]. The modelling procedure and dynamic response of different box-girder bridges are proposed [20, 21]. It has been observed that a lot of studies are done to analyse this type of bridges. Also, a standard procedure for the design of such bridges is specified in AASTHO specification but Indian codes are silent about the same. These observations motivate to carry out the investigation of curved box-girder bridges. Also, some equations are proposed for prestressed curved bridge under dead load, prestress load and IRC class-A load. The obtained results are validated with the published results, before evaluating the results.
2 Validation A model [6] of prestressed box-girder bridge with mentioned material properties, as shown in Fig. 1, is used for the validation. The path of strand cables is straight and the boundary condition of the deck is simply supported. For loading conditions, 25 kN/m2 gravity load, is acting on the top of deck, and 50 kN tensile force is applied on each tendons. A 4-noded plate element having 6 degrees of freedom at each node is considered for discretisation. A 100 mm size of mesh is chosen in the present investigation. The resultant stress distribution in exterior and interior top slab and soffit slab under dead and prestress loads for different curve angles (α) is
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Fig. 1 Cross-section of box-girder bridge for validation 3.0
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calculated and compared. Figure 2 shows the exterior and interior top and soffit slab stress under prestress load. The percentage error in the results is about 5%, so the modelling procedure is acceptable for further investigation.
3 Methodology The investigation of prestressed trapezoidal box-girder bridge (40 m span) is performed by using CSiBridge v.20.0.0. In this study, response of prestressed boxgirder bridge is analysed by varying curve angle. Figure 3 demonstrates the crosssection of a simply supported bridge, at support (a) and at mid span (b), with the
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(a) Cross-section at support
(b) Cross-section at mid span
Fig. 3 Geometrical properties of box-girder bridge
(a) Shell element for straight bridge
(b) Shell element for curved bridge
Fig. 4 Finite element model of bridge
parabolic cable arrangement, and material properties. In this study, 3-lane IRC classA load (IRC:6–2017) is considered. Figure 4 shows the finite element model of the bridges.
4 Result and Disscusion 4.1 Effect of Curve Angle The impact of varying curvature on maximum values of bending moment (M), vertical deflection (δ), stress at top slab (σt ), and stress at the bottom slab (σb ) are investigated under dead load (DL), prestress load (PL), and live load (LL). Figure 5 shows the variation of responses (bending moment, vertical deflection, and stresses at top and bottom of slab) for different curve angles under DL. It is observed that the maximum values of bending moment, stresses at top and bottom slab increase with the curve angle, but the value of deflection decreases with the curve angle under DL. The bottom slab stress is much more than the top slab stress of prestressed box-girder bridge. The values of responses up to 12° are not significant. When the curvature
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is changed from 0° to 60°, the bending moment is found to increase by 19% with respect to those in the straight bridge, while, the respective changes in the top slab stress and bottom slab stress are about 10% and 26% respectively. The deflection is decreased by 35% under DL. The variation of maximum values of bending moment, vertical deflection, and stresses at top and bottom of slabs under PL is shown in Fig. 6. It is observed that there is decrement in bending moment with the curve angle up to 36°, after that it rises rapidly. The values of vertical deflection and stress at bottom of the slab increase considerably with the curve angle but the nature of these responses under prestress load is opposite to those values under dead load. The σt shows a mixed trend. Up to 24° the value of stress decreases after that it increases considerably. The σb is higher than stress at top slab. When the curvature is changed to 60° from 0°, the M, σt, and σb with respect to a straight bridge are not significant. But the deflection is increased by 19% under prestress load. Figure 7 shows the variation of bending moment, vertical deflection, stresses at top and bottom slab with a curve angle under LL. For smaller curve angles (up to 12°), the variation in responses is not significant. The responses (bending moment, vertical deflection, stresses at top and bottom slab) increase with the curve angle. All these responses under live load are lesser than those values under dead and prestress loads. The stress at bottom of the slab is lesser than the stress at top slab. When the curvature is changed from 0° to 60°, the bending moment is found to be increased by 13% compared to the straight bridge, while, the respective changes in the vertical
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deflection, top slab stress, and bottom slab stress are about 115%, 18% and 7% respectively.
4.2 Proposed Equation Some of the relations are suggested to show the impact of curvature on the bending moment ratio (BMR), stress ratio at top (SRt ), stress ratio at bottom (SRb ) and vertical deflection ratio (VDR). Here, BMR is the ratio of the maximum bending moment of curved prestress box-girder bridge to the straight bridge. All other ratios are defined and adopted similarly. The equations for different loads i.e., dead, prestress and live loads are presented in Table 1. The verification of these proposed equations for prestressed box-girder bridge is presented in Table 2 with percentage error compared to finite element results.
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Table 1 Proposed equations For dead load
B M R = 1 + 5.408 × 10−5 α 2 S R t = 0.9982 + 2.574 × 10−5 α 2 S R b = 1.0086 + 6.977 × 10−5 α 2 V D R = 1 − 0.0027α − 5.1505 × 10−5 α 2
For prestress load
B M R = 1 + 8.335 × 10−8 α 3 − 2.8527 × 10−4 α S R t = 1 + 3.0941 × 10−6 α 3 − 2.8802 × 10−8 α 4 − 7.372 × 10−5 α 2 S R b = 1 + 8.598 × 10−6 α 2 V D R = 1 + 5.368 × 10−5 α 2
For live load
B M R = 1 + 3.7727 × 10−5 α 2 S R t = 1 + 0.00184α + 5.3029 × 10−9 α 4 S R b = 1 + 3.1718 × 10−7 α 3 V D R = 1 + 0.00401α + 4.1533 × 10−6 α 3
5 Conclusions The conclusions based on the investigation of the curved prestressed box-girder bridge are as follows: . The effect of curve angle up to 12° is not significant, so these bridges can be analysed as straight bridge.
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. The maximum values of BM increases considerably with the curve angle under dead and live load, while it decreases up to 36° with the curve angle under prestress load. . The maximum values of VD decreases with the curve angle under dead load; while, for prestress and live load, it increases with the cure angle. But the nature of VD under prestress load is different compared to live load. . For both DL and LL, the CS rises considerably with the curvature. For prestress load, the CS falls up to 24°, and after than it increases. . The maximum values of TS increase with the curve angle under all loads (dead, prestress, and live loads). The nature of TS under prestress load is opposite compared to the other two loads. . The effect of curvature on responses under prestress load is not significant. . The results of the proposed equations are very close to their finite element results. So, the response of a curved bridge can be determined directly using the equations.
References 1. Bazant ZP, Nimeiri ME (1974) Stiffness method for curved box girders at initial stress. J Struct Div 100:2071–2090 2. Heins CP, Oleinik JC (1976) Curved box beam bridge analysis. Comput Struct 6:65–73 3. Barr PJ, Eberhard MO, Stanton JF (2001) Live-load distribution factors in prestressed concrete girder bridges. J Bridg Eng 6(05):298–306 4. DeSantiago E, Mohammadi J, Albaijat HMO (2005) Analysis of horizontally curved bridges using simple finite-element models. Pract Period Struct Des Constr 10(01):18–21 5. Samaan M, Sennah K, Kennedy JB (2005) Distribution factors for curved continuous composite box-girder bridges. J Bridg Eng 10:678–692 6. Khaloo AR, Kafimosavi M (2007) Enhancement of flexural design of horizontally curved prestressed bridges. J Bridg Eng 12(05):585–590 7. Kim SJ, Kim JJ, Yi ST, Noor NB, Kim SC (2016) Structural performance evaluation of a precast PSC curved girder bridge constructed using multi-tasking formwork. Int J Concret Struct Mater 10(03):1–17 8. Samaan M, Kennedy JB, Sennah K (2007) Impact factors for curved continuous composite multiple-box girder bridges. J Bridg Eng 12(01):80–88 9. Hodson DJ, Paul JB, Halling MW (2012) Live-load analysis of posttensioned box-girder bridges. J Bridg Eng 17:644–651 10. Cho D, Park S, Kim W (2013) Live load distribution in prestressed concrete girder bridges with curved slab. Appl Mech Mater 284:1441–1445 11. Kim WS, Laman JA, Linzell DG (2007) Live load radial moment distribution for horizontally curved bridges. J Bridg Eng 12(06):727–736 12. Tiwari S, Bhargava P (2017) Load distribution factors for composite multicell box girder bridges. J Inst Eng (India): Ser A 98(04):483–492 13. Gupta T, Kumar M (2018) Flexural response of skew-curved concrete box-girder bridges. Eng Struct 163:358–372 14. Gupta N, Agarwal P, Pal P (2019) Analysis of RCC curved box girder bridges. J Appl Innovat Res 1:153–159 15. Gupta N, Agarwal P, Pal P (2019) Free vibration analysis of RCC curved box girder bridges. Int J Technol Innovat Modern Eng Sci 5(02):1–7
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16. Agarwal P, Pal P, Mehta PK (2019) Analysis of RC skew box girder bridges. Int J Sci Innovat Eng Technol 6:1–8 17. Agarwal P, Pal P, Mehta PK (2020) Finite element analysis of skew box-girder bridges under IRC-A loading. J Struct Eng (Madras) 47(3):243–258 18. Agarwal P, Pal P, Mehta PK (2020) Parametric study on skew-curved RC box girder bridges. Structures 28:380–388 19. Agarwal P, Pal P, Mehta PK (2020) Computation of design forces and deflection in reinforced concrete skew-curved box-girder bridges. Struct Eng Mech 78(3):255–267 20. Agarwal P, Pal P, Mehta PK (2022) Box-girder bridges - modelling and analysis. Int J Eng Model 35(1):19–42 21. Agarwal P, Pal P, Mehta PK (2022) Free vibration analysis of RC box-girder bridges using FEM. Sound Vibrat 56(2):105–125 22. IRC 6: 2014 (2014) Standard specifications and code of practice for road bridges, Section II, Loads and stresses
Design, Analysis and Optimization of Chassis for a Utility Vehicle Bhaskarabhatla Jahnavi Manaswini, Kolakotla Abhishek, Nuthalapati Hemanth, B. Anjaneya Prasad, and A. Somaiah
Abstract A utility vehicle, usually motorized, is designed to do a particular purpose more efficiently than a passenger vehicle. Utility trucks can easily navigate rugged terrain. They are used to convey things in various environments such as farms, industrial sites, and parks. The chassis provides the strength required to sustain the various vehicular components and the cargo and contributes to the vehicle’s rigidity and stiffness. As a result, the chassis is an integral component of the entire safety system. Our team relentlessly worked towards the objective from square one. This paper shows the details of design considerations and methodology used in modeling the design and development. SOLIDWORKS 2021 and ANSYS 2020 R1 are used to design and evaluate the medium. The chassis is designed to run on human power and green energy. Keywords ANSYS · Chassis · SOLIDWORKS · Utility vehicle · Computer aided engineering · AISI 4130
1 Introduction The primary goal is to minimize the vehicle weight by using lighter but more robust materials and equipment and optimizing it for a lightweight automobile. Now is the time to build alternative and green transportation requirements for a better future. Different technologies were investigated, and a tadpole model with two front wheels & one rear wheel was chosen based on research aspects such as handling, turning radius, stability, and simplicity of maneuvering. The vehicle has a revolutionary tadpole shape that is ergonomically built and simple to produce. The B. J. Manaswini (B) · K. Abhishek · N. Hemanth · A. Somaiah Institute of Aeronautical Engineering, Hyderabad, Telangana 500043, India e-mail: [email protected] B. Anjaneya Prasad JNTUH College of Engineering, Hyderabad, Telangana 500085, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_24
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structure was modeled using SOLID WORKS 2021, and numerous structural tests were performed in ANSYS R19.2 software.
2 Design Constraints • The vehicle’s geometry should be in the form of a tadpole (2F1R). • The width of the vehicle is limited to 60 inches. The vehicle’s length should be within 100 inches. • The vehicle must be suitable for carrying two riders, each of whom must be at least 6 feet 3 inches tall and weigh 115 kg. • In both static and dynamic conditions, the driver’s body should have a minimum clearance of 76.2 mm with any component of the vehicle. • All rigid components of the vehicle must have a minimum clearance of 152.4 mm from the ground.
3 Design Methodology 3.1 Chassis Design The vehicle is entirely reliant on the best frame structure, so it is only then that the entire vehicle can be built to exact measurements. While attempting to turn, the front end of such a tadpole vehicle accelerates less than the rear end—this remarkable speed and consistency. As a consequence, the complete frame was subjected to a FEM study.
3.2 Design Procedure • Chassis Design is a single solid part made in SOLIDWORKS software. • The required number of reference planes are created with respect to one of the principal planes. • Using the Line tool, different 2D sketches are sketched on the plane. • Using the 3D sketch tool, all the 2D sketches on different planes are linked, and a complete 3D sketch model of the chassis is formed. • Using the Weldments tool and with the appropriate pipe dimensions, weldments are created for each member of the 3D sketch. • Using Corner treatment, the trim order is adjusted accordingly. • The complete design is formed as shown in [Fig. 1].
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Fig. 1 Isometric view of the chassis
3.3 Use of Different Cross Section The cross-section of the structure should be chosen based on the loads operating on the various elements of the structure. The use of circular cross-section pipes and square pipes to construct a frame is popular, but one should optimize it to weigh less while maintaining the same strength. The cross-section of pipes should be selected based on the moments and forces. In general, larger bending strengths could be attained simply by changing either the thickness or diameter, or both, where it occurs. Furthermore, you may use CAE to determine the weak link and then attempt to increase the diameter or thickness of those elements. This procedure should be iterated till the maximum deformation at this portion of the structure is within acceptable limits. It is recommended that larger diameter or thicker-walled pipes be used solely for load-carrying members to enhance the longevity of the members recognized as supporting the tires and seats. Other portions may be kept as light as possible. Moreover, having too many section sizes will cause difficulty while procuring. As a result, following optimization, the portion size can be standardized, and no more than 3 section sizes should be determined.
3.4 Track Width and Wheel Base The distance between the centers of the front wheels is measured as track width, whereas the wheelbase is the length from the center of the rear wheel to the center of the front wheels.
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Track width influences the degree of weight transfer laterally. The track width and wheelbase were finalized after multiple design iterations at 54 and 91 inches, respectively.
4 Material Selection The frame may be made of various materials with varying cross-sections and diameters. The choice of the material might make a significant difference in decreasing or increasing the overall weight. The use of lighter materials that do not compromise strength and safety can be advantageous. • When compared to other carbon steels, materials such as AISI 4130 have a higher yield strength. • Alternative materials, such as aluminum alloys, are also preferable. Aluminum is about one-third the mass of steel in the very same cross-section. However, we must use larger diameter and thicker aluminum pipes for more strength, but the mass of the needed aluminum will be much lower than that of steel.
5 Structural Analysis The frame must be designed so that driver protection may be provided without the need for additional protective members in the structure. For example, vertical and horizontal elements bent toward the vehicle’s exterior can expect external items not directly to impact riders. So, we performed design optimization by iterating multiple times. A bending test was performed using ANSYS software to determine if the frame can sustain the driver’s weight and vehicle components under static and dynamic circumstances. Structural Analysis is performed in the Static Structural system after inputting all the mechanical properties of the materials used in our chassis into the engineering data. The Geometry model is imported as a STEP file previously designed in SOLIDWORKS. In Model, we perform the meshing with tetrahedral elements.
5.1 Front Impact Analysis 5.1.1
Frontal Impact Load Calculation
The maximum weight (m) of the vehicle with drivers in it is 330 kg. The maximum speed of the vehicle is 35 km/h (9.7222 m/s), and it will be treated as the initial velocity (u) during collision. After a collision, the vehicle will come to a halt; thus,
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the final velocity (v) is 0 m/s. Work done (W) can be calculated as the change in Kinetic energy, which is: W= W=
| 1| 2 mu − mv2 2
| 1| 300(9.722)2 − 300(0)2 2
W = 15595.35 Kgm2 /s2 Consider the impact time(t) to be 0.3 s, when a collision takes place from the front. The distance (D) can be calculated as the product of initial velocity and impact time: D = v(t) D = 9.722 × 0.3 D = 2.9166 m From Work and Force relations, W = F(D), F = F=
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5.1.2
Boundary Conditions
The boundary condition of fixed support is provided at the onset of the rear wheel and a force of 5347.2N is delivered to the front element of the chassis in Frontal Impact analysis.
5.1.3
Results
After solving the boundary conditions, a total deformation of 6.46 mm, an equivalent stress of 406.9 MPa and an equivalent elastic strain of 0.002 were observed (Figs. 2, 3, and 4).
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Fig. 2 Total deformation of frontal impact analysis
5.2 Side Impact Analysis 5.2.1
Side Impact Load Calculation
The maximum weight (m) of the vehicle with drivers in it is 330 kg. The maximum speed of the vehicle is 35 km/h (9.7222 m/s), it will be treated as the initial velocity (u) during collision. After collision, the vehicle will come to a halt; thus, the final velocity (v) is 0 m/s. Work done (W) can be calculated as the change in Kinetic energy, which is: W= W=
| 1| 2 mu − mv2 2
| 1| 300(9.722)2 − 300(0)2 2
W = 15595.35 Kgm2 /s2
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Fig. 3 Equivalent stress of frontal impact analysis
Considering the impact time(t) to be 0.3 s, when a collision takes place from the front. The distance (D) can be calculated as the product of initial velocity and impact time: D = v(t) D = 9.722 × 0.3 D = 2.9166 m From Work and Force relations, W = F*D, F = F=
15595.35 2.9166
F = 5347.2 N
w D
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Fig. 4 Equivalent elastic strain of frontal impact analysis
5.2.2
Boundary Conditions
The boundary condition of fixed support is provided at one side of the side protection members and a force of 5347.2N is delivered to the other side protection member of the chassis in Side Impact analysis.
5.2.3
Results
After solving the boundary conditions, a total deformation of 64.4 mm, an equivalent stress of 1268.7 MPa and an equivalent elastic strain of 0.006 were observed (Figs. 5, 6, and 7).
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Fig. 5 Total deformation of side impact analysis
5.3 Roll Over Analysis 5.3.1
Roll-Over Impact Load Calculation
The maximum weight (m) of the vehicle with drivers in it is 330 kgs. The maximum height (h) of the vehicle is 1.143 m. Applying energy conservation method during rollover, i.e., Kinetic Energy is equal to Potential Energy, i.e., mgh = 21 mv2 v = v =
/ 2gh
√ 2 × 9.81 × 1.143
v = 15.857 m/s 1 2
done| can be calculated as the change in Kinetic energy, which is: W = | Work mu2 − mv2
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Fig. 6 Equivalent stress of side impact analysis
W=
| 1| 300(15.857)2 − 300(0)2 2
W = 41488.334 Kgm2 /s2 Considering the impact time (t) to be 0.3 s, when a collision takes place from the front. The distance (D) can be calculated as the product of initial velocity and impact time: D = v(t) D = 15.857 × 0.3 D = 4.76 m From Work and Force relations, W = F(D), F = F=
41488.334 4.76
F = 8716.04 N
w D
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Fig. 7 Equivalent elastic strain of side impact analysis
5.3.2
Boundary Conditions
The boundary condition of fixed support is provided at the base of the chassis and a force of 8716.04 N is delivered to the top protection member of the chassis in Roll Over analysis.
5.3.3
Results
After solving the boundary conditions, a total deformation of 69.44 mm, an equivalent stress of 7099.4 MPa and an equivalent elastic strain of 0.038 were observed (Figs. 8, 9 and 10).
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Fig. 8 Total deformation of roll over analysis
Fig. 9 Equivalent stress of roll over analysis
6 Conclusion Following the theoretical study, we chose AISI 4130 as our material. The basic design for this material was iterated and optimized at all loading sites, and the optimal design was examined for the most negligible deformation and greatest load distribution.
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Fig. 10 Equivalent elastic strain of roll over analysis
References 1. Tiwari A, Singh A, Das S, Jaswal I (2017) Design of efficycle-human powered light weight hybrid tricycle with inbuilt rear wheel steering and use of universal joint in front axle, vol 2, no 3. pp 12–20 2. Chaudhari K, Joshi A, Kunte R, Nair K (2013) Design and development of roll cage for an all-terrain vehicle 3. Kumar MD, Teja PS, Krishna R, Sreenivasan M Design optimisation and simulation analysis of formula SAE frame using chromoly steel. https://doi.org/10.21272/jes.2019.6(2).d2 4. Mat MH, Ghani ARA (2012) Design and analysis of ‘Eco’car chassis. Procedia Eng 41:1756– 1760 5. Chouhan M, Gangrade K, Sharma S (2019) Design & optimization of roll cage. Int J Eng Trends Technol (IJETT)–67(6) 6. More A, Chavan C, Patil N, Ravi K (2017) Design, analysis and optimization of space frame chassis. Int J Eng Technol 9(2):1411–1422 7. Riley WB, George AR (2002) Design, analysis and testing of a formula SAE car chassis. SAE Technical paper series 2002–01–3300 8. Yembarwar S, Kamble B, Pande P, Wankhade A Design and finite element analysis of roll cage of all-terrain vehicle. Int J Res Sci & Eng 2(3):30–37
Design and Development of Semi-automated Manual Transmission Kamlesh Sasane, Aqleem Siddiqui, Suryateja Chadalawada, Shanto Mathai, Quirenius Mendonsa, Aldrin Rego, and Melissa Vazapully
Abstract The transmission system used in an automobile transmits power from engine to wheels. The automated transmission system has no clutch pedal and gear lever while the manual transmission system incorporated the same. To modify the conventional transmission system into the automated transmission system a selector and a shifter mechanism is required which needs a minimum of 2 actuators, one for selection and other for shifting. But since this mechanism is interlinked with each other it is required to develop a complex method which could both shift and select the gears. In this paper have used a single linear actuator which could both shift and select the gears with the help of a selector sleeve. INDICA V2 car with manual transmission is selected, designed, modelled and the gear shifter mechanism fabricated. Gear engaging algorithms are developed for designed shifter mechanism. INDICA V2 engine is taken out of the car and assembled with the gear box with a designed shifter mechanism and tested for gear shifting leads to provide plug in solution to modify manual transmission. Keywords Gear shifter · Selection of actuators · Clutch actuation
1 Introduction An automatic gearbox, or automatic transmission system, is a gearbox that, after switching on the gear, does not require manual actuation of the gear lever or clutch. The primary function of an AMT is to automate manual transmissions, which means K. Sasane · A. Siddiqui · S. Chadalawada · S. Mathai (B) · Q. Mendonsa · A. Rego · M. Vazapully Department of Mechanical Engineering, Fr. C. Rodrigues Institute of Technology, University of Mumbai Vashi, Navi Mumbai 400703, India e-mail: [email protected] K. Sasane e-mail: [email protected] A. Siddiqui e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_25
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the driver is no longer responsible for pressing the clutch and shifting gears with a stick. In this paper, a manual transmission gearbox of INDICA V2 is used to develop a plugin AMT system, thus providing the benefits of an Automatic Transmission (AT).
2 Literature Review A detailed introduction to AMT system’s working principles is provided here that represents a relative simple but effective Model based dynamic model. The shift behaviours are also successfully verified through [1]. The authors have constructed a model of AMT vehicle transmission which provides an essential base to understand the relation between dynamic response and control of clutch, TPS, and shifting strategy [2], and through research work it has been concluded that with respect to manual transmission, the AMT allows to improve the driving comfort, increase in fuel efficiency and gear shift quality [3]. This study enlightens on detailed comparison between the experimental and simulated results showing the influence of the actuators for gear shift control on the gear shift quality and performance [4]. We have developed a model of the hydraulic system that was also verified using experimental results that were constructed for the purpose of validation [5], A mathematical model was developed and the results were compared to the experimental data [6]. We have calculated the parameters and characterized the trend pattern of the speed, torque, current and back EMF constant as well as the efficiency [7]. The task of automating the shifting process and the shifting logic for EcoCAR vehicle was accomplished and the challenge they faced was the lack of inbuilt fault diagnosis on the SCUguide, and the management of the workstation to choose the best priority sequencing rule among the available alternatives for processing the jobs with maximum benefit [8]. On reviewing the following literature, it was concluded that although the AMT systems are well studied not much work is done in developing plug-in solutions to convert manual cars to AMT. Development on such modules was present but solutions required extensive modifications and were complicated. From the “Shift performance test and analysis of multipurpose vehicle” paper which conducted Gear shift quality assessment (GSQA) we took the design parameters for this paper. The mechanism of the gear shifter was inspired after examining the other articles as the shift behaviours were effectively verified through experimental data. As a result of the investigation, we gained a better understanding of the dynamic reaction and shifting approach. It has been determined that AMT can improve driving comfort, fuel efficiency, and gear shift quality.
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3 Requirements for Conversion of Manual to Semi-automatic It requires to study the motions involved in gear shifting, selecting suitable mechanisms to imitate/recreate those movements, Studying the forces involved and making sure wear on the gearbox is minimum, selecting mechanisms that are most versatile for the plugin module and designing the mechanism such that calibration is easy in order to convert the manual transmission system to semi-automatic the difference between two methods in analysed in Table 1. To focus on gear shifting actuation method the available actuators are compared in Table 2. Comparative study of the various available systems was done by studying the working, advantages and disadvantages of hydraulic, pneumatic and electromechanical AMT systems, and we have decided to go forward and design an electro–mechanical AMT system. Table 1 Manual versus Semi-automatic transmission system Manual transmission
Semi-automatic transmission
Gear shift Linkage working
The gear shift is performed by means of linkages or cables connected to a selector
Automation by means of electrohydraulic pistons and solenoids
Clutch actuator
Cables are connected to the clutch pedal
Processors and sensors automatically depress the clutch when shifted
Controller
The driver manually shifts the gears
Micro controller shifts gears according to driver inputs
Table 2 Actuation method used in Gear shifting mechanism Hydraulic amt system
Pneumatic amt system
Electro–Mechanical amt system
The gear shifting process is much smoother due to the absence of linkages and cables
Control of pneumatic systems is easier. Pneumatic systems are simple and economical
Highly precise and accurate shifting can be achieved by using linkages
Precise shifting movement by high torque and shifting force
The operating pressure required is 5–6 bar. High-speed operation is possible
Energy is easily stored and resupplied. Control flexibility with mechanical systems
A significant amount of space is required for accommodating the hydraulic pump, oil reservoir, and oil tank
Pneumatic systems exhibit spongy characteristics due to the compressibility of air
Produces very small torqude compared to their size and weight
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Fig. 1 Dismantling of gear box from Indica V2
4 Technical Specifications of Gearbox 4.1 Transaxle Front-wheel drive-through constant velocity joints, Model: TA65-STAR/3.64 with overdrive, Type: Synchromesh on all forward gears, Sliding mesh for reverse gear, No. of gears: 5 Forward,1 Reverse, Gear ratios (NA): (1st–3.64, 2nd–1.95, 3rd–1.27 4th–0.88,5th–0.636, Rev.–3.58 and Final drive ratio: 4.4). Gear Shift Floor mounted with International “H” pattern, with Fifth and Reverse in line with interlock to prevent accidental engagement from 5th to reverse (Fig. 1).
4.2 Clutch Type: Single plate dry friction diaphragm type, Outside dia. of clutch lining 190 mm (NA/MPFI). Friction area: 285 sq.cm (NA / MPFI). Based on the reverse engineering, the studied literature review indicates that the Force required for shifting gears is 400 N and the Angle of rotation of the Selector is 10°, Ideal time for gear shifting is 0.25 s and Travel Length of the Shifter rod is 22 mm.
5 Design of Gear Shifter In the manual gearbox, the shifter rods work in engaging the gears with the help of selector forks attached on the dog clutch. For the design automation we needed a selector and a shifter mechanism which requires a minimum of 2 actuators, one for selection and other for shifting. But since this mechanism is interlinked with each
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Fig. 2 Sleeve
Fig. 3 Bearing plate
other it is required to develop a complex method which could both shift and select the gears. In the design have used a single linear actuator which could both shift and select the gears with the help of a selector sleeve.
5.1 The Shifter A Sleeve shown in Fig. 2 with step diameter is mounted on a DGBB Bearing. The Bearing is press fit on the bearing plate shown in Fig. 3 such that it is free to rotate. Further the sleeve is welded to a Spur Gear. This sub assembly is mounted on the cover plate. The cover plate is mounted in such a way that it bears the load of the whole assembly. A slot is made on the sleeve from the other end opposite to the bearing. The shifted rod is passed through the sleeve hole with clearance fit. Then a pin is screwed on the shifter rod restricting the to and fro movement of the shifter rod till the slot length. One end of the shifter rod is connected to the changer Rod and the other end is connected to the Linear actuator shaft. A Linear Actuator of 200N force is selected for the shifting mechanism.
5.2 The Selector A Servo Motor shown in Fig. 4 with 40 Kg-cm is used for the selection mechanism. A spur pinion is mounted on the motor shaft with a grub screw. The pinion is selected
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Fig. 4 Servo motor
such that the centre to centre distance between the gear and pinion shown in Fig. 5 s 60 mm which is the constraint. The motor is mounted on a holding clamp which is welded to a mounting bracket. The gear is then meshed with the pinion with the help of slots on the mounting bracket. The Gear ratio selected is 2. The 3D model of gear shifter mechanism and its fabricated set up is shown in Fig 6 and Fig 7, respectively.
Fig. 5 Motor clamp
Fig. 6 Gear shifter attachment
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Fig. 7 Fabricated model of Gear shifter attachment
6 Working Gear shift mechanism is assembled with INDICA V2 and the following working has been observed based on the designed gear shifting algorithms,
6.1 For Gear Shifts not Involving Change of Shift Gates Assuming that no gear is engaged and the gear changer is in the Central Position of Neutral, it is estimated that the angle between the Central Position and side positions are 10 degrees to both sides. So first the servo is calibrated such that it turns the pinion to rotate the gear mounted on the sleeve. The pinion is turned clockwise in such a way that the gear turns 10 degrees and engages with the shift gate of the 1st and 2nd gear. Then the linear actuator pulls the shifter rod backward. This motion engages the first gear. For 1–2 gear shifts, again the linear actuator pushes the shifter rod in the forward direction engaging the 2nd gear (Fig. 8).
6.2 For Gear Shifts Involving Change of Shift Gates Now that the 2nd gear is engaged, the driver wants to upshift from 2nd to 3rd. The linear actuator pulls back to the priory calibrated neutral position of the 1st and 2nd gear, then the gear changer has to change from 1st to 2nd gate. This change
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Fig. 8 Gear shifter attachment with gear box
of shift gate is done by a pinion mounted on a servo motor. The pinion turns anticlockwise towards the left position engaging with the 2nd shift gate neutral position. The Actuator pushes the shifter rod in the forward direction engaging the synchro of the third gear.
6.3 For Engaging Reverse Gear The shifter is in central Neutral Position N. The servo mounted pinion rotates the gear clockwise towards the left position by 12.5 degrees. The shifter is in the 3rd gate position. Then the Linear actuator pushes the selector rod in the forward direction till the reverse gear is engaged.
7 Conclusion In this paper, have presented and tested an approach for the development of semiautomated manual transmission, which enables the drivers to carry out the task of shifting the gear effortlessly. Indica V2 transaxle gearbox was dismantled and used for the automation purpose. We have designed and developed a mechanism for shifting gears by employing an electromechanical actuator and a servo motor. The algorithm developed has to be integrated with the sensors. One of the major challenges faced was the unavailability of data pertaining to the force and torque required for shifting.
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So, we had to reverse engineer the gearbox and look for data in the reference papers. Frequent breakdown of the gearbox and wearing of parts due to aging were the setbacks faced. Integrated closed loop system could not be developed due to absence of motion sensors to detect gear changes inside the gearbox. As the fully automatic cars are quite expensive in the modern world. Drivers who want to cherish this experience can retrofit our design to their manual transmission system. It’ll also be helpful for the Design and Development of Semi-Automated Manual Transmission for the injured population who face trouble during shifting the gear manually and can switch to our one push button strategy
References 1. Fischer R, Schneider G (2002) The XSG family–Dry clutches and electric motors as core elements of the future automated gearbox. In: 7th LuK symposium 2. Oh J-Y, Park YJ, Lee G-H, Song C-S (2012) Modeling and validation of a hydraulic systems for an AMT. Int J Precis Eng Manufacturing Int J Precis Eng Manuf 13(5). https://doi.org/10. 1007/s12541-012-0091-6 3. Kumar S, Reddy PN, Basha P (2014) Fabrication of pneumatic gear changer. IOSR J Mech Civ Engineering (IOSR-JMCE) 11(3). https://doi.org/10.9790/1684-11355363 4. Kumbhar MS, Panchagade DR, Baidya K (2014) Development of actuator control strategy for DC motor controlled automated manual transmission (AMT). Int J Recent Technol Eng (IJRTE) 3(1). ISSN 2277–3878 5. .Krishnaraj G (2003) Characteristics of linear actuator for an automated manual transmission. The Ohio State University 6. Yang C, Hua L, Wang Z, He Y (2014) Shift performance test and analysis of multipurpose vehicle Hindawi Publishing Corporation. Adv Mech Eng 2014. Article ID 378176. https://doi. org/10.1155/2014/378176 7. Glielmo L, Iannelli L, Vacca V, Vasca F (2006) Gearshift control for automated manual transmissions. IEEE/ASME Trans Mechatron 11 (1) 8. Huang H, Nowoisky S, Knoblich R, Gühmann C (2015) Multi-domain modelling and simulation of an automated manual transmission system based on Modelica. Int J Simul Process Model 10(3). 9. Tseng C, Hsieh M (2005) Analysis and optimization of clutch actuator on automated manual transmission system. SAE Technical Paper 2005–01–1782. https://doi.org/10.4271/2005-011782 10. Lucente G, Montanari M, Rossi C (2007) Modelling of an automated manual transmission system. Mechatronics:0957–4158. http://dx.doi.org/10.1016/j.mechatronics.2006.11.002 11. Modak A (2017) Modeling and control of an automated manual transmission for EcoCAR 3 Vehicle. The Ohio State University 12. Yang C, Hua L, Wang Z, He Y Shift performance test and analysis of multipurpose vehicle. Hubei key laboratory of advanced technology of automobile components. Wuhan, Hubei 430070, China 13. Yang C, Hua L, Wang Z, He Y (2014) Shift performance test and analysis of multipurpose vehicle. Adv Mech Eng. https://doi.org/10.1155/2014/378176
Analysis of Interdependence of Structural Irregularity in Connected Buildings Rakesh Pasunuti
and M. Abdul Akbar
Abstract Connected buildings are gaining popularity due to increase in accessibility, improved aesthetics and touristic value attached to the connecting bridge. However, the structural impact of connecting two buildings/towers needs a careful study especially if there is irregularity/asymmetry in one which may affect the structural behavior of the other. In this study, two symmetrical buildings with nine stories and nine bays along each direction connected with a 4.5 × 7 m sky bridge at its 7th floor is analyzed using ETABS. After design checks as per IS codes and validation of the base model, one building was modified to bring in irregularities. Five different types of irregularities viz., geometric irregularity, mass irregularity, reentrant irregularity, torsional irregularity and weak story irregularity were introduced in the building, with two levels of irregularities for each of the irregularities. The overall building response was observed based on the results of the time period and lateral displacement and inferences drawn. Keywords Interconnected buildings · Static analysis · Irregularities · Unsymmetrical buildings · ETABS
1 Introduction As earthquakes are unpreventable and unexpected, the only alternative to safeguard human life is proper design and construction of earthquake-resistant structures. To tackle rising population and limited land resources, engineers are designing high-rise buildings and connected high-rise buildings. These connected high-rise buildings are gaining popularity due to their practical advantages; however, they are at risk of being damaged during earthquakes and this risk will increase for irregular buildings. There are different types of irregularities under the category of plan and vertical irregularity. R. Pasunuti (B) · M. A. Akbar Department of Civil Engineering, Dr B R Ambedkar National Institute of Technology, G.T. Road, Amritsar Bypass, Jalandhar- 144011, Punjab, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_26
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As a result, such irregular structures should be carefully designed to account for their dynamic behavior. Sayed Mahmoud et al. (2016) [1] reports on the seismic behavior of two super-tall structures in Malaysia with a connecting skybridge—The Petronas Twin Towers. In this research, response in the direction of the connecting bridge as well as in the perpendicular direction is studied and it was observed that lower the location of the connecting bridge, lesser is the induced straining action values, and vice versa. Varadharajan et al. (2011) [2] have summed up previous studies on the subject of structural irregularities, including both plan and vertical irregularities. It was observed that in comparison to plan irregularities, there are lesser research studies on vertical irregularities. This research found that the effect of irregularity was dependent on the extent and location of the irregularity, and the variation in seismic response parameters was identified around the irregularity location. Titiksh (2017) [3] studied a series of irregular structures to check displacements, drift and base shear in different seismic zones. This research shows that for all models, base shear and lateral displacements increase significantly as zone factors increase. Nagare et. al (2015) [4] found many buildings in India have soft story resulting from functional requirements of the building. Their research suggested two methods, Central Concrete Core method or providing additional strength to the columns to eliminate the effect of this soft story. Poonam et. al (2012) [5] studied a 10 story building with mass and weak story irregularity by comparing base shear, displacement and drift. Building with floating columns shows maximum story drifts in soft storys and that can lead to damage in the structure. Buildings with heavy loads experience maximum story shears, which should be included into column design. Mu et. al (2011) [6] compared the performance of double-tower connected structures with connected trusses to a single tower without connected trusses, showing that the double-tower connected structures with connected trusses have better performance and structural effectiveness. The connected trusses transfer internal force and coordinate the overall structure’s deformation. Ying Zhou et al. (2011) looked at a multi-tower building with long-span trusses and large openings in the floor slabs. Due to the fact that strong earthquakes could cause a lot of vertical acceleration, it is suggested that the long-span connecting truss be made stronger and stiffer. Authors are yet to see studies which deal with the interdependence of irregularities. There is a need to study the factors that have to be considered for designing the structures which are connected to each other with at least one building being irregular. In this study, the effect of displacement on the regular building due to the irregularity induced in the connected building has been studied by incorporating five different kinds of irregularities viz., geometric irregularity, mass irregularity, re-entrant irregularity, torsional irregularity and weak story irregularity.
2 Modeling in ETABS This study is carried out using ETABS by analyzing a series of irregular buildings. The building models were developed by changing a regular-configuration building
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(termed as base model). Two base models were modeled, one is a single nine story building and other is two nine story buildings connected with bridge at 7th floor. This connected bridge dimensions are taken as 4.5 m wide (one bay) and 7 m long to avoid pounding effect between two buildings [7, 8]. Table 1 shows the details of the base model. For the purpose of result interpretations the first building is named as P and the second building named as Q(Fig. 1). Each irregularity is applied with an increasing severity of two levels which are level 1 and level 2. A live load of 4 kN/m2 and a superimposed dead load of 1 kN/m2 was applied on all floors in addition to self weight. After analysis, design of beams and columns were checked as per IS: 456 (2000) [9]. The models were checked for values of gravity loads and seismic loads from ETABS with values derived by manual computation, and other checks were done for each model according to IS Code recommendations, including beam and column design check, drift check, and diaphragm assignment in each floor. The value obtained from ETABS for gravity loads and seismic loads were 373,644.30 and 27,248.87 kN, respectively & the value obtained by manual calculation for gravity loads and seismic loads were 370,165.75 and 27,107.86 kN respectively. The percentage difference in gravity loads and seismic loads were 0.94 and −0.52%, respectively. For geometric irregular models, certain bays as shown in the figure (Fig. 2a) were removed in 8 and 9th floor of building Q. For mass irregularity, a live load of 10 and 15 kN/m2 were applied instead of 4 kN/m2 on specified floors for level 1 and 2 respectively in building Q(Fig. 2b). For re-entrant irregular models, some of the panels were removed in all floors of building Q and made to look like a plus shaped building (Fig. 2c). For torsional irregular model, the shear wall of building Q is moved one bay in both directions individually for level 1 and 2 (Fig. 2d). In order to introduce a weak story, some of the columns were removed in the ground floor of the building Q (Fig. 2e). Table 1 Properties of base model
Property
Description
Grade of concrete for columns, shear wall
M30
Grade of concrete for beams, slabs
M25
Grade of steel
Fe 500
Column sizes
0.50 × 0.50 m
Beam sizes
0.4 × 0.80 m
Grid size
4.5 m
Slab thickness
0.15 m
Number of stories
9
Number of bays along X-direction in each building
9
Number of bays along Y-direction in each building
9
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Fig. 1 Plan and 3D view of regular connected building
3 Results and Discussions After analyzing, all the models were checked for mode shape. Mode shapes describe the configurations into which a structure will naturally displace and it is a pattern of structural deflection model that corresponds to each natural frequency. For instance, the 8th mode shape of mass irregularity of level 2 has shown in Fig. 3. In this, building (P) which has constant mass shows a deflected mode shape and building Q, higher mass shows a regular mode shape. The twelve models including two base models have been studied, and their lateral story displacements, story drifts, and time period were computed in order to investigate the response of the first building due to the irregularity induced in the second building. The effect on change in the time period on irregularities was almost negligible in both the buildings. As per 1893:2016 (part 1) [8], story drift has been checked in all models with 0.004 times the height of the building and the values were found to be within the limits. Further, in order to check the effect of one building irregularity on the other building, displacements were tabulated in table: building (P) alone and then for the entire structure (PQ).
Analysis of Interdependence of Structural Irregularity in Connected …
a. Geometric irregularity
b. Mass irregularity
c. Re-entrant corner irregularity
d. Torsional irregularity
e.Weak storey irregularity
Fig. 2 Irregularities considered in this study
Fig. 3 Mode shape model
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Percentage increase in displacement
40 30
LEVEL 1
LEVEL 2
20 10 0
Displacement increase in building P Displacement increase in connected building
Fig. 4 Irregularity effect in level 1 and 2
Due to the irregularity in building Q, displacements are greater than the base model (without irregularities), and those higher displacements are obtained in building Q. Figure 4 shows the effect of irregularity on regular building due to bridge connection and Table 2 shows the displacement values. In geometric irregularity, the displacement increased by 8.37% in level 1 and 14.77% in level 2 for both overall and P building alone. This shows that there is 100% transfer of effect from Q building to P building. Further, in mass irregularity, displacement increased by 5.5% in the overall building and 1.4% in building P for level 1 and 21.7% in the overall building while 5.2% in building P for level 2. It shows that 25% effect is transferred to regular building (P) due to bridge connection. Surprisingly, re-entrant irregularity follows the same trend of geometric irregularity by transferring the complete effect to regular building (P) with an increase of 1.29 and 3.6% displacements in level 1 and level 2 respectively for both overall and P building alone. However the pattern of increase in displacements was quite different in the case of torsional irregularity. In torsional irregularity, 18.3 and 12.1% increased in displacements for the overall building and regular building (P) in level 1 and 36.1 and 23.2% increased in displacement for the overall building and regular building (P) in level 2. It is concluded that 65% of the effect was transferred on the regular building (P) from the irregular building (Q). Finally, in weak story irregularity, after removing some columns there is no much significant change in displacement but in terms of effect transferred, it is 43%. The displacements increased are 0.67% in overall building, 0.28% in building P for level 1 and 0.77% for overall building and 0.33% for building P for level 2.
4 Conclusion The study was carried out to understand the behavior of regular buildings when connected with irregular buildings. In connected buildings drifts were within the limits and the time period is similar to a regular building. But higher displacement
Level 2
18.59
18.59
Weak
18.64
18.83
20.84
Re-entrant 18.59
18.59
Torsional
20.14
18.85
Geometric 18.59
Mass
18.71
22.0
18.83
19.61
20.14
43
65
100
25
100
18.59
18.59
18.59
18.59
18.59
18.65
22.91
19.26
19.55
21.33
18.73
25.31
19.26
22.63
21.33
Displacement in Displacement in Displacement in Percentage of Displacement in Displacement in Displacement in base (mm) P building (mm) P and Q effect base (mm) P building (mm) P&Q building building (mm) transferred to (mm) regular building (%)
Level 1
Table 2 Displacement in level 1 and 2
43
65
100
25
100
Percentage of effect transferred to regular building (%)
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values are always obtained in Q building because of induced irregularity. Considering the story displacement, it is observed that the effect of irregularity can transfer to a regular building with connection (bridge). The effect of one building irregularity on the other connected building is unique. There is min 25%−max 100% effect transferring due to bridge connection. Connected buildings with one regular building should not take as regular building as adjacent irregular building can transfer irregularity, so designers need to consider connected buildings as special buildings if there is irregularity in one building and need to design both as irregular buildings.
References 1. Mahmoud S, Abdallah W, Hanna N, Abdelaal A (2016) Seismic response evaluation of connected super-tall structures. Proc Inst Civ Eng Struct Build 169:840–852. https://doi.org/10.1680/jstbu. 15.00085 2. Varadharajan S, Sehgal VK, Saini B (2013) Review of different structural irregularities in buildings. J Struct Eng 39:538–563 3. Titiksh A (2017) Effects of irregularities on the seismic response of a medium rise structure, Asian. J Civ Eng 18:1307–1314 4. Nagare KP, Bahaskar PG, Nair V (2015) Seismic response of RC frame building with first soft storey 2 assistant. Int Res J Eng Technol 2:1762–1779 5. Gupta A, Kumar A, Gupta AK (2012) Study of response of structurally irregular building frames to seismic excitations. Int J Civ, Struct, Environ Infrastruct Eng Res Dev 2:25–31. https://www. researchgate.net/publication/309378985 6. Mu Z, Wang L, Fan Z (2011) Analysis of stress response for the super high rise double-tower connected structure with the trusses of changing rigidity. Adv Mater Res 291–294:1559–1563. https://doi.org/10.4028/www.scientific.net/AMR.291-294.1559 7. Bhatt AD, Lamichhane GP (2019) Study and analysis of pounding effect between adjacent RC buildings. Tech J 1:123–132. https://doi.org/10.3126/tj.v1i1.27710 8. IS (1893) Indian standard criteria for earthquake resistant design of structures—Part 1. Bureau of Indian Standards 9. IS 456 (2000) Indian standard code of practice for plain and reinforced concrete. Bureau of Indian Standards
Numerical Modeling and Analysis
Numerical Investigation on the Effect of Inclination Angle of the Wall Fin on the Hydrogen–Air Micro-combustion K. A. Srinivasa Raghavan, E. Rajesh, V. R. K. Raju, and S. Srinivasa Rao
Abstract In this present study, combustion is investigated in a micro-scale channel with an internal triangular wall fin numerically. Numerical simulations have been performed for micro-combustion with triangular wall fin for fin-inclination angles of 40°, 50°, and 60° at different inlet velocities. Two small-sized recirculation zones are formed one behind each wall fin so that the flame is anchored and offers more residence time for stable reaction. The average combustion efficiency is observed to be greater than 97% for all the inclination angles in the considered range of inlet velocities. Though the effect of the inclination angle on the mean temperature on the outer wall is insignificant in terms of magnitude, the uniformity in its distribution is affected by the fin-inclination angle. The uniformity measured in terms of the uniformity index (UI) and standard deviation (SD) is better for higher inclination angles or lower inlet velocities. It is also demonstrated that the 50° inclination angle gives the highest average and maximum wall temperature in most inlet velocities. Keywords Micro-combustor · Wall-fin inclination angle · Wall temperature uniformity
1 Introduction With the increased concern about depleting energy supplies and a dire need for lighter and long-lasting electronics, combustion in micro-channels has emerged as a key research area. Many combustion based micro-generators are being produced worldwide to meet the rising need for smaller size and greater energy density power supplies. Kaisare et al. [1] defined power MEMS (Micro Electro Mechanical Systems) as microsystems for energy conversion and production. With the progressing technology of MEMS, different small-scale systems including micro K. A. Srinivasa Raghavan · E. Rajesh · V. R. K. Raju (B) · S. Srinivasa Rao National Institute of Technology, Warangal 506004, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_27
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aircrafts and turbines, miniature internal combustion engines, portable communication devices and electronic devices such as thermo-photovoltaic and thermoelectric micro-structures, are constantly evolving for human and industrial applications. Numerous techniques have been proposed to improve the combustion characteristics by modifying the combustion domain as well as the combustor wall configurations. A few of them include insertion of bluff body, wall cavity, wall fins, etc. Wan et al. [2] proposed the development of bluff body augmented combustors inspired from the propulsion systems. The performance enhancement achieved was attributed to the formation of a re-circulation zone. The effect of the relative size of the bluff was studied by Fan et al. [3] numerically. The blow-off limits increased when the size of the bluff body was increased. This was due to the combined effect of increase in the size of re-circulation zone followed by the presence of key radicals in it. The impact of variation of bluff body angle as well as controllable-flow ratio in the slotted bluff body combustor were examined by Yan et al. [4]. This configuration was noticed to provide enhanced combustion efficiency owing to prolonged residence time and flow of large volumes of unburnt gas into the re-circulation zone. Controllable flow ratio was found to significantly influence the combustion characteristics. Also, the combustion efficiency improved along with narrowed flame extinction limits with the increasing angle of bluff body. Yunfei et al. [5], studied the combustion characteristics for varied slit-widths and the flow angle for a bluff body (with slit) augmented micro-combustor. The controllable slit width was found to significantly influence the combustion phenomenon. The reduction in combustion efficiency at higher values of the controllable slit angle (β) was attributed to weakened re-circulation zones. The combination of bluff body-cavity augmentations was numerically investigated by Zheng et al. [6, 7]. The bluff body’s influence was noticed to be predominant only in the region behind it, and the cavity was proposed to be located in that region. The influence of insertion of the bluff body prior to the cavity was also numerically studied. The impact of wall-bluff body’s shape considering combustors with bluff bodies of different (circle, diamond, ellipse, semi-circular, triangle, half ellipse, wall blade, crescent and arrowhead) shapes was numerically studied by Bagheri et al. [8]. Qamareen et al. [9] examined the influence of wall fin geometry which is placed after the step. It was observed that the maximum exterior wall temperature decreases if the wall-fin is moved towards the outlet because of the reduction in the effective reaction zone. The radiation heat transfer efficiency improvement was also observed in the micro-combustor with wall fin compared to the one without fin. Though few studies have been performed by considering the wall-fin, the studies pertaining to its geometric parameters are due. The present work intends to numerically study the effect of the wall-fin inclination angle on the combustion characteristics.
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2 Geometry and Numerical Scheme 2.1 Geometry Figure 1 depicts the schematic diagram of the geometrical model of the micro-channel with an internal wall fin. The combustion chamber length (L0 ) and height (W1 ) considered for the study are 8 and 1 mm respectively. Furthermore, the combustion channel is designed such that the wall thickness (W3 ) is 0.2 mm, the gap from the inlet to the wall-fin (L1 ) is 1 mm with the hypotenuse of the triangle (W2 ) being 0.4 mm. The inclination angle (θ) varies in the range of 40°−60°, increased in steps of 5°.
2.2 Governing Equations For the micro-scale combustion chamber considered in this study, the Knudsen number is 90° to horizontal: Dg/ p a − Dg/ p uc x g/ p uc = ∗ sin θg/ p us 2 Dg/ p uc + ∗ cos θg/ p uc − 90 2 Dg/ p a − Dg/ p uc yg/ p uc = ∗ cos θ gp us 2 Dg/ p uc + ∗ cos θg/ p uc − 90 2
(7)
(8)
(9)
(10)
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3.3 Coordinate Equations for the Lower Concave Circular Arc Segment of the Complete Double Circular Arc Firstly, we have to find the θls (which is the angle between the line connecting center of the lower circular arc and the origin point to the vertical X-axis zeroth line). As we know the radius of the lower circular arc and the connecting coordinate point of circular arc to the involute from this we can find the pressure angle at which the involute ends from the geometric analysis. Therefore, at the meeting point the x and y coordinate values are the same, i.e., the ending coordinate of involute and starting coordinate of the lower circular arc same. From this condition, we can equate the below equation to the obtained unknown angle. x g/ p i @ θg/ p i = lower concave circular arc @ θg/ p uc x g li @θi =
Dg
d
+ Dg lc 2
∗ sin θg ls −
Dg
lc
2
∗ cos θg lc
(11)
Here, θi = θls = 18.312061, so from the above equations we can find the unknown angle, i.e., θls x p i @ θi = lower concave circular arc @ θ p lc
xp
li
@θi =
Dp
a
− D p lc 2
∗ sin θ p ls −
Dp
lc
2
∗ cos θ p lc
(12)
Here, θi = θ p ls = 10.92041405, so from the above equations we can find the unknown angle i.e., θls The lower circular should end at the center between two teeth and should be with the dedendum circle, the end point of the circular arc which makes an angle with the horizontal line is shown in the above 2D figure. When (θ)lc varies between starting point of arc and 89° to horizontal:
x g/ p
lc
=±
Dg/ p
d
+ Dg/ p lc 2
∗ sin θg/ p ls +
Dg/ p
lc
2
∗ cos θg/ p lc (13)
yg/ p
lc
=
Dg/ p
d
+ Dg/ p 2
lc
∗ cos θg/ p ls −
Dg/ p 2
lc
∗ sin θg/ p lc (14)
Gear Pair Analysis: Double Circular Arc with Involute Profile
577
When (θ)lc is 90° to horizontal:
yg/ p
x g/ p
lc
=
lc
=±
Dg/ p
d
+ Dg/ p lc
Dg/ p
d
+ Dg/ p
2 lc
2
∗ sin θg/ p ls
∗ cos θg/ p ls −
(15)
Dg/ p 2
lc
(16)
When (θ)lc is >90° to horizontal:
x g/ p
lc
=± −
yg/ p
lc
Dg/ p
Dg/ p
−
d
d
∗ sin θg/ p ls
∗ sin θg/ p lc − 90
lc
Dg/ p 2
+ Dg/ p lc
2
2
=
Dg/ p
+ Dg/ p lc
2 lc
(17)
∗ cos θg/ p ls
∗ cos θg/ p lc − 90
(18)
The above equations are applicable for both pinion and gear parameters which are considered in this paper. Here we were considering only one side of the tooth profile because the other side is symmetric about the created double circular and the remaining teeth were symmetric about the designed tooth in circular patterns (see Table 1). A simulation analysis and comparison study are done in between the gear pairs, i.e., one is involute profiled gear and the other one is modified double circular arc gear profile with same parameters like pressure angle, number of teeth on both the gears, module same applied torque, etc., but the only difference is the profile of the gear tooth. This is how we verify, compare, and validate our results as here the simulation is done in the Solidworks. The material which is used for both the gear pairs is Alloy steel, its yield strength: 620 MPa, the properties were shown in Figs. 3 and 4 and Table 2.
4 Conclusion The modified version of double circular arc profile is proposed, and the stress developed with the proposed DCA profile external-external gear pair is compared with that of gear pairs having standard involute tooth profile. The proposed DCA profile
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Table 1 Parameters for developing modified DCA profile Sl. no.
Parameter
Value
Involute profile 1
Zp
38
2
Zg
135
3
m
3.5
4
α Dg p Dg a Dg d Dg b Dp p Dp a Dp d Dp d
20o
5 6 7 8 9 10 11 12 13 14 15 16 17 18
472.5 mm 479.5 mm 463.75 mm 444.004 mm 133 mm 140 mm 124.25 mm 124.979 mm
Parameter
Value
Modified DCA profile x g ui 4.375282 1.9824747 x p ui 2.3913511 x g li 0.14105888 x p li 18.312–22.435 θg i 10.9204–26.3437 θp i 22.435–91.52 θg uc θg lc 18.312–90.19 θ p uc 26.3437–93.182 θ p lc 10.9204–88.486 Dg uc 4.1769 mm Dg lc 3.436 mm 4.102 mm D p uc 3.731 mm D p lc 4.375282 x g ui @θi 1.9824747 x p ui @θi x g li @θi 2.3913511 x p li @θi 0.14105888
Fig. 3 Simulation analysis of gear pairs with involute profile and modified DCA profile
Gear Pair Analysis: Double Circular Arc with Involute Profile
(a) Bending stress
579
(b) Radial Stress
(c) Maximum Principal Stress Fig. 4 Stress Comparison between results with modified DCA profile and Involute profile
is having two circular arcs towards addendum and dedendum of a gear tooth and standard 20° full depth involute profile is considered near the pitch point of gear tooth. The result shows that the stress developed in a gear pair with modified double circular arc profile is less than that with involute profile. These stresses were generated under same torque, and every parameter is same for both the gears except the profile of the tooth.
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Table 2 Stress developed in gear pair with involute profile and modified DCA profile S. no.
1 2
Torque (Nm)
50 100
Gear type
Normal stresses (MPa)
Principle Stresses (MPa) σ1 > σ2 > σ3
Bending
σ1
Radial
σ2
σ3
Involute
3.6
1.9
5.51
1.99
1.68
DCA
3.3
1.7
5.6
1.7
1.5
Involute
7.2
3.98
11.2
3.98
3.37
DCA
7
3.5
11.7
3.5
2.5
10.8
5.98
16.5
5.98
5
3
150
Involute DCA
10
5.1
16.8
5.1
3.46
4
200
Involute
14.4
7.97
22
7.9
6.7
DCA
13.3
6.83
22.3
6.8
4.6
Involute
18
9.96
27.5
9.96
8.43
DCA
16.5
8.4
27.6
8.4
5.7
21.6
11.9
11.9
10.1
5
250
6
300
Involute DCA
20.6
10.3
34
10.3
7.3
7
350
Involute
25.2
13.9
38.5
13.9
11.8
DCA
22.8
11.6
38.3
11.6
7.9
Involute
28.8
15.9
44
15.9
13.4
DCA
26
13.2
43.5
13.2
9
32.4
17.9
49.6
17.9
15.1
8
400
33
9
450
Involute DCA
29.2
14.9
49
14.8
10.1
10
500
Involute
36
19.9
55.1
19.9
16.8
DCA
33.9
16.9
56.6
16.9
12.1
References 1. Hebbal MS, Ishwar TM, Rayannavar P, Prakash KH (May 2014) Reduction of root fillet stress by alternative root fillet profile. Int J Res Eng Technol 3:823–826 2. Chironis NP (1967) Gear design and application. McGRAW-HILL Book Company, New York 3. Zhang H, Hua L, Han XH (2010) Computerized design and simulation of meshing of modified double circular-arc helical gears by tooth end relief with helix. Mech Mach Theory 45:46–64 4. Wang J, Luo SM, Su DY et al (2015) Geometric design and simulation of tooth profile using elliptical segments as its line of action. J Cent South Univ 22:2119–2126 5. Fuentes A, Ruiz-Orzaez R, Gonzalez-Perez I (2014) Computerized design, simulation of meshing, and finite element analysis of two types of geometry of curvilinear cylindrical gears. Comput Method Appl M 272:321–339 6. Bahk CJ, Parker RG (2013) Analytical investigation of tooth profile modification effects on planetary gear dynamics. Mech Mach Theory 70:298–319 7. Chen BK, Liang D, Li ZY (2014) A study on geometry design of spiral bevel gears based on conjugate curves. Int J Precision Eng Manuf 15:477–482 8. Zhu HL, Ning P, Zou M et al (2013) A gear pump based on harmonic gear drive. Proc IMechE Part C J Mech Eng Sci 227:2844–2848
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9. Lin C, Gong H, Nie N et al (2013) Geometry design, three-dimensional modeling and kinematic analysis of orthogonal fluctuating gear ratio face gear drive. Proc IMechE Part C J Mech Eng Sci 227:779–793 10. Yeh T, Yang DCH, Tong SH (2001) Design of new tooth profile for high-load capacity gears. Mech Mach Theory 36(10):1105–1120 11. Litvin FL (1989) Theory of gearing. NASA Publication, Washington 12. Litvin FL, Fuentes A (2004) Gear geometry and applied theory, 2nd edn. Cambridge University Press, New York 13. Dooner DB, Seireg AA (1995) The kinematic geometry of gearing: a concurrent engineering approach. Wiley, New York 14. Barone S (2001) Gear geometric design by B-spline curve fitting and sweep surface modelling. EWC 17:66–74. https://doi.org/10.1007/s003660170024 15. Routh B, Sahoo V, Sobczyk AS (2021) Performance analysis of asymmetric toothed strain wave gear. Proc IMechE (UK) J Mech Eng Sci Part C 235(23):7314–7328. https://doi.org/10. 1177/09544062211015784 16. Sahoo V, Maiti R (2018) Load sharing by tooth pairs in involute toothed harmonic drive with conventional wave generator cam. Meccanica 53:373–394. https://doi.org/10.1007/s11012017-0698-x 17. Sahoo V, Maiti R (2016) Static load sharing by tooth pairs in contact in internal involute spur gearing with thin rimmed pinion, Proc IMechE (UK) J Mech Eng Sci Part C 230(4):485–499. https://doi.org/10.1177/0954406215618424 18. Luo SM, Wu Y, Wang J (2008) The generation principle and mathematical models of a novel cosine gear drive. Mech Mach Theory 43:1543–1556 19. Ishibashi A, Yoshino H (1987) Design and manufacture of gear cutting tools and gears with an arbitrary profile. JSME Int J 30(265):1159–1166 20. Vijayakar SM, Sarkar B, Houser DR (1988) Gear tooth profile determination from arbitrary rack geometry. Gear Technol 5(6):18–23 21. Yoshino H, Shao M, Ishibashi A (1992) Design and manufacture of pinion cutters for finishing gears with an arbitrary profile. JSME Int J Ser 3 Vib Control Eng Eng Ind 35(2):313–319 22. Zhang GH, Xu HB, Long H (1995) Double involute gear with ladder shape of tooth. Chin J Mech Eng 31:47–52 23. Xin H (2011) Design for basic rack of harmonic drive with double-circular-arc tooth profile. China Mech Eng 22(6):656–662 24. Chen G, Li H, Liu Y (2019) Double-arc harmonic gear profile design and meshing analysis for multi-section conjugation. Adv Mech Eng 11(5):1–14
Performance Analysis of Thermal Characteristics of Plate Heat Exchanger Using Al/H2 O Nanofluid: An Experimental Study Shubhamshree Avishek and Pankaj Kumar
Abstract Enhancing the efficiency of plate heat exchangers (PHE) lowers the consumption of energy in the thermal processing of food, beverages, and dairy units. An experimental study is incorporated to analyze the performance of thermal characteristics of PHE using alumina/water (Al2 O3 /H2 O) nanofluid used during the mango juice thermal processing. The setup comprises two flow loops for hot fluid (nanofluid) and cold fluid (mango juice). The inlet temperature of cold fluid is kept constant with a varying inlet temperature of hot fluid from 50 to 60 °C and a mass flow rate from 4 to 7 LPM. The experimental results show an improvement in the thermal performance especially the heat transfer of nanofluid upon increasing the particle concentration. For Re = 350, the improvement in heat transfer coefficient is achieved to be twice for 0.2% and 2.5 times for 0.3% with respect to that of 0.1% particle concentration. Keywords Alumina nanoparticles · Nanofluid · Plate heat exchanger · Mango juice · Thermal processing · Convective heat transfer
1 Introduction Today, industries are very concerned about energy-saving techniques which suit their respective application. Plate heat exchangers (PHE) have captured a large area in the field of dairy and food industries. To date, there are very limited studies on their flow performance and heat transfer performance. PHE is employed in this field due to its flexibility in the assembly, higher rate of heat transfer, hygienic operations, i.e., ease of cleaning, requirement of less space, and modular parts which can be changed individually making its maintenance cheaper. They can be heaters, S. Avishek (B) Department of Mechanical Engineering, National Institute of Technology Meghalaya, Shillong, Meghalaya 793003, India e-mail: [email protected] P. Kumar Section Engineer, Indian Railways, Sonpur, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_56
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coolers, condensers, etc. Generally, PHEs are employed for the liquid-to-liquid flow condition. There are two methods active and passive employed for the improvement of thermal performance in heat exchangers. In active methods, external forces are used while in the passive methods, innovative heat transfer fluid (nanofluid) or modification of heat transfer surfaces (fins) is required. Nanofluids have enhanced heat transfer performance in contrast to that of conventional fluids. Here metals like Cu, Al, Ag, and Au; metal oxides like Al2 O3 , TiO2 , CuO, SiO2 , CeO2 , and ZnO; and carbon like a diamond, CNT, MWCNT, SWCNT, etc. are used as nanoparticles, and water, ethylene glycol, propylene glycol, and transformer oil are different base fluids. Past studies show the significant importance of using nanoparticles to enhance the heat transfer rate. Nanoparticles are also mainly used to make a compact heat exchanger with an additional heat transfer rate, such as a study made by Stogiannis [1] found that by using SiO2 35% there was advancement in the heat transfer rate as compared to the ideal fluid water. Also, in one of the experiments made using CuO/H2 O, it has been observed that the efficacy of nanofluid depends on the flow type, where laminar flow is seen to be the most efficient [2]. Moreover, the behavior of the improvement of heat transfer is not always the same for different types of heat exchangers; in this context, one study was made where ZnO/H2 O was used to compare the performance of PHE and concentric Tube Heat Exchanger, where it was found that the PHE had the higher efficiency of about 20% than that of concentric heat exchanger with 14% [3]. Farajollahi et al. [4] experimentally analyzed and compared two nanofluids, Al2 O3 /H2 O and TiO2 /H2 O for their heat transfer performance in a Shell and Tube Heat Exchanger where it was seen upgradation of rate f heat transfer due to the increment of Peclet number. Also, nanoparticles possessing low mean dia was having lower optimum concentrations. It was also observed that the TiO2 /H2 O performance for heat transfer was better for a low concentration whereas Al2 O3 /H2 O performed better heat transfer rate for a higher heat transfer concentration [5]. All these experiments show that nanoparticles help in increasing the thermal performance especially heat transfer coefficient, thermal conductivity, and rate of heat transfer [6]. Till now the use of nanofluid in PHE’s thermal performance during the thermal processing of mango juice has not been performed. In this regard, the current work aims to study the effect of nanofluid on the thermal characteristics performance of PHE. The thermal performance like rate of heat transfer, heat transfer coefficient, and overall heat transfer coefficient of Al2 O3 /water nanofluid at different concentrations and for variable flow rate, keeping the temperature at the inlet of cold fluid constant (20 °C) with a varying inlet temperature of nanofluid was investigated.
Performance Analysis of Thermal Characteristics of Plate Heat …
585
Table 1 The specifications of α-Al2 O3 nanoparticle Purity
Average particle size
Molecular mass
Density
Thermal conductivity
Specific heat
99.50%
30–50 nm
101.96 g/mol
3970 kg/m3
40 W/m.K
753 J/kg.K
2 Experimental Investigation 2.1 Nanofluid Preparation To produce nanofluid a Two-Step method is undertaken where α-Al2 O3 nanoparticle with specification as shown in Table 1 is added into distilled water. A digital balance is used to note the required mass of the Al2 O3 nanoparticle which is then mixed gradually with the distilled water where the solution is mechanically stirred for an hour. After this process, the solution is ultra-sonicated for about 3 h in a continuous manner using a ultrasonic bath at 100 W to ensure consistent diffusion of the two materials. After 6 days of the production of the nanofluid, it was observed that there were no sediments and the pH reading was 4.9 indicating the nanofluid to be stable as it is not anywhere closer to the iso-electric point (IEP). The mixture having a pH range of 3–5 is understood to be stable as per past research [7–9].
2.2 Experimental Setup and Procedure Figure 1 describes the experimental setup of PHE comprising two main loops, a completely closed loop for passing the hot fluid which is the prepared nanofluid, comprising 1L & 3 kW capacity geyser, an insulated tank, and a regulating valve used to modulate the hot fluid rate of flow, and then an open-loop for cold fluid (mango juice) comprising a centrifugal pump with regulating valve, two 35L capacity tanks, and a PHE as a test section. A counter-flow heat exchanger arrangement is accounted and the geometric specifications are shown in Table 2. A digital temperature indicator and four pressure gauges were installed at the heat exchangers’ inlet and outlet connections to measure temperature and pressure. The volume flow rate is determined by the flow meter installed on both fluids outlets. Once the inlet temperature is set, the pump fitted was started where the nanofluid is tend to be circulated. The mango juice goes from the insulated tank to the plate heat exchanger where it develops heat while passing through the PHE from the nanofluid. The pressure and temperature of the fluid were recorded when the entire system achieves a steady state. The experiments were done for 4, 5, 6, and 7 LPM volume flow rates for both the fluids, while taking 20 °C constant inlet temperature and varying the inlet temperature of nanofluids, i.e., 50, 55, 60, 65 °C for three separate concentrations of nanoparticle 0.1, 0.2, and 0.3%.
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Fig. 1 Schematic figure of experimental setup
Table 2 PHE geometric characteristic
Length of plates
336 mm
Width of plates
120 mm
Spacing between plates
2.5 mm
Thickness of plates
0.5 mm
Total number of plates
8
Thermal conductivity of plate material
0.63 W/m.K
2.3 Data Reduction The heat transfer rate was examined to analyze the thermal characteristics of nanofluid using experimental data. The inlet and outlet temperatures are noted during the experiment. All the fluid properties taken into account are at the mean temperature as Th,avg =
Thi + Tho 2
(1)
Tc,avg =
Tci + Tco 2
(2)
The heat lost by nanofluid (Qh ) and the heat gained by the mango juice (Qc ) can be calculated using measured temperature and volume flow rates.
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Q h = ρh v˙h C p,h (Thi − Tho )
(3)
Q c = ρc v˙c C p,c (Tco − Tci )
(4)
Q avg =
Qc + Qh 2
(5)
where, v˙ c and v˙ h are volume flow rates of cold and hot fluid. ρ density of fluids. Cp specific heat capacity. The overall heat transfer coefficient is computed as U=
Q avg AF/\T L M T D
A = Nt H W
(6) (7)
F is a correction factor for temperature, F can be taken as unity for counter-flow, and LMTD /\TLMTD is calculated as /\T L M T D =
(Tho − Tci ) + (Thi − Tco ) ho −Tci ) ln (T (Thi −Tco )
(8)
While the other thermo-physical properties like density and the thermal conductivity of both fluids are experimentally evaluated. Thermal conductivity is measured using a guarded hot plate apparatus, viscosity using Viscometer, and density using a hydrometer.
3 Results and Discussions 3.1 Convective Heat Transfer Coefficient Figure 2. defines that heat transfer augmentation for 0.1% nanoparticle concentration is less compared to that of 0.2 and 0.3% concentrations. As the Reynolds number at lower Re number increases, the heat transfer coefficient also increases, i.e., for Re = 260 the enhancement for 0.2 and 0.3% concentration with respect to the 0.1% concentration are, respectively, 891 and 1618 W/m2 .K. and for Re = 350. The maximum enhancement observed in 0.3% concentration is about 2650 W/m2 .K. For Re = 350
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S. Avishek and P. Kumar Re vs h 4500 0.1% 0.2% 0.3%
4000 3500
h(W/m2.K)
3000 2500 2000 1500 1000 500 200
250
300
350
400
450
Re
Fig. 2 Heat transfer coefficient versus Reynolds number at 65 °C with varying mass flow rates
the improvement in heat transfer coefficient is seen to be twice for 0.2% and 2.5 times for 0.3% with respect to 0.1% particle concentration. It is also observed that as the particle concentration and Re no. increase, the coefficient of heat transfer increases. It also increases with increasing temperature. Results show that the maximum heat transfer coefficients for 0.3% at 65 °C, 60 °C, 55 °C, and 50° C are, respectively, around 4100, 3600, 3300, and 3100 W/m2 .K. It is also observed that for 0.3% concentration at 65 °C there is a maximum increment in heat transfer coefficient of 2650 W/m2 .K. This may be due to the fact that with the rise in temperature Brownian motion of nanoparticle get intensified which delays the formation of the boundary layer at the entrance leading to an increment in the heat transfer coefficient. Figure 3 depicts the correlation of Reynolds number and heat transfer coefficient. It shows as the Re increases with temperature, the heat transfer coefficient also enhances. Additionally, it also depends on the concentration of nanoparticles which means, as the nanoparticle concentration increments the heat transfer coefficient also enhances.
3.2 Overall Heat Transfer Coefficient Figure 4 shows a similar pattern with overall heat transfer coefficient as well. This means as the concentration of nanoparticle and Re increases, the overall heat transfer
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coefficient also increases. The increment for 0.2 and 0.3% nanoparticle concentration is higher than that of 0.1% concentration. For example, the heat transfer coefficient increment for 0.1, 0.2, and 0.3% concentrations are, respectively, 600, 940, and 950 W/m2 .K.
4 Conclusions This current experimental work focuses on analyzing the thermal performance of PHE used with Al2 O3 /DW during mango juice thermal processing. The experiment was done for 0.1%, 0.2%, and 0.3 vol.% particle concentration, varying the volume flow rate 4–7 LPM and varying the inlet temperature of 50–65 °C of the hot fluid where the cold fluid is kept at a constant inlet temperature at 20 °C. . The heat transfer rate enhances with the incrementation of the Re number and increasing the concentration of the particle for different volume flow rates at all temperature ranges from 50–65 °C. . The thermal characteristics are observed to elevate with the inflation of the concentration of nanoparticles at different flow rates and with varying temperatures which happens due to the incrementation of fluid’s thermal conductivity with the addition of nanoparticles in the distilled water.
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. A comparatively larger improvement in the heat transfer coefficient was observed as 2650 W/m2 .K for 0.3% concentration at a nanofluid inlet temperature of 65 °C. This may be due to the fact that the fluid’s viscosity decreases with a rise in the temperature and in turn enhances the Re which increases the Nu number, and finally, the heat transfer coefficient improves. . For Re = 350, the heat transfer coefficient is observed to be improved twice for 0.2% and 2.5 times for 0.3% with respect to that of 0.1% particle concentration. . The plate heat exchanger exhibits minimum pressure drop, i.e., 7 Pa on the change of volume flow rate from 4 to 7LPM. . A minimum pressure drop of 7 Pa is observed with the change in volume flow rate.
References 1. Stogiannis IA, Mouza AA, Paras SV (2015) Efficacy of SiO2 nanofluids in a miniature plate heat exchanger with undulated surface. Int J Therm Sci 92:230–238 2. Pantzali MN, Mouza AA, Paras SV (2009) Investigating the efficacy of nanofluids as coolants in plate heat exchangers (PHE). Chem Eng Sci 64:3290–3300 3. Fard MH, Talaie MR, Nasr S (2011) Numerical and experimental investigation of heat transfer of ZnO/water nanofluid in the concentric tube and plate heat exchangers. Therm Sci 15(1):183–194
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4. Farajollahi B, Etemad SGh, Hojjat M (2011) Heat transfer of nanofluids in a shell and tube heat exchanger. Int J Heat Mass Transf 53:12–17 5. Tiwari AK (2015) Thermal performance of shell and tube heat exchanger using nanofluids. Int J Adv Prod Mech Eng (IJAPME) 1(1):2394–6210 6. Javadi FS, Sadeghipour S, Saidur R, BoroumandJazi G, Rahmati B, Elias MM, Sohel MR (2013) The effects of nanofluid on thermophysical properties and heat transfer characteristics of a plate heat exchanger. Int Commun Heat Mass Transf 44:58–63 7. Singh PK, Harikrishna PV, Sundararajan T, Das SK (2012) Experimental and numerical investigation into the hydrodynamics of nanofluids in microchannels. Exp Thermal Fluid Sci 42:174–186 8. Chandrasekar M, Suresh S, Bose AC (2010) Experimental investigations and theoretical determination of thermal conductivity and viscosity of Al2 O3 /water nanofluid. Exp Thermal Fluid Sci 34:210–216 9. Qu J, Wu H-Y, Cheng P (2010) Thermal performance of an oscillating heat pipe with Al2 O3- water nanofluids. Int Commun Heat Mass Transf 37:111–115
Wear and Corrosion Behaviour of WS2 Reinforced Al-Based Composites Sweta Rani Biswal and Seshadev Sahoo
Abstract This paper validates the potentiality of WS2 as a solid lubricant at high weight concentration in 78% Al-10% Al2 O3 -12%WS2 hybrid composite fabricated in powder metallurgy method through tribological and corrosion analysis. Porosity is found to be 1.72% with an average hardness value of 61.46 HV. The wear loss is studied with 5 N and 1.5 m/s sliding load and velocity, respectively. The average wear loss is found to be 0.0095 gms. The coefficient of friction and the minimum corrosion rate is 0.161 and 0.01465 mm/year for 120 h, respectively. Corrosion analysis indicates the decrement in corrosion rate as the exposure duration increases. Keywords Solid lubricant · Wear · Corrosion · Hybrid metal matrix composite · Automotive
1 Introduction In the genre of sustainability and environmental friendliness, Hasan [1], Khoshsima [2], Aabid [3], Kumar [4], Danappa [5], and Omrani et al. [6] have suggested that Aluminium composites reinforced with solid lubricant plays a pivotal role in automotive industries due to their antifriction and corrosion resistance property. These properties not only increase the life of the frictional parts but also help in reducing fuel consumption. It’s a boon for the manufacturing industry whose main concern is to make cost-effective frictionless components for the automotive system. Many Researchers like Sahoo and Samal [7], Sahoo [8], Furlan [9], and Rengifo et al. [10] have reported that Solid lubricants like dichalcogenides are reinforced in the composite to improve the tribological properties of Aluminium composites. These dichalcogenides are two-dimensional (2D) structure, which creates tribofilm during friction and helps to improve the tribological properties of the aluminium composite without any external lubrication system. The most popular are MoS2 and S. R. Biswal (B) · S. Sahoo Department of Mechanical Engineering, ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar 751030, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_57
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WS2 . Although the potential of 2D-WS2 is more than that of MoS2 , there are very less investigations on the 2D-WS2 , which is sustainability in higher temperature conditions unlike MoS2 . Whereas, the earlier investigation is mainly focused on WS2 reinforced Al-MMCs or Al-MMCs with other reinforcements as suggested by Nakayama [11], Niste [12], Vaziri [13], and Biswal and Sahoo [14]. According to the earlier investigation of Biswal and Sahoo [14], they have found that there is an increment in physical and mechanical properties like density, hardness, and porosity up to the 6 wt. concentration of WS2 in Al Hybrid composite along with alumina. However, on the further addition of WS2 , there are no substantial results. The tribological and corrosion aspects were also not studied before at varying concentrations of WS2 addition in Al hybrid composite. This provides a huge gap in the study of different aspects of reinforcing 2D-WS2 as a hybrid combination in Al-MMCs. This paper tried to establish the gap to study the different aspects such as tribological, mechanical, and corrosion behaviour of 2D-WS2 reinforced Al hybrid composite at higher weight fraction, i.e., more than 6 wt.%. The fabrication route adopted here is the powder metallurgy method because of the reactive nature of WS2 at molten state as reported by Niste [12].
2 Experimental Procedure The solid processing fabrication method is adopted for the fabrication of hybrid composite, which is known as powder metallurgy. For the making of a hybrid composite, Aluminium (Al) powder in 78 weight percentage, alumina (Al2 O3 ) powder in 10 weight percentage, and tungsten disulphide (WS2 ) in 12 weight percentage have been taken. The material specification of the hybrid composite is given in Table 1. Synthesis of a hybrid composite is done by three significant steps of the powder metallurgy process, i.e., powder mixing, compaction, and sintering. The process flow diagram for fabrication is represented in Fig. 1. The parameters considered for fabrication are 600 MPa compaction pressure and 600 °C sintering temperature as suggested by Biswal and Sahoo [15]. The fabrication process is schematically represented in Fig. 1. Theoretical Density has been calculated using the rule of the mixture as per Chawla [16]. Porosity has been calculated as explained by Biswal and Sahoo [15]. Table 1 Specification of hybrid composite Composition
Concentration in weight %
Purity in %
Density in gm/cm3
Melting point in °C
Average Particle size in microns
Al
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99
2.71
660
80
Al2 O3
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99.9
3.95
2072
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12
99.9
7.5
1250
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Fig. 1 Process flow diagram of powder metallurgy method
Studying the different physicomechanical properties of the composite has been done through different characterizations such as SEM, XRD, and EDS with colour mapping. These characterizations are used to introspect the presence of chemical composition taken for the hybrid composite after fabrication. The hardness test for the five samples of the same composition has been done in Vickers’s Hardness tester to evaluate the consistency of results. The parameters for hardness testing are taken as 50gf loading for 15 s of indentation. Finally, wear and corrosion wear tests have been done through pin on disc wear test rig and ASTMG31 procedure [17], respectively, to study the wear and corrosion behaviour of the hybrid composite. The parameters considered for the wear test are 5N sliding load, 1.5 m/s sliding velocity, and 11.11 sliding time. A test has been performed to evaluate its tribological characteristics. For the corrosion test, samples are kept at room temperature and done in 3.5% (weight) NaCl solution kept for 24, 48, 72, 96,120 h.
3 Results and Discussions The theoretical density as calculated using the rule of the mixture is 3.04 gm/cm3 . The after sintering density and porosity are expressed in Table 2. The patterns of XRD analysis in the hybrid composites are represented in Fig. 2 in two different conditions, i.e., before sintering (green pallet) and after sintering (sintered pallet). The significant peaks are visible for Al and Al2 O3 whereas low-intensity WS2 peaks are found in the XRD patterns before and after sintering. This is due to the lower concentration of WS2 as compared to the Al matrix in the hybrid composite. Lowintensity but strong peaks are visible at low phase angles at 14.4° and 28.814°. The formation of WO3 is not visible in the XRD, which indicates the controlled reaction at high-temperature sintering process. The colour mapping of the hybrid composite is shown in Fig. 3, which indicates the presence of all the elements in the composite. The materials are uniformly distributed throughout the composite and there is no sign of agglomeration, which improves the compactness of the composite by reducing the porosity. The average porosity is 1.72%, which is very less. Looking into the results of hardness tests, the average hardness value is found 61.46 HV, i.e., more than the hardness value 52.2 ± 0.729 HV of Aluminium hybrid
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Table 2 Properties of hybrid composite Composition
Porosity in %
Sintered density in gm/cm3
Hardness in HVN
Wear loss in grams
Sp1
1.5
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Fig. 2 XRD of hybrid composite a Green pallet and b Sintered pallet
Fig. 3 EDS of hybrid composite with colour mapping
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composite combined with alumina at 10 weight percentage and Tungsten disulphide at 6 weight percentage as presented by Biswal and Sahoo [14]. This indicates that WS2 at high weight concentration which is taken as 12 wt.% improves the mechanical properties, i.e., hardness of the as-taken hybrid composite. As suggested by Biswal and Sahoo [14], the reaction between WS2 with Al and Al2 O3 helps to improve the mechanical properties by forming chemical tribofilm during high-temperature sintering process at high weight fractions too. These findings conflict with the results found by Kanthavel et al. [18], Rouhi et al. [19], and Liu et al. [20]. According to them, the addition of dichalcogenide MoS2 improves the properties of hybrid composite until 4 wt.% reinforcement. However, tungsten disulphide gives better results than the most common dichalcogenides like MoS2 at high weight percentage reinforcement without agglomeration. Figures 4 and 5 illustrate the wear and corrosion behaviour of Aluminium hybrid composite. The coefficient of friction (COF) is 0.161 as shown in the graph of Fig. 4. The average of wear loss is observed at 0.0095gms. The wear loss is less than that of MoS2 reinforced Aluminium hybrid composite with Alumina as reported by Kanthavel et al. [18]. These findings reveal the potentiality of 2D-WS2 as compared to other solid lubricants of the 2D-TMDs family at a high weight fraction. Al2 O3 and 2D-WS2 have a consistent distribution of hard particles in the hybrid composite. During friction, hard particles are exposed which helps to prevent the composite from degrading. According to Wei, adding W and S to a sandwich construction enhances wear resistance [21]. As per the reports, Aluminium performs outstandingly in the corrosive environment, which creates a huge market for its alloy and composite. However, the addition of solid lubricants has a huge impact on its corrosion resistance as reported by Hihara and Bakkar [22], Zakaria and Zakaria [23]. The corrosion test result illustrated in Fig. 5 indicated a decremental trend of corrosion rate in mm/year with
Fig. 4 COF versus Time graph of hybrid composite
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Fig. 5 Corrosion analysis of hybrid composite
respect to the exposure duration. The minimum value found in the hybrid composite is 0.01465 mm/year. The enhanced corrosion resistance of the Al matrix observed in this work is due to the high interfacial interaction of aluminium with Al2 O3 , and WS2 . Additionally, for the study of the properties, the pure Al matrix has been used which clarifies there are no intermetallic phases in the Al matrix. Therefore, the Al2 O3 and WS2 particles are believed to act as physical barriers in the Aluminium hybrid composite.
4 Conclusions A novel fabrication technique, i.e., powder metallurgy is adopted to fabricate 78 wt.% Al-5 wt.% Al2 O3- 12 wt.% WS2 hybrid composites. The different properties of the as-taken hybrid composites were calculated and provided the following conclusions: (1) The hybrid composite was successfully fabricated through the powder metallurgy method without agglomeration and reaction. (2) The porosity is 1.72%, which indicated the compactness of the hybrid composite. (3) There is a consistent distribution of alumina and tungsten disulphide in the Aluminium matrix. Microstructural characterization indicates the presence of reinforced particles without any secondary phases. (4) The value of average hardness was improved to 61.46 HV which is more than the WS2 concentration at 6 wt. as found by Biswal and Sahoo [14]. (5) The average wear loss is found to be 0.0095 gms at 0.161 coefficient of friction. (6) The corrosion rate is degraded as the exposure duration increases. Its minimum value is 0.01465 mm/year at 120 h.
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Acknowledgements The authors are extremely thankful to Dr. Natraj Yedla, Assistant Professor, Department of Metallurgical and Materials Engineering, National Institute of Technology, and his research group for their assistance in the characterization of the samples.
References 1. Hasan MS, Wong T, Rohatgi PK, Nosonovsky M (2022) Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models. Tribol Int 170:107527. https://doi.org/10.1016/j.triboint.2022.107527 2. Khoshsima S, Mertdinç S, Motallebzadeh A, Altınta¸s Z, A˘gao˘gulları D, Balcı-Ça˘gıran Ö (2022) Enhanced hardness and wear resistance of Al-based hybrid MMCs by using of composite metal boride reinforcement particles. Mater Chem Phys 126377. https://doi.org/10.1016/j.mat chemphys.2022.126377 3. Aabid A, Murtuza MA, Khan SA, Baig M (2022) Optimization of dry sliding wear behavior of aluminium-based hybrid MMC’s using experimental and DOE methods. J Mater Res Technol 16:743–763. https://doi.org/10.1016/j.jmrt.2021.12.005 4. Kumar J, Singh D, Kalsi NS, Sharma S, Mia M, Singh J, Rahman MA, Khan AM, Rao KV (2021) Investigation on the mechanical, tribological, morphological and machinability behavior of stir-casted Al/SiC/Mo reinforced MMCs. J Market Res 12:930–946. https://doi.org/10.1016/ j.jmrt.2021.03.034 5. Danappa GT, Raghavendra CR, Swamy RP, Naik K (2021) Dry sliding wear behaviour of Al7075/Gr/nano TiO2 MMC using RSM. Mater Today Proc 38:2797–2802. https://doi.org/10. 1016/j.matpr.2020.08.736 6. Omrani E, Moghadam AD, Menezes PL, Rohatgi PK (2016) Influences of graphite reinforcement on the tribological properties of self-lubricating aluminum matrix composites for green tribology, sustainability, and energy efficiency—a review. Int J Adv Manuf Technol 83:325–346. https://doi.org/10.1007/s00170-015-7528-x 7. Sahoo S, Samal S, Bhoi B (2020) Fabrication and characterization of novel Al-SiC-hBN selflubricating hybrid composites. Mater Today Commun 25:10142. https://doi.org/10.1016/j.mtc omm.2020.101402 8. Sahoo S (2021) Self-lubricating composites with 2D materials as reinforcement: a new perspective. Reinf Plast 65(2):101–103. https://doi.org/10.1016/j.repl.2020.06.007 9. Furlan KP, De Mello JDB, Klein AN (2018) Self-lubricating composites containing MoS2: a review. TriboL Int 120:280–298. https://doi.org/10.1016/j.triboint.2017.12.033 10. Rengifo S, Zhang C, Harimkar S, Boesl B, Agarwal A (2017) Effect of WS2 addition on tribological behavior of aluminum at room and elevated temperatures. Tribo Lett 65(3):76. https://doi.org/10.1007/s11249-017-0856-2 11. Nakayama N, Sakagami S, Horita M, Miki H, Takahashi A, Hashimoto AK (2014) Fabrication of WS2 -dispersed Al composite material by compression shearing method at room temperature. Key Eng Mater 622–623:1066–1074. https://doi.org/10.4028/www.scientific.net/KEM. 622-623.1066 12. Niste VB, Ratoi M, Tanaka H, Xu F, Zhu Y, Sugimura J (2017) Self-lubricating Al-WS2 composites for efficient and greener tribological parts. Sci Rep 7(1):1–14. https://doi.org/10. 1038/s41598-017-15297-6 13. Vaziri SH, Shokuhfar A, Afghahi SSS (2019) Investigation of mechanical and tribological properties of aluminum reinforced with Tungsten Disulfide (WS2 ) nanoparticles. Mater Res Express 6(4):045018. https://doi.org/10.1088/2053-1591/aafa00/meta 14. Biswal SR, Sahoo S (2022) Structural and mechanical properties of a novel Al-Al2 O3 -WS2 hybrid composites. Mater Lett 307:131017. https://doi.org/10.1016/j.matlet.2021.131017
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15. Biswal SR, Sahoo S (2020) Fabrication of WS2 dispersed Al-based hybrid composites processed by powder metallurgy: effect of compaction pressure and sintering temperature. J Inorg Organomet Polym 30:2971–2978. https://doi.org/10.1007/s10904-020-01450-8 16. Chawla KK (2012) Composite materials: science and engineering. Springer, New York 17. Standard practice for laboratory immersion corrosion testing of metals A.S.T.M., G31-72 (2004). https://doi.org/10.1520/G0031-72R04 18. Kanthavel K, Sumesh KR, Saravanakumar P (2016) Study of tribological properties on Al/Al2O3/MoS2 hybrid composite processed by powder metallurgy. Alex Eng J 55(1):13–17. https://doi.org/10.1016/j.aej.2016.01.024 19. Rouhi M, Moazami-Goudarzi M, Ardestani M (2019) Comparison of effect of SiC and MoS2 on wear behavior of Al matrix composites. Trans Nonferrous Metals Soc China 29(6):1169. https://doi.org/10.1016/S1003-6326(19)65025-9 20. Liu S, Wang Y, Muthuramalingam T, Anbuchezhiyan G (2019) Effect of B4 C and MOS2 reinforcement on micro structure and wear properties of aluminum hybrid composite for automotive applications. Compos B Eng 176:107329. https://doi.org/10.1016/j.compositesb.2019. 107329 21. Wei Z, Li B, Xia C, Cui Y, He J, Xia JB, Li J (2018) Various structures of 2D transitionmetal dichalcogenides and their applications. Small Methods 2(11):1800094. https://doi.org/ 10.1002/smtd.201800094 22. Hihara LH, Bakkar A (2016) Corrosion of metal matrix composites. In: Hashmi S, (ed) Reference module in materials science and materials engineering. Elsevier, Oxford, pp 1–28. https:// doi.org/10.1179/imr.1994.39.6.245 23. Zakaria H, Zakaria M (2014) Microstructural and corrosion behavior of Al/SiC metal matrix composites. Ain Shams Eng J 5(3):831. https://doi.org/10.1016/j.asej.2014.03.003
Optimization of Non-traditional Machining Processes: Application of a Simple Optimization Algorithm Joji Thomas, Vivek Kumar Chouhan, Anshuman Kumar Sahu, and Siba Sankar Mahapatra
Abstract Non-traditional machining has become a popular choice for machining of hard and difficult-to-machine materials for generating complex profiles with high dimensional accuracy and surface integrity. In order to improve the performance of the processes, key machining parameters need to be optimized as the costs involved in these processes are comparably high. In this paper, four non-traditional machining processes are considered to optimize using a recently developed metaheuristic technique known as the simple optimization technique (SOPT). The performance of SOPT is compared with the performance of some well-known metaheuristic techniques used to solve the same problems. It is observed that SOPT performs in a superior manner in all these problems as compared to other metaheuristic algorithms. Being one of the simplest algorithms and ability to reach near-optimal solutions for complex, constrained optimization problems makes this algorithm a good choice for solving real-world optimization problems. Keywords Simple optimization (SOPT) · Non-traditional machining processes · Metaheuristic algorithms
J. Thomas (B) Department of Mechanical Engineering, Chouksey Engineering College, Bilaspur, Chhattisgarh, India e-mail: [email protected] V. K. Chouhan Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing, Kancheepuram, Chennai, India A. K. Sahu · S. S. Mahapatra Department of Mechanical Engineering, National Institute Technology Rourkela, Rourkela, Odisha, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_58
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1 Introduction Optimization is an act of finding the best solution from a number of alternative solutions available. To get effective utilization of resources and thus increase the productivity, an organization should apply the process of optimization. In a manufacturing process, there are different parameters that affect the quality and productivity of the process. It is possible to optimize the value of process parameters, which results in increased productivity without affecting the quality of the product. The first step in an optimization process is to formulate a mathematical model. Forming an optimization model needs a better understanding of the process and methodology. The model comprises of one or more objective functions and a set of constraints that can be of linear or nonlinear type. There are traditional techniques available for solving such types of problems. These methods give exact solutions to the problem until the mathematical model is simple in nature but these methods cease to reach optimal solutions as the complexity of the model increases. Therefore, more attention is now given toward the research in developing metaheuristic algorithms that are able to get solutions that are close to the optimum solutions for a wide variety of complex optimization problems. Metaheuristic algorithms are nature-inspired algorithms that are a higher level of heuristic which is simply a method of obtaining solution by trial and error. Whereas heuristics are developed for a particular type of problem, metaheuristics are for a wide variety of problems including continuous and discrete types. In the recent years, a large number of metaheuristic algorithms are developed which are successfully applied to solve many engineering optimization problems. All these algorithms require generating random solutions at the initial stage, and this initial set of solutions is called population. In later stages, this population evolved to the best solution by passing through generations. Genetic algorithm (GA) is an evolutionary algorithm based on the genetic evolution theory proposed by Charles Darwin [1]. Here, the randomly produced initial solution is improved in each generation by using the selection, crossover, and mutation operators. After several generations, a new optimized solution is evolved. Particle swarm optimization (PSO) algorithm modeled the social behavior of a flock of bird or a school of fish [2], and it is the most popular algorithm based on the intelligence of a swarm. Another swarm intelligence-based algorithm is the artificial bee colony (ABC), which mimics the foraging behavior of a honeybee colony [3]. The musical process of searching for a perfect state of harmony is used to develop the Harmony search (HS) algorithm [4]. A memetic metaheuristic algorithm is Shuffled Frog Leaping Algorithm (SFLA). In this algorithm, the meme transfer mechanism of virtual frogs is used for the global exchange of information among the population [5]. Based on the interaction of masses and the law of gravitation, a gravitational search algorithm (GSA) is developed [6]. Fireworks algorithm (FWA) is inspired by the fireworks explosion in the night sky [7]. The annealing process in metallurgy is a process in which a material is heated and then cooled in a controlled manner to increase the size of its crystals and reduce their defects. This phenomenon is the basis of developing the Simulated Annealing (SA) algorithm [8].
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SA especially gives good results if traditional optimization methods get stuck in the local optimum. Teaching learning-based optimization (TLBO) is inspired by the teaching–learning process [9]. TLBO algorithm is characterized by the absence of control parameters except for the size of the initial population.
2 Simple Optimization (SOPT) Algorithm Simple Optimization (SOPT) is also metaheuristic algorithm based on population in which a randomly populated solution is generated [10]. There are only two steps to be applied in each iteration of the SOPT algorithm for the exploration and exploitation of the solution. The first step is the exploration stage in which the algorithm explores to get a new better solution using Eq. (1). The second step is the exploitation stage in which a similar Eq. (2) is used to search better solution nearby as the value of the control parameter is half of the one used in the exploration step. For the search of a new better solution, both steps utilize the best solution of the previous iteration. In each step, if newly obtained solution is better than the worst solution of the population, then the new solution will replace the worst solution in the population. This process will continue till the number of iterations reaches to maximum iterations set by the user for a particular problem. Xk,new = Xk,best + C1 × Rk
(1)
Xk,new = Xk,best + C2 × Rk
(2)
Equations (1) and (2) used in the algorithm are the same except for the controlling parameters. Parameter C1 is used in Eq. (1) which is a positive constant between 1 and 2 while parameter C2 is used in Eq. (2) whose value is half of the value of C1 . Xk,new and X k,best are the kth variable of the new solution and the best solution of the population, respectively. Rk is a random number that is normally distributed with a mean zero and standard deviation σk . Standard deviation σk is obtained by calculating the standard deviation of kth variable of all the members in the population. 1. For instance, consider j = 1,2…M optimization variables and i = 1,2,…N candidate solutions then (X11 , X12 , X13 …X1M ), (X21 , X22 , X23 …X2M )……(XN1 , XN2 , XN3 …XNM ) represent the candidate solutions where N is population size. For variable k, σk can be calculated from the set (X1k , X2k , X3k …XNk ). Initially, when solutions are dispersed widely in solution space, the value of σk shall be more. As the search progresses, solutions come closer together toward the optimum solution and thus the value of σk will be reduced which causes smaller values of random numbers and small movement of new solutions. Other algorithms keep the
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value of random numbers within a fixed range, e.g. in PSO random numbers are generated in the range [0, 1]. The SOPT algorithm was effectively used to solve unconstrained problems [11] but to get quality solutions for a constrained optimization problem one needs to apply constraint handling techniques judiciously.
2.1 Handling of Constraints in the SOPT Algorithm There are different methodologies available for handling constraints in an optimization problem, e.g. rejection of solutions violating constraint, repair of infeasible solutions, apply penalty to the solution violating the constraint, and apply some rules to rank the available solutions [12]. Based on the methodology to develop rules for ranking the available solutions, a constraint fitness priority-based ranking method developed by Dong et al. [13] is used. The methodology is incorporated into the SOPT algorithm for handling the constraints.
2.2 SOPT Algorithm The basic steps of the SOPT algorithm are as follows: 1. 2. 3.
Initialization of the population. Calculation of fitness function of each candidate solution in the population. Calculation of constraint fitness function of each candidate solution in the population using the methodology proposed by Dong et al. [13]. 4. Repeat. 5. Sort the population in descending order of constraint fitness function and ascending order of fitness value providing priority to the constraint fitness function. 6. Generate a new solution by Eq. (1). 7. Compare the worst solution of the population with a new one and replace it if the new solution is superior to existing solutions in the population. 8. Generate a new solution by Eq. (2). 9. Compare the worst solution of the population with a new one and replace it if the new solution is superior to existing solutions in the population. 10. Store the best solution found so far. 11. Replace the best solution of the population with randomly generated solution if it remains best for limiting number of iterations (Replacement_counter). 12. Until the maximum number of iterations is not reached. In the basic algorithm, a minor alteration is made by performing the exploration step first as against the exploitation step which is performed first by the original algorithm [10]. For selecting the best solution in the population, all the solutions
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are ranked based on the constraint fitness function. The solution with the highest constraint fitness value will be in first position. If two different solutions have the same value of constraint fitness, then the one with a lower value of the objective function is kept prior to the other. The advantage of this method is that feasible solutions which always have a constraint fitness value higher than the infeasible solutions will get a higher ranking in the list. Therefore, during the iterations, the best feasible optimal solution is obtained by maintaining both feasible solutions as well as better infeasible solutions.
2.3 Settings for SOPT Metaheuristic algorithms are usually sensitive to the control parameters. If there are many control parameters, then it would be very difficult to get a correct set of parameters for a particular problem. In SOPT algorithm, there is only one independent control parameter C1 . By conducting various experiments, it has been observed that the value of C1 taken between 1 and 2 gives good results in less computational effort. In the present work, the value of C1 is taken as 1.6 because this value gives better results in most of the problems. The size of the population is maintained at 50 candidate solutions. The algorithm terminates after completing 5000 iterations. As there are only two function evaluations in each iteration, 5000 iterations are equivalent to about 10,000 function evaluations.
3 Non-traditional Machining Processes In order to test the proposed algorithm, four standard machining optimization processes such as ultrasonic machining (USM), abrasive jet machining (AJM), wire electric discharge machining (WEDM), and water jet machining (WJM) are selected. A brief discussion on different processes is made below.
3.1 Ultrasonic Machining Process (USM) It is a non-traditional machining process in which a tool of the desired shape vibrates over a work piece at an ultrasonic frequency range. Water-based abrasive particles are provided to the machining zone. Mechanics of material removal is due to fracture caused by hitting action of abrasive particles onto the work surface. USM is characterized by its low material removal rate (MRR). Therefore, it is necessary to maximize the MRR without affecting the surface finish. Some of the process parameters that affect MRR and surface finish in an USM process are amplitude of tool fluctuation,
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types of abrasive, size of abrasive grain, amount of abrasive in water-based slurry, feed force, etc. A mathematical model representing USM process available in literature was attempted to optimize the process using GA [14]. The same model was used by Rao et al. [15] to optimize the process using SA and a comparison with GA was reported [15]. Further work was performed in experiments to improve the optimized value of MRR using three different metaheuristic algorithms such as ABC, HS, and PSO and the results were compared with the results obtained from GA [16]. TLBO algorithm was applied to optimize the models and reported better results as compared to other mentioned algorithms [9]. The performances of GSA and FWA were tested in obtaining maximum MRR under a given constraint of surface roughness (SR). The result of GSA was reported better than that obtained by GA, SA, ABC, HS, and PSO but inferior to the result obtained by TLBO, while FWA delivered results even better than TLBO [17].
3.2 Abrasive Jet Machining Process (AJM) It is a mechanical-type machining process where material removal is achieved by impinging high-speed abrasive particles coming out of a nozzle, which strike the target surface and materials get eroded. MRR and SR are two important performance measures of this process and these performance measures are affected by the grit size of abrasive, distance of nozzle tip, spray angle, velocity of jet, size of nozzle, carrier fluid pressure, etc. This process is well suited for generating complex cavities and holes of any shape in both ductile and brittle materials. A model to study the behavior of brittle material was formulated by Sarkar et al. [18]. Similarly, a model for ductile material was formulated by Hutchings [19]. These two models were used by Jain et al. [17] to optimize the MRR for the AJM process by GA. Rao et al. [15] have utilized SA algorithm to improve the value of MRR. TLBO algorithm was used for both brittle and ductile material separately and reported better results than that obtained from GA and SA [9].
3.3 Wire Electric Discharge Machining Process (WEDM) WEDM is adapted from the conventional electrical discharge machining (EDM) in which an electrode made of thin copper or brass in the form of wire is utilized. Discharge between continuously moving wire electrode and work piece occurs on the application of a proper voltage. The gap between the electrode and work piece is flooded with the deionized water. A series of sparks between work piece and wire causes the material to be eroded ahead of the wire. WEDM is well suited for generating intricate shapes in difficult-to-machine hard materials. The process
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parameters that affect the machining speed (proportional to MRR) considering SR as constraint are pulse on time, pulse off time, servo feed, and peak current. Rao and Pawar [20] modeled the WEDM process based on the response surface modeling (RSM) approach and the optimum setting of these parameters was obtained by using ABC algorithm. The results of the optimization of WEDM process were compared for maximum machining speed by four different non-traditional algorithms namely PSO, HS, SA, and shuffled frog leaping (SFL) [21]. Rao and Kalyankar [9] showed the result obtained by TLBO in optimizing machining speed by keeping SR within a prescribed limit is better than other well-known algorithms.
3.4 Water Jet Machining (WJM) Water jet machining process is also a non-traditional cutting process that is specially applied for destructive or precision cutting of materials like wood, ceramics, rubber, etc. In this process, high energy of high-pressure water jet is utilized to cut the materials. The process is fast, precise, and eco-friendly. The common operations performed by this process are cleaning, descaling, drilling, deburring, and cutting. There is no heat affected zone produced in this process; therefore, cut edges of work material do not get hardened. Hashish and Du Plessis [22] have established a mathematical model to represent the WJM process. The model provides results in good agreement with the experimental results. The same mathematical model is used to maximize the MRR using GA [14]. To calculate MRR, it is assumed that the width of the cut in the cutting process is equal to the water jet diameter, and the maximum power consumed in the process is limited to 50 kW.
4 Optimization Models for Non-traditional Processes 4.1 Optimization Model for USM Mathematical model considered by the Jain et al. [14] is utilized to test the proposed SOPT algorithm. There are five decision variables affecting MRR of USM process. They are the maximum displacement of the vibrating tool from its mean position Av (mm), frequency of the vibrating tool fv (Hz), mean diameter of abrasive grain dm (mm), the concentration of abrasive particles by volume in slurry Cav, and static feed force applied to the tool Fs (N). The mathematical model representing relation of MRR with five decision variables is given by Eq. (3)
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0.75 | × K usm 4.963 × A0.25 t 0.25 × Fs0.75 × A0.75 Maximize MRR = × Cav × dm × f v v σ f w (1 + λ)0.75 (3)
|
Subject to the surface roughness constraint given by Eq. (4) | 1−
| | | Fs × Av × dm 0.5 1154.7 × ≥0 At × σ f w (1 + λ)0.5 × (Ra )max Cav
(4)
Ranges of values of the decision variables are 0.005 ≤ Av ≤ 0.1; 10,000 ≤ fv ≤ 40,000; 0.007 ≤ dm ≤ 0.15; 0.05 ≤ Cav ≤ 0.5; 4.5 ≤ Fs ≤ 45. In the above equations, the constants used are the same as those used by Jain et al. [14]. These are (i) cross-sectional area of the cutting tool At which is taken as 20 mm2 , (ii) flow stress of abrasive particles made of silicon carbide σft as 28,000 MPa, (iii) flow stress of work material which is tungsten carbide σfw as 6900 MPa, (iv) a constant of proportionality equal to the ratio of the diameter of projections on abrasive grain to the square of the mean diameter of abrasive grains Kusm as 0.1 mm−1 , (v) allowable surface roughness value (Ra )max as 0.8 μm, and (vi) Indentation ratio λ as 0.246. The result obtained by solving the problem using the SOPT algorithm is shown in Table 1 along with the solutions obtained by other algorithms. It clearly depicts that the SOPT is able to give a comparable result. In this case, the algorithm is able to reach the maximum MRR of 4.0064 mm3 /s without violating the constraint. Convergence curve shown in Fig. 1 indicates that the final solution is reached within 250 iterations; that means the algorithm required less than 500 function evaluations to reach the final solution.
4.2 Optimization Model for AJM of Brittle Material For optimizing the MRR of brittle work piece material by AJM process, the mathematical model proposed by Sarkar and Pandey [18] is used. There are three decision variables, mass flow rate of abrasive particles Ma (kg/s), mean radius of abrasive particles rm (mm), and velocity of abrasive particles va (mm/s). The model for brittle material is represented by Eq. (5) |
ηa Maximize MRR = 0.0035 × 0.25 σ 0.75 f w × ρa
| × Ma × va1.5
(5)
Subject to the surface roughness constraint given by Eq. (6) | 1−
| | | ρa 0.5 18.26 × × rm × va ≥ 0 (Ra)max σfw
(6)
0.479
10.8
C av
f s (N)
MRR
0.783
0.1336
d m (mm)
3.553
39,333.9
f v (Hz)
Ra (μm) ≤ 0.8
0.0263
Av (mm)
(mm3 /s)
GA [14]
Parameter
0.798
3.941
16.4
0.5
0.15
40,000
0.0167
ABC [16]
0.78
3.870
4.5
0.5
0.15
40,000
0.0582
HS [16]
0.792
3.95
4.5
0.5
0.15
40,000
0.06
PSO [16]
Table 1 Comparisons of result of USM with the previous results
0.793
3.894
12.78
0.5
0.14
40,000
0.0227
SFL [16]
0.793
3.660
4.53
0.5
0.114
40,000
0.077
SA [15]
0.799
3.96
16.251
0.5
0.1441
39,876
0.0176
GSA [17]
0.8
4.0061
10.707
0.5
0.1500
39,956.3
0.0257
FWA [17]
0.799
4.004
4.5
0.5
0.15
40,000
0.0611
TLBO [9]
0.8
4.0064
4.5132
0.5
0.15
40,000
0.061
SOPT
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Fig. 1 Convergence curve for USM
Having variable range as 0.0000167 ≤ Ma ≤ 0.0005; 0.005 ≤ rm ≤ 0.075; and 150,000 ≤ va ≤ 400,000. Various constants and their values used in the equation are (i) density of abrasive particles made of alumina ρa (3.85 × 10–6 kg/mm3 ), (ii) proportion of abrasive particles effectively participating in the machining process ηa (0.7), (iii) flow stress of work material (glass) σfw (5000 MPa), and (iv) allowable surface roughness value (Ra)max (0.8 μm). The results of optimization are shown in Table 2. It is evident that SOPT algorithm is able to deliver a better MRR value of 8.258 mm3 /s as compared to GA and SA. TLBO gives better results as compared to the result obtained by SOPT but it is violating the constraint of surface roughness, which needs to be kept below 0.8 μm. The convergence curve for the process is shown in Fig. 2. Table 2 Comparisons of the result of AJM (Brittle Material) with the previous results Parameters M a (kg/s) r m (mm) va (mm/s)
GA [14] 0.0005 0.005 315,504.3
SA [15] 0.0005 0.005 315,764.8
TLBO [9]
SOPT
0.00049
0.0005
0.005
0.005
333,982
315,770
MRR (mm3 /s)
8.236
8.257
8.9769
8.258
Ra (μm) ≤ 0.8
0.799
0.799
0.846
0.799
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Fig. 2 Convergence curve for AJM (brittle material)
4.3 Optimization Model for AJM of Ductile Material For ductile material, the model proposed by Hutchings [19] is used in this model. There are three decision variables, mass flow rate of abrasive particles Ma (kg/s), mean radius of abrasive particles rm (mm), and velocity of abrasive particles va (mm/s). The objective function is given in Eq. (7). Maximize MRR = 1.0436 × 10−6 × ζ ×
|
| ρw × Ma × va3 2 × H 1.5 × ρ 0.5 δcw a dw
(7)
With the surface roughness constraint given in Eq. (8) | | | ρa 0.5 25.82 × 1− × rm × va ≥ 0 Hdw (Ra)max |
(8)
Decision variables are taken in the range 0.0000167 ≤ Ma ≤ 0.0005; 0.005 ≤ rm ≤ 0.075; 150,000 ≤ va ≤ 400,000. Constants used in the model are (i) density of abrasive particles (glass bead) ρa (2.48 × 10–6 kg/mm3 ), (ii) density of work piece (Al-6061-T6) ρw (2.7 × 10–6 kg/mm3 ), (iii) critical plastic strain or erosion ductility of work piece δcw (1.5), (iv) dynamic hardness of work piece Hdw (1150 MPa), (v) amount of indentation volume plastically-deformed ζ (1.6), and (vi) allowable surface roughness (Ra)max (2 μm).
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Table 3 Comparisons of the result of AJM (ductile) with the previous results Parameters M a (kg/s) r m (mm) va (mm/s)
GA [14] 0.0005 0.005 333,214.7
SA [15] 0.0005 0.005 333,549.08
TLBO [9]
SOPT
0.0005
0.0005
0.005
0.005
354,360
333,600
MRR (mm3 /s)
0.6025
0.6053
0.7258
0.6056
Ra (μm) ≤ 2
1.997
1.999
2.123
1.999
Fig. 3 Convergence curve for AJM (ductile material)
The result of the AJM process for ductile material is tabulated in Table 3. Once again, the SOPT algorithm is able to reach better value compared to GA and SA, but the result of SOPT is inferior to the result of TLBO, which delivers better results at the cost of constraint violation. The convergence curve for this process is shown in Fig. 3.
4.4 Optimization Model for WEDM Using RSM approach, Rao and Pawar [20] derived the Eqs. (9) and (10) for machining speed (Vm ) and surface roughness (Ra ), respectively, for a WEDM process. There are four decision variables such as pulse on time (Ton ) in μs, pulse off time (Toff ) in μs, peak current (Ip ) in A, and servo feed setting (F). The objective function is
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Maximize Vm = 1.555 + 0.1095 x1 − 0.187x2 + 0.0929x3 + 0.1279x4 + 0.0393 x1 x2 − 0.0793 x1 x3 − 0.01188x1 x4 − 0.01688x2 x3 − 0.0493x2 x4 − 0.06061x3 x4 − 0.03219(x1 )2 + 0.02031(x2 )2 − 0.0909(x3 )2 − 0.06061(x4 )2
(9)
Subject to the constraint of surface roughness (Ra) given by the Eq. (10). The value of Ra should not exceed the permissible roughness (Ra)max of 2 μm, i.e. Ra ≤ (Ra)max Ra = 3.6 + 0.2971 x1 − 0.2979 x2 − 0.1479 x3 − 0.03542 x4 + 0.02185 x1 x2 − 0.2031 x1 x3 + 0.04062 x1 x4 + 0.01562 x2 x3 − 0.1531 x2 x4 − 0.1031 x3 x4 − 0.3182( x1 )2 − 0.3807( x2 )2 − 0.4057( x3 )2 − 0.2682( x4 )2
(10)
The variables x1 , x2 , x3 , and x4 are the coded values of Ton , Toff , Ip , and F, respectively, the same as those used by Rao and Pawar [20]. Ranges of decision variables are 4 ≤ Ton ≤ 8; 10 ≤ Toff ≤ 30; 90 ≤ Ip ≤ 140; 30 ≤ F ≤ 50. Results for the optimization of the velocity of machining are shown in Table 4. From Table 4, it is clear that the SOPT algorithm performs better than the PSO, HS, SA, and SFL algorithms and it is equally comparable to the ABC algorithm but inferior to the TLBO algorithm. TLBO algorithm delivers a value of machining speed of 1.4287 mm/min as against 1.4242 mm/min delivered by SOPT, but at the cost of a small constraint violation. The convergence curve shown in Fig. 4 indicates that the SOPT algorithm reaches the optimum value before 400 iterations. Table 4 Comparisons of the result of WEDM with previous results Parameters
ABC [20]
PSO [21]
HS [21]
SA [21]
SFL [21]
TLBO [9]
SOPT
Pulse-on time (μs)
8
4
8
8
7.972
4
8
Pulse-off time (μs)
30
23.33
29.66
29.66
29.8
22.937
30
Peak current I p (amp)
132.57
140
134.15
134.15
133.37
140
132.531
Pulse-on time (μs)
50
50
50
50
50
50
50
Machining speed, V m (mm/min)
1.422
1.420
1.420
1.414
1.419
1.4287
1.422
Ra (μm) ≤ 2
1.998
1.992
1.973
1.973
1.995
2.019
1.999
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Fig. 4 Convergence curve for WEDM
4.5 Optimization Model for Water Jet Machining In this work, the model established by Hashish and Du Plessis [22] is used for optimization. The model consists of two objectives; one is to maximize MRR and the second is to minimize specific energy consumption. Both the objectives need to be optimized simultaneously. Jain et al. [14] have considered only to maximize MRR as the objective and solved the problem by GA. Decision variables that affect the MRR are (i) nozzle exit pressure Pw in MPa, (ii) nozzle diameter at exit dwn in mm, (iii) traversing rate of nozzle fn in mm/s, and (iv) stand-off-distance (SOD) X in mm. The objective function is represented by Eq. (11) Maximize MRR = where ϕ =
2 K1
| | || C f w Pw ϕ σ yw 0.297 1.5 2 (11) 1 − e−2256.76 ηw fn dwn f n X 0.5 ψ 3 1 − Cfw 2Pw ϕ
/ | | | 0.5 − 0.57ψ + 0.2ψ 2 and ψ = 1 − P1w K1 =
σcw K 1 2
|
X · · · C D ≈ 0.7 Xi
Subjected to the power consumption constraint as 2 1.11 × 10−1.5 C D dwn Pw1.5 ≤ Pmax
(12)
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Ranges of variables are 1 ≤ Pw ≤ 400; 0.05 ≤ dwn ≤ 0.5; 1 ≤ fn ≤ 300; 2.5 ≤ X ≤ 50. Values of constants for work piece (maple wood) are drag coefficient, Cfw = 0.005; coefficient of damping for work piece, ηw = 2357.3 kg mm−2 s−1 ; yield strength under compression of work piece, σcw = 26.2 MPa; yield strength under tension of work piece, σyw = 3.6 MPa; length of initial region of water jet, Xi = 20 mm; and allowable power consumption, Pmax = 50 kW. The results of applying SOPT to get maximized MRR for the WJM process are shown in Table 5. Better MRR as compared to GA and SA is obtained. Further to check the effectiveness of SOPT algorithm, all the above-mentioned problems are run for 30 times with different seeds of random number generator. The time taken to reach the best solution is also recorded and shown in Table 6. SOPT algorithm is implemented in MATLAB 8.5.0(R2015a) and experiments are conducted in a system with Intel® Core™ i3-6100U CPU @2.50 GHz, 4.00 GB RAM operating platform. Results indicate that the mean value is almost equal to the best result with a very small deviation in the results. Also, the time taken by the algorithm to reach the solution is very small. In all these problems, the number of iterations required to reach the solution by SOPT algorithm may be higher. However, the number of function evaluations in SOPT is less because it requires only two function evaluations in each iteration while all other algorithms require at least the number of function evaluations Table 5 Optimization results for WJM process Parameters
GA [14]
SA [16]
SOPT
Nozzle exit pressure (MPa)
397
398.12
400
Nozzle diameter at exit (mm)
0.5
0.5
0.5
Traversing rate of nozzle (mm/s)
214.41
215.63
300
Stand-off-distance (mm)
2.54
2.5
2.5
MRR (mm3 /s)
139.79
140.25
140.48
Power consumption (constraint) (kW)
48.345
48.8
49.14
Table 6 Results of 30 runs by SOPT USM
AJM(Brittle)
AJM(Ductile)
WEDM
WJM
MRR (mm3 /s)
MRR (mm3 /s)
MRR (mm3 /s)
Machining speed (mm/min)
MRR (mm3 /s)
4.0064
8.258
0.6056
1.422
140.48
Mean result 3.997
8.258
0.60557
1.420
140.48
Std. deviation
0.017
0
3.84337E-05
0.001955
9.65527e-06
Mean CPU time (s)
0.21
0.23
0.070295
0.23
0.019485
Best result
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equal to the size of the population, and in the case of TLBO, it is more than twice the size of the population. Therefore, SOPT is computationally less expensive compared to other algorithms.
5 Conclusion In this work, a simple and efficient SOPT algorithm to solve constrained optimization problem is proposed. To handle the constraint, a constraint fitness priority-based ranking method is used. SOPT algorithm consists of two simple equations with only two function evaluations in one iteration. To check the effectiveness of SOPT to get the optimum solution, three test problems of constrained optimization are solved. SOPT algorithm is further applied to optimize the four non-traditional machining processes, USM, AJM, WEDM, and WJM taking examples of each. Models selected for this work are the same as those used by the other researchers to solve by some common metaheuristic techniques. Comparing the results obtained by SOPT shows that it delivers better results compared to almost all other algorithms. Being a simple algorithm with only one independent control parameter, it is able to reach its best solution in very few function evaluations. Thus, SOPT algorithm can be a good choice for using it to solve complex optimization problems with constraints. It can be further explored and modifications can be done to improve its ability to solve more and more engineering application problems. The ability of the SOPT algorithm to solve multi-objective problems can also be explored.
References 1. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95– 99 2. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan 3. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132 4. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902– 3933 5. Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38:129–154 6. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248 7. Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: Advances in swarm intelligence, pp 355–364 8. Deb K (2012) Optimization for engineering design: algorithms and examples, PHI Learning Pvt. Ltd 9. Rao RV, Kalyankar VD (2013) Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Eng Appl Artif Intell 26:524–531
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10. Hasançebi O, Azad SK (2012) An efficient metaheuristic algorithm for engineering optimization: SOPT. Int J Optim Civil Eng 2:479–487 11. Thomas J, Mahapatra SS (2016) Improved simple optimization (SOPT) algorithm for unconstrained non-linear optimization problems. Perspect Sci 8:159–161 12. Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287 13. Dong Y, Tang J, Xu B, Wang D (2005) An application of swarm optimization to nonlinear programming. Comput Math Appl 49:1655–1668 14. Jain NK, Jain VK, Deb K (2007) Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms. Int J Mach Tools Manuf 47:900–919 15. Rao RV, Pawar PJ, Davim JP (2009) Optimisation of process parameters of mechanical type advanced machining processes using a simulated annealing algorithm. Int J Mater Prod Technol 37:83–101 16. Rao RV, Pawar PJ, Davim JP (2010) Parameter optimization of ultrasonic machining process using nontraditional optimization algorithms. Mater Manuf Process 25:1120–1130 17. Goswami D, Chakraborty S (2015) Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms. Ain Shams Eng J 6:315–331 18. Sarkar PK, Pandey PC (1976) Some investigations on abrasive-jet machining. Institution of Engineers (India). J Mech Eng Div 56:284–287 19. Hutchings IM (1981) A model for the erosion of metals by spherical particles at normal incidence. Wear 70:269–281 20. Rao RV, Pawar PJ (2009) Modelling and optimization of process parameters of wire electrical discharge machining. Proc Inst Mech Eng Part B J Eng Manuf 223:1431–1440 21. Rao RV (2010) Advanced modeling and optimization of manufacturing processes: international research and development, Springer Science & Business Media 22. Hashish M, Du Plessis MP (1979) Prediction equations relating high velocity jet cutting performance to standoff distance and multipasses. J Eng Ind 101:311–318
Storage, Handling, and Disposal of Hazardous Waste Sonali Goel
and Renu Sharma
Abstract For many decades, waste management strategies have been a major source of concern. Waste management strategies consist of hazardous waste and non-hazardous waste. Hazardous waste is defined as waste that poses a significant or potential threat to the environment as well as human health. Hazardous waste handling may be quite difficult. Even on a small scale, improper hazardous waste management can threaten both human health and the environment. The current research is based on the management of radioactive waste generated in ‘Nuclear Medicine Department’ and the Department of ‘Radiation Therapy’ of Siksha ‘O’ Anusandhan (SOA) Deemed to be University, Bhubaneswar. At the hospital, radioactive materials are employed in both therapeutic and diagnostic procedures, and exposure to it is generally harmful to human health. Although radioactive wastes cannot be eliminated, waste minimization, waste reduction, and safe disposal should be implemented by India’s Atomic Energy Regulatory Board (AERB) norms. Keywords Radioactive waste management · Storage · Handling · Disposal · Environmental effect
1 Introduction Waste management is a challenging issue in many cities throughout the world. Waste generated from various human activities, industrial, household, and various other sources, can pose health risks and have a harmful influence on the environment if not managed properly. It is our responsibility to develop an effective process for waste management. It is necessary to first understand the waste generated, the availability of S. Goel (B) · R. Sharma Department of Electrical Engineering, Institute of Technical Education and Research, SOA (Deemed to be University), Bhubaneswar, Odisha 751030, India e-mail: [email protected] R. Sharma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. Tripathy et al. (eds.), Recent Advances in Mechanical Engineering, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-19-9493-7_59
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resources, and the environmental circumstances of a given community. Various kinds of waste generated are categorized as solid waste, liquid waste, biomedical waste, construction waste, hazardous waste, and e-waste. To avoid the harmful effects of e-waste management, sufficient attention must be paid to the disposal methods [1, 2]. Waste cooking oil is a type of hazardous waste that is produced at alarming rates across the world. The disposal of hazardous waste cooking oil (WCO) causes major environmental risks [3]. Because of its great toxicity to the environment and humans, hazardous waste management is difficult to identify, distribute, and treat [4]. Due to an inadequate collection system, household hazardous waste (HHW) accounts for about 1% of municipal solid waste (MSW) produced in many countries throughout the world [5]. Some household wastes are harmful; therefore, they must be handled with caution. These wastes are known as household hazardous wastes (HHHW) [6]. Waste disposal is a burning issue, posing threats to human health, the environment, and the economy. Waste valorization is a process that adds value to waste materials and has emerged as an effective method to handle this issue as well as maximize environmental and social benefits [7]. Hazardous waste might be in solid, liquid, or gas form, depending on the source of creation, and each phase requires a separate treatment process [8, 9]. This waste is mostly generated by companies, research laboratories, and hospitals [10]. The majority of this waste is composed of liquid acid and dangerous inorganic compounds having corrosive characteristics [11]. In SOA deemed to be University, the hazardous waste gets generated only at SUM ULTIMATE MEDICARE. In this hospital, the source of hazardous waste is generated in the Departments of ‘Nuclear Medicine’ and ‘Radiation Therapy’. The majority of hospital waste is a radioactive waste with low-level and occasionally medium-level waste having short half-lives. At the hospital, radioactive substances are used in both therapeutic and diagnostic procedures. The majority of the radiation therapy is used in the nuclear medicine unit of the hospital. When radioactive materials are inserted into human bodies, such as iodine to cure a damaged thyroid gland or iridium pellets, body parts and fluids can become radioactive. The following are the classifications for radioactive waste (Fig. 1).
Fig. 1 Classification of radioactive waste
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2 Methodology Various quantitative data has been gathered from two hospitals of SOA (‘Nuclear Medicine Department’ and ‘The Radiation Therapy Department’) involved in producing radioactive wastes. Various management techniques have been adapted for monitoring the waste generated. The radioactive waste management process involves proper collection and disposal. This will ensure a proper monitoring mechanism thereby improving the environmental performance of the University.
3 Hazardous Waste Management in Nuclear Medicine Department Unused radioisotopes are the main and only hazardous waste in Nuclear Medicine Department, produced due to various diagnostic and therapeutic procedures involving radiopharmaceuticals. Radioactive waste is usually categorized as solid waste and liquid waste. These are sealed or unsealed in nature and contain clinical radioisotopes, e.g. Tc99m, I-131, F-18, Ga68, Lu177, or QA radioisotopes like Ge68. The solid waste is generated while the manipulation, preparation, and administration of radiopharmaceuticals in the patient, and it contains cotton swabs, absorbent sheets, glass vials, used syringes, lines, and needles. Sealed sources also come in the category of solid waste post its useful life, e.g. Ge68 cylindrical phantoms used in QA of PET/CT equipment. Liquid waste consists of leftover activity inside glass vials in liquid form. The International Commission on Radiation Units and Measurement (ICRU) periodically analyzes and updates principles of radiation physics that are crucial for radioactive waste management [12, 13]. The radioactive waste stored in radioactive waste storage rooms is shown in Fig. 2. Becquerel (Bq) is the standard unit of radioactivity and is defined as the activity of radioactive material in which one nucleus decays at each second [14]. Different forms of radiation like Gamma rays, X-rays, Alpha rays, Beta rays, Neutrons, etc. can cause biological harm due to differences in their capabilities of ionization. Exposure to a
Fig. 2 Radioactive waste stored in radioactive waste storage rooms
622 Table 1 Half-life of commonly used isotopes [15]
S. Goel and R. Sharma Sl. no.
Radio isotopes
Half-life
1
Technetium-99 m (Tc-99 m)
6h
2
Iodine-131 (I-131)
8 days
3
Fluorine-18 (F-18)
110 min
4
Cobalt-60 (Co-60)
5.271 years
unit of gamma or X-ray radiation is less harmful than a unit of alpha-ray exposure. Some commonly used isotope’s half-life is mentioned in Table 1. Within the work area, radioactive waste should be recognized and separated. Solid radioactive waste should be collected in foot-operated collection bins with disposable polythene linings, and liquid waste should be collected in polythene carboys. It is best to avoid collecting radioactive waste in glassware [15]. Incinerators may be used to separate combustible radioactive waste from non-combustible waste. When two different isotopes with differing half-lives, such as Tc-99 m and I-131, are used, separate waste-collecting bags and containers should be employed. Each bag or container must be labeled with the isotope’s name, amount of activity, and monitoring date. The radioactive waste management process in the nuclear medicine department consists of four steps:
3.1 Storage Radioactive waste segregated in terms of biomedical waste color coding is stored inside specially designed lead-lined waste bins kept inside a radioactive waste storage room.
3.2 Collection Radioactive waste is collected following the principles and guidelines of biomedical waste storage. Radioactive waste is segregated and collected in color-coded biomedical waste containers.
3.3 Disposal Radioactive waste stored in radioactive waste storage rooms is left for adequate decay. Measurements for surface exposure are taken, and when the exposure rates are equal to background readings, waste is treated as biomedical waste and is sent for disposal to an authorized biomedical waste handling agency Sanni Clean Pvt. Ltd. Record
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for disposal is maintained. For the disposal of sealed sources, AERB authorization is obtained and sealed sources are disposed of at the manufacturer’s waste handling facilities as per the mandate of the Atomic Energy Regulatory Board (AERB). The details of the quantity of radioactive waste are recorded and are mentioned in Table 2. Table 2 Radioactive waste disposal record from the Department of Nuclear Medicine Article
Isotope
Date and time of segregation and packing (Storage)
Exposure rate @1mt distance (mR/h)
Date and time of disposal
Exposure rate @1mt distance (mR/h)
Remark
Solid waste PET-CT/SPECT/Low Dose Beta Therapy Empty vials
F18, Tc99m
25.1.21
0.89
2.2.21
0.002
Radioactive content in the waste is Nil
Liquid waste
Tc99m
1.2.21
1.29
4.2.21
0.003
Nil
Solid waste PET-CT/SPECT/Low Dose Beta Therapy Empty vials
F18, Tc99m I131
Used syringes
Ga68
Liquid waste
Left over activity of Tc99m, Ga68
28.2.21
0.97
18.3.21
0.001
Nil
28.2.21
0.83
20.2.21
0.004
Nil
Solid waste PET-CT/SPECT/Low Dose Beta Therapy Empty vials
F18, Tc99m, Ga68
30.3.21
0.27
6.4.21
0.001
Nil
Liquid waste
F18, Tc99m, Ga68
22.3.21
0.34
6.4.21
0.012
Nil
13.29
21.5.21
0.17
Nil
0.89
15.5.21
0.2
Nil
Solid Waste PET-CT/SPECT/Low Dose Beta Therapy Empty vials
F18, 20.4.21 Tc99m, I-131, Ga68
Liquid waste
Ga68, Tc99m, I-131
28.4.21
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3.4 Waste Handling Agency After adequate decay of radioactive content, radioactive waste is handled as biomedical waste and is disposed of at the waste disposal facility of M/s Sanni Clean Pvt. Ltd. Bhubaneswar.
4 Hazardous Waste Management in the Radiation Therapy Department Radioactive waste is identified first and then segregated in the place of the work area. Color codes for different types of waste with disposable polythene are used to collect solid radioactive waste while polyethylene carboys are used to collect liquid waste. It is forbidden to collect radioactive waste in glassware. Before deciding on the way of disposal, each package is checked and marked for activity level. Tc-99 m and I-131, two distinct isotopes with differing half-lives, are used, with separate wastecollecting bags and containers for each. Each container must be identified with the name of the isotope, activity level, and monitoring date. Decayed sealed radioactive sources (Ir192) are the only hazardous waste in Radiation Therapy Department. For the disposal of decayed sealed sources, AERB authorization is obtained and sealed sources are disposed of at the manufacturer’s waste handling facilities by CURIUM, The Netherlands, as per the mandate of the Atomic Energy Regulatory Board (AERB). The radioactive waste management process in the radiation therapy department consists of four steps.
4.1 Radioactivity Survey The measuring of radioactivity associated with a waste package is an essential component of any radioactive waste plan. The measurement, also known as a survey, is performed as part of the waste pre-treatment process. When it is first collected, it is repeated every time whenever the waste package is handled. This protects personnel handling the package, aids in the prevention of contamination, and enables an independent check of the record-keeping system. Measuring radioactivity within waste is critical for managing. The initial measurement activity provides information on the degree of radioactivity present allowing additional treatment requirements to be determined. Where waste is to be discharged at clearance levels, a second measurement is necessary.
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4.2 Record Keeping A logbook is used to keep proper records. The acquisition and administration of diagnostic and therapeutic radioisotopes are documented. The records also contain information on the waste created at the time of disposal. Before discharging into the public sewage system, the activity levels of the effluent are measured. The entire yearly activity disposed of in the sewage system is reported. The names of those who are authorized to administer and dispose of radioisotopes are documented.
4.3 Waste Storage To minimize cross-contamination, radioactive medical waste is separated from other forms of medical waste and kept in containers with lead shielding to prevent radiation dispersion. The containers are clearly labeled with the universal ‘radioactive sign’, the kind of radioisotope within, and the date the waste was generated, allowing the duration of its half-life to be traced more easily.
4.4 Waste Disposal The hospital has employed a Radiation Safety Officer (RSO) with the minimum qualification required by the appropriate authority. The RSO advises and assists the employer in the safe disposal of radioactive waste by the recommendations of the competent authorities from time to time. The RSO is responsible for ensuring all elements of radiation safety at the institution, including the proper disposal of radioactive waste. The radioactive waste collected is disposed of in two ways, i.e. by the Dilute and disperse method and Delay and decay method.
4.4.1
Dilute and Disperse Method
If the low activity solid article does not exceed 1.35 micro-curies, then it is discarded as ordinary hospital waste. This category includes vials, syringes, cotton swabs, tissue sheets, and other similar items. Liquid radioactive waste with less than micro-curie is disposed of in the sanitary sewer system. The total amount of liquid radioactive material discharged into the sanitary sewage system shall not exceed the permissible limitations as shown in Table 3.
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Table 3 Maximum limit of disposal for sanitary sewage system [16] Radionuclide
Maximum limit on total discharge/day (MBq)
Average monthly concentration of radioactivity in the discharge
H3
92.5
3700
C14
18.5
740
Na24
3.7
222
P32
3.7
S35
18.5
Cl36
0.37
18.5 74 74
Ca45
3.7
10.1
Co60
0.37
37.0
0.37
11.1
Sr89 Sr90
+
Y90
0.037
0.148
Zr95 + Nb96
3.7
74
Mo99 + Tc99m
3.7
185
Ru106
+
Rh106
0.37
14.8
Sb124
0.37
25.9
I 125
3.7
22.2
I131
3.7
22.2
Cs137 + Ba137m
0.37
14.8
Ba140 + La140
0.37
29.6
Ce144 Tm170
+
Pr144
0.37
11.1
3.7
37.0
Ir192
3.7
37.0
Po210
0.037
0.74
4.4.2
Delay and Decay Method
Radioactive waste with medium activity and half-lives of less than a month is kept in the storage area, well ventilated by an exhaust system that passes through a duct line to a rooftop exit. To avoid radiation leakage, the storage areas and doors have suitable lead shielding (10 HVL) as shown in Fig. 3. The radioactive wastes are kept for at least 10 half-live, when just 0.1 percent of original activity remains after decay. After testing for residual activity, the waste is disposed of as low activity solid or liquid waste. SUM ULTIMATE hospital is associated with the company VARIAN Medical System for the safe disposal of waste generated from the hospital in the Netherlands, where radioactive and other highly toxic wastes are stored underground for a long time. The waste is transported in the most efficient modes of transportation depending on the half-life of the waste generated from the medical.
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Fig. 3 Doors with lead shielding to prevent radiation leakage
5 Conclusion Hazardous waste management systems have far-reaching implications for the environment, health, and safety. Radioisotopes are increasingly being used in hospitals for diagnostic and therapeutic purposes. Radioactive wastes cannot be eliminated, and unscientific handling of hazardous waste can create threats to everyone. This waste must be disposed of in accordance with India’s Atomic Energy Regulatory Board (AERB) norms. Therefore, the Institute should adopt standard operating procedures for safely disposing of waste in the allied departments. Acknowledgements The assistance provided by the authorities of Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, in getting the survey data is highly acknowledged. This research did not receive any specific grant from any funding agencies in the public, commercial, or not-for-profit sectors.
References 1. Zhang Z, Malik MZ, Khan A, Ali N, Malik S, Bilal M (2022) Environmental impacts of hazardous waste, and management strategies to reconcile circular economy and ecosustainability. Sci Total Environ 807:150856 2. Misra V, Pandey SD (2005) Hazardous waste, impact on health and environment for development of better waste management strategies in future in India. Environ Int 31(3):417–431 3. Hosseinzadeh-Bandbafha H, Li C, Chen X, Peng W, Aghbashlo M, Lam SS, Tabatabaei M (2022) Managing the hazardous waste cooking oil by conversion into bioenergy through the application of waste-derived green catalysts: a review. J Hazard Mater 424:127636 4. Su P, Zhang J, Yang B (2022) The current status of hazardous waste management in China: identification, distribution, and treatment. Environ Eng Sci 39(1):81–97
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5. Gholampour Arbastan H, Gitipour S (2022) Evaluating the consequences of household hazardous waste diversion on public health and ecological risks of leachate exposure. Int J Environ Sci Technol 19(5):4407–4420 6. El-Khateeb M (2022) Household hazardous waste: handling, precaution and hazard reduction. Egypt J Chem 7. Wang X, Li C, Lam CH, Subramanian K, Qin ZH, Mou JH, Jin M, Chopra SS, Singh V, Ok YS, Yan J, Lin CSK (2022) Emerging waste valorisation techniques to moderate the hazardous impacts, and their path towards sustainability. J Hazard Mater 423:127023 8. Alawa B, Galodiya MN, Chakma S (2022) Source reduction, recycling, disposal, and treatment. In: Hazardous waste management. Elsevier, pp 67–88 9. Zangina AS, Ali AF (2020) An overview of hazardous waste management in Nigeria 10. Rani L, Srivastav AL (2022) Hazardous waste monitoring and transboundary movement. In: Hazardous waste management. Elsevier, pp 275–287 11. Lara ER, De la Rosa JR, Castillo AIR, de Jesús Cerino-Córdova F, Chuken UJL, Delgadillo SSF, Rivas-García P (2017) A comprehensive hazardous waste management program in a Chemistry School at a Mexican university. J Clean Prod 142:1486–1491 12. International Commission on Radiation Units and Measurement (ICRU) Recommendations (1980) 13. Cherry SR, Sorenson JA, Phelps ME (1980) Physics on nuclear medicine, 3rd edn. Saunders, Philadelphia, Pennsylvania 14. Kovler K (2012) Radioactive materials. In: Toxicity of building materials. Woodhead Publishing, pp 196–240 15. Khan S, Syed AT, Ahmad R, Rather TA, Ajaz M, Jan FA (2010) Radioactive waste management in a hospital. Int J Health Sci 4(1):39 16. https://dae.gov.in/writereaddata/AE(SDRW)Rules%201987.pdf