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Lecture Notes in Mechanical Engineering

Nik Ahmad Ridhwan Nik Mohd. Shabudin Mat   Editors

Proceedings of the 2nd International Seminar on Aeronautics and Energy ISAE 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 Mohamed Haddar, National School of Engineers of Sfax (ENIS), Sfax, Tunisia 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 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

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Nik Ahmad Ridhwan Nik Mohd. · Shabudin Mat Editors

Proceedings of the 2nd International Seminar on Aeronautics and Energy ISAE 2022

Editors Nik Ahmad Ridhwan Nik Mohd. UTM Aerolab, Institute for Vehicle System and Engineering (IVeSE) Universiti Teknologi Malaysia Johor Bahru, Johor, Malaysia

Shabudin Mat UTM Aerolab, Institute for Vehicle System and Engineering (IVeSE) Universiti Teknologi Malaysia Johor Bahru, Johor, Malaysia

Department of Aeronautics Automotive and Naval Engineering Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor, Malaysia

Department of Aeronautics Automotive and Naval Engineering Faculty of Mechanical Engineering Universiti Teknologi Malaysia Johor, Malaysia

ISSN 2195-4356 ISSN 2195-4364 (electronic) Lecture Notes in Mechanical Engineering ISBN 978-981-99-6876-3 ISBN 978-981-99-6874-9 (eBook) https://doi.org/10.1007/978-981-99-6874-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 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 Paper in this product is recyclable.

Organization Committee

Conference Chairman Nik Ahmad Ridhwan Nik Mohd, Universiti Teknologi Malaysia

Conference Co-chairman Muhammad Faruq Foong Mohamad Faiz Foong, Universiti Teknologi Malaysia

Scientific Committee Shabudin Mat, Universiti Teknologi Malaysia Mohd Nazri Mohd Nasir, Universiti Teknologi Malaysia Mastura Ab Wahid, Universiti Teknologi Malaysia Norazila Othman, Universiti Teknologi Malaysia Iskandar Shah Ishak, Universiti Teknologi Malaysia Haris Ahmad Israr Ahmad, Universiti Teknologi Malaysia

v

Preface

Under the auspices of the UTM Aerolab Committee, the Second International Seminar on Aeronautics and Energy (ISAE) was held at the Universiti Teknologi Malaysia on 17 September 2022. The overall objective of the seminar was to bring together international scientists and engineers, physicists, and mathematicians in academia and industry in the fields related to aeronautics, aerospace, and energy. One of the goals of the conference is to foster collaboration among international scientists and engineers from a variety of disciplines who are engaged in aeronautics and energy-related research. The seminar covers all aspects of aeronautical engineering, including flying vehicle aerodynamics; subsonic and high Mach number flight, CFD, and wind tunnel testing; aircraft control; flight mechanics; drone technologies; aircraft structure; aircraft and rocket propulsion; and topics in energy such as green energy technology for aerospace and aeronautics application and renewable energy. Nik Ahmad Ridhwan Nik Mohd. served as Chairman of the ISAE 2022 seminar. Other scientific committee members include the following individuals: Shabudin Mat, Mohd Nazri Mohd Nasir, Faruq Foong., Mastura Ab Wahid, Norazila Othman, Iskandar Shah Ishak, and Haris Ahmad Israr Ahmad. The program for the seminar included 23 contributions, which were presented orally during the online session. The organizing committee is grateful to the keynote speaker, Associate Professor Andrea Da Ronch from the University of Southampton, UK, Prof. Ir. Dr. Shuhaimi Mansor from Universiti Teknologi Malaysia, and Mr. Reza from Un-manned System and Technology, Malaysia. Special appreciation is extended to the members of the Scientific Committee of ISAE 2022 for assessing and evaluating the scientific merits of the articles included in this edition. The articles were accepted for inclusion in the conference proceedings and presentation at the conference venue based on reviewers’ comments and the submitted manuscripts’ scholarly merits. The accepted papers contribute to advancing knowledge in all important fields of study and are highly scientific.

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Preface

The organizing committee would like to thank all contributors to this volume and those who presented their research at the conference. Johor Bahru, Malaysia January 2023

Nik Ahmad Ridhwan Nik Mohd.

Contents

CFD and Experimental Aerodynamics Experimental Studies of the Effect of Rectangular-Shaped Canard on a Generic Blended Wing Body (BWB) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fareez Sanim, Abiola Imisi-Oluwa, Shabudin Mat, Haris Fadzilah, and Khushairi Amri Kasim Blowing Active Flow Control on FFA-W3-270 Airfoil Model . . . . . . . . . . . Kabiilesh Kathiresan, Abiola Imisi-Oluwa, Shabudin Mat, Abdullahi Yahaya Daura, Seyed-Reza Jafari-Gahraz, Tholudin Mat Lazim, and Nur Amalina Musa

3

19

Experimental Investigation of a Generic Light Aircraft Model with Dual External Stores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soh Khai Yuet, Abiola Imisi-Oluwa, and Shabudin Mat

31

Computational Analysis on Aerodynamics of a Boxfish-Inspired Airship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nur Asyiqin Ahmad Kamal and A. S. M. Harithuddin

47

Computational Fluid Dynamics Analysis of High Aspect Ratio Wing Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ainaa Nabilah Mohd Nazri, Norzaima Nordin, Norazrina Mat Jali, Baizura Bohari, and Mohammad Yazdi Harmin Effect of Surface Roughness Size on the Skin Friction Drag for NACA0012 Airfoil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Noor H. Dhaher, M. A. H. Al-fahham, and Mohammed Hameed Mohammed Noise Estimation of NACA 0012 Airfoil Using DES Method . . . . . . . . . . . Jafirdaus Jalasabri, Mohamed Sukri Mat Ali, Fairuz Izzuddin Romli, and Nurshafinaz Mohd Maruai

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83

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Contents

Design and Optimization Numerical Study of Air-Intake Performance of a Scramjet with Various Cowl Lip Length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zahraa Hamzah Hasan, Nik Ahmad Ridhwan Nik Mohd., Shabudin Mat, Abdul Rahim Abu Talib, and Nik Mohd Izual

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Design and Numerical Study of High-Speed Water Tunnel with Interchangeable Test Section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Ibrahim Madan and Chang-Ren Chen Avionics and System Integration on UAV/Drone Development of an Autonomous RC Catamaran for Surveillance and Pollution Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Faisal Baki and Nazri Nasir Development of Autonomous Battery Charging Station for Campus Surveillance UAV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Ahsan Habib Rifat, Mohammed Raihan, and Nazri Nasir Preliminary Development and Performance Testing of an Autonomous Battery Charging System for a Two Legs Multicopter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Mohammed Raihan and Nazri Nasir Assessing Drone-Based Last-Mile Logistics—A Hybrid Solution . . . . . . . 161 Bruno Lamiscarre, Innocent Davidson, Georges Mykoniatis, Luis Gustavo Zelaya Cruz, and Felix Mora-Camino Rocket and Missile Flight Performance and Trajectory Prediction of a 2.75-Inch Solid Propellant Rocket . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Nur Syahirah Shafek Hamlan, Ezanee Gires, Kamarul Ariffin Ahmad, Faizal Mustapha, Norkhairunnisa Mazlan, Noorfaizal Yidris, and Adi Azriff Basri Control and Navigation A Study on Aircraft Pitch Control in Rejecting Disturbances . . . . . . . . . . 187 Salihu Abdulmumini Jalo, Mohammed Ahmed, Abdulqadiri Bello Abdulqadiri, Muhammad Usman Ilyasu, Isa Mohammed Inuwa, and Garba Elhassan Experimental Analysis on Pitching Moment for Embedment Cylinder to Flat Plate High Altitude Platform Station . . . . . . . . . . . . . . . . . 197 Ummi Zuhairah Zaimi, Hidayatullah Mohammad Ali, and Azmin Shakrine Mohd Rafie

Contents

xi

Reaching Law Controller for Backlash Compensation in Parabolic Antenna Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Salihu Abdulmumini Jalo, Mohammed Ahmed, Abdulqadiri Bello Abdulqadiri, Muhammad Usman Ilyasu, Isa Mohammed Inuwa, and Garba Elhassan Aircraft Structure and Aero-elasticity Numerical Investigation of Stresses on the Composite Aircraft Fuselage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Muhammad Irfan Naufal and Faruq Muhammad Foong Aeroelastic Optimization Using Laminate Fiber Orientation on a Composite Wing Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Angga Dwi Saputra, Ilham Akbar A. Satriya, R. Wibawa Purabaya, Syariefatunnisa, Zuhdhy Masfuri, Dimas Sangaji, and Farhan Muzzammil Ali Sustainability Numerical Modeling of Flow-Induced Instabilities in a Cage-Type Steam Turbine Control Valve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Mohd Rais Ramli, Nik Ahmad Ridhwan Nik Mohd, Shabudin Mat, and Mohd Nazri Mohd Nasir Effect of Scruton Number on Energy Harvesting Utilizing Flow-Induced Vibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Azalia Sharmine Saiful, Mohamed Sukri Mat Ali, Nursyafinaz Maruai, and Salehuddin Muhammad Study the Implementation of Hybrid PV/Wind System in Hot Region, Low Wind Speed; Challenges, Obstacles, and Prospects; Case of Samawah, Iraq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 Fazila Mohd Zawawi and Mustafa Abdulkareem Hussein A Methodology for Evaluating Aviation Sustainability Perspectives . . . . 277 H. Karam, E. Anwama, I. E. A. Davidson, H. Alfazari, F. Krykhtine, and F. Mora-Camino

CFD and Experimental Aerodynamics

Experimental Studies of the Effect of Rectangular-Shaped Canard on a Generic Blended Wing Body (BWB) Fareez Sanim, Abiola Imisi-Oluwa, Shabudin Mat, Haris Fadzilah, and Khushairi Amri Kasim

Abstract The purpose of this study is to investigate effects of canard on Universiti Teknologi Malaysia Blended Wing Body model (UTM-BWB). A model of generic blended wing body has been designed and fabricated locally in UTM. The experiment was conducted in Universiti Teknologi Malaysia Low Speed Wind Tunnel (UTMLST) with maximum speed of 80 m/s. During the experiment, the model was mounted on the steady balance measurement equipment located underneath the wind tunnel. Two measurement techniques were employed on the wing, the first one was the turf thread to observe the flow separation above the wing while the second experiment was the steady balance measurement to obtain the aerodynamic characteristics of model. The experiment has been performed at the speed of 30 m/s in two phases. The first phase was the experiment without the canard while the second phase was the experiment with the canard. The canard has been designed with interchangeable angles of 0, 10, and −10°. The experiments were conducted at the angle of attack, α varies from −3 to 15° and sideslip angle, β from −8 to 8°. The Aerodynamic data obtained were plotted in order to obtain aerodynamic characteristics of the BWB model with and without canard configurations at certain angle of attack and sideslip angle. Keywords Aerodynamics · Blended wing body · Canard · Wind tunnel

F. Sanim · A. Imisi-Oluwa · S. Mat (B) · H. Fadzilah · K. A. Kasim Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Bahru, Johor, Malaysia e-mail: [email protected] S. Mat UTM Aerolab, Institute for Vehicle System and Engineering, Universiti Teknologi Malaysia, 81310 Bahru, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_1

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F. Sanim et al.

1 Introduction Blended wing body (BWB) is an aircraft whose main body and wing are merged together without any distinct separation line. BWB can be described to be a revolutionary concept of aircraft design and maybe the future of aircraft design because the BWB design can reduce the drag coefficient of the aircraft when compared to the conventional aircraft whose fuselage contribute less lift and provide major source of drag. In BWB, the entire body provides high lift coefficient and minimize the drag developed. The configuration of blended wing body (BWB) aircraft can provide reduction in fuel consumption and noise by reducing drag [1]. The blended wing body (BWB) aircraft is an unconventional aircraft that offers the aerodynamics performance advantages compared to the conventional aircraft [2, 3]. However, BWB is having a certain problem that is related to the design. Based on the feasibility study, the chance for BWB to replace the current aircraft is less because of difficulty in manufacturing process due to limiting constraints of the structure, less stability, and control due to absence of tail, problem about cabin pressurization, and a large drag due to large frontal area of the engine that is installed in the wing-body [4, 5]. The installation of the canard as a control surface may would improve the aerodynamic efficiency in BWB [6, 7].

1.1 Adverse Pressure Gradient Adverse pressure gradient is developed when the flow over a surface body has an increasing pressure distribution in the flow direction [8]. It caused by the excessive momentum loss near the wall as the boundary layer move downstream against increasing pressure area with equation [4]. dp >0 dx

(1)

The adverse pressure gradient always occurs at the back of the body [9]. When the adverse gradient strength increases, the point of inflection will occur in the boundary layer, and it increase the distance from the wall. Weak adverse pressure gradient will not cause the flow to separate yet although it is vulnerable to transition into turbulence flow. Then, moderate adverse pressure gradient where the critical condition is achieved with zero wall shear is given by the Eq. (2). ∂u =0 ∂y

(2)

It can be defined as the separation point because when the pressure gradient increases any further it will cause backflow at the surface wall and the boundary layer will thickens and the flow will be detached from the surface wall.

Experimental Studies of the Effect of Rectangular-Shaped Canard …

5

1.2 Flow Separation Flow separation is the flow that separated from the surface due to reverse flow phenomenon and it will create a large circulating flow region downstream of the surface [8]. The flow separation is developed when the flow past through a blunt body [10]. Firstly, the flow will undergo pressure drop which will affect the flow to decelerate as pressure increase. So, the particle of the flow cannot flow into the region of increasing pressure. Hence, the particle undergoes a standstill and being pushed backwards into the motion by the pressure of the flow and develop a separation point which means the flow will start to separate from the surface body.

1.3 The Effect of Canard on BWB The installation of the canard in front of the BWB model will affect the aerodynamic characteristic of the BWB. As shown in the Fig. 1, the lift coefficient increases when the angle of attack is increased. There is a sudden change of lift between 8 and 10° depend on the canard setting angle. After this point, lift increases until it stalls at 48 to 50°. From the figure, it can be observed that the canard is effective at moderate and high angles of attack. The characteristic of drag is shown in Fig. 2, from the figure it can be observed the drag coefficient versus angle of attack graph is parabolic. It shows that when angle of attack increases, the drag coefficient increased. The installation of canard has caused the drag to increase. From Fig. 3, it can be seen that blended wing body both without and with canard configurations have reduced the stability due to absence of vertical stabilizer. The application of canard as a horizontal control surface could be used to supplement stability [1, 2, 8, 9].

2 Methodology The experiment has been conducted at Universiti Teknologi Malaysia Low Speed Wind Tunnel (UTM-LST) using blended wing model (BWB) model. As mentioned earlier, two measurement techniques were employed on the wing. The first one was the flow turf thread technique to visualize the flow characteristics of the BWB model with and without canard. The second experiment was the steady balance data, the model was attached to UTM-LST external strain gauges that can measure the forces and moments in 3 dimensional. The model has been designed with canard deflection angles of 0, 10, and −10°. The model was tested at the speeds of 30 m/s corresponding to Reynolds number of 2.2 × 106 . The angle of attack, α was varied from −3 to 15° while the yawing angle, β was varied from −8 to 8°.

6

Fig. 1 CL versus α [2]

Fig. 2 CD versus α [2]

F. Sanim et al.

Experimental Studies of the Effect of Rectangular-Shaped Canard …

7

Fig. 3 CM versus α [1]

2.1 Model and Experimental Setup The model has been installed in UTM-LST test section area with 1.5 × 2.0 × 5.8 m. The model is attached to UTM-LST heavy capacity load cell located underneath the test section as shown in Fig. 4. This load cell is capable of measuring six-degrees of freedom forces and moment to obtain the aerodynamic characteristic. Table 1 shows the specification of the UTM BWB model. The installation of the model is shown in Figs. 5, 6, and 7 respectively representing the clean wing and with canard configuration. The experiment was carried out in several stages. The first test that has conducted was on the clean wing configuration where the measurement of tare values for force and pressure distribution were taken. These values are obtained at wind speed, v = 0 m/s, angle of attack, α = 0°, and yaw angle, β = 0°. Once the tare result was done, the wind tunnel was run at 30 m/s wind speed to carry out following test parameters of varying angle of attack and yaw angle. Finally, the test was repeated for the model with the canard tested at different angles of attack.

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Fig. 4 Steady balance system

Table 1 UTM BWB model configuration

UTM BWB model configuration Total length

0.850 m

Max. wing span

1.700 m

Mean aerodynamic chord

0.423 m

Height

0.153 m

Wing area

0.582 m2

Aspect ratio

2.93

Thickness to chord ratio

0.178

Fuselage volume

0.14135 m3

Wing volume

0.1496 m3

3 Result and Discussion This section discusses the results obtained from the two experiments. For the flow visualization, the sample images are shown in Figs. 7 and 8. Flow visualization technique is used to show the instantaneous flow pattern formed by the tufts on the BWB model surface. Figure 7 shows the flow pattern at 30 m/s and BWB body in all configurations at angle of attack of 0°. It is observed that early flow separation occurred only on the BWB-canard body with a deflection angle of positive 10°. Positive canard deflection promotes early flow separation. From Fig. 8 shows the flow pattern at 30 m/s and BWB body in all configurations at angle of attack of 9°. It is observed that early flow separation occurred in all

Experimental Studies of the Effect of Rectangular-Shaped Canard …

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Fig. 5 BWB clean wing configuration

Fig. 6 BWB with canard configuration

BWB-canard body configurations. Canard promotes early flow separation compared to clean configuration. Figure 9 shows the flow pattern at 30 m/s and BWB body in all configurations at angle of attack of 15°. It is observed that early flow separation occurred in both clean BWB configuration and all BWB-canard body configurations. At this angle of attack, maximum lift is already reached, and stalling occurs.

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Fig. 7 Flow visualization for all BWB configuration at zero angle of attack

Figure 10 shows the graph of lift coefficient against angle of attack. The graph shows that the lift coefficient is increased when angle of attack increased for all BWB configurations. The results here consistent with numerical work by Ali et al. [8]. For the angle of attack between −3 and 9°, the lift coefficient increases consistently for all the BWB configuration. However, at high angle of attack, the no canard configuration shows the lowest lift coefficient while canard −10° configuration has the highest lift coefficient. The results obtained here has suggested that the coefficient of lift is increased when the canard is installed on the wing. The configuration at canard +10° has recorded the lowest lift coefficient when compared to other BWB canard configurations. Thus, adding canard to the BWB with negative deflection angle will give more lift compared to canard with positive deflection angle. Based on the Fig. 11, the drag coefficient increases when the angle of attack is increased. The Canard +10° configuration has recorded the highest drag compared to other BWB configurations while the lowest drag is recorded for the canard with 0° configuration. This is because Canard +10° configuration has more wetted area than other BWB configurations, this situation will increase the friction on the BWB and

Experimental Studies of the Effect of Rectangular-Shaped Canard …

11

Fig. 8 Flow visualization for all BWB configurations at 9° angle of attack

influence the drag force to increase. The trend for all BWB configuration has recorded almost similar drag except for canard +10° configuration. Thus, by additional canard with zero and negative deflection angle, it does not significantly affect the drag produced on the BWB model. The drag polar is shown in Fig. 12. From Fig. 12, the drag polar is almost consistent for all canard configurations. Initially, the drag coefficient is constant while the lift coefficient is increasing. At certain point, the lift coefficient is directly proportional to the drag coefficient. The model with no canard configuration shows the lowest lift and drag contribution compared to the other configurations. The canard +10° configuration provides the poorest aerodynamic performance compared to other BWB canard configurations as it recorded highest drag coefficient with the lowest lift coefficient. The best configuration is the wing with canard −10°, this is

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Fig. 9 Flow visualization for all BWB body configurations at 15° angle of attack 0.8 0.7 Lift Coefficient, C L

0.6 0.5 0.4 0.3 0.2 0.1 0 -4

-2

-0.1 0

2

4

6

8

10

12

14

16

Angle of Attack, α No Canard (Clean Wing)

Fig. 10 CL versus α

Canard = +10

Canard = 0

Canard = -10

Experimental Studies of the Effect of Rectangular-Shaped Canard …

13

0.3

Drag Coefficient, C D

0.25 0.2 0.15 0.1 0.05 0 -4

-2

0

2

4

6

8

10

12

14

16

Angle of Attack, α No Canard (Clean Wing)

Canard = +10

Canard = 0

Canard = -10

Fig. 11 CD versus α

because from the figure, this configuration has produced the highest lift coefficient and the drag coefficient is the configuration without canard. Thus, it shout be noted that the canard with negative angle provide a better aerodynamic performance for BWB model [9]. Figure 13 shows at lower angle of attack between −3 and 1° for no canard and canard +10° configurations, the L/D increases steadily to a peak value and then decreases. While, canard 0° and canard −10° configurations are increasing from angle of attack −3 to 3°. This shows that the maximum aerodynamic performance for both no canard and canard +10° configurations are at angle of attack 1° while both canard 0° and canard −10° configurations are at angle of attack 3°. Furthermore, 0.8 0.7 Lift Coefficient, C L

0.6 0.5 0.4 0.3 0.2 0.1 0 -0.1

0

0.05

0.1

0.15

0.2

0.25

0.3

Drag Coefficient, C D No Canard (Clean Wing)

Fig. 12 Drag polar

Canard = +10

Canard = 0

Canard = -10

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F. Sanim et al. 12 10 8

L/D

6 4 2 0

-4

-2

0

2

-2 No Canard (Clean Wing)

4

6

8

10

12

14

16

Angle of Attack, α Canard = +10

Canard = 0

Canard = -10

Fig. 13 L/D versus α

the maximum L/D for the canard 0° is highest compared to other BWB configurations and canard +10 configuration has the lowest maximum L/D. However, after canard 0° configuration reach maximum L/D, it decrease drastically compared to the canard −10° configuration which decreases steadily after reaching maximum L/D. Hence, canard −10° configuration give better performance compared to other BWB configurations since higher the L/D results in better aerodynamic performance. The pitching moment characteristics is shown in Fig. 14. The trend for all BWB configurations have shown a negative slope which shows that the model is stability in pitching. The configuration with no canard has shown that it has the most negative slope when compared to BWB with canard configurations. This shows that this configuration is the most stable BWB. The results have shown that the canard configuration will reduce the stability of the model. Yawing moment is shown in Fig. 15. From the figure, yawing moment coefficient for all BWB conditions increases when the sideslip angle is increased. The results for all BWB configurations is almost similar except for the canard +10° configuration. The canard +10° configuration has recorded the highest yawing moment when compared to the other configurations. The rolling moment is shown in Fig. 16, the rolling moment coefficient decreases as the sideslip angle increases. Figure 16 has suggested that rolling moment is consistent for all configurations but the model without the canard has recorded highest rolling moment when the yawing angle is increased. The reason for this unknown at this stage.

Experimental Studies of the Effect of Rectangular-Shaped Canard …

15

0 -2

Pitching Moment Coefficient, C M

-4

0

2

4

6

8

10

12

14

16

-0.02 -0.04 -0.06 -0.08 -0.1 -0.12

Angle of Attack, α

No Canard (Clean Wing)

Canard = +10

Canard = -10

Canard = 0

Fig. 14 CM versus α

Yawing Moment Coefficient, C N

0.02

-10

0.015 0.01 0.005

-8

-6

-4

0 -2 0 -0.005

2

4

6

8

10

-0.01 -0.015 Sideslip Angle, β No Canard (Clean Wing)

Fig. 15 CN versus α

Canard = +10

Canard = 0

Canard = -10

F. Sanim et al.

Rolling Moment Coefficient, C R

16

-10

-8

-6

-4

No Canard (Clean Wing)

0.05 0.04 0.03 0.02 0.01 0 -2 -0.01 0 2 -0.02 -0.03 -0.04 -0.05 -0.06 Sideslip Angle, β Canard = +10

4

Canard = 0

6

8

10

Canard = -10

Fig. 16 CR versus α

4 Conclusion A detailed investigation on the effect of canards configuration on blended wing body (BWB) was successfully carried out in this project. Flow visualization of BWB body in both configurations showed that canard promotes early flow separation thereby increasing drag slightly. Furthermore, experimental results have showed that the canard has increased the lift on the blended wing body (BWB) in all deflections. The canard also provides an additional control surface on the BWB model. Several other main observations to note from this study is that the wing with zero canard give BWB body a better aerodynamic efficiency than clean wing BWB configuration. In conclusion, canard configuration can improve aerodynamic performance of the wing, but it could affect the stability of the model. Acknowledgements This research was funded by the Universiti Teknologi Malaysia (Grant 21H05). The authors also would like to express their appreciation to the technical staff of the Universiti Teknologi Malaysia Aeronautical Laboratory (UTM Aerolab).

References 1. Nasir R, Mazlan N, Ali Z, Wisnoe W, Kuntjuro W (2016) A blended wing body airplane with a close-coupled, tilting tail. In: IOP conference series: material science and engineering 2. Ali ZM, Kuntjoro W, Wisnoe W (2012) Effect of canard to the aerodynamic characteristics of blended wing body airplane. In: IEEE symposium on business, engineering and industrial applications 3. Ali ZM, Kuntjoro W, Wisnoe W (2015) Wind tunnel testing for blended wing body aircraft with canard. In: Proceedings of the fourth international conference on advances in mechanical, aeronautical and production techniques-MAPT

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4. Katon M, Kuntjoro W (2011) Airframe design for modularity of the BWB Baseline II-E2 UAV. In: 2011 international conference on business, engineering and industrial applications. IEEE, pp 73–77 5. Sharma A, Alva T, Srinivas G (2015) Comparative study and CFD analysis of blended wing body (BWB). Int J Sci Technol Eng 6. Nasir R, Kunjoro W, Wisnoe W (2012) Longitudinal static stability of a blended wing-body unmanned aircraft with canard as longitudinal control surface. J Mech Eng (JMechE) 9(1): 99–121 7. Pate S (2013) Design and analysis of modified blended wing body canard configuration. J Aeronautical Aerospace Eng 2(5):1000121 8. Ali ZM, Kuntjoro W, Wisnoe W, Ishak I, Katon M, Ahmad N (2020) Aerodynamics analysis on the effect of canard aspect ratio on blended wing body aircraft using CFD simulation. In: IOP conference series: materials science and engineering, vol 834, no 1. IOP Publishing, p 012015 9. Ali ZM, Kuntjoro W, Wisnoe W, Rizal E, Ismail I (2017) Numerical study of aerodynamic characteristics on blended wing body aircraft with small canard. Pertanika J Sci Technol 25 10. Schlichting H, Gersten K (2017) Boundary-layer theory, vol 9, p 39

Blowing Active Flow Control on FFA-W3-270 Airfoil Model Kabiilesh Kathiresan, Abiola Imisi-Oluwa, Shabudin Mat, Abdullahi Yahaya Daura, Seyed-Reza Jafari-Gahraz, Tholudin Mat Lazim, and Nur Amalina Musa

Abstract This paper discusses about the installation of active flow control technique on FFA-W3-270 Airfoil model. The main objective is to investigate either this flow control technique can delay or promote the flow separation that occurs on the model. Flow separation is detachment of the airflow from the airfoil surface, which may lead to loss in lift and increase in pressure drag. Active flow control is a technique used to add energy to the flow by expenditure of power from other source which are able to delay the flow separation. The aim of this study is to investigate the effect on flow separation when blowing technique is used as an active flow control method. In this experiment, the air from compressor passed through pressure regulator has been channeled to pressure tubing of FFA-W3-270 in order to create blowing conditions on the model surface. During the experiments, the blowing has been conducted at three different location across the wing. The experiment has been conducted at two Reynolds numbers of Re = 0.8 × 106 and 1.0 × 106 , the angle of attack was varied from 0° until 14°. The result has shown that the blowing flow control has significantly delay the flow separation at low angle of attack but it promotes the flow separation at high angle of attack and Reynolds number. Keywords Active flow control technique · Flow separation · FFA-W3-270 airfoil · Wind tunnel experiments

K. Kathiresan · A. Imisi-Oluwa · S. Mat (B) · A. Y. Daura · S.-R. Jafari-Gahraz · T. M. Lazim Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Bahru, Johor, Malaysia e-mail: [email protected] S. Mat UTM Aerolab, Institute for Vehicle System and Engineering, Universiti Teknologi Malaysia, 81310 Bahru, Johor, Malaysia N. A. Musa Faculty of Engineering, City University, Petaling Jaya, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_2

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1 Introduction This paper discussed the flow separation characteristics above FFA-W3-270 airfoil when the active flow control is applied on its surface. An airfoil is a shape that capable to produce lift with relatively high efficiency as it passes through the air. An airfoil can have many cross-sectional shapes and form the basic unit. The designers choose the shape that has the best aerodynamic characteristics to suit the purpose, weight, and speed of the aircraft. Furthermore, drag accompanies lift due to flow separation and adverse pressure gradient. When the flow separates from the surface as a result of strong adverse pressure gradient in the flow direction, transition to turbulent and reverse flows occur. Flow control technique is used for flow control to delay the flow separation, reduce pressure drag and to reduce noise of an airfoil [6, 7]. According to Anderson [1], when the flow starts to move from stagnation point which is at the leading edge, the pressure would decrease drastically to the minimum value at the peak of the airfoil upper surface. This usually occurs at 10% of the chord length. After this point, pressure will start to increase gradually until the trailing edge. This increase is called as adverse pressure gradient. This effect becomes stronger when angle of attack increased. Furthermore, according to Munson et al. [5], the laminar flow that detached from the airfoil surface due to adverse pressure gradient is highly sensitive to disturbances. This result in transition from laminar state to turbulent state. The transition region is located away from the airfoil at the outer boundary of separated flow area. Anderson [1] stated that for an attached flow on the airfoil, there would be a pressure acting on the leading edge surface. The pressure on trailing edge surface produces a net force to counteract the leading edge pressure force, which results in zero pressure drag. However, for separated flow, the pressure acting on aft surface is insufficient to counter the pressure on forward surface [1]. There are two types of flow control techniques namely active and passive methods. Active flow control is modifying the flow by adding a momentum through to expenditure the energy. Passive flow control is modifying the flow by using geometrical shapes on the airfoil. Active flow control is easily method because the air can be controlled easily. In this experiment, active flow control by blowing technique is carried out on FFAW3-270 airfoil model, where the wing is assumed infinite in spanwise direction. The FFA-W3-270 airfoil is an aerodynamic efficient airfoil with high lift, low drag, and structurally efficient due to its shape [4]. Hence this airfoil is used widely in wind engineering. Active flow control is a method to delay the separation of flow, by adding energy through the expenditure of power from other sources to the flow before it reaches the separation point. This method will maintain the continuity of the flow without being separated from the airfoil surface. The most common active flow control techniques are suction and blowing. Constant blowing technique is used by Timo [8], in his research of active flow separation control on a high lift wing-body configuration. Blowing technique is used to delay the flow separation at the flap of wings by creating a slot. Timo [8] stated that strong adverse gradient is the reason for the

Blowing Active Flow Control on FFA-W3-270 Airfoil Model

21

flow separation, as the flow does not have enough dynamic energy to withstand the pressure. Control flow through the slotted actuators leads to re-energization of the boundary layer and therefore enforces the ability to withstand separation tendencies. Suction pressure compartment is similar to suction slot, where suction pressure is applied to the protruded area instead of slot. Example of suction pressure compartment is holes that have suction pressure outlet. In the research by Azim et al. [2], few pressure holes along profile of an airfoil are created to suck the flow out by suction pressure. They has shown that the separation is delayed until the end of trailing edge when suction active flow control is introduced to the airfoil. Smoother streamlines are observed around the airfoil. Azim et al. [2] also showed that the farther the point of active suction flow control, the better result produced.

2 Methodology The experiments is carried out at Universiti Teknologi Malaysia Low Speed Wind Tunnel (UTM-LST) with maximum speed of 80 m/s. The FFA-W3-270 airfoil model was attached to UTM-LST heavy capacity load cell located underneath to test section. The designed active flow control system which is a pneumatic system was connected to compressor in order to obtain the blowing mechanism. The experiments were done at various angles of attack in the range of 0–14°, with increment of 2°. The experiment is conducted at two different set of Reynolds number, which are 0.8 × 106 and 1.0 × 106 . Both parameters helped to understand the nature of flow separation and effect of active flow control towards it. Parameters chosen for the research was based on the previous work done by Jafari Gahraz et al. [4] by introducing active flow control to observe if better airfoil aerodynamic efficiency can be reached. In order to understand the effects of flow control technique, the blowing mechanism was positioned at 3 locations above the wing namely AFC 1, AFC 2, and AFC 3. Table 1 shows the location of blowing position across the upper surface of the FFA-W3-270 airfoil. Two measurement techniques were used in this experiment, the first was pressure measurement and followed by steady balance data experiments. For the first measurement, the pressure was measured by FKPS 30DP electronic pressure scanner Table 1 Blowing active flow control injection point on FFA-W3-270 airfoil model Active flow control

Chord wise location

AFC 1

Flow injected at pressure tapping 0.01c (pressure tap 1) and 0.02c (pressure tap 2)

AFC 2

Flow injected at pressure tapping 0.03c (pressure tap 3) and 0.05c (pressure tap 4)

AFC 3

Flow injected at pressure tapping 0.066c (pressure tap 5) and 0.084c (pressure tap 6)

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and data was recorded by LabVIEW application, which is a built-in software on the wind tunnel facility. For the second measurement, the force and moment acting on the airfoil model during airfoil is measured by using UTM-LST heavy capacity load cell located underneath the wind tunnel. Important aerodynamic forces and moments that measured in this experiment were then converted into coefficients. The aerodynamic characteristics of the model, with and without active flow control, are then analyzed.

2.1 FFA-W3-270 Airfoil Model The model used in this project is the FFA-W3-270 airfoil model. This model was fabricated by Jafari-Gahraz [3], to conduct the research on aerodynamics performance of the airfoil in Universiti Teknologi Malaysia (UTM). The installation of the model in wind tunnel testing is shown in Fig. 3. The length of the span of the model is 750 mm and chord length is 500 mm. The model fabricated from fiberglass. The model consists of 30 pressure taps, which is located at 50% of the wingspan. There are 17 pressure taps on upper surface and 12 pressure taps on lower surface of the model. Another one pressure tap is a stagnation point, which is located at the front of leading edge. This can be seen Fig. 2. The model has two plates on top and bottom to cancel the 3D flow effect in spanwise direction, which also shown in the figure. The wing model mounted vertically across the flow field. Figure 1 shows the dimension of FFA-W3-270 airfoil model while Fig. 2 is location of pressure taps on FFA-W3-270 airfoil. Table 2 shows the specifications of the airfoil model.

3 Result and Discussion The results obtained steady balance and pressure measurement experiments is now discussed in this section.

3.1 Steady Balance Data The characteristics of CL and CD are shown in Figs. 4 and 5 for Re = 0.8 × 106 and Re = 1.0 × 106 respectively. From the figures, it should be noted that blowing method is able to improve aerodynamic characteristic of airfoil section. From the figures, the flow control has delayed the flow separation thus the CL is increases. Moving the blowing further backward from the trailing edge has produced better effect in flow separation. This mean that AFC 3 is more effective to control the flow separation. However, at high Reynolds number, AFC 1 and AFC 2 produce more drag force compared to clean wing configurations. This situation happened because at high

Blowing Active Flow Control on FFA-W3-270 Airfoil Model

Fig. 1 Dimension FFA-W3-270 airfoil model

Fig. 2 Location of pressure taps in FFA-W3-270 airfoil model

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Fig. 3 Placement of FFA-W3-270 airfoil model in wind tunnel

Table 2 Specification of FFA-W3-270 airfoil model [4] FFA-W3-270 wing model specification Airfoil

FFA-W3-270

Wing span

75 cm

Chord length

50 cm

Number of pressure tap

30 (29 measure pressure, 1 stagnation point)

angle of attack, blowing flow control has promoted the flow separation to occur in front of leading edge instead of delaying and thus the pressure drags increases. From the figures, an increasing in Reynolds number has a small increased in the coefficient of lift and coefficient of drag.

3.2 Pressure Distribution Data Figure 6 shows the pressure distribution on clean wing configuration of FFA-W3270 airfoil model. It shows that constant pressure line between 26% of chord and 38.6% of chord, which indicates the flow separation occurred in the location (6). This is called laminar separation bubbles, where flow separated for specific length and reattached to the airfoil surface as turbulent flow. There are lot of wake and vortices inside laminar separation bubbles, which causes pressure drag formation. The sample results when blowing active flow control is applied on the model is shown in Figs. 7 and 8. From Fig. 7, the laminar separation delayed to further aft of the wing. When the position of the blowing is moved further behind from

Blowing Active Flow Control on FFA-W3-270 Airfoil Model

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Fig. 4 Aerodynamic characteristics of airfoil at Re = 0.8 × 106

Fig. 5 Aerodynamic characteristics of airfoil at Re = 1.0 × 106

leading edge of the airfoil, the laminar separation bubbles are not visible. However, full separation happened near trailing edge, which showed that laminar separation bubbles again has been delayed. Figure 7 shows pressure distribution at low angles of attack. From this figure, most significant changes can be seen on AFC 3, where the separation delayed near to trailing edge of the airfoil, which is in the region from 64.2% to 95% of chord. Similar trend can be observed at low angle of attack, α = 2, 6, and 8°. Changes in Reynolds number has increased the magnitude of coefficient of pressure Cp , slightly.

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Fig. 6 Coefficient of pressure, Cp versus x/c for clean wing configuration

Fig. 7 Pressure distribution results at low angles of attack

The sample results at high angle of attack of α = 10 and 14° is shown in Fig. 8, at these conditions the flow separation is not delayed as it does for law angle of attacks. Flow separation occurs earlier at front of the leading edge in the ranges of 5–18% of chord. This phenomenon occurred because, at high angle of attack, the leading edge of airfoil is in nose-up condition. This is due to the front part of the leading edge being highly curvature. This profile will increase the circulation of flow and thus increase the velocity in the region. Munson et al. [5] stated that, this would happen before the aerodynamic center or quarter chord of the airfoil. Blowing flow control, will add energy or momentum to the circulation of airflow. Thus, it induced

Blowing Active Flow Control on FFA-W3-270 Airfoil Model

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Fig. 8 Pressure distribution at high angles of attack

the transition from laminar to turbulent boundary layer to occur at very early stage. Besides that, pressure differential between frontal surface and aft surface will also increase. The situation will increase the pressure rag and reduce the lift force. This is the main reason for the reduction in lift and increased in drag for higher Reynolds number case. Pressure distribution at high angles of attack shown in Fig. 8.

3.3 Surface Contour Plot This section discussed 3D contour plot that can provide the nature of flow around airfoil. This is done by plotting the chord wise location in x-axis, the coefficient of pressure in y-axis and angle of attack in z-axis. Sample results are shown in Figs. 9, 10 and 11. From the figures, several distinct differences are observed when compare the results with and without flow control. This surface plot is able to visualize the delay of flow separation clearly. Figure 9 shows the surface contour plot for clean wing configuration. The width of blue colored contour on top of the plot shows the flow separation. The steepness of the surface curvature from 1% of chord to 14% of chord indicates the adverse pressure gradient contour. The steepness of curvature of surface from 18% of chord to 26% of chord indicates the favorable pressure gradient. The distinct difference is shown by AFC 3 in Fig. 12, where the width of flow separation or width of blue colored contour, reduced drastically. Besides that, the steepness of curvature for adverse pressure gradient and curvature of favorable pressure gradient became steeper. This indicates the flow separation has been delayed further behind. Surface flow contour plot for AFC1 in Fig. 10 and AFC 2 in Fig. 11 show nominal effect in flow separation when compared to AFC 3.

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Fig. 9 Surface contour plot in clean wing

Fig. 10 Surface contour plot in AFC 1

Fig. 11 Surface contour plot in AFC 2

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Fig. 12 Surface contour plot in AFC 3

4 Conclusion The main objective of this project is to investigate the effects of blowing control techniques in flow separation around FFA-W3-270 2D airfoil model has been achieved. From the results obtained, it can be concluded blowing method is able to delay the flow separation or laminar separation bubbles. The effectiveness of the blowing technique is more effective if it is been located further aft of the wing. The blowing technique is ineffective at high angle of attack and high Reynolds number as it promotes the flow separation to occur at the leading edge of the wing. Acknowledgements This research was funded by the Universiti Teknologi Malaysia (Grant 21H05). The authors also would like to express their appreciation to the technical staff of the Universiti Teknologi Malaysia Aeronautical Laboratory (UTM Aerolab).

References 1. Anderson JD (2014) Fundamental of aerodynamics, 6 edn. McGraw-Hill, New York, pp 381–392 2. Azim R, Hasan M, Ali M (2014) Numerical investigation on the delay of boundary layer separation by suction for NACA 4412. In: 6th BSME international conference on thermal engineering (ICTE 2014) 3. Jafari-Gahraz S, Mat L, Darbandi M (2017) Wind tunnel study of effect zigzag tape on aerodynamics performance of wind turbine airfoil. J Adv Res Fluid Mech Thermal Sci 41(1): 1–9 4. Jafari-Gahraz S, Mat Lazim TE, Schneider G, Darbandi M (2016) Experimental study on aerodynamic performance of FFA-W3–270 airfoil for axial wind turbine blade. In: 3rd international conference on fluid flow, heat and mass transfer (FFHMT’16), Paper No. 171, pp 1–6 5. Munson B, Young D, Okiishi T (2002) Fundamental of fluid mechanics, 1st edn. Wiley, New York, p 2002 6. Souri M, Sarallah A (2020) Reducing aerodynamic noise in a rod-airfoil using suction and blowing control method. Int J Appl Mech 12(4):2050036

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7. Stroh A, Georg F, Frohnapfel B, Atzori M, Vinuesa R, Schlatter P, Gatti D (2021) Investigation of blowing and suction for turbulent flow control on airfoils. AIAA J 59(11):4422–4436 8. Timo K (2011) Active flow separation control on a high-lift wing-body configuration Part 1: baseline flow and constant blowing. American Institute of Aeronautics and Astronautics

Experimental Investigation of a Generic Light Aircraft Model with Dual External Stores Soh Khai Yuet, Abiola Imisi-Oluwa, and Shabudin Mat

Abstract The paper discusses the effect of dual external stores attached beneath the wing of a generic subsonic light aircraft model. An experimental investigation is conducted in UTM Low Speed Wind Tunnel to determine the aircraft model’s aerodynamic characteristics and flow distribution. The interest in installing external stores on light aircraft has not been discussed in detail to date. Few light aircraft have recently been installed with external stores available in the market. In this experiment, the aircraft model was tested at three configurations: the model with clean wing, the model with single, and finally, the model with dual external stores. These configurations were tested at the same Reynolds number of 0.46 × 106 with angle of attacks ranging from −4 to 15° while the yaw angles ranging from −10 to 10°. Measurement techniques were performed on the model; the first was the external balance measurement, and the final was the surface distribution measurement. The results showed that the installation of dual external stores had reduced the lift coefficient by 13% compared to the clean wing configuration. Similar to the drag, it reduces because the induced drag is increased. The longitudinal static stability is reduced when the external stores are installed. Another observation is that the trim angle of the dual external stores configuration is greater than the clean wing configuration. In contrast, the rolling stability is improved, when the stores is installed. However, the effect of external stores is not significant in directional static stability. For pressure distribution, the external stores have delayed the flow separation to occur at a higher angle of attack. Keywords External store · Light aircraft model · Wind tunnel testing · Aerodynamic characteristics

S. K. Yuet · A. Imisi-Oluwa Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Bahru, Johor, Malaysia S. Mat (B) UTM Aerolab, Institute for Vehicle System and Engineering, Universiti Teknologi Malaysia, 81310 Bahru Johor, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_3

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1 Introduction Light aircraft is defined as an aircraft with a maximum take-off weight of less than 12,500 lbs. or 5670 kg as stated by the Federal Aviation Administration (FAA), United States Department of Transport. External stores are comprised of the fuel tank, missiles, bombs, and cameras installed externally on the aircraft as stated by Turner [10]. The interest in installing external stores on light aircraft is increasing; for example, the United States Air Force, USAF, has funded a Light Attack/Armed Reconnaissance (LAAR) program to enable external stores to be installed on suitable light aircraft [9]. The advantages of light aircraft include lightweight, low manufacturing and maintenance cost, and high performance in combat missions, resulting in high interest in converting light aircraft to combat aircraft. Several examples of light aircraft with external stores are Douglas A-1 Skyraider (AD-1) in Korean War, and Beechcraft AT-6, Pilatus PC-9 and KAI KA-1. When the external stores are mounted underneath the wing, it can cause changes on the airflow to its surrounding components such as control surfaces. Consequently, it may affect the aircraft’s performance such as changes in the aerodynamic characteristics and promote flow separation [4, 12]. For such reason, many researchers have conducted experiments and simulations to study the effect of external stores on the aerodynamic performances of light aircraft [6, 11]. Whoric [11] carried out an experiment to investigate the static stability of fighter aircraft when external stores are installed in various configurations. The result shows that the aircraft’s longitudinal stability and directional stability have been reduced; in contrast, rolling stability is increased with the installation of an external store. Lazim et al. [2] simulated a semi-span model of fighter aircraft with external stores where the results from both numerical and experimental were compared. It was found that external stores have affected the pressure distribution, especially on the lower surface of the aircraft wing. Another research done by Manaf et al. [5] to study the effect of a single external store on a light aircraft model was conducted at Universiti Teknologi Malaysia Low-Speed Wind tunnel (UTM-LST). The results from the study have found that external stores have reduced the lift and increased the drag of the aircraft, especially at high angles of attack. Intensive flow visualization studies were also performed by Manaf et al. [5] and this can be seen in Fig. 1 where at high angle of attack, the flow distribution across the wing is separated and large wake is produced at the trailing edge of the wing at high. The study on the effect of dual external stores on a full light aircraft model is yet to be conducted. Thus, in this project, the experimental work has been carried out to analyze the aerodynamic performances of the aircraft with external stores.

Experimental Investigation of a Generic Light Aircraft Model with Dual …

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Fig. 1 Flow visualisation of light aircraft with external store at different angle of attack [5]

2 Methodology The experiment is conducted in Universiti Teknologi Malaysia Low-Speed Wind Tunnel (UTM-LST) using a generic light aircraft model, as shown in Fig. 1. The project’s objective was to investigate the effect of dual external stores installed in the light aircraft model. Thus, the model was tested with three configurations. The first configuration was the clean wing configuration; the second was the model with a single external store configuration; and finally, the model with dual external store configuration. Two measurement techniques were applied to obtain the aerodynamic characteristics and the pressure distribution above the wing. The first method was the UTM-LST external force balance measurement and the second method was surface pressure measurement.

2.1 Wind Tunnel Test As mentioned earlier, two measurement techniques were employed on the model to obtain the aerodynamic characteristics effect of the external store. The measurement techniques external balance and surface pressure measurement. The wind tunnel has a

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test section of dimension 1.5 m (H) × 2.0 m (W) × 5.8 m (L) with a maximum speed of 80 m/s. Mansor [3] stated that UTM-LST is equipped with a 6-component balance to obtain load measurements. The balance is capable of measuring aerodynamic forces and moments in 3-dimensional axes at varying wind directions as the test section is installed with a turntable to enable the model to be rotated with an accuracy of 0.04%. For the surface pressure measurement, an electronic pressure scanner is used where all the pressure tap tubes on the aircraft wing model were connected to the pressure scanner. The aircraft model consists of 14 pressure taps along the wing’s chord, distributed as seven pressure taps on the upper surface and seven pressure taps on the lower surface. All the data obtained from the electronic scanner is processed through computational software called LabVIEW.

2.2 Test Configurations The experiment has been conducted with parameters shown in Table 1 for all configurations. All experiments were conducted at 30 m/s at a constant Reynolds number of 0.46 × 106 based on the mean aerodynamic, c of the wing.

2.3 External Store Design The external store and pylon design were done in computer-aided design software (CAD) called SolidWorks. The external stores have been designed to be attached underneath the wing. Based on the project’s objective which was to analyse the effect of dual external stores in parallel attachment configuration, the vertical distance between the two external stores was 20 mm. The material used in making the external store is wood block. The aircraft model, with single external store and dual external stores, was also drawn in CAD software prior to the manufacturing process of the model. Figure 2 shows the dual external stores configuration attached by three struts as in the wind tunnel test section. The location of the external stores on each wing for both single and dual external store configurations is about 25% of the wing roots. Table 1 Test parameters

Description

Values

Angle of attack, α

−4, 0, 3, 6, 8, 10, 12, 14, and 15°

Sideslip angle, β

−10, −5, 0, 5, and 10°

Reynolds number

0.46 × 106

Wind speed, v

30 m/s

Experimental Investigation of a Generic Light Aircraft Model with Dual …

35

Fig. 2 Dual external stores configuration on light aircraft model in CAD software

2.4 Experiments on Light Aircraft Model The attachment of the model in the test section of UTM-LST is shown in Fig. 3. It is supported by three struts supports. The first test conducted was on the clean wing configuration, where the measurements of tare values for force and pressure distribution were taken. These values are obtained at wind speed, v = 0 m/s, angle of attack, α = 0°, and yaw angle = 0°. Once the tare result was done, the wind tunnel was run at 30 m/s wind speed to carry out the following test parameters: varying angle of attack and yaw angle. Finally, the test was repeated for single external store and dual external store configurations. Fig. 3 The installation of UTM-LST light aircraft model with dual external stores configuration

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3 Result and Discussion The results obtained were corrected according to the Anderson [1] where the blockage correction such as solid and wake blockage were considered to increase the accuracy of the results based on Rae and Pope [8].

3.1 Effect of Stores on Aerodynamic Characteristics The result obtained from the external balance were converted into the coefficients of lift (CL ), Coefficient of drag (CD ), and pitching moment coefficient (CM ). Figures 4 and 5 show that installing external stores has reduced the CL and increased the CD of the aircraft. A gradual decrease in CL can be seen from clean wing configuration to dual external configurations. Similar to the drag, the CD increase from clean wing configuration to dual external configuration, as shown in Fig. 5. The maximum lift coefficients for all configurations decreased consistently at about a percentage of 5%, starting from clean wing configuration to dual storage. An interesting flow physics is observed for the stalling angle. The stall angle is different for each configuration, where both clean wing and single external store configurations have the same stall angle at 12° whereas the stall angle for dual external stores is at 7°. The effect of external stores is more significant at a higher angle of attack for both lift and drag. This is due to the increase in the frontal area of the aircraft model and another factor is that the external store behind the aircraft generates addition wake. The drag polar for CD versus CL 2 is also plotted in Fig. 6 to investigate the effect of external stores on induced drag. The results have shown that the induced drag reduces when external stores are installed, especially for dual external stores configuration, where there was a reduction of 24%, as observed from the graph. This phenomenon can be related to the CL characteristic of this configuration, here, the dual external stores generated significantly less lift among these three configurations. In order to investigate the stability of the model, the pitching moment (CM ), yawing moment (CN ), and rolling moments (CR ) are plot in Figs. 7, 8 and 9. The longitudinal stability of aircraft shows that CM increases when external stores are installed as shown in Fig. 7. However, the CM for configurations of single external store and dual external stores have recorded a small change each other at percentage of 0.5% compared to clean wing configuration of 6%. From the results, it can be concluded that the effect of additional external stores on the model does not significantly affect the longitudinal stability of the aircraft. Overall, the aircraft is still longitudinally stable as the negative slope is obtained stated by Nelson [7]. Yawing moment coefficient (CN ) is shown in Fig. 8 below to determine an aircraft’s directional static stability. From the results obtained, the aircraft is directionally stable as the slope of yawing moment coefficient against the sideslip angle is positive. The results for yawing moment coefficients also have shown that the external stores have not significantly effecting the directional stability. Figure 9 shows the rolling moment (CR ) coefficient

Experimental Investigation of a Generic Light Aircraft Model with Dual … 1.2 1 LIFT COEFFICIENT, CL

Fig. 4 Lift coefficient against angle of attack at various yaw angles, a −5°, b 0°, and c 10°

37

0.8 0.6 0.4 0.2 0

-5

-0.2

0

5

-0.4 clean wing

10

ANGLE OF ATTACK single store

15

20

dual stores

(a) 1.2 LIFT COEFFICIENT, CL

1 0.8 0.6 0.4 0.2

-5

0 -0.2 0

5

-0.4

10

15

20

ANGLE OF ATTACK single store

clean wing

dual stores

(b) 1.2

LIFT COEFFICIENT, CL

1

-5

0.8 0.6 0.4 0.2 0 -0.2

0

-0.4

5

10

15

20

ANGLE OF ATTACK clean wing

single store

dual stores

(c)

graph at various sideslip angles. Three negative slopes are observed in the graph that indicate the aircraft is stable in rolling stability at all these configurations. However, it is interesting to find that the rolling stability of the aircraft is improved with the installation of the external stores of single and dual configurations. The graph shows a more linear slope for single and dual external stores configuration than clean wing configuration. The reason for this can be linked with the dihedral effect through the installation of stores, which enable the restoring moment for aircraft to be stable.

38 0.3 DRAG COEFFICIENT, CD

Fig. 5 Drag coefficient against angle of attack at various yaw angles, a −5°, b 0°, and c 10°

S. K. Yuet et al.

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3.2 Effect of Stores on Pressure Distribution This section presents the results obtained from surface pressure measurement experiment. Figures 10 and 11 show the pressure distribution at low angles of attack of −4 and 0° while Figs. 12 and 13 is the results at high angles of attack of 12° and 14° respectively. The yawing angles were taken at −10, 0, and 10°. Based on Figs. 10, at −10° yaw angle and −4° angle of attack, the flow separation does not occur when dual external stores are installed on the wing. It can also be seen that

Experimental Investigation of a Generic Light Aircraft Model with Dual … Fig. 6 Drag polar, CD versus CL 2 curve at yaw angle = 0°

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Fig. 7 Pitching moment coefficient against angle of attack at various yaw angles, a −5°, b 0°, and c 10°

single store

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Fig. 8 Yawing moment coefficient against sideslip at angle of attack 0°

S. K. Yuet et al. YAWING MOMENT COEFFCIENT, CN

40

-15

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ROLLING MOMENT COEFFICIENT, CL

-15

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Fig. 9 Rolling moment coefficient against sideslip angle with angle of attack 0°

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the lift is generated from the wing’s leading edge to trailing edge. Compared to the other two configurations (single external store and clean wing configurations), both surface distributions give the similar pattern where the lift is not produced at the wing’s leading edge because the pressure at upper surface is greater than the lower surface. When compared yawing angle at 0° and 10° respectively, all these configurations give the same pressure distribution pattern with no lift generated at the leading edge. The lift is generated after 30% of chord wing from leading edge. However, at 0° angle of attack as shown in Fig. 11, all configurations at all yaw angles give similar pressure distributions with no flow separation occurring at the wing. The results at high angles of attack is shown in Figs. 12 and 13. At a high angle of attack, the results showed that the flow separation has moved forward in the leading edge for all the configurations. However, the installation of external stores has delayed the flow separation as shown in Fig. 12 at yaw angle of −10°, it has created 16.7% different in coefficient of pressure. Interestingly, at 0° yaw angle, only dual external stores configuration can delay the flow separation compared to single external store and clean wing configuration having the same flow separation activities. When the angle of attack increases to 14°, the pressure distribution is shown in Fig. 13. The flow separation happened more rapidly at the leading edge. The installation of external stores has delayed the flow separation to occur. Both single external stores and clean wing have experienced a complete flow separation across the wing, but the aircraft with dual external stores has flow attached on the wing. Also, at 10° yaw angle, the

Experimental Investigation of a Generic Light Aircraft Model with Dual …

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PRESSURE COEFFICIENT ,CP

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Fig. 10 Pressure distribution at low angle of attack due to effect of stores at yaw angle, a −10°, b 0°, and c 10° at α = −4°

external stores for single and dual configurations have delayed the flow separation. The pressure distribution at the lower wing surface is not affected by the external stores as all configurations show the same curve and values in figure above.

42

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Fig. 11 Pressure distribution at low angle of attack due to effect of stores at yaw angle a −10°, b 0°, and c 10° at α = −4°

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Upper surface clean wing -3.5

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Fig. 13 Pressure distribution at low angle of attack due to effect of stores at yaw angle, a −10°, b 0°, and c 10° at α = 14°

4 Conclusion The study on the effect of dual external stores on a generic light aircraft model was successfully carried out at UTM Low Speed Wind Tunnel, and this project’s objective was achieved. A noticeable effect is found on lift coefficient as it is reduced by 13%

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when the dual external stores are installed on the wing compared to the clean wing configuration. In addition, there is also a reduction of 23% in induced drag when dual external stores are installed. In terms of stability, external stores are not affected much because the aircraft model is still in stable condition for directional and rolling stability. However, longitudinal stability is reduced with an increase in trim angle. The study also shows that the installation of external stores has delayed the flow separation to occur mainly at high angle of attack and positive yaw angle, whereas at low angle of attack, only at angle of attack at −4° and yaw angle at −10° promotes the delay of flow separation. Furthermore, external stores only affect the wing’s upper surface, whereas consistent results are obtained in the wing’s lower surface. Acknowledgements This research was funded by the Universiti Teknologi Malaysia (Grant 21H05). The authors also would like to express their appreciation to the technical staff of the Universiti Teknologi Malaysia (UTM Aerolab).

References 1. Anderson JD (2010) Fundamentals of aerodynamics, 5th edn. McGraw-Hill Education 2. Lazim T, Shabudin M, Saint H (2003) Computational fluid dynamic simulation (CFD) and experimental study on wing-external store aerodynamic interference of a subsonic fighter aircraft. Acta Polytechnica 43(5) 3. Mansor S (2008) Introduction to UTM low speed wind tunnel 4. Manaf Z, Shabudin M, Shuhaimi M, Nazri N, Tholudin M (2017) Influences of external store on aerodynamic performance of UTM-LST generic light aircraft model. J Adv Res Fluid Mech Thermal Sci 39(1):36–46 5. Manaf Z, Shabudin M, Shuhaimi M, Nazri N, Tholudin M (2018) Wind tunnel experiment of UTM-LST generic light aircraft model with single external store. Int Rev Mech Eng (IREME) 12(3) 6. Marsden P, Haines A (1967) Aerodynamic loads on external stores: a review of experimental data and method of prediction. Citeseer 7. Nelson RC (1998) Flight stability and automatic control, vol 2. McGraw Hill, New York 8. Rae W, Pope A (1984) Low-speed wind tunnel testing. Wiley 9. Tittel S (2009) Cost, capability, and the hunt for a lightweight ground attack aircraft. Fort Leavenworth, US Army Command and General Staff College 10. Turner C (1982) Effect of store aerodynamics on wing/store flutter. J Aircr 19(7):574–580 11. Whoric J (1977) Static stability and drag effects of various external store configurations on the F-15 aircraft at Mach numbers from 0.6 to 1.3. ARO INC ARNOLD AFS TN 12. Yoon Y (2009) Experimental study for the safety analysis of an external store separation from fighter aircraft. J Korean Soc Aeronautical Space Sci 37(3):232–239

Computational Analysis on Aerodynamics of a Boxfish-Inspired Airship Nur Asyiqin Ahmad Kamal and A. S. M. Harithuddin

Abstract Boxfish’s unusual rigid-like shape is commonly thought as the limitation of its movement within its aquatic surroundings. Despite that, recent studies on its hydrodynamic characteristics has proved its hydrodynamic capability with some claiming that the unique boxfish shape produces less drag and helps maneuverability. These qualities are desirable for hydrodynamic and aerodynamic vehicles. Applying a biomimetic approach, this attempts to adapt the boxfish shape to airship design in order to improve its flight performance. A boxfish-shaped airship hull is designed with an approximation of the carapace shape of a yellow boxfish (Ostracion cubicus Linnaeus 1728). For aerodynamics characterization comparison, a typical ellipsoidal hull shape with a similar fineness ratio is chosen. Both airship’s hulls are analyzed using Ansys Fluent, a solver system for aerodynamic performance analysis with computational fluid dynamics approach. The drag force, lift force, yaw moment, and pitch moment is chosen as these are the qualities that relate to airship maneuverability. The results show that the boxfish-inspired airship’s hull generates better maneuverability in the yaw plane but higher drag association compared to the ellipsoid airship’s hull. Additionally, the boxfish-shaped airship’s hull promotes better stability in the pitch plane. This study demonstrates that the biomimetic airship’s hull has huge potential in the airship’s development. It is hoped that study will provide useful resources for future development of biomimetic application in lighter-than-air vehicle design. Keywords Biomimetic · Lighter-than-air vehicle · Airship · Aerodynamics performance · Computational fluid dynamics · Boxfish

N. A. A. Kamal · A. S. M. Harithuddin (B) Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_4

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1 Introduction Biomimetic is the study of adapting ideas, designs, and strategies found in nature in solving engineering problems. Nature has developed structures, designs, materials, and strategies that maximize efficiency through millions of years of natural selection. By mimicking nature’s design, as the idea goes, one can find a solution that has gone through a long process of ‘testing’ and thus be applied to optimize a function [1]. Aerospace engineering has particularly benefited from biomimetic. Examples are aplenty: from the upturned wing tip of soaring birds which was found to reduce drag, the halters of winged insects that inspired vibrational gyroscopes, and also the fact that the heavier-than-air flight was inspired by birds [2]. Lighter-than-air flight has been often overlooked by aircraft designers, especially from the lens of biomimetic. After all, there are no animals that are actually lighter than the air itself. One problem of airships, a type of lighter-than-air aircraft, is its maneuverability in air. Its flight is known to be sluggish and susceptible to wind gush with only propellers to guide its motion [3]. An airship concept that will be explored is one with smaller size that can be maneuvered in tight places such as indoors and functioning as drones [4]. The inspiration is not coming from flying animals but from under the sea [5]. A fish and an airship both depend on a common trait in their motion through space: buoyancy. Looking into nature for inspiration, one question arises on the animal that closely resembles the shape and motion of an airship. The answer may be found in yellow boxfish (Ostracion cubicus Linnaeus 1728). They both have the same structure (the carapace of a yellow boxfish is analogous to the hull of a blimp) and same method of motion control (fins for yellow boxfish and propellers of blimps) (Fig. 1). Literature has shown that boxfish’s carapace shape provides lower drag production and improved maneuverability for the species to swim through the limited space of coral reefs under the sea [8–10]. The carapace shape can be adapted to design the hull of an airship in order to improve its maneuverability. The purpose is to investigate whether a boxfish-inspired airship design promotes better aerodynamics performance. It is crucial to decide on the methodology to provide this research with a reasonable or logical solution. Inspired by the yellow boxfish, this research explores a new design of airship by mimicking the fish’s carapace as its hull design. The study of this boxfish-inspired

Fig. 1 A yellow boxfish (left) and a common airship/blimp (right) [6, 7]

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airship’s hull involves matching the yellow boxfish’s aerodynamic characteristics (or more accurately the ‘hydro’-dynamics) in the hope of copying its motion efficiency in water. Another airship hull design, ellipse-shaped was also sketched using CATIA. To evaluate the aerodynamic performance of a boxfish-shaped airship’s hull, its drag, lift, moment forces, and coefficients are analyzed using a computational fluid dynamics method with Ansys Fluent. In the end, both models will be compared to determine the ideal performances. Hence, the influence of a boxfish-shaped airship’s hull on its aerodynamics performance and maneuverability can be determined.

2 Methodology 2.1 Validation The boxfish model to be validated was a 3-dimensional scanned model from an actual Ostracion cubicus Linnaeus, 1758 [9]. Meanwhile, the validation boxfish model in this research is an approximated sketch made using CATIA. Its dimension resembles the 3-dimensional scanned model in the study that will be validated (Figs. 2 and 3). The simulation was done using Ansys Fluent under transient flow and turbulence model of Transition SST, with water density of 998.2 kg/m3 and dynamic viscosity of 1.001 Pa·s. The meshing generated from the research to be validated was 10 million elements [9]; meanwhile, the meshing generated for validation is only 1.3 million elements. The boundary condition includes 1 velocity inlet of 0.5 m/s and 5 pressure outlets. The simulation setup for validation was done with the same setup as the research paper referred for 0° yaw with variation of velocity from 0.2 to 0.5 m/s. The maximum percentage error for drag force varies from 16 to 17%. However, there is no specific range of percentage error as every study has distinct parameters and assumptions [11]. In this study validation, an approximated 3-dimensional boxfish model is sketched using CATIA; meanwhile, the research to be validated

Fig. 2 Dimension of 3d-scanned model (left) and sketched model (right) [10]

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Fig. 3 Domain and boundary condition of analysis setup [10]

used a 3-dimensional scanned boxfish. Therefore, there exists a little bit of difference in the geometry since a 3-dimensional scanned model gives a more accurate body design compared to the approximated one. Moreover, in terms of practicality, the airship will most likely be built based on the approximated computer model rather than an exact scaled-up copy of a yellow boxfish.

2.2 Reynolds Number Determination In order to simulate the airship’s hull under the same fluid condition and achieve the equal performance of a boxfish, the determination of Reynolds number to be applied is crucial. The Reynolds number, Re is a dimensionless number used to compare a real situation with a scale model. In this case, it is used to compare aerodynamic characteristics of a waterborne boxfish with an airborne blimp airship. Reynolds number is defined as: Re =

ul ρul = µ ν

(1)

where ρ is the density of the fluid, u is the velocity of the object, l is the length of the object, ν is the kinematic viscosity, and μ is the dynamic viscosity of the fluid. The characteristic length and speed of the airship is found by comparison of boxfish’s length and speed in seawater with the help of Reynolds number. To determine the best speed, Reynolds number, and size of the airship, a range of boxfish’s speed between 0.05 and 0.50 m/s [12] and length between 10 and 45 cm were applied to the equation [6]. We assumed that both boxfish and airship float at a temperature of 25 °C. Therefore, the density of water for the boxfish is 1023.38 kg/ m3 with viscosity of 9.370 × 10–7 Pa·s; meanwhile, the density of air for the airship is 1.184 kg/m3 with viscosity of 1.562 × 10–5 Pa·s.

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To best imitate the boxfish, the intended airship is designed as a smaller type with a typical length between 1 and 3 m and a fineness ratio (the ratio of the length of a body to its maximum width) of approximately 3.7. Therefore, the range of speed for this boxfish-inspired airship design will be about 1–3 m/s. Hence, a boxfish-inspired design with a length of 1 m and 1.46 m/s speed is simulated using Ansys.

2.3 Computational Aided Drawing (CAD) The shape of the airship is a solid of revolution about a line through its longitudinal axis, which means that the height and width are equivalent. The approximated boxfish-inspired airship and ellipse-shaped airship are 1 m long with both height and width of 0.27 m. Both models were sketched using CATIA (Fig. 4).

2.4 Computational Fluid Dynamics (CFD) Analysis The CAD models were analyzed using Ansys R1 Fluid Flow Fluent system. They were imported into Ansys DesignModeler where a small cuboid domain with dimension of 7 × 1 × 1 m was made for mesh refinement and a bigger fluid domain with dimension of 11.2 × 2.8 × 2.8 m was produced (Fig. 5). The meshing method used for the 3-dimensional analysis of the airship models was tetrahedrons with high smoothing quality and element size of 300 mm. Inflation layer was added to the airship’s surface with a growth rate of 1.2 and maximum layers of five. In addition, body sizing meshing was applied to the bigger domain body under the body influence of the smaller domain with element size of 100 mm for further meshing improvement. The completed meshing has an average skewness of 0.23 and average orthogonal quality of 0.77. These qualities are excellent for analysis using Ansys Fluent (Fig. 6).

Fig. 4 Boxfish-inspired design (left) and ellipse-shaped design (right)

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Fig. 5 Domain configuration

Fig. 6 The completed meshing for boxfish-inspired design (left) and ellipse-shaped design (right)

The models are then moved into Ansys Fluent setup analysis to calculate for the solution. The flow is set to be transient and the model used was the Transition SST turbulence model. The velocity of the inlet is set to be 1.46 m/s, considering the selected airship’s hull speed from the Reynolds number evaluation. The fluid applied was air with a density of 1.184 kg/m3 and viscosity of 1.562 × 10–5 Pa·s. The boundary condition setup is the same as in validation (see Fig. 3). The outlet pressure was set to 0 Pa and the simulation was run for 1000 iterations for 0° angle of attack condition as all residuals, except for continuity reaches the order of magnitude of 10–3 and converged (see Fig. 7).

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Fig. 7 Residual plot for convergence

For further analysis, drag force, lift force, and moment in pitch and yaw directions were defined into the setup and were plotted. Other simulations were also made with different yaw angles (15, 30, and 45°) to study the effect of changes in flow direction during the turning maneuver.

3 Results and Discussion 3.1 Boxfish-Shaped and Ellipsoid-Shaped Airship’s Hulls at 0° Yaw Angle Both boxfish and ellipse hulls generated a drag coefficient of 0.0217 and 0.0176, respectively (see Fig. 8). The ellipse-shaped hull has a lower drag association compared to the boxfish-shaped hull, considering its streamline or ‘aerodynamics geometry’ frontal. In terms of lift force and coefficient, the ellipsoid-shape hull produced a low positive lift; meanwhile, the boxfish-shaped hull produced a low negative lift. Since airships usually gained their lift from the ‘buoyant gasses’; hence, the low negative lift value is acceptable as it possesses low significance on the airship’s buoyant lift force [13].

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Fig. 8 Aerodynamics coefficients comparison between boxfish-inspired and ellipse-shaped airships

For maneuverability, the boxfish-shaped hull promotes turning in yaw direction due to its positive value; meanwhile, the ellipsoid-shaped hull promotes stability for its negative yaw coefficient (see Fig. 8). A positive yaw coefficient shows that the airship tends to turn away from its flight path or is unstable; hence, promoting maneuverability [14]. In contrast, a negative yaw moment causes the airship to counteract the changes in its flight path; therefore, promoting weathercock stability but low maneuverability. The boxfish-inspired airship promotes stability in pitch rotation for its negative pitch moment. This is desired for an airship as pitch rotation is not needed in airship operation since the buoyant force around its body is equivalent; hence, it will ascend and descend equally from its frontal surface to rear surface [15]. Meanwhile, the ellipsoidal hull enhances pitching rotation as it generates a positive pitch moment. To conclude the discussion, these findings fit with the purpose to design a small airship that has more airborne maneuverability for an indoor environment with tight spaces, while maintaining the pitch stability against oscillation.

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Fig. 9 Contours of velocity magnitude of boxfish-shaped (top) and ellipse-shaped (bottom)

The velocity magnitude (see Fig. 9) and static pressure contours (see Figs. 10 and 11) are visualized. It can be seen that maximum static pressure acts on the frontal area of both models, where the velocity is at its minimum value.

3.2 Boxfish-Shaped Airship’s Hull Under Variation Yaw Angle Figure 12 shows a directly proportional relationship between yaw coefficient and yaw angle. The maximum yaw coefficient is around 0.1214 for 45° yaw angle. This is due to the manoeuvrability of the boxfish-inspired airship’s hull which promotes positive yaw moment. The same relationship is produced for drag coefficient 0.165 for 45° yaw angle (see Fig. 13). As the airship turns, a large frontal surface associates with the incoming flow; hence, increases the generated drag force.

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Fig. 10 Contour of static pressure for boxfish-inspired airship’s hull

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Fig. 11 Contour of static pressure for ellipse-shaped airship’s hull

Fig. 12 The relationships between aerodynamics moments of a boxfish-inspired airship with yaw angle of attack

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Fig. 13 The relationships between aerodynamics forces of a boxfish-inspired airship with yaw angle of attack

4 Conclusion The aerodynamics characteristics of both boxfish-inspired airship’s hull and ellipseshaped airship’s hull were studied to evaluate the difference between both shapes and determine the best option for airship’s maneuverability. The application of biomimetic or bio-inspired design was applied to discover its importance in improving the airship’s efficiency and functionality. Moreover, computational fluid dynamics has provided the analysis and evaluation of the aerodynamics performance for both boxfish-shaped and ellipse-shaped airship’s hulls. From the analysis, the boxfish-shaped airship’s hull promotes maneuverability, pitch stability, and a low drag coefficient production of 0.0217. In addition, a boxfishshaped airship is a non-aerodynamics shape yet provides maneuverability in yaw direction; hence, improving the efficiency and functionality of its flight. However, in terms of aerodynamics capability, an ellipse-shaped airship has a better quality in terms of drag and lift production since it is an aerodynamics geometry. This highly maneuverable lighter than air vehicle design is foreseen to be useful for indoor airships moving in tight spaces. This performance is crucial to airship technology; hence, the research and development of boxfish-shaped airship’s hulls should be made in the future to produce an efficient airship.

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References 1. Hwang J, Jeong Y, Park JM, Lee KH, Hong JW, Choi J (2015) Biomimetics: forecasting the future of science, engineering, and medicine. Int J Nanomed 10(1):5701–5713 2. Sedan MF, Malek A, Harithuddin ASM (2018) Development of attitude control system for hybrid airship vehicle. Int J Eng Technol 7(4.13): 99–106 3. https://www.rawpixel.com/search/april%203.%20the%20airship%20is%20visiting%20u.s.% 204th%20fleet%20headquarters%20for?page=1&sort=curated 4. Harithuddin ASM, Sedan MF, Ali SAM, Mansor S, Jifroudi HR, Adam SN, Khuzaimah Z (2019) Lighter-than-air (LTA) unmanned aerial system (UAS) carrier concept for surveillance and disaster management. In Seminar Nasional Geomatika 3:1255–1264 5. Bechert D, Bruse M, Hage W, Meyer R (2000) Fluid mechanics of biological surfaces and their technological application. Naturwissenschaften 87: 157–171.https://doi.org/10.1007/s00 1140050696 6. McGrouther AM (n.d.) Yellow Boxfish, ostracion cubicus linnaeus, 1758. The Australian Museum. https://australian.museum/learn/animals/fishes/yellow-boxfish-ostracion-cubicus/#: ~:text=Fast%20Facts&text=The%20species%20grows%20to%2045%20cm%20in%20length 7. rawpixel (n.d.) April 3. the airship is visiting U.S. 4th Fleet headquarters for images: Free photos, PNG Stickers, wallpapers & backgrounds. 8. Chowdhury H, Islam R, Hussein M, Zaid M, Loganathan B, Alam F (2019) Design of an energy efficient car by biomimicry of a boxfish. Energy Procedia 160:40–44 9. Vincent JF, Bogatyreva OA, Bogatyrev NR, Bowyer A, Pahl AK (2006) Biomimetics: its practice and theory. J R Soc Interface 3(9):471–482 10. Van Wassenbergh S, van Manen K, Marcroft TA, Alfaro ME, Stamhuis EJ (2015) Boxfish swimming paradox resolved: forces by the flow of water around the body promote manoeuvrability. J R Soc Interface 12(103):20141146. https://doi.org/10.1098/rsif.2014.1146 11. Majidian H, Re: what is the acceptable limit of difference between numerical and experimental work for validation? https://www.researchgate.net/post/What_is_the_acceptable_limit_of_d ifference_between_numerical_and_experimental_work_for_validation/5f44d69f61acf92222 3c7648/citation/download 12. Hove JR, O’Bryan LM, Gordon MS, Webb PW, Weihs D (2001) Boxfishes (Teleostei: Ostraciidae) as a model system for fishes swimming with many fins: kinematics. J Exp Biol 204(8):1459–1471 13. Liao L, Pasternak I (2009) A review of airship structural research and development. Progr Aerospace Sci 45(4–5): 83–96 14. Kobayashi T, Katsuyama E, Sugiura H, Ono E, Yamamoto M (2018) Efficient direct yaw moment control: tyre slip power loss minimisation for four-independent wheel drive vehicle. Veh Syst Dyn 56(5):719–733 15. De Paiva E, Bueno S, Bergerman M (1999) A robust pitch attitude controller for AURORA’s semi-autonomous robotic airship. In: The 13th lighter-than-air systems technology conference, p 3907

Computational Fluid Dynamics Analysis of High Aspect Ratio Wing Application Ainaa Nabilah Mohd Nazri, Norzaima Nordin, Norazrina Mat Jali, Baizura Bohari, and Mohammad Yazdi Harmin

Abstract The aviation industry is attempting to enhance the aerodynamic performance by increasing the aspect ratio of the wing, which can be associated with the usage of the High Aspect Ratio (HAR) wing. Aerodynamic performance can be analyzed using different approaches and one of the approaches is through the Computational Fluid Dynamics (CFD) analysis. However, prior research demonstrates a vague technique for CFD analysis, which makes it challenging for new researchers to learn the precise steps using the CFD approach. Therefore, this study aims to demonstrate the process of CFD analysis in a detailed technique using Ansys software and compare the aerodynamic performance at three options domain sizes. The aspect ratio of AR-16 was used with the Spalart–Allmaras turbulence model and the result of the mesh independency study was validated with the lift coefficient. The best mesh was verified with different turbulence models either using k–ω SST or Standard k–ε. The result shows that the best mesh for the HAR wing is the base mesh with a low percentage difference compared to fine mesh. In the domain sizes, the second option with the reduction of 20% domain size produced a higher lift-to-drag ratio than first and third options with a percentage error of less than 6% at the angle of attack, AoA 9°. Moreover, the 20% domain size reduction can reduce approximately 20 min of computational time, as well as contribute to the computational time efficiency in the CFD analysis of the HAR wing. Keywords Computational fluid dynamic · Domain size · High aspect ratio wing · Mesh dependency · Turbulence model A. N. M. Nazri Department of Mechanical Engineering, Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Kem Perdana Sungai Besi, 57000 Kuala Lumpur, Malaysia N. Nordin (B) · N. M. Jali · B. Bohari Department of Aeronautical Engineering and Aviation, Faculty of Engineering, Universiti Pertahanan Nasional Malaysia, Kem Perdana Sungai Besi, 57000 Kuala Lumpur, Malaysia e-mail: [email protected] M. Y. Harmin Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_5

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1 Introduction Over the last few centuries, the aircraft design revolution has evolved to enhance the aerodynamic performance. Aerodynamic performance can be related in many aspects, such as lift, drag and the lift-to-drag ratio (L/D), which can be defined as a ratio of lift coefficient to drag coefficient [1]. One of the methods to improve aerodynamic performance is to increase the aspect ratio of the wing. Aspect Ratio (AR) is the ratio between the wing span and the wing chord [2] and a High Aspect Ratio (HAR) wing is able to reduce the overall drag coefficient, thus providing the aircraft with a higher lift-to-drag ratio [3]. This can be proven by the overall drag coefficient expressed in Eq. (1), where the overall drag coefficient can be defined as follows: C D = C D0 + C Di = C D0 +

(C L )2 π e' A R

(1)

where CDO is the profile drag coefficient, CDi is the induced drag coefficient, CL is the lift coefficient, e' is the span coefficient factor and AR is the aspect ratio of the wing. This equation shows that the overall drag coefficient is inversely proportional to AR. The increasing wing aspect ratio will reduce the overall drag coefficient, thus resulting in a higher lift-to-drag ratio. Aerodynamic performance can be analyzed through analytical, experimental and numerical approaches. An analytical approach can be performed using a formula such as lift and drag coefficient. Meanwhile, for the experimental approach; flight and wind tunnel tests are applicable methods for analyzing aerodynamic performance [4]. As for the numerical approach, the aerodynamic performance can be analyzed using computational software such as Fluid Structure Interaction (FSI) analysis and Computational Fluid Dynamics (CFD) analysis. CFD analysis comprises selecting the domain, determining the domain size, meshing and selecting the turbulence model. A few different types of fluid domains have been used in CFD analysis such as the rectangular domain, the C-type domain, the O-type domain and the H-type domain [5–8]. The C-type domain has high accuracy and computational efficiency, and the results are consistent with experimental results [5–7]. Furthermore, the selection of domain size has a significant influence on the determination of flow features, particularly at the wall boundary [9]. The preferred domain size is to allocate a domain at a distance of about 20 chord from the airfoil to allow good flow development around the wing [10–12]. Nevertheless, an appropriate domain size may also depend on the requirements of the analysis, and it is essential to vary the domain size when carrying out the analysis to determine the optimal domain size [13]. In addition, meshing is one of the criteria that must be considered in the CFD analysis. Two types of mesh can be used in CFD analysis, (a) structured mesh and (b) unstructured mesh. According to Danardono et al. [5], the unstructured mesh is preferred it is more suitable for wing airfoil and blended winglets due to the curve shaped structure. Also, this finding supported where the unstructured mesh is less computational time while maintaining its accuracy [7].

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Another essential requirement in meshing is the range of the wall y+ value and the skewness [10]. In the viscous sublayer area where the viscous stress dominates the wall shear, the value for the wall y+ was determined to be less than one, obviating the need for wall functions that tend to overestimate the viscous drag in comparison [10, 14]. The next step is selecting the turbulence model for the CFD analysis. Generally, there are two categories of turbulence models: the one-equation turbulence model and the two-equation turbulence model [15]. The Spalart–Allmaras turbulence model rep- resents a one-equation turbulence model while Standard k-ε, Realizable k-ε, k-ω SST, Standard k-ω represent in the form of two-equation turbulence models. Spalart-Allmaras and k-ω SST models are known as suitable turbulence models for aerodynamic performance analysis [16, 17]. Fairuz et al. [18] found that the Spalart– Allmaras turbulence model can predict an unsteady flow and aerodynamic force of the wing. Hence, it is preferable to use the Spalart–Allmaras turbulence model to analyze the aerodynamic performance. This finding is supported by Efthekhari et al. [12]. However, the previous study has demonstrated a vague technique of CFD simulation, which challenges the new researcher to learn and perform CFD analysis especially in the HAR wing application. Therefore, the present study will provide a clear technique for per- forming the CFD analysis in terms of the selection of domain size and the simulation setup on the aerodynamic performance of HAR wing model.

2 Methodology ANSYS software was used to perform CFD analysis for a HAR wing model with the aspect ratio of AR-16. The simulation underwent a few phases, such as the HAR wing modeling, selection of fluid domain, mesh independency study, turbulence model selection and simulation setup (see Fig. 1).

2.1 HAR Wing Modeling The present study modeled the HAR wing with a symmetrical NACA0012 airfoil. The airfoil coordinates were imported from the airfoil generator into the Ansys design modeler via Microsoft Office Excel. The imported airfoil coordinates were plotted and linked to form an airfoil curve and extruded according to the wingspan length. The HAR wing was modeled with the aspect ratio of AR-16 (see Fig. 2). The HAR wing model was constructed based on the Nordin et al. [3] wing model with the wing- span length, chord length and span width are 0.8 m, 0.05 m and 0.025 m, respectively.

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Selection of fluid domain: Selection of domain type (C-type) Selection of domain size: (Option 1 – 100% domain size) (Option 2 – reduce 20% of the original size) (Option 3 – reduce 40% of the original size)

NACA 0012 airfoil was used with wing model (AR-16) Import airfoil coordinate from airfoil gener- ator to ANSYS design modeler

Turbulence model & simulation setup: Turbulence model (Spalart-Allmaras) Set up the boundary condition

Mesh independency study:

Verification with different turbulence model (k

turbulence model)

Validation with Cl value (AoA 0°)

Meshing type (unstructured mesh) Body sizing and inflation layer mesh + Targeted value of wall y 7. Temperature also can indicate water quality because it influences the density of water, chemical reactions, and biological activity [5, 6]. These two parameters will be focused on in this study which will involve the usage of a DS18B20 waterproof temperature sensor and an analogue pH sensor [10, 11].

Fig. 1 Percentage of river condition in Malaysia

Development of an Autonomous RC Catamaran for Surveillance …

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According to the first Newton’s law of motion, a force must be applied to an object to move its body. Therefore, using the installed propulsion system, a boat produces a force or thrust to move its body from its static condition. According to Rawson and Tupper, the rotating propeller creates an area of a pressure difference between the frontal and rear of the propeller, which makes the boat move forward [12]. For the propulsion system of the boat, several different propellers were used, for example, fixed-pitch propellers, ducted propellers, and airboat propellers [13, 14]. However, the airboat propeller is the most suitable for this study since it can prevent the propeller from getting entangled in aquatic weeds and mud at the bottom of the lake. A boat or a ship turns using its rudder; it works by inducing the directional or rudder force that can induce a rudder moment. In addition, the rudder moment induces a drift angle and a slight surge velocity before the hull exerts a force on water particles to produce the opposite force to the hull. Since the hull’s bow has a larger surface area than the stern, the resultant force makes the hull turn [9]. However, a rudder is not used in the study since it might entangle aquatic weeds or mud. Hence, the propeller will produce a directional thrust to induce the rudder moment as same as the rudder. On the other note, a ship or boat hull is one of the components that create the boat or hull itself. It is an enclosure structure to protect the ship’s components. Not only that, but the buoyancy force of the boat also depended on the hull’s volume. The buoyancy force cannot support the hull’s weight if the hull does not have sufficient volume. There are two hull categories: planning and displacement hull [3]. A planning hull is a type of hull that the weight is supported by hydrodynamic pressure at high speed, making it lift at the bow of the hull, for example, flat bottom hull and deep vee hull [15]. Also, there is another type of hull, the displacement hull, that uses buoyancy to support its weight, and examples are round bottom hull and multi-hull or catamaran [16]. In the present study, an autonomous RC catamaran was proposed to assess the water quality in the Universiti Teknologi Malaysia (UTM) water reservoir. As stated before, the type of hull chosen is a catamaran equipped with a flight controller for autonomous navigation. It also has a water quality monitoring system consisting of pH and temperature sensors. The data was recorded in real-time and saved using mobile storage for easy access.

2 Methodology The methodology for the experiment consists of 3 categories: catamaran fabrication, water monitoring sensor assembly, and autopilot navigation system assembly. However, all three categories were executed separately and eventually integrated into a complete system. For a better understanding, Fig. 2 shows the flow chart involved in the preparation. The boat carries essential sensors onboard to ensure the system works perfectly on the water’s surface and records data at any location. Therefore, the catamaran or

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Fig. 2 Setup flowchart

multi-hull type was chosen as the main hull since this design offers stability and a larger deck area that can store all the components and sensors. Not only that, but it also provides low resistance when it travels through wavy water around UTM lake (Figs. 3 and 4). There were two containers involved in keeping the electronic components. The upper container was used to house all the related components for the autopilot navigation system, while the bottom cylindrical container stored water monitoring sensors, such as the analogue pH sensor and DS18B20 sensor. The primary material for the container is made from thermoset polymer with a non-deformable shape and geometry, especially if exposed to heat. However, both hulls were custom-made using a 3D printer with polylactic acid (PLA) material. Fig. 3 Catamaran view 1

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Fig. 4 Catamaran frontal view 2

Newton’s third law states that every force acting on a body has a reaction force. This law applies to the propulsion system being used, which is the airboat propeller. The propeller will provide both thrusts for the boat and directional thrust to steer the boat in any direction. Figure 5 shows the connections of the motor and servo (Fig. 6). MG996R servo was used to provide torque to the rod connector by using a push rod. The SunnySky X3525 520kV brushless motor with an 8-inch propeller connected to the aluminium rod will turn in the direction the servo turns. Both servo and motor were connected to the FrSky X8R receiver at SBUS pins 2 and 1. However, the speed of the motor is managed and controlled by the Skywalker 60A UBEC electronic speed controller (ESC). Lastly, the system can be powered with a battery as the

Fig. 5 Servo connection

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Fig. 6 Motor and propeller

power source. With this configuration, the boat can be manually manoeuvred and operated without the assistance of an autonomous system. An autopilot and autonomous navigation system are needed to ensure the system can be deployed without human intervention. Various equipment and sensors communicate to ensure the autopilot system always provides the best navigation accuracy. Hence, some essential components must be considered to make the system work. Pixhawk flight controller was chosen to pair with the global positioning system (GPS) module to capture satellite data and communicate with the flight controller for positioning and control. Radio telemetry sends all the autopilot information to the ground system from the flight controller. Next, the system was powered by four cells 4400 mAh lithium polymer battery manufactured by Gens ace. Lastly, a power module was used to reduce the battery’s voltage to a voltage compatible with the battery. It is usually 5V and used to measure battery capacity and voltage. The wire connection for the catamaran’s autopilot and navigation system is shown in Fig. 7. All the previously described components and equipment were connected to a single Pixhawk flight control powered by a four cells lithium polymer battery. A power module was used to lower the voltage to a level compatible with the motor, usually 5 V, and it also can measure the voltage and capacity of the battery. Each of the two hull servos was connected to the receiver and then to the Pixhawk through channel RC. The Pixhawk includes a dedicated pin for the GPS module with a built-in compass. The autopilot system was programmed using Mission Planner, a software used to program the waypoint, proportional-integral-derivative (PID) tuning, and compatible with Pixhawk controller hardware (Fig. 8). As discussed earlier, various methods are used to monitor water quality; however, this study used an autonomous boat loaded with sensors required. The sensor set and equipment were kept inside the catamaran body. The data recorded in real-time, including pH value and temperature, was stored in mobile storage, a secure digital (SD) card for further analysis. Several sensors were used: an analogue pH sensor

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Fig. 7 Autopilot navigation system

and the DS18B20 sensor. The analogue pH sensor is used to measure pH value by measuring the voltage produced by the acidity of water and the difference between its known solution voltage. DS18B20 sensor is a sensor used to measure the temperature of the water. In addition, Arduino Uno R3 connected all the system’s sensors and components and helped them to communicate. The SD card module will read and write data from the SD card to Arduino or vice versa. Since a 22.2 V battery powers the system, a voltage regulator is needed to reduce and step down the voltage into a compatible voltage for the system. A switch regulated the system from taking the data. Figure 9 shows the connection of all the components and sensors involve in the system. The sensors, such as the analogue pH sensor and temperature sensor, were connected to the Arduino analogue pin A0 and digital pin 8, respectively. The data from the sensor was sent to the Arduino and then stored on the SD card. Only the SD card module (LM2596) can read the SD card attached to numerous digital output pins, as shown in Fig. 9. The power supply used is the same 14.8 V lithium polymer

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Fig. 8 Mission planner waypoint

battery that powered up the autopilot and navigation system. The high voltage can be minimized by decreasing the voltage from 14.8 to 5 V, the operating voltage for all the devices (Fig. 10 and Table 1).

3 Result and Discussion According to the observation, the catamaran’s stability and buoyancy are sufficient to cruise at an average speed of 0.25 m/s. Initial data was collected based on manual control only, imitating an autonomous system following the waypoint given. The ground speed was measured using GPS and can be monitored using the Mission Planner dashboard. The pilot controlling the catamaran tried to get as close as possible to the 0.25 m/s speed limit. However, the speed fluctuation still varies from 0.13 m/s to 0.43 m/s at an average of 0.25 m/s with a standard deviation of 0.07. Since the speed was measured using GPS, the accuracy of GPS also needs to be considered, as the throttle percentage also affects the motor output speed (Fig. 11). GPS accuracy depends on the number of satellites captured (red line); the higher the number of satellites detected, the better its accuracy. From Fig. 13, the maximum number of satellites detected is 22. According to Fig. 12, the catamaran’s speed fluctuates at an average speed of 0.25 m/s. The green line in Fig. 13 is the horizontal dilution of precision (HDOP) used to measure the error by the relative position of the satellites. The higher the number of satellites detected, the lower the HDOP and the more accurate the GPS is. In addition, the motor’s throttle percentage also influenced the catamaran’s speed. According to Fig. 14, the average throttle percentage is 13% only. After the catamaran was armed, the throttle percentage fluctuated because

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Fig. 9 Sensor connection

Fig. 10 Testing location Table 1 The information regarding the testing site for the catamaran

Location

Universiti Teknologi Malaysia (UTM) lake

Operation area

9340 m2

Coordinate

1.555510,103.637515

Travel distance

130 to 150 m

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Fig. 11 Waypoint recorded by the flight controller

the pilot tried to adjust the catamaran’s speed as close as possible to 0.25 m/s. It can be seen in Fig. 12, where speed fluctuation occurs at the catamaran’s initial stage. However, a steady speed was achieved when the throttle percentage was 13%, whereby the catamaran started to cruise at an average speed of 0.25 m/s. Since the catamaran was powered by a four cells LiPo battery, the voltage and total current used also need to be considered to determine the catamaran’s endurance and how long the operation period for each charge. According to Fig. 15, the battery voltage decreases linearly for 10 min of operation from 16.1 to 16 V. It consumes only 0.1 V for the mission, as recorded using the Pixhawk flight controller (Fig. 16). As the battery voltage decreases, the total current increases linearly up to 9.5 mAh within 10 min of operation. Theoretically, the operation can last up to 57 h using just four cells LiPo battery. Since this is the initial data collected without considering full autonomous function, the battery voltage and the total current used is too small,

Fig. 12 Catamaran speed over time

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Fig. 13 Number of satellites and HDOP

Fig. 14 Throttle percentage

Fig. 15 Battery voltage over time

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Fig. 16 Total current used over time

considering all the components installed in the system. The experiment was repeated to verify the energy consumption. Next, water temperature is a crucial parameter for water pollution since it can influence water density, chemical reactions, and biological activity. Hence, a DS18B20 sensor attached at the bottom of the catamaran will give an accurate water temperature reading in this study. The sensor was programmed by recording the data every 5 s. While the catamaran travelled following its waypoint along the path, the temperature sensor recorded the data and stored it in the SD card. Based on the Fig. 17, the temperature reading across the lake is relatively constant, with an average temperature of 28 °C for 10 min of recording time. Wagner et al. stated that the water temperature should always range between 0 to 40 °C [17]. Therefore, based on the Interim National Water Quality (INWQ) Standard by the Department of Environment, the water temperature should be not more than 29 °C. Based on Fig. 18, the pH value fluctuated slightly over time but maintained a consistent average reading of 8. Since the fluctuation value of pH is the same as the fluctuation of temperature value simultaneously, as Wagner et al. stated, the water’s temperature might influence the acidity (pH), density, and chemical reaction [17]. However, according to the INWQ, the pH value of the UTM lake is still within the acceptable range, indicating the water is clean.

Fig. 17 Temperature versus time

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Fig. 18 pH value versus time

4 Conclusion In conclusion, the catamaran moved stably during autonomous function with 0.5 m/s configured using the Mission Planner motor configuration. However, the route taken by the catamaran must be adjusted and tuned so it can follow the original route even if it reaches the desired waypoint. This type of unmanned surface vessel (USV) has an enormous potential to be used in future in various kinds of missions, either in water reservoirs or in the ocean itself. The small development might become significant in the industry because future technologies can deliver new USVs. Besides that, from water monitoring or surveillance, USV also has potential in the military. For example, it could be used as a stealth mission or defends the mission from any incoming attack throughout the sea border.

References 1. Chang H-C et al. (2021) Autonomous water quality monitoring and water surface cleaning for unmanned surface vehicle. Sensors 21(4). https://doi.org/10.3390/s21041102 2. Afroz R et al (2014) Water pollution: challenges and future direction for water resource management policies in Malaysia. Environ Urban ASIA 5(1):63–81. https://doi.org/10.1177/097542 5314521544 3. Goi CL (2020) The river water quality before and during the Movement Control Order (MCO) in Malaysia. Case Stud Chem Environ Eng 2 2. https://doi.org/10.1016/j.cscee.2020.100027 4. Afroz R, Rahman A (2017) Health impact of river water pollution in Malaysia. Int J Adv Appl Sci 4(5):78–85. https://doi.org/10.21833/ijaas.2017.05.014 5. Naz N, Karim MM (2017) Investigation of hydrodynamic characteristics of high speed multihull vessels including shallow water effect. Proced Eng 194:51–58. https://doi.org/10.1016/j. proeng.2017.08.116 6. Pule M et al (2017) Wireless sensor networks: a survey on monitoring water quality. J Appl Res Technol 15(6):562–570. https://doi.org/10.1016/j.jart.2017.07.004 7. National Water Quality Standards For Malaysia. Department of Environment. https://www. doe.gov.my/standard-dan-indeks-kualiti/standard-kualiti-air-kebangsaan-5/ 8. Tuna G et al (2013) Navigation system of an unmanned boat for autonomous analyses of water quality. Electron Electr Eng Elektronika ir Elektrotechnika 19(8):3–7. https://doi.org/10.5755/ j01.eee.19.8.5387

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9. Molland AF, Turnock SR (2007) Marine rudders and control surfaces: principles, data, design and applications. Butterworth-Heinemann, Cambridge, MA, USA. https://www.sciencedirect. com/book/9780750669443/marine-rudders-and-control-surfaces#book-info 10. Caraballo G (2015) An arduino based control system for a brackish water desalination plant. M.S. thesis, Department of Electrical Engineering, University of North Texas, Denton, Texas, USA. https://digital.library.unt.edu/ark:/67531/metadc804931/m2/1/high_res_d/thesis.pdf 11. Khan S (2017) Development of a CAD system for parametric and attribute-based modification of yacht hull models. M.S. thesis, Department of Mechanical Engineering, Istanbul Technical University, Istanbul, Turkey. https://www.researchgate.net/publication/324011552_Develo pment_of_a_CAD_System_for_Parametric_and_Attribute-Based_Modification_of_Yacht_ Hull_Models 12. Rawson KJ, Tupper EC (2001) Powering of ships: general principles. In; Basic ship theory, 5th ed. Butterworth-Heinemann, Cambridge, MA, USA, pp 365–410. https://www.sciencedirect. com/book/9780750653985/basic-ship-theory#book-info 13. Carlton JS (2019) Marine propellers and propulsion, 4th ed. Butterworth-Heinemann, Cambridge, MA, USA. https://books.google.com.my/books?id=2drWDgAAQBAJ&pri ntsec=frontcover&dq=Marine+propellers+and+propulsion&hl=en&sa=X&redir_esc=y#v= onepage&q=Marine%20propellers%20and%20propulsion&f=false 14. Kaizu Y et al (2011) Development of unmanned airboat for water-quality mapping. Biosys Eng 109(4):338–347. https://doi.org/10.1016/j.biosystemseng.2011.04.013 15. Wagner RJ et al (2000) Guidelines and standard procedures for continuous water-quality monitors: site selection, field operation, calibration, record computation, and reporting. USA. https:// pubs.usgs.gov/wri/2000/4252/report.pdf 16. Dandabathula G et al (2021) Design and development of aquayaan: an IoT based robotic boat for inland water surveys. India. https://doi.org/10.13140/RG.2.2.32247.14247 17. Wagner RJ et al (2006) Guidelines and standard procedures for continuous water-quality monitors: station operation, record computation, and data reporting. USA. https://pubs.er.usgs.gov/ publication/tm1D3

Development of Autonomous Battery Charging Station for Campus Surveillance UAV Ahsan Habib Rifat, Mohammed Raihan, and Nazri Nasir

Abstract Unmanned Aerial Vehicle (UAV) plays an essential role in various industries. It has been used in surveillance, photography, reconnaissance, environmental data collection, agriculture, and many other purposes. However, the main problem of UAVs is that, it is time-consuming to manually recharge the battery and natural phenomena like sunray and rain while operating in an open area. Thus, an autonomous battery charging station can solve the problem as it does not require human interaction and can protect the drone from natural phenomena. In this work, an autonomous charging station for UAV is designed, fabricated, and developed. The main focus of the charging station is the door mechanism, platform mechanism, and positioning mechanism. The Hilux bed size is the primary concern for sizing the charging station as it will be used for transporting, and the dimensions for the charging station is as follows length, width, and height (1450 mm × 1400 mm × 1200 mm). The parallel positioning mechanism has been used to control the UAV on the charging station platform. However, a linear actuator has been used to control the door mechanism of the charging station. After fabrication of the charging station, it can be seen that it can operate in any weather while protecting the UAV from rain and sunray and the operating time of the charging station is less than 90 s. Keywords UAV · Charging station · Drone · Landing platform · Positioning mechanism

A. H. Rifat · M. Raihan · N. Nasir (B) Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia e-mail: [email protected] N. Nasir UTM Aerolab, Institute for Vehicle System and Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_11

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1 Introduction Over the last decade, the use of Unmanned Aerial Vehicles (UAVs) has increased significantly in various application areas owing to their maneuverability and compact size [1]. UAVs have been commonly used for surveillance, aerial photography, environmental data collection, agriculture, geographic mapping, search and rescue, and law enforcement [2]. UAVs are a part of an Unmanned Aircraft System (UAS), consisting of a ground-based controller and a communication system with the UAVs [3]. With the increasing number of uses of UAV, they are facing new challenges, including continuous UAV operation. To address this issue, the concept of charging stations has been employed by several researchers [4]. A charging station is a UAV platform that enables self-contained and continuous UAV operations [5]. It can also be used as a storage and charging system for UAVs. Apart from that, the charging station should protect the UAVs from natural phenomena like rain and sunray while operating in an open area [6]. Nonetheless, the current charging stations for UAV have some limitations. For instance, UAV’s batteries must be manually replaced after each mission, which takes time. Furthermore, due to the battery’s weight, it is impossible to add an extra battery to onboard UAV, limiting the flight duration of the UAV [7]. These current limitations can be overcome by developing a charging station that can serve as a chamber for UAV as well as an autonomous charging station. The fundamental mechanism in the charging station relies on the UAV platform, position on the platform, and door operation [8]. The linear actuator, scissor lifting, and linear ball screw are used to move the platform. The landing platform consists of both active and passive positioning mechanisms [9]. In the active positioning mechanism, the parallel and rotary pushers control the UAV to move towards the center of the platform [10]. The landing platform and UAV body need modification according to the user’s needs for the passive positioning mechanism [11]. However, current charging stations are made of heavy components, including materials and power supplies [12]. Consequently, they are inconvenient for users to move from one place to another. Moreover, all of the available charging stations are expensive. The main purpose of the charging station is to properly charge the batteries of UAV as well as to protect the UAV. However, due to inaccurate landing on the platform, UAV may experience battery charging issues. Furthermore, while transporting the charging station from one location to another, the UAV frame may be damaged due to the faulty positioning mechanism that holds the UAV. In this study, a portable lightweight charging station has been designed that can be used for both charging and protecting UAV. In addition, the efficiency of the charging station is measured while operating. Eventually, the performance of the charging station’s platform, positioning, and door mechanism is evaluated during UAV operation.

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2 Methodology In this section, a design process of a portable lightweight charging station for a hexacopter UAV will be discussed. The development of a charging station needs to undergo with several steps to accomplish a suitable design. The UAV has two-legged landing gear for better structural support.

2.1 Research Flow Chart Figure 1 depicts the flowchart of the charging station. The initial stage of the design process was to determine the design of the charging station that meets all the standards. At the same time landing platform, positioning mechanism, and door mechanism also have been designed.

Fig. 1 Project flow chart

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Fig. 2 Circuit diagram of charging station

2.2 Circuit Diagram One of the most critical aspects of the charging station is connecting all of the motors, linear actuator, and linear ball screw to the driver and power source. Figure 2 depicts the circuit diagram, which includes the connecting wires to the motors, a linear actuator, and a linear ball screw. The connection between all components also has been discussed in the figure. The Stepper motor and linear ball screw have four wires to command the working principle. However, the linear actuator has only two wires to control the movement.

2.3 Pick up Car Bed Size The primary goal of this charging station is to make it portable. The pickup car has been used to transport the charging station for every operation. At the first stage all information such as dimensions for bed size of pickup car and took the average value have been collected. It has been decided that the Toyota Hilux 2021 was a suitable dimension to place the charging station box. Figure 3 shows the Toyota Hilux 2021 bed size with maximum clearance to the charging station frame with the car body. Lastly, the wheel part is vital while designing the charging station box.

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Fig. 3 Hilux bed dimensions Fig. 4 Two and four-legged landing gear

2.4 Drone Legged Landing Gear The UAV landing gear technology has been developed to provide the structural support to the UAV body while landing. However, the function of UAV legged can be used in structural support to heavy payload and autonomous charging system to the UAV battery. Figure 4a illustrates the two-legged landing gear that is mostly used in surveillance, photography, data collection, and mapping. Moreover, Fig. 4b depicts the four-legged landing gear that is mostly used for heavy payloads in agriculture and military industries. The landing gear is also a vital part while designing the charging station as it will be mainly used to charge the drone battery autonomously.

2.5 SolidWorks Design Figure 5 illustrates the 3D solid works drawing of the charging station for the campus surveillance UAV. There will be two box parts in the charging station, and the design was prepared considering the pickup car bed size and UAV dimension. The platform design with a linear ball screw and stepper motor has been finalized according to

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Fig. 5 A 3D design of SolidWorks design

the requirements of the charging station. From the design of the solid works, the weight of the charging station was initially calculated 55 kg with all the components installed.

2.6 Parts Specification The charging station consisted of both mechanical and electrical parts. While, mechanical parts include frame, platform, linear ball screw, linear actuator, and parallel pusher. On the other hand, the electrical components include a motor, battery, power supply, driver, and wires. The charging station’s frame will be made of aluminum extrusion. Aluminum extrusion is the process of pushing aluminum alloy material through a die with a specific cross-sectional profile. A linear ball screw will be used to move the charging station platform. The platform can move upward and downward direction with the help of a linear ball screw. It is a heavy mechanism and simple to operate with help of motor driver. The linear actuator will be used for the door mechanism of the charging station. In contrast to a standard electric motor’s circular motion, a linear actuator generates motion in a straight line. Linear actuators are widely employed in machine tools and industrial machineries, such as disc drives and printers, valves and dampers, and a variety of other applications. The NEMA 23 motor will be used to control the pushers on the platform. The NEMA 23 stepper motor has a 2.3 × 2.3-inch faceplate and a high torque hybrid bipolar motor. The step angle of this motor is 1.8°, which means it has 200 steps each revolution, and each step covers 1.8°.

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The TB6600 is a simple professional stepper motor driver that can handle twophase stepper motors. It works with Arduino and other microcontrollers capable of producing a 5V digital pulse signal. The TB6600 stepper motor driver has a wide power input range. Moreover, it can output a 4A peak current, which is enough for most stepper motors. A power supply will be used in charging station to control electrical components. The primary role of a power supply is to convert electric current from a source to the appropriate voltage, current, and frequency to power the load. As a result, power supplies are sometimes known as power converters. Furthermore, four rollers will be used at the bottom of charging station for smooth movement. The rollers are capable of carrying the heavy weight of charging station.

2.7 Charging Station Fabrication The fabrication of charging station started with the installation of aluminum extrusion. Figure 6 shows the fabrication process of the charging station frame from SolidWorks design. An aluminum extrusion with 20 × 20 mm and 20 × 40 mm has been used to fabricate the frame of the charging station. This 20 × 40 mm aluminium extrusion has been used to strengthen the structure. The centralized load of 20 × 40 mm aluminium extrusion is 182 N, and the evenly distributed load is 364 N. for the other components, the 20 × 20 mm aluminium plate has been used with the centralized load is 99 N. In order to connect all extrusions, the L-shaped bracket have been used. In order to attached the L-shaped bracket to the other structure, the M5 T nut and washer were used. Figure 7 depicts the installation of a linear ball screw for the platform of the charging station. The three linear ball screws are used to hold the platform and control movement. The linear ball screw is the heavy mechanism and requires extra support structure. Therefore, aluminum extrusion is used to support the linear ball screw.

Fig. 6 Charging station base fabricating

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Fig. 7 Installation linear ball screw

3 Results and Discussion Figure 8 shows the complete fabrication work of the charging station. Where an acrylic sheet was used to cover the frame of the charging station. Acrylic sheet is a transparent thermoplastic and difficult to shatter. In addition, the charging station consists of two parts, namely the upper box part and the lower box part. The dimension of the lower part was reduced during fabrication to accommodate the Toyota Hilux bed size. The lower box section was used to store electrical parts and the power supply. The upper box part, on the other hand, was used for UAV landing and charging. Apart from that, four rollers were installed at the lower part of the charging station to facilitate easy movement. The mechanical and electrical components of the charging station are depicted in Fig. 9. The UAV platform was moved with three linear ball screws. A NEMA 23 motor drives all three linear ball screws. One driver can typically control two motors. As such, eight drivers were used to control all of the motors in this work. Fig. 8 Front view of charging station

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The door of the charging station opened from both sides, so two linear actuators were used to control the door from both sides. Moreover, a limit switch was used for the smooth operation of the linear actuator, which fixed the boundary of expansion and contraction of the linear actuator. Figure 10 depicts the UAV charging station platform. The platform was made of plywood, and a carbon fiber sticker was added to allow the UAV to move smoothly on it. To control the UAV, eight motors were used to control the clamp of the positioning mechanism, which was made of hollow rectangle aluminum. The clamp brought the UAV to the center of the platform. Furthermore, the positioning mechanism is an active parallel pusher, and the pusher moves the UAV at the center of the platform synchronously. The positioning mechanism was able to bring the UAV to the center of the platform. Fig. 9 Charging station mechanical and electrical parts

Fig. 10 Platform with positioning mechanism

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Table 1 Charging station operating time Door mechanism

Positioning mechanism

Platform mechanism

Open (Seconds)

Close (Seconds)

Open (Seconds)

Close (Seconds)

Up (Seconds)

Down (Seconds)

1

49

52

15

18

22

21

2

50

53

14

19

23

21

3

50

52

15

18

24

21

4

51

54

15

20

22

21

5

50

53

15

18

23

22

6

52

52

15

19

22

21

7

50

53

15

18

22

22

8

51

54

16

18

24

22

9

50

55

15

18

22

21

10

50

52

16

20

22

20

Average

50

53

15

19

23

21

No

During the operation, the propeller and UAV body did not contact the frame of the charging station. Moreover, the clamp of the positioning mechanism was able to hold the UAV during transport to a different location. Data collected from the charging station operating time in three categories is presented in Table 1. The efficiency of charging station performance is evaluated in three categories, such as door mechanism, positioning mechanism, and platform mechanism. Table 1 depicts the operation time for each mechanism. The maximum time required for the door opening mechanism is 52 s, and the minimum time required is 49 s. However, the maximum and minimum required times for the door closing mechanism were 55 and 52 s, respectively. The average time required to operate the door mechanism, positioning mechanism, and platform movement was 50, 17, and 22 s, respectively. Based on Table 1, the charging station can be operational in less than 90 s.

4 Conclusion The main focus of this research was to design a suitable charging station for campus surveillance UAV. In this research, various scope was tried to produce a design for the UAV autonomous battery charging station. The main problem was making the charging station portable, and the pickup car bed’s dimension was measured. Along with the UAV specification and pickup car dimension design has been built for an autonomous battery charging station. Finally, the fabrication of the charging station has been completed. An acrylic sheet has been used to cover the charging station. The operating time for the positioning mechanism is 15 s to open and 19 s

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to close. During the closing operation, it has to push the UAV, which delays about a few seconds. Furthermore, the platform mechanism required 23 s to operate, and the door mechanism required 50 s to operate. Lastly, the UAV autonomous battery charging station can operate in less than 90 s which is acceptable considering the weight and portable station. Acknowledgements This work was funded by Universiti Teknologi Malaysia (UTM) to Nazri Nasir through the UTM RA iconic research grant (Vot number: PY/2020/04477). This research was also supported by UTM Aeronautics Laboratory (Aerolab). Most of the data and information included in this research article were extracted from Ahsan Habib Rifat, A.H.R.’s thesis. We would like to thank the assistant engineer, Mr Zurueng Ajim, for his assistance and guidance throughout the project.

References 1. Mahony AR, Kumar V (2012) Aerial robotics and the quadrotor. IEEE Robot Autom Mag 19(3):20–32. https://doi.org/10.1109/mra.2012.2208151 2. Lima P, Ribeiro MI (2003) Santos-victor the rescue project-cooperative navigation for rescue robots. In: Proceedings of the 1st international workshop on advances in service robotics, Verona, Italy, 13–15 March 2003 3. Dale DR (2007) Automated ground maintenance and health management for autonomous unmanned aerial vehicles. Master’s Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA 4. Junaid AB, Lee Y, Kim Y (2016) Design and implementation of autonomous wireless charging station for rotary-wing UAVs. Aerosp Sci Technol 54:253–266 5. Choi CH, Jang HJ, Lim SG, Lim HC, Cho SH, Gaponov I (2016) Automatic wireless drone charging station creating essential environment for continuous drone operation. In: Proceedings of the 2016 international conference on control, automation and information sciences, ICCAIS 2016, Ansan, Korea, 27–29 October 2016 6. Feng Y, Zhang C, Baek S, Rawashdeh S, Mohammadi A (2018) Autonomous landing of a UAV on a moving platform using model predictive control. Drones 20:34 7. A station with mechanized battery replacement [E-resource] // Airobotics - Access Mode: https://www.airoboticsdrones.com/. Address date 1 April 2021 8. Kartoun U, Stern H, Edan Y, Feied C, Handler J, Smith M, Gillam M (2006) Vision-based autonomous robot selfdocking and recharging. In: 2006 world automation congress, pp 1–8. IEEE 9. Galimov M, Fedorenko R, Klimchik A (2020) UAV positioning mechanisms in landing stations: classification and engineering design review. Sensors 20(13):3648. https://doi.org/10.3390/s20 133648 10. Hao Z (2016) Unmanned Plane interface automatic butt system. CN patent 205790709-U, 7 December 2016 11. Godzdanker R, Valavanis KP, Rutherford MJ (2014) Intelligent self-leveling docking system. U.S. Patent 20140124621-A1, 8 May 2014 12. Al-Obaidi MR, Mustafa MA, Wan Hasan WZ, Azis NB, Sabry AH, Ang SP, Hamid ZHA (2018) Efficient charging pad for unmanned aerial vehicle based on direct contact. In Proceedings of the 2018 IEEE 5th international conference on smart instrumentation, measurement and application, ICSIMA 2018, Kuching, Sarawak, 24–26 July 2018

Preliminary Development and Performance Testing of an Autonomous Battery Charging System for a Two Legs Multicopter Mohammed Raihan and Nazri Nasir

Abstract Although the demand for autonomous drones is rising, drone charging systems are still executed manually. This study aims to design and fabricate an autonomous charging system for a two-legged multicopter UTM JagaDrone, including identifying the charging box size. Its propulsion system uses a six-cell Lithium polymer battery with 12,000 mAh, and the charging box should be transportable by using a Toyota Hilux 4-wheeler vehicle. After an extensive literature review, we designed a mechanical aligner to centre the multicopter on the charging pad. Afterwards, a Battery Management System (BMS) is used to monitor the charging of the battery. The system was designed to charge the drone battery within 90 s, which previously required about 15 min using manual execution. Since the 12A fast charging mode reduces the life cycle of the battery, a balance charge with 1A current is used. Using balance charge mode, we achieved about 1.1 V within just 1.6 h. Overall, the designed autonomous charging system reduces the charging procedure by almost 81.81%. Keywords Battery · Charging system · Multicopter · Autonomous · Drone · Unmanned aerial vehicle (UAV)

M. Raihan · N. Nasir (B) Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia e-mail: [email protected] N. Nasir UTM Aerolab, Institute for Vehicle System and Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_12

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1 Introduction Over the last decade, Unmanned Aerial Vehicles (UAVs), particularly rotary-winged multicopters, have seen exponential growth, especially in agriculture, surveillance, maintenance, transportation, delivery, and search-and-rescue vehicles [1, 2]. A multicopter is a UAV with more than two powerful rotors and has high mobility and acceleration capability. Multicopters balance autonomously with the aid of an electronic control system and sensors. Multicopters can be operated for indoor and outdoor purposes because of their high mobility, low cost, compact and VTOL (Vertical Take-Off and Landing) ability. Despite the meteoric rise in interest in multicopters for various purposes, the most significant constraint of any multicopter for long-term operations is their limited onboard power supply. The flight time of a multicopter is limited by its weight and battery lifespan as the energy storage system. The multicopter can only fly for 20 min in a controlled environment, even with a fully charged battery [1, 2]. As a result, significant advancements in the power supply systems of multicopters are required to conduct long-term and durable flight missions. As of now, several types of batteries have been commonly used to power multicopters, including lithium-sulfur (Li–S), nickel metal hydride (NiMH), nickel– cadmium (NiCd), and lithium-polymer (LiPo) batteries [3]. Li–S are the light power source and have a high capacity compared to NiMH batteries with a high energy density capability and are environmentally friendly. NiCd batteries, on the other hand, provide stable and high energy densities. Nonetheless, most of these batteries are expensive, have a fast discharge rate, and have a limited lifespan [3, 4]. On the contrary, LiPo has several advantages, including high charging efficiency, lightweight, and extremely high energy densities [5]. These batteries can either be manually recharged after battery drain. The battery life limitation and the requirement for manual charging are two significant barriers to their widespread deployment of a fully automated system [6]. Due to weight and space constraints, additional batteries cannot be added to the power supply of a multicopter. As a result, multicopters must have a backup power source to compensate for their limitations and extend flight time [7–9]. Therefore, numerous studies have been conducted to charge the battery wirelessly [10– 13]. Although this technology appears to be very promising, it does have some limitations, such as inefficient wireless power transfer and requiring complex electronic systems. Alternatively, the battery can be automatically replaced, reducing the system’s reliability and increasing cost [14]. Most previous multicopter charging systems required the battery to be disassembled and charged separately. In that case, the battery may not be fully charged during unavoidable circumstances, such as a sudden surveillance mission. Nonetheless, a manual charger requires a unique individual setting which the system cannot automatically detect the battery’s specifications. In this work, we fabricated a charging station using pogo pins and its socket and a battery management system for a twolegged multicopter UTM JagaDrone used for security surveillance. This multicopters propulsion system is powered by six-cell LiPo batteries with 12,000 mAh capacity.

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2 Methodology The methods of this research will be focused on the design of the autonomous battery charger for two legs multicopter aircraft. This project begins with a feasibility study of the existing charging station model available in the market. In this project, UTM JagaDrone has been used as a testing multicopter, which led to identifying the suitable charging box size. The drone also uses six lithium polymer batteries in its propulsion system with a capacity of 12,000 mAh. The software used in this project namely Arduino and C++ programming. SolidWorks has been used in this project as software for designing and drafting. Figure 1 shows the process flowchart that illustrates the activities involve in the present study. The literature review is done after the problems and requirements have been identified. After that, the frame assembly is executed. Then we set up the charging system that charges the drone autonomously, and finally, we will proceed to data collection, analysis and validation.

2.1 Charging Station Working Procedure Figure 2a shows the flow diagram of the battery management system, where B stands for the Automatic Charging Connection Point. The drone is placed on the platform, directly connected to the battery charging point. The drone is set to charge the battery when the battery percentage is below 30%. When the charging process is completed or at least 80%, the drone will be on standby mode, waiting for the command from Security Monitoring Operation Centre (SMOC) to perform the flight.

Fig. 1 Research flowchart

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Fig. 2 Landing mechanism flow of drone. a Battery management system (BMS) and b land mechanism flow of drone

Figure 2b indicates that the drone is in the fly state. If the drone’s battery percentage drops below 30%, a command signal will be sent to SMOC through the battery management system. The drone is then commanded to land by SMOC, the canopy will autonomously open, and the linear actuators will start working slowly.

2.2 Hardware Requirements Numerous pieces of hardware are required to run an autonomous charging system: a magnetic pogo pin, a battery management system, a pogo pin socket and a power supply. We used the Daly BMS system. The charging and draining of rechargeable batteries are monitored and controlled electronically by a Battery Management System (BMS). BMS protect the rechargeable batteries (cells or battery packs) from operating outside their safe operating condition, monitor the states, calculates secondary data, and reports that information. They also control the environment in which batteries operate, authenticate them, and balance them. With an external communication data channel, a battery pack with a battery management system is a smart battery pack. To recharge a smart battery pack, we must use a smart charger. A power supply is a piece of equipment used to provide electricity to another electrical device. The primary function of a power supply is to regulate the voltage, current,

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and frequency of the electric current so that it can give power to the load. To charge the multicopter autonomously, we used a Pogo pin. It is a special connection that combines power with signal transfer and can be mated and unmated easily.

2.3 Solid Works Design Pogo Pin Connector Design Figure 3a shows the magnetic touch pin connector attached to the platform, and Fig. 3b shows the magnetic touch pin connector attached to the drone’s legs. These connectors are connected using two magnets as the platform aligner is in the closed position. Finally, Fig. 4 illustrates the connector’s assembly during the final stage of the multicopter aligning on the charging platform.

Fig. 3 Adjuster 3D parts a connector pin for aligner shaft b connector pin for drone leg Fig. 4 3D parts for charge connector

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Fig. 5 Charging platform design, a fully opened aligner and b closed aligner

Platform and Aligner Design Figure 5a shows that the aligner of the charging platform is open, and Picture (b) shows that the aligner is closed. Note that the charging connector (Pogo pin) is attached to the aligner shaft, which will automatically be connected to the drone’s legs when the aligner is in the closed position. Then the drone started charging automatically.

2.4 Schematic Diagram Schematic Diagram of Charging Connector Here, Fig. 6 illustrates a schematic diagram of the charging connector. Using this schematic diagram, we connected the pogo pin charging system. The charging connector is connected to the drone leg (as the power receiver) and the platform aligner. Schematic Diagram of Aligner Figure 7 illustrates the schematic diagram for the platform aligner. Using this schematic diagram, we autonomously connected the aligner system with the overall charging system.

2.5 Experimental Setup Figure 8 shows the experimental setup for the charging system. We did several experiments before finalizing the design used in our charging station. After landing

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Fig. 6 Pogo charging system schematic diagram

Fig. 7 Platform aligner schematic diagram using stepper motors

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Fig. 8 Experimental setup with electric components

Fig. 9 Battery management system (BMS) testing

on the platform, we first tested the aligning system and then moved the connector for the pogo pin to the multicopter leg for the charging process. We tested our charging system’s battery management system (BMS) to send the battery status to the operator’s dashboard as the battery life indicator (Fig. 9).

3 Result and Discussion Experiments were conducted to determine the time required to charge the battery for UAVs, as shown in Fig. 10. A constant current, 1 A used to charge the 12,000 mAh, and the six-cell battery using balance charging mode. However, the current fluctuated during the experiment, perhaps caused by the increased number of battery cells. During the measurement, the battery was already ~60%, corresponding to ~23.81 V. For the initial set of tests, a charging time duration of zero to ~120 min

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Fig. 10 Battery voltage and current versus time graph using pogo pins during regular charging

was initiated while measuring the voltage. Figure 10 demonstrates that the battery’s voltage increases as charging time increases. After charging the battery for 120 min, the voltage reached a maximum value of ~24.6 V, while the voltage increased by 1.1 V. Nevertheless, by increasing the charging current, the battery can be charged more rapidly. However, charging the battery with a low current value of around 1 A is recommended to increase the battery’s life expectancy and maintain a balanced charging rate. According to Fig. 11, we get several static analysis data. For the current consumption against time, we get a mean of 0.824 and a standard deviation of 0.034. In the graph voltage against time, we get a mean of 24.27 and a standard deviation of 0.33. The voltage has a range of 1.253, and the current supply has a range of 0.931 A. Likewise, in Fig. 10, the graph illustrates the normal charging mode charging data pattern. In a regular charging system, the current flow fluctuates throughout the charging time. From our findings, the cause of the current supplied fluctuation in both charging modes is the current flow to every cell of the battery for balancing. Cell balancing is a technique for increasing battery life by increasing the capacity of a battery pack by connecting multiple cells in series and ensuring that all the energy is available for use. Therefore, when we used the balanced mode, the current flow for the battery was not fluctuate compared to the normal charging mode the current flow.

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Fig. 11 Battery voltage and current versus time graph using pogo pin during balance charging

4 Conclusion This work aimed to pave the way to addressing the limited flight duration of UAVs caused by their low battery capacity. In this work, an automated charging station with a battery management system is designed and fabricated. In addition, experiments were conducted to determine the time required to charge the battery for UAVs. This design could charge the battery with endurance and persistence, allowing longduration missions to be carried out. This system is an efficient step towards developing a reliable solution for UAV flight time restrictions. This autonomous charging system will save 15 min of manual work required to charge a drone’s battery. The process of manual charging is repetitive and requires human intervention. It takes about 90 s to complete the procedure, so the battery is ready to be recharged. This design could charge the battery with endurance and persistence, allowing long-duration missions to be carried out. An automated charging station with a battery management system is designed and fabricated. In addition, experiments were conducted at a constant current to determine the time required to charge UAVs’ batteries fully. This charging system will help the relevant operator to reduce time by 81.81% in the charging process and faster the charging time with autonomy. Acknowledgements This work was funded by Universiti Teknologi Malaysia through the UTM RA Iconic research grant (vot number: PY/2020/04477). This research was also supported by UTM Aeronautics Laboratory (Aerolab) and all the facilities. Special thanks to the assistant engineer, and technician for his assistance and guidance throughout this study.

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References 1. Junaid AB, Lee Y, Kim Y (2016) Design and implementation of autonomous wireless charging station for rotary-wing UAVs. Aerosp Sci Technol 54:253–266 2. Jawad AM et al (2019) Wireless power transfer with magnetic resonator coupling and sleep/ active strategy for a drone charging station in smart agriculture. IEEE Access 7:139839–139851 3. Cwojdzi´nski L, Adamski M (2014) Power units and power supply systems in UAV. Aviation 18(1):1–8 4. Townsend A et al (2000) A comprehensive review of energy sources for unmanned aerial vehicles, their shortfalls and opportunities for improvements. Heliyon 6(11):e05285 5. Yan H et al (2021) Optimum battery weight for maximizing available energy in UAV-enabled wireless communications. IEEE Wireless Commun Lett 10(7):1410–1413 6. Khonji M et al (2017) Autonomous inductive charging system for battery-operated electric drones. In: Proceedings of the eighth international conference on future energy systems 7. Verstraete D, Lehmkuehler K, Wong K (2012) Design of a fuel cell powered blended wing body UAV. In: ASME international mechanical engineering congress and exposition 2012. American Society of Mechanical Engineers 8. Kim T, Kwon S (2012) Design and development of a fuel cell-powered small unmanned aircraft. Int J Hydrogen Energy 37(1):615–622 9. Rhoads G et al (2010) Design and flight test results for a 24 hour fuel cell unmanned aerial vehicle.In: 8th annual international energy conversion engineering conference 10. Faraci G et al (2020) Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry. Appl Energy 259:114204 11. Mostafa TM, Muharam A, Hattori R (2017) Wireless battery charging system for drones via capacitive power transfer. IEEE PELS workshop on emerging technologies: wireless power transfer 12. Choi CH et al (2016) Automatic wireless drone charging station creating essential environment for continuous drone operation. International conference on control, automation, and information sciences (ICCAIS) 13. Kim SJ, Lim GJ (2018) Drone-aided border surveillance with an electrification line battery charging system. J Intell Rob Syst 92(3):657–670 14. Fujii K, Higuchi K, Rekimoto J (2013) Endless flyer: a continuous flying drone with automatic battery replacement. International conference on ubiquitous intelligence and computing and 2013 IEEE 10th international conference on autonomic and trusted computing

Assessing Drone-Based Last-Mile Logistics—A Hybrid Solution Bruno Lamiscarre, Innocent Davidson, Georges Mykoniatis, Luis Gustavo Zelaya Cruz, and Felix Mora-Camino

Abstract Last mile delivery (LMD) refers to the transfer of goods from the final dispatch point to a transportation centre. This is the most difficult and expensive component of the logistics chain for many logistics disciplines. New technologies enable new methods of information collection, distribution, and provision of logistical services. For instance, the enhanced drone technology of the present day, with its now-acceptable payload, autonomy, and collision avoidance capabilities, can facilitate logistical solutions. This strategy has the potential to reduce last-mile logistics costs and contribute to environmental objectives. The demand for LMD services can be divided into two categories: planned demand and unexpected demand. The purpose of this study is to develop a hybrid solution for LMD. The solution consists of ground vehicles for planned demand and unmanned aerial vehicles for unplanned demand. The ultimate objective of this study is to estimate the generated UAV traffic in urban areas by employing a worldwide continuous technique for sizing both fleets. The ground LMD routing and scheduling activities are globally analysed using an empirical formula that relates the lowest cost route operation in a congested metropolitan region to the mean length required to service a customer, while the UAV LMD activity is globally analysed using a stochastic model. As a consequence of this LMD solution, this study generates estimates of traffic intensity, enabling the computation of performance indices related to service quality and environmental impact. Keywords Last mile logistics (LMD) · Unmanned aerial vehicles (UAV) · Sustainability · Fleet sizing · BHH theorem · Stochastic processes B. Lamiscarre · F. Mora-Camino (B) Neometsys, Toulouse, France e-mail: [email protected] B. Lamiscarre · I. Davidson · F. Mora-Camino Durban University of Technology, DUT, Berea, South Africa G. Mykoniatis · F. Mora-Camino ENAC-Toulouse, Toulouse, France L. G. Z. Cruz · F. Mora-Camino Universidade Federal Fluminense, UFF, Niterói, Brazil © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_13

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1 Introduction Last-mile delivery (LMD) patterns have changed in urban areas as a consequence of global trends towards increased urbanisation and the expansion of e-commerce. According to the United Nations [1], by 2030, 60% of the world’s population will reside in cities, and by 2025, 200 billion shipments will have been delivered worldwide [2]. Amazon, a significant participant in urban logistics, delivers approximately 86% of shipments weighing less than 2.27 kg. Despite the fact that urban logistics contribute significantly to air pollution, the major industrialised nations have also adopted a long-term decarbonization strategy. Therefore, reorganising urban logistical operations around greener vehicles seems reasonable. UAVs appear to be outstanding candidates for providing at least a portion of this solution. After analysing the extant trends in last-mile delivery (LMD), this paper proposes a general approach for the administration of UAV traffic dedicated to LMD in urban areas. Then, it is demonstrated that the constraint of dimensionality can be avoided by employing a continuous modelling approach to obtain approximate estimates of the generated traffic, costs, and benefits associated with LMD hybrid solutions (ground vehicles and UAVs).

2 Last-Mile Delivery Last-mile delivery or last-mile logistics refers to the transportation of goods from distribution centres to the final delivery destination, typically the customer’s address. With a few minor exceptions in remote locations, this activity has remained grounded until now. The objective of last-mile delivery operations is to deliver the designated product as affordably, quickly, and as precisely as possible. Last-mile delivery is a viable option for businesses that distribute products directly to consumers. A few examples include direct-to-consumer retailers, food delivery services, thirdparty logistics providers, couriers, supermarkets, pharmacies, restaurants, department stores that offer delivery, florists, and e-commerce. E-commerce has recently increased B2B (Business-to-Business) and B2C (Business-to-Consumer) transactions’ last-mile delivery. Last-mile ground delivery is the distribution system’s most expensive link. It accounts for approximately half of all transportation costs. This delivery cost increases the overall price for consumers, reducing the demand for this service and the profit margins of merchants. This rising price is due to a number of factors, including: • The final mile of delivery is conducted at slower speeds because it occurs in urban areas or because local roadways may be used to reach consumers. • Last-mile delivery necessitates extended idling due to traffic conditions (traffic signals, traffic congestion) and ultimate product delivery.

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• It is difficult to deliver tens of thousands of products to their final destination in a single day, necessitating complex routes with multiple delivery sites that may result in excess kilometres. • Frequent delivery failures to final customers. • When demand for delivery occurs in real-time and is indiscriminately distributed across the delivery space, it is challenging to select optimal routes and designate the fleet of vehicles in an efficient manner. In recent decades, last-mile delivery has been a thriving industry, but its growth has accelerated during the current pandemic. Significant prospects for the development of urban logistics based on the operation of unmanned aerial vehicles (UAVs) are consolidating as businesses are able to profit from formerly unused urban airspace and alleviate ground traffic by reducing the need for ground-based logistic transportation, which is one of the major contributors to traffic congestion and pollution [3–7]. Numerous studies have been conducted in the past on the design of effective UAVbased urban logistics systems, but traffic volumes and capacities have rarely been considered [6–8]. Other studies [9] predict that high traffic densities of drones operating in urban airspace will occur within the next few decades, making the effective design and organisation of this type of traffic imperative and urgent. When analysing the demand for products stored in a logistic centre, it is presumed that this demand is distributed stochastically across the investigated urban space and at various times of the day. The anticipated demand represents the portion of this demand that is known in advance due to its periodicity or previous request. Unplanned demand is another form of online demand that develops stochastically. Location, volume, and weight of the product to be delivered are factors that are common to both scheduled and unanticipated demands. However, their performance indices differ. In the case of planned demand, it is presumed that delivery must be completed within a large time range while minimising delivery costs, whereas in the case of unplanned demand, delivery must be completed as soon as possible after the delivery request is sent. In this communication, a generic hybrid LMD solution employing two categories of last-mile delivery vehicles, namely terrestrial vehicles and UAVs, is investigated. These vehicles are distinguished by their payload volume and weight capacity, nominal speed, operational expenses (travel cost and energy consumption per unit of distance and cargo), as well as loading and unloading times and mean velocities. Ground logistics vehicles have two significant advantages over UAVs: they can deliver larger or heavier cargo, and the scale effect enables reduced unit transportation costs. However, they considerably contribute to urban traffic congestion and pollution. In addition, they lack flexibility, and the cost of their expedited transport of individual requests is prohibitive. Nevertheless, ground-based last-mile delivery will be of interest when it is possible to combine known requests in advance in order to maximise the effect of scale, optimise delivery routes to make better use of the fleet, and ensure high-quality service.

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Significant literature exists for addressing vehicle routing problems (VRP) with the objective of minimising logistical costs through delivery route planning in relation to anticipated demand. Braekers et al. [10] describes a recent investigation of several VRP variants and accompanying algorithms. Due to the focus on the main variables that affect the efficacy of a delivery system, no specific routing algorithm is considered; instead, an empirical formula [11] based on the BHH theorem [12] is used to approximate the mean distance to be travelled. The theory is implemented by establishing N evenly spaced sites in a given region from an external depot. Given the proportions of ground-based and air-based LMD operations, it is possible to estimate not only the global delivery costs of the logistics company, but also the environmental impact resulting from the energy used for delivery and emissions. When the requirements of various logistics companies in the same region are independent, the results above can be repeated and added. Regarding unscheduled demand, it is presumed that each request is fulfilled by a UAV making a round journey from a depot. In addition, it is presumed that the distances travelled are consistent with the drones’ autonomy and that battery charging and replacement occurs at the depot. The available information on unanticipated demand can be used to design the warehouse or the depot’s local manufacturing unit, which can be regularly supplied by large-capacity ground vehicles. To address the issues of sizing a fleet of UAVs operating from a given location and the associated energy consumption, a stochastic model is used to represent demand and delivery service, allowing analytical results to be obtained on the performance of the UAVbased delivery system (service times, waiting times, energy) for a given UAV fleet size. In terms of service quality, environmental impact, and energy consumption, the hybrid solution for LMD under consideration can be compared to a conventional ground-based solution.

3 The Proposed Strategy for UAVs in LMD Urban UAV traffic managers, both on the ground and in the air, must forecast demand for available space. Consequently, it is essential to predict how the primary consumers of the 3D urban landscape would generate traffic demand. This would allow local governments to develop a traffic plan to influence the 3D urban environment, harmonising projected traffic demand with public acceptance and urban quality of life. For instance, a logistics company’s last mile delivery traffic demand will be the result of its solution to the following interrelated problems: location and dimensioning of the logistics centres, size and composition of the delivery fleet, policy for handling delivery requests, fleet management, scheduling, and routing; whereas the underlying generating process will be the final delivery demand, which will be characterised by its spatial and temporal distributions as well as its demand characteristics.

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Depending on the decision problem under consideration, different logistics urban landscape representations are better adapted to contribute to the solution. The logistics urban space consists of the urban transport space and the urban demand space, also known as the urban transport space and urban demand space, respectively. Both regions have geographical associations and are inextricably linked. You can use discrete or continuous representations to depict the urban transit space and the urban demand space. A 2D graph representation can be based on the grids of streets and lanes in the city via its ground circulation plan to provide transit opportunities for ground delivery vehicles, while a 3D graph model can be used for organised airspace [9]. The operational period can be viewed as a continuous span of time or as a series of discrete intervals corresponding to distinct demand and traffic patterns. On the basis of discrete representations of space and time, existing models can be used to address problems such as scheduling and routing of products by ground or air, where operations costs must be minimised while serving a given demand [10]. However, if the delivery fleet must include a large number of UAVs, numerical complexity issues will arise unless the urban logistics space is subdivided into multiple subareas. It is important to note that, in general, these methodologies, which are complicated by delivery timeframes and capacity constraints, evaluate only the peripheral environmental issues. It is presumed that the location of the logistics centre is already suitable and that additional locations for UAV ground operations are accessible close to the facility at the logistics centre. The location of logistical hubs is frequently chosen to be close to the delivery region and easily accessible by wholesale supply chains. All of this suggests that distribution hubs for last-mile deliveries are frequently located on the periphery of cities. Ground vehicles and unmanned aerial vehicles are the two categories of LMD vehicles investigated. They differ in payload volume and weight capacity, nominal speed, operating expenses (travel cost per unit distance and refund cost), and loading and offloading times. As a scheduled demand, parcels with a volume greater than Vmax or a weight greater than Mmax are expected to be delivered by ground transportation the following day, whereas smaller or lighter items may be delivered by ground transportation or UAVs. Smaller or lighter products that are part of the anticipated demand but are inaccessible by ground vehicles or whose inclusion on a scheduled delivery route would be prohibitively expensive can be added online to the unplanned request record at a time when UAVs should be available. Then, to enhance the effectiveness of the LMD system, an intelligent dispatcher system is required that can classify any new request as planned or unforeseen. This method is illustrated in Fig. 1.

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Fig. 1 Principle of a cargo dispatcher system

4 Ground Last-Mile Delivery for Planned Demand There is a substantial body of literature on vehicle routing problems (VRP) with the objective of minimising logistical costs via delivery route planning. For instance, in [11] there is a current study on the numerous variants of the VRP and the accompanying algorithms. Due to the fact that this study concentrates on the primary factors that affect the efficacy of a delivery system, no specific algorithm is examined. Regarding planned demand, an empirical formula [12] based on the BHH theorem can be used to approximate the mean distance to be travelled by assigning uniformly distributed N points in a square area A (in square metres) from and outside the depot located a distance l from the centre of the area (see Fig. 2). This formula implies delivery routes that minimise travel distances.

LN

√ N +ρ· N · A = 2l · C

)) (( ) ( 1 1 + √ C N

(1)

where ρ is a distribution area shape parameter and C is the ground vehicle capacity (the utmost number of standard parcels a vehicle can convey in a single tour). This formula considers N to be greater than C. This formula has been employed to optimise the layout of urban delivery subareas. The distance between the depot and the

External depot

l

A Delivery area with tours

Fig. 2 Organization of ground delivery for planned demand

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√ metropolitan area is the initial distance term. The second term (ρ · N · A/C) represents the average √ distance travelled to reach a delivery location during a tour, and the third term (ρ · AN ) represents the average distance detoured to reach each delivery location. Let VO and VI represent, respectively, the average speed used to reach the delivery region from the depot and the average speed used during delivery. Therefore, the average time to service N delivery sites is calculated as follows: √ N +ρ· N · A d N = 2l · C · VO

)) (( ) ( 1 1 / VI + √ C N

(2)

which has an increasing relationship with N. If T is the daily operating period, then the average number of delivery locations that a vehicle with capacity C can serve is such that: NT = N ·

T dN

(3)

or √ N T = α(A, l, C, VO , VI ) + β( A, l, C, VO , VI ) · T − α(A, l, C, VO , VI ) (1 + γ · T )

(4)

with α( A, l, C, VO , VI ) = C 2

ρ2 A )2 ( √ 2 · ρ A + 2l/( VVOI )

C · VI ) β(A, l, C, VO , VI ) = ( √ ρ A + 2l/( VVOI ) ( ) √ VO VI ρ A + 2l/( γ (A, l, C, VO , VI ) = ) C · ρ2 · A VI

(5)

(6)

(7)

The size of the fleet required to complete the ground delivery is then determined by: FG (T ) = DT /N T

(8)

where DT is the total demand considered during the period T. The number of delivery operations D N is then approximated as follows: D N = DT /C The approximate total distance travelled at speed VO is calculated by:

(9)

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VO = FG (T ) · 2l · (N /C)

(10)

The estimated total distance travelled at speed VI is calculated as follows: VI = FG (T ) · ρ · N ·

√

(( ) ( )) 1 1 A + √ C N

(11)

Under the assumption that the ground vehicles leave the depot with a complete load C, the total distance travelled is FG (T ) · l · (N /C) and the same distance will be travelled when the ground vehicles are empty. In addition, it is reasonable to presume that the vehicle is progressively unloaded during a delivery route. All of these elements enable the logistics company to not only assess approximately global delivery costs, but also the environmental impact of the energy required for delivery and emissions, if any (electric vehicles). If a large number of logistics companies operate in the same region, the previous findings can be repeated and supplemented if their needs vary.

5 UAVs Last-Mile Delivery for Unplanned Demand Regarding unforeseen demand, it is presumed that each request is fulfilled by a UAV making a round-trip from a depot. In addition, it is presumed that the distances travelled are adequate with the autonomy of the drones and that battery charging and replacement procedures occur at the depot. Statistics on unanticipated demand should be used to determine the scale of the depot’s warehouse or local manufacturing facility, which must be regularly supplied by large capacity ground vehicles. The depot is therefore deemed to be located within the metropolitan area that the delivery service will serve (see Fig. 3). The issue of the location of the UAV depot is not addressed here, but the following factors must be taken into account: the geographic distribution of demand, the structure of the airway network that UAVs can use in urban areas, the range of the considered UAVs, and the available landing sites. Fig. 3 Organization of delivery for unscheduled demand

UAV base and depot Ground bulk supply

Delivery area by UAVs

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To address the problem of sizing a fleet of UAVs operating from a given location, a stochastic model is used to represent demand and delivery service, enabling analytical results to be derived on the performance of the UAV-based delivery system for a given fleet size of UAVs. It is anticipated that delivery requests will be generated by a stochastic process, and that when a request is received, a UAV will be dispatched to the location of the request as soon as possible. In addition, it is assumed that the metropolitan region’s demand is distributed according to another stochastic process. In this example, it is presumed that the rate, λ of delivery requests follows a Poisson process, while the time required to reach the request location follows an exponential distribution parameter μ. The Poisson assumption for request timing is a standard approach that can be utilised over a variety of time intervals when the flux of requests is nearly constant. Other stochastic models could have been utilised, but for the sake of simplicity, the aforementioned hypotheses were utilised, resulting in analytical advancements despite the need to investigate somewhat complex formulae in the end. Regarding the demand exponential distribution supposition, this can correspond to a centrally located facility in the urban area under consideration. Taking into account a fleet of f UAVs used to process queries in real time, the assumptions used configure a classical Markov queuing system of class M/M/ f with some peculiarities. In this instance, once a request has been fulfilled, i.e., a cargo has been delivered to a customer by a UAV, this UAV must return to the depot in order to be available for the fulfilment of a new request. Considering that the consumer begins to be served when the UAVs depart the depot to attend to him, the following definition is applicable: 1 = 2 · δ/ VI μ

(12)

where VI is the mean speed of the UAVs over the course of their journey and δ is the mean distance to the depot. The loading and offloading periods of the UAV’s control are deemed negligible in comparison to the duration of the trip and are therefore disregarded. If f > λ/u, the delivery procedure has attained a probabilistic stationary state, and the waiting time to be served (in the queue) can be calculated as follows: Wq = π0

ρ( f · ρ) f λ · (1 − ρ)2 · f !

(13)

where ρ is the utilisation rate and the probability that no customer is awaiting the assignment of a UAV to his request. These are furnished by: ⎛ ⎞ f f −1 λ k ∑ (μ) ( μλ ) 1 λ ⎠ ρ= and π0 = 1/⎝ + f ·μ k! f ! 1 − (λ/ f μ) k=0

(14)

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The average response time for a customer’s package is determined by: TU =

ρ( f · ρ) f 1 + π0 2μ λ · (1 − ρ)2 · f !

(15)

The probability for a client to wait more than t to get a parcel is given by: ⎛

⎞ −μt ( f −1−λ/μ 1 − e ⎠ · P(W > t) = e−μt ⎝1 + f !(1 − ρ) f − 1 − λ/μ π0 · ( μλ )

f

(16)

These last two indices can be used to determine the fleet size based on the service level specified by a value TU or a value p such that: P(W > tmax ), p ∈ [0, 1]

(17)

where tmax is a guaranteed delivery time. It is also possible to demonstrate that TU and P(W > tmax ) are decreasing functions of f and VI , implying that increasing the number of UAVs or the speed is one method to reduce the average response time or the likelihood of a timeout. The formula yields the average total distance dU travelled by the UAVs at a given speed, VI and time Δt is as shown: ⎛ ⎞ f −1 +∞ ∑ ∑ dU = 2δμ · ⎝ n · Pn + f Pn ⎠ · Δt (18) n=0

n= f

where Pn is the probability of receiving n requests is determined by the classic formula M/M/ f : ⎧ ( )n ⎨ π0 . λ /n! f or 0 ≤ n < f (μ )n Pn = ⎩ π0 · λ /( f ! · f n− f f or n ≥ f μ

(19)

It can be seen that when f is sufficiently large, dU converges to 2δμΔt, which corresponds to the case where there are no outstanding requests during the considered time period.

6 Conclusion This study focused on the development of a method for designing last-mile logistics based on a hybrid solution comprising ground vehicles for scheduled demand and unmanned aerial vehicles (UAVs) for unscheduled demand. This strategy combines

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the scale effects of ground vehicles carrying out multiple deliveries with the adaptability and responsiveness of UAVs specialising in delivery. Ground last mile delivery routing and scheduling activities were evaluated globally using an empirical formula relating minimum cost route operation in a congested urban environment with the mean length required to serve a customer, whereas UAV LMD activity was evaluated globally using a stochastic model. In both instances, the generated traffic was computed, and performance indices relating to service quality and environmental impact were developed. At this time, it seems prudent to ensure the effectiveness of the proposed hybrid solution by developing an intelligent dispatcher system capable of classifying each new request, whether scheduled or unanticipated, online. As UAVs progress in terms of safety, range, and payload, it is anticipated that they will capture a larger share of the LMD market.

References 1. United Nations, Department of Economic and Social Affairs, Population Division. The World’s Cities in 2018—Data Booklet (ST/ESA/ SER.A/417) (2018) 2. Shipping Index, https://www.pitneybowes.com/us/shipping-index.html. Accessed 13 July 2022 3. Bhawesh S, Rohit Gupta R, Bani-Hani D (2020) Analysis of barriers to implement drone logistics. Int J Log Res Appl 24(6):531–550 4. Eun J, Song BD, Lee S, Lim DE (2019) Mathematical investigation on the sustainability of UAV logistics. Sustainability 11(21):5923 5. Goodchild JA, Toy A (2018) Delivery by drone: an evaluation of unmanned aerial vehicle technology in reducing CO2 emissions in the delivery service industry. Transp Res Part D Transp Environ 61:59–67 6. Park J, Kim S, Suh K (2018) A comparative analysis of the environmental benefits of dronebased delivery services in urban and rural areas. Sustainability 10:888 7. Koiwanit J (2018) Analysis of environmental impacts of drone delivery on an online shopping system. Adv Clim Chang Res 9(3):201–207 8. Mora-Camino F, Lamiscarre B, Mykoniatis G (2021) Structuring air logistics networks in the urban space. In: SID2021, Valencia 9. Gan X, Wang Y, Li S, Niu B (2012) Vehicle routing problem with time windows and simultaneous delivery and pick-up service based on MCPSO. Math Probl Eng 2012:104279 10. Braekers K, Ramaekers K, Van Nieuwenhuyse I (2016) The vehicle routing problem: stateof the art classification and review. Comput Ind Eng 99:300–313 11. Eilon S, Watson-Gandy CDT, Christofides N (1971) distribution management: mathematical modelling and practical analysis. Hafner Editor, New York 12. Beardwood J, Halton JH, Hammersley JM (1959) The shortest path through many points. Math Proc Cambridge Philos Soc 55:299–327

Rocket and Missile

Flight Performance and Trajectory Prediction of a 2.75-Inch Solid Propellant Rocket Nur Syahirah Shafek Hamlan, Ezanee Gires, Kamarul Ariffin Ahmad, Faizal Mustapha, Norkhairunnisa Mazlan, Noorfaizal Yidris, and Adi Azriff Basri

Abstract The flight performance and flight trajectory of any unguided rocket should be determined because its capability and flight trajectory are uncountable in flights. The purpose in this study was to develop a performance and trajectory prediction program for an unguided 2.75-inch solid propellant rocket and to perform a parametric analysis. The program uses the Runge–Kutta Fehlberg (or RKF45) method, and it was developed using Python. It requires multiple geometric designs and motor parameters as the program input. The program was then verified and validated with previous experimental flight data obtained from literature papers. Then, a parametric analysis (also called as sensitivity analysis) was done to analyze how significant these parameters affect the flight of a rocket, compared with the results based on the baseline rocket. Based on the prediction results, the baseline rocket flies with a range of 3040 m, reaches up to 3307 m in altitude and has a peak velocity of 747.554 m/s. In parametric analysis, the parameter that gave the most significant difference was motor grain configuration, followed by launch angle and nozzle expansion ratio. Keywords Rocket · Trajectory · Missile · Prediction

N. S. S. Hamlan · E. Gires (B) · K. A. Ahmad · F. Mustapha · N. Mazlan · N. Yidris · A. A. Basri Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia e-mail: [email protected] N. S. S. Hamlan e-mail: [email protected] E. Gires · K. A. Ahmad · F. Mustapha · N. Mazlan · N. Yidris · A. A. Basri Aerospace Malaysia Research Center (AMRC), Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_14

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1 Introduction Rockets (known as missiles) are self-propelled, guided, or unguided military weapons propelled by rocket propellants. Unguided rockets do not have a controlled flight path, while guided rockets have their flight path programmed with instructions. Rocket propellants come in two different types, liquid and solid, but most rockets nowadays use solid rocket motors [1]. Just like guided rockets, engineering an unguided rocket should be taken seriously as well. Since it is ‘unguided’, the flight performance and flight trajectory of any rocket should be determined because its capability and flight trajectory are uncontrollable in flight. Besides that, flying an unguided rocket without knowing where it will land may be very dangerous. In a study about risk analysis of rocket trajectories, Engelen and Mooij highlighted that there is a serious probability that the risks are very high when launching large amateur rockets, leading to difficulties in convincing the range safety personnel of the launch site that it is safe to launch [2]. Anyone who builds rockets must utilize all resources to identify how big uncertainties in their simulation and input parameters will be. Estimating aerodynamic data and thrust misalignments can be quite challenging. The main objective of this study is to predict the flight performance and trajectory of an unguided solid propellant rocket. Specifically, to develop a program that calculates and predicts, to set a baseline rocket design and to perform the parametric study. The program was developed using numerical methods and the theory of rocket dynamics. A conceptual baseline rocket body and rocket motor were designed and applied to the program. The validity of the program was tested by comparing it with actual flight data obtained from literature papers. Then, a parametric study was done to identify the sensitivity of certain design parameters in affecting the flight and trajectory of a solid propellant rocket. When designing a rocket, many design parameters of a rocket (launch conditions, rocket body, and motor) play an important role in determining how the rocket flies and behaves. The sensitivity of these parameters may vary. Small changes can drastically increase the flight range or velocity of the rocket, or they may do not affect anything at all.

1.1 Methods for Performance and Trajectory Prediction There are various approaches for predicting the trajectory of a rocket. Keeports developed a 2-dimensional model with the assumption of a frictionless launch rod [3]. However, this method is only limited to vertically launched rockets. Besides that, a paper by Khalil et al. explained a much different approach by using a six-degree of freedom model with more complexity taken into account [4]. The complexity of this method may result in getting errors in calculation results. Another method by Akgul and Karasoy uses state vector equations, assuming that the object flies in a free-flight phase over an elliptical and non-rotating earth [5]. This method may be

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easy and straightforward, but it can only be done if satellite or radar data is available. Moreover, the data obtained from satellites or radar contains noise, which can cause a decrease in accuracy in prediction. Hence, filtering is necessary in order to get better accuracy in results. Lastly, Box et al. suggested using numerical solutions, specifically Runge–Kutta-Fehlberg or RKF45 method to predict the trajectory of a rocket, assuming that the rocket’s rigid body is axisymmetric and dynamic and aerodynamic characteristics are set as default input [6, 7].

1.2 Rocket Design The design of baseline rocket will be based on some well-known and established designs used by military organizations around the world. One of them is the Hydra-70 rocket, which has a diameter of 2.75-inch or 70 mm. It is classified as a fin-stabilized unguided rocket mainly used for air-to-land or land-to-land launches. Some of these rockets have different types of warheads installed according to their use and purposes, such as air bursting or detonating. This rocket was first developed by the US Navy to be used during World War II. The 2.75-inch or 70 mm diameter is a standardized caliber set by the North Atlantic Treaty Organization (NATO). This caliber is not only adopted by the NATO member states but also by more than 50 countries and 70 military organizations worldwide. Hence, this caliber is considered an international caliber [8]. The Mighty Mouse rocket (see Fig. 1) was developed by the US Air Force and Marine Corps in the 1950s and had a 2.75-inch caliber. This rocket has a length of 1.2 m and weighs 8.4 kg. The design of this rocket is considered bad as its speed and spin rate were unsatisfactory when countering the gravitational pull, wind directions, and dispersion. This missile was designed to adapt for air-to-land use as a main weapon installed in helicopters, having the ability to fly at a maximum range of 6000 m and an average range of 3400 m [9]. Advanced Precision Kill Weapon System (APKWS) is a guided missile developed by the US Army in 1966 to improve the capability and cost compared with the initial Hydra-70 rockets, which was made as a baseline design. This missile is

Fig. 1 Mighty mouse MK 4 rocket [9]

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smaller and has higher accuracy compared to Hydra-70. To meet the APKWS rocket characteristics, this modified Hydra-70 rocket was applied with a new guided model known as LCPK (Low-Cost Precision Kill). Besides that, a standardized MK 66 motor was used in this rocket with a newly developed compatible warhead that can be used with other 2.75-inch rockets used by the US Army. The MK 66 motor is a US standardized motor consisting of a longer motor case and spring-loaded fins and has a thrust of up to 1300-pound force.

2 Methodology 2.1 Development of the Performance and Trajectory Program The program was developed using Python programming. Rocket geometric design and thrust profile are needed as input for the program to run its calculations using the Runge–Kutta-Felhberg method, or RKF45. The thrust profile can be obtained from static flight tests or simulation software. In this study, a rocket motor simulator called Burnsim is used. Figure 2 shows the flowchart of the calculation to predict the trajectory of the rocket. It is assumed that the rocket’s rigid body is axisymmetric as well as dynamic and aerodynamic characteristics of its body are identified as a set of program input. The simulation starts with a definition of initial states and time that will pass through the RKF45 block, which will then pass through the rocket’s dynamic model. In that particular process, the equation of motion of the rocket will be solved to obtain the state derivatives with respect to time using the program input. The output from the dynamic model passes through the state derivative calculation. These state derivative components will be returned to the Runge–Kutta-Fehlberg block.

Fig. 2 Flowchart of the program [6]

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2.2 Runge–Kutta-Fehlberg Method One of the ways to solve an initial value problem is to calculate it twice using two-step sizes, h and h/2. Then, these answers were compared at mesh points corresponding to the larger step size. However, this method takes a lot of time to compute for smaller step sizes and must be recalculated if the agreement is unsatisfactory. The Runge–Kutta-Fehlberg method, or for short, RKF45, is one of the ways to solve this issue. It works by identifying if the calculation is using a proper step size. For each step, the solutions will come out with two different guesses. If both of them come to a close agreement, the guess is accepted. Otherwise, the step size is decreased. If the guesses agree to more significant digits than required, the step size should be bigger.

2.3 Design of the Rocket Body and Motor The rocket motor is a solid rocket motor. Beckstead et al. stated that variations of chemical ingredients and their amounts would affect the physical and chemical properties of the propellant, combustion characteristics, and performance [10]. It consists of ammonium perchlorate (AP) as the oxidizer and aluminium powder (Al) as fuel. Hydroxyl-terminated polybutadiene (HTPB) is used as a binder, and toluene diisocyanate (TDI) as a curing agent to cure the binder. Lastly, dibutyl sebacate (DBS) is used in the formulation as a plasticizer. Ammonium perchlorate (AP) is one of the most commonly used oxidizers because of its excellent properties [11, 12]. In solid rocket motor formulations, aluminium and boron are the commonly used metals for fuel [13, 14]. The presence of fuel in solid rocket propellants will increase the heat and temperature of combustion, the density of propellant, as well as specific impulse, Isp . Hydroxyl-terminated polybutadiene (HTPB) has been excessively used for many years ago due to its allowability in higher solid fractions (between 88 to 90% of ammonium perchlorate and aluminium) and its fairly good physical characteristics [15, 16]. Table 1 shows the parameters of the baseline rocket body design and motor design. As previously discussed, the rocket will have a diameter of a 2.75-inch or 70 mm, which is the most common missile caliber used in military organizations. There were three parameters that will be studied in this parametric analysis: launch angle, nozzle expansion ratio, and grain configuration. For every setting in the parametric study, the rocket flight simulation was done the same way as how the simulation of the baseline rocket was done. Other parameters that will not be studied will be kept constant to maintain the consistency of the results.

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Table 1 Design details of the baseline rocket body and motor Item

Unit

Value

Item

Unit

Value

Empty mass

kg

4.607

Propellant density

lb/in3

9.84 × 10–4

lb

Body diameter

m

0.07

Propellant mass

Body length

m

1.07

Core shape

Carbon fiber

Grain outer dia

Body material

3.438 BATES

m

0.063

Ogive

Grain inner dia

m

0.020

Nose cone length

m

0.15

Grain length

m

0.30

Nozzle throat diameter

m

0.018

Characterized Isp

s

154.9

Nozzle exit diameter

m

0.060

Characteristic velocity

ft/s

4982.7

Nose cone shape

2.4 Validation Simulation data are approximate values of real-world situations, and they do not generate accurate and precise results. Because of that, the simulation program developed should be verified and validated to a certain extent for the sole purpose and application. The validation process will provide accurate, clean, and complete set of data by eliminating errors that exist. Several experimental flight data were obtained and compared with the developed simulation program in this research. Figure 3 shows the experimental trajectories of commercial rocket motors compared with calculated trajectories using RKF45. Brown et al. launched a ballistic rocket with an altimeter sensor built in [17]. The rocket uses a hobby rocket motor manufactured by Aerotech, model J425. Figure 2 (left) shows an altitude-time comparison between the experimental rocket flight trajectories by Brown et al. compared with the calculated rocket flight trajectory done by the program. During the flight, a parachute was deployed once the rocket reached its maximum altitude, which explains why the second half of the flight path is linear-like.

Fig. 3 The trajectory of the Aerotech J425 rocket by Brown et al. (left) and the Aerotech G80T rocket by Migliorino et al. (right) compared with the calculated trajectory [17, 18]

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Another comparison was made by Migliorino et al. [18], using a rocket powered by another hobby rocket motor manufactured by Aerotech, model G80T, shown in Fig. 3 (right). Similarly, the rocket also deployed a parachute after it reached its maximum altitude, making the second half of the flight looks like a linear flight path. Unlike Fig. 3 (left), there is a slight difference in the actual flight trajectory of the Aerotech G80T rocket compared to the calculated trajectory from 50 to 450 m altitude. Many factors can cause these differences. For example, the rocket propellant characteristics and burning of the motor were obtained and done using software such as ProPep3 and Burnsim, respectively. In general, simulations are meant to be a prediction only and do not promise an accurate outcome since they have their own limitations. For example, Burnsim does not consider erosive burning and ignition in their simulations, which will slightly affect the thrust-time results [8]. Besides that, all of the simulations presented in this writing also do not take into account the variability of ambient and atmospheric conditions such as wind speed, wind direction and minute changes in air density.

3 Result and Discussion In this chapter, the calculation results for performance and trajectory prediction of the baseline rocket are presented and discussed together. Figure 4 below shows the plot of the trajectory and velocity of the baseline rocket at different launch angles. By using a baseline launch angle of 30° this parametric study will observe the trajectory and flight performance of the rocket at launch angles from 10° until 80° (from the vertical axis). It can be observed that the highest altitude reached is when the rocket is launched at a 10° angle. The apogee decreases as the launch angle increases, and this trend also can be seen in the total time of flight. However, the trend for flight range is the opposite. The flight range increases as the launch angle increases. Rockets launched at angles from 10° to 80° hit their maximum velocities right before motor burnout at 4.73 s. The rocket continues to decelerate after motor burnout hitting its lowest velocity, which is where the rocket hits its highest altitude

Fig. 4 Plot of trajectory (left) and velocity (right) of the baseline rocket at different launch angles

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Fig. 5 Plot of trajectory (left) and velocity (right) of the baseline rocket at different nozzle expansion ratios

and accelerates again in a free-fall motion towards the ground. Figure 5 shows the plot of the trajectory and velocity of the baseline rocket at different nozzle expansion ratios. The baseline expansion ratio is 11.11, and other parameters that were introduced in the simulation are kept constant. In this study, the only change made to the nozzle expansion ratio was the nozzle throat diameter, while the nozzle exit diameter, including other parameters, was kept constant. There is a fluctuating trend on the apogee while the flight range decreases as the nozzle expansion ratio reduces. There is a constant decreasing trend in the rocket motor burnout and peak velocity, even though the mass and the composition of the propellant are kept constant throughout this study. The baseline rocket with the highest nozzle expansion ratio burns out the fastest and generates the highest peak velocity, approximately 982 m/s, while the rocket with the smallest expansion ratio has the lowest peak velocity, approximately 631 m/s. However, there is an increasing trend in the total time of flight. The highest nozzle expansion ratio has the shortest duration of flight (57 s), while the lowest nozzle expansion ratio has the longest duration of flight (59 s). All of these differences were heavily affected by the nozzle expansion ratio that caused differences in the pressure in the burning chamber, changing the burn flow rate of the propellant, the flight performance, and the trajectory of the rocket. Figure 6 shows the plot of the trajectory and velocity of the baseline rocket with different grain configurations.

Fig. 6 Plot of trajectory (left) and velocity (right) of the baseline rocket with different motor grain configurations

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The obvious feature in the simulation results is that the star grain can be generally beneficial when it comes to getting the furthest range and highest altitude compared to BATES baseline grain. Star grain with 10 points has the furthest flight range, 35.58% further than the baseline grain (BATES), while the star grain with 4 points reached the highest altitude and the longest duration of flight, among others. When these plots were observed visually, there is no doubt that the baseline rocket with star grain configuration has a massive difference in the flight path characteristics compared with the baseline rocket with BATES grain. In general, when Fig. 6 is observed, the star grain travels faster than the baseline grain, almost 32% faster than the rocket with BATES grain. In a paper by Stein [19], he emphasized that the star grain has the ability to boost the flight range of a sounding rocket due to its impressive burning performance. To give the idea, the star grain has a bigger initial burning surface area that increases the startup pressure. Hence, that is why the peak velocity of the star grain is much higher than the peak velocity of the BATES grain.

4 Conclusion Identifying the trajectory and flight performance of an unguided rocket is important to avoid unwanted consequences, such as design failure, that may put people at risk or destroy buildings if it lands in an unwanted location. Hence, this study has successfully developed a program to simulate the flight performance and trajectory of an unguided solid propellant rocket using the RKF45 numerical method in Python. To ensure the accuracy of the developed program, the simulated results were verified and validated with experimental results obtained from various papers from the past. This study has also designed a baseline rocket, and the simulation results were obtained based on its body and motor design characteristics. A parametric analysis has been done in this study to discover the sensitivity of the parameters and how much it affects the flight performance and trajectory of a solid propellant rocket. It has been found that the launch angle plays a big part in determining the flight range of the rocket, with the contribution of the thrust generated by the rocket motor. While the nozzle expansion ratio parameter also has a massive effect due to the difference in the burning performance of the motor, resulting in significant differences in flight range and velocity. Finally, it can be concluded that the star grain configuration is able to give a 35.58% increase over the baseline in the flight range of a rocket due to its impressive burning performance. Thus, the objectives of the paper have been achieved. This study can be improved with the addition of better approaches in methodology. Compared to the simulation, a static test of the baseline rocket can be done to obtain higher accuracy experimental thrust data with fewer limitations. A flight test on the baseline rocket can also be done to validate the trajectory prediction results. This paper can be beneficial for future studies as a motivation for improving any research related to this field or as a reference to designing a solid propellant rocket to meet specific requirements for in-flight performance and trajectory.

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References 1. Sutton GP, Biblarz O (2017) Rocket propulsion elements (7th ed.). Wiley 2. Engelen FM, Mooij E (2011) Quantitative risk analysis of rocket trajectories 10:8190–8200 3. Keeports D (1990) Numerical calculation of model rocket trajectories. Phys Teacher 28(5):274– 280. https://doi.org/10.1119/1.2343024 4. Khalil M, Abdalla H, Kamal O (2009) Trajectory prediction for a typical fin stabilized artillery rocket. International conference on aerospace sciences and aviation technology, 13 (AEROSPACE SCIENCES), 1–14. https://doi.org/10.21608/asat.2009.23742 5. Akgül A, Karasoy S (2005) Development of a tactical ballistic missile trajectory prediction tool 5:1463–1467 6. Box S, Bishop CM, Hunt H (2011) Stochastic six-degree-of-freedom flight simulator for passively controlled high-power rockets. J Aerosp Eng 24(1):31–45. https://doi.org/10.1061/ (asce)as.1943-5525.0000051 7. Galvez D, Bosquet J (2021) (rep.). Development of a performance simulator of solid propellant rocket motors. Barcelona 8. Thales Group (n.d.) Weapon systems & munitions. Retrieved February 23, 2022, from https://www.thalesgroup.com/en/markets/defence-and-security/air-forces/weapon-systemsmunitions 9. Ordway F (1960) International missile and spacecraft guide (1st ed.). McGraw Hill Book Company 10. Beckstead MW, Puduppakkam K, Thakre P, Yang V (2007) Modeling of combustion and ignition of solid-propellant ingredients. Prog Energy Combust Sci 33(6):497–551 11. Chaturvedi S, Dave PN (2011) Nano metal oxide: potential catalyst on thermal decomposition of ammonium perchlorate. J Exp Nanoscience, 1–27 12. Chaturvedi S, Dave PN (2012) A review on the use of nanometals as catalysts for the thermal decomposition of ammonium perchlorate. J Saudia Chem Soc (In Press) https://doi.org/10. 1016/j.jscs.2011.05.009 13. Galfetti L, De Luca L, Severini F, Maggi F, Marra G, Meda L (2003) Explosion and Shock Waves 41(6):680 14. Galfetti L, Severini F, DeLuca LT, Marra GL, Meda L, Braglia R. (2004) Ballistics and combustion residues of aluminized solid rocket propellants. Proceedings of the 9-IWCP, novel energetic materials and applications, vol 18 15. Galfetti L, De Luca LT, Marra G, Meda L, Severini F, Cerri S, Lentini L, Babuk V (2006) Intl astronautical congress IAC 2–6 Oct. 2006, Valencia, C4.3.03 16. Meda L, Marra G, Galfetti L, Inchingalo S, Severini F, Deluca L (2005) Nano-Composites for rocket solid propellants. Compos Sci Technol 65(5):769–773. https://doi.org/10.1016/j.com pscitech.2004.10.016 17. Brown W, Wiesneth M, Faust T, Huynh N, Montalvo C, Lino K, Tindell A (2018) Measured and simulated analysis of a model rocket. Proc Inst Mech Eng Part G J Aerosp Eng 233(4):1397– 1411.https://doi.org/10.1177/0954410017752730 18. Migliorino MT, Aiello M, Berti M, Rotondi M, D’Alessandro S, Bianchi D, Jahjah M, Pizzarelli M (2022) Student firing tests and launches with commercial and self-made solid rocket motors. Acta Astronaut 197:23–34. https://doi.org/10.1016/j.actaastro.2022.04.025 19. Stein SD (n.d.) Benefits of the star grain configuration for a sounding rocket. American institute of aeronautics and astronautics

Control and Navigation

A Study on Aircraft Pitch Control in Rejecting Disturbances Salihu Abdulmumini Jalo, Mohammed Ahmed, Abdulqadiri Bello Abdulqadiri, Muhammad Usman Ilyasu, Isa Mohammed Inuwa, and Garba Elhassan

Abstract An introductory study on simplified aircraft pitch control considering the different variants of the well-known proportional-integral-derivative (PID) controller is presented in this paper. Different control methods have been applied in the aircraft systems control. The linear control methods are not suitable due to highly nonlinear nature of the plant. The intelligent methods lack comprehensive models hampering more rigor research. The nonlinear require higher computing time making their real implementation impossible. The model of the aircraft is a simplified analytical model derived using one of Boeing aircraft models. Investigated are the performances of the PID, PI, and PD controllers. A pulse disturbance was considered to mimic wind, gust, or sudden torque change. Sensor error was considered in the evaluation. The work was achieved by simulation using MATLAB/SIMULINK software. Results showed that PID and PD controllers have similar results while the PI controller has oscillations. Further results show that both the PID and PD are able to suppress the disturbance whose value can result in deviation equals to the reference pitch angle that is; 11° to about 22°. The two controllers however show very poor performance in rejecting sensor errors that is; from 2.2° to just 2°. The PD controller can serve same purpose as the PID controller in aircraft pitch control. Consequently, the study

S. A. Jalo (B) · I. M. Inuwa State Polytechnic Yola, Jimeta, Adamawa, Nigeria e-mail: [email protected] M. Ahmed Abubakar Tafawa Balewa University, P. M. B. 0248, Bauchi, Bauchi, Nigeria A. B. Abdulqadiri Cadatral Zone A00 Central Business District FCT Abuja, Nigerian Midstream and Downstream Petroleum Regulatory Authority, Plot 1012, Abuja 1012, Nigeria M. U. Ilyasu Federal Polytechnic Bali, Bali, Taraba, Nigeria G. Elhassan Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_15

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would help to serve as basis for research in aircraft pitch control for beginning as well as new comers and naïve in the area of flight control. Keywords Aircraft pitch control · Proportional-integral-derivative control · Proportional-derivative control · Proportional-integral control · Disturbance rejection · Sensor error

1 Introduction According to current studies the rise in aircraft accidents is alarming. Almost half was attributed to mainly malfunctioning of the aircraft system. One-third arises from the propulsion system malfunctions. Others are increasing in complexities aircraft systems and reduction of cost of maintenance. Therefore, it means there is the need for more intelligent, effective automation, and control strategies; for better fault detection and diagnosis, reduction or correction, and isolation [1]. Studies and improving aircraft control systems is essential for safe conveyance humans and other important missions in life [2]. Different control methods have been applied in the aircraft systems control. In the work of Gupta [1], the model predictive combined with Kalman’s filter was presented. The proportional-integral feedback control method was investigated on high order rotorcrafts. In the works of Wibowo [3], the linear quadratic regulator, linear quadratic Gaussian and adaptive control schemes are studied. In the work of Caughey [4], the linear quadratic optimal technique was presented. The proportional-derivative, linear quadratic regulator, and the nonlinear port-Hamiltonian energy-based control law were examined in the works of Gresham et al. [5]. Husek and Narenathreyas [6], applied the fuzzy logic for longitudinal control of an aircraft. In another studies conducted by Mohamed and Madhavan [7], a Luenberger observer based optimal flight controller was proposed. The linear control methods are not suitable due to highly nonlinear nature of the plant. The intelligent methods lack comprehensive models hampering more rigor research. The nonlinear require higher computing time making their real implementation impossible. Modern aircrafts keep becoming more sophisticated, with more accidents recorded and enhancement of their control systems could aid in providing solutions. Therefore, one of the ways to improve on controlling such systems could be by applying a simplified nonlinear control method. That is reducing the computational burden and making it in simpler form by making it having characteristics in between the linear and nonlinear methods. Hence, as starting point towards achieving that goal the different variants of the simplest form of well-known proportional-integral-derivative (PID) control method was explored.

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2 Methodology 2.1 The Aircraft Model Aircraft dynamics is governed by six coupled nonlinear equations. The equations can be decoupled and converted to linear under certain assumptions. The separation can be into the longitudinal and lateral dynamics. The aircraft pitch control falls under the longitudinal dynamics [8]. As shown in Fig. 1, there are basically four forces acting on an aircraft; thrust, lift, drag, and weight. The lift is an upward force which originates as a result of the wings; it keeps an aircraft in flight. The weight is opposite of the lift, which is a downwards force whose source is the aircraft and load weights. Therefore, the lift has to be equal or greater than the weight for planes to keep moving in flight. Otherwise the aircraft is pulled down. Thrust is the force generated by the engines which pushes the aircraft forward. The drag is the force which opposes the thrust; it is therefore a force which resists forward movement in aircrafts. Ideally, the aircraft can move in three directions; upwards or downwards, leftwards or right wards (sideways), and forwards or backwards directions or positions. Angular movements are also categorized into three; the pitch, yaw, and roll. Angular changes upwards and downwards are regarded as the pitch (nose, tail positions); turning of the aircraft from forwards and backwards. Sideways tilting or angular movements or angles is referred to as the roll; it tends to turn the aircraft upside down clockwise or anticlockwise by the sides. Yaw angle is the turning angle sideways turning; left or right angular turning [9]. Assuming the aircraft is in the state of steady-cruise maintaining a constant altitude and velocity. Therefore, the thrust, the drag, the weight, and the lift forces would balance each other in the x-direction as well as the y-direction. Another assumption is that a change in the pitch angle will not affect or alter the speed of the aircraft. Thus, all these simplified problem to the minimum and the aircraft longitudinal dynamic equations of motion become simplified. In the case under consideration the input is the deflection angle and the output is the aircraft pitch angle. Upon insertion of numerical values from the data taken from a Boeing’s commercial aircraft [8], the equations becomes as given by (1)-(3). Fig. 1 An illustration of forces acting on an aircraft

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Where a is the deflection angle. Taking the Laplace transform yields Eqs. (4) to (6) and solving the equations in order to obtain the transfer function finally resulted in Eq. (7). a˙ = −0.313a + 56.7q + 0.232δ

(1)

q˙ = −0.0139a − 0.426q + 0.0203δ

(2)

θ˙ = 56.7q

(3)

s A(s) = −0.313A(s) + 56.7Q(s) + 0.232Δ(s)

(4)

s Q(s) = −0.0139A(s) − 0.426Q(s) + 0.0203Δ(s)

(5)

sΘ(s) = 56.7Q(s)

(6)

1.151s + 0.1774 Θ(s) = 3 Δ(s) s + 0.739s 2 + 0.921s

(7)

2.2 The Control Scheme The simplest configurations of the proportional-integral-derivative controllers considered are the proportional-integral-derivative (PID), proportional-integral (PI), and proportional-derivative (PD). Their control laws are given by Eqs. (8), (9), and (10) respectively. U (s) P I D = K P E(s) + K I

E(s) + K D s E(s) s

U (s) P I = K P E(s) + K I

E(s) s

U (s) P D = K P E(s) + K D s E(s)

(8) (9) (10)

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2.3 The Control System It shows the dynamics of the system in terms of the pitch angle Θ(s) in the frequency domain. The different controllers are given by the Eqs. (8), (9), and (10). Their task is to compensate for changes in the system by always trying to reduce the error E(s) to zero. Wind gust is characterized as a sudden disturbance [10–12]. Step signals can be used as test signals for systems characterized by disturbances that are sudden in nature [13, 14]. A step signal was also harnessed by Hong et al. in their studies on aircraft [15]. Pulse of a second duration and amplitude equal to the desired pitch angle was used for testing the effect of forward path disturbance. It was a pulse causing a 2.2 error in the reverse/feedback path (representing the sensor error). This is an initial phase of the studies effort would be made to make the aircraft and disturbance models to be close their actual forms as the research progress. Meaning the study will keep becoming refined as it progressed as this is just the beginning or the basis of the exploration in order to improve on the system performance.

3 Result and Discussion 3.1 Simulation Results Without Disturbance Figure 2 shows the aircraft step response and Fig. 3 depicts the error and RMS error plots. It can be seen that the response with the PID and the PD controllers almost overlap while that of the PI controller is oscillatory. Oscillations are highly undesirable in such system which may worsen in the presence of nonlinearities considering the fact that simplest form of the system is considered for the study. The PID has the least amount of error followed by the PD and the PI which is very high. The error margin between the PID and the PD is very small. The errors resulted with the different schemes die-off with time indicating stability likelihood.

3.2 Simulation Results with Pulse Disturbance The system response with pulse disturbance between 3 and 4 s is as portrayed by Fig. 4 while the error graphs are given by Fig. 5. The PID and the PD reduced the effect of the disturbance with the PID producing lesser amount of error. The errors of both the methods used decline with time which infer that the system maybe stable with high probability.

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Fig. 2 No disturbance aircraft step response

25 Reference position Position with PID Position with PI Position with PD

P os ition (D eg)

20

15

10

5

0

1

2

3

4

5 time (s)

6

7

8

9

10

20 P os ition error (D eg)

Fig. 3 No disturbance error response

0

Position error with PID Position error with PI Position error with PD

10 0 -10 -20

0

1

2

3

4

5 time (s)

6

7

8

9

10

P os ition error (D eg)

15 Position error with PID Position error with PI Position error with PD

10

5

0

0

1

2

3

4

5 time (s)

6

7

8

9

10

3.3 Simulation Results with Sensor Error Figure 6 shows the plots of the system response with introduced sensor error. Figure 7 is that of the resulted errors. Both controllers indicated the ability compensate for

A Study on Aircraft Pitch Control in Rejecting Disturbances Fig. 4 Pulse disturbance step response

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25 Reference position Position with Disturbance Position with PID Position with PD

P o s ition (D eg )

20

15

10

5

0

1

2

3

4

5 time (s)

6

7

8

9

10

15 P os ition error (Deg)

Fig. 5 Pulse disturbance error response

0

Position error with PID Position error with PD

10 5 0 -5

0

1

2

3

4

5 time (s)

6

7

8

9

10

P os ition error (Deg)

15 Position error with PID Position error with PD

10

5

0

0

1

2

3

4

5 time (s)

6

7

8

9

10

error to some extent with the PID being better than that of the PD controller. In both cases the error decline indicating the likelihood of stability of the system.

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Fig. 6 Sensor disturbance step response

14

12

P o s itio n (D eg)

10 Reference position Position with Disturbance Position with PID Position with PD

8

6

4

2

0 0

2

3

4

5 time (s)

6

7

8

9

10

15 P os ition error (D eg)

Fig. 7 Sensor disturbance error response

1

Position error with PID Position error with PD

10 5 0 -5

0

1

2

3

4

5 time (s)

6

7

8

9

10

P os ition error (D eg)

15 Position error with PID Position error with PD

10

5

0

0

1

2

3

4

5 time (s)

6

7

8

9

10

3.4 Summary of Discussion A close examination of the results indicated that with the PID and PD controllers have results which are close. The system with the PI controller is oscillatory in nature. The results also portrayed that with both the PID and PD controllers were

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able to suppress the disturbance whose value can result in deviation equals to the reference pitch angle that is; from about 11° to about 22°; introducing an error of about 100% of the desired pitch angle. Hence, indicating high level of robustness in reducing disturbance originating from the feed-forward direction. However, both the controllers show very poor performance in rejecting sensor errors that is; from 2.2 (introducing an error of sensing of about 20%) to just 2° (-2°) with the PD and the PID. That is a 9% sensor error suppressing ability; meaning the introduction of a sensor error above 1.21°-1.54° will make the system steady state error to be beyond the allowable value of ±2% − ±5% in control systems. It means that using only the PI controller may be difficult to be used for aircraft pitch control.

4 Conclusion The introductory study on a simplified aircraft model pitch control considering the different variants of the proportional-integral-derivative (PID) controller was successfully presented. The model of the aircraft is a simplified analytical model derived using one of Boeing aircraft models. The performances of the PID, PI, and PD controllers were examined using pulse disturbance was considered to mimic wind, gust or sudden torque change, and sensor error. The work was done utilizing the MATLAB/SIMULINK software and simulation based. Results showed that PID and PD controllers have results are a bit similar while the PI controller is associated with oscillations. Further results show that both the PID and PD are able to suppress the disturbance whose value can result in deviation equals to the reference pitch angle that is; from about 11° to about 22°. However, both the controllers however show very poor performance in rejecting sensor errors that is; from 2.2° to just 2°. It means that using only the PI controller may not be difficult to be used for aircraft pitch control. The PD controller can serve almost same purpose as the PID controller in aircraft pitch control. Hence, there is need for further improvement for successful to such class of systems; that is aircrafts. The study could help to serve as basis for research in not only aircraft pitch control for beginners as well as new comers and naïve in the area of flight control. It may provide information for experienced researchers.

References 1. Gupta A (2015) Modeling and control of an aircraft system (for Aerospace Applications). Master of Technology Dissertation, Department of Electrical Engineering, National Institute of Technology Rourkela, Rourkela-769008, Odisha, India 2. Gruszecki J, Rogalski T (2012) The method of aircraft control system evaluation. In: ITI 2012 34TH international conference on information technology interfaces, Cavtat, Croatia, 2012, pp 429–434

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3. Wibowo SS (2007) Aircraft flight dynamics, control and simulation using MATLAB and SIMULINK: cases and algorithm approach 4. Caughey DA (2011) Introduction to aircraft stability and control course notes for M&AE 5070,” N. Y.-. Sibley School of Mechanical & Aerospace Engineering Cornell University Ithaca, Ed., ed 5. Gresham JL, Fahmi J-MW, Simmons BM, Hopwood JW, Foster W, Woolsey CA (2021) Flight test approach for modeling and control law validation for unmanned aircraft. AIAA SCITECH, American Institute of Aeronautics and Astronautics 6. Husek P, Narenathreyas K (2016) Aircraft longitudinal motion control based on takagi–sugeno fuzzy model. Appl Soft Comput 49:269–278 7. Mohamed M, Madhavan G (2020) Reduced order model based flight control system for a flexible aircraft. IFAC PapersOnLine 53:75–80 8. Michigan UO (2022) Aircraft pitch: system modeling. Available: https://ctms.engin.umich.edu/ CTMS/index.php?example=AircraftPitch§ion=SystemModeling. Accessed 66 June 2022 9. Chaitanya R, Model based aircraft control system design and simulation. European masters in design and technology of advanced vehicle systems (EUROMIND)—Aeronautics, Department of Management and Engineering, Linköping University 10. Hu W, Letson F, Barthelmie R, Pryor S (2018) Wind gust characterization at wind turbine relevant heights in moderately complex terrain. J Appl Meteorol Climatol 57:1459–1476 11. Knigge C, Raasch S (2016) Improvement and development of one-and two-dimensional discrete gust models using a large-eddy simulation model. J Wind Eng Ind Aerodyn 153:46–59 12. Seregina LS, Haas R, Born K, Pinto JG (2014) Development of a wind gust model to estimate gust speeds and their return periods. Tellus A: Dyn Meteorol Oceanogr 66:22905 13. Ogata K (2010) Modern control engineering vol. 5: Prentice hall Upper Saddle River, NJ 14. Nise NS (2020) Control systems engineering. Wiley 15. Hong S, Jeong J, Kim S, Suk J, Jung JI (2013) Longitudinal flight dynamics of a single tilt-wing unmanned aerial vehicle. IFAC Proc Vol 46:60–65

Experimental Analysis on Pitching Moment for Embedment Cylinder to Flat Plate High Altitude Platform Station Ummi Zuhairah Zaimi, Hidayatullah Mohammad Ali, and Azmin Shakrine Mohd Rafie

Abstract Due to the evolution of advanced technologies and their broad range of applications in numerous domains such as military, industrial, and scientific purposes, worldwide interest in High Altitude Platform Stations (HAPS) continue to rise. Researchers and experts worldwide have taken the initiative and conducted studies to create and build high-efficiency and safe HAPS. However, far too little attention has been paid to designing HAPS by embedding cylinders into a flat plate. The primary concern of the design is whether the approach of injecting the Magnus effect would be able to improve the aerodynamics and stability of the flat plate. To date, a new Cylinder to Flat Plate to Cylinder (CyFlaP) HAPS has been designed that incorporates the Magnus effect on a bluff body with the goal of increasing its aerodynamic performance. This project intends to analyze the pitching moment coefficient due to the rotational direction and rotational speed of the rotating cylinders. The tests were conducted for different Reynolds numbers (Re) ranging from Re = 2.55 × 105 to 9.10 × 105 which implicitly implied different free stream velocities varying from 2.80 to 10.05 m/s for different angles of attack ranging from α = −20° to α = 20°. Data for this study were collected using experimental analysis of the model in the closed-loop wind tunnel. Among the documented data, only the ones that had a higher rotational speed with clockwise rotating cylinders displayed a negative slope of moment coefficient over the specified range of angle of attack, with the highest negative moment coefficient recorded to be C M = −0.04 at the angle of attack , α = 10°. The analysis shows that the CyFlaP is able to fly in a longitudinal static stable state at 10.05 m/s (Re = 9.10 × 105 ) when the rotating cylinders are rotated in the clockwise-clockwise direction at a speed of 2322 RPM. This highlights the capability of the CyFlaP to fly steadily. U. Z. Zaimi · H. M. Ali · A. S. M. Rafie (B) Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia e-mail: [email protected] U. Z. Zaimi e-mail: [email protected] H. M. Ali e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_16

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Keywords Wind tunnel testing · Longitudinal static stability · Rotating cylinder · Magnus effect · High altitude platform station

1 Introduction The development of the High-Altitude Platform Station (HAPS) started in the 1900s [1]. HAPS network involves the integration of both terrestrial and satellite radio access networks. HAPS can be found in various types of aircraft such as airplanes, balloons, or airships. Commonly, HAPS are placed in the stratosphere which ranges from 17 km up to 22 km. The stratosphere is an atmospheric layer where the wind and temperature are typically constant. The mission of HAPS is to provide broadband access, weather monitoring, security monitoring, and data collecting from the acquisition of city remote sensing data [2]. Up until now, there is still a growing list of research being done to improve and develop the HAPS mission and network communication. Cylinder-Flat Plate-Cylinder (CyFlaP) embedment consists of a flat plate in between two rotating cylinders, which is designed for HAPS. This design was inspired by past research from Wolf [3], who proposed the Leading Edge Cylinder Embedment (LECA). Similar to LECA, Ali et al. [4] have conducted a numerical analysis on the embedment of a rotating cylinder at the leading edge of a Selig S1223 airfoil, with various rotational speeds at different free stream flows starting from 5 to 30 m/s. The result shows an improvement in the lift coefficient, C L by approximately 23%. Magnus effect is an old physics phenomenon that was first observed and explained by Isaac Newton in 1672, while watching a tennis game at the University of Cambridge [5]. Since the nineteenth century, there were a few famous inventions utilizing the Magnus effect such as the Flettner-rotor and Plymouth A-A-2004 [6]. Magnus effect can be defined as a phenomenon when a spinning body in fluid forms a vortex of fluid around itself and experiences a force perpendicular to the direction of motion. The behaviour of the Magnus effect is different from the airfoil, as the circulation is generated by mechanical rotation rather than airfoil action. Magnus effect can be described in Bernoulli’s principle, which asserts that an increase in velocity is accompanied by a decrease in pressure within an area of a moving fluid [7]. Furthermore, in 2021, a computational analysis was conducted by Ali et al. on the embedment of the rotating cylinder to a flat plate [8]. The CyFlaP model, which consists of a flat plate in between two rotating cylinders was analysed using Ansys Workbench 2019. Compared to a model without momentum injection, the embedment of the rotating cylinders to a flat plate resulted in producing higher lift and lower drag of up to 51% and 99% respectively. Besides that, the stall angle was also delayed by approximately 78%. The longitudinal static stability of an aircraft depends on the aerodynamic characteristics, specifically the moment coefficient of the aircraft. The plot of the moment coefficient over the angle of attack, C Mα of the aircraft will determine whether the

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aircraft is stable, unstable or neutral in terms of its longitudinal static stability [9]. Apparently, while the rotation of the cylinder does generate a high lift force, there is a drawback from the Magnus effect which results in the tendency of the CyFlaP to rotate about its lateral axis during the increment of the angle of attack due to the centrifugal force. The findings of the computational analysis of the CyFlaP’s pitching moment indicate that it is statically unstable [10]. In order to experimentally analyze the stability of the CyFlaP, the CyFlaP will be tested in various conditions in the closed-loop wind tunnel. Strain gauge balance are used to measure the aerodynamic loads. If the result shows a negative slope of C Mα curve, the CyFlaP is statically stable whereas, a positive slope of C Mα curve would indicate that the CyFlaP is statically unstable. This study aims to provide the experimental analysis of the longitudinal static stability of the CyFlaP at varied rotational directions and varied rotational speeds of the rotating cylinders.

2 Methodology The methodology applied in this study will be carefully explained and depicted in this section, starting with the fabrication of the model, assembly, component balance calibration and lastly, the wind tunnel testing.

2.1 Model Fabrication and Assembly The CyFlaP is designed to provide a high lift coefficient, C L by applying the Magnus effect, in which the flat plate is embedded with two rotating cylinders. A large gap between the rotating cylinders and the stationary body will result in the reduction of the efficiency of the rotating cylinders [11]. Therefore, the clearance between the rotating cylinders and the flat plate is kept within approximately 0.005 m. The size of the CyFlaP is designed to fully utilize the wind tunnel test section. The wind tunnel model of the CyFlap, depicted in Fig. 1 is 1.490 m × 0.957 m and weighs approximately 10 kg. The dimension of the flat plate is 1.330 m × 0.954 m, embedded with two rotating cylinders with a radius of 0.080 m and a length of 0.873 m. Material selection was done carefully to ensure a sustainable and lightweight model which can satisfy all aerodynamic configurational requirements while also being cost-effective. The flat plate and cylinders are fabricated using Styrofoam whereas the side plates are made of aluminium. Three aluminium ribs are embedded into the flat plate and attached to the side plates using screws. The cylinders are held by a holder and a motor is attached on each side to rotate the cylinder. The cylinder holder was 3-D printed using Polycarbonate (PC) material for its impact resistance. Two aluminium test rigs were used to hold the CyFlaP in the wind tunnel during testing at various configurations as can be seen in Fig. 2.

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Fig. 1 Wind tunnel model of the CyFlaP

Rotating cylinder

Flat plate

Side plate

Fig. 2 The CyFlaP wind tunnel model attached to the test rig

2.2 Control System In this experiment, propulsion and component balance are controlled using electrical and electronic components to regulate the desired input. Propulsion. The block diagram in Fig. 3 illustrates the process of the control system in propulsion. Component Balance. Load cell is a type of transducer that converts force into a measured electrical output. A load cell operates by transforming mechanical force into digital data which can be monitored and recorded by the user. The embedded Wheatstone bridge consists of four strain gauges that converts deformation into

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Fig. 3 Block diagram of the propulsion control system

electrical signals. The load cell consists of four wires. The red and black wire is used to induce excitation whereas the remaining wires are used for the output. Strain gauge load cells feature strain gauges that generate voltage irregularities when loaded. The degree of voltage change is represented as a weight in a digital readout [12]. Before the load cells are used for the experiment, the load cells need to be calibrated using a 0.5 kg dead weight. The maximum weight distribution can be applied to the load cell is 40.0 kg. Each load cell is calibrated separately. The calibration constant can be determined by placing the dead weight on the load cell and changing the calibration constant until the calculated weight output is equivalent to the dead weight applied. Component Balance Calibration. In this experiment, two load cells for each side are used to measure the forces. To ensure that the vertical force will deform the middle section of the load cell, the load cells are screwed between a beam and a rod which will transfer the force to the load cells. Figure 4 shows load cell C and D attached to the test rig. The same attachment for load cells A and B is placed on the other side of the test rig. The rod is attached to the middle rib of the CyFlaP. The movement of the CyFlaP will transfer the centrifugal force to the rod and then to the load cell. The force will then be converted into electrical signals. This position of the load cells only allows measurement of vertical loads. In order to read the value that is closest to the actual value, the horizontal and vertical forces needed to be separated before measurements were made and transmitted to the sensors [13].

Fig. 4 Force acting on the component balance during pitching

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2.3 Wind Tunnel Assembly The experimental model is placed in the test section of a closed-loop wind tunnel, supported by the rig depicted in Fig. 5. The position of the load cells attached to the outer side of the rig are based on the forces and moments that needs to be determined. The load cells will be installed horizontally to measure lift force and moments whereas vertically to measure drag force. The motor is powered by a power supply and controlled by a controller. All wires and screws are ensured to be properly connected and tightened before turning on the power. Changes of Rotational Direction. The CyFlaP will be adjusted to a certain angle starting from α = −20° to α = 20° with an increment of 5°. The rotational speed of the cylinder is fixed at 1727 RPM. The rotational direction is varied as clockwiseclockwise (CW-CW), clockwise-counterclockwise (CW-CCW), counterclockwiseclockwise (CCW-CW) and counterclockwise-counterclockwise (CCW-CCW). The motor capacity is inputted into the Data Acquisition (DAQ) system with increments of 5% at each angle, ranging from 5 to 20% of the total motor capacity. Changes of Rotational Speed. The same procedure as in the experiment of changes of rotational direction is used. However, the rotational direction of the cylinder is fixed as CW-CW to ensure the momentum injection on the front cylinder flows in the direction of the free stream flow. The first set of data is recorded by setting the rotational speed of the motor at 1727 RPM followed by a higher rotational speed of the motor which is 2322 RPM. Figure 6 shows the side view of the CyFlaP at α = 20° during the wind tunnel test. The tests were conducted at different free stream speed, U∞ which are 2.80, 5.10, 7.50 and 10.05 m/s. This is equivalent to Re = 2.55 × 105 , 4.64 × 105 , 6.83 × 105 and 9.10 × 105 respectively, which is within the range of low stratospheric level (refer Fig. 7).

Fig. 5 The model is attached on the test rig in wind tunnel test section

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Fig. 6 Side view of the CyFlaP at α = 20° Fig. 7 Average windspeed at different altitudes [14]

Figure 8 illustrates the condition of air in various altitude. Based on the figure shown, the temperature at the stratosphere level is constant at approximately -50 °C. This also indicates low air pressure in the stratosphere level. However, due to the limitation of the closed-loop wind tunnel, it is difficult to satisfy the similitude of the stratospheric level air condition during the test. The

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Fig. 8 Air conditions in various altitude [15]

Table 1 Air condition during testing

Properties

Value

Temperature (°C)

30.93

Pressure (kPa) Air density (kg/m3 ) Air humidity (%)

1.013 1.225 53.55

freestream air is directly supplied from the surrounding air. The air conditions are as in Table 1.

3 Results and Discussion Based on the wind tunnel test, the data are recorded and analyzed. The results will be presented in proper graphs and further explained in this section.

3.1 Experimental Analysis The data obtained from two analysis which are the effects of different rotational directions on the pitching moment coefficient and the effects of different rotational speeds on the pitching moment coefficient are presented.

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Pitching Moment coefficient

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Fig. 9 Changes of pitching moment coefficient, C M over angle of attack, α with CW-CW rotational direction

Effects of Different Rotational Directions on Pitching Moment Coefficients. Based on the data, the pitching moment coefficients, C M are determined and plotted as in Figs. 9, 10, 11 and 12. Figure 13 shows the schematic diagram of the various rotational direction of rotating cylinders. Based on the results obtained, CW-CW shows a stable curve

Pitching Moment coefficient

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Fig. 10 Changes of pitching moment coefficient, C M over angle of attack, α with CW-CCW rotational direction

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Fig. 11 Changes of pitching moment coefficient, C M over angle of attack, α with CCW-CW rotational direction

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Fig. 12 Changes of pitching moment coefficient, C M over angle of attack, α with CCW-CCW rotational direction

Fig. 13 Schematic diagram of different rotational direction of rotating cylinder

despite the changes of wind speed compared to other rotational direction. CW-CCW and CCW-CW shows an opposite trend where the curve of CW-CCW is stable in the negative α region and fluctuates in the positive α region whereas the CCW-CW curve is stable in the positive α region and fluctuates in the negative α region. However, both curves show a neutrally stable longitudinal static stability at a wind speed of approximately 10.05 m/s. CCW-CCW shows fluctuating curves as α increases. However, as the wind speed increases, the CyFlaP started to achieve a neutral stability of longitudinal static stability. When a rotating cylinder is rotating in the CW direction, the induced flow aids the free stream flow at the top of the cylinder, thus the velocity on the top of the cylinder is higher and pressure is lower compared to the bottom of the cylinder. Thus, more lift is produced. However, when a rotating cylinder is rotating in the CCW direction, the induced flow opposes the free stream flow at the top of the cylinder. Consequently,

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the velocity on the top of the cylinder is lower, which cause the pressure to be higher there compared to the bottom of the cylinder, resulting in less generation of lift. The streamlines around the cylinder are altered due to the rotation of the cylinder and the free stream flow. The formation of eddies behind the body at different angle of attacks also affects the results. Effects of Different Rotational Speeds on Pitching Moment Coefficients. Based on the data, the pitching moment coefficients, C M are determined and plotted as in Figs. 14 and 15. Lower rotational speeds show a positive slope for the C Mα curve (see Fig. 14). The difference of wind speed causes a slight change on the C Mα curve at lower rotational speeds. Higher rotational speeds display a negative slope for the C Mα curve (see Fig. 15). At a negative angle of attack, the curve shows that the CyFlaP is unstable, and it starts to gain stability starting from α = −5° up to α = 10°. The gradient of the C Mα curve increases as the wind speed increases. This shows that the CyFlaP is more stable when the rotating cylinders rotate at higher rotational speeds provided by higher wind speeds. Higher rotational speeds result in higher restoring moment that is sufficient to bring the CyFlaP back to its equilibrium condition. The results obtained may be explained by discussing the degree of longitudinal static stability (see Fig. 16). The first line shows a very stable aircraft. The nose-up

Pitching Moment coefficient

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Fig. 14 Changes of pitching moment coefficient, C M over angle of attack, α at 1727 RPM

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Fig. 15 Changes of pitching moment coefficient, C M over angle of attack, α at 2322 RPM

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Fig. 16 Degree of longitudinal static stability [9]

disturbance will cause the angle of attack, α, to increase and the pitching moment coefficient, C M , to reduce. The aircraft develops a restoring moment to nose down and tends to return to its equilibrium condition. So, it is possible to state the need for stable trim at the incidence angle, αe . The second line shows a stable aircraft which has the same conclusion as the first line. However, it is easier to control the aircraft to its trim state compared to the first line. This suggests that a very stable aircraft will be more resilient to disturbance. Stronger control actions will thus be necessary to allow the aircraft to move or alter its trim state [9]. The magnitude of the restoring moment decreases when the stability degree or stability margin is reduced until it drops to zero at neutral stability. The third line shows a neutrally stable aircraft. When there is any disturbance, the aircraft will stay at the disturbed position. The fourth line shows an unstable aircraft. The nose-up disturbance will cause C M to increase with α. The aircraft will continue to nose up and diverge. It has no tendency to return to its equilibrium condition.

4 Conclusion To conclude, the fabrication of the wind tunnel model of the CyFlaP was completed successfully. Apart from that, the experimental analysis of pitching moment coefficient of a rotating cylinder embedment onto a flat plate for HAPS resulted in a tremendous effect on the aerodynamic performance of the CyFlaP. The effects of different rotational directions and rotational speeds of the rotating cylinder on the longitudinal static stability of the CyFlaP were investigated. Among all the documented data, only the cylinders with a higher rotational speed and a CW-CW rotational configuration displayed a negative slope for the C Mα curve. The gradient of the C Mα curve becomes steeper as the Reynolds number increases. Overall, it was shown that the CyFlaP achieved optimal stable flying at a wind speed of 10.05 m/s, (Re = 9.10 × 105 ) when the rotating cylinder rotates at a speed of 2322 RPM in the clockwise-clockwise direction. One limitation of this study was that the

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air flow provided for the wind tunnel test could not entirely replicate the air condition at the stratospheric level. Despite this limitation, the study has demonstrated the successful application of the Magnus effect and the potential of the CyFlaP to achieve stable flight condition. Nevertheless, there is still room for further research to determine the stability and aerodynamic efficiency of the CyFlaP at high altitude conditions which would be advantageous for an integrated application in the aerospace industry. Acknowledgements This research work is supported by the Ministry of Education (MoE) Malaysia under Fundamental Research Grant Scheme (FGRS) Phase 1/2018 with MoE ref code. FRGS/1/ 2018/TK09/UPM/02/2. The support is gratefully acknowledged.

References 1. Joner B, Schneider J (1975) Evaluation of advanced airship concepts. In: Lighter than air technology conference. p 930 2. Kurt GK, Khoshkholgh MG, Alfattani S, Ibrahim A, Darwish TS, Alam MS, Yanikomeroglu H, Yongacoglu A (2021) A vision and framework for the high altitude platform station (HAPS) networks of the future. IEEE Commun Surv Tutor 23(2):729–779 3. Wolff EB (1925) Preliminary investigation of the effect of a rotating cylinder in a wing. No. NACA-TM-307 4. Ali HM, Rafie ASM, Ali SAM (2021) Numerical analysis of leading edge cylinder Aerofoil on Selig S1223 for moving surface boundary control. J Aeronaut Astronaut Aviat 53(2):143–153 5. Štˇepánek A (1988) The aerodynamics of tennis balls—the topspin lob. Am J Phys 56(2):138– 142 6. Inc.Seifert J (2012) A review of the Magnus effect in aeronautics. Prog Aerosp Sci 55:17–45 7. Clarke CW, Batko JS, Smith Jr, KW (2015) Magnus effect in duct flow 8. Ali HM, Rafie ASM, Ali SAM, Gires E (2021) Computational analysis of the rotating cylinder embedment onto flat plate. CFD Lett 13(12):133–149 9. Cook M (1997) Flight dynamics principles: a linear systems approach to aircraft stability. 2nd edn. Butterworth-Heinemann 10. Mahammad Noor M (2021) Computational analysis of pitching moment on cylinder-flat plate-cylinder embedment (CyFlaP) using computational fluid dynamics (CFD). (Unpublished thesis). Universiti Putra Malaysia, Malaysia 11. Al-Garni AZ, Al-Garni AM, Ahmed SA, Sahin AZ (2000) Flow control for an airfoil with leading-edge rotation: an experimental study. J Aircr 37(4):617–622 12. Load Cells and Force Sensors. https://www.omega.com/en-us/resources/load-cells. Accessed 10 Mar 2022 13. Bueno Tintoré I (2018) Design of a three-axis wind tunnel force balance. (Doctoral dissertation). University of Zagreb. Faculty of Transport and Traffic Sciences. Division of Aeronautics 14. Lee YC, Ye H (1998) Sky station stratospheric telecommunications system, a high speed low latency switched wireless network. In: 17th AIAA International Communications Satellite Systems Conference and Exhibit. p 1394 15. The Stratosphere. https://scied.ucar.edu/learning-zone/atmosphere/stratosphere. Accessed 15 June 2022

Reaching Law Controller for Backlash Compensation in Parabolic Antenna Systems Salihu Abdulmumini Jalo, Mohammed Ahmed, Abdulqadiri Bello Abdulqadiri, Muhammad Usman Ilyasu, Isa Mohammed Inuwa, and Garba Elhassan

Abstract Presented is a study on the application of the reaching law controller for suppressing the effect of backlash disturbance in parabolic antenna systems. The parabolic antennas are reflector antennas specifically designed to work at line-ofsight, otherwise there might be interruption or stoppage of communication. Backlash is one of the interferences that may affect the operation of such systems, and researches deduce that it could be reduced using suitable control systems. This will result in a reduced down time, maintenance costs, handling system complexities and enhanced life span. Many control methods have been tested on such systems ranging from linear methods to non-linear approaches. The antenna model in this study is adopted from a previous work. This study targets the performances of the controller in the presence of backlash disturbance which was then compared with that of the PID controller. The simulation work was done using MATLAB/SIMULINK software. Results portrayed that the Constant Rate Reaching Law Controller (CRC) was able to completely compensate for the disturbance created by the presence of backlash in the system. The PID compensation was not up to that of the CRC. Therefore, the CRC showed better performance in terms of robustness. Furthermore, it indicated the suitability of the scheme for this class of plants; that is parabolic antenna systems. S. A. Jalo (B) · I. M. Inuwa State Polytechnic Yola, Yola, Adamawa State, Nigeria e-mail: [email protected] M. Ahmed Abubakar Tafawa Balewa University, P. M. B 0248 Bauchi, Bauchi State, Nigeria A. B. Abdulqadiri Cadatral Zone A00 Central Business District FCT Abuja, Nigerian Midstream and Downstream Petroleum Regulatory Authority, Plot 1012, Nigeria M. U. Ilyasu Federal Polytechnic Bali, Bali, Taraba State, Nigeria G. Elhassan Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_17

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Further studies may aid in achieving simple as well as robust control system for such systems. Keywords Parabolic dish antenna system · Constant rate reaching law · Backlash · Proportional-integral-derivative control

1 Introduction The parabolic dish antenna systems consist of reflector type antennas which are specifically designed to work at line-of-sight. Any little disruption might lead to interruption or stoppage of communication completely [1]. There exist factors that interfere with smooth operations in these systems which include wind, gust, friction as well as backlash [2, 3]. Precision is essential in such systems and researches give credit to the applications of control systems for its enhancement [2, 3]. Backlash is an internal disturbance which originates from the gearing arrangement that aids in the positioning of the system. If neglected, the backlash may negatively affects the smooth operation of such systems. Researchers showed that it could be reduced using suitable control systems, thereby, allowing for schedule maintenance hence, reducing down time, maintenance costs, handling system complexities and prolonged life span of the system. Gawronski et al. [4] explored the Linear Quadratic Gaussian (LQG) and Feed forward Control methods on the DSS-13 Antenna. In another work by same authors, H-Infinity was examined for wind rejection [5] on telescopic antenna. The Proportional-Integral, LQG, Feedforward as well as the H-Infinity control techniques were investigated for the antenna systems [6]. Chan-Ho et al. in their work applied the H-Infinity for step tracking control of parabolic antennas mounted on moving vehicle [7]. The Model Predictive Control (MPC) scheme proposed by Ghahramani et al. [8, 9] and the Ant Colony algorithm for optimizing PID controller was explored for the control of large communication parabolic dish antenna system [10]. The fuzzy logic was utilized in the works of Ahmed and Noor [11, 12] for friction and backlash compensation in parabolic dish antennas. Several control strategies have been examined on such systems. The linear methods of control has the disadvantage of requiring more precise sensors and may not cope with recent trends of newer versions with attributes such as increased sizes and speed of operation. The main downside of the intelligent methods is the absence of readily available mathematical models, reducing the chances for in-depth research. Nonlinear control methods require higher computation time, hence imposing the problem on implementation. Therefore, the control scheme proposed in this study is expected to have the simplicity of the PID control schemes which falls under the linear control methods. Another important property is the robustness of the sliding mode control techniques which are categorized under nonlinear control schemes.

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2 Materials and Methods A simple configuration of the system is shown in Fig. 1. The reference position indicates where the desired antenna location will be fixed. The parabolic antenna system block represents the plant which is the complete antenna system. The antenna position is the system’s response. The error ‘e’ is the difference between the real antenna position and the reference or desired position. The controller is the subsystem that always tries to accomplish the compensation task such that the antenna position is always at the desired location. Therefore, the controller would always try to minimize the value of the error. The simulation of the controller will be conducted using SIMULINK/MATLAB software. The proposed constant rate reaching law controller (CRC) will be compared with the Proportional-Integral-Derivative (PID) Controller.

2.1 The Parabolic Dish Antenna Model The parabolic dish antenna system is a telescopic antenna proposed by Nise [13]. The system’s transfer function is as given by Eq. (1), where θ O (s) is desired position and θ I (s) is the current position of the antenna. 6.628 θ O (s) = 3 θ I (s) s + 101.71s2 + 171s

(1)

2.2 The Controller Starting with the Proportional-Integral-Derivative (PID) controller design, the popular PID controller control law is given by Eq. (3), where e is the error signal with respect to time t, θd is the desired antenna postion, θ0 is the current antenna postion, K P is the controller proportional gain, K I is the controller integral gain and K D is the controller derivative gain. e(t) = θd (t) − θ0 (t)

Fig. 1 An illustration of the system

(2)

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u(t) = K P e(t) + K D

de(t) + KI dt

{ e(t)dt

(3)

Next is the Constant Rate Reaching Law Controller (CRC) which is governed by Eq. (6). The Constant Rate Reaching Law can be modelled by Eq. (4), and it is dependent on the sliding surface given by Eq. (5) [14–16]. In these equations, y and c are constants which are chosen in a way such that they are greater than zero and suit the system’s operation. Constant rate reaching law = −y ∗ sign(shi )

(4)

shi(t) = ce(t) + e(t) ˙

(5)

u = y ∗ sign(shi )

(6)

3 Results and Discussions 3.1 System Response Without Disturbance The antenna system response is demonstrated in Fig. 2 and the error in the system is shown in Fig. 3. The system with the PID controller has an overshoot of about 2.0°, a settling time of about 3.0 s, a rise time of 1.25 s and a steady state error of about 1.0°. The corresponding parameters for the CRC are 0.0°, 1.4 s, 0.8 s and 0.2°. Further analysis indicate that the Root Mean Square Error (RMSE) of the PID controller and the CRC is about 1.0° and 0.2° respectively. This suggest that the resulting parameters of the system with the CRC are better than that with the PID controller. The summation of the absolute value of the error with the PID is 5357.8° while with the CRC is 3439.6° which further strengthen the superiority of the CRC over the PID controller in terms of accuracy. In both cases, the error signal keeps declining with time which indicates the likelihood of system stability.

3.2 System Response with Backlash Disturbance The antenna system response is illustrated in Fig. 4 and the error in the system by Fig. 5. The effect of the backlash disturbance has introduced about a 30° change above the desired position. Both the PID controller and the CRC were able to maintain the value of the parameters close to the previous case without disturbance. How, the increase in the number of errors for the PID controller (10,111°) was significantly

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60

50

Reference position Position with PID Position with CRC

Position (Deg)

40

30

20

10

0

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

time(s)

Fig. 2 Antenna system response without disturbance

higher than the CRC (5881.7°), suggesting that the CRC is more robust. This also indicates that the PID has grown three times compared to that of the CRC. Here, the errors also reduces with time, portraying a stable system.

3.3 Brief Explanation on Results The results showed that the antenna system response in terms performance and ability to curb the effect of backlash disturbance in the system are better with the CRC compared to the PID controller. The information from the error plots also showed that the system might be stable for both cases.

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Position error (Deg)

60 Position error with PID Position error with CRC

40 20 0 -20

0

0.5

1

1.5

2

2.5 time(s)

3

3.5

4

4.5

5

Position error (Deg)

60 Position RMS error with PID Position RMS error with CRC

40

20

0

0

0.5

1

1.5

2

2.5 time(s)

Fig. 3 Antenna system response errors without disturbance

3

3.5

4

4.5

5

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90 80 70

Position (Deg)

60 50 40

Reference position Position with PID Position with CRC Disturbance signal

30 20 10 0

0

0.5

1

1.5

2

2.5

time(s) Fig. 4 Antenna system response with disturbance

3

3.5

4

4.5

5

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60 Position error with PID Position error with CRC

40 20 0 -20

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Position error (Deg)

time(s) 60 Position RMS error with PID Position RMS error with CRC

40

20

0

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

time(s) Fig. 5 Antenna system response errors with disturbance

4 Conclusion The study on the application of the Constant Rate Reaching Law Controller for suppressing the effect of backlash disturbance in parabolic antenna systems was successfully accomplished. Parabolic antennas are designed to operate at line-ofsight configuration. Backlash is a serious internal disturbance that disrupts the positioning of such systems, which could be suppressed by applying suitable control systems. Performances of the system with and without backlash disturbance were examined and were compared with that of the PID controller using MATLAB/ SIMULINK software. Results showed that the constant rate reaching law controller (CRC) was able to compensate for the disturbance created by the presence of backlash in the system better than the PID. Hence, the CRC demonstrated better performance, indicating its suitability for application to this class of systems. Further studies in this area could lead to a more simple and robust control systems, which may be applicable to other similar systems.

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References 1. 2. 3. 4. 5. 6. 7. 8.

9.

10.

11.

12.

13. 14. 15. 16.

Balanis CA (2005) Antenna theory: analysis and design. Wiley, New Jersey Gawronski W (2004) Control and pointing challenges of antennas and (radio) telescopes Gawronski W (2008) Modeling and control of antennas and telescopes. Springer Gawronski W, Racho CS, Mellstrom JA (1994) Linear Quadratic Gaussian and feedforward controllers for the DSS-13 antenna Gawronski W (1996) An H-infinity controller with wind disturbance rejection properties for the DSS-13 antenna Gawronski W (2001) Antenna control systems: from PI to H-infinity. IEEE Antenna Propag Mag 43 Chan-Ho C, Cheal L, Sang-Hyo L, Tae-Yong K (2003) Antenna control system using step track algorthm h-infinity controller. Int J Control Autom Syst 1:83–92 Ghahramani A, Karbasi T, Nasiran M, Sedigh AK (2011) Predictive control of earth station antenna (XY Pedestal). In: Second international conference on control instrumentation and automation (ICCIA). pp 344–349 Ghahramani A, Karbasi T, Nasirian M, Seqigh AK (2011) Predictive control of a two degrees of freedom XY Robot (satellite tracking pedestal) and comparing GPC and GIPC algorithms for satellite tracking. In: Proceedings of second international conference on control, instrumentation and automation (ICCIA). pp 865–870 Ahmed M (2013) Optimal design of PID controller for position control of parabolic dish antenna using ant colony optimization. Master’s thesis, Abubakar Tafawa Balewa University, Bauchi, Nigeria Ahmed M, Noor SBBM (2014) Fuzzy control of parabolic antenna with friction compensation. In: 5th International conference on intelligent and advanced systems (ICIAS). Kuala Lumpur Convention Centre, Malaysia Ahmed M, Noor SBBM (2014) Fuzzy control of parabolic antenna with backlash compensation. In: International Conference on mathematics, engineering and industrial applications (ICoMEIA2014). Penang, Malaysia Nise NS (2019) Control systems engineering. Wiley, New Jersey Slotine JJE, Li W (1991) Applied nonlinear control. Prentice-Hall Englewood Cliffs, New Jersey Khalil HK (2002) Nonlinear systems, 3rd edn. Prentice Hill, New Jewsey Liu J, Wang X (2012) Advanced sliding mode control for mechanical systems: design, analysis and MATLAB simulation. Springer Science and Business Media

Aircraft Structure and Aero-elasticity

Numerical Investigation of Stresses on the Composite Aircraft Fuselage Muhammad Irfan Naufal and Faruq Muhammad Foong

Abstract Improvement of an aircraft’s efficiency and performance can be achieved by decreasing the aircraft weight through considerable usage of composite materials in the aircraft fuselage. Composite materials are relatively new and there is still very few research on the stress distributions of composite fuselage. This study focuses on the effects of pressure and thermal loadings on the hoop stress of a carbon fiber fuselage skin at a cruising altitude of 11,000 m through finite element simulation. Model used in this analysis consists of a quarter of the cylindrical fuselage with two window cut-outs. Analysis shows that the addition of frame onto the fuselage was able to significantly reduce the maximum stress levels by approximately 23.5% and the addition of stringers further reduced the stress levels by another 29.2%. Further analysis shows that the frame and stringers mostly reduce the hoop stresses on the fuselage that were caused by pressure loadings, whereas minimal reduction was observed for the stresses from the thermal loadings. Finally, a comparison demonstrated that an aluminum alloy fuselage would experience an approximately 221.0% increase in maximum stress at cruising altitude and a 43.0% increase in mass for the same fuselage if compared to the carbon fiber material. Nevertheless, if compared to the relative yield strength of each material, the aluminum alloy achieved a 43.0% larger safety factor than the carbon fiber material. Keywords Stress · Fuselage · Composites · Finite element analysis

M. I. Naufal · F. M. Foong (B) Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia e-mail: [email protected] F. M. Foong UTM Aerolab, Institute for Vehicle System and Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_18

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1 Introduction The main purpose of commercial aircrafts is to transport either passengers or cargos to a desired location. The fuselage of an aircraft plays an important role in accommodating the weight of the aircraft and the payload to ensure comfort and safety for passengers or shipments. During flight, the fuselage is subjected to pressure forces and thermal stresses due to high velocities and temperature differences at high altitudes. According to [1], fighter jets have been using composite fuselages since the late 1930s with the prime example being the Hughes flying boat, H-4. The composite fuselage market rose when Boeing featured the Boeing 787 Dreamliner where most of its major component were made from composite material [2]. Nevertheless, the weight difference between fighter jets and commercial jets are vastly different that to apply the same material for commercial jetliners requires a thorough analysis before the technology becomes stable. Currently, non-composite fuselages still dominate the commercial aircraft industry. Hiken [3] states that only the fuselage of the Airbus A350 and the Boeing 787 contains more than 50% composite material, as of the year 2015. However, due to the advantages of composite materials, Vasiliev [4] claimed that its usage in the aircraft industry has been steadily increasing. Despite the superior properties of composite, Campos et al. [5] argued on the drawbacks of this material such as vulnerability to electromagnetic interference, lack of lightning protection, expensive machinery and many more. Additionally, there is still a broad research gap on the structural analysis of composite fuselages. The objective of this study is to analyze the hoop stress of a carbon fiber aircraft fuselage under pressure and thermal loading through finite element analysis. The effects of frame and stringers on the fuselage skin were also considered in this work. Finally, the performance of the carbon fiber fuselage was compared to an aluminum alloy fuselage. The study conducted here provides an important insight on the amount of hoop stress that can be reduced with the addition of frames and stringers and which form of loadings, either pressure or thermal loadings, dominate the hoop stresses in the fuselage.

2 Finite Element Modelling The design process for the fuselage was done using SolidWorks software. A crown skin panel assembly was designed from the existing data of the Boeing 787 Dreamliner. The assembly consists of three parts, which are the frame, stringers and skin. Three models were designed, where the first model (Model 1) consists of only the fuselage skin, the second model (Model 2) consists of the fuselage skin and frame and the third model (Model 3) includes the fuselage skin, frame and stringer as shown in Fig. 1. In order to simplify the simulation, a few assumptions were made which are:

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Fig. 1 Model 3 consisting of the fuselage skin, frame and stringers

1. The cross-section of the aircraft is considered circular, 2. The aircraft’s passenger floor will not be included in the simulation 3. The stringers are equally spaced and the joining are considered perfect. The skin was designed with 3.0 mm thickness, 1878.6 mm radius and 3500.1 mm length with two windows cutouts of 271.8 mm wide and 467.4 mm height. The frame was modelled with thickness of 2.6 mm. The model consists of a total of seven frames with 508.0 mm intervals while the stringers formed will be 2.6 mm thick with total of 14 stringers at equal intervals of 167.6 mm.

2.1 Mesh and Boundary Conditions Figure 2 shows the meshing of Model 3 which consists of the skin, frame and stringers. The mesh used for all models was curvature-based mesh with a maximum element size of 55.3 mm and minimum element size of 18.4 mm. This will produce a total of 91,086 nodes and 48,312 elements after meshing. For Model 1, a total of 47,728 nodes and 23,226 elements were generated when meshed and for Model 2 has a total of 67,902 nodes and 35,091 elements. The same boundary conditions were applied to all models where an X-symmetry boundary was applied to the left and right side of the models, a Z-symmetry boundary condition was set to the top and a Y-symmetry boundary was assigned to the bottom of all three models.

2.2 Material and Loadings Two types of material were assigned to the model to compare the stresses experienced by each material. Here, the two materials assigned are the standard CF UD (Carbon Fiber) and the AL-7075T6 (Aluminum alloy) material which are both commonly used in the fuselage of commercial aircrafts. Table 1 describes the mechanical properties of these materials.

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Fig. 2 Meshing and boundary conditions for Model 3

Table 1 Material properties of standard CF UD and AL-7075T6 Properties Density

(kg/m3 )

Carbon fiber

Aluminum alloy

1600

2810

Poisson’s ratio

0.30

0.33

Yield strength (MPa)

110

505

Young’s modulus (GPa)

135

72

Thermal expansion coefficient (µK−1 )

4.0

23.5

Two different types of loading were considered separately in the simulation where the first loading was a purely mechanical loading that arises from an internal pressure exerted inside the fuselage due to the pressurized cabin and an external pressure exerted on the outer skin of the aircraft at a cruising altitude of 11,000 m. This results in an internal pressure of 101.3 kPa and an external pressure of 26.7 kPa. The second type of loading was purely thermal where stresses occur due to the temperature differences between the inner and outer layer of the fuselage. Similarly, the second type of loading was also analyzed at cruising altitude. The internal cabin temperature was set to 15.0 °C whereas the external temperature was set to −54.6 °C.

2.3 Model Validation In this study, only the hoop stress of the fuselage was analyzed. To validate the model, the hoop stress of the skin under pure pressure loading was compared with Eq. (1) obtained from Hearn [6] σh =

ri2 Pi − ro2 Po (Pi − Po )ri2 ro2 + 2 2 ro − ri (ro2 − ri2 )r 2

(1)

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Table 2 Model validation with analytical equations Loading

Simulation σ h (MPa)

Analytical σ h (MPa)

Error (%)

Pressure

47.3

47.2

0.2

Thermal

28.7

26.8

7.1

and the hoop stress under thermal loading was compared with stress due to temperature gradients for a thin wall object obtained from Wilson et al. [7] as shown in Eq. (2). σh =

Eα∆T 2(1 − v)

(2)

where for Eq. (1), σh is the hoop stress, ri , ro and r are the inner radius, outer radius and radius of interest of the fuselage and Pi and Po are the pressures exerted on the inner and outer surfaces of the fuselage. For Eq. (2), E, α and v are the Young’s modulus, thermal expansion coefficient and Poisson’s ratio of the fuselage material and ∆T is the temperature difference between the fuselage’s inner and outer surfaces. Only Model 1 was used to validate the simulation since Eqs. (1) and (2) is only valid for thin wall structures without the presence of frames or stringers. Initially, the carbon fiber material was assigned to the model. Table 2 demonstrates the comparison of hoop stresses between the simulation and the analytical equations for both type of loadings. For both types of loadings, the difference between the simulation and analytical results are relatively small, being less than 8.0%. This is especially true for the pure pressure loading where the error was less than 1.0%. Hence, it suffices to say that the simulation has been validated.

3 Result and Discussion The simulation in Sect. 2.2 was then repeated by combining both loadings together onto Model 1 to observe how each individual loadings and their combined effects contribute to the stress of the fuselage. Figure 3 displays the comparison of these loadings for carbon fiber material. The stresses were taken along the longitudinal length of the fuselage. Two peaks were observed at a longitudinal distance of 1500 and 2000 mm. These peaks correspond to the high stresses at the corners of the window cutouts. The carbon fiber material demonstrates that both type of loadings results in a similar stress output where the hoop stresses due to the pressure loading is only slightly larger than the stresses from the thermal loadings as seen in Fig. 3. This indicates that both loadings have significant effects on the carbon fiber fuselage. The combined loading was the simulated for Model 2 and Model 3 to observe how the addition of frame and stringer influences the stresses on a carbon fiber fuselage.

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Fig. 3 Combined loading effects on carbon fiber fuselage

Figure 4 reveals this comparison. The addition of the frame onto the fuselage reduces the maximum stress by approximately 23.5% whereas the extra inclusion of stringers further reduces the maximum stress level by another 29.2%. Considering Model 3 which displayed the lowest stress levels, the material was then changed to aluminum allowed and re-simulated to compared under the combined

Fig. 4 Comparison between combined loading effects for Models 1, 2, and 3 for carbon fiber fuselage

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loading scenario. Figures 5 and 6 below displays the effect of the combined loadings on the stresses on Model 3 for the carbon fiber and the aluminum alloy material. One important observation that can be made here is that the addition frame and stringers to the fuselage significantly reduces the stresses due to pressure loadings, but not so much the stresses due to thermal loadings. This makes sense since Eq. (1)

Fig. 5 Stresses due to combined loadings for Model 3 carbon fiber fuselage

Fig. 6 Stresses due to combined loadings for Models 3 aluminum alloy fuselage

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Table 3 Comparison between carbon fiber and aluminum alloy fuselage Maximum σ h (MPa)

Material

Mass (kg)

Carbon fiber

557.4

63.9

1.7

Safety factor

Aluminum alloy

979.0

205.1

2.5

suggests that the hoop stress due to pressure loadings are dependent on the inner and outer radius of the fuselages, which changes when frames and stringers are added On the other hand, Eq. (2) shows that stresses from thermal loadings are more material dependent instead. Nevertheless, Fig. 5 indicates that thermal loadings are the prime source of stresses in typical commercial aircraft fuselage at high altitudes. Figure 6 demonstrates a similar trend to Fig. 5 where the stresses due to the pressure loadings were significantly reduced with the inclusion of frame and stringer. However, the aluminum allow material displayed a much larger stress from thermal loadings compared to the carbon fiber fuselage, recording 221.0% larger combined stress. This is mostly due to the thermal characteristics of the alloy itself where the thermal expansion coefficient for aluminum alloy is much higher than carbon fiber. In addition, for the same fuselage size, the carbon fiber fuselage would weigh 43.0% lighter than the aluminum alloy as derived from Table 3. Nevertheless, if both materials were to be compared in terms of their respective yield stresses from Table 1, the aluminum alloy material demonstrates a 47.1% larger safety factor than the carbon fiber material.

4 Conclusion Overall, the finite element analysis shows that the addition of frame and stringer has effectively reduced the maximum hoop stress on the fuselage skin up to a total of 45.8%, where a majority of the reduced stresses comes from the stresses induced by the pressure loadings. The usage of the carbon fiber material was shown to be superior to aluminum alloy in terms of mass and stress, recording a 43.0% lower mass and a 68.8% lower stress levels. However, the aluminum alloy material displayed a 47.1% larger safety factor compared to the carbon fiber material. Additionally, in terms of cost, carbon fiber is known to be quite expensive and can cost two to five times more than the aluminum alloy. Further study in the field would be a more detailed stress analysis where other forms of stresses experienced by the fuselage are considered. Acknowledgements The authors would like to thank to Fundamental Research Grant Scheme (FRGS) from Ministry of Higher Education (MOHE) Malaysia, Grant No: FRGS/1/2021/TK0/ UTM/02/3, for funding this research.

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References 1. A Brief History of Aircraft Materials (2019) https://www.thomasnet.com/insights/a-brief-his tory-of-aircraft-materials/. Accessed 15 Dec 2021 2. Sloan J (2018) The first composite fuselage section for the first composite commercial jet. https://www.compositesworld.com/articles/the-first-composite-fuselage-section-forthe-first-composite-commercial-jet. Accessed 21 Dec 2021 3. Hiken A(2018) The evolution of the composite fuselage: a manufacturing perspective. Aerosp Eng 4. Vasiliev V, Jones RM, Man LI (2017) Mechanics of composite structures. Mech Compos Struct 1–506 5. Campos SJ, Aurelio M, de Almeida SFM (2009) Design and analysis of a composite fuselage. In: Brazilian Symposium on Aerospace Engineering and Applications, vol 3 6. Hearn E (2001) Mechanics of materials, vol 1, 3rd edn. Butterworth-Heinemann, pp 215 7. Wilson D, Filion YR, Moore ID (2015) Identifying factors that influence the factor of safety and probability of failure of large-diameter, cast iron water mains with a mechanistic, stochastic model: a case study in the City of Hamilton. Procedia Eng 119(1):130–138

Aeroelastic Optimization Using Laminate Fiber Orientation on a Composite Wing Structure Angga Dwi Saputra, Ilham Akbar A. Satriya, R. Wibawa Purabaya, Syariefatunnisa, Zuhdhy Masfuri, Dimas Sangaji, and Farhan Muzzammil Ali

Abstract Composite materials use increases significantly in aerospace structures, especially wing structures, primarily due to their attractive strength-weight ratios. Nevertheless, the increasing use of light and slender wings in modern unmanned aerial vehicles (UAVs) leads to structural configurations featuring low natural frequencies and high flexibility, which can easily experience aeroelastic phenomena that might cause potentially catastrophic failure. This paper aims to present an investigation to achieve an optimal fiber orientation of laminates layup for the UAVs’ wing skin to meet aeroelastic design requirements. The wing ribs and spars are made from aluminum alloy, while the wing skin is a composite plate made of woven carbon laminates. The flutter speeds for each configuration layup were analyzed numerically using MSC Nastran. The doublet-lattice method is adopted to predict threedimensional unsteady aerodynamic forces acting on the oscillating wing. According to the numerical analysis, it is evident that the fiber orientations of the wing skin influence the critical flutter onsets. Since the fiber orientation change can significantly affect the wing’s stiffness, particularly in the torsion modes. Thus, the aeroelastic performance can be improved without increasing the mass by modifying the fiber orientation on the wing skin laminates. The angle of 45° can improve the critical flutter speed by more than 400 m/s and static-divergence up to 330 m/s or 50% higher in static-divergence speed and more than 260% in flutter speed compared to angles of 0 and 90°. Keywords Aeroelastic · Flutter · Static-divergence · Composite · Fiber orientation

A. D. Saputra (B) · I. A. A. Satriya · R. W. Purabaya · Syariefatunnisa · Z. Masfuri · D. Sangaji · F. M. Ali National Research and Innovation Agency, Puspiptek, Serpong 15314, Indonesia e-mail: [email protected] R. W. Purabaya Sepuluh Nopember Institute of Technology, Surabaya, Indonesia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_19

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1 Introduction Composite materials are now widely used in various aircraft components due to advancements in material technology. The strength-to-weight ratio of composite materials is excellent. Composites are lightweight but structurally rigid. Furthermore, composites have greater manufacturing feasibility for complex parts, unique contours, and special features, particularly in aircraft components [1]. The composite structure comprises laminates with varying fiber orientations that can be adjusted to provide various material properties. Different material structures’ orientations and lamination can be adjusted to produce an optimal composite structure. Due to manufacturing constraints, fiber orientation angles are typically limited to 0, 45, and 90°, with the layer thickness remaining constant [2]. The orientation and lamination of different material structures can be adjusted to produce an optimal composite structure. However, the growing use of light and high aspect ratio wings results in structural configurations with low natural frequencies susceptible to aeroelastic instability [3]. Divergence and flutter are the most dangerous aeroelastic phenomena. Flutter is a dynamic structural instability caused by the interaction of aerodynamic, elastic, and inertial forces that can lead to catastrophic failure [4]. Divergence occurs when structural displacement caused by aerodynamic forces changes the wing’s angle of attack. The lift force increases when the angle of attack changes. The increased lift causes the structure to be displaced further, causing the wing surface to curve upwards. Von Mises stress on the wing caused by the continuous increase in the lift will result in wing structure failure [5]. Flight tests, wind tunnels, and numerical analyses are some methods used to predict an aircraft structure’s aeroelastic behaviour. However, because the wind tunnel model is expensive and time-consuming, the engineer cannot test and analyze a wide range of designs during the optimization process. As a result, numerical simulations are the most effective and efficient method for analyzing flutter behaviour during the design phase [6, 7]. The current industry standard for aeroelastic analysis in subsonic regimes is to generate the unsteady aerodynamic interaction with the aircraft structure using traditional potential flow models such as the doublet-lattice method (DLM) and strip theory [8]. The DLM is a theory of an unsteady tridimensional lifting surface. The linearized compressible aerodynamic potential theory for subsonic flow is the DLM’s theoretical foundation. The undisturbed flow is uniform and either steady or harmonically varying (gusting). All lifting surfaces parallel the incoming flow and are flat [9]. The DLM provides a more robust approach for non-stationary aerodynamic prediction and a faster method of computing unsteady aerodynamic loads than fluid–structure interaction (FSI) procedures based on the finite element method (FEM) and computational fluid dynamics (CFD) [10]. This paper aims to present an investigation into achieving an optimal fiber orientation of laminates layup for the wing skin of UAVs to meet the aeroelastic design requirements. The wing ribs and spars are made of aluminum alloy, while the wing skin is a woven carbon laminate composite plate. The flight envelope of the analyzed

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UAV wing is in the subsonic regime. As a result, the DLM method can still calculate the flutter boundary.

2 Aeroelastic Theory The term aeroelasticity is related to aerodynamic, elastic, and inertial forces. The relations between these three main disciplines are developed in various analysis types. The aeroelastic equation of motion is assembled with a separation of structural and aerodynamic operators [10–12]. It can be written as: Aq¨ + (ρV B + D)q˙ + (ρV 2 C + E)q = 0

(1)

where A, B, C, D, and E are the structural inertia, aerodynamic damping, aerodynamic stiffness, structural damping, and structural stiffness matrices, respectively, and q is the generalized coordinates (typically modal coordinates). ρ and U are the air density and airspeed, respectively. It is important to note that the B and C matrices of aerodynamic forces are functions of the reduced frequency k. k = ωb/U

(2)

where ω is the oscillation frequency of a particular mode, and b is the half of the chord line length. As these aeroelastic equations have a zero right-hand side (and are homogeneous), it is impossible to determine the model response’s absolute values. Instead, the system’s stability needs to be evaluated using an eigenvalue approach. The aeroelastic Eq. (1) can be solved efficiently for an N-DoF system using an eigenvalue solution to determine the system frequencies and damping ratios at a particular flight condition (airspeed and altitude). Introducing the (trivial) expression: [

I 0 0 A

]{ } { } ]{ } [ q q˙ 0 0 I = − q¨ 0 −(ρU 2 C + E) −(ρU 2 B + D) q˙

(3)

where I is the N × N identity matrix, combining it with Eq. (1) in partitioned form gives the formulation. { } [ ]{ } q˙ q 0 I − =0 q¨ −A−1 (ρU 2 C + E) −A−1 (ρU 2 B + D) q˙

(4)

{ } q = z, and also assuming a harmonic in the form z = z o eiωt thus, Eq. (3) q˙ becomes: If

(Q − I λ)z o = 0

(5)

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where: [ Q=

0 I −A−1 (ρU 2 C + E) −A−1 (ρU 2 B + D)

] (6)

For an oscillatory system, such as the aeroelastic system considered here, the eigenvalues λ of the system matrix Q occur in complex conjugate pairs and are in the form. / λ j = ζ j ω j ± i ω j 1 − ζ j2 (7) where ωj are the natural frequencies and ζ j are the damping ratios. Aeroelastic stability analysis is performed based on the eigensolution of the flutter equation. The system becomes unstable if the real part of the complex eigenvalues is positive [13, 14].

3 Finite Element Model The four main components of the wing design of a half-wing medium altitude long endurance aircraft are the front spar, rear spar, skin, and ribs. The elastic modulus of the woven carbon fiber composite material (0.380 mm/ply) to which the ribs and skin were applied was 77,400 MPa in the fiber direction, 72,900 MPa in the transverse direction, and 3150 MPa in the shear direction. Meanwhile, the elastic modulus of Aluminium 2024-T3 was 73,100 MPa, and the shear modulus was 2800 MPa when applied to the front and rear spars. The composite fiber with those properties has been chosen because of its high availability in the market. Figure 1 depicts the design of the half-wing component. The wing structure is subjected to four boundary conditions: the two holes in the front spar’s root and two others in the holes of the rear spar’s root. Both boundary

(a)

(b)

Fig. 1 a Entire half-wing structure design and b internal half-wing structure

Aeroelastic Optimization Using Laminate Fiber Orientation … Table 1 The thickness of each segment on the spars

237

No. segment

Front/rear spar thickness (mm)

Joint

20.00

1

3.00

2

2.75

3

2.50

4

2.25

5

2.00

6

1.75

7

1.50

8

1.25

conditions were of the pin support type, representing the actual condition of the wingfuselage joint. The wing structure, including spars and the skin, was divided into eight segments with varied thicknesses on each segment. The length of each segment was dependent on the rib’s spaces. Both front and rear spars were made from aluminum 2024-T3 with varied thicknesses on every segment, going thicker from the tip to the root. Table 1 details the difference in thickness of each spar segment. In contrast, the thickness of each segment on the ribs and the skin were uniform. There were nine ribs on the wing structure, and each rib was made of four plies laminate with a total thickness of 1.52 mm. Similarly, the skin was made of four plies laminate with a uniform segment thickness of 1.52 mm. The computations were carried out with different setup laminates of the wing’s skin to analyze the effects of fiber orientation on the critical flutter speed. The wing’s skin laminates remained uniform with four plies for all segments, but every case had a different configuration of fiber orientation which presents in Table 2. In Table 2, the fiber orientation of each ply can be identified. Case 1 performed the setup laminate with uniform orientation for all plies, and the other cases carried out computation with two opposite angles on the middle layers. Table 2 Skin configuration of fiber orientation for Cases 1–7

Case

Orientation of skin (degree)

1

0/0/0/0

2

0/15/-15/0

3

0/30/-30/0

4

0/45/-45/0

5

0/60/-60/0

6

0/75/-75/0

7

0/90/-90/0

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4 Results and Discussion The normal analysis was carried out using MSC NASTRAN SOL 103 with varied laminate structures of the wing skin. Table 3 presents the normal mode computation results. It can be seen clearly that Case 1 and Case 4 result in very different frequencies on each mode. Therefore, it is necessary to analyze Case 1 and Case 4 more to identify the effect of the fiber orientations on the dynamics behavior of the structure. The result of the normal mode analysis for Case 1, shown in Fig. 2, the first bending mode has the lowest frequency at 2 Hz, and the first torsion mode is 21.4 Hz. Whereas in Case 4, the first bending has a lower frequency than Case 1 at 1.8 Hz and the first bending is much higher than Case 1 at 39.9 Hz. According to normal analysis results, the fiber orientation on its skin significantly influences the dynamic properties of the wing structure, particularly in torsion stiffness. According to Table 3, the variations of the fiber orientations 0° can affect the bending stiffness considerably, by which the eigenfrequencies of bending modes also increase slightly. On the other hand, the fiber orientation of the skin laminates will influence the torsion stiffness remarkably. It can be seen in the change of torsion Table 3 Normal modes analysis results Frequency (Hz)

Mode 1st bending

Case 1

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

2.0

2.0

1.9

1.8

1.9

1.9

2.0

2nd bending

6.3

6.4

6.2

6.1

6.2

6.3

6.3

3rd bending

9.0

8.9

8.4

8.1

8.4

8.8

8.9

4th bending

23.8

23.8

22.8

22.0

22.7

23.6

23.6

1st torsion

21.4

28.4

36.7

39.9

36.7

28.3

21.3

2nd torsion

41.4

54.2

70.7

76.2

70.7

54.2

41.3

(a) 1st bending mode (Mode 1) Fig. 2 Results of normal mode analysis for Case 1

(b) 1st torsion mode (Mode 4)

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mode frequencies for each case. The laminate configuration 0/45/-45/0 produces the highest torsion stiffness and eigenfrequency. The futter solution is obtained using the p-k method with matched-method calculation in this work (see Sect. 2 for more detail). The futter solution is obtained at a fixed Mach number and different flight altitudes in the matched method. In MSC NASTRAN SOL 145, the unsteady aerodynamic forces are calculated using the DLM approach in which the wing model is simplified as a thin plate. The Aerodynamics forces determined by the DLM approach are substituted in the aeroelastic equation. Then, the frequencies and damping of each mode can be evaluated by calculating eigenvalues. Since Case 1 and Case 4 produce the most distinctive structural properties compared to the other cases, thus they also produce very different aeroelastic behavior. In addition, comparing Case 1 and Case 4 can clearly show the torsional stiffness’s effect on the critical flutter speed. The effect of the skin’s fiber orientation on flutter and static-divergence speed for Cases 1 and 4 are plotted in Figs. 4 and 5. For all the cases, the analyses were carried out in ranging wind speeds of 5–400 m/s as the maximum diving wind speed of the UAV is only 150 m/s. We can find the static-divergence and flutter phenomena in both Figs. 3 and 4. The flutter occurs when the damping goes down and eventually become zero because of coupling between two modes or more. Comparatively, static divergence occurs when the structural modes’ frequencies are zero [15]. In Case 1, flutter occurs at a wind speed of 220 m/s because of the interaction between the first torsion (Mode 4) and third bending modes (Mode 3). The static divergence of Case 1 happens at 220 m/s. At this wind speed, frequencies and damping of the third bending dip to zero abruptly. The interaction between the first bending and first torsion can be found, as seen in Fig. 3. Those modes are coupled at 290 m/ s, and then they separate again. As those frequencies move away from each other, the torsion damping returns to the negative value. In Case 4 (Configuration 0/45/-45/0), the flutter is not found at a wind speed of up to 400 m/s. While the static-divergence speed takes place when the frequency of the first bending mode drops to zero at 330 m/s. Unlikely Case 1, the first bending mode

(a) 1st bending mode (Mode 1)

Fig. 3 Results of normal mode analysis for Case 4

(b) 1st torsion mode (Mode 6)

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(a) Frequency

(b) Damping Fig. 4 Flutter analysis result of Case 1

contributes to the static-divergence phenomena for Case 4. It is evident that applying a fiber angle of 45° can strengthen the torsion stiffness and prevent the structure from undergoing coupled flutter. At the same time, it also improves the static divergence speed as high torsion stiffness can prevent a high deflection in a rotation direction, increasing the lift force on the wing. Table 4 presents the critical speed for all laminate configurations. The laminates with fiber orientation 0/45/-45/0 have the best aeroelastic performance. The configuration can withstand aeroelastic loads until two times the maximum operational speed. The fiber orientation effect on the skin is evident in the torsion stiffness matrix of a laminate. The torsion frequency increases significantly with the application of composite skins between the ribs and spars. Increasing the torsion stiffness makes the structure more resistant to flutter instability.

Aeroelastic Optimization Using Laminate Fiber Orientation …

241

(a) Frequency

(b) Damping Fig. 5 Flutter analysis result of Case 4 Table 4 Flutter analysis results Fiber orientation

Static-divergence speed (m/s)

Flutter speed (m/s)

Case 1

0/0/0/0

220

220

Case 2

0/15/-15/0

280

300

Case 3

0/30/-30/0

320

390

Case 4

0/45/-45/0

330

>400

Case 5

0/60/-60/0

320

390

Case 6

0/75/-75/0

270

300

Case 7

0/90/90/0

220

220

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5 Conclusion According to numerical analysis results, the fiber orientation on its skin significantly influences the dynamic properties of the wing structure, particularly in torsion stiffness. The laminates with fiber orientation 0/45/-45/0 have the best aeroelastic performance, as applying an angle of 45° can improve the torsion stiffness matrix of a laminate. Increasing the torsion stiffness makes the structure more resistant to flutter instability and prevents static divergence until two times the maximum operational speed. The angle of 45° can improve the critical flutter speed by more than 400 m/ s and static-divergence up to 330 m/s or 50% higher in static-divergence speed and more than 260% in flutter speed compared to angles of 0 and 90°. Acknowledgements This work was supported through funding from the National Research and Innovation Agency under the Developing of MALE Unmanned Aerial Vehicle (UAV) Program.

References 1. Basri EI, Sultan MTH, Mustapha F, Basri AA, Abas MF et al (2019) Performance analysis of composite ply orientation in aeronautical application of unmanned aerial vehicle (UAV) NACA4415 wing. J Mater Res Technol 8(5):3822–3834 2. Seresta O, Gürdal Z, Adams DB, Watson LT (2007) Optimal design of composite wing structures with blended laminates. Compos B Eng 38(4):469–480 3. Natella M (2020) Aeroelastic tailoring of composite aircraft. Dissertation, TU Delft 4. Hodges DH, Pierce GA (2011) Aeroelastic flutter in introduction to structural dynamics and aeroelasticity. Cambridge University Press, pp 175–177 5. Moon, Scott (2009) Aero-structural optimization of divergence-critical wings. University of Toronto 6. Kim JY, Kwon HJ, Kim KS, Lee I, Han JH (2005) Numerical investigation on the aeroelastic instability of a complete aircraft model. JMSE Int J Ser B 48(2):212–217 7. Ghalandari M et al (2022) Aeroelastic optimization of the high aspect ratio wing with aileron. J Comput Mater Contin 70(3) 8. Valente C, Wales C, Jones D, Gaitonde A, Cooper JE, Lemmens Y (2017) An optimized doublet-lattice method correction approach for a large civil aircraft. Int Forum Aeroelasticity Struct Dyn 9. Eduardo Manuel Pizarro Gomes Pepe (2015) Numerical implementation of a frequency-domain panel method for flutter prediction of a 3D Wing. Master of Science Thesis, Tecnico Lisboa 10. Berci M, Torrigiani F (2020) Multifidelity sensitivity study of subsonic wing flutter for hybrid approaches in aircraft multidisciplinary design and optimization. MDPI Aerosp J 7:161 11. Fung YC (1993) An introduction to the theory of aeroelasticity. Dover Publications Inc, New York 12. Bisplinghoff RL, Ashley H (1962) Principles of aeroelasticity. Dover Publications Inc., New York 13. Wright JR, dan Cooper JE (2007) Introduction to aircraft aeroelasticity and loads. Wiley, West Sussex 14. De Leon DM et al (2012) Aeroelastic tailoring using fiber orientation and topology optimization. J Struct Multidiscip Optim 46:663–667 15. Akcabay DT, Young YL (2019) Steady and dynamic hydroelastic behavior of composite lifting surfaces. J Compos Struct 227:111240

Sustainability

Numerical Modeling of Flow-Induced Instabilities in a Cage-Type Steam Turbine Control Valve Mohd Rais Ramli, Nik Ahmad Ridhwan Nik Mohd, Shabudin Mat, and Mohd Nazri Mohd Nasir

Abstract Flow-induced vibration is a low-frequency failure phenomenon caused by the interaction between fluid flow and physical structure. This work aims to investigate the potential of flow-induced instability within a cage-type steam turbine control valve in which the throttle valve stem failed due to flow-induced vibration. In this work, a computational fluid dynamic (CFD) analysis was carried out based on a 3-dimensional, unsteady, finite volume method that solves Reynold-Averaging Navier Stokes at the cell-centred and Realizable k-ε as the turbulent modelling. In the present work, the double-seated throttle valve was utilized to regulate steam flow with a capacity of 10 kg/s, a rated pressure of 44 bar, and a temperature of 410 °C entering the control valve system from a vertical inlet to the horizontal outlet into the turbine. Based on the CFD analysis performed, it was found that the CFD predicted the existence of significant vortex-shedding activities due to strong aerodynamic interaction between the cage and the throttle valve component. The dominant broadband frequency determined by the unsteady calculation closely matches the frequency measured through the field measurement. Keywords Flow-induced instability · Control valve · Double-seated valve

1 Introduction Control valves are one of the fundamental elements that make up a control system, and it plays a crucial role, especially in the industrial flow processes. Its function is to regulate the amount of fluid passing through, either flowing to a turbine or directly downstream by throttling the valve. This throttling process will create a flow M. R. Ramli · N. A. R. N. Mohd (B) · S. Mat · M. N. M. Nasir Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia e-mail: [email protected] N. A. R. N. Mohd · S. Mat · M. N. M. Nasir UTM Aerolab, Institute for Vehicle Systems and Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_20

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constriction that will accelerate the fluid flow and cause a pressure drop across the valve and as well as a dissipation process, in which the fluid’s energy is converted into kinetic energy and some vibrations [1]. This vibration will somehow reach the internal components inside the control valve system and may cause resonance which will shorten their lifespan and eventually cause fatigue failure. Control valves sometimes operate under severe service conditions, making them experience a wide range of problems such as excessive noise and vibrations that eventually result in component failure [2]. In 2018, a field case study was conducted to find the root cause of the repetitive structural failure experienced by the throttle valve stem in one of the operating steam turbine control valve systems. The control valve systems operate at an undisclosed petroleum refinery complex whose function is to supply the highly-dense energy steam to the turbine for powering the complex. They usually work below the medium opening, which this range is responsible for many failures and vibration-related problems as a result of large pressure drops that will lead to significant pressure fluctuations, and large static and dynamic forces act on the components inside the control valves [3, 4]. Figure 1 shows the failure event related to the steam turbine system in 10 years from 2008 until 2018 in the facility. The system installed is a double-seated plug, cage-type control valve in which the linear horizontal movement is controlled pneumatically, and the demand signal from the controller dictates the position (Fig. 2). It was reported that the throttle valve oscillates at a commanded position of 31 percent lift during the operation. A radial wear defect on the throttle valve stem was observed at the bush support end of the assembly, as shown in Fig. 3. Initial field vibration measurement data of the throttle valve component in several units showed that each exhibited a prominent frequency ranging from 233 to 455 Hz, which was picked up by the sensor during the turbine operating at 4515 rpm. Laboratory analysis of the impact testing was done on the throttle valve-cage assembly revealed that the critical range of resonant frequency occurs around 270–290 Hz,

Fig. 1 Failure event survey of control valve system recorded for duration of 10 years [5]

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Fig. 2 The schematic diagram of the OEM control valve system [5]

Fig. 3 Throttle valve stem defect [6]

which corresponds to the 1st bending mode and thus opens the possibility of resonance occurrence during the system operating and leads to the component’s failure. Based on the early hypothesis, this work conducted a CFD analysis to assess the behavior of highly turbulent steam flow, particularly in characterizing the flowinduced vibration phenomenon in a real-scale double-seated cage-type steam turbine control valve.

1.1 Previous Cases of Control Valve Failure In 2018, Singapore Refining Company Pte. Ltd. reported two similar failure events which involved one of their operated steam turbine control valves that happened in 2012 and 2014. The system was commissioned in 1993 and was retrofitted in 2006 to operate at a higher load capacity. Before 2012, some reported issues concerning the abnormal wear defect were observed at the governor valve stem bushing. In 2012, the governor valve, a double-seated plug-type valve, was reported to break at the

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Fig. 4 Schematic diagram of the component modification [7]

threaded area section outside the chest and a significant wear defect was observed at both the front and rear support bush. A similar scenario recurred again in 2014, except this time, the valve stem broke inside the chest. Analysis from the 2012 case of the valve stem’s surface fracture ruled out the possibility of a metallurgical defect and concluded that the stem was broken due to fatigue. This finding was supported by the data from both vibration measurement and impact testing, which revealed the valve vibrated at a relatively significant vibration level with a peak amplitude of around 25–30 mm/s in which the dominant frequency was captured at around 200 Hz which is close to its natural frequency of 212 Hz. Later in 2014, a vibration measurement of the valve stem recorded a vibrational amplitude of above 1000 mm/ s that manifested in the form of higher noise and vibration, which immediately indicated the occurrence of the resonance phenomenon. This issue has been resolved by introducing a few modifications, particularly increasing the valve stem’s diameter at the centre part (Fig. 4), which has further separated the flow’s frequency and the valve’s natural frequency despite the current vibrational level being relatively the same as the previous [7].

2 Methodology The dimensions of the computational domain were based on the measurement of the actual components and their drawings. Figure 5 shows the fluid domain of the inside control valve body assembly. The type of valve used in this study is a doubleseated valve with a combination of two different valve head designs based on the characterization made by Domnick and Brillert, which are can-shaped type and flatshaped type [1]. In the present study, the current computations were performed using a commercial CFD solver that solves the unsteady Reynold-averaged Navier–Stokes equation using finite volume method on the cell centre. The turbulence in the flow was modelled using the Realizable k-ε model with the effect of temperature included. The calculation was solved with the 2nd order discretization, and the convergence criterion was achieved at around 1 e−5 for momentum and continuity. The steam flow in the chest is quite complex with turbulence, temperature, pressure gradients, and the highly flow curvature, which is needed to take care of from the start to ensure a high-fidelity flow study. A spatial discretization based on the cut-cell method was employed in which a higher resolution mesh was constructed around

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Fig. 5 Mesh topology build based on the cut-cell method

the throttle valve stem to capture vortex-shedding activity and fluctuations within the flow. The height of the first cell initial around the valve and cage was set at 0.5 mm and 0.7 mm respectively, while the global cell size of the chest is 2 mm with the treatment at the near wall region being set to standard wall function. The inlet and outlet boundaries were set to the mass flow rate and pressure conditions according to the actual operating parameters with a no-slip wall condition was imposed on the wall of all the components. Furthermore, due to some difficulties in obtaining the actual flow conditions at the inlet section of the control valve, the inlet flow conditions were considered uniform and the effect of upstream pressure fluctuations has not been present in this analysis. The fluid medium was modelled based on the characteristics obtained from IAPWS-IF97 steam data properties. The unsteady calculation was performed with the time step size dt = 0.0005 s and 2000 samples that covered a 1-s flow.

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3 Result and Discussion 3.1 Fourier Transform (FT) Analysis A CFD simulation was conducted based on the actual operating parameters to replicate the flow behavior inside the control valve. Several assumptions have been made to perform this simulation that have already been mentioned in the methodology section, affecting the accuracy of the data generated. Due to this reason, a validation study was done by comparing the numerical frequency data with the field vibration measurement to ensure the integrity of the generated result and to see how much the result deviated by these assumptions. In the present study, the flow-induced instability was monitored through aerodynamic forces (axial) fluctuation on the throttle valve stem. The frequency spectrum of the transient force on the throttle valve stem solved for 1024 samples is presented in Fig. 6, while the frequency spectral of the field vibration measurement is shown in Fig. 7. Based on Fig. 7, two peak frequencies have picked up from the throttle valve during system operation which are at 75 and 233 Hz, corresponding to the turbine speed and the component’s vibration respectively. FT data generated from CFD analysis suggested that two dominant frequencies at 109 and 219 Hz occurred in the axial direction which the broadband frequency is approximately 6% off from the measured value. However, the peak frequency of 109 Hz does not correspond to any frequencies picked up in the field measurement data which is believed that it may be the result of the assumptions that have been made.

Fig. 6 Predicted frequency spectra obtained from CFD calculation

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Fig. 7 Measured frequency spectra

3.2 Flow Streamlines The flow field of steam at different cross sections along the throttle valve is visualized by streamlines in Fig. 8. In general, it is tough to conclude the dynamic flow behavior in overall since every section exhibits a distinct flow pattern that is influenced by each of the components’ profile, pressure and velocity distribution, and the condition at the valve-seat opening. But looking at every section, the CFD simulation predicted that there is a strong aerodynamic interaction between the cage and the throttle valve stem itself. Quantitatively, the steam that came from the upstream was split instantaneously into three different paths within the chest cavity. The first portion of the steam split and flowed toward the downstream chest by passing through the passage between the cage and the chest wall; the second portion of the steam flowed down and interacted directly with the cage and the last portion of the steam straightly pass through the cage and engulfed the throttle valve directly. These portions of steam interacted with each other, forming multiple reversed flow circulation zones. For example, in Fig. 8c– e, vortices induced were observed at the lower cage bars, which seem trapped and constrained by the space between the throttle valve and the lower cage bars. This condition put the vortex in close proximity to the throttle valve, which is feared will cause it to vibrate in a close frequency range or within its structural mode.

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(a)

(b)

(c)

(d)

(e)

Fig. 8 Steam flow visualized by streamlined profile

4 Conclusion The CFD analysis was conducted with the primary objective of assessing the flow instability inside the steam turbine control valve system in which the throttle valve stem failed due to flow-induced vibration. The CFD predicted a strong aerodynamic interaction between cage bars and the throttle valve, which has nurtured the vortexshedding development. The discrete Fourier transform analysis suggested that the load fluctuation in the axial direction has two dominant frequencies in which the peak broadband frequency is 219 Hz, 6% off from the measured value of 233 Hz.

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Acknowledgements The authors would like to thank the Universiti Teknologi Malaysia (UTM) Encouragement Research Grant, Grant No: Q.J130000.2651.17J60, for funding this research.

References 1. Domnick CB, Brillert D (2018) Flow induced steam valve vibrations— a literature review of excitation mechanisms, preventive measures and design improvements. J Eng Gas Turbines Power 141. https://doi.org/10.1115/1.4041253. 2. Roth KW, Stares JA (2001) Avoid control valve application problems with physics-based models. Hydrocarbon Processing, August. [Online]. https://www.hydrocarbonprocessing. com/magazine/2001/august-2001/special-report-fluid-flow/avoid-control-valve-applicationproblems-with-physics-based-models 3. Yonezawa K, Ogawa R, Ogi K, Takino T, Tsujimoto Y, Endo T, Tezuka K, Morita R, Inada F (2012) Flow-induced vibration of a steam control valve. J Fluids Struct 35:76–88. https://doi. org/10.1016/j.jfluidstructs.2012.06.003 4. Zeng L-F, Liu G-W, Mao J-R, Wang S-S, Yuan Q, Yuan H, Wang K-G, Zhang J-J, Xu Y-T (2015) Flow-induced vibration and noise in control valve. Proc Inst Mech Eng, Part C: J Mech Eng Sci 229. https://doi.org/10.1177/0954406215570386 5. Yusoff MD (2019) GPS PT-XXXX Governor Valve Root Cause Study. Petronas Gas Berhad. unpublished 6. UTM Aeronautics Laboratory (2019) CFD and FEA evaluation of governor valve design for GPP-B steam turbine (Phase 2): Identification of Induced Vortex. unpublished 7. Bhat V, Suthan T (2018) Steam turbine steam control valve failure. Presented at the Asia Turbomachinery and Pump Symposium. Singapore

Effect of Scruton Number on Energy Harvesting Utilizing Flow-Induced Vibration Azalia Sharmine Saiful, Mohamed Sukri Mat Ali, Nursyafinaz Maruai, and Salehuddin Muhammad

Abstract The consumption of renewable energy has shown a positive impact on society and industries. Renewable energy is used widely in electronic devices such as sensors. The aim of this study is to help increase the amount of consumption of wind energy but on a smaller scale for small electronic devices. The study focus on the effect of Scruton number on energy harvesting. Reynolds number of 6.277 × 103 were used in this study corresponding to reduced velocity of 10 m/s. Numerical simulation using OpenFOAM was conducted for this study and flow visualization is generated to understand the flow physics. A strong correlation was found between Scruton numbers with energy being harvested. The highest harvested energy was 1.6 mW corresponding to the highest Scruton number. Detailed analysis showed that the increases in harvested energy was not mainly due to change in amplitude vibration, but it was due to increase in the vibration frequency. Flow visualization showed that the increases of frequency vibration was due to the increase Karman vortex strength in the wake. Keywords Energy harvesting · Flow-induced vibration · Scruton number

1 Introduction Renewable energy has been the focus of study globally. It is also one of the goals (number 7) in the global Sustainable Development Goals, (SDGs) which is to ensure people get the access to affordable, reliable, sustainable, and modern energy. According to report from the United Nations Sustainable Development Goals [1], A. S. Saiful · M. S. M. Ali (B) · N. Maruai Malaysia-Japan International Institute of Technology, UniversitiTeknologi Malaysia, Jalan Semarak, 54100 Kuala Lumpur, Malaysia e-mail: [email protected] S. Muhammad UTM Razak Faculty of Technology and Informatics (RFTI), Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_21

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the consumption of renewable energy is increasing at a very slow phase from 16.4% in 2010 to 17.1% in 2018. By the end of 2019, some of the developing countries have reached 219 Watts capacity of renewable energy per capita which equals to 7% increment over the year but it is still less than the previous year, 2018 which they reached 8.8% increment of capacity per capita. According to Zyadin et al. [19], one of the reason main challenges for renewable energy developments is its technologies are immature and expensive. Thus, a simple and less expensive energy harvesting system is preferable. In this paper, an energy harvesting system utilizing flow-induced vibration (FIV) has been proposed. This concept is applicable for both water (river or wave) and air (wind), where the same vibrations behavior can be found if the Scruton and Reynolds numbers are set the same [9, 17, 18]. Flow-induced vibration method involves flow over a bluff body. Bluff body is defined by significant flow separations on its surfaces. This separated flow is shedding downstream to form alternate vortex streets [13]. The occurrence of alternating vortex formation generates fluctuating lift. If the bluff body is placed on an elastic support system, flow-induced vibration can be observed [6, 12, 16]. When the natural frequency of the body corresponds with the frequency of the vibration, the interaction of flow over an elastic bluff body causes a considerable transverse motion, called the galloping [15]. Many efforts have been made to limit and control this phenomenon, because excessive vibration bring a safety issue to civil structures [7]. However, in this paper, following the study of Maruai et al. [11], a technique utilizing vibration phenomenon for energy harvesting has been proposed in this project. The main objective of this study is to investigate the effect of Scruton number on energy harvesting from flow-induced vibration. Study by Ismail et al. [6], shows Scruton number significantly affects the vibration amplitude of the elastically support body. However, there is still no study to relate the effect of Scruton number to the energy being harvested.

2 Methodology 2.1 Model Geometry The problem geometry under investigation is a square cylinder supported with a spring-damping system in one-degree of freedom. The cylinder is placed in a uniform flow as shown in Fig. 1. This configuration is set the same as has been studied by Maruai et al. [11], who also investigates the energy harvesting system from flowinduced vibration. However, their focus of investigation is on placing another body downstream and the Scruton number is set constant. The condition being set for the current study is listed in Table 1.

Effect of Scruton Number on Energy Harvesting Utilizing Flow-Induced …

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Fig. 1 Sketch of problem geometry, similar to Ref. [11]

Table 1 Key parameters [11] Parameters

Non-dimensional

Magnitude

Reduced velocity, U R

U fn D UD v m ρ D2 L k ρU 2 L δ 2π π ∗ 2m ζ

10

Reynolds number, Re Mass ratio, m* Spring constant, k* Damping ratio, ζ Scruton number, Sc Natural frequency, f n

(

/ ) k 1 2π m / fn

6.26 × 103 566.74 223.79 (4.38 to 32.70) × 10–3 3.896 to 25.923 1

2.2 Numerical Simulation This study has been conducted numerically using OpenFOAM CFD software. The flow solutions are obtained by solving the Unsteady Reynolds-averaged Navier– Stokes (URANS) equations. For the turbulence model, k-ω Shear Stress Transport (SST) proposed by Menter [14], has been chosen. Figure 2 shows the computational domain used for the numerical simulation. A structured coarse mesh is used for the computational domain due to hardware and computing capacity restrictions. When opposed to unstructured mesh, structured mesh provides a greater level of quality and control in terms of node placement. Furthermore, because structured grid lines and flow follow the contours of the geometry, it may result in more accurate results and better convergence for solvers. Multiple organized hexagonal blocks are used to create the computational domain.

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Fig. 2 Schematic diagram of the computational domain and boundary conditions for isolated single square cylinder

As it gets closer to the square cylinder, the number of cells steadily rises. Figure 3 show the distribution of mesh near the square cylinder. The challenging part for simulating flow-induced vibration is to create a moving mesh. In order to effectively deal with the problem, an automatic mesh motion method has been used [2, 10]. Based on the Laplace smoothing equation, the mesh enclosing the cylinder is allowed to deform automatically. This is to avoid any instability in the computation of the Navier–Stokes equations for continuity and incompressibility.

Fig. 3 Mesh around the square cylinder

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∆.(γ ∆u) = 0

(1)

Where u is the node mesh deformation velocity and γ is displacement diffusion which is the square of the inverse of the cell volume. γ = 1/l 2 is used for this study. The distance l, is in between the cell center and the nearest cylinder edges given by 1. As a result, the cell quality near the square cylinders is sustained only when the cell distance from the cylinder surfaces is large. The OpenFOAM software is used to solve the important equations. In order to solve the problem in this study, a combination of Pressure Implicit Split Operator (PISO) and Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) method from the PIMPLE algorithm is applied for the pressure-momentum coupling in onetime step. The shape of the domain is predicted to vary in response to the cylinder motion. Providing an effective solution to the problem, an automatic mesh motion algorithm is used. Based on the Laplace equation, the mesh around the cylinder is allowed to deform automatically.

2.3 Validation Table 2 compares the amplitude vibration (yr ms /D) obtained by the current study with previous similar studies (isolated square cylinder, Ur = 10). The current study is able to reproduce the amplitude vibration in the range obtained by the previous studies. Experiment result by Kawabata et al. [7], is closer to the current study. The same value of Scruton number was used by both studies. Referring to [6], results by Kawabata et al. has the effect of spanwise instability during their experimental work that affect their results slightly. This may the reason why the current study is not be able to get the exact value of yr ms similar to Kawabata et al. [7], comparing the current results with Ismail et al. [6], their amplitude vibration is higher than the current study. This is expected, as the Scruton number used in the current study is a slightly less than Ismail et al. (Sc = 4.315). Table 2 Comparison of current numerical result with other similar studies

Author

yr ms D

(10–3 )

Experimental by Ismail et al. [6]

4.59

Numerical by Ismail et al. [6]

6.03

Experimental by Kawabata et al. [7] Current numerical

11.42 8.9

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3 Result and Discussion At Ur = 10, the vibration of the cylinder is governed by the Karman vortex [2]. Figure 4 shows instantaneous vorticity contours near the vibrating cylinder. The flow visualization is taken during the oscillation is at a maximum displacement. All cases (different Scruton number) showing the formation of Karman vortex shedding downstream of the cylinder. This suggests that, at all Scruton numbers under investigation, the vibration is still being generated by the shedding of Karman vortices. The strength of the vortex shedding can be presented in the form of power spectrum density. Referring to study by Ali et al. [3], a tonal can be observed for signals generate from a Karman vortex. Figure 5 shows the current study is able to replicates the previous results and it shows a strong tonal for all different Scruton numbers under investigation. At this analysis, although the tonal frequency is almost the same, strength of the vibrating signal is different for each case under investigation. The strongest signal is for case 2 (Sc = 4.376) and the lowest is for case 4 (Sc = 29.114). This relates the strength of Karman vortex with the amplitude vibration being generated. Figure 6 shows the change of amplitude vibration with Scruton number. The trend is coincided with the previous discussion on the Karman vortex strength. Except for case 1 (Sc = 3.896), the amplitude vibration decreases when Scruton number of the elastic system is increased. Referring to Khalak and Wiliamson [8], the mode

(a) Sc = 3.896

(b) Sc = 4.376

(c) Sc = 16.763

(d) Sc = 29.114

Fig. 4 Instantaneous vorticity contour taken at y = ymax

Effect of Scruton Number on Energy Harvesting Utilizing Flow-Induced …

(a) Sc = 3.896

(b) Sc = 4.376

(c) Sc = 16.763

(d) Sc = 29.114

261

Fig. 5 Power spectrum density analysis for vibrating signals

response of the vibration is altered when the mass damping, m∗ ζ of the system is changed. At Ur = 10, the mode response of interest is in the lower branch mode. Citing the argument of Khalak and Wiliamson [8], case 1 (Sc = 3.896) may have entered different onset of vibration mode, as case one is the lowest m∗ ζ in the current study. Fig. 6 Graph of yr ms for vibration

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Fig. 7 Fundamental (tonal) frequency of vibration

Case 1 (Sc = 3.896) also exhibits a unique feature for the frequency of vibration. This is done in order to confirm the statement in the previous argument. However, the current study is focus on the effect of Scruton number on the energy that can be harvested from the vibration. The study on the mode of vibration response is beyond the scope of the current study. Figure 7 shows the fundamental frequency of the vibration. Except for case 1, increasing the Scruton number increases the vibration of the cylinder. The frequencies also are greater than the natural frequency. This is to confirm early discussion that the vibration of the cylinder is driven by the Karman vortex strength and not by galloping ( f = f n ). The change of amplitude and frequency of vibration with Scruton number significantly affect the harvested energy. Figure 8 shows the relation between Scruton number with power being harvested. The power being harvested is calculated numerically according to Refs. [4, 5, 12]. Phar v = 8π 3 m ∗ ζ ymax f 2 f n

(2)

Generally, the harvested energy can be increased by increasing the Scruton number of the elastic system. Although the amplitude of vibration is decreased with the Scruton number, the harvested power still increases due to increase in frequency of the vibration. This suggests that the amplitude vibration is not heavily influenced on the energy harvesting but the Scruton number heavily affects the frequency of vibration ( f ) that responsible for the change in the energy harvesting.

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Fig. 8 Magnitude of root mean generated power (Watt)

4 Conclusion The main purpose of this study is to improve the energy harvesting from flowinduced vibration by studying the effect of Scruton number on energy harvesting and determine the physics flow behavior for energy harvesting when Scruton number is varied while other parameters are kept constant. This study is conducted numerically using computational fluid dynamics software, OpenFOAM. Based on the results obtained, the potential power harvested value can be increased by increasing the Scruton number. It should be noted that the increase in power harvested is not due the amplitude vibration, but mainly due to the increase in the frequency of the vibration. Acknowledgements This research is supported by UTM Fundamental Research, reference number PY/2019/01810 and cost number Q.K130000.2543.21H16.

References 1. Global sustainable development goals: Goal 7 (2021). https://sdgs.un.org/goals/goal7 2. Adzlan A, Ali MSM, Zaki SA (2021) Temporal evolution of lift in a pure cruciform system for energy harvesting. Ocean Eng 223:108648 3. Ali MSM, Doolan CJ, Wheatley V (2012) Low reynolds number flow over a square cylinder with a detached flat plate. Int J Heat Fluid Flow 36:133–141 4. Bernitsas MM, Raghavan K, Ben-Simon Y, Garcia E (2008) Vivace (vortex induced vibration aquatic clean energy): A new concept in generation of clean and renewable energy from fluid flow. J Offshore Mech Arctic Eng 130(4) 5. Ding L, Zhang L, Wu C, Mao X, Jiang D (2015) Flow induced motion and energy harvesting of bluff bodies with different cross sections. Energy Convers Manage 91:416–426 6. Ismail MH, Ali MSM, Salim SAZS, Shirakashi M, Muhamad S (2006) Flow induced vibration of a square cylinder with high scruton number. Ratio 1000:2

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7. Kawabata Y, Takahashi T, Haginoya T, Shirakashi M (2013) Interference effect of downstream strip-plate on the crossflow vibration of a square cylinder. J Fluid Sci Technol 8(3):348–363 8. Khalak A, Williamson CH (1999) Motions, forces and mode transitions in vortex- induced vibrations at low mass-damping. J Fluids Struct 13(7–8):813–851 9. Maruai NM, Ali MSM, Ismail MH, Ihsan NM, Sadzli FNH, Salim S (2017) Wind energy harvesting from wind-induced vibration. Chem Eng Trans 56:451–456 10. Maruai NM, Ali MSM, Ismail MH, Salim SAZS, Shirakashi M, Muhamad S (2006) Comparative study on energy extraction from vibrating square cylinder 11. Maruai NM, Ali MSM, Ismail MH, Zaki SA (2018) Flow-induced vibration of a square cylinder and downstream flat plate associated with micro-scale energy harvester. J Wind Eng Ind Aerodyn 175:264–282 12. Maruai NM, Mat Ali MS, Ismail MH, Shaikh Salim SAZ (2018) Downstream flat plate as the flow-induced vibration enhancer for energy harvesting. J Vibr Control 24(16):3555–3568 13. Mat Ali MS, Doolan CJ, Wheatley V (2011) Low reynolds number flow over a square cylinder with a splitter plate. Phys Fluids 23(3):033602 14. Menter FR (1994) Two-equation eddy-viscosity turbulence models for engineering applications. AIAA J 32(8):1598–1605 15. Methal Z, Ali MSM, Maruai NM, Abd Ghani R (2018) Microwatt energy harvesting by exploiting flow-induced vibration. J Adv Res Fluid Mech Thermal Sci 47(1):25–34 16. Mohamed A, Zaki SA, Shirakashi M, Ali MSM, Samsudin MZ (2021) Experimental investigation on vortex-induced vibration and galloping of rectangular cylinders of varying side ratios with a downstream square plate. J Wind Eng Ind Aerodyn 211:104563 17. Williamson C, Govardhan R (2004) Vortex-induced vibrations, annual review of fluid mechanics 18. Williamson C, Govardhan R (2008) A brief review of recent results in vortex-induced vibrations. J Wind Eng Ind Aerodyn 96(6–7):713–735 19. Zyadin A, Halder P, Kahkonen T, Puhakka A (2014) Challenges to renewable energy: a bulletin of perceptions from international academic arena. Renew Energy 69:82–88

Study the Implementation of Hybrid PV/ Wind System in Hot Region, Low Wind Speed; Challenges, Obstacles, and Prospects; Case of Samawah, Iraq Fazila Mohd Zawawi and Mustafa Abdulkareem Hussein

Abstract This work is conducted to analyze the economic feasibility and profitability of a hybrid energy system (consisting of PV panels, wind turbines, hydrogen fuel cells, lithium-ion batteries, and converter) to provide clean electrical power and store the excess electricity for later uses. To achieve the purpose of this research, the HOMER optimizations and simulations software was used. The major results of this study revealed the levelized cost of energy. LCOE and economic feasibility of a hybrid system consisting of wind turbine, lithium-ion batteries, hydrogen fuel cells, and converter reached a value of 0.05172 USD/kWh, indicating higher profitability and improved feasibility. Also, the LCOE and economic feasibility of case 1 consisting of a hybrid system consisting of solar PV panels, wind turbine, lithiumion batteries, hydrogen fuel cells, and converter reached a value of 0.05229 USD/ kWh, indicating less profitability and improved feasibility than case 1. Furthermore, findings confirmed that using a wind turbine, solar PV panels, hydrogen fuel cells, and lithium-ion batteries provides higher economic feasibility, which is mirrored by a low value of the LCOE. In addition, the study affirmed that there is no greenhouse gas (GHG) emissions resulted from using solar PV system and wind turbines throughout the year, as no diesel generator is used. Finally, results indicated that using energy storage technology such as hydrogen fuel cells and lithium-ion batteries would achieve higher system profitability due to the storage of clean electrical power for later use. Keywords Hybrid energy system · HOMER · LCOE · Economic feasibility

F. M. Zawawi (B) · M. A. Hussein Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_22

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1 Introduction Several academics, including Dodson et al. [1], reported the importance of using various energy resources to counter the enormous and swift growth of the world’s population. This rapid and large growth caused an increasing demand on the global energy resources. The massive consumption of energy resources led to a significant pressure on fossil fuel resources, such as crude oil, heavy oil, natural gas, gasoline, and diesel fuel [2]. However, due to the large utilization of fossil fuel resources, several environmental issues have been emerged, especially climate change and global warming. In this context, using renewable energy resources such as wind farms can mitigate fossil fuels’ environmental concerns and achieve sustainability [3]. Wind energy offers clean and accessible electrical power and does not harm the environment or cause greenhouse gas (GHG) emissions [4]. Samawah, located in Iraq, is considered an Iraqi province that heavily relies on fossil fuels. Samawah uses heavy oil and diesel to generate electrical power to cover residential electricity demand, resulting in high pollution and environmental problems. Hence, it is vital to introduce the renewable energy projects to mitigate these environmental issues. One of these renewable systems is the wind energy. Installing wind farms at Samawah requires from engineers and energy projects managers to investigate the wind speeds potential in this area to figure out the economic feasibility of clean electrical power generation from wind farms at Samawah. As indicated in Table 1, the average wind speeds in Samawah are insufficient to generate electrical power from wind turbines. Thus, other renewable energy sources are needed. This work investigates the profitability and clean electrical power generation from a hybrid energy system that combines wind with solar and fuel cells. The aim is to increase the system’s reliability and effectiveness. Using the HOMER software, a system modelling, simulation, and optimization are accomplished. The main study’s goal is to investigate a hybrid energy system consisting of wind energy system with PV modules and fuel cells. The PV modules can provide a second renewable energy source, while fuel cells are used to store electrical power and consume it later at night or cloudy days.

1.1 Wind Energy in Iraq: Status and Prospects A study conducted by Al-Taai et al. [6], aimed to investigate and assess the viability as well as evaluate the feasibility of wind energy in Iraq for generating clean electrical power via wind turbines. To achieve their study goal, they conducted measurements of data related to the wind speeds between 1 April 2011 and 1 April 2012, through 15 different stations in several areas in Iraq, considering 3 heights, which were 12, 50, and 100 m. They also conducted an experimental approach, through which a wind turbine having a capacity of 3 kW was installed and electrical power generated was measured across the same period. Results of the analysis revealed that there is

Study the Implementation of Hybrid PV/Wind System in Hot Region … Table 1 Average wind velocity of Samawah [5]

267

Month

Average Wind Velocity (m/s)

January

3.43

February

3.66

March

4.48

April

4.03

May

3.36

June

3.95

July

4.22

August

3.99

September

3.46

October

3.17

November

3.04

December

3.38

Mean Annual Wind Velocity

3.68

effective system profitability and economic effectiveness due to the maximum wind velocities, which were attained at Basrah (Al-Brjsuh), whilst the medium velocity values were attained at Baghdad (Abughraib). In addition, the lowest values of wind velocity were recorded in the northern zone of Mosul (Bashiqah). A study was conducted to examine and evaluate locations in Iraq that have higher rate of wind speeds and can be appropriate to install wind turbines and wind farms for effective generation of clean electrical power. To accomplish their study goal, researchers developed a program via MATLAB software, through which a graphical user interface was implemented for determining the values of wind speed and Weibull distribution of wind at different locations in Iraq. The analysis indicated that using wind energy system is effective due to large wind speeds. In addition, results of their analysis revealed that the wind energy intensity ranges between roughly 18 to 110 kWh/m2 depending on the location and month, which is illustrated in Fig. 1. From Fig. 1, it can be indicated that Amara has the maximum wind energy intensity having a value of approximately 110 kWh/m2 in July in comparison to other regions measured in Iraq. Basra follows Amara, and then Al hay comes in the third place, which is followed by Nasiriyah in the fourth place.

1.2 A Hybrid Energy System Talaat et al. [7], discussed the dynamic modeling and control carried out on a new hybrid power system comprising a fuel cell (FC), which can be effectively combined with several renewable energy sources of different nature such as solar and wave energy, with battery banks used as energy sources backup for electric power production. Furthermore, it extracts the full potential of the three sources using a new, fast,

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Fig. 1 Monthly wind energy intensity in Iraq

and high-precision controller based on buck-boost technology that simultaneously controls the maximum power of the used power conversion systems. The battery bank will feed the system directly when there is a problem with one of the sources. These sources have different characteristics to test the developed controller under different conditions and develop a reliable power system. Wave energy is currently one of the most promising renewable sources. It has seen significant developments recently to increase its efficiency by incorporating new technologies, such as Savonius turbine which is previously used to generate wind energy. In this study, a two-stage Savonius rotor was used in the wave generator. The simulation model for the entire hybrid power system was produced using MATLAB/Simulink and experimentally validated in a remote area under different atmospheric conditions. The controller succeeded in keeping the hybrid system voltage constant at 11.8 V close to the desired value of 12 V, with an efficiency of 98%.

1.3 Wind Energy Levelized Cost of Energy A study conducted by Andersen [8], aimed to investigate the levelized cost of energy that can be used for wind energy. To achieve the study goal, Andersen conducted a case study, through which he investigated the concept of the LCOE and the formula that can be used for wind energy. Andersen found that the term “levelized cost of energy” denoted by “LCOE”, is widely used by scholars and engineers for the analysis and comparison of the economic feasibility of several renewable energy systems, and for finding the cost effectiveness of these systems. Furthermore, this study found that for wind energy projects, the value of the LCOE can cover the lifecycle cost related to the installation and operation of wind energy projects, taking into account that the cost related to operation and maintenance (O&M) is computed annually. Besides

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269

these results, this study found that larger wind power systems have lower values of the LCOE. In addition, improvements related to the quantities of the LCOE could be obtained when wind turbines are located and installed at places, where higher wind speed is available, which in turn can enhance the level of capacity factor related to wind power system. Globally, the mean value of the LCOE of onshore wind power plants has dropped roughly 39% between 2010 and 2019, from a value of 0.086 USD/ kWh in 2010, to 0.053 USD/kWh in 2019 [9]. While the total initial cost, the project economic lifespan cost, wind lifecycle capacity factor, and the O&M cost are major key indices in calculating the LCOE value, there are some other parameters that should be considered for accurate calculation of the LCOE, such as the location, cost of land, wind availability around the year, place where wind turbines are installed, and time of the year [9]. A study conducted by Ragnarsson et al. [10], aimed to investigate the potential cost and capability of wind energy generation at Búrfell, which is located in the south of Iceland. To achieve their study goal, this study conducted a mathematical modelling to analyzed different equations for determining the best formula that can be used for calculating the value of the LCOE of wind energy systems. The analysis revealed an equation that was developed for effective calculations of the value related to the LCOE for several wind energy projects. The equation can be expressed by the formula: Σn It +Mt +Ft (1+r )t Et t=1 (1+r )t

t=1

LC O E = Σn

(1)

where n is the lifetime of the wind farm project, r is the weighted mean cost of the capital, E t is the annual energy production corresponding to year t, Ft is the fuel expenditure during the year t, and Mt is the operation and maintenance cost of wind system.

1.4 Wind Turbine Efficiency A study conducted by Blackwood [11], aimed to develop an equation for calculating the efficiency of wind turbine that can help determine its power generation performance. To achieve the study goal, Blackwood investigated the efficiency formula developed by Albert Betz in 1919. Betz computed the efficiency of wind turbine depending on a number of variables. Betz found that the wind turbine having exemplary conditions and effective performance would achieve an efficiency having maximum value of 59.3%, which is called the Betz Limit, at which just 59.3% of overall kinetic energy in the winds would be capable to rotate the wind turbine for offering electrical power.

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2 Methodology 2.1 Theory and Mathematical Model To compute the hybrid system size, HSS, Eq. (2) is utilized [12]: H SS =

ηS B

E DL × P S I × K L O SS × HT ilt

(2)

where, E DL PSI ηS B K L O SS HT ilt

daily energy load (kWh), peak solar intensity at earth’s surface (kW/m2 ), efficiency of the system balance, loss factor related to the higher temperature values and dust, and. average daily tilted solar irradiation (kWh/m2 /day).

η S B is evaluated via the relationship [12]: η S B = ηConver ter × ηW ir e

(3)

where, ηConver ter the efficiency of the system converter and. ηW ir e the efficiency of the system wires. K L O SS is computed via the expression [12]: K L O SS = (1 − TLosses ) × (1 − D Losses ) × (1 − P VLosses )

(4)

where, TLosses losses related to higher temperature values, D Losses losses due to the soiling and dust,and. P VLosses tolerance of the PV panel. To calculate the number of PV modules, N P V −Modules [12]: N P V −Modules =

PV S PV W

(5)

To estimate the converter capacity, CC, the Eq. (6) is used [12]: CC = P V S · S F

(6)

where SF is the safety factor (1.25). To calculate the charge controller current, ICC, the Eq. (7) is used [12]:

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ICC = N S · I SC · S F

(7)

where, N S strings number and I SC short circuit current. To calculate the payback period of the system, PBP, the Eq. (8) is used [12]: PBP =

T SC AS RC

(8)

where, T SC total system cost and AS RC annual system running cost. To calculate the fuel cell voltage, the Eq. (9) is employed: VFC = E − V Act − VOhm − VConc

(9)

where, E V Act VOhm VConc

open circuit voltage, activation loss voltage, ohmic loss voltage, and concentration loss voltage.

To locate the lithium-ion battery’s amperage, BA, for the hybrid energy system, the Eq. (10) is applied [12]: BA =

E DL · AD D O Dmax · η B · N V

(10)

where, AD D O Dmax ηB NV

autonomy days, maximum depth of discharge of the lithium-ion battery, lithium-ion battery’s efficiency, and battery’s nominal voltage.

The number of Li-ion batteries is evaluated using the Eq. (11) [12]: NB = where, B A total battery amperage and A S B amperage of the chosen battery.

BA AS B

(11)

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Fig. 2 The definition of Samawah location in the HOMER software

2.2 Location Definition The location of the study at which the research is conducted is at Samawah. The latitude and longitude of Samawah are determined in the HOMER software. Samawah’s latitude is 31°19.1’N. While its longitude is 45°16.8’E. Based on these data, the resources of solar irradiation, air (ambient) temperature, and wind speed are downloaded from the NASA websites to help calculate the system profitability. Figure 2 illustrates the definition of Samawah location in the HOMER software.

2.3 Ambient Temperature Resources The definition of ambient temperature is also applied in the HOMER software. Ambient temperature indicates the level of PV modules that have higher performance and whether their effectiveness is affected due to higher temperature values. Figure 3 shows the ambient temperature monthly profile of Samawah.

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Fig. 3 The definition of Samawah average air temperature in the HOMER software

2.4 Wind Speeds Resources Another resource is vital to determine the potential of wind turbine and its power output. Here, the average wind speeds are determined from the NASA wind database. These values can help predict the power output of wind system. Figure 4 presents the average wind speeds of Samawah.

Fig. 4 The definition of Samawah average wind speed in the HOMER software

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Table 2 Major financial data outputs of the first case

Total NPC

$483,483,000.00

Levelized COE

$0.05172

Operating cost

$10,645,030.00

3 Result and Discussion After conducting simulation, optimization and sensitivity analysis via the HOMER software, it was found that there are 6,736 simulated solutions. 720 of them were feasible, while 4,694 were infeasible due to the capacity shortage constraint. In addition, 5,478 solutions were infeasible due to the maximum unmet hydrogen. Also, 1,620 solutions were infeasible due to the hydrogen tank level constraints. Moreover, the HOMER software obtained major cases that have the lowest values of the LCOE and net present cost (NPC) compared to all solutions found through the simulations and optimizations.

3.1 Case 1 Main Result This case consists of only wind turbine to provide clean electrical power, while the energy storage technologies used in this scenario are fuel cell and lithium-ion battery. The NPC of this case amounts to 483,483,000 USD. Furthermore, the LCOE and operating cost are presented in Table 2. Further financial results associated with this case are presented in Table 3. It is inferred from Table 3, that the largest initial cost of the first case’s components is linked to the cost of wind turbine. It is followed by the cost of the battery. The lowest initial cost is linked to the cost of hydrogen tank. The wind turbine also has Table 3 Further fiscal data on the first scenario Component

Capital (USD)

Converter

123,828,941 39,477,576

32,016,014

0

22,248,146 173,074,385

Wind turbine

221,093,600 70,486,265

57,163,823

0

39,723,531 309,020,158

Battery

931,618

92,536

0

74,392

Replacement Operation Fuel Salvage (USD) and (USD) (USD) maintenance (USD)

395,261

Total (USD)

1,345,023

Electrolyzer 15,000

6,364

10,342

0

1,198

Hydrogen tank

50

0

12,928

0

0

System

345,869,209 110,365,466

89,295,643

0

62,047,267 483,483,051

30,508 12,978

Study the Implementation of Hybrid PV/Wind System in Hot Region …

275

Fig. 5 Monthly electric production of the wind turbine for the first case

a higher replacement cost and operation and maintenance cost compared to other components. The results of the HOMER software related to the first case revealed the monthly electric production of the wind turbine, which is presented in Fig. 5. It is inferred from Fig. 5, that the maximum electric production from the wind turbine can be attained in June and July due to higher wind speeds. In addition, it is concluded that the lowest electric production of the wind turbine for the first case is during January, February, November, and December.

4 Conclusion This work is conducted to analyze the economic feasibility and profitability of a hybrid energy system (consisting of PV panels, wind turbines, hydrogen fuel cells, lithium-ion batteries, and converter) to provide clean electrical power and store the excess electricity for later uses. To achieve the purpose of this research, the HOMER simulations software was used. The major results of this study can be summarized through the following articles: 1. The levelized cost of energy, LCOE and economic feasibility of a hybrid system consisting of wind turbine, lithium-ion batteries, hydrogen fuel cells, and converter reached a value of 0.05172 USD/kWh, indicating higher profitability and improved feasibility. 2. The LCOE and economic feasibility of a hybrid system consisting of solar PV panels, wind turbines, lithium-ion batteries, hydrogen fuel cells and converter reached a value of 0.05229 USD/kWh, indicating less profitability and improved feasibility than case 1. 3. Using wind turbine, solar PV panels, hydrogen fuel cells, and lithium-ion batteries provide higher economic feasibility, which is mirrored by very low values of the LCOE.

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4. Using energy storage technology such as hydrogen fuel cells and lithium-ion batteries would achieve higher system profitability due to the storage of clean electrical power for later use.

References 1. Dodson J, Dérer P, Cafaro P, Götmark F (2020) Population growth and climate change: addressing the overlooked threat multiplier. Sci Total Environ 748:141346. https://doi.org/ 10.1016/j.scitotenv.2020.141346 2. Rashedi A (2020) On Reduced consumption of fossil fuels in 2020 and its consequences in the global environment and exergy demand. Energies 2020:6048. https://doi.org/10.3390/en1322 6048 3. Sher F, Curnick O, Azizan MT (2021) Sustainable conversion of renewable energy sources. Sustainability 13(5):2940. https://doi.org/10.3390/su13052940 4. Nelson V, Starcher K (2018) Wind energy: renewable energy and the environment. reading: textbook, 3rd edn. CRC Press, Publisher 5. Ministry of Transport and General Authority for Meteorology and Seismic Monitoring (2019) Wind Speed Data of Samawah 6. Al-Taai OT, Wadi QM, Al-Tmimi AI (2014) Assessment of a viability of wind power in Iraq. Am J Electr Power Energy Syst 3(3):60–70 7. Talaat M, Elgarhy A, Elkholy MH, Farahat MA (2021) Integration of fuel cells into an off-grid hybrid system using wave and solar energy. Int J Electr Power Energy Syst 130:106939. https:// doi.org/10.1016/j.ijepes.2021.106939 8. Andersen AE (2017) The potential for implementing wind into hybrid renewable energy systems in UNDP country offices, University of Copenhagen, Denmark. Master Thesis 9. IRENA RES (2020) The international renewable energy agency, Abu Dhabi. Renewable power generation costs in 2019 10. Ragnarsson B, Oddsson G, Unnthorsson R, Hrafnkelsson B (2015) Levelized cost of energy analysis of a wind power generation system at Búrfell in Iceland. Energies 8:9464–9485. https:// doi.org/10.3390/en8099464 11. Blackwood M (2016) Maximum efficiency of a wind turbine. Undergrad J Math Model One + Two 6(2):2 12. Jogunuri S, Kumar R, Kumar D (2017) Sizing an off-grid photovoltaic system (A case study). In: International conference on energy, communication, data analytics and soft computing (ICECDS). https://doi.org/10.1109/icecds.2017.8389927

A Methodology for Evaluating Aviation Sustainability Perspectives H. Karam, E. Anwama, I. E. A. Davidson, H. Alfazari, F. Krykhtine, and F. Mora-Camino

Abstract This study addresses the sustainability of aviation in the coming decades. In order to establish a modelling approach for the global air transport sector, the prospective contribution of air transport to fuel consumption and environmental impacts over several decades is considered. To achieve ambitious sustainability goals, it is believed that new aeronautical technologies must be developed and air transport operations must be continuously optimised. This should necessitate substantial investments in research and development (R&D) and equipment by aeronautical manufacturers and air transport operators (airline fleets and airport infrastructures), and queries such as what, how much, and when must be answered. The proposed framework permits levels of detail (air transport services, aircraft classes, and technologies) compatible with strategic decision-making aimed at meeting the demand for air transport services while meeting sustainability goals. Once informed, this framework will enable simulation testing of possible coherent solution scenarios or formulation of global optimisation decision-making problems pertaining to R&D investment in civil aeronautics, fleet renewal by air transport operators, and airport modernization. Keywords Aviation sustainability · Energy · Environment impacts · Systems analysis · Modelling · Fuzzy sets H. Karam · E. Anwama · I. E. A. Davidson · F. Mora-Camino (B) Durban University of Technology, DUT, Durban, South Africa e-mail: [email protected] F. Mora-Camino Universidade Federal Fluminense, UFF, Rio de Janeiro, Brazil H. Alfazari Sohar University, Sultanate of Oman, Sohar, Oman F. Krykhtine Universidade Federal do Rio de Janeiro, UFRJ, Rio de Janeiro, Brazil F. Mora-Camino Ecole Nationale de l’Aviation Civile, ENAC, Toulouse, France © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 N. A. R. Nik Mohd. and S. Mat (eds.), Proceedings of the 2nd International Seminar on Aeronautics and Energy, Lecture Notes in Mechanical Engineering, https://doi.org/10.1007/978-981-99-6874-9_23

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1 Introduction The definition of sustainability for humanity is meeting our own requirements without compromising future generations’ ability to meet their own needs [1]. The aviation industry, which is a direct polluter of the earth’s atmosphere due to its emissions of CO2 , NOx, and other particles, pollution, and the formation of contrails that contribute to the greenhouse effect [2–5], must now prioritise sustainability. Prior to more than a decade ago, with the exception of aeronautical pollution, the aeronautical industry did not prioritise environmental impact reduction. Governments, consumers, employees, and investors have exerted increasing pressure on aircraft manufacturers and airlines over the past several decades to act assertively on environmental issues and to promote and attain sustainable environmental goals and objectives. Faced with an extended global trend of sustained growth in air transport over the future decades, aircraft and engine manufacturers, as well as service providers (airports and airlines), viewed sustainability as both a necessity and an immense business opportunity [6, 7]. However, during the Covid-19 pandemic, air traffic decreased significantly, temporarily mitigating this industry’s environmental impact. Today, fragile airlines are battling a steep increase in the price of traditional aviation fuel, and the issue of sustainability is momentarily less important to them.

2 The Proposed Methodological Approach With a timeline of several decades, a modelling approach that takes into account the global air transportation sector is developed. This approach considers the potential contribution of air travel to fuel consumption and environmental impacts. The main actors of the air transport service supply are considered globally (aircraft manufacturers, engine manufacturers, fuel producers, airlines, airports operators, and air traffic management), while a new unit is introduced to allow a coherent treatment of the energy issue. It is believed that to meet ambitious sustainability goals, new aeronautical technologies must be developed, and air transport operations must be continuously optimized. Due to this, there should be a significant need for investment in research and development (R&D) from aerospace manufacturers and for equipment by air transport operators (airline fleets, airport infrastructure), and questions like what, how much, and when should be addressed. Sustainable aviation fuel (SAF) is a fuel technology that has received large attention since it requires rather limited investments to be implemented [8–10]. However, many other aeronautical technologies related to the fields of aerodynamics, structure, and materials, propulsion, must be reviewed. While remaining generic at this stage of the study, the proposed framework allows for levels of specificity (air transport services, aircraft types, and technologies) that are suitable for strategic decision-making, with the objective of generating the supply necessary to satisfy demand for air transport services while achieving sustainability

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objectives. This framework enables the formulation of global optimisation decisionmaking problems pertaining to R&D investment in civil aviation, fleet renewals for air transport operators, and terminal enhancements when properly informed. Beginning in year 0, the timeline for the intended time horizon consists of K years. The following are the primary actors and processes considered in this analysis: – Let M represent the number of classes of air transportation services developed by civil aviation, including very short, short, medium, and long-range passenger/ cargo air transportation. – Let N represent the number of air transport vehicle classes that may be considered. These classes are distinguished by their rotor/fixed wing configurations, single/ multiple engines, piston/jet engines, narrow/medium/large body configurations, and aircraft fuels. It is presumed that M < N in light of the multitude of extant aircraft designs for a given class of air transport services. Each class of aircraft n is dedicated to a class m of air transportation services, allowing the set {1, . . . , N } to be partitioned into M subsets S1 , . . . , Sm , . . . , S M . – For each class of aerial vehicle n ∈ {1, . . . , N } with I n = I En ⊕ I Dn ⊕ I Nn , a set of aeronautical technologies (aircraft characterised by their propulsion systems, size, and aerodynamics) is considered as A set I n , where I En is the set of technologies currently in operation, I Dn is the set of technologies already under development, and I Nn is the set of potentially new technologies which could be developed and implemented within the considered time horizon. – Let L represent the number of prospective fuel/energy types that can be utilised with various aircraft classes and technologies. – Let L in be the group of fuels utilised by aircraft of class n equipped with technology i. It is assumed that when various fuels are used, their proportions are fixed because of the adopted technology.

3 Quantifying Air Transport Services, Fuel, Emissions While the current units employed in air transportation (tonnes or passengers per kilometre) are related to customer revenue, it would be beneficial to introduce a new unit to quantify air transport services based on energy consumption.

3.1 Air Transport Service Quantification Let’s define the special unit used in this study to quantify the supply and demand for aviation transport services in terms of energy and the environment: tonnes per equivalent kilometre (TEK) measured in tonnes per km. Consider an air service of class m performed by an aircraft of type n equipped with technology i and a maximum payload P L inm . Let for a corresponding standard flight LCinm be the mean length of a cruise expressed in kilometres, EC Dinm (k) be

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the energy consumption (expressed in energy units) of the aircraft during takeoff, climb to cruise level, and descent from cruise level to landing, EC Rinm (k) be the energy cruise consumption at cruise per flown kilometre for period k, Eq. (1) gives the equivalent kilometer, E K inm for the flight: ( E K inm (k)

=

LCinm

·

1 + EC Dinm (k) EC Rinm (k)

) (1)

Pin (k) is the total capacity in payload in tons per equivalent kilometer (TEK) of aerial vehicle of class n equipped with technology i for period k is then given by: Pin (k) = N Ainm (k) · N Finm · P L inm · E K inm (k)

(2)

where N Finm is the greatest number of flights an aircraft of type n equipped with technology i can perform per year for class m air transportation services. This number should be adjusted to account for network effects, which can be evaluated using current statistics on global operations. Here N Ainm (k) is the number of aircraft of type n equipped with technology i that were available to perform air transport service m during period k. The capacity of the air transport sector in year k, P(k) expressed in TEK, is the aggregate of the contributions of each current technology for each class of aerial vehicle: P(k) =

N Σ Σ n=1

Pin (k), k ∈ I n (k) ⊂ I n , n ∈ {1, . . . , N }

(3)

i∈I n (k)

while the total TEK capacity of the class n of aerial vehicle is given by: P n (k) =

Σ

Pin (k), n ∈ {1, . . . , N }

(4)

i∈I n (k)

and the total TEK capacity available for air transport service m is given by: Am (k) =

Σ

P n (k), m ∈ {1, . . . , M}

(5)

n∈Sm

3.2 Quantifying Energy and Environmental Impact The environmental impact of each aeronautical technology is believed to be composed of J distinct components, most of which are related to the type of propellant used and are specific to a single TEK. It is assumed that each aeronautical technology

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receives continuous enhancements proportional to its degree of maturity. The expressions for fuel consumption per TEK in energy units or equivalent and environmental emissions per TEK in tons are:: ( ) E Ninl k − kin for k ≥ kin , l ∈ {1, . . . , L}, i ∈ I (k), n ∈ {1, . . . , N } nj (

E Ii

) k − kin for k ≥ kin , j ∈ {1, . . . , J }, i ∈ I (k), n ∈ {1, . . . , N }

(6) (7)

where kin = 0 if i ∈ I En and is the year of introduction of the novel aeronautical tech( ) ) nj ( nology i ∈ I Dn ⊕ I Nn into operations. The E Ninl k − kin and E Ii k − kin variables are assumed to be decreasing functions of time. The reference cruise consumption for aircraft type n equipped with technology i for the year k is calculated as follows: EC Rinm (k) =

Σ

( ) E Ninl k − kin

(8)

I ∈L in

Consequently, the operational costs associated with a produced TEK (no scale economics are considered in the present model) follow decreasing functions of time tending to a limit value and representing enhanced procedures through learning: ( ) Cin k − kin for k ≥ kin , j ∈ {1, . . . , J }, i ∈ I n (k)

(9)

nj

This anticipated decrease in functions E i and Cin are the consequence of experimental knowledge and training that has enhanced operational conditions. Following [11], the contribution of aeronautical technology i to fuel consumption l and environmental impact j for aircraft of class n during period k is given in year k as follows: ( ) f inl (k) = E Ninl k − kin · Pin (k), l ∈ {1, . . . , L}, i ∈ I n (k) nj

nj (

) k − kin · Pin (k), j ∈ {1, . . . , J }, i ∈ I n (k)

ei (k) = E Ii

(10) (11)

The contribution of air transport service m to fuel consumption l and environmental impact j at period k is determined by the following equations: ϕ ml (k) =

Σ Σ

( ) E Ninl k − kin · Pin (k), l ∈ {1, . . . , L}, m ∈ {1, . . . , M} (12)

n∈Sm i∈I n (k)

ξ m j (k) =

Σ Σ

nj (

Eli

) k − kin · Pin (k), j ∈ {1, . . . , J }, m ∈ {1, . . . , M} (13)

n∈Sm i∈I n (k)

When selective fuel restrictions are applied to specific air transport services or globally to the entire sector, the following constraints must be taken into account:

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Σ

ϕ ml (k) ≤ Φlmax (k) for some m ∈ {1, . . . , M}

(14) (15)

m∈{1,...,M} ml where ϕmax (k) is the upper limit of fuel type l available for air transport service m l and Φmax (k) is the total amount of fuel type l available for the entire air transport service sector.

4 Characterizing Potential Investments and Associated Costs Adopting a standard development model for each technology in development, I Dn or to be developed I Nn . • To develop the new technology, a minimum amount of investment, I Ninmin in terms of the base year 0 is required. • A minimum development delay Tinmin must be taken into account. If z in is the year in which the development of technology i began (z in is negative for i ∈ I Dn ) for aircraft of class n, the following conditions must be considered: kin = 0 for i ∈ I En and kin ≥ z in + Tinmin for i ∈ I Dn ⊕ I Nn

(16)

n

ki Σ I Nik (k) ≥ I Ninmin , i ∈ I Dn ⊕ I Nn , I Nin (k) = 0, k ≥ ki , i ∈ I n (k) k + ρ) (1 k=z n

(17)

i

where ρ is a discount rate based on the scenario and I Nin (k) is the level of investment in technology i for vehicle type n at period k. If technology (i, n) depends on the availability of technology (u, m) with m /= n, u ∈ I Dm ⊕ I Nm , antecedence restrictions is required such as: z in ≥ kum

(18)

Observe that this type of constraints enables the consideration of situations in which a technological advancement is advantageous for various categories of air vehicles or air transport services. The following equations describe the development of the production potential of each technology, i ∈ I n (k), for each class of aerial vehicle n ∈ {1, . . . , N }: Pin (k) = Pin (0)

if k ≤ kin

(19)

A Methodology for Evaluating Aviation Sustainability Perspectives

Pin (k) = Pin (k − 1) − Rin (k) + Nin (k) if k > kin

283

(20)

where Pin (0) is the initial contribution of technology I, Rin (k) is the TEK capacity of technology (i, n) removed from operation of class n during period k, and Nin (k) is the operational capacity of technology (i, n) during year k. The average number of aircraft involved is: R Ain (k) = N Ain (k) = (

N Finm

Rin (k) · P L inm · E K inm

(21)

N Finm

Rin (k) ) · P L inm · E K inm

(22)

Associated with fleet restructure decisions are unit costs C Rin and C Nin . Unit costs, C Rin , reflect the residual value of aircraft that have been retired from service. It is assumed that C Nin not only the acquisition cost of new aircraft to perform their reference air transport service, but also the costs supported by airports to permit the operation of new aircraft of type n equipped with technology i, are included. Over period [0, K], the total investment costs, which consist of R&D investments, aircraft fleets renewal, and airport improvement expenses, are expressed in the base year 0 as follows: ⎛ K ⎞ Σ Σ I Ni (k) ⎜ ⎟ k ⎜ ⎟ Σ ⎜ k=0 i∈I n (k) (1 + ρ) ⎟ ⎜ ⎟ C T ot = ( ) ( ) ⎜ ⎟ K n n n n n ⎜ Σ Σ Ci (k − ki ) + C Ri · R Ai (k) + C Ni · N Ai (k) ⎟ n∈{1,···,N }⎝ ⎠ + (1 + ρ)k k=1 i∈I n (k)

(23)

The operational revenues and expenses of airlines and facilities are not considered here. While this complex issue is related to the future economic climate, it can be observed that historically global profit/loss margins remain low in comparison to the global revenues and expenses of the air transport industry. Investment funds are always constrained, which impacts the selection of technologies to be developed, the scheduling of this development, and the renewal of aircraft fleets and airport upgrades. In the air transportation industry, these upper limits are unclear because they may be subject to decisions based on global politics. When considering global investments in R&D in the aeronautical sector devoted to air transport, it appears possible to relate these levels to a certain percentage of the total GDP of industrialised nations, whereas fleet and airport investments can be related to the total cash flow of air transport operators (air carriers and airports).

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5 Quantifying Sustainability Objectives Regarding the issue of sustainability, the following constraints can be considered: – First, when flow constraints regarding environmental impacts at the end of the considered time horizon are taken into account, they are expressed as follows: N Σ Σ

nj (

E Ii

) j K − kin · Pin (K ) ≤ Fmax , j ∈ {1, . . . , J }

(24)

n=1 i∈I n (K ) j where Fmax j ∈ {1, . . . , J } are target upper values for each flow of them, this type of objective having been considered in different aviation forums. – When the second objective is to limit the global pollution levels caused by the repetitive impact of the air transportation industry on the environment, the following equations can be considered:

) ( L j (k) = 1 − σ j · L j (k − 1) +

Σ

Σ

nj

πi (k),

n∈{1,...,N } i∈I n (K )

k ∈ {1, . . . , K }, j ∈ {1, . . . , J }

(25)

where L j (0) is the initial level of pollution of type j and σ j is a natural weathering rate for component j. Then, for example, the final pollution level constraints are written as: j L j (k) ≤ L max , j ∈ {1, . . . , J }

(26)

j where L max j ∈ {1, · · · , J } are the desired maximum levels for each category of environmental impact. This performance metric is difficult to evaluate due to the limited availability of reliable models and the difficulty of distinguishing it from other sources of pollution. – Third, when environmental objectives are classified by class of air transportation services, final flow level restrictions can be written as follows: Σ Σ ) nj ( jm E Ii K − kin · Pin (K ) ≤ Fmax , j ∈ {1, . . . , J }, m ∈ {1, . . . , M} n∈Sm i∈I n (K )

(27) jm

where L max j ∈ {1, . . . , J } is the objective upper limits for the level of each form of environmental impact caused by the air transport service m, m ∈ {1, . . . , M}.

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6 Global Air Transportation Service Market After introducing fuzzy representation of demand along the total time horizon, this paper considers supply–demand constraints for the air transport sector as represented by its primary services.

6.1 Expressing Future Demand for Air Transportation Services The demand for each class of air transport service, expressed in TEK, is anticipated to evolve in accordance with a potential trend and uncertainties resulting from the dynamic economic and environmental conditions. Although it is not possible to obtain accurate estimates of the demand for air transport not only over the long term but also over the medium term, different scenarios can be generated [12]. Here, four near trend scenarios are considered for each class of air transport service to generate a fuzzy representation of air transport demand along the planning horizon [13, 14]: m m m m Dmin (k) ≤ D− (k) ≤ D+ (k) ≤ Dmax (k), k ∈ {1, . . . , K }, m ∈ {1, . . . , M}

(28)

in the case in which yearly rates are considered, these demand levels can be written for k ∈ {1, . . . , K }, m ∈ {1, . . . , M} as: ) ( m m m Dmin (k) = 1 + αkm · Dmin (k − 1) with Dmin (0) = D m (0)

(29)

) ( m m m D− (k) = 1 + βkm · D− (k − 1) with D− (0) = D m (0)

(30)

) ( m m m D+ (k) = 1 + γkm · D+ (k − 1) with D+ (0) = D m (0)

(31)

) ( m m m Dmax (k) = 1 + δkm · Dmax (k − 1) with Dmax (0) = D m (0)

(32)

where D m (0) is the current level of demand for TPK with class n aerial vehicle at the beginning of the considered time horizon with: αkm ≤ βkm ≤ γkm ≤ δkm for k ∈ {1, . . . , K }, m ∈ {1, . . . , M}

(33)

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6.2 Supply Constraints Now, by employing a fuzzy notation for each air transport demand, the capacity constraints can be formulated so that each demand class m is approximatively met: Σ

P n (k) ≥ D˜ m (k) for k ∈ {1, . . . , K }, m ∈ {1, . . . , M}

(34)

n∈Sm

The above inequalities encompass the four distinct situations for air transport service m during period k. m Dmin (k) ≤

Σ

m P n (k) < Dmi− (k)

(35)

n∈Sm

In such a circumstance, it is acceptable for the offered capacity to fall marginally short of demand. Σ m m D−in P n (k) < D+ (36) (k) ≤ (k) n∈Sm

In this circumstance, it is believed that the offered capacity will be sufficient to satisfy demand. m D+ (k) ≤

Σ

m P n (k) < Dmax (k)

(37)

n∈Sm

In this instance, it is believed that the offered capacity may be marginally greater than demand. Σ m Dmax P n (k) (38) (k) ≤ n∈Sm

In this scenario, it is believed that the offered capacity will exceed demand.

7 Analysis and Discussion This document proposes a generic framework to support strategic analysis and decision making regarding the future of aviation transport as a whole, taking energy and sustainability concerns into account. Here, a dynamic model is in play, which, starting from an initial situation, takes into account a series of technological development decisions leading to a range of possible industrial solutions that should be able to satisfactorily meet the demand for air transport, with all of its particularities, over the

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considered time horizon. The generated solutions must meet global or sectoral environmental protection objectives. Introduced are two classes of decision variables: variables associated with technological development, such as timing variables, the kin and investment variables for each conceivable technology, I Nin (k) and variables associated with the size and composition of air vehicle fleets Rin (k) and Nin (k). The values assigned to these two categories of variables will affect the environmental impact of the aviation industry. In addition, the implementation of a common unit, tonnes per equivalent kilometre (TEK), makes it possible to return evaluation and comparison of the various categories of aviation vehicles under consideration to a common basis. This model should enable the identification of strategies that not only allow for the fulfilment of anticipated air transport requirements, but also allow for the achievement of emission reduction goals for the period under consideration. This enables the formulation of constraints on the levels of the air transport service offer and the level of environmental impacts that must be satisfied by both the scenario simulation and optimization approaches. Using mathematical programming techniques and the discounted total cost of investment, C T ot , these solutions satisfying these two types of constraints can be evaluated from an economic standpoint. Nonetheless, this approach disregards the numerous economic actors concerned. Thus, the total cost could relate only to technological development and the installation of industrial tools for the production of new aircraft and their components (engines), while the sector of air transport operators would be exempt from satisfying a minimum level of profitability, which opens the door to the thorny problem of pricing, particularly when viewed over the long term. It should be noted that the process of technological development and the establishment of industrial tools for the production of aircraft, as well as their proper production, have a significant impact on the environment, and that, beyond the air transportation sector, consideration should be given to the sector’s overall impact. Consequently, this component could be added to the environmental impact assessment module outlined here. Validation and quantification of the model proposed here are challenging due to the model’s multidisciplinary nature and reliance on sectoral data that are not readily available. The a priori selection of classes of services, vehicles, and technologies may also have difficult-to-evaluate consequences. The selection of the different levels of environmental impact is also difficult to determine, and there may be incompatibility between a priori established political objectives and what is actually realisable, necessitating an iterative process in the application of the proposed model to identify solutions that are both politically acceptable and actually realisable. Lastly, this model presupposes a convergence and comprehensive cooperation of efforts that should make air transport more sustainable, excluding competition between states, aircraft manufacturers, and air transport operators.

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8 Conclusion In this paper, a generic framework is proposed to facilitate strategic analysis and decision-making regarding the future of aviation transport as a whole, taking energy and sustainability concerns into account. This model can be improved in a number of ways, such as by recognising that the demand for air transport services is not entirely exogenous and is dependent on its performance, by taking network effects into account when analysing the production of air transport operators, and by incorporating the energy consumption of ground vehicles at airports and other air terminals. The information of such a model is a complex endeavour involving numerous sources of technological, operational, and economic knowledge, as well as a number of uncertainties regarding, for instance, the efficacy of prospective technologies. Once this model is informed and the different levels of constraints along the considered time horizon are selected, the feasibility of different strategies can be evaluated, and a series of mixed integer optimisation problems can be formulated and solved to approach the definition of a sustainable future for air transportation. This generic framework should contribute to the design of effective and consistent approaches for the implementation of global long-term policies aimed at ensuring the sustainability of air transport.

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