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Unmanned Aerial Vehicle, range, endurance, design of experiment, genetic optimization
The widespread adoption of Unmanned Aerial Vehicles (UAVs) can be traced to its flexibility and wide adaptability to various operating conditions and applications, comparably low cost of construction and maintenance and environmental friendliness as they can be easily configured for electric power. The use of electric power also favours its low noise applications such as surveillance. A major issue associated with surveillance, as addressed in this study is the compromise between Range and Endurance operation modes. The Range mode relates to being able to cover longer distances while the Endurance mode relates to spending longer times in the atmosphere for a fixed charge. Trying to balance the interplay of these parameters gave rise to a multi-objective optimization where the objectives are somewhat conflicting. This resulted in a set of Pareto solutions which are a set of design parameters (primarily angle of attack) that satisfy the joint requirements of the performance parameters of Range and Endurance. This study first considered a baseline aerodynamic design using traditional design methods. Design of Experiment techniques were then used to select the most favourable design points. This model was then used to build an input framework for Genetic Optimization algorithm deployed in the Global Optimization Toolbox of MATLAB. The result of this research shows that most of the region associated with medium angle of attack (AOA) setting (7 degrees) jointly satisfies good Range and Endurance performances with an average lift-to-drag ratio of 20 in the flight configuration considered. The implication of this result is that low velocity drag encountered in surveillance that requires a high AOA is largely reduced with the medium setting, albeit stabilized with other structural and aerodynamic settings, namely an aspect ratio of 13 and a taper ratio of 0.6.
Ogedengbe I. I., Akintunde M. A., Dahunsi O. A., Bello E. I.a, Bodunde P., "Multi-objective Optimization of Subsonic Glider Wing Using Genetic Algorithm", International Journal of Intelligent Systems and Applications(IJISA), Vol.14, No.2, pp.14-25, 2022. DOI: 10.5815/ijisa.2022.02.02
 M. T. Khot, “CFD Based Airfoil Shape Optimization for Aerodynamic Drag Reduction”, MSc. Dissertation, College of Engineering, Sharjah Univ., Sharjah, IND, 2012.
 P. B. Howard, Y. Z. Beckett and W. Z. David, “Airfoil Optimization Using Practical Aerodynamic”, AIAA Journal of Aircraft, vol. 47, no. 5, pp 1707-1726, October 2010.
 H. X. Chen, Y. F. Zhang, W. S. Zhang and S. Fu, “GA Optimization Design of Multi-Element Airfoil”, Proc. 7th International Conference on Computational Fluid Dynamics - ICCFD7, July 2012, pp 1-6.
 K. Norbert, R. G. Nikolas, B. Joel, D. Richard, F. Antonio, V. Daniel, B. Klaus, B. Holger, S. Volker and H. Subhendu, “Flow simulation and shape optimization for aircraft design”, Journal of Computational and Applied Mathematics, vol. 203, no. 2, pp. 397 – 411, June 2007.
 K. Dalamagkidis, “Aviation History and Unmanned Flight. In: On Integrating Unmanned Aircraft Systems into the National Airspace System. Intelligent Systems, Control and Automation: Science and Engineering”, ISCA book series: vol. 54, pp. 11 -42. 2012.
 Read, B. (2018), “Silent Revolution - Glider Technology”, https://www.aerosociety.com/news/silent-revolution-glider-technology/, July 7, 2018.
 E. Alpman (2013), “Airfoil Shape Optimization Using Evolutionary”, https://pdfs.semanticscholar.org/babc/9c5e8d20d23dc0d7fb99ce446bfd50b7493d.pdf, October 2, 2017.
 Mehdi J. Marie, Safaa S.Mahdi, Esraa Y. Tarkan, " Intelligent Control for a Swarm of Two Wheel Mobile Robot with Presence of External Disturbance", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.11, pp. 7-12, 2019.DOI: 10.5815/ijmecs.2019.11.02
 Basem E. Elnaghi, Reham H. Mohammed, Sobhy S. Dessouky, Mariam K. Shehata," Load Test of Induction Motors Based on PWM Technique using Genetic Algorithm", International Journal of Engineering and Manufacturing (IJEM), Vol.9, No.2, pp.1-15, 2019.DOI: 10.5815/ijem.2019.02.01
 C. Jianfeng, N. Vivek and M. Tim, “Beyond evolutionary algorithms for search-based software engineering”, Journal of Information and Software Technology, vol. 95, no. 1, pp 281-294, March 2018.
 H. Yilei, Q. Qiulin and K. A. Ramesh,”Shape Optimization of an Airfoil in Ground Effect for Application to WIG Craft”, Journal of Aerodynamics, Hindawi, Volume 2014, December 2014.
 X. C. Neto, G. J. P. Da Silva, O. P. Ferreira and J. O. Lopes, “A Subgradient Method for Multiobjective Optimization”, International Journal of Computational Optimization and Applications, vol. 54, no. 3, April 2013.
 P. Kyoungwoo, S. K. Byeong, L. Juhee and S. K. Kwang, “Aerodynamics and Optimization of Airfoil Under Ground Effect”, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, vol. 03, no. 04, November 2009.
 K. Gaetan, W. Kenway, R. R. Joaquim, A. Martins, “Multipoint Aerodynamic Shape Optimization Investigations of the Common Research Model Wing”, Aerospace Research Central, vol. 54, no. 1, pp. 113 – 129, December 2015.
 E. Dileep, M. Nebish and V. Loganathan, “Aerodynamic Performance Optimization of Smart Wing Using SMA Actuator”, Research Journal of Recent Sciences, vol. 2, no. 6, pp 17-22, June 2013.
 L. Ciprian, C. Lucian, D. Maria and M. Stefan, “Wing Lift-Drag Optimization”, Journal of Scientific Research and Education in the Airforce, Romania- AFASE2018, pp 191-196, 2018.
 S. Kandasamy, K. Nandan, and J. Umakanth, “Parametric Optimization of High Aspect Ratio Wing Using Surrogate Model”, International Federation of Automatic Control, hosting by Elsevier Ltd. – IFAC PapersOnLine vol. 51, no. 1, pp 231-236, 2018.
 X. Chai, X. Yu, Y. Wang, “Multipoint Optimization on Fuel Efficiency in Concept Design of Wide-body Aircraft”, Chinese Journal of Aeronautics, Chinese Society of Aeronautics and Astronautics, hosting by Elsevier Ltd., vol. 31, no. 1, pp 99-106, October 2017.