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Redesign of Morphing UAV for Simultaneous Improvement of Directional Stability and Maximum Lift/Drag RatioARIK, S. , TURKMEN, I. , OKTAY, T.
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unmanned aerial vehicles, stability, artificial intelligence, neural networks, optimization
neural(9), optimization(8), design(7), technology(5), networks(5), aerospace(5), performance(4), oktay(4), morphing(4), lift(4)
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About this article
Date of Publication: 2018-11-30
Volume 18, Issue 4, Year 2018, On page(s): 57 - 62
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.04007
Web of Science Accession Number: 000451843400007
SCOPUS ID: 85058778064
This paper presents a novel method based on the artificial intelligence to redesign of morphing Unmanned Aerial Vehicle (UAV) for improvement of index consisting of directional stability and maximum lift/drag (L/D) ratio. In this study, Artificial Neural Network (ANN) based objective function is optimized with Artificial Bee Colony (ABC) algorithm. Firstly, the sweep angle is selected as input parameter and directional stability and maximum L/D ratio are selected as output parameters for ANN. ANN is trained with a small number of data obtained by the computational fluid dynamics method and the trained ANN is used for multiplying these data. Two ABC optimization algorithms with different objective functions are used to improve the index consisting of directional stability and maximum L/D ratio: While the first is used the adjustment of the ANN weights, the second is used the optimization of the ANN based objective function. Simulation results realized with limited data show that although directional stability and maximum L/D ratio have inverse relation, they are optimized equally and simultaneously. Thus, the artificial intelligence techniques provide fast and accurate determination of the optimal aerodynamic shape of UAV without time consuming and complexity caused by theoretical calculations.
|References|||||Cited By «-- Click to see who has cited this paper|
| J. Mariens, "Wing Shape Multidisciplinary Design Optimization," pp. 12-61, Master Thesis, Delft University of Technology, 2012.
 H. Yeo, W. Johnson, "Performance and Design Investigation of Heavy Lift Tilt-Rotor with Aerodynamic Interference Effects," Journal of Aircraft, vol. 46, no. 4, pp. 1231-1239, 2009.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 35]
 C. W. Jr. Acree, W. Johnson, "Performance, Loads and Stability of Heavy Lift Tiltrotors," in AHS Vertical Lift Aircraft Design Conference, San Francisco, CA, United States, 2006.
 W. Wisnoe, R. E. M. Nasir, W. Kuntjoro, A. M. I. Mamat, "Wind tunnel experiments and CFD analysis of Blended Wing Body (BWB) Unmanned Aerial Vehicle (UAV) at mach 0.1 and mach 0.3," in 13th International Conference on Aerospace Sciences & Aviation Technology, 2009, p. 14.
 S. Huang, L. Miller, J. Steck, "An exploratory application of neural networks to airfoil design," in 32nd Aerospace Sciences Meeting and Exhibit, American Institute of Aeronautics and Astronautics, 1994.
 T. Rajkumar, J. Bardina, "Prediction of Aerodynamic Coefficients Using Neural Networks for Sparse Data," in Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference, Pensacola Beach, Florida, USA, 2002, pp. 242-246.
 M. H. Djavareshkian, A. Esmaili, "Heuristic optimization of submerged hydrofoil using ANFIS-PSO," Ocean Engineering, vol. 92, pp. 55-63, 2014.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 7]
 A. Hacioglu, "Augmenting Genetic Algorithm with Neural Network and Implementation to the Inverse Airfoil Design," 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Multidisciplinary Analysis Optimization Conferences, 2004.
 A. Hacioglu, "Fast evolutionary algorithm for airfoil design via neural network," AIAA Journal, vol. 45, no. 9, pp. 2196-2203, 2007.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 27]
 N. R. Secco, B. S. de Mattos, "Artificial neural networks to predict aerodynamic coefficients of transport airplanes," Aircraft Engineering and Aerospace Technology, vol. 89, no. 2, pp. 211-230, 2017.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]
 J. Brett, A. Ooi, "Effect of Sweep Angle on the Vertical Flow over Delta Wings at an Angle of Attack of 10°," Journal of Engineering Science and Technology, vol. 9, no. 6, pp. 768-781, 2014.
 R. C. Nelson, "Flight Stability and Automatic Control", pp. 67-71, WCB/McGraw Hill, 1998.
 D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Technical Report-TR06, 2005.
 D. Karaboga, B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007.
[CrossRef] [Web of Science Times Cited 2470] [SCOPUS Times Cited 3233]
 D. R. Hush, B. G. Horne, "Progress in supervised neural networks," IEEE Signal Processing Magazine, vol. 10, no. 1, pp. 8-39, 1993.
[CrossRef] [Web of Science Times Cited 472] [SCOPUS Times Cited 847]
 S. Haykin, "Neural Networks: A Comprehensive Foundation", pp. 23-270, Macmillan, 1994.
 E. Oztemel, "Yapay Sinir Aglari", pp. 23-113, Papatya Bilim, 2003.
 L. H. Tsoukalas, R. E. Uhrig, "Fuzzy and neural approaches in engineering", pp. 191-229, Wiley, 1997.
 T. Oktay, S. Coban, "Simultaneous Longitudinal and Lateral Flight Control Systems Design for Both Passive and Active Morphing TUAVs," Elektronika ir Elektrotechnika, vol. 23, no. 5, pp. 15-20, 2017.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]
 T. Oktay, M. Konar, M. A. Mohamed, M. Aydin, F. Sal, M. Onay, M. Soylak, "Autonomous flight performance improvement of load-carrying unmanned aerial vehicles by active morphing," International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, vol. 10, no. 1, pp. 123-132, 2016.
 T. Oktay, S. Arik, I. Turkmen, M. Uzun, H. Çelik, "Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio," Aircraft Engineering and Aerospace Technology, 2018.
[CrossRef] [Web of Science Times Cited 1]
 T. Oktay, M. Uzun, "Aerodynamic Tailcone Shape Optimization for Autonomous Navigation Performance Maximization of Morphing Aerial Robot," presented at the International Conference on Engineering and Natural Science, Sarajevo, 2016.
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