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Permissible Wind Conditions for Optimal Dynamic Soaring with a Small Unmanned Aerial Vehicle

Liu Duo-Neng1,2, Hou Zhong-Xi1, Guo Zheng1, Yang Xi-Xiang1, Gao Xian-Zhong1

College of Aerospace Sciences and Engineering, National University of Defense Technology, Changsha, Hunan, 410073, China
Corresponding author. Email: liuduoneng@nudt.edu.cn.

Computer Modeling in Engineering & Sciences 2016, 111(6), 531-565. https://doi.org/10.3970/cmes.2016.111.531

Abstract

Dynamic soaring is a flight maneuver to exploit gradient wind field to extend endurance and traveling distance. Optimal trajectories for permissible wind conditions are generated for loitering dynamic soaring as well as for traveling patterns with a small unmanned aerial vehicle. The efficient direct collection approach based on the Runge-Kutta integrator is used to solve the optimization problem. The fast convergence of the optimization process leads to the potential for real-time applications. Based on the results of trajectory optimizations, the general permissible wind conditions which involve the allowable power law exponents and feasible reference wind strengths supporting dynamic soaring are proposed. Increasing the smallest allowable wingtip clearance to trade for robustness and safety of the vehicle system and improving the maximum traveling speed results in shrunken permissible domain of wind conditions for loitering and traveling dynamic soaring respectively. Sensitivity analyses of vehicle model parameters show that properly reducing the wingspan and increasing the maximum lift-to-drag ratio and the wing loading can enlarge the permissible domain. Permissible domains for different traveling directions show that the downwind dynamic soaring benefitting from the drift is more efficient than the upwind traveling pattern in terms of permissible domain size and net traveling speed.

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Cite This Article

Duo-Neng, L., Zhong-Xi, H., Zheng, G., Xi-Xiang, Y., Xian-Zhong, G. (2016). Permissible Wind Conditions for Optimal Dynamic Soaring with a Small Unmanned Aerial Vehicle. CMES-Computer Modeling in Engineering & Sciences, 111(6), 531–565.



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