Time-history performance optimization of flapping wing motion using a deep learning based prediction model
作者机构：School of Aeronautics and AstronauticsZhejiang UniversityHangzhou 310027China Huanjiang LaboratoryZhuji 311800China
出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)
年 卷 期：2024年第37卷第5期
页 面：317-331页
核心收录：
学科分类：08[工学]
基 金：This work was supported by the specialized research projects of Huanjiang Laboratory and the Defence Industrial Technology Development Programme China(Nos.JCKY2019205A006 JCKY2021205B003).
主 题：FWMAV Flapping wing motion Deep learning Unsteady aerodynamic performance Optimization Time-history curve
摘 要：Flapping Wing Micro Aerial Vehicles(FWMAVs)have caused great concern in various fields because of their high efficiency and maneuverability.Flapping wing motion is a very important factor that affects the performance of the aircraft,and previous works have always focused on the time-averaged performance optimization.However,the time-history performance is equally important in the design of motion mechanism and flight control system.In this paper,a time-history performance optimization framework based on deep learning and multi-island genetic algorithm is presented,which is designed in order to obtain the optimal two-dimensional flapping wing motion.Firstly,the training dataset for deep learning neural network is constructed based on a validated computational fluid dynamics method.The aerodynamic surrogate model for flapping wing is obtained after the convergence of training.The surrogate model is tested and proved to be able to accurately and quickly predict the time-history curves of lift,thrust and moment.Secondly,the optimization framework is used to optimize the flapping wing motion in two specific cases,in which the optimized propulsive efficiencies have been improved by over 40%compared with the baselines.Thirdly,a dimensionless parameter C_(variation)is proposed to describe the variation of the time-history characteristics,and it is found that C_(variation)of lift varies significantly even under close time-averaged performances.Considering the importance of time-history performance in practical applications,the optimization that integrates the propulsion efficiency as well as C_(variation)is carried out.The final optimal flapping wing motion balances good time-averaged and time-history performance.
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