空气动力学学报2024，Vol.42Issue(10)：84-97,14.DOI:10.7638/kqdlxxb-2024.0123
基于深度强化学习的三维变形机翼反设计方法
An inverse design method for three-dimensional morphing wings based on deep reinforcement learning
摘要
Abstract
It is of great significance to find out how to deform a three-dimensional deformed wing independently to meet the requirements of aerodynamic performance and the basic function of mission adaptive deforming under variable operating conditions.In this study,an RLID(reinforcement learning inverse design)framework is proposed and applied to the reverse design of three-dimensional morphing wings for adaptive morphing flight missions under variable operating conditions.The CST(class-shape function transformation)parameterization method is chosen to define three-dimensional morphing wings,and the Latin hypercube sampling method is used to sample in the design space and generate sample points.Computational fluid dynamics simulations are performed to obtain corresponding aerodynamic parameters,and the deep belief network surrogate model is constructed to map the input-output relationship between morphing design parameters and aerodynamic parameters.To address the variable operating conditions,a DQN(deep Q-network)reinforcement learning agent,leveraging unsupervised learning,is used to provide real-time morphing strategies,and the results meet about 70%of the expected aerodynamic performance requirements,and the average aerodynamic performance reaches more than 98%of the requirements.Furthermore,the design results via the DQN agent are compared with those via the G-CGAN(greedy-based conditional generative adversarial network)agents.The results indicate that the proposed RLID framework efficiently obtains a satisfactory strategy of morphing wings under variable operating conditions and that the DQN agent focuses more on overall task rewards than the G-CGAN agent.关键词
变形机翼/强化学习/深度Q网络/代理模型/可变工况Key words
morphing wings/reinforcement learning/deep Q-network/surrogate models/variable operating conditions分类
航空航天引用本文复制引用
苏敬,孙刚,陶俊..基于深度强化学习的三维变形机翼反设计方法[J].空气动力学学报,2024,42(10):84-97,14.基金项目
飞行器基础布局全国重点实验室开放基金项目(ZYTS-202501) （ZYTS-202501）
