基于深度强化学习的多攻角翼型流动控制研究  

Flow Control of Multi-attack Angle Airfoils Based on Deep Reinforcement Learning

作　　者：尤孙钰 靳智昊 董祥瑞 宋杨 蔡小舒[1,2] YOU Sunyu;JIN Zhihao;DONG Xiangrui;SONG Yang;CAI Xiaoshu(School of Energy and Power Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Key Laboratory of Multiphase Flow and Heat Transfer for Power Engineering,Shanghai 200093,China)

机构地区：[1]上海理工大学能源与动力工程学院,上海200093 [2]上海市动力工程多相流传热重点实验室,上海200093

出　　处：《动力工程学报》2024年第12期1887-1897,共11页Journal of Chinese Society of Power Engineering

基　　金：上海市自然科学基金资助项目(21ZR1443700);上海市晨光计划资助项目(21CGA54)。

摘　　要：针对弱湍流条件下大攻角翼型产生的流动分离现象,采用柔性演员评论家(Soft Actor-Critic,SAC)深度强化学习(Deep Reinforcement Learning,DRL)算法训练神经网络,并对其进行闭环主动流动控制策略研究。在复杂环境下,通过增添零质量射流约束,采用三射流进行优化策略研究,获得不同攻角条件下的平均阻力系数。结果表明:使用DRL训练得到的策略控制射流速度,可以高效抑制翼型流动分离;在单射流控制、翼型攻角分别为10°、13°、15°时,平均阻力系数分别减少25%、15.3%、11.7%;在大攻角条件下,基于DRL的主动流动控制方法具有良好的效果,验证了该方法在抑制翼型流动分离中的高效性;合成射流的引入也可以使智能体寻找到更好的控制策略,使阻力系数进一步减小。In order to solve the problem of flow separation phenomenon caused by high-angle-of-attack airfoils under weak turbulent conditions,a soft actor-critic(SAC)deep reinforcement learning(DRL)algorithm was used to train a neural network for closed-loop active flow control strategy.In complex environments,an optimized strategy was developed by introducing a zero-mass jet constraint and utilizing three jets,obtaining average drag coefficient reductions for different angles of attack.Results show that using the DRL training strategy to control the jet velocity can inhibit the flow separation of airfoil effectively.When single-jet control is used and airfoil attack angles are 10°,13°and 15°,the average drag coefficients are reduced by 25%,15.3%and 11.7%,respectively.Under the condition of large angle of attack,the DRL-based active flow control method has a good effect,which verifies high efficiency of the method in restraining flow separation of airfoil.The introduction of the synthetic jet also enables the agent to find a better control strategy and further reduce the drag coefficient.

关 键 词：流动控制 深度强化学习 弱湍流条件 多攻角翼型 

分 类 号：TK2[动力工程及工程热物理—动力机械及工程]