| 深度强化学习在翼型分离流动控制中的应用 |
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| 引用本文： | 姚张奕,史志伟,董益章.深度强化学习在翼型分离流动控制中的应用[J].实验流体力学,2022,36(3):55-64. |
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| 作者姓名： | 姚张奕 史志伟 董益章 |
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| 作者单位： | 南京航空航天大学 非定常空气动力学与流动控制工业和信息化部重点实验室，南京 210016 |
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| 摘 要： | 搭建了基于深度强化学习（DRL）的射流闭环控制系统，在NACA0012翼型上开展了大迎角分离流动控制实验研究。NACA0012翼型弦长200 mm，实验风速10 m/s，雷诺数1.36×105。射流激励器布置在翼型上表面，通过电磁阀进行无级控制。将翼型表面的压力系数和智能体自身的动作输出作为智能体的观测量，以翼型后缘压力系数为奖励函数，对智能体进行训练。结果表明:经过训练的智能体成功地抑制了大迎角下的流动分离，比定常吹气的费效比降低了50%；智能体可以将翼型后缘压力系数稳定地控制在目标值附近；状态输入和奖励函数的改变会对最终的训练效果产生不同影响。
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| 关 键 词： | 人工智能 深度强化学习 射流控制 流动控制 流动分离 |
| 收稿时间： | 2021-08-04 |
Deep reinforcement learning for the control of airfoil flow separation |
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| Affiliation: | Key Laboratory of Unsteady Aerodynamics and Flow Control, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Abstract: | A jet closed-loop control system based on Deep Reinforcement Learning （DRL） was built, and an experimental study was carried out on the separation flow control at high angles of attack on the NACA0012 airfoil. The airfoil chord length is 200 mm and the wind speed was 10 m/s. The Reynolds number was 1.36×105 based on the chord length. The jet actuator was arranged on the upper surface of the airfoil and the solenoid valve was used for stepless control. The pressure coefficient of the airfoil surface and the action output of the agent itself were taken as the observation of the agent. The pressure coefficient of the trailing edge of the airfoil was used as the reward function to train the agent. Our results showed that the trained agent successfully suppresses the flow separation at high angles of attack and the cost-effectiveness ratio is reduced by 50% compared with steady blowing. At the same time, the agent could also stabilize the pressure coefficient of the trailing edge near the target value. The state input and the change of the reward function also have different effects on the final training effect. |
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