Experimental study on Q-learning control of airfoil trailing-edge flow separation using plasma synthetic jets

物理 翼型 等离子体驱动器 后缘 雷诺数 阻力 Chord(对等) 流动分离 流量控制(数据) 失速(流体力学) 空气动力学 湍流 控制理论(社会学) 机械 强化学习 等离子体 人工智能 计算机科学 介质阻挡放电 控制(管理) 量子力学 分布式计算 计算机网络
作者
Haohua Zong,Yun Wu,Hua Liang,Zhi Su,Jinping Li
出处
期刊:Physics of Fluids [American Institute of Physics]
卷期号:36 (1) 被引量:8
标识
DOI:10.1063/5.0185853
摘要

In this paper, a continuous Q-learning algorithm is deployed to optimize the control strategy of a trailing-edge airfoil flow separation at a chord-based Reynolds number of 2×105. With plasma synthetic jets issued at the middle chord and a hot wire placed in the separated shear layer acting as the actuator and sensor, respectively, a high-speed reinforcement learning control at an interaction frequency of 500 Hz is realized by a field-programmable gate array. The results show that in the Q-learning control, the controller only needs several seconds to elevate the instantaneous reward to a level close to the final mean reward, and convergence of the control law typically takes less than 100 s. Although the relative drag reduction achieved by Q-learning control (10.2%) is only slightly higher than the best open-loop periodical control at F∗=4 (9.6%), the maximum power saving ratio is improved noticeably by 62.5%. Physically, Q-learning control creates more turbulent fluctuations, earning more rewards by increasing the transition possibilities toward high-value states. With increasing penalty strength of plasma actuation, the final control laws obtained from Q-learning exhibit a decreasing number of active states. Detailed comparisons between the open-loop and Q-learning control strategies show that the statistics of the controlled velocity fields remain similar, yet the turbulent fluctuations contributed by the vortex shedding mode are reduced by constant-frequency plasma actuation.
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