论文标题
实验中主动流控制的强化学习
Reinforcement Learning for Active Flow Control in Experiments
论文作者
论文摘要
我们通过实验证明，通过自动发现主动控制策略，而无需对流动物理学的任何事先了解，可以在流量控制问题中应用强化学习（RL）的可行性。我们认为湍流经过圆柱体，目的是通过正确选择两个小直径圆柱体的旋转速度来减少圆柱阻力或最大化功率增长效率，这些旋转速度平行于较大的圆柱体的下游和下游。鉴于经过适当设计的奖励和降噪技术，在经过数十次牵引实验之后，RL代理可以发现最佳控制策略，可与最佳静态控制相媲美。虽然已经发现RL在最近的计算机流仿真研究中有效，但这是第一次在实验中证明其有效性，为探索复杂流体力学应用中的新最佳主动流控制策略铺平了道路。
We demonstrate experimentally the feasibility of applying reinforcement learning (RL) in flow control problems by automatically discovering active control strategies without any prior knowledge of the flow physics. We consider the turbulent flow past a circular cylinder with the aim of reducing the cylinder drag force or maximizing the power gain efficiency by properly selecting the rotational speed of two small diameter cylinders, parallel to and located downstream of the larger cylinder. Given properly designed rewards and noise reduction techniques, after tens of towing experiments, the RL agent could discover the optimal control strategy, comparable to the optimal static control. While RL has been found to be effective in recent computer flow simulation studies, this is the first time that its effectiveness is demonstrated experimentally, paving the way for exploring new optimal active flow control strategies in complex fluid mechanics applications.
