We have demonstrated the effectiveness of reinforcement learning (RL) in bluff body flow control problems both in experiments and simulations by automatically discovering active control strategies for drag reduction in turbulent flow. Specifically, we aimed to maximize the power gain efficiency by properly selecting the rotational speed of two small cylinders, located parallel to and downstream of the main cylinder. By properly defining rewards and designing noise reduction techniques, and after an automatic sequence of tens of towing experiments, the RL agent was shown to discover a control strategy that is comparable to the optimal strategy found through lengthy systematically planned control experiments. Subsequently, these results were verified by simulations that enabled us to gain insight into the physical mechanisms of the drag reduction process. While RL has been used effectively previously in idealized computer flow simulation studies, this study demonstrates its effectiveness in experimental fluid mechanics and verifies it by simulations, potentially paving the way for efficient exploration of additional active flow control strategies in other complex fluid mechanics applications.

我们通过自动发现湍流减阻的主动控制策略，在实验和模拟中证明了强化学习 (RL) 在钝体流动控制问题中的有效性。具体来说，我们的目标是通过正确选择与主气缸平行且位于主气缸下游的两个小气缸的转速来最大化功率增益效率。通过正确定义奖励和设计降噪技术，并经过数十次牵引实验的自动序列，RL 代理发现了一种控制策略，该策略可与通过冗长的系统计划的控制实验找到的最佳策略相媲美。随后，这些结果通过模拟得到验证，使我们能够深入了解减阻过程的物理机制。虽然强化学习之前已在理想化计算机流动仿真研究中得到有效使用，但本研究证明了其在实验流体力学中的有效性，并通过仿真进行了验证，可能为在其他复杂流体力学应用中有效探索其他主动流量控制策略铺平道路。