Reinforcement learning for bluff body active flow control in experiments and simulations.

ABSTRACT: 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 in

SUBMITTER: Fan D 

PROVIDER: S-EPMC7585013 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature