Gradient-free learning of a closed-loop wall controller for turbulent drag reduction

Closed-loop wall control learnt by multi-agent reinforcement learning can lower skin-friction drag in turbulent channels, but these gradient-based policies are trained on small periodic boxes and exhibit reduced performance when carried over to a larger domain. We recently showed that such policies are also prone to saturated bang-bang actuations that collapse into standing streamwise waves whose scale is set by the computational box rather than by the near-wall cycle, and proposed architectural fixes that avoid these degeneracies.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

• agents
• reinforcement-learning