Gradient-free learning of a closed-loop wall controller for turbulent drag reduction
Closed-loop wall controllers learnt by multi-agent reinforcement learning are usually trained on periodic boxes far smaller than the flows they are meant to drive, and a large part of their drag reduction is lost when they are carried across. Retraining on the target domain is not an affordable remedy: the centralised critic that assigns credit to each wall patch degrades as patches are added, the zero-net-mass constraint couples the patches it is asked to separate, and the episodes must be collected in sequence at a cost that grows with the domain. We propose instead a short gradient-free refinement stage, applied to the transferred policy on the domain it will drive. An evolution strate...
| Authors | Giorgio Maria Cavallazzi, Miguel Pérez Cuadrado, Alfredo Pinelli |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 14 July 2026 |
| Last updated at source | 27 August 2026 |
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