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. Here, we employ Evolution Strategy (ES) to optimise a recurren
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- PossiblePossibly related (embedding) · 51%AgileRL/AgileRL →
- LinkedLinked via arxiv author · 85%Giorgio Maria Cavallazzi →
“Gradient-free learning of a closed-loop wall controller for turbulent drag reduction”
- LinkedLinked via arxiv author · 85%Miguel Pérez Cuadrado →
“Gradient-free learning of a closed-loop wall controller for turbulent drag reduction”
- LinkedLinked via arxiv author · 85%Alfredo Pinelli →
“Gradient-free learning of a closed-loop wall controller for turbulent drag reduction”