Deep reinforcement learning for active flow control in a turbulent separation bubble

Bernat Font (), Francisco Alcántara-Ávila, Jean Rabault, Ricardo Vinuesa () and Oriol Lehmkuhl
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Bernat Font: Delft University of Technology
Francisco Alcántara-Ávila: KTH Royal Institute of Technology
Jean Rabault: Independent researcher
Ricardo Vinuesa: KTH Royal Institute of Technology
Oriol Lehmkuhl: Barcelona Supercomputing Center

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract The control efficacy of deep reinforcement learning (DRL) compared with classical periodic forcing is numerically assessed for a turbulent separation bubble (TSB). We show that a control strategy learned on a coarse grid works on a fine grid as long as the coarse grid captures main flow features. This allows to significantly reduce the computational cost of DRL training in a turbulent-flow environment. On the fine grid, the periodic control is able to reduce the TSB area by 6.8%, while the DRL-based control achieves 9.0% reduction. Furthermore, the DRL agent provides a smoother control strategy while conserving momentum instantaneously. The physical analysis of the DRL control strategy reveals the production of large-scale counter-rotating vortices by adjacent actuator pairs. It is shown that the DRL agent acts on a wide range of frequencies to sustain these vortices in time. Last, we also introduce our computational fluid dynamics and DRL open-source framework suited for the next generation of exascale computing machines.

Date: 2025
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DOI: 10.1038/s41467-025-56408-6

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