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

Author

Listed:
  • Bernat Font

    (Delft University of Technology
    Barcelona Supercomputing Center)

  • Francisco Alcántara-Ávila

    (KTH Royal Institute of Technology)

  • Jean Rabault

    (Independent researcher)

  • Ricardo Vinuesa

    (KTH Royal Institute of Technology)

  • Oriol Lehmkuhl

    (Barcelona Supercomputing Center)

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.

Suggested Citation

  • Bernat Font & Francisco Alcántara-Ávila & Jean Rabault & Ricardo Vinuesa & Oriol Lehmkuhl, 2025. "Deep reinforcement learning for active flow control in a turbulent separation bubble," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56408-6
    DOI: 10.1038/s41467-025-56408-6
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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