Suárez Pol, Alcántara-Ávila Francisco, Rabault Jean, Miró Arnau, Font Bernat, Lehmkuhl Oriol, Vinuesa Ricardo
FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden.
Independent researcher, Oslo, Norway.
Commun Eng. 2025 Jun 18;4(1):113. doi: 10.1038/s44172-025-00446-x.
Active flow control strategies for three-dimensional bluff bodies are challenging to design, yet critical for industrial applications. Here we explore the potential of discovering novel drag-reduction strategies using deep reinforcement learning. We introduce a high-dimensional active flow control setup on a three-dimensional cylinder at Reynolds numbers (Re) from 100 to 400, spanning the transition to three-dimensional wake instabilities. The setup involves multiple zero-net-mass-flux jets and couples a computational fluid dynamics solver with a numerical multi-agent reinforcement learning framework based on the proximal policy optimization algorithm. Our results demonstrate up to 16% drag reduction at Re = 400, outperforming classical periodic control strategies. A proper orthogonal decomposition analysis reveals that the control leads to a stabilized wake structure with an elongated recirculation bubble. These findings represent the first demonstration of training on three-dimensional cylinders and pave the way toward active flow control of complex turbulent flows.
用于三维钝体的主动流动控制策略设计具有挑战性，但对工业应用至关重要。在此，我们探索使用深度强化学习发现新型减阻策略的潜力。我们在雷诺数（Re）为100至400的三维圆柱体上引入了高维主动流动控制装置，涵盖向三维尾流不稳定性的转变。该装置涉及多个零净质量通量射流，并将计算流体动力学求解器与基于近端策略优化算法的数值多智能体强化学习框架相结合。我们的结果表明，在Re = 400时减阻高达16%，优于经典的周期性控制策略。适当正交分解分析表明，该控制导致尾流结构稳定，回流泡拉长。这些发现首次展示了在三维圆柱体上的训练，并为复杂湍流的主动流动控制铺平了道路。
