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This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at \(Re=100\) , the DRL approach achieved a \(9.32\%\) drag reduction and a \(78.4\%\) decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between the two instances, making it scalable to more complex flows and higher Reynolds numbers.

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Towards Active Flow Control Strategies Through Deep Reinforcement Learning

  • 章节

  • 发表日期：

    2026年

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  • Ricard Montalà,
  • Bernat Font,
  • Pol Suárez,
  • Jean Rabault,
  • Oriol Lehmkuhl,
  • Ricardo Vinuesa,
  • Ivette Rodriguez

摘要

This paper presents a deep reinforcement learning (DRL) framework for active flow control (AFC) to reduce drag in aerodynamic bodies. Tested on a 3D cylinder at \(Re=100\) , the DRL approach achieved a \(9.32\%\) drag reduction and a \(78.4\%\) decrease in lift oscillations by learning advanced actuation strategies. The methodology integrates a CFD solver with a DRL model using an in-memory database for efficient communication between the two instances, making it scalable to more complex flows and higher Reynolds numbers.