Towards Active Flow Control Strategies Through Deep Reinforcement Learning

Ricard Montalà, Bernat Font, Pol Suárez, Jean Rabault, Oriol Lehmkuhl +1 more
11/8/2024
cs.LGphysics.flu-dyn

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Abstract

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

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Cite this paper

@article{montal2024towards,
  title  = {Towards Active Flow Control Strategies Through Deep Reinforcement Learning},
  author = {Ricard Montalà and Bernat Font and Pol Suárez and Jean Rabault and Oriol Lehmkuhl and Ivette Rodriguez},
  year   = {2024},
  eprint = {2411.05536},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.05536v1}
}

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