Towards Active Flow Control Strategies Through Deep Reinforcement Learning
Ricard Montalà (Universitat Politecnica de Catalunya)
Bernat Font (TU Delft - Mechanical Engineering)
Pol Suárez (KTH Royal Institute of Technology)
Jean Rabault (Independent researcher)
Oriol Lehmkuhl (Barcelona Supercomputing Center)
Ricardo Vinuesa (University of Michigan, KTH Royal Institute of Technology)
Ivette Rodriguez (Universitat Politecnica de Catalunya)
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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 the two instances, making it scalable to more complex flows and higher Reynolds numbers.