Towards Active Flow Control Strategies Through Deep Reinforcement Learning
R. Montalà (Universitat Politecnica de Catalunya)
B. Font (TU Delft - Mechanical Engineering)
P. Suárez (KTH Royal Institute of Technology)
J. Rabault (Independent researcher)
O. Lehmkuhl (Barcelona Supercomputing Center)
R. Vinuesa (KTH Royal Institute of Technology)
I. 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.