Towards Active Flow Control Strategies Through Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2411.05536v1
• Date: Fri, 08 Nov 2024 12:49:24 GMT
• Title: Towards Active Flow Control Strategies Through Deep Reinforcement Learning
• Authors: Ricard Montalà, Bernat Font, Pol Suárez, Jean Rabault, Oriol Lehmkuhl, Ivette Rodriguez,
• Abstract summary: This paper presents a deep reinforcement learning 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.
• Score: 0.0
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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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