Towards Active Flow Control Strategies Through Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework for active flow control that effectively reduces drag and lift oscillations on a 3D cylinder, demonstrating significant aerodynamic improvements.

Contribution

It presents a novel DRL-based active flow control method integrated with CFD simulations, achieving notable drag reduction and oscillation decrease.

Findings

9.32% drag reduction achieved

78.4% decrease in lift oscillations

Effective learning of advanced actuation strategies

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