Towards Active Flow Control Strategies Through Deep Reinforcement Learning
This addresses drag reduction for aerodynamic applications, but it is incremental as it applies an existing DRL method to a specific flow control problem.
The paper tackled active flow control to reduce drag in aerodynamic bodies using deep reinforcement learning, achieving a 9.32% drag reduction and a 78.4% decrease in lift oscillations on a 3D cylinder at Re = 100.
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