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

翻译：本文提出了一种用于主动流动控制（AFC）的深度强化学习（DRL）框架，旨在降低空气动力学体的阻力。通过在雷诺数Re = 100条件下的三维圆柱体上进行测试，该DRL方法通过学习先进的驱动策略，实现了9.32%的阻力降低和78.4%的升力振荡减小。该方法通过内存数据库将计算流体力学（CFD）求解器与DRL模型集成，以实现两者间的高效通信。