The aim of this study is to discover new active-flow-control (AFC) techniques for separation mitigation in a two-dimensional NACA 0012 airfoil at a Reynolds number of 3000. To find these AFC strategies, a framework consisting of a deep-reinforcement-learning (DRL) agent has been used to determine the action strategies to apply to the flow. The actions involve blowing and suction through jets at the airfoil surface. The flow is simulated with the numerical code Alya, which is a low-dissipation finite-element code, on a high-performance computing system. Various control strategies obtained through DRL led to 43.9% drag reduction, while others yielded an increase in aerodynamic efficiency of 58.6%. In comparison, periodic-control strategies demonstrated lower energy efficiency while failing to achieve the same level of aerodynamic improvements as the DRL-based approach. These gains have been attained through the implementation of a dynamic, closed-loop, time-dependent, active control mechanism over the airfoil.

翻译：本研究旨在探索雷诺数为3000的二维NACA 0012翼型分离抑制的新型主动流动控制技术。为寻找这些主动流动控制策略，研究采用基于深度强化学习智能体的框架来确定施加于流场的控制策略。控制动作涉及翼型表面射流的吹吸作用。流动模拟采用低耗散有限元数值代码Alya在高性能计算系统上完成。通过深度强化学习获得的不同控制策略实现了43.9%的阻力降低，部分策略使气动效率提升58.6%。相比之下，周期性控制策略在未能达到深度强化学习方法同等气动改进水平的同时，表现出更低的能量效率。这些性能增益是通过在翼型表面实施动态、闭环、时变的主动控制机制实现的。