Deep-reinforcement-learning-based separation control in a two-dimensional airfoil
Xavier Garcia (KTH Royal Institute of Technology)
Arnau Miró (Universitat Politecnica de Catalunya, Barcelona Supercomputing Center)
Pol Suárez (KTH Royal Institute of Technology)
Francisco Alcántara-Ávila (KTH Royal Institute of Technology)
Jean Rabault (Independent researcher)
Bernat Font (TU Delft - Ship Hydromechanics)
Oriol Lehmkuhl (Barcelona Supercomputing Center)
Ricardo Vinuesa (KTH Royal Institute of Technology)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
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.
help_outline