Deep-reinforcement-learning-based separation control in a two-dimensional airfoil

TL;DR

This paper presents elsarticle.cls, a LaTeX document class designed to streamline formatting for Elsevier journal submissions by building on article.cls and minimizing package conflicts with common tools. It documents dependency on standard packages (e.g., natbib, geometry, hyperref) and outlines how the class accommodates various submission formats, including preprint and final styles, while providing convenient environments for theorems and structured front matter. The authors compare elsarticle.cls with the older elsart.cls, emphasizing reduced clashes and improved compatibility with widely-used LaTeX packages. The paper also provides practical installation guidance via Elsevier and CTAN resources, enabling researchers to efficiently prepare manuscripts that conform to Elsevier’s formatting requirements.

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.