Vinuesa, R.; Lehmkuhl, O.; Lozano-Durán, A.; Rabault, J. Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning. Fluids2022, 7, 62.
Vinuesa, R.; Lehmkuhl, O.; Lozano-Durán, A.; Rabault, J. Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning. Fluids 2022, 7, 62.
Vinuesa, R.; Lehmkuhl, O.; Lozano-Durán, A.; Rabault, J. Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning. Fluids2022, 7, 62.
Vinuesa, R.; Lehmkuhl, O.; Lozano-Durán, A.; Rabault, J. Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning. Fluids 2022, 7, 62.
Abstract
In this review we summarize existing trends of flow control used to improve the aerodynamic efficiency of wings. We first discuss active methods to control turbulence, starting with flat-plate geometries and building towards the more complicated flow around wings. Then, we discuss active approaches to control separation, a crucial aspect towards achieving high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modelling. Finally, we thoroughly revise data-driven methods, their application to flow control, and focus on deep reinforcement learning (DRL). We conclude that this methodology has the potential to discover novel control strategies in complex turbulent flows of aerodynamic relevance.
Keywords
turbulence; flow control; simulation; aerodynamics; machine learning; deep reinforcement learning
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
