Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning
Name
fluids-07-00062.pdf
Size
5.98 MB
Format
Adobe PDF
Checksum (MD5)
5dd421a78b080fce970b1ac1a913f813
Author(s)
Vinuesa, Ricardo
Lehmkuhl, Oriol
Lozano-Durán, Adrian
Rabault, Jean
Date Issued
February 1, 2022
Journal
Fluids
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Vinuesa, R.; Lehmkuhl, O.; Lozano-Durán, A.; Rabault, J. Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning. Fluids 7 (2): 62 (2022)
Version
Final published version
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 a high aerodynamic efficiency. Furthermore, we highlight methods relying on turbulence simulation, and discuss various levels of modeling. Finally, we thoroughly revise data-driven methods and 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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/fluids7020062
