Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning

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...

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Published in:Fluids (Basel) 2022-02, Vol.7 (2), p.62
Main Authors: Vinuesa, Ricardo, Lehmkuhl, Oriol, Lozano-Durán, Adrian, Rabault, Jean
Format: Article
Language:English
Subjects:
Active control
Aerodynamics
Aircraft
Aviation
Control methods
Coronaviruses
COVID-19
Deep learning
Deep reinforcement learning
Design optimization
Efficiency
Emissions
Energy
Flow control
Friction
Machine learning
Numerical analysis
Pandemics
Reynolds number
Simulation
Turbulence
Velocity
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cited_by cdi_FETCH-LOGICAL-c408t-d7a86fcbad398269b7f9e8ca1653d9896285eca4c894790537eb085d9073acf23
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container_end_page
container_issue 2
container_start_page 62
container_title Fluids (Basel)
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creator Vinuesa, Ricardo
Lehmkuhl, Oriol
Lozano-Durán, Adrian
Rabault, Jean
description 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.
doi_str_mv 10.3390/fluids7020062
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subjects Active control
Aerodynamics
Aircraft
Aviation
Control methods
Coronaviruses
COVID-19
Deep learning
Deep reinforcement learning
Design optimization
Efficiency
Emissions
Energy
Flow control
Friction
Machine learning
Numerical analysis
Pandemics
Reynolds number
Simulation
Turbulence
Velocity
title Flow Control in Wings and Discovery of Novel Approaches via Deep Reinforcement Learning
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