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Reinforcement-learning-based actuator selection method for active flow control

Published online by Cambridge University Press:  12 January 2023

Romain Paris Open the ORCID record for Romain Paris [Opens in a new window] ,
Samir Beneddine Open the ORCID record for Samir Beneddine [Opens in a new window]  and
Julien Dandois Open the ORCID record for Julien Dandois [Opens in a new window]
Romain Paris*
Affiliation:
DAAA, ONERA, Université Paris Saclay, F-92190 Meudon, France
Samir Beneddine
Affiliation:
DAAA, ONERA, Université Paris Saclay, F-92190 Meudon, France
Julien Dandois
Affiliation:
DAAA, ONERA, Université Paris Saclay, F-92190 Meudon, France
*
†Email address for correspondence: romain.paris@onera.fr
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Abstract

This paper addresses the issue of actuator selection for active flow control by proposing a novel method built on top of a reinforcement learning agent. Starting from a pre-trained agent using numerous actuators, the algorithm estimates the impact of a potential actuator removal on the value function, indicating the agent's performance. It is applied to two test cases, the one-dimensional Kuramoto–Sivashinsky equation and a laminar bidimensional flow around an airfoil at $Re=1000$ for different angles of attack ranging from $12^{\circ }$ to $20^{\circ }$, to demonstrate its capabilities and limits. The proposed actuator-sparsification method relies on a sequential elimination of the least relevant action components, starting from a fully developed layout. The relevancy of each component is evaluated using metrics based on the value function. Results show that, while still being limited by this intrinsic elimination paradigm (i.e. the sequential elimination), actuator patterns and obtained policies demonstrate relevant performances and allow us to draw an accurate approximation of the Pareto front of performances versus actuator budget.

JFM classification

Flow Control: Drag reduction Flow Control: Instability control Mathematical Foundations: Machine learning
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 955 , 25 January 2023 , A8
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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References

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