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Comparative analysis of machine learning methods for active flow control

Published online by Cambridge University Press:  10 March 2023

Fabio Pino Open the ORCID record for Fabio Pino [Opens in a new window] ,
Lorenzo Schena Open the ORCID record for Lorenzo Schena [Opens in a new window] ,
Jean Rabault Open the ORCID record for Jean Rabault [Opens in a new window]  and
Miguel A. Mendez Open the ORCID record for Miguel A. Mendez [Opens in a new window]
Fabio Pino*
Affiliation:
EA Department, von Kármán Institute for Fluid Dynamics, 1640 Sint Genesius Rode, Belgium Transfers, Interfaces and Processes (TIPs), Université libre de Bruxelles, 1050 Brussels, Belgium
Lorenzo Schena
Affiliation:
EA Department, von Kármán Institute for Fluid Dynamics, 1640 Sint Genesius Rode, Belgium Department of Mechanical Engineering, Vrije Universiteit Brussels, 1050 Brussels, Belgium
Jean Rabault
Affiliation:
Norwegian Meteorological Institute, 0313 Oslo, Norway
Miguel A. Mendez
Affiliation:
EA Department, von Kármán Institute for Fluid Dynamics, 1640 Sint Genesius Rode, Belgium
*
†Email address for correspondence: fabio.pino@vki.ac.be
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Abstract

Machine learning frameworks such as genetic programming and reinforcement learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, benchmarking some of their most representative algorithms against global optimization techniques such as Bayesian optimization and Lipschitz global optimization. First, we review the general framework of the model-free control problem, bringing together all methods as black-box optimization problems. Then, we test the control algorithms on three test cases. These are (1) the stabilization of a nonlinear dynamical system featuring frequency cross-talk, (2) the wave cancellation from a Burgers’ flow and (3) the drag reduction in a cylinder wake flow. We present a comprehensive comparison to illustrate their differences in exploration versus exploitation and their balance between ‘model capacity’ in the control law definition versus ‘required complexity’. Indeed, we discovered that previous RL control attempts of controlling the cylinder wake were performing linear control and that the wide observation space was limiting their performances. We believe that such a comparison paves the way towards the hybridization of the various methods, and we offer some perspective on their future development in the literature of flow control problems.

JFM classification

Flow Control: Flow Control

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JFM Papers
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Journal of Fluid Mechanics , Volume 958 , 10 March 2023 , A39
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© The Author(s), 2023. Published by Cambridge University Press

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References

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