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Deep reinforcement transfer learning of active control for bluff body flows at high Reynolds number

Published online by Cambridge University Press:  20 October 2023

Zhicheng Wang Open the ORCID record for Zhicheng Wang [Opens in a new window] ,
Dixia Fan Open the ORCID record for Dixia Fan [Opens in a new window] ,
Xiaomo Jiang ,
Michael S. Triantafyllou Open the ORCID record for Michael S. Triantafyllou [Opens in a new window]  and
George Em Karniadakis
Zhicheng Wang
Affiliation:
Laboratory of Ocean Energy Utilization of Ministry of Education, Dalian University of Technology, Dalian 116024, PR China School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, PR China
Dixia Fan
Affiliation:
School of Engineering, Westlake University, Hangzhou 310024, PR China
Xiaomo Jiang
Affiliation:
School of Energy and Power Engineering, Dalian University of Technology, Dalian 116024, PR China State Key Lab of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Provincial Key Lab of Digital Twin for Industrial Equipment, Dalian University of Technology, Dalian 116024, PR China
Michael S. Triantafyllou*
Affiliation:
Department of Mechanical Engineering, Massachusetts Institute Technology, Cambridge, MA 02139, USA MIT Sea Grant College Program, Cambridge, MA 02139, USA
George Em Karniadakis
Affiliation:
Division of Applied Mathematics and School of Engineering, Brown University, Providence, RI 02912, USA
*
†Email address for correspondence: [email protected]
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Abstract

We demonstrate how to accelerate the computationally taxing process of deep reinforcement learning (DRL) in numerical simulations for active control of bluff body flows at high Reynolds number ($Re$) using transfer learning. We consider the canonical flow past a circular cylinder whose wake is controlled by two small rotating cylinders. We first pre-train the DRL agent using data from inexpensive simulations at low $Re$, and subsequently we train the agent with small data from the simulation at high $Re$ (up to $Re=1.4\times 10^5$). We apply transfer learning (TL) to three different tasks, the results of which show that TL can greatly reduce the training episodes, while the control method selected by TL is more stable compared with training DRL from scratch. We analyse for the first time the wake flow at $Re=1.4\times 10^5$ in detail and discover that the hydrodynamic forces on the two rotating control cylinders are not symmetric.

JFM classification

Flow Control: Drag reduction Turbulent Flows: Turbulence simulation Mathematical Foundations: Machine learning
Type
JFM Papers
Information
Journal of Fluid Mechanics , Volume 973 , 25 October 2023 , A32
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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Footnotes

‡

Equal contribution.

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