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Learn to flap: foil non-parametric path planning via deep reinforcement learning

Published online by Cambridge University Press:  27 March 2024

Z.P. Wang Open the ORCID record for Z.P. Wang [Opens in a new window] ,
R.J. Lin ,
Z.Y. Zhao ,
X. Chen ,
P.M. Guo Open the ORCID record for P.M. Guo [Opens in a new window] ,
N. Yang ,
Z.C. Wang  and
D.X. Fan Open the ORCID record for D.X. Fan [Opens in a new window]
Z.P. Wang
Affiliation:
School of Engineering, Westlake University, Hangzhou, Zhejiang 310024, PR China School of Mechanical and Material Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
R.J. Lin
Affiliation:
University of Chinese Academy of Sciences, Beijing 100049, PR China Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China
Z.Y. Zhao
Affiliation:
University of Chinese Academy of Sciences, Beijing 100049, PR China Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China
X. Chen
Affiliation:
Taihu Laboratory of Deepsea Technological Science, Wuxi, Jiangsu 214000, PR China
P.M. Guo*
Affiliation:
School of Engineering, Westlake University, Hangzhou, Zhejiang 310024, PR China School of Mechanical and Material Engineering, Queen's University, Kingston, ON K7L 3N6, Canada
N. Yang*
Affiliation:
University of Chinese Academy of Sciences, Beijing 100049, PR China Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China
Z.C. 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
D.X. Fan*
Affiliation:
School of Engineering, Westlake University, Hangzhou, Zhejiang 310024, PR China
*
†Email addresses for correspondence: guopengming@westlake.edu.cn, ning.yang@ia.ac.cn, fandixia@westlake.edu.cn
†Email addresses for correspondence: guopengming@westlake.edu.cn, ning.yang@ia.ac.cn, fandixia@westlake.edu.cn
†Email addresses for correspondence: guopengming@westlake.edu.cn, ning.yang@ia.ac.cn, fandixia@westlake.edu.cn
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Abstract

To optimize flapping foil performance, in the current study we apply deep reinforcement learning (DRL) to plan foil non-parametric motion, as the traditional control techniques and simplified motions cannot fully model nonlinear, unsteady and high-dimensional foil–vortex interactions. Therefore, a DRL training framework is proposed based on the proximal policy optimization algorithm and the transformer architecture, where the policy is initialized from the sinusoidal expert display. We first demonstrate the effectiveness of the proposed DRL-training framework, learning the coherent foil flapping motion to generate thrust. Furthermore, by adjusting reward functions and action thresholds, DRL-optimized foil trajectories can gain significant enhancement in both thrust and efficiency compared with the sinusoidal motion. Last, through visualization of wake morphology and instantaneous pressure distributions, it is found that DRL-optimized foil can adaptively adjust the phases between motion and shedding vortices to improve hydrodynamic performance. Our results give a hint of how to solve complex fluid manipulation problems using the DRL method.

JFM classification

Vortex Flows: Vortex interactions Flow Control: Control theory

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JFM Papers
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Journal of Fluid Mechanics , Volume 984 , 10 April 2024 , A9
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© The Author(s), 2024. Published by Cambridge University Press

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Footnotes

‡

These authors contributed equally to this work.

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