Deep Reinforcement Learning: A New Beacon for Intelligent Active Flow Control
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Published:2023-02-16
Issue:
Volume:1
Page:
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ISSN:2813-6209
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Container-title:Aerospace Research Communications
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language:
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Short-container-title:Aerosp. Res. Commun.
Author:
Xie Fangfang,Zheng Changdong,Ji Tingwei,Zhang Xinshuai,Bi Ran,Zhou Hongjie,Zheng Yao
Abstract
The ability to manipulate fluids has always been one of the focuses of scientific research and engineering application. The rapid development of machine learning technology provides a new perspective and method for active flow control. This review presents recent progress in combining reinforcement learning with high-dimensional, non-linear, and time-delay physical information. Compared with model-based closed-loop control methods, deep reinforcement learning (DRL) avoids modeling the complex flow system and effectively provides an intelligent end-to-end policy exploration paradigm. At the same time, there is no denying that obstacles still exist on the way to practical application. We have listed some challenges and corresponding advanced solutions. This review is expected to offer a deeper insight into the current state of DRL-based active flow control within fluid mechanics and inspires more non-traditional thinking for engineering.
Funder
Natural Science Foundation of Zhejiang Province
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Frontiers Media SA
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