机器学习在流动控制领域的应用及发展趋势

doi:10.7527/S1000-6893.2020.24686

Abstract

Abstract: As a multidisciplinary field in fluid mechanics, flow control has played a key role in both scientific research and engineering applications. Due to complicated features of flow systems such as strong nonlinearity, flow control, especially closed-loop control, has been a challenging issue in the past decades. Recently, the rapid developing machine learning has brought new methods, new perspectives, and new views to diverse fields, and also to flow control. This article reviews three distinct ideas that involve machine learning into flow control, so as to demonstrate an overall view of machine learning in flow control, and furthermore, to outline some trends for this field.

Key words: flow control, machine learning, reduced order modeling, genetic programming, deep reinforcement learning

CLC Number:

V211.3

REN Feng, GAO Chuanqiang, TANG Hui. Machine learning for flow control: Applications and development trends[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524686-524686.

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Machine learning for flow control: Applications and development trends

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