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机器学习在流动控制领域的应用及发展趋势

发布日期: 2021-06-10 | 浏览次数: 692
机器学习在流动控制领域的应用及发展趋势

任峰1, 高传强1,2, 唐辉1,3                          

1. 香港理工大学 机械工程学系 流固耦合研究中心, 香港;
2. 西北工业大学 航空学院, 西安 710072;
3. 香港理工大学 深圳研究院, 深圳 518057


Machine learning for flow control: Applications and development trends

REN Feng1, GAO Chuanqiang1,2, TANG Hui1,3        

1. Research Center for Fluid-Structure Interactions, Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China;
2. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
3. Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518057, China

                        

摘要: 流动控制作为流体力学中的重要跨学科领域,一直是科学研究和工程应用关注的焦点之一。由于流动系统具有强非线性等复杂特征,对流动的控制尤其是闭环控制,一直颇富挑战性。近年来机器学习的迅速发展为许多学科带来了新的方法、新的视角和新的观点,对于流动控制领域亦是如此。通过回顾现阶段三类基于机器学习的流动控制方法,为主动流动控制领域的研究者展示机器学习在流动控制中应用的整体概况,进而勾勒出本领域的发展趋势。

关键词: 流动控制, 机器学习, 降阶模型, 遗传规划, 深度强化学习

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


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