典型文献
Recent progress of machine learning in flow modeling and active flow control
文献摘要：
In terms of multiple temporal and spatial scales,massive data from experiments,flow field measurements,and high-fidelity numerical simulations have greatly promoted the rapid development of fluid mechanics.Machine Learning(ML)provides a wealth of analysis methods to extract potential information from a large amount of data for in-depth understanding of the underlying flow mechanism or for further applications.Furthermore,machine learning algorithms can enhance flow information and automatically perform tasks that involve active flow control and optimization.This article provides an overview of the past history,current development,and promising prospects of machine learning in the field of fluid mechanics.In addition,to facilitate understanding,this article outlines the basic principles of machine learning methods and their applications in engineering practice,turbulence models,flow field representation problems,and active flow control.In short,machine learning provides a powerful and more intelligent data processing architecture,and may greatly enrich the existing research methods and industrial applications of fluid mechanics.
文献关键词：
中图分类号：
作者姓名：
Yunfei Li;Juntao Chang;Chen Kong;Wen Bao
作者机构：
School of Energy Science and Engineering,Harbin Institute of Technology,Harbin 150001,China
文献出处：
引用格式：
[1]Yunfei Li;Juntao Chang;Chen Kong;Wen Bao-.Recent progress of machine learning in flow modeling and active flow control)[J].中国航空学报（英文版）,2022(04):14-44
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0.625555
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