Recent progress of machine learning in flow modeling and active flow control

TLDR

Large, multi‑scale experimental and simulation datasets have accelerated fluid‑mechanics research, and machine learning provides powerful analysis tools that extract insights, enhance flow information, and enable automated active flow control and optimization, enriching both research and industry. This article provides an overview of the past history, current development, and promising prospects of machine learning in the field of fluid mechanics. The article outlines the basic principles of machine learning methods and their applications to engineering practice, turbulence modeling, flow‑field representation, and active flow control.

Abstract

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.