Wavelet Transforms and Machine Learning Methods for the Study of Turbulence

Author:

Alam Jahrul M1ORCID

Affiliation:

1. Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada

Abstract

This article investigates the applications of wavelet transforms and machine learning methods in studying turbulent flows. The wavelet-based hierarchical eddy-capturing framework is built upon first principle physical models. Specifically, the coherent vortex simulation method is based on the Taylor hypothesis, which suggests that the energy cascade occurs through vortex stretching. In contrast, the adaptive wavelet collocation method relies on the Richardson hypothesis, where the self-amplification of the strain field and a hierarchical breakdown of large eddies drive the energy cascade. Wavelet transforms are computational learning architectures that propagate the input data across a sequence of linear operators to learn the underlying nonlinearity and coherent structure. Machine learning offers a wealth of data-driven algorithms that can heavily use statistical concepts to extract valuable insights into turbulent flows. Supervised machine learning needs “perfect” turbulent flow data to train data-driven turbulence models. The current advancement of artificial intelligence in turbulence modeling primarily focuses on accelerating turbulent flow simulations by learning the underlying coherence over a low-dimensional manifold. Physics-informed neural networks offer a fertile ground for augmenting first principle physics to automate specific learning tasks, e.g., via wavelet transforms. Besides machine learning, there is room for developing a common computational framework to provide a rich cross-fertilization between learning the data coherence and the first principles of multiscale physics.

Funder

NSERC

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Mechanical Engineering,Condensed Matter Physics

Reference109 articles.

1. The Local Structure of Turbulence in the Incompressible Viscous Fluid for very Large Reynolds Number;Kolmogorov;C. R. Acad. Sci. U.S.S.R.,1941

2. Lumley, J.L. (1967, January 15–22). The structure of inhomogeneous turbulent flows. Atmospheric Turbulence and Radio Wave Propagation. Proceedings of the International Colloquium, Moscow, Russia.

3. Wavelet transforms and their application to turbulence;Farge;Annu. Rev. Fluid Mech.,1992

4. Davidson, P.A. (2004). Turbulence—An Introduction for Scientists and Engineers, Oxford University Press.

5. Enhancing computational fluid dynamics with machine learning;Vinuesa;Nat. Comput. Sci.,2022

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3