Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Series Vol. 4 , 30 May 2023


Open Access | Article

Review on the coupling and promotion of machine learning methods to CFD

Haocheng Zheng * 1
1 Hohai university, Nanjing, Jiangsu Province, China

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 4, 52-57
Published 30 May 2023. © 30 May 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Haocheng Zheng. Review on the coupling and promotion of machine learning methods to CFD. ACE (2023) Vol. 4: 52-57. DOI: 10.54254/2755-2721/4/20230345.

Abstract

As the computer level advances in 1990s, CFD (the computational fluid dynamics, for short) has promoted fast. The process of using CFD to analyze complex or ideal conditions has a very wide application background. However, confronted by cases with higher accuracy as well as larger number of samples, CFD needs to spend much time and money for the sake of resolving the issues. ML (Machine learning, for short) approach provides a promising choice for CFD. This paper reviews the coupling of ML and CFD and the progress in promoting the application of CFD. This paper briefly introduces CFD along with ML approaches, such as supervised learning, unsupervised learning and reinforcement learning. This article also discusses challenges and issues with the aim of being resolved in the research of ML model based on CFD, such as using multiple machine learning models or hybrid models to solve problems and quantifying the uncertainty of machine learning models. If these problems are solved, ML method can provide a promising development prospect for CFD.

Keywords

Machine Learning, CFD, Promotion

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-915371-55-3
ISBN (Online)
978-1-915371-56-0
Published Date
30 May 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/4/20230345
Copyright
30 May 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated