Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches
Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/7/1440 |
| _version_ | 1797588738863267840 |
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| author | Yi Zhang Dapeng Zhang Haoyu Jiang |
| author_facet | Yi Zhang Dapeng Zhang Haoyu Jiang |
| author_sort | Yi Zhang |
| collection | DOAJ |
| description | Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development. |
| first_indexed | 2024-03-11T00:56:18Z |
| format | Article |
| id | doaj.art-fe2313168e054f83929b0924c173847c |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T00:56:18Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-fe2313168e054f83929b0924c173847c2023-11-18T20:00:19ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-07-01117144010.3390/jmse11071440Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning ApproachesYi Zhang0Dapeng Zhang1Haoyu Jiang2Ship and Maritime College, Guangdong Ocean University, Zhanjiang 524088, ChinaShip and Maritime College, Guangdong Ocean University, Zhanjiang 524088, ChinaSchool of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, ChinaEngineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development.https://www.mdpi.com/2077-1312/11/7/1440data-driven machine learningdevelopment processguide for developmentlimitationsturbulence modeling |
| spellingShingle | Yi Zhang Dapeng Zhang Haoyu Jiang Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches Journal of Marine Science and Engineering data-driven machine learning development process guide for development limitations turbulence modeling |
| title | Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches |
| title_full | Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches |
| title_fullStr | Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches |
| title_full_unstemmed | Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches |
| title_short | Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches |
| title_sort | review of challenges and opportunities in turbulence modeling a comparative analysis of data driven machine learning approaches |
| topic | data-driven machine learning development process guide for development limitations turbulence modeling |
| url | https://www.mdpi.com/2077-1312/11/7/1440 |
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