LAPSE:2023.3752
Published Article
LAPSE:2023.3752
Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review
Bin Yang, Xin Zhu, Boan Wei, Minzhang Liu, Yifan Li, Zhihan Lv, Faming Wang
February 22, 2023
Abstract
Heat dissipation in high-heat flux micro-devices has become a pressing issue. One of the most effective methods for removing the high heat load of micro-devices is boiling heat transfer in microchannels. A novel approach to flow pattern and heat transfer recognition in microchannels is provided by the combination of image and machine learning techniques. The support vector machine method in texture characteristics successfully recognizes flow patterns. To determine the bubble dynamics behavior and flow pattern in the micro-device, image features are combined with machine learning algorithms and applied in the recognition of boiling flow patterns. As a result, the relationship between flow pattern evolution and boiling heat transfer is established, and the mechanism of boiling heat transfer is revealed.
Keywords
complex structural microchannels, computer vision, fluid flow, heat transfer, Machine Learning
Suggested Citation
Yang B, Zhu X, Wei B, Liu M, Li Y, Lv Z, Wang F. Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review. (2023). LAPSE:2023.3752
Author Affiliations
Yang B: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China [ORCID]
Zhu X: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China [ORCID]
Wei B: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
Liu M: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
Li Y: School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China
Lv Z: College of Art, Uppsala University, s-75105 Uppsala, Sweden [ORCID]
Wang F: Department of Biosystems, Katholieke Universiteit Leuven, BE-3001 Leuven, Belgium [ORCID]
Journal Name
Energies
Volume
16
Issue
3
First Page
1500
Year
2023
Publication Date
2023-02-02
ISSN
1996-1073
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Original Submission
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PII: en16031500, Publication Type: Review
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LAPSE:2023.3752
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https://doi.org/10.3390/en16031500
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