IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1500-d1056090.html
   My bibliography  Save this article

Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review

Author

Listed:
  • Bin Yang

    (School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China)

  • Xin Zhu

    (School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China)

  • Boan Wei

    (School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China)

  • Minzhang Liu

    (School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China)

  • Yifan Li

    (School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin 300384, China)

  • Zhihan Lv

    (College of Art, Uppsala University, s-75105 Uppsala, Sweden)

  • Faming Wang

    (Department of Biosystems, Katholieke Universiteit Leuven, BE-3001 Leuven, Belgium)

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.

Suggested Citation

  • Bin Yang & Xin Zhu & Boan Wei & Minzhang Liu & Yifan Li & Zhihan Lv & Faming Wang, 2023. "Computer Vision and Machine Learning Methods for Heat Transfer and Fluid Flow in Complex Structural Microchannels: A Review," Energies, MDPI, vol. 16(3), pages 1-24, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1500-:d:1056090
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1500/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/3/1500/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rafiul K. Rasel & Shah M. Chowdhury & Qussai M. Marashdeh & Fernando L. Teixeira, 2022. "Review of Selected Advances in Electrical Capacitance Volume Tomography for Multiphase Flow Monitoring," Energies, MDPI, vol. 15(14), pages 1-22, July.
    2. Boštjan Zajec & Leon Cizelj & Boštjan Končar, 2022. "Experimental Analysis of Flow Boiling in Horizontal Annulus—The Effect of Heat Flux on Bubble Size Distributions," Energies, MDPI, vol. 15(6), pages 1-12, March.
    3. Jose Loyola-Fuentes & Luca Pietrasanta & Marco Marengo & Francesco Coletti, 2022. "Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes," Energies, MDPI, vol. 15(6), pages 1-20, March.
    4. Mahsa Dehghan Manshadi & Majid Ghassemi & Seyed Milad Mousavi & Amir H. Mosavi & Levente Kovacs, 2021. "Predicting the Parameters of Vortex Bladeless Wind Turbine Using Deep Learning Method of Long Short-Term Memory," Energies, MDPI, vol. 14(16), pages 1-17, August.
    5. Fei, Yu & Xiao, Qingtai & Xu, Jianxin & Pan, Jianxin & Wang, Shibo & Wang, Hua & Huang, Junwei, 2015. "A novel approach for measuring bubbles uniformity and mixing efficiency in a direct contact heat exchanger," Energy, Elsevier, vol. 93(P2), pages 2313-2320.
    6. Dongkwon Han & Sunil Kwon, 2021. "Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs," Energies, MDPI, vol. 14(12), pages 1-24, June.
    7. Donghui Zhang & Haiyang Xu & Yi Chen & Leiqing Wang & Jian Qu & Mingfa Wu & Zhiping Zhou, 2020. "Boiling Heat Transfer Performance of Parallel Porous Microchannels," Energies, MDPI, vol. 13(11), pages 1-17, June.
    8. Daehoon Kang & Jooyoung Lee & Anirban Chakraborty & Sang-Eui Lee & Gildong Kim & Choongho Yu, 2022. "Recent Advances in Two-Phase Immersion Cooling with Surface Modifications for Thermal Management," Energies, MDPI, vol. 15(3), pages 1-16, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peng, Weike & Gao, Jiaxin & Chen, Yuntian & Wang, Shengwei, 2024. "Bridging data barriers among participants: Assessing the potential of geoenergy through federated learning," Applied Energy, Elsevier, vol. 367(C).
    2. Jun Yang & Biao Li & Hui Sun & Jianxin Xu & Hua Wang, 2023. "Experimental Measurement and Theoretical Prediction of Bubble Growth and Convection Heat Transfer Coefficient in Direct Contact Heat Transfer," Energies, MDPI, vol. 16(3), pages 1-19, January.
    3. Mahsa Dehghan Manshadi & Milad Mousavi & M. Soltani & Amir Mosavi & Levente Kovacs, 2022. "Deep Learning for Modeling an Offshore Hybrid Wind–Wave Energy System," Energies, MDPI, vol. 15(24), pages 1-16, December.
    4. Ali Javaid & Umer Javaid & Muhammad Sajid & Muhammad Rashid & Emad Uddin & Yasar Ayaz & Adeel Waqas, 2022. "Forecasting Hydrogen Production from Wind Energy in a Suburban Environment Using Machine Learning," Energies, MDPI, vol. 15(23), pages 1-13, November.
    5. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
    6. Ali Akbar Firoozi & Farzad Hejazi & Ali Asghar Firoozi, 2024. "Advancing Wind Energy Efficiency: A Systematic Review of Aerodynamic Optimization in Wind Turbine Blade Design," Energies, MDPI, vol. 17(12), pages 1-30, June.
    7. Han, Sun & Zhenghao, Meng & Meilin, Li & Xiaohui, Yang & Xiaoxue, Wang, 2023. "Global supply sustainability assessment of critical metals for clean energy technology," Resources Policy, Elsevier, vol. 85(PB).
    8. Liaofei Yin & Zhonglin Yang & Kexin Zhang & Yingli Xue & Chao Dang, 2023. "Heat Transfer of Water Flow Boiling in Nanostructured Open Microchannels," Energies, MDPI, vol. 16(3), pages 1-11, January.
    9. Paweł Madejski & Tomasz Kuś & Piotr Michalak & Michał Karch & Navaneethan Subramanian, 2022. "Direct Contact Condensers: A Comprehensive Review of Experimental and Numerical Investigations on Direct-Contact Condensation," Energies, MDPI, vol. 15(24), pages 1-31, December.
    10. Enas Taha Sayed & Abdul Ghani Olabi & Abdul Hai Alami & Ali Radwan & Ayman Mdallal & Ahmed Rezk & Mohammad Ali Abdelkareem, 2023. "Renewable Energy and Energy Storage Systems," Energies, MDPI, vol. 16(3), pages 1-26, February.
    11. Lixia Kang & Wei Guo & Xiaowei Zhang & Yuyang Liu & Zhaoyuan Shao, 2022. "Differentiation and Prediction of Shale Gas Production in Horizontal Wells: A Case Study of the Weiyuan Shale Gas Field, China," Energies, MDPI, vol. 15(17), pages 1-13, August.
    12. Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1500-:d:1056090. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.