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Advanced big-data/machine-learning techniques for optimization and performance enhancement of the heat pipe technology – A review and prospective study

Author

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  • Wang, Zhangyuan
  • Zhao, Xudong
  • Han, Zhonghe
  • Luo, Liang
  • Xiang, Jinwei
  • Zheng, Senglin
  • Liu, Guangming
  • Yu, Min
  • Cui, Yu
  • Shittu, Samson
  • Hu, Menglong

Abstract

A heat pipe (HP) is a passive heat transfer device able to transmit heat a few meters or several hundred meters away from the heat source without use of external energy. This paper presents a critical review of the HP technologies. It is found that the heat transfer performance of a HP is highly dependent upon its geometrical and operational conditions, whilst the existing computerized analytical and numerical models for the HP require a huge number of parametrical data inputs, and therefore is extremely time-consuming and impractical. Furthermore, the measurement results of the HPs vary time by time and show certain disagreement with the simulation prediction, giving a high uncertainty in characterisation of the HP. Development of a machine learning algorithm and associated models based on the structured HP database is a solution to tackle these challenges, which is able to provide the dimensionless and multiple-factors-considering solution for HP structural optimization and performance prediction. A review on big-date/machine-learning technology for HP application was undertaken, indicating that a database covering the HP parametrical data, operational variables and associated performance results has not yet been established. Challenges for the HP structural optimization and performance prediction using the big-data-trained machine learning technology lie in: (1) complex and unregulated HP data; (2) unidentified analytic algorithm for HP structural optimization; and (3) unidentified data-driven algorithm for HP performance prediction. This review-based study provides the potential future research directions for development of the big-data-trained machine learning technology for HP structural optimization and performance prediction.

Suggested Citation

  • Wang, Zhangyuan & Zhao, Xudong & Han, Zhonghe & Luo, Liang & Xiang, Jinwei & Zheng, Senglin & Liu, Guangming & Yu, Min & Cui, Yu & Shittu, Samson & Hu, Menglong, 2021. "Advanced big-data/machine-learning techniques for optimization and performance enhancement of the heat pipe technology – A review and prospective study," Applied Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:appene:v:294:y:2021:i:c:s030626192100444x
    DOI: 10.1016/j.apenergy.2021.116969
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    2. Chen, Hao & Zhang, Chao & Yu, Haizeng & Wang, Zhilin & Duncan, Ian & Zhou, Xianmin & Liu, Xiliang & Wang, Yu & Yang, Shenglai, 2022. "Application of machine learning to evaluating and remediating models for energy and environmental engineering," Applied Energy, Elsevier, vol. 320(C).
    3. Wang, Xianling & Yang, Jingxuan & Wen, Qiaowei & Shittu, Samson & Liu, Guangming & Qiu, Zining & Zhao, Xudong & Wang, Zhangyuan, 2022. "Visualization study of a flat confined loop heat pipe for electronic devices cooling," Applied Energy, Elsevier, vol. 322(C).
    4. Liang, Lin & Zhao, Yaohua & Diao, Yanhua & Ren, Ruyang & Zhu, Tingting & Li, Yan, 2023. "Experimental investigation of preheating performance of lithium-ion battery modules in electric vehicles enhanced by bending flat micro heat pipe array," Applied Energy, Elsevier, vol. 337(C).
    5. Anish Nair & Ramkumar P. & Sivasubramanian Mahadevan & Chander Prakash & Saurav Dixit & Gunasekaran Murali & Nikolai Ivanovich Vatin & Kirill Epifantsev & Kaushal Kumar, 2022. "Machine Learning for Prediction of Heat Pipe Effectiveness," Energies, MDPI, vol. 15(9), pages 1-14, April.
    6. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).

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