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

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.

Citation

Wang, Z., Zhao, X., Han, Z., Luo, L., Xiang, J., Zheng, S., …Hu, M. (2021). Advanced big-data/machine-learning techniques for optimization and performance enhancement of the heat pipe technology – A review and prospective study. Applied energy, 294, Article 116969. https://doi.org/10.1016/j.apenergy.2021.116969

• Journal Article Type: Article
• Acceptance Date: Apr 16, 2021
• Online Publication Date: Apr 28, 2021
• Publication Date: Jul 15, 2021
• Deposit Date: May 19, 2021
• Publicly Available Date: Apr 29, 2022
• Journal: Applied Energy
• Print ISSN: 0306-2619
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 294
• Article Number: 116969
• DOI: https://doi.org/10.1016/j.apenergy.2021.116969
• Keywords: Heat pipe; Big data; Machine learning; Optimization; Prediction; Algorithm
• Public URL: https://hull-repository.worktribe.com/output/3772165