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., Liu, G., Yu, M., Cui, Y., Shittu, 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