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

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

Applied Energy, 2021, vol. 294, issue C, No S030626192100444X

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.

Keywords: Heat pipe; Big data; Machine learning; Optimization; Prediction; Algorithm (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192100444X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:294:y:2021:i:c:s030626192100444x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2021.116969

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().