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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

Listed:
  • 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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    as
    1. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).
    2. Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
    3. Ghasemaghaei, Maryam & Calic, Goran, 2019. "Does big data enhance firm innovation competency? The mediating role of data-driven insights," Journal of Business Research, Elsevier, vol. 104(C), pages 69-84.
    4. Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
    5. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    6. He, Wei & Hong, Xiaoqiang & Zhao, Xudong & Zhang, Xingxing & Shen, Jinchun & Ji, Jie, 2015. "Operational performance of a novel heat pump assisted solar façade loop-heat-pipe water heating system," Applied Energy, Elsevier, vol. 146(C), pages 371-382.
    7. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    8. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    9. Yaqoob, Ibrar & Hashem, Ibrahim Abaker Targio & Gani, Abdullah & Mokhtar, Salimah & Ahmed, Ejaz & Anuar, Nor Badrul & Vasilakos, Athanasios V., 2016. "Big data: From beginning to future," International Journal of Information Management, Elsevier, vol. 36(6), pages 1231-1247.
    10. Singh, Randeep & Mochizuki, Masataka & Mashiko, Koichi & Nguyen, Thang, 2011. "Heat pipe based cold energy storage systems for datacenter energy conservation," Energy, Elsevier, vol. 36(5), pages 2802-2811.
    11. Shafieian, Abdellah & Khiadani, Mehdi & Nosrati, Ataollah, 2018. "A review of latest developments, progress, and applications of heat pipe solar collectors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 273-304.
    12. Liu, Yuting & Yang, Xu & Li, Junming & Zhao, Xudong, 2018. "Energy savings of hybrid dew-point evaporative cooler and micro-channel separated heat pipe cooling systems for computer data centers," Energy, Elsevier, vol. 163(C), pages 629-640.
    Full references (including those not matched with items on IDEAS)

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    2. 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.
    3. 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).
    4. 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).
    5. 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).
    6. 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).

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