Open Access

ARTICLE

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

by Mamdouh El Haj Assad¹, Ibrahim Mahariq², Raymond Ghandour², Mohammad Alhuyi Nazari³, Thabet Abdeljawad^4,5,6,*

1 Sustainable and Renewable Energy Engineering Department, University of Sharjah, P. O. Box 27272, Sharjah, UAE
2 College of Engineering and Technology, American University of the Middle East, Kuwait
3 Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
4 Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia
5 Department of Medical Research, China Medical University, Taichung, 40402, Taiwan
6 Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

* Corresponding Author: Thabet Abdeljawad. Email:

(This article belongs to the Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)

Computers, Materials & Continua 2022, 70(1), 361-374. https://doi.org/10.32604/cmc.2022.019048

Received 31 March 2021; Accepted 01 May 2021; Issue published 07 September 2021

Abstract

Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their forecasting and modeling are proposed. According to the reviewed works, concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the models. Moreover, by using other effective factors, the accuracy and comprehensive of the models can be modified. Finally, some suggestions are offered for the upcoming works in the relevant topics.

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