Artificial intelligence and numerical study of the heat transfer and entropy generation analysis of NEPCM-MWCNTs-Water Hybrid Nanofluids inside a quadrilateral enclosure

Bouzidi, M; Alimi, F; Alasmari, A; Islam, MS; Talebizadehsardari, P; Shafi, J; Ghalambaz, M

Artificial intelligence and numerical study of the heat transfer and entropy generation analysis of NEPCM-MWCNTs-Water Hybrid Nanofluids inside a quadrilateral enclosure

Bouzidi, M Alimi, F Alasmari, A Islam, MS Talebizadehsardari, P Shafi, J Ghalambaz, M

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Case Studies in Thermal Engineering, 2024, 63, pp. 105258
Issue Date:: 2024-11-01

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Field	Value	Language
dc.contributor.author	Bouzidi, M
dc.contributor.author	Alimi, F
dc.contributor.author	Alasmari, A
dc.contributor.author	Islam, MS
dc.contributor.author	Talebizadehsardari, P
dc.contributor.author	Shafi, J
dc.contributor.author	Ghalambaz, M
dc.date.accessioned	2025-02-04T21:59:02Z
dc.date.available	2025-02-04T21:59:02Z
dc.date.issued	2024-11-01
dc.identifier.citation	Case Studies in Thermal Engineering, 2024, 63, pp. 105258
dc.identifier.issn	2214-157X
dc.identifier.uri	http://hdl.handle.net/10453/184950
dc.description.abstract	In the present investigation, the primary objective is to assess the synergistic impact of a novel hybrid nanofluid composed of nano-enhanced phase change material, multi-walled carbon nanotubes, and water. The governing equations are transformed into dimensionless forms for a more generalized analysis. To solve the problem, the Galerkin finite element method is employed, offering a robust numerical approach. A dataset of 1000 records was created by numerically solving the model for various combinations of control parameters. Using the dataset, a neural network was trained to learn the relationship between control parameters and heat transfer rate. The evaluation outcomes are comprehensively illustrated through key parameters, including local and average Nusselt numbers, total entropy generation, contours, and streamlines in the range of 103<Rayleigh number<105, 0<nanotube concentration<0.025, 0<nano capsule concentration <0.025, and 0.1<non-dimensional fusion temperature <0.7. Remarkably, the results indicate a substantial improvement in the heat transfer rate of the suspension for a hybrid concentration of 5 %, showcasing an impressive 13 % enhancement in contrast to the water host fluid's performance. Notably, the observed rise in entropy generation is relatively moderate, only a 5 % increase.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Case Studies in Thermal Engineering
dc.relation.isbasedon	10.1016/j.csite.2024.105258
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	4012 Fluid mechanics and thermal engineering
dc.title	Artificial intelligence and numerical study of the heat transfer and entropy generation analysis of NEPCM-MWCNTs-Water Hybrid Nanofluids inside a quadrilateral enclosure
dc.type	Journal Article
utslib.citation.volume	63
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-02-04T21:58:53Z
pubs.publication-status	Published
pubs.volume	63

Abstract:

In the present investigation, the primary objective is to assess the synergistic impact of a novel hybrid nanofluid composed of nano-enhanced phase change material, multi-walled carbon nanotubes, and water. The governing equations are transformed into dimensionless forms for a more generalized analysis. To solve the problem, the Galerkin finite element method is employed, offering a robust numerical approach. A dataset of 1000 records was created by numerically solving the model for various combinations of control parameters. Using the dataset, a neural network was trained to learn the relationship between control parameters and heat transfer rate. The evaluation outcomes are comprehensively illustrated through key parameters, including local and average Nusselt numbers, total entropy generation, contours, and streamlines in the range of 103

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http://hdl.handle.net/10453/184950