LAPSE:2023.0819
Published Article
LAPSE:2023.0819
Machine Learning-Based Approach for Modeling the Nanofluid Flow in a Solar Thermal Panel in the Presence of Phase Change Materials
February 21, 2023
Abstract
Considering the importance of environmental protection and renewable energy resources, particularly solar energy, the present study investigates the temperature control of a solar panel using a nanofluid (NFD) flow with eco-friendly nanoparticles (NPs) and a phase change material (PCM). The PCM was used under the solar panel, and the NFD flowed through pipes within the PCM. A number of straight fins (three fins) were exploited on the pipes, and the output flow temperature, heat transfer (HTR) coefficient, and melted PCM volume fraction were measured for different pipe diameters (D_Pipe) from 4 mm to 8 mm at various time points (from 0 to 100 min). Additionally, with the use of artificial intelligence and machine learning, the best conditions for obtaining the lowest panel temperature and the highest output NFD temperature at the lowest pressure drop have been determined. While the porosity approach was used to model the PCM melt front, a two-phase mixture was used to simulate NFD flow. It was discovered that the solar panel temperature and output temperature both increased considerably between t = 0 and t = 10 min before beginning to rise at varying rates, depending on the D_Pipe. The HTR coefficient increased over time, showing similar behavior to the panel temperature. The entire PCM melted within a short time for D_Pipes of 4 and 6 mm, while a large fraction of the PCM remained un-melted for a long time for a D_Pipe of 8 mm. An increase in D_Pipe, particularly from 4 to 6 mm, reduced the maximum and average panel temperatures, leading to a lower output flow temperature. Furthermore, the increased D_Pipe reduced the HTR coefficient, with the PCM remaining un-melted for a longer time under the panel.
Keywords
collector, eco-friendly nanoparticles, Machine Learning, PCM, solar energy
Subject
Suggested Citation
Alqaed S, Mustafa J, Almehmadi FA, Alharthi MA, Sharifpur M, Cheraghian G. Machine Learning-Based Approach for Modeling the Nanofluid Flow in a Solar Thermal Panel in the Presence of Phase Change Materials. (2023). LAPSE:2023.0819
Author Affiliations
Alqaed S: Mechanical Engineering Department, College of Engineering, Najran University, P.O. Box 1988, Najran 61441, Saudi Arabia [ORCID]
Mustafa J: Mechanical Engineering Department, College of Engineering, Najran University, P.O. Box 1988, Najran 61441, Saudi Arabia [ORCID]
Almehmadi FA: Department of Applied Mechanical Engineering, College of Applied Engineering, Muzahimiyah Branch, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia [ORCID]
Alharthi MA: Department of Chemical Engineering, College of Engineering at Yanbu, Taibah University, P.O. Box 4050, Yanbu Al-Bahr 41911, Saudi Arabia [ORCID]
Sharifpur M: Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria 0002, South Africa; Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404, Taiwan [ORCID]
Cheraghian G: Institut für Chemie and IRIS Adlershof, Humboldt-Universität zu Berlin, 12489 Berlin, Germany; Department of Chemistry, King’s College London, London WC2R 2LS, UK [ORCID]
Journal Name
Processes
Volume
10
Issue
11
First Page
2291
Year
2022
Publication Date
2022-11-04
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr10112291, Publication Type: Journal Article
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LAPSE:2023.0819
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https://doi.org/10.3390/pr10112291
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