Staff View: Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system

Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system

As the world transitions to more renewable and clean energy sources, energy storage systems become crucial. They assist in storing surplus energy produced during peak production times and release it when there is a high energy demand. Phase change material (PCM) based latent heat storage systems are...

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Main Authors:	Maalla, Allam, Dahari, Mahidzal, Chaturvedi, Rishabh, Fouad, Yasser, Abdullaev, Sherzod, Farag, Ahmed, Alkhayyat, Ahmed, Abdo, G.M.
Format:	Article
Published:	Elsevier 2024
Subjects:	TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/45239/ https://doi.org/10.1016/j.est.2024.111256
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spelling	my.um.eprints.452392024-09-19T01:56:27Z http://eprints.um.edu.my/45239/ Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system Maalla, Allam Dahari, Mahidzal Chaturvedi, Rishabh Fouad, Yasser Abdullaev, Sherzod Farag, Ahmed Alkhayyat, Ahmed Abdo, G.M. TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering As the world transitions to more renewable and clean energy sources, energy storage systems become crucial. They assist in storing surplus energy produced during peak production times and release it when there is a high energy demand. Phase change material (PCM) based latent heat storage systems are appropriate candidates for efficient thermal energy storage. Their ability to harness the latent heat of phase transitions provides energy density, temperature control, and reduced heat loss advantages, making them well-suited for various applications. In the current research, a novel plate latent heat storage system (P-LHS) inspired by plate heat exchangers was introduced to enhance heat absorption. Besides, to facilitate the limited thermal conductivity of the PCM and increase natural convection phenomena, several fins were applied within the P-LHS system. Artificial neural network (ANN) models were exerted to anticipate the duration needed for melting of 70 % (phi = 0.7) and 100 % (phi = 1) of the PCM. The input variables of the ANN models were the fin angle (beta), the distance between two fins (D), and the fin's trail angle (alpha). This research examined different configurations of the fins within the P-LHS system to find the optimal one. In all the designed Tests, the heat absorption and temperature distribution within the PCM were accelerated; thereby, the melting time up to phi = 0.7 and phi = 1 was reduced remarkably compared to those in the fin-less P-LHS system. Besides, for the fastest full melting of the material in the P-LHS system, the optimal design 2 (OD2) was proposed, in which the fins should have the dimensions of alpha = 171.450 degrees, beta = 97.540 degrees, and D = 27.545 mm. In OD2, the time needed until the melting of 70 % of the PCM was decreased by approximately 65.68 %. While, the necessary duration for the PCM to melt entirely was 1574 s, approximately 66.80 % less than the time needed for melting the substance in the system without fins (4741 s). This reduction in melting time was about 53 min, which is a significant amount of time in energy storage systems. Elsevier 2024-05 Article PeerReviewed Maalla, Allam and Dahari, Mahidzal and Chaturvedi, Rishabh and Fouad, Yasser and Abdullaev, Sherzod and Farag, Ahmed and Alkhayyat, Ahmed and Abdo, G.M. (2024) Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system. Journal of Energy Storage, 86 (B). p. 111256. ISSN 2352-152X, DOI https://doi.org/10.1016/j.est.2024.111256 <https://doi.org/10.1016/j.est.2024.111256>. https://doi.org/10.1016/j.est.2024.111256 10.1016/j.est.2024.111256
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Maalla, Allam Dahari, Mahidzal Chaturvedi, Rishabh Fouad, Yasser Abdullaev, Sherzod Farag, Ahmed Alkhayyat, Ahmed Abdo, G.M. Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system
description	As the world transitions to more renewable and clean energy sources, energy storage systems become crucial. They assist in storing surplus energy produced during peak production times and release it when there is a high energy demand. Phase change material (PCM) based latent heat storage systems are appropriate candidates for efficient thermal energy storage. Their ability to harness the latent heat of phase transitions provides energy density, temperature control, and reduced heat loss advantages, making them well-suited for various applications. In the current research, a novel plate latent heat storage system (P-LHS) inspired by plate heat exchangers was introduced to enhance heat absorption. Besides, to facilitate the limited thermal conductivity of the PCM and increase natural convection phenomena, several fins were applied within the P-LHS system. Artificial neural network (ANN) models were exerted to anticipate the duration needed for melting of 70 % (phi = 0.7) and 100 % (phi = 1) of the PCM. The input variables of the ANN models were the fin angle (beta), the distance between two fins (D), and the fin's trail angle (alpha). This research examined different configurations of the fins within the P-LHS system to find the optimal one. In all the designed Tests, the heat absorption and temperature distribution within the PCM were accelerated; thereby, the melting time up to phi = 0.7 and phi = 1 was reduced remarkably compared to those in the fin-less P-LHS system. Besides, for the fastest full melting of the material in the P-LHS system, the optimal design 2 (OD2) was proposed, in which the fins should have the dimensions of alpha = 171.450 degrees, beta = 97.540 degrees, and D = 27.545 mm. In OD2, the time needed until the melting of 70 % of the PCM was decreased by approximately 65.68 %. While, the necessary duration for the PCM to melt entirely was 1574 s, approximately 66.80 % less than the time needed for melting the substance in the system without fins (4741 s). This reduction in melting time was about 53 min, which is a significant amount of time in energy storage systems.
format	Article
author	Maalla, Allam Dahari, Mahidzal Chaturvedi, Rishabh Fouad, Yasser Abdullaev, Sherzod Farag, Ahmed Alkhayyat, Ahmed Abdo, G.M.
author_facet	Maalla, Allam Dahari, Mahidzal Chaturvedi, Rishabh Fouad, Yasser Abdullaev, Sherzod Farag, Ahmed Alkhayyat, Ahmed Abdo, G.M.
author_sort	Maalla, Allam
title	Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system
title_short	Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system
title_full	Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system
title_fullStr	Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system
title_full_unstemmed	Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system
title_sort	artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system
publisher	Elsevier
publishDate	2024
url	http://eprints.um.edu.my/45239/ https://doi.org/10.1016/j.est.2024.111256
_version_	1811682107302346752
score	13.211869

Artificial neural network-based optimization of heat absorption process of phase change materials in a novel-designed finned-plate latent heat storage system

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