Machine learning-based prediction of transient latent heat thermal storage in finned enclosures using group method of data handling approach: a numerical simulation

DOI10.1016/J.ENGANABOUND.2022.06.009OpenAlexW4282981314WikidataQ113875197 ScholiaQ113875197MaRDI QIDQ2085877

Meysam Alamshenas, Masoud Afrand, Vahid Safari, Leila Darvishvand, Babak Kamkari

Publication date: 19 October 2022

Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.enganabound.2022.06.009

zbMATH Keywords

computational fluid dynamics (CFD)artificial neural network (ANN)phase change material (PCM)latent heat storagemachine learning (ML)group method of data handling (GMDH)

Mathematics Subject Classification ID

Fluid mechanics (76-XX) Classical thermodynamics, heat transfer (80-XX)

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Cites Work

• Interaction of a pair of in-line bubbles ascending in an Oldroyd-B liquid: a numerical study
• Simulating gas bubble shape during its rise in a confined polymeric solution by WC-SPH
• Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network

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