Multi-Objective Artificial Neural Network-Genetic Algorithm Optimization of Transient Thermal Storage Device in a Hexagonal Phase Change Material System

dc.authoridhttps://orcid.org/0000-0002-7222-3014
dc.contributor.authorLi, Yonghui
dc.contributor.authorBasem, Ali
dc.contributor.authorZhang, Haibo
dc.contributor.authorKhlifi, Mohamed Arbi
dc.contributor.authorAbed Balla, Hyder H.
dc.contributor.authorKhan, Mohammad Nadeem
dc.contributor.authorAbu-Zinadah, Hanaa
dc.contributor.authorRakhmonov, Farkhod
dc.contributor.authorFouad, Yasser
dc.contributor.authorMahariq, İbrahim
dc.date.accessioned2026-08-10T12:21:15Z
dc.date.issued2026
dc.departmentMühendislik ve Mimarlık Fakültesi
dc.description.abstractLatent thermal energy storage systems based on phase change materials (PCMs) play a vital role in improving energy efficiency, mitigating peak thermal loads, and facilitating the integration of renewable energy sources. Their ability to store large amounts of heat within a narrow temperature range makes them particularly attractive for applications in solar energy systems, waste heat recovery, and thermal management technologies. Nevertheless, the inherently low heat-conduction of most PCMs significantly limits charging rates, thereby restricting their practical performance. In response to these challenges, this work introduces a novel hexagonal thermal energy storage device inspired by honeycomb architectures and conventional shell-and-tube heat exchangers. The design integrates two concentric hexagonal frameworks within a unified outer shell, interconnected by structural bases rather than conventional fins. This monolithic configuration not only increases the effective heat-transfer surface but also creates extended conductive pathways that accelerate melting during charging cycles. The segmented internal domain further enables the use of multiple PCMs to meet varying thermal demands, while improving mechanical integrity and reducing leakage risks. To identify the optimal configuration for maximum energy storage, an artificial neural network predictive model is coupled with a multiobjective genetic algorithm, with the first and second hexagon lengths and the system inclination angle as key design variables. Two distinct optimal cases were identified by single-objective optimization (Optimal Cases 1 and 2). Another optimal case was derived from multi-objective optimization and the TOPSIS method, referred to as Optimal Case 3. By 5 h, all three optimized cases had fully melted (liquid fraction = 1). In contrast, the Core Case exhibited significantly slower melting behavior with the liquid fraction of 0.505.
dc.identifier.doi10.1016/j.icheatmasstransfer.2026.111842
dc.identifier.issn0735-1933
dc.identifier.issueP5
dc.identifier.scopus2-s2.0-105043553588
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/11363/12169
dc.identifier.volume178
dc.indekslendigikaynakScopus
dc.institutionauthorMahariq, İbrahim
dc.institutionauthoridhttps://orcid.org/0000-0002-7222-3014
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofInternational Communications in Heat and Mass Transfer
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectThermal energy storage
dc.subjectMulti-objective optimization
dc.subjectArtificial neural network
dc.subjectGenetic algorithm
dc.subjectPareto front analysis
dc.subjectPhase change material
dc.titleMulti-Objective Artificial Neural Network-Genetic Algorithm Optimization of Transient Thermal Storage Device in a Hexagonal Phase Change Material System
dc.typeArticle

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