An intelligent approach to investigate the effects of container orientation for PCM melting based on an XGBoost regression model

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dc.authoridErtam, Fatih/0000-0002-9736-8068
dc.authoridKIYAK, Burak/0000-0001-9088-9154
dc.authorwosidErtam, Fatih/V-5288-2018
dc.authorwosidKIYAK, Burak/W-4102-2018
dc.contributor.authorKiyak, Burak
dc.contributor.authorOztop, Hakan F.
dc.contributor.authorErtam, Fatih
dc.contributor.authorAksoy, I. Gokhan
dc.date.accessioned2024-08-04T20:55:05Z
dc.date.available2024-08-04T20:55:05Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe orientation of the container filled with phase change material (PCM) is a critical parameter that significantly effects the performance of thermal energy storage systems. In this study, the Computational Fluid Dynamics (CFD) method is utilised to analyse the effects of container position on the melting process of PCM. Unlike conventional methods, the melting process of PCM was conducted using the hot air jet impingement method. The study investigated the impact of two various Reynolds numbers (2235 and 4470) and three different H/D ratio (the ratio of the distance between the jet and the container to the container diameter) which were 0.4, 0.5, and 0.6, on the PCM melting process. In addition, regression analysis was executed using the Extreme Gradient Boosting algorithm (XGBoost). The outcomes unveiled that the artificial intelligence model attained a minimum accuracy of 97.89 % and reached a maximum accuracy of 99.35 % across the 12 datasets for comparing performance metrics. These results serve as a testament to the prowess of the XGBoost algorithm in providing precise predictions of the target variable within a notably extensive range of accuracy for the datasets under consideration.en_US
dc.description.sponsorshipScientific Research Foundation of Inonu University [2022/3092]en_US
dc.description.sponsorshipThis study was supported by the Scientific Research Foundation of Inonu University (Project No: 2022/3092) .en_US
dc.identifier.doi10.1016/j.enganabound.2024.01.018
dc.identifier.endpage213en_US
dc.identifier.issn0955-7997
dc.identifier.issn1873-197X
dc.identifier.scopus2-s2.0-85184080722en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage202en_US
dc.identifier.urihttps://doi.org/10.1016/j.enganabound.2024.01.018
dc.identifier.urihttps://hdl.handle.net/11616/101797
dc.identifier.volume161en_US
dc.identifier.wosWOS:001178733600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofEngineering Analysis With Boundary Elementsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPhase change materialen_US
dc.subjectHot air jet impingementen_US
dc.subjectThermal energy storageen_US
dc.subjectCFDen_US
dc.subjectContainer positionen_US
dc.subjectXGBoosten_US
dc.subjectArtificial intelligenceen_US
dc.titleAn intelligent approach to investigate the effects of container orientation for PCM melting based on an XGBoost regression modelen_US
dc.typeArticleen_US

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