Cooling performance prediction of a metal foam internal heat exchanger: An artificial neural network approach

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dc.contributor.authorParmaksızoğlu, İsmail Cem
dc.contributor.authorİpekoğlu, Mehmet
dc.date.accessioned2024-04-04T17:35:51Z
dc.date.available2024-04-04T17:35:51Z
dc.date.issued2023
dc.departmentTAÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractAlthough HFC refrigerants have high global warming potential (GWP) values, they are preferred due to their satisfactory cooling performance and A1 fire protection classification. If possible, alternatives of HFC-type refrigerants should be used; if not, they should be used with the least charge value. In this study, the effect of metal foam heat exchanger was investigated to reduce the amount of refrigerant in the refrigeration system. The performance of the metal foam incorporated internal heat exchanger (IHX) was estimated by trained artificial neural networks (ANNs) using the correlations given in the literature, and the results were compared with the experimental data presented in the literature. For the same cooling capacity, a higher performance is achieved by using IHX with metal foam additives. Although the developed correlation has been extracted for IHX, it could be applied for all HE with gas flow.
dc.identifier.citationParmaksızoğlu, İsmail C. ve İpekoğlu, M. (2023). Cooling performance prediction of a metal foam internal heat exchanger: An artificial neural network approach. Heat Transfer Research, 54 (15), 1-11.
dc.identifier.doi10.1615/HeatTransRes.2023045436
dc.identifier.endpage11en_US
dc.identifier.issn1064-2285
dc.identifier.issue15en_US
dc.identifier.scopus2-s2.0-85170826264
dc.identifier.scopusqualityQ3
dc.identifier.startpage1en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1019
dc.identifier.volume54en_US
dc.identifier.wosWOS:001055091600001
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.relation.ispartofHeat Transfer Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectRefrigeration cycleen_US
dc.subjectInternal heat exchangeren_US
dc.subjectMetal foamen_US
dc.subjectGWPen_US
dc.titleCooling performance prediction of a metal foam internal heat exchanger: An artificial neural network approach
dc.typeArticle

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