An artificial neural network-based numerical estimation of the boiling pressure drop of different refrigerants flowing in smooth and micro-fin tubes

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dc.authorid0000-0001-9297-8134en_US
dc.authorid0000-0003-3157-1992en_US
dc.authorid0000-0002-8843-8191en_US
dc.authorid0000-0002-5743-3937en_US
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorBacak, Aykut
dc.contributor.authorKayacı, Nurullah
dc.contributor.authorDalkılıç, Ahmet Selim
dc.date.accessioned2024-02-26T11:47:44Z
dc.date.available2024-02-26T11:47:44Z
dc.date.issued2024en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractIn thermal engineering implementations, heat exchangers need to have improved thermal capabilities and be smaller to save energy. Surface adjustments on tube heat exchanger walls may improve heat transfer using new manufacturing technologies. Since quantifying enhanced tube features is quite difficult due to the intricacy of fluid flow and heat transfer processes, numerical methods are preferred to create efficient heat exchangers. Recently, machine learning algorithms have been able to analyze flow and heat transfer in improved tubes. Machine learning methods may increase heat exchanger efficiency estimates using data. In this study, the boiling pressure drop of different refrigerants in smooth and micro-fin tubes is predicted using an artificial neural network-based machine learning approach. Two different numerical models are built based on the operating conditions, geometric specifications, and dimensionless numbers employed in the two-phase flows. A dataset including 812 data points representing the flow of R12, R125, R134a, R22, R32, R32/R134a, R407c, and R410a through smooth and micro-fin pipes is used to evaluate feed-forward and backward propagation multi-layer perceptron networks. The findings demonstrate that the neural networks have an average error margin of 10?percent when predicting the pressure drop of the refrigerant flow in both smooth and micro-fin tubes. The calculated R-values for the artificial neural network’s supplementary performance factors are found above 0.99 for all models. According to the results, margins of deviations of 0.3?percent and 0.05?percent are obtained for the tested tubes in Model 1, while deviations of 0.79?percent and 0.32?percent are found for them in Model 2.en_US
dc.identifier.doi10.1515/kern-2023-0087en_US
dc.identifier.scopus2-s2.0-85183853128en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7155
dc.identifier.urihttps://doi.org/10.1515/kern-2023-0087
dc.identifier.wosWOS:001152457800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherDe Gruyteren_US
dc.relation.ispartofKerntechniken_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectartificial neural network; machine learning; micro-fin tube; pressure drop; two-phase flowen_US
dc.titleAn artificial neural network-based numerical estimation of the boiling pressure drop of different refrigerants flowing in smooth and micro-fin tubesen_US
dc.typeArticleen_US

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