Prediction of heat transfer in a circular tube with aluminum and Cr-Ni alloy pins using artificial neural network

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dc.contributor.authorBerber, Adnan
dc.contributor.authorGurdal, Mehmet
dc.contributor.authorBagirsakci, Kazim
dc.date.accessioned2024-02-23T14:20:16Z
dc.date.available2024-02-23T14:20:16Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractThis work aims to estimate the experimental heat transfer coefficients of a circular channel using artificial neural network. The experiments are carried out at a forced turbulent flow regime of 10,000 < Re <50,000. The obtained experimental Nusselt numbers are compared using the ANN (Artificial Neural Network). In the developed ANN structure are showed mean square error (MSE), average relative deviation (ARD %), and correlation coefficient (R-2) in modeling of overall experimental datasets of Nusselt number. As a result, it is observed that the heat transfer correlation predicted by ANN are sufficiently consistent with the experimental results.en_US
dc.identifier.doi10.1080/08916152.2020.1793826
dc.identifier.endpage563en_US
dc.identifier.issn0891-6152
dc.identifier.issn1521-0480
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85088295373en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage547en_US
dc.identifier.urihttps://doi.org/10.1080/08916152.2020.1793826
dc.identifier.urihttps://hdl.handle.net/20.500.12452/13097
dc.identifier.volume34en_US
dc.identifier.wosWOS:000550706700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofExperimental Heat Transferen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHeat Transferen_US
dc.subjectArtificial Neural Network [Ann)en_US
dc.subjectTurbulent Flowen_US
dc.subjectPinen_US
dc.subjectForced Convectionen_US
dc.titlePrediction of heat transfer in a circular tube with aluminum and Cr-Ni alloy pins using artificial neural networken_US
dc.typeArticleen_US

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