Prediction of nanofluid flows' optimum velocity in finned tube-in-Tube heat exchangers using artificial neural network

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dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorMercan, Hatice
dc.contributor.authorAçlkgöz, Özgen
dc.contributor.authorDalklllç, Ahmet Selim
dc.contributor.authorWongwises, Somchai
dc.date.accessioned2023-01-31T08:10:09Z
dc.date.available2023-01-31T08:10:09Z
dc.date.issued2022en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractThe average flow velocity in heat exchangers is considered less often and thus needs further and detailed investigation because of its crucial influence on the overall thermal performance of the application. The use of nanofluids has similar influences to finned tube designs. Considering the rise in heat transfer and pressure drop, uncertainties in cost analyses with the uses of fins and nanoparticles, evaluation of optimum operating velocity of the fluids is necessary. On the contrary, there aren’t enough experimental, parametric, or numerical investigations present on this subject. The use of machine learning techniques to heat transfer applications to make optimization becomes popular recently. In this work, important factors of the process as tube number, cleanliness factor, and overall cost as output factors have been estimated by an artificial intelligence method using 339 data points. The influence of input factors of Reynolds number, thermal conductivity, specific heat, viscosity, and total fin surface efficiency on the outputs have been studied. Total tube number, cleanliness factor, and total cost analysis have been determined with deviations of ?0.66%, 0.001%, and 0.12% as a result of the solution with 6 inputs, correspondingly.en_US
dc.identifier.doi10.1515/kern-2022-0097en_US
dc.identifier.scopus2-s2.0-85145203759en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6180
dc.identifier.urihttps://doi.org/10.1515/kern-2022-0097
dc.identifier.wosWOS:000901732800001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWalter de Gruyter GmbHen_US
dc.relation.ispartofKerntechniken_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectANN; cost analysis; finned double-pipe heat exchanger; Levenberg–Marquardt; MLPen_US
dc.titlePrediction of nanofluid flows' optimum velocity in finned tube-in-Tube heat exchangers using artificial neural networken_US
dc.typeArticleen_US

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