Numerical determination of condensation pressure drop of various refrigerants in smooth and micro-fin tubes via ANN method

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dc.authoridDalkilic, Ahmet Selim/0000-0002-5743-3937
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorColak, Andac Batur
dc.contributor.authorCelen, Ali
dc.contributor.authorDalkilic, Ahmet Selim
dc.date.accessioned2024-11-07T13:32:51Z
dc.date.available2024-11-07T13:32:51Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn the current work, the pressure drop of the refrigerant flow in smooth and micro-fin pipes has been modeled with artificial neural networks as one of the powerful machine learning algorithms. Experimental analyses have been evaluated in two groups for the numerical model such as operation parameters/physical properties and dimensionless numbers used in two-phase flows. Feed forward back propagation multi-layer perceptron networks have been developed evaluating the practically obtained dataset having 673 data points covering the flow of R22, R134a, R410a, R502, R507a, R32 and R125 in four different pipes. The outputs acquired from the artificial neural network have been evaluated with the target ones, and the performance factors have been estimated and the prediction accuracy of the network models has been resourced comprehensively. The results revealed that the neural networks could predict the pressure drop of the refrigerant flow in smooth and micro-fin pipes between 10% deviation bands.
dc.identifier.doi10.1515/kern-2022-0037
dc.identifier.endpage519
dc.identifier.issn0932-3902
dc.identifier.issn2195-8580
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85139962584
dc.identifier.scopusqualityQ4
dc.identifier.startpage506
dc.identifier.urihttps://doi.org/10.1515/kern-2022-0037
dc.identifier.urihttps://hdl.handle.net/11480/15651
dc.identifier.volume87
dc.identifier.wosWOS:000810148600001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWalter De Gruyter Gmbh
dc.relation.ispartofKerntechnik
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectANN
dc.subjectmachine learning
dc.subjectmicro-fin pipe
dc.subjectpressure drop
dc.subjecttwo-phase flow
dc.titleNumerical determination of condensation pressure drop of various refrigerants in smooth and micro-fin tubes via ANN method
dc.typeArticle

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