Using the artificial neural network model for modeling the performance of the counter flow vortex tube

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dc.authoridKIRMACI, Volkan/0000-0001-7076-1911
dc.contributor.authorUluer, Onuralp
dc.contributor.authorKirmaci, Volkan
dc.contributor.authorAtas, Safak
dc.date.accessioned2024-09-29T15:55:24Z
dc.date.available2024-09-29T15:55:24Z
dc.date.issued2009
dc.departmentKarabük Üniversitesien_US
dc.description.abstractIn this study, the effect of the nozzle number and the inlet pressure on the heating and cooling performance of the counter flow type vortex tube has been modeled with artificial neural networks (ANN) by using the experimentally obtained data. ANN has been designed by Pithiya software. In the developed system output parameter temperature gradient between the cold and hot outlets (Delta T) has been determined using inlet parameters such as the inlet pressure (P(inlet)), nozzle number (N), and cold mass fraction (mu(c)). The back-propagation learning algorithm with variant which is Levenberg-Marquardt (LM) and Fermi transfer function have been used in the network. in addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R(2)), the root means square error (RMSE) and the mean absolute percentage error (MAPE). R(2), RMSE and MAPE have been determined for Delta T as 0.9947, 0.188224, and 0.0460, respectively. (C) 2009 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2009.04.061
dc.identifier.endpage12263en_US
dc.identifier.issn0957-4174
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-69249205468en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage12256en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2009.04.061
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4622
dc.identifier.volume36en_US
dc.identifier.wosWOS:000270646200028en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectVortex tubeen_US
dc.subjectCooling performanceen_US
dc.subjectANNen_US
dc.titleUsing the artificial neural network model for modeling the performance of the counter flow vortex tubeen_US
dc.typeArticleen_US

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