The prediction of heat transfer and fluid characteristics for equilateral triangular bodies in tandem arrangement by artificial neural networks

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dc.authorid37111080900
dc.authorid23982535500
dc.authorid55195748600
dc.authorid6603381007
dc.authorid55195389500
dc.authorid6602825059
dc.contributor.authorManay E.
dc.contributor.authorGüneş S.
dc.contributor.authorAkçadirci E.
dc.contributor.authorÖzceyhan V.
dc.contributor.authorÇakir U.
dc.contributor.authorÇomakli O.
dc.date.accessioned20.04.201910:49:12
dc.date.accessioned2019-04-20T21:44:39Z
dc.date.available20.04.201910:49:12
dc.date.available2019-04-20T21:44:39Z
dc.date.issued2012
dc.departmentBayburt Üniversitesien_US
dc.description.abstractThe objective of this study is to investigate the effect of the spacing between equilateral dual triangular bodies symmetrically placed into the channel axis under steady state conditions on heat transfer and fluid characteristics by using artificial neural networks (ANN). The Back Propagation (BP) training algorithm was applied to train the model. The successful application proved that ANN model can be used for predicting the Nusselt number and skin friction coefficient as a convenient and effective method. The distribution of local Nusselt number, skin friction coefficient along the channel wall and overall enhancement ratio of all investigated cases are presented.en_US
dc.identifier.endpage517
dc.identifier.issn1303-9709
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84860135371en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage505
dc.identifier.urihttps://hdl.handle.net/20.500.12403/904
dc.identifier.volume25
dc.identifier.wosWOS:000421136900028en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofGazi University Journal of Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural network
dc.subjectHeat transfer enhancement
dc.subjectTandem arrangement
dc.subjectTriangular body
dc.subjectBodies in tandem
dc.subjectChannel axis
dc.subjectChannel wall
dc.subjectEnhancement ratios
dc.subjectHeat Transfer enhancement
dc.subjectLocal Nusselt number
dc.subjectSkin friction coefficient
dc.subjectSteady-state condition
dc.subjectTandem arrangement
dc.subjectTraining algorithms
dc.subjectTriangular body
dc.subjectFriction
dc.subjectNusselt number
dc.subjectPipe flow
dc.subjectSkin friction
dc.subjectNeural networks
dc.subjectArtificial neural network
dc.subjectHeat transfer enhancement
dc.subjectTandem arrangement
dc.subjectTriangular body
dc.subjectBodies in tandem
dc.subjectChannel axis
dc.subjectChannel wall
dc.subjectEnhancement ratios
dc.subjectHeat Transfer enhancement
dc.subjectLocal Nusselt number
dc.subjectSkin friction coefficient
dc.subjectSteady-state condition
dc.subjectTandem arrangement
dc.subjectTraining algorithms
dc.subjectTriangular body
dc.subjectFriction
dc.subjectNusselt number
dc.subjectPipe flow
dc.subjectSkin friction
dc.subjectNeural networks
dc.titleThe prediction of heat transfer and fluid characteristics for equilateral triangular bodies in tandem arrangement by artificial neural networksen_US
dc.typeArticleen_US

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