Artificial neural networking estimation of skin friction coefficient at cylindrical surface: a Casson flow field

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dc.contributor.authorRehman, Khalil Ur
dc.contributor.authorShatanawi, Wasfi
dc.contributor.authorÇolak, Andaç Batur
dc.date.accessioned2023-10-30T12:28:04Z
dc.date.available2023-10-30T12:28:04Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractIn this article, we constructed Artificial Neural Networking (ANN) models to predict values of the skin friction coefficient for two different flow regimes of non-Newtonian fluid. More specifically, flow of Casson fluid is considered toward an inclined surface with stagnation point and mixed convection effects. Energy equation is considered by means of thermal radiations, viscous dissipation, heat generation and temperature-dependent variable viscosity effects. The flow regime is carried as a two various models namely Model-I: Casson fluid flow in the presence of magnetic field and Model-II: Casson fluid flow in the absence of magnetic field. Mathematical formulation is presented for each model, and shooting method is used to obtain the numerical data of skin friction coefficient. In contrast to the Casson fluid, mixed convection, and velocities ratio parameters, the skin friction coefficient exhibits a direct relationship with the magnetic field parameter and the curvature parameter. The MoD values for both models (I, II) show that there is relatively little variation between targeted and the projected values produced from the constructed ANN models.en_US
dc.identifier.doi10.1140/epjp/s13360-023-03704-zen_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85146782337en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6847
dc.identifier.urihttps://doi.org/10.1140/epjp/s13360-023-03704-z
dc.identifier.volume138en_US
dc.identifier.wosWOS:000921890600006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEuropean Physical Journal Plusen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.titleArtificial neural networking estimation of skin friction coefficient at cylindrical surface: a Casson flow fielden_US
dc.typeArticleen_US

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