Analyzing activation energy and binary chemical reaction effects with artificial intelligence approach in axisymmetric flow of third grade nanofluid subject to soret and dufour effects

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dc.authorid0000-0001-9433-4981
dc.contributor.authorShafiq, Anum
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.date.accessioned2024-10-12T19:43:07Z
dc.date.available2024-10-12T19:43:07Z
dc.date.issued2023
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractThe use of nanotechnology has led to the design of many modern and more cost-effective implementation, such as solar power generation, the redevelopment of heat exchangers, and the modernization of the medical and pharmaceutical industries. In this study, the combined effects of activation energy with binary chemical reactant in a steady magnetohydrodynamic mixed convective third-grade nanofluid flow by radially radiative stretching plate has been analyzed with an artificial intelligence approach. Heat transfer analysis was conducted with heat generation, Joule heating, and Soret and Dufour effects. By incorporating appropriate transformations, the initial nonlinear coupled partial differential equations expressing the fluid model were formed as a comparable nonlinear ordinary differential equations system. Three different artificial neural network models were proposed in order to predict the skin friction, Nusselt number, and Sherwood number values of the fluid model by the Shooting Runge-Kutta Fehlberg 4, technique using the data set created by taking various values of the relevant parameters. It is worthy of noting that the average deviation values for each output parameter remained less than 5%. Furthermore it is also observed that mean square error values for skin friction coefficient, local Nusselt number, and local Sherwood number values were attained as 3.63 x 10(-3), 4.03 x 10(-4), and 8.62 x 10(-3), respectively. The obtained results show that artificial neural networks are an engineering tool that can be used with high accuracy to estimate the combined effects of activation energy and binary chemical reaction in a fixed magnetohydrodynamic mixed convective third-grade nanofluid flow with a radial radiative stretched plate.en_US
dc.identifier.citationShafiq, A., Çolak, A. B., & Sindhu, T. N. (2023). Analyzing activation energy and binary chemical reaction effects with artificial intelligence approach in axisymmetric flow of third grade nanofluid subject to Soret and Dufour effects. Heat Transfer Research, 54(3).
dc.identifier.endpage94en_US
dc.identifier.issn1064-2285
dc.identifier.issn2162-6561
dc.identifier.issue3en_US
dc.identifier.startpage75en_US
dc.identifier.urihttps://hdl.handle.net/11467/8773
dc.identifier.volume54en_US
dc.identifier.wosWOS:001031629000003en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherBegell House Incen_US
dc.relation.ispartofHeat Transfer Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArrhenius Activation Energyen_US
dc.subjectBinary Chemical Reactionen_US
dc.subjectArtificial Neural Networks (ANN)en_US
dc.subjectAxisymmetric Mixed Convection Flowen_US
dc.subjectThird-Grade Nanofluiden_US
dc.titleAnalyzing activation energy and binary chemical reaction effects with artificial intelligence approach in axisymmetric flow of third grade nanofluid subject to soret and dufour effectsen_US
dc.typeArticleen_US

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