CONSTRUCTION OF NEURAL NETWORK BASED INTELLIGENT COMPUTING FOR TREATMENT OF DARCY-FORCHHEIMER SISKO NANOFLUID FLOW WITH ROSSELAND'S RADIATIVE PROCESS

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dc.authoridColak, Andac Batur/0000-0001-9297-8134|Sindhu, Tabassum/0000-0001-9433-4981
dc.contributor.authorShafiq, Anum
dc.contributor.authorColak, Andac Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.date.accessioned2024-10-12T19:43:07Z
dc.date.available2024-10-12T19:43:07Z
dc.date.issued2023
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractA generalization of Newtonian and power-law fluids is the Sisko model. It foretells dilatants and fluid pseudoplas-ticity. It was first suggested to use the Sisko fluid model to gauge high shear rates in lubricating greases. Three constants in this model are easily selectable for certain fluids, and it is demonstrated that the model is a good predictor of shear thickening and thinning. The study of nanofluids is gaining popularity quickly because of unique thermal, mechanical, and chemical characteristics of nanomaterials. Sisko nanofluids are also required for the production of nanoscale materials because of the superb wetting and dispersing capabilities they possess. In the present investigation, the Levenberg-Marquardt method with backpropagated neural networks is used to evaluate the nanomaterial flow of Darcy-Forchheimer Sisko fluid model. Thermophoresis and Brownian motion effects are considered when develop-ing the nanofluid model. By applying the necessary transformations, the original nonlinear coupled partial differential system representing fluidic model are converted to an analogous nonlinear ordinary differential system. For different fluid model scenarios, a dataset for the proposed multilayer perceptron artificial neural network is produced by altering the necessary variables via the Galerkin weighted residual approach. An artificial neural network called a multilayer perceptron has been created in order to forecast the multilayer perceptron values.en_US
dc.identifier.endpage98en_US
dc.identifier.issn1064-2285
dc.identifier.issn2162-6561
dc.identifier.issue9en_US
dc.identifier.startpage77en_US
dc.identifier.urihttps://hdl.handle.net/11467/8766
dc.identifier.volume54en_US
dc.identifier.wosWOS:001016462600004en_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.snmzWoS_2024en_US
dc.subjectDarcy-Forchheimeren_US
dc.subjectSisko modelen_US
dc.subjectthermal radiationen_US
dc.subjectartificial neural networken_US
dc.subjectweighted residual methoden_US
dc.subjectGauss-Laguerre formula (GLF)en_US
dc.titleCONSTRUCTION OF NEURAL NETWORK BASED INTELLIGENT COMPUTING FOR TREATMENT OF DARCY-FORCHHEIMER SISKO NANOFLUID FLOW WITH ROSSELAND'S RADIATIVE PROCESSen_US
dc.typeArticleen_US

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