Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling

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dc.authoridSindhu, Tabassum/0000-0001-9433-4981
dc.authoridShafiq, Anum/0000-0001-7186-7216
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorShafiq, Anum
dc.contributor.authorColak, Andac Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.contributor.authorAl-Mdallal, Qasem M.
dc.contributor.authorAbdeljawad, T.
dc.date.accessioned2024-11-07T13:35:04Z
dc.date.available2024-11-07T13:35:04Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg-Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.
dc.identifier.doi10.1038/s41598-021-93790-9
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid34267255
dc.identifier.scopus2-s2.0-85110633390
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-021-93790-9
dc.identifier.urihttps://hdl.handle.net/11480/16324
dc.identifier.volume11
dc.identifier.wosWOS:000675827900008
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectStagnation Point Flow
dc.subjectBoundary-Layer-Flow
dc.subjectThermal-Conductivity
dc.subjectHeat-Transfer
dc.subjectNanofluids
dc.subjectPrediction
dc.subjectViscosity
dc.subjectTherapy
dc.titleEstimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling
dc.typeArticle

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