Artificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects

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dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridRehman, Khalil Ur/0000-0002-4218-6582
dc.authoridShatanawi, Wasfi/0000-0001-7492-4933
dc.contributor.authorRehman, Khalil Ur
dc.contributor.authorColak, Andac Batur
dc.contributor.authorShatanawi, Wasfi
dc.date.accessioned2024-11-07T13:35:07Z
dc.date.available2024-11-07T13:35:07Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe convective heat transfer in non-Newtonian fluid flow in the presence of temperature stratification, heat generation, and heat absorption effects is debated by using artificial neural networking. The heat transfer rate is examined for the four different thermal flow regimes namely (I) thermal flow field towards a flat surface along with thermal radiations, (II) thermal flow field towards a flat surface without thermal radiations, (III) thermal flow field over a cylindrical surface with thermal radiations, and (IV) thermal flow field over a cylindrical surface without thermal radiations. For each regime, a Nusselt number is carried out to construct an artificial neural networking model. The model prediction performance is reported by using varied neuron numbers and input parameters, and the results are assessed. The ANN model is designed by using the Bayesian regularization training procedure, and a high-performing MLP network model is used. The data used in the creation of the MLP network was 80 percent for model training and 20 percent for testing. The graph shows the degree of agreement between the ANN model projected values and the goal values. We discovered that an artificial neural network model can provide high-efficiency forecasts for heat transfer rates having engineering standpoints. For both flat and cylindrical surfaces, the heat transfer normal to the surface reflects inciting nature towards the Prandtl number and heat absorption parameter, while the opposite is the case for the temperature stratification parameter and heat generation parameter. It is important to note that the magnitude of heat transfer is significantly larger for Flow Regime-IV in comparison with Flow Regimes-I, -II, and -III.
dc.identifier.doi10.3390/math10142394
dc.identifier.issn2227-7390
dc.identifier.issue14
dc.identifier.scopus2-s2.0-85136935224
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/math10142394
dc.identifier.urihttps://hdl.handle.net/11480/16343
dc.identifier.volume10
dc.identifier.wosWOS:000832110800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofMathematics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectconvective heat transfer
dc.subjecttemperature stratification
dc.subjectthermal radiations
dc.subjectmixed convection
dc.subjectartificial neural networking
dc.titleArtificial Neural Networking (ANN) Model for Convective Heat Transfer in Thermally Magnetized Multiple Flow Regimes with Temperature Stratification Effects
dc.typeArticle

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