Designing artificial neural network of nanoparticle diameter and solid-fluid interfacial layer on single-walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles

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dc.authoridShafiq, Anum/0000-0001-7186-7216
dc.authoridSindhu, Tabassum/0000-0001-9433-4981
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorShafiq, Anum
dc.contributor.authorColak, Andac Batur
dc.contributor.authorNaz Sindhu, Tabassum
dc.date.accessioned2024-11-07T13:32:55Z
dc.date.available2024-11-07T13:32:55Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this study, an artificial neural network (ANN) has been developed to predict the boundary layer flow of a single-walled carbon nanotubes nanofluid toward three different nonlinear thin isothermal needles of paraboloid, cone, and cylinder shapes with convective boundary conditions. Different effects of particle diameter and solid-fluid interface coating have been taken into account in the thermal conductivity model of nanofluid in which ethylene glycol has been used as the base fluid. Single and dual phase approach is used to establish the management model under the phenomenon of zero heat and mass flux. A dataset has been developed for different scenarios of the fluid model by changing the relevant parameters with the Runge-Kutta based shooting technique. Two different ANN models have been developed to predict Nusselt number and skin friction coefficient (SFC) values. The values obtained from ANN models have been compared with the numerical data, which are the target values. In addition, mean square error and R values have also been examined in order to analyze the prediction performance of ANN models more comprehensively. The calculated R values for Nusselt number and SFC were obtained as 0.9999. The results obtained showed that ANN can predict Nusselt number and SFC values with high accuracy.
dc.identifier.doi10.1002/fld.5038
dc.identifier.endpage3404
dc.identifier.issn0271-2091
dc.identifier.issn1097-0363
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85113134761
dc.identifier.scopusqualityQ1
dc.identifier.startpage3384
dc.identifier.urihttps://doi.org/10.1002/fld.5038
dc.identifier.urihttps://hdl.handle.net/11480/15673
dc.identifier.volume93
dc.identifier.wosWOS:000686952500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal For Numerical Methods in Fluids
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectartificial neural network
dc.subjectinterfacial layer
dc.subjectthin slendering needle
dc.subjectzero heat and mass flux
dc.titleDesigning artificial neural network of nanoparticle diameter and solid-fluid interfacial layer on single-walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles
dc.typeArticle

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