Modeling of Darcy-Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural network

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dc.contributor.authorShafiq, Anum
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.date.accessioned2023-06-23T11:13:31Z
dc.date.available2023-06-23T11:13:31Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractModern industries face a new challenge in cooling processes. Traditional cooling lubricants have limited heatconducting capacity. The development of nanofluids possessing superior properties such as high thermal conductivity, homogeneity, and long-term stability has revolutionized the cooling lubrication industry. The literature reports a wide range of applications of nanofluid, such as cooling devices, peristaltic pumps for diabetic treatments, accelerators, reactors, petroleum industry applications, solar collectors and so forth. Nanofluids like Williamson nanofluid are very important non-Newtonian fluids that have pseudoplastic properties. Williamson nanofluid has a number of applications in the medical and engineering sciences. It is used in food processing, inkjet printing, adhesives and emulsions, coated photographic films, and many other applications. In the current study, the nanomaterial flow of the Darcy-Forchheimer Williamson nanofluid model is evaluated using the Levenberg–Marquardt approach with backpropagated neural networks. Thermalphoresis and Brownian motion are incorporated into the nanofluid model. This system is converted into an analogous nonlinear ordinary differential system through the application of necessary transformations. A dataset for the proposed multilayer perceptron artificial neural network is generated by altering the necessary variables through a Runge–Kutta fourth-order shooting procedure. It has been created an artificial neural network called a multiple-layer perceptron in order to forecast the values of the multiple-layer perceptron. It is discovered that the (Formula presented.) parameter had the highest mean deviation of (Formula presented.) and the (Formula presented.) parameter had the lowest mean deviation of (Formula presented.). Furthermore, MSE value of ANN model developed to estimate the skin friction coefficient value as (Formula presented.) and R value as 0.99954 whereas MSE and R values of the ANN model developed for the estimation of the LNN value were obtained as (Formula presented.) and 0.99999, respectively.en_US
dc.identifier.doi10.1002/fld.5216en_US
dc.identifier.scopus2-s2.0-85160665185en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6676
dc.identifier.urihttps://doi.org/10.1002/fld.5216
dc.identifier.wosWOS:000995333700001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofInternational Journal for Numerical Methods in Fluidsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectartificial neural network; Darcy-Forchheimer; first order chemical reaction; nonlinear stretching sheet; thermal radiation; viscous dissipation; Williamson modelen_US
dc.titleModeling of Darcy-Forchheimer magnetohydrodynamic Williamson nanofluid flow towards nonlinear radiative stretching surface using artificial neural networken_US
dc.typeArticleen_US

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