Shafiq, AnumColak, Andac BaturNaz Sindhu, Tabassum2024-11-072024-11-0720210271-20911097-0363https://doi.org/10.1002/fld.5038https://hdl.handle.net/11480/15673In 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.eninfo:eu-repo/semantics/closedAccessartificial neural networkinterfacial layerthin slendering needlezero heat and mass fluxArticle93123384340410.1002/fld.50382-s2.0-85113134761Q1WOS:000686952500001Q3