https://doi.org/10.1140/epjp/s13360-023-03704-z
Regular Article
Artificial neural networking estimation of skin friction coefficient at cylindrical surface: a Casson flow field
1
Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia
2
Department of Mathematics, Air University, PAF Complex E-9, 44000, Islamabad, Pakistan
3
Department of Mathematics, Faculty of Science, The Hashemite University, P.O. Box 330127, 13133, Zarqa, Jordan
4
Information Technologies Application and Research Center, Istanbul Commerce University, 34445, Istanbul, Turkey
Received:
4
October
2022
Accepted:
13
January
2023
Published online:
23
January
2023
In this article, we constructed Artificial Neural Networking (ANN) models to predict values of the skin friction coefficient for two different flow regimes of non-Newtonian fluid. More specifically, flow of Casson fluid is considered toward an inclined surface with stagnation point and mixed convection effects. Energy equation is considered by means of thermal radiations, viscous dissipation, heat generation and temperature-dependent variable viscosity effects. The flow regime is carried as a two various models namely Model-I: Casson fluid flow in the presence of magnetic field and Model-II: Casson fluid flow in the absence of magnetic field. Mathematical formulation is presented for each model, and shooting method is used to obtain the numerical data of skin friction coefficient. In contrast to the Casson fluid, mixed convection, and velocities ratio parameters, the skin friction coefficient exhibits a direct relationship with the magnetic field parameter and the curvature parameter. The MoD values for both models (I, II) show that there is relatively little variation between targeted and the projected values produced from the constructed ANN models.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
