Design of inverse multiquadric radial basis neural networks for the dynamical analysis of MHD casson nanofluid flow along a nonlinear stretchable porous surface with multiple slip conditions

The present research, a numerical approach to examine magnetohydrodynamics (MHD) Casson nanofluid flow in a porous medium along a stretchable surface with different slips using artificial neural networks (ANNs) by taking inverse multiquadric (IMQ) radial basis function (RBF) as an activation function. i.e. ANNs-IMQ-RBF. The hybridization of genetic algorithms (GAs) and sequential quadratic programming (SQP) is used for learning in ANNs-IMQ-RBF. The PDEs representing the fluid flow are converted into a nonlinear system of dimensionless form of ODEs through an appropriate transformation while effects of variation in the values of Casson parameter (beta), Brownian motion parameter (Nb), Prandtl number (Pr), stretching parameter (n), porosity parameter (P), Lewis number (Le) along with temperature slip parameter (lambda(2)) on velocity, temperature and nanofluid concentration are depicted through graphs. The effectiveness, convergence and accuracy of the proposed solver are validated evidently through boxplot analysis, histograms and cumulative distribution function (CDF) plots. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This research investigates the magnetohydrodynamics (MHD) Casson nanofluid flow in a porous medium along a stretchable surface with different slips using artificial neural networks (ANNs) and inverse multiquadric (IMQ) radial basis function (RBF) as an activation function. The effects of various parameters on the velocity, temperature, and nanofluid concentration are analyzed through graphs. The proposed solver is validated through boxplot analysis, histograms, and cumulative distribution function (CDF) plots.