An Implicit Finite Difference Scheme and Neural Network Approach for Non-Newtonian Nanofluid Flow Using Induced Magnetic Field

Abstract

The aim of this contribution is to propose a numerical scheme for solving linear and nonlinear boundary value problems. The scheme is implicit and it is constructed on three grid points. The stability of the proposed implicit scheme is provided. In addition to this, a mathematical model for heat and mass transfer using induced magnetic field (IMF) is modified. Furthermore, this model is transformed into boundary value problems by employing similarity transformations. The dimensionless model of boundary value problems is solved using the proposed numerical scheme. The scheme is applied with a combination of a shooting approach and an iterative method. From the obtained results, it can be seen that velocity profile declines with enhancing Weissenberg number. The results are also compared with those given in past research. In addition to this, a neural network approach is applied that is based on the input and outputs of the considered model with specified values of parameters.