Exploring the impact of Hall and ion slip effects on mixed convective flow of Casson fluid Model: A stochastic investigation through non-Fourier double diffusion theories using ANNs techniques

Abstract

The analysis of Non-Newtonian Casson fluid flow using Artificial Neural Networks (ANNs) has enticed the attention of researchers and scientists due to their tremendous role in modern technologies and development. The generalised Ohm law with mass and thermal transport is employed. The mechanism of mass and energy transmission is based on generalised Fick’s and Fourier laws. An incompressible magnetohydrodynamics (MHD) boundary layer flow of Darcy-Forchheimer Casson fluid across a stretching sheet is elaborated in the current study. The flow incorporates the effects of Hall and ion slip phenomena and occurs steadily over a stretchable linear surface through porous medium. The Casson fluid flow has been expressed in form of system of PDEs, which are further simplified to non-linear system of ODEs through similarity replacements. The ANN-LMBOA algorithm is used to crack these equations (ODEs). To validate the ANN-LMBOA results, the dataset is produced by employing the (FDM) finite difference method (Lobatto IIIA) in Matlab package using bvp4c-solver. The dataset is generated for diverse scenarios of flow constraints, testing, and validation of the neural network. The accurateness of the ANN-LMBOA (Artificial Neural Network- Levenberg Marquardt Backpropagation Optimization Algorithm) is evaluated via several statistical neural network tools, i.e., RP, MSE, EH and CF graphs.