Stochastic numerical computing to study MHD micropolar nanofluidic system along a rotating disk in the presence of partial slip conditions

Abstract

The goal of investigation is to look at how a rotating disc affects the motion of magnetohydrodynamics micropolar nanofluid flow model (MHD-MPNFFM) in applied magnetic field and partial slip condition. Using suitable transformations, the PDEs of considered flow system are converted to ODEs. These obtained non-linear ODEs are then processed using the computational power of Lobatto IIIA approach to obtain a dataset of Artificial Intelligence neural networks model backpropagated with Bayesian Regularization procedure (AINM-BBRP) for variation of velocity slip parameters, magnetic field parameter, material parameters, Brownian motion parameter, thermophoresis parameter and Lewis number. To verify the current findings, a contrast to past study is included. Graphical representations of regression, efficiency, fit curve, error bars, and trained state analysis are presented using nftool. The velocity, temperature, microrotation, and concentration profiles were computed, and the findings were presented through graphs and tables. This is observed that velocity and microrotation or spin components are degraded by the slip action and magnetic field. Brownian motion parameter, thermophoresis parameter, and Lewis number all have rising temperature fields, however Brownian motion parameter and Lewis number have decreasing concentration profiles. The accuracy achieved in terms of relative error demonstrates the validity and significance of the solution process.