MHD triple diffusion due to the impulsive flow of micropolar nanofluids around a cone: Mangler's transformations and ANN analyses

Abstract

An implicit finite difference (FD) and artificial neural network (ANN) techniques are applied to study the triple diffusion and non-linear mixed convection flow around a vertical cone. The forced flow is due to an impulsive motion of a micropolar nanofluid while the buoyancy-driven flow is obtained using the quadratic form of Boussinesq approximation. Two governing equations are introduced for the species concentrations; those include non-linear chemical reactions. It is focused on the cases of the weak concentration of microelements, opposing and assisting flow, and the roles of the magnetic field, viscous dissipation, and convective boundary conditions are examined. The solution methodology is based on Mangler's transformations. At the same time, the effective ANN is used to predict some important physical quantities such as heat transfer rate against some key factors such as Biot number, Eckert number, and magnetic coefficient. Remarkably, the flow rate in the assisting flow is up to 0.95% higher than in the opposing flow. Across all cases, an increase in the vortex parameter ($K=0.1-1.2$) enhances fluid friction near the cone surface by 63.1%. These findings are particularly relevant for industrial applications involving heat and mass transfer in nanofluid systems, such as microreactors, biomedical engineering, and thermal energy storage.