Simulation of Magnetic Field Effects on Heat and Mass Transfer in a Porous Spline Half-Cylinder Using ANN and ISPH Approaches

Abstract

This study utilizes Artificial Neural Networks (ANNs) and Incompressible Smoothed Particle Hydrodynamics (ISPH) simulations to explore the effects of magnetic fields on heat and mass transfer in a porous spline half-cylinder filled with Nano-Encapsulated Phase Change Material (NEPCM). Simulations were conducted over a range of physical parameters: buoyancy ratio () from −2 to 2, Darcy number () from 10⁻⁵ to 10⁻², Hartmann number () from 0 to 50, Rayleigh number (Ra) from 10³ to 10⁶, and fusion temperature from 0.05 to 0.9. The results demonstrate that increasing the Ha reduces heat transfer efficiency by up to 15%, as the magnetic field stabilizes fluid flow and enhances conduction. Conversely, increasing the Ra improves heat transfer efficiency by approximately 25% due to enhanced convection and mixing. The buoyancy ratio significantly influences fluid flow, with higher values favoring concentration-driven buoyancy, while lower values enhance temperature-driven convection. The Da affects permeability, with higher values promoting convective heat transfer and dynamic flow, whereas lower values result in stable, conductive heat transfer. Fusion temperature impacts phase change behavior, affecting heat capacity and flow dynamics through latent heat absorption. These insights underscore the critical role of optimizing these parameters to enhance performance in applications such as thermal energy storage and industrial processes involving phase change materials.