numerical and artificial neural network analysis of magnetohydrodynamic natural convection in a nano-encapsulated phase change suspension filled quadrantal circular enclosure

This article deals with the heat transfer dynamics of a quadrantal circular enclosure filled with nano-encapsulated phase change material (nepcm) suspension. central to the investigation is the examination of nanoparticles that exhibit phase transitions and their consequential effects on the thermophysical attributes of the nepcm suspension. the transformative states of these nanoparticles - solid, liquid, or phase change - are pivotal in modifying localized heat capacity, influenced by various parameters. among these, the concentration of nepcm particles, the rayleigh number, the hartmann number, and the fusion temperature stand out as critical to heat transfer dynamics. the governing equations for fluid flow and heat transfer of nepcm suspension are introduced in the form of partial differential equations and were solved using the finite element method. the results showed that while an increase in the rayleigh number augments heat transfer rates, a rise in the hartmann number reduces it due to the lorenz force’s influence on fluid velocity. optimal fusion temperature values, crucial to heat transfer, have been pinpointed at θf = 0.35 and θf = 0.675. the study further unveils the augmentation in heat transfer rates with an increased concentration of nanoparticles, particularly at the wall’s commencement. a significant contribution is developing a neural network model, demonstrating high predictive accuracy for heat transfer rates. this model, through contour diagrams, offers insights into heat transfer dynamics, sidestepping the need for extensive numerical simulations.