Artificial neural network-based comparative investigation of magnetohydrodynamic natural convection in triple-partitioned triangular cavities with sinusoidal and rotating partitions

Abstract

This study investigates natural convection in a triple-partitioned triangular cavity under the influence of an external magnetic field, considering both wavy (WP) and rotating (RP) partition configurations. The effects of key parameters-including the Rayleigh number (Ra), magnetic field strength (Hartmann number, Ha), magnetic field inclination (gamma), WP wave amplitude (A(f)) and wave number (N-f), and RP rotational speed (Omega) and size (R)-on the flow, thermal fields, and overall cooling performance-are analyzed by using finite volume method. Cooling performance of WP and RP is evaluated through an artificial neural network (ANN)-based approach. The flow and thermal fields in the triple-partitioned cavity are strongly affected by the partition type. WP produces the lowest average Nu, followed by circular-stationary and flat partitions, with reductions of 32%-8.6% at Ra=105, while RP enhances Nu by 138.4% and 38% at Ra=104 and Ra=105. At the highest Ha, Nu decreases by 1%-22% depending on the partition, with WP reducing Nu by 13%-31% and RP increasing it by 9%-71%. Increasing A(f) to 0.3 reduces Nu by 20.7% and 24.4% for N-f=1 and 3, while at A(f)=0.1, Nu is nearly unaffected by the wave number. At Omega=-100 and 100, Nu increases by 73.8% and 60.1%; a larger circular interface boosts Nu by 111.6% when rotating but lowers it by 11% when stationary. ANN predictions indicate that WP lowers Nu by 0.3% at low Ra and 36.9% at high Ra, whereas RP raises it by 76.8% and 36.1%.