Thermosolutal convection and entropy generation in hybrid nanofluids: FEM and ANN analysis of a magnetized wavy enclosure

This study investigates the thermosolutal convection and entropy generation in a magnetohydrodynamic (MHD) hybrid nanofluid-filled enclosure featuring wavy vertical walls and a centrally placed star-shaped cylinder. The problem addresses the critical need to optimize heat and mass transfer in engineering systems, such as thermal management, energy storage, and electronic cooling. A hybrid nanofluid composed of alumina ($A{l}_2{O}_3$) and copper ($Cu$) nanoparticles in water is analyzed using the Finite Element Method (FEM) via COMSOL Multiphysics, complemented by an Artificial Neural Network (ANN) model to predict the Nusselt number and validate results. Key findings reveal that increasing the Rayleigh number (Ra) from 10³ to 10⁵ enhances the average Nusselt number (Nu_Avg) by 16.27 % and total entropy generation (E_Total) by 93.53 %, driven by intensified buoyancy-driven convection. Conversely, a stronger magnetic field (Ha = 50) suppresses fluid motion, reducing (Nu_Avg) by 18.03 % and Sherwood number (Sh_Avg) by 10.83 %, while increasing E_Total by 80.83 % due to Lorentz forces. Hybrid nanoparticles (2 % volume fraction) improve (Nu_Avg) by 6.63 % compared to pure fluid, demonstrating their thermal enhancement potential. The Lewis number (Le) and buoyancy ratio (N) significantly influence mass transfer, with (Sh_Avg) rising by 41.60 % at (Le = 10) and 32.90 % at (N = 10). The ANN model achieves exceptional accuracy (R = 1, MSE = 2.47 × 10⁻⁹) in predicting thermal behavior, reducing computational effort. Novelty lies in the combined analysis of hybrid nanofluids, star-shaped geometry, and MHD effects using FEM-ANN integration a configuration unexplored in prior literature. This work provides actionable insights for designing energy-efficient systems with optimized entropy generation and enhanced thermal performance.