AI-enhanced prediction and analysis of heat and mass transfer in NEPCM-filled wavy star-shaped cavities under magnetic, radiative, and chemical influences

Abstract

Nano-Encapsulated Phase Change Material (NEPCM)-based thermal systems play a crucial role in enhancing heat and mass transfer performance in energy storage and chemical processing applications. This study investigates the thermal and solutal behavior of a wavy star-shaped cavity filled with NEPCM, influenced by magnetic fields, thermal radiation, and chemical reactions. The mathematical model incorporates the effects of double diffusion, including Soret and Dufour phenomena, along with buoyancy-driven convection. The research methodology integrates numerical simulations with an artificial intelligence (AI) framework, specifically utilizing the XGBoost regression model to predict the average Nusselt ($N{u}_{\text{avg}}$) and Sherwood ($S{h}_{\text{avg}}$) numbers. The results reveal that increasing the Soret number enhances particle dispersion, improving $N{u}_{\text{avg}}$ and $S{h}_{\text{avg}}$ by up to 40% and 45%, respectively. Thermal radiation improves uniformity in heat and mass transfer by approximately 30%, while magnetic fields enhance these processes by 20 %. Additionally, higher chemical reaction rates amplify heat and mass transfer by up to 60 %, highlighting the significance of reactive transport. The findings emphasize the critical role of initial conditions, with the configuration featuring high-temperature and high-concentration solid particles achieving superior performance. These insights contribute to optimizing NEPCM-based systems for thermal energy storage, advanced heat exchangers, and chemical process efficiency.