Employing ISPH simulations and artificial intelligence for enhanced heat and mass transfer in a core reactor with nano-enhanced phase change materials

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This research investigates the heat and mass transfer characteristics of nano-enhanced phase change material (NEPCM) within a core reactor, employing both the incompressible smoothed particle hydrodynamics (ISPH) simulations and artificial intelligence (AI) analysis. NEPCM holds promise for enhancing thermal energy storage and transfer efficiency, with applications spanning various fields, including nuclear reactors. Through ISPH simulations, this study delves into the intricate fluid dynamics and phase change phenomena within the reactor, providing valuable insights into the thermal behavior of NEPCM. Eight embedded rods are initially settled inside a reactor with high temperature and concentration on four rods and the others are maintained at low temperature and concentration. The effects of various parameters including the Cattaneo–Christov heat parameter ( δ Heat ) , Cattaneo–Christov mass parameter ( δ Mass ) , Dufour number ( Du ) , Hartmann number ( Ha ) , Soret number ( Sr ) , and thermal radiation ( Rd ) are conducted in this work. δ Heat and δ Mass play critical roles in evaluating heat and mass transfer efficiencies. Raising the Dufour number improves temperature distribution while having a minimal impact on concentration. Additionally, increasing the Dufour number from 0 to 1.5 leads to a 30% increase in the velocity field. Elevating the Soret number significantly enhances velocity and concentration, while increasing the thermal radiation parameter improves temperature distribution. These findings are pertinent across multiple domains like combustion, chemical engineering, and geophysics, aiding in process optimization and system design. The ANN model exhibits high precision in predicting Nusselt and Sherwood numbers, highlighting its effectiveness in forecasting.