Heat and mass transfer in wavy star-shaped cavities with nano-enhanced phase change material: numerical simulation and machine learning applications

Purpose This study aims to investigate coupled heat and mass transfer processes in a wavy star-shaped cavity filled with nano-enhanced phase change materials (NEPCMs) and porous media. It examines the influence of magnetic fields and chemical reactions on transport phenomena and explores the role of key parameters such as amplitude (A), frequency (f), Darcy number (Da), Hartmann number (Ha), thermal radiation (Rd), Rayleigh number (Ra), Soret (Sr) and Dufour (Du) numbers. Design/methodology/approach The investigation uses the incompressible smoothed particle hydrodynamics (ISPH) method, integrated with an extreme gradient boosting (XGBoost) machine learning model, to study thermal and solutal transport. Periodic boundary conditions are imposed on the inner circular cylinder’s temperature and concentration, modeled as sinusoidal oscillations to simulate dynamic thermal and solutal sources. Findings The results demonstrate that increasing the amplitude parameter enhances thermal gradients by 50% and concentration gradients by 30%, whereas higher frequency parameters intensify oscillations in velocity and temperature fields by 40%. Low Darcy numbers significantly reduce permeability, leading to a 40% decrease in heat and mass transfer. Magnetic fields suppress convective transport, reducing peak velocity and average Nusselt (Nu_avg) and Sherwood (Sh_avg) numbers by 30%. The XGBoost model predictions align closely with the ISPH simulations, showing high accuracy for Nu_avg and Sh_avg metrics. Originality/value This study provides a comprehensive analysis of the interplay between physical parameters and heat and mass transport phenomena in NEPCM-filled porous cavities under dynamic conditions. The integration of numerical methods with machine learning enhances predictive accuracy and understanding of complex transport processes, contributing valuable insights for the design of efficient thermal systems. Future research directions include exploring dynamic boundary conditions and multi-phase effects to optimize thermal performance further.