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This study investigates the double-diffusion phenomenon of nano-enhanced phase change material within connected porous channels using a hybrid incompressible smoothed particle hydrodynamics (ISPH) method and Extreme Gradient Boosting (XGBoost) machine learning model. Key parameters, including buoyancy ratio ( N  = −2 to 2), radii of inner cylinders ( Rc 1  = 0.25–3.0,  Rc 2  = 0.5–5.0), Darcy number (Da = 10 −4  to 10 −2 ), Dufour number (Du = 0.01–1), Soret number (Sr = 0.01–2), and fusion temperature ( θf  = 0.1–0.9) were analyzed to understand their effects on the heat capacity ratio (Cr), temperature (θ\theta), velocity (VV), and concentration (ϕ\phi) at various dimensionless times ( τ  = 0.1, 5, 10). The XGBoost model accurately predicted the average Nusselt (Num) and Sherwood (Shm) numbers, validated by metrics such as mean squared error, mean absolute deviation, coefficient of determination ( R2 ), and root mean squared error. The results revealed that positive buoyancy enhances mixing and diffusion by up to 34%, while larger radii increase diffusion efficiency by 30%. Higher Darcy numbers and Soret effects boost transport processes by 70 and 25%, respectively. The hybrid ISPH and XGBoost frameworks effectively capture the interplay between physical and geometric parameters, offering insights into optimizing thermal and solutal systems for industrial applications.

Double diffusion of nano-enhanced phase change materials in connected porous channels: A hybrid ISPH-XGBoost approach