This research investigates heat and mass transfer behavior in nano-enhanced phase change materials (NEPCM) within reactive systems using incompressible smoothed particle hydrodynamics (ISPH) coupled with artificial neural network (ANN) predictions. It introduces a novel model comprising six vertical rods and one curved rod, forming a unique Ш-Chip configuration. The complexity of the NEPCM model demonstrates its relevance in fluid dynamics and heat transfer analyses applicable across diverse fields including food processing, electronics cooling, chip manufacturing, heat exchangers, and solar energy systems. The study explores the influence of various dimensionless parameters such as the Frank-Kamenetskii number (Fk), Hartmann number (Ha), Soret and Dufour numbers (Sr&Du), Lewis number (Le), Rayleigh number (Ra), and fractional order parameter (α) on heat and mass transfer phenomena. Heat/mass sources from embedded vertical and curved rods filled with NEPCM are considered within the model. An artificial neural network (ANN) model employing a multilayer perceptron (MLP) structure is utilized to accurately predict the average Nusselt (Nu‾) and Sherwood (Sh‾) numbers, demonstrating its applicability in fluid dynamics and heat transfer analyses. Key insights highlight the role of Fk and Ra in enhancing convection flow and nanofluid velocity within the curved Ш-Chip. Furthermore, variations in Ha lead to observable velocity reductions due to intensified Lorentz forces, while increased Le values result in decreased velocities as thermal diffusion becomes dominant. The fractional order parameter (α) aids in the transition from unsteady to steady state. The unique configuration of the curved Ш-Chip, combined with the incorporation of heat/mass sources from embedded particles, offers promising applications across diverse fields such as food processing, electronics cooling, chip manufacturing, heat exchangers, and solar energy systems. This research underscores the crucial role of understanding the pertinent parameters to ensure the effective development of systems tailored to specific needs. Additionally, it emphasizes the practical value of employing ANN models in predictive modeling to tackle the complexities encountered in engineering applications.
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