Dynamics outlook of hybrid nanoparticle-enhanced milk flow in a rapidly activated electromagnetically integrated conduit under quadratic thermal ramp-up and oscillatory pressure variations using Python AI-facilitated computational model

This study investigates the flow and thermal dynamics of Casson milk enhanced with silver-magnesium oxide hybrid nanoparticles within a rapidly activated electromagnetically actuated conduit under quadratic thermal ramping and oscillatory pressure forcing. A physics-based model incorporating thermal radiation, volumetric heat absorption, and Darcy porous drag is solved analytically using the Laplace transform technique, with predictions validated by a Python-based artificial neural network (ANN). The electromagnetic conduit flow is mathematically modeled, with solutions obtained via Laplace transform analysis. Results reveal that nanoparticle inclusion significantly improves effective thermal conductivity and alters viscosity, enhancing heat transfer efficiency while modifying velocity profiles. Key parametric trends show that the modified Hartmann number amplifies flow momentum, whereas wider electrode spacing attenuates it. Increased thermal radiation reduces fluid temperature, while a larger Casson parameter abates shear stress (SS). The radiation parameter positively augments the rate of heat transfer (RHT). The developed ANN model demonstrates exceptional predictive accuracy, achieving over 99.93% agreement with analytical results across training, validation, and test datasets for both SS and RHT predictions. These findings highlight the synergistic potential of hybrid nanofluids and AI-driven modeling for optimizing thermal processing, improving energy efficiency, and advancing sustainable practices in the dairy industry.