Machine learning based prediction of Williamson-Casson fluid flow with Cattaneo-Christov heat transfer over a curved stretching sheet.

Accurate prediction of non-Newtonian nanofluid flow and heat transfer under melting heat conditions is essential for applications in advanced thermal management systems, polymer extrusion and coating processes, metallurgical melting operations, biomedical fluid transport, micro- and nano-scale heat exchangers, and energy systems. Motivated by these practical applications, the current work systematically examines the Williamson-Casson nanofluid flow over a slippery curved expanding surface while simultaneously incorporating the Cattaneo-Christov heat flux model. The flow is affected by melting heat transfer conditions at boundary, activation energy, chemical reactivity and Soret/Dufour effects. The modeled equations have been solved through the bvp4c approach in dimensionless form. The solution obtained from this approach is then used to provide a dataset for Artificial Neural Network approach. It has discovered in this work that velocity profiles are augmented with growth in thermal buoyancy factor and velocity slip parameter, while declining with augmentation in magnetic parameter, ferrohydrodynamic interaction and Weissenberg number. Thermal profiles escalated with augmentation in heat source/sink parameter, heat dissipation factor, unsteadiness factor, radiation factor and Dufour number, while declining with augmentation in melting heat parameter, thermal buoyancy factor and thermal relaxation factor. The analysis of the histograms across the entire dataset by plotting the frequency of errors within discrete intervals confirms the validity and reliability of the model. With growth in local Weissenberg number, Casson factor and curvature parameter there is augmentation in skin friction. For higher Prandtl number and melting heat parameter there is intensification in Nusselt number. With escalation in Schmidt number and Soret number there is augmentation in Sherwood number while higher factor of activation energy causes reduction in it.