Research Information System
Multiscale and Multidisciplinary Modeling, Experiments and Design, vol.8, no.9, 2025 (ESCI, Scopus)
This research looks into the three-dimensional Williamson nanofluid flowing over a stretching sheet as influenced by the Cattaneo–Christov heat and mass fluxes. Advanced mathematical modeling transforms governing partial differential equations into ordinary differential equation using similarity transformations. Equations are solved with the use of bvp4c of MATLAB along with artificial neural network for the validation and optimization of numerical solutions. Key parameters include thermal radiation, thermophoresis, and Brownian motion, the effect of which is investigated on the profiles of temperature, velocity, and concentration. The findings show that the Williamson parameter for non-Newtonian fluids increases fluid viscosity, reducing velocity; the Cattaneo–Christov flux is fastened to heat transfer and raises the temperature profile. The artificial neural network prediction has been compared with numerical results, and it was seen that artificial neural network-based models effectively capture the complex fluid dynamics. These findings have implications for maximizing thermal management in industrial operations, including polymer extrusion and porous media applications. These parameters of interest are examined to monitor their effect on velocity, temperature, and concentration profiles, by examining the effect of the Williamson parameter on velocity and that of the Cattaneo–Christov flux on temperature. As clear from the table, the results predicted by artificial neural network are highly precise with high value of regression in terms of unity and mean square error is as small as possible.