AI-neural network modelling of Williamson blood flow in porous medium Soret-Dufour effects with tetra hybrid nanoparticles

Abstract

This manuscript delineates a thorough study on the heat and mass transfer phenomenon of the Williamson fluid flow embedded with a tetra-hybrid nanofluid evaluating a wide range of considered and physical effects such as magnetohydrodynamics (MHD), porous medium, radiative heat flux, Joule heating, Soret and Dufour effects, and a Stefan blowing parameter at the boundary, and the rest. A tetra-hybrid nanofluid containing nanofluid gold (Au), silver (Ag), titanium dioxide (TiO2), and aluminium oxide (Al2O3) is used for the improvement of significant thermal and mass transport characteristics. In the back, the demand for efficient thermal systems relates to sets with multiple, integrated transport mechanisms; however, the synergistic transport mechanisms have been largely unexplored, and the coupled hybrid advanced dimensions nanofluids have been unexplored in terms of their combined influences on these parameters. The core target was to examine the active relationships within the physical dynamics parameters while also evaluating the relative increases in the velocity, temperature, and concentration. This paper employs a robust computational approach to the study by solving the governing systems of non-linear ordinary differential equations using an appropriate method of similarity transformation and subsequent numerical techniques. The integration of artificial neural network (ANN) models within this spectrum for the first time, with predictions and optimization set for the outputs, adds a new dimension to this work. The data show that incorporating Soret and Dufour effects, along with the tetra-hybrid nanoparticles, markedly increases the Nusselt and Sherwood numbers, indicating improved heat and mass transfer rates. Furthermore, streamline plots are created to illustrate alterations in the flow structure induced by the Soret and Dufour parameters. This research makes valuable contributions to the development of refined cooling technologies, particularly in energy, chemical, and other process-oriented industries, highlighting the practical utility and innovation potential of the synergistic application of artificial neural networks alongside sophisticated nanofluid models.