The role of machine learning in analyzing magnetic field localization and vortex generation in tetra-hybrid nanofluid: A numerical study

This research distinguishes itself by integrating machine learning algorithms to assess the impact of confined magnetic fields on vortex dynamics in hybrid nanofluid flow, through the inclusion of both vertical and horizontal magnetic field strips. Specifically, the study focuses on a vertical cavity with an aspect ratio of 1:5, where a bottom lid moves horizontally from left to right to drive the flow. We have applied a limited magnetic field consisting of vertical, and horizontal strips. The authors have developed MATLAB codes to implement an algorithm for solving the governing equations of the nanofluid flow and heat transfer. The algorithm is based on the Stream-Vorticity formulation and uses a finite difference method. The algorithm can examine how several parameters, such as magnetic field strength (0–300), nanoparticle volume fraction (0–20%), and Reynolds number (Re) (1–50), impact the characteristics of nanofluids in terms of flow and thermal properties. The results demonstrate that magnetic fields influence the stress distribution of the flow pattern and the temperature distribution. Further, the presence of a magnetic field also affects stress distribution. Moreover, it has been determined that the Nusselt number (Nu) experiences a 60% increase due to the magnetic field, while there is a remarkable rise attributed to the Re. Similarly, significant changes are observed in skin friction under both parameters. These findings carry implications for designing and operating devices.