MHD radiant couple stress tetrahybridized nanofluid streaming inside slanted rotating micro-parallel plates subject to Hall currents: a neuro-computing approach

The hydrothermal dynamical effectiveness and usefulness of highly responsive spinning mechanisms under slanted Hall currents is a significant issue in several manufacturing and experimental functions. Hybridized nanoparticles have novel properties that are advantageous for a range of technical uses. Compared to trihybrid, bihybrid, or mono-nanofluid, tetrahybrid nanofluid (Tetra HNF) is a new idea in research that enables a faster cooling process. These motivate us to research the effects of oblique Hall currents on a non-Newtonian couple stress tetrahybrid nanofluid flow in an oblique channel with oscillatory heating under strong external magnetic attraction with Hall currents in a magneto-gyrating environment. To create tetrahybrid nanofluids (Cu–TiO\(_2\)–Ag–Al\(_2\)O\(_3\)/WEG), copper, titania, silver, and alumina nanopowder forms are dispersed in a colloidal solution of water and ethylene glycol (vol. 60–40\(\%\)). We discuss four kinds of nanoparticles: spheres, bricks, cylinders, and platelets. Mechanical circumstances and presumptions are used to build the partial differential equations (PDEs) that describe the mechanical problems. The dimensionless energy and momentum with related wall constraints are resolved using an analytical approach. Multiple kinds of graphic representations and tabulated data are presented to fully accomplish and demonstrate the mechanical aspects of important developing parameters on the hydrothermal trends and their practical significance. Our results demonstrate that the resultant velocity rapidly rises over growing changes in inclined Hall currents. The velocity profile gets an elevation for the inclination of the channel in the range \(\pi /4<\alpha <\pi /2\), but reversal flow occurs for a slight angle of inclination (\(0<\alpha <\pi /4\)). Platelet-shaped NPs transport higher heat than other shapes (spherical, brick shaped, or cylindrical). Tetrahybrid nanofluid achieves higher heat transport than other base fluid types (pure WEG or mono/bi/trihybrid nanofluids). An artificial neural network (ANN) model is also developed based on testing datasets generated via the analytical evaluation. This ANN architecture achieves an astounding \(99.98\%\) accuracy in predicting critical flow amounts. Our simulations can be applied to the development of reliable oblique Hall sensors and to several manufacturing procedures, including the interaction of nano-polymers and the use of composite nano-lubricants in regulating temperature.