Numerical and neural-network analysis of Casson hybrid nanofluid flow and heat transfer in a sinusoidally stenosed artery.

We investigate unsteady flow and heat transfer in a blood-based Casson hybrid nanofluid containing Al2O3 and Fe3O4 nanoparticles as it passes through a rigid artery with a mild sinusoidal stenosis. The formulation includes a transverse magnetic field, Hall-current effects, viscous dissipation, and Joule heating. Under the long-wavelength approximation, the governing equations reduce to local radial initial-boundary-value problems. These problems are mapped onto a fixed domain and solved with a forward-time central-space (FTCS) scheme that uses a separate stencil at the centreline. Particular attention is given to the effects of the Casson, magnetic, Hall, Reynolds, Prandtl, and Eckert parameters, as well as the two nanoparticle volume fractions, on the velocity, temperature, wall shear stress, and local Nusselt number. We interpret the observed trends through the balance among viscous resistance, Lorentz damping, radial diffusion, internal heating, and the effective properties of the suspension. Parameter-specific Levenberg-Marquardt backpropagation neural networks are also trained to interpolate the FTCS data. In most of the sampled cases, the network follows the numerical profiles closely. However, a small number of negative centreline-temperature predictions make clear that a low overall error does not always imply physically acceptable behaviour at every point. The neural-network results should therefore be viewed as interpolation within the sampled ranges, while the physical conclusions remain tied to the assumptions of the idealized single-phase arterial model. Clinical trial registration: Not applicable.