Computational insights and artificial neural network modeling of radiative boundary layer flows in tangent hyperbolic nanofluid

This research examines the boundary-layer flow of a tangent hyperbolic nanofluid over a moving wedge, considering both viscous and radiative effects, in order to evaluate nanoparticle-enhanced thermal properties and non-Newtonian dynamics. The study combines nanofluid heat enhancement with non-Newtonian flow behavior, radiative thermal processes, and motile organism patterns to create an integrated mathematical framework that addresses current research gaps while proposing applications ranging from cooling systems to automotive thermal management, biomedical technology, and energy system design. The differential equations are transformed using similarity transformations before being solved numerically using MATLAB's fourth-order Runge-Kutta technique. The study uses artificial neural networks for prediction validation and findings via contour plots, three-dimensional graphs, and simplified visuals. The study shows that Weissenberg numbers increase fluid elasticity while decreasing drag, heat radiation effects expand temperature profiles while increasing thermal boundary thickness and shifts in thermophoresis, and Lewis numbers have a significant impact on chemical distributions by improving industrial studies of fluid dynamics.