Numerical Modelling and Deep learning-based analysis of unsteady micropolar nanofluid flow with heat radiation over a riga plate

The fluid flow analysis past a Riga plate is significantly considered in engineering and physics, particularly used for examining the boundary layer nanofluid flow. The Riga surface is normally used for estimation and regulating the properties of turbulent and laminar flow. Determining the behavior of fluid flow across a Riga plate is important in different sectors, such as heat transfer, industrial processes, and aerodynamics. The current study investigates the unsteady flow of a Micropolar nanofluid (UMNF) over a vertical non-linear extensible Riga plate. The implications of variable thermal conductivity, Brownian diffusion, and thermophoresis are also addressed. The modeled equations of UMNF flow are simplified into the dimensionless form of ODEs (ordinary differential equations) by using the similarity substitution. The dataset for the Levenberg-Marquardt Back Propagation (LMBP) based on Artificial Neural Networks (ANNs) is generated through the numerical approach BVP4c. The upshot of physical flow dimensionless constraints on energy, microrotation, velocity, and concentration distributions are illustrated graphically. The numerical findings for skin friction, energy, and mass transfer are displayed in tabular form. It can be observed that the effect of Hartmann number drops, while the variation in buoyancy parameter enhances the fluid velocity. The thermal field of UMNF increases with the influence of variable thermal conductivity.