Stagnation Point Hybrid Nanofluid Flow and Entropy Production with Influence of Nonlinear Thermal Convection on a Riga Plate with Application of Neural Network Technique

This study examines the generation of irreversibility and the behavior of stagnation point hybrid nanofluid flow on a Riga plate. The effects of nonlinear thermally convective and solar radiation are incorporated in the modeled equations. The nanoparticles of (Cu) and (Al2O3) are mixed with Glycol (C3H8O2) to hybridized it. The leading equations have been changed to dimension-free form by using the set of appropriate variables and then have been evaluated by Artificial Neural Network (ANN) approach. It is revealed in this work that, the velocity panels are amplified with expansion in Grashof number and electromagnetic factor while declined with escalation in magnet/electrode factor and nanoparticles concentration. Upsurge, in Eckert number for both the scenarios (Ec 0) and (Ec 0), the radiation factor and nanoparticles concentration cause augmentation in thermal characteristics. Radiation factor has positive impacts on Bejan number and generation of Entropy. Moreover, Bejan number is retarded while entropy is augmented with growth in Brinkman number. It is also established in this work that the principle of entropy generation for hybrid nanoparticles supports the efficient delivery of drug in cancer treatment.