Artificial Neural Network-Based Flow and Heat Transfer Analysis of Williamson Nanofluid over a Moving Wedge: Effects of Thermal Radiation, Viscous Dissipation, and Homogeneous-Heterogeneous

There is a need for accurate prediction of heat and mass transfer in aerodynamically designed, non-Newtonian nanofluids across aerodynamically designed, high-flux biomedical micro-devices for thermal management and reactive coating processes, but existing work is not uncharacteristically remiss regarding viscoelasticity, radiative heating, viscous dissipation, and homogeneous–heterogeneous reactions within a single scheme that is calibrated. This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation, thermal radiation, and homogeneous-heterogeneous reactions. The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations. RK4 is the technique for gaining numerical solutions, but with the addition of ANNs, there is an improvement in prediction accuracy and computational efficiency. The study investigates the influence of wedge angle parameter, along with Weissenberg number, thermal radiation parameter and Brownian motion parameter, and Schmidt number, on velocity distribution, temperature distribution, and concentration distribution. Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns, but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena. This research develops findings that are of enormous application in aerospace, biomedical (artificial hearts and drug delivery), and industrial cooling technology applications. New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.