MHD Convective Flow of Chemically Reacting Viscoelastic Fluid Through an Infinite Inclined Plate via Machine Learning

This study investigates the influence of various parameters such as the Grashof number, corrected Grashof number, permeability of the porous material, angle of inclination, magnetic parameter, heat source parameter, Prandtl number, radiation parameter, radiation absorption parameter, Schmidt number, and chemical reaction parameters on the flow behaviour of viscoelastic fluids past inclined plates. To improve the accuracy and predictive capacities of the models employed in the study, Machine Learning techniques are applied. The results show that increasing the Grashof number, corrected Grashof number, permeability, and angle of inclination results in higher velocities, however the existence of a magnetic parameter decreases velocity. The temperature increases as the heat source parameter increases, but lowers when the Prandtl number, radiation parameter, and radiation absorption parameter increases. Furthermore, as the Schmidt number and chemical reaction parameters increase, the concentration falls. This research advances our understanding of viscoelastic fluid flow and highlights the power of Machine Learning in predicting and analysing complex fluid flow events.