Optimization of the numerical treatment of the Darcy–Forchheimer flow of Ree–Eyring fluid with chemical reaction by using artificial neural networks

In this study, Darcy Forchheimer flow paradigm, which is a useful paradigm in fields such as petroleum engineering where high flow velocity effects are common, has been analyzed with artificial intelligence approach. In this context, first of all, Darcy–Forchheimer flow of Ree–Eyring fluid along a permeable stretching surface with convective boundary conditions has been examined and heat and mass transfer mechanisms have been investigated by including the effect of chemical process, heat generation/absorption, and activation energy. Cattaneo–Christov heat flux model has been used to analyze heat transfer properties. Within the scope of optimizing Darcy–Forchheimer flow of Ree–Eyring fluid; three different artificial neural network models have been developed to predict Nusselt number, Sherwood number, and skin friction coefficient values. The developed artificial neural network model has been able to predict Nusselt number, Sherwood number, and skin friction coefficient values with high accuracy. The findings obtained as a result of the study showed that artificial neural networks are an ideal tool that can be used to model Darcy–Forchheimer Ree–Eyring fluid flow towards a permeable stretch layer with activation energy and a convective boundary condition.