Chemical and MHD Effects on Mixed Oscillatory Flow Dynamics: A Neural Network Study

This paper investigates in detail the thermal and chemical effects of an unstable magnetohydrodynamic (MHD) mixed oscillatory flow. The temperature, velocity, and concentration profiles can be investigated in detail by transforming the governing equations into a dimensionless system. These equations are solved using the perturbation method, which reveals details about the significant effects and connections between the variables being examined. The velocity, temperature, and concentration are found to decrease as the magnetic field, heat radiation, and chemical reaction rise. Additionally, artificial neural network (ANN) approaches are applied to these ordinary differential equations (ODEs), and the outcomes are contrasted with numerical simulations. This work illustrates the ANN model's capacity to produce extremely precise heat transfer rate forecasts from an engineering standpoint. This method improves knowledge of complex fluid magnetohydrodynamics and porous medium flows by incorporating artificial intelligence.