Impact of thermal energy on MHD Casson fluid through a Forchheimer porous medium with inclined non-linear surface: A soft computing approach

In this work, the influence of thermal energy in term of heat source, thermal radiation and chemical reaction on magneto hydrodynamic Casson fluid flow model (MHD-CFM) over a nonlinear slanted extending surface with slip velocity in a Forchheimer permeable medium is numerically studied using the Levenberg Marquardt methodology with backpropagated learning mechanism. It is valuable to evaluate the flow of Cason fluids based on materials (such as drilling muds, clay coatings, various suspensions and certain lubricating oils, polymeric melts, and a wide range of colloids) in the occurrence of heat transfer. Using efficient data, PDEs of (MHD-CFM) were converted to ordinary differential equations. These obtained non-linear ODEs are then rectified using the computational power of the Lobatto IIIA approach to obtain a dataset of Levenberg Marquardt algorithm based trained neural networks (LMA-TNN) for six scenarios of this presented model, which were graphically represented using nftool to obtain regression, efficiency, fit curve, error bars, and trained state analysis. The velocity, temperature, and concentration profiles were computed, and the findings were presented. Additionally, the skin friction coefficient, Nusselt number, & local Sherwood number are explored. The graphs show that when values of radiation parameter and the Forchheimer porous media parameter increase, the temperature of the plate drops. In the existence of a chemical process and a high Schmidt number, the concentration drops. The accuracy achieved in terms of relative error demonstrates the validity and significance of the solution process.