OPTIMIZATION OF DARCY-FORCHHEIMER SQUEEZING FLOW IN NONLINEAR STRATIFIED FLUID UNDER CONVECTIVE CONDITIONS WITH ARTIFICIAL NEURAL NETWORK

Abstract

In cases when high velocity occurs, non-Darcy phenomena are essential for explaining fluid motion in porous media and have wide range of applications. The present study displays the magnetohydrodynamic (MHD) squeezing flow of fluid through a non-Darcian medium towards a stretched permeable surface. The heat and mass procedures are investigated using convective conditions and nonlinear stratification. The radiation and viscous dissipation phenomena are implemented to enhance the heat transfer. The nonlinear simplified equations are evaluated using a numerical Runge-Kutta fourth-order approach via the shooting process. To see the variation in the relevant fields, graphs of essential parameters have been provided. The Sherwood number, Nusselt number, and the skin friction coefficient were calculated numerically for various parameters and three different artificial neural networks (ANNs) were developed with the obtained data. The obtained results have shown that artificial neural networks can make predictions and optimizations with high accuracy.