Application of feedforward neural network in the study of dissociated gas flow along the porous wall

This paper concerns the use of feedforward neural networks (FNN) for predicting the nondimensional velocity of the gas that flows along a porous wall. The numerical solution of partial differential equations that govern the fluid flow is applied for training and testing the FNN. The equations were solved using finite differences method by writing a FORTRAN code. The Levenberg–Marquardt algorithm is used to train the neural network. The optimal FNN architecture was determined. The FNN predicted values are in accordance with the values obtained by the finite difference method (FDM). The performance of the neural network model was assessed through the correlation coefficient (r), mean absolute error (MAE) and mean square error (MSE). The respective values of r, MAE and MSE for the testing data are 0.9999, 0.0025 and 1.9998 · 10−5.

► The major objective of the study presented in this paper was to construct a high-quality FNN model to predict the nondimensional velocity of the dissociated gas that flows along a porous wall.
► The proposed approach can be applied to predict the other characteristics of the boundary layer.
► Soft programming methods such as FNN can be use to predict new values from generated data, thus saving computational time and reducing cost of studies.