Artificial neural network model for prediction of friction factor in pipe flow

Abstract

Determination of friction factor is an essential prerequisite in pipe flow calculations. The Darcy-Weisbach equation and other analytical models have been developed for the estimation of friction factor. But these developed models are complex and involve iterative schemes which are time consuming. In this study, a suitable model based on artificial neural network (ANN) technique was proposed for estimation of factor to friction in pipe flow. Multilayered perceptron (MLP) neural networks with feed-forward back-propagation training algorithms were designed using the neural network toolbox for MATLAB®. The input parameters of the networks were pipe relative roughness and Reynold’s number of the flow, while the friction factor was used as the output parameter. The performance of the networks was determined based the mean on absolute percentage error (MAPE), mean squared error (MSE), sum of squared errors (SSE), and correlation coefficient (R-value). Results have shown that the network with 2-20-31-1 configuration trained with the Levenberg-Marquardt 'trainlm' function had the best performance with R-value (0.999), MAPE (0.68%), MSE (5.335xI0-7), and SSE (3.414x10-4). A graphic user interface (GUI) with plotting capabilities was developed for easy application of the model. The proposed model is suitable for modeling and prediction of friction to factor in pipe flow for on-line computer-based computations.