Abstract

CASTILLO-CALDERON, Jairo; SOLORZANO-CASTILLO, Byron  and  MORENO-MORENO, José. DESIGN OF A NEURAL NETWORK FOR THE PREDICTION OF THE COEFFICIENT OF PRIMARY LOSSES IN UTRBULENT FLOW REGIME. Ingenius [online]. 2018, n.20, pp.21-27. ISSN 1390-860X.  https://doi.org/10.17163/ings.n20.2018.02.

This investigation is focused on the design of a neural network for the prediction of the friction factor in turbulent flow regime, being this factor indispensable for the calculation of primary losses in closed ducts or pipes. MATLAB® Neural Networks Toolbox is used to design the artificial neural network (ANN), with backpropagation. The database includes 724 points obtained from the Moody diagram. The Reynolds number and the relative roughness of the pipe are the input variables of the ANN, the output variable is the coefficient of friction. The Levenberg-Marquardt algorithm is used for training the ANN by using different topologies, varying the number of hidden layers and the number of neurons that are hidden in each layer. The best result was obtained with a 2-30-30-1 topology, exhibiting a mean squared error (MSE) of 1.75E-8 and a Pearson correlation coefficient R of 0.99999 between the neural network output and the desired output. Furthermore, a descriptive analysis of the variable was performed in the SPSS® software, where the mean relative error obtained was 0.162 %, indicating that the designed model is able to generalize with high accuracy.

Keywords : Moody diagram; friction factor; head loss; artificial neural network; backpropagation; turbulent flow.

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