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B-Jump: Roller length, sequent depth, and relative energy loss using Artificial Neural Networks

Author(s): F.Y. Yazdandoost; S. M. Bateni; M. FAZELI

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Keywords: Hydraulic jump; neural networks; back propagation algorithm; orthogonal least square algorithm; Roller length; sequent depth; energy loss

Abstract: The phenomenon of the hydraulic jump is so complex that despite considerable laboratory and prototype studies, estimation of its main characteristics in a generalized and accurate form is still difficult. The Artificial Neural Network (ANN) approach aims at limiting the needs for costly and timeconsuming experiments. In this study, twoANN models, multi-layer perceptron using back propagation algorithm (MLP/BP) and radial basis function using orthogonal least-squares algorithm (RBF/OLS), were used to predict the roller length, sequent depth, and the relative energy loss of the B-jump. Based on a pre-specified range of jump parameters, the input vectors include: upstream bed slope (tan ?), inflow depth h1, approach velocity V1 and elevation of jump toe from the datum plane z1, generated from the experimental data of Hager (J. Hydraul. Res., IAHR, 26(5), 539–558, 1988) and Kawagoshi and Hager (J. Hydraul. Res., IAHR, 28(4), 461–480, 1990). Once the network is trained to an acceptable level of accuracy, it produces an output of jump roller length, sequent depth, and relative energy loss for any input vector. The predicted values agree well with measurements. Sensitivity analysis was performed to investigate the importance of each input neuron. Finally a matrix of weights was specified for use at any given location


Year: 2007

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