An experimental study on plunging depth of density currents

Mass density of the current flows is the one of the important problem in the hydraulics of the dam reservoir. Plunge point occurs when the mass density current penetrates in the stagnant fluid. Recognition the place of this point is very important because of clearing the boundary of the density current flow and ambient fluid. In this study the influences of bed slope and hydraulic parameters on plunging depth were experimentally investigated. The results show that the slope has a minor effect on the plunging depth. The height of plunging depth is increased by increasing the density of the current flow. Also increasing the densimetric Froude number caused of decreasing the plunging depth. Finally an equation was proposed to estimate the plunging depth using as function of flow characteristics.     

Applications of soft computing techniques for prediction of energy dissipation on stepped spillways

In this study, numbers type of soft computing including artificial neural network (ANN), support vector machine (SVM), multivariate adaptive regression splines (MARS), and group method of data handling (GMDH) were applied to model and predict energy dissipation of flow over stepped spillways. Results of ANN indicated that this model including hyperbolic tangent sigmoid as transfer function obtained coefficient of determination (R ² = 0.917) and root-mean-square error (RMSE = 6.927) in testing stage. Results of development of SVM showed that developed model consists of radial basis function as kernel function achieved R ² = 0.98 and RMSE = 2.61 in validation stage. Developed MARS model with R ² = 0.99 and RMSE = 0.65 has suitable performance for predicating the energy dissipation. Results of developed GMDH model show with R ² = 0.95 and RMSE = 5.4 has suitable performance for modeling energy dispersion. Reviewing of results of prepared models showed that all of them have suitable performance to predict the energy dissipation. However, MARS and SVM are more accurate than the others. Attention to structures of GMDH and MARS models declared that Froude number, drop number, and ratio of critical depth to height of step are the most important parameters for modeling energy dissipation. The best radial basis function was found that as best kernel function in developing the SVM.

     

Numerical modeling of cavitation on spillway’s flip bucket

Numerical modeling of hydraulic phenomenon by computational fluid dynamic (CFD) approaches is one of the main parts in the high cost hydraulic structure studies. In this paper, using Flow 3D as CFD commercial tool, the cavitation phenomenon was assessed along spillway's flip bucket of the Balaroud dam. Performance of numerical modeling was compared to the physical model, which was constructed to this purpose. During numerical modeling, it was found that RNG turbulence model is a suitable performance for modeling the cavitation. Physical modeling shows that minimum cavitation index is about 0.85 and minimum cavitation index based on Flow 3D results is about 0.665, which was related to the flood discharge with return period of 10000 years. The main difference between numerical and physical modeling is related to the head of velocity, which is considered in physical modeling. Results of numerical simulation show that occurrence of cavitation based on cavitation index equal to 0.25 is not possible along the spillway.     

Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS)

Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems (ANFIS). To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network (MLP) was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.