Modeling River Mixing Mechanism Using Data Driven Model

Modeling river mixing mechanism in terms of pollution transmission in rivers is an important subject in environmental studies. Dispersion coefficient is an important parameter in river mixing problem. In this study, to model and predict the longitudinal dispersion coefficient (D _L ) in natural streams, two soft computing techniques including multivariate adaptive regression splines (MARS) as a new approach to study hydrologic phenomena and multi-layer perceptron neural network as a common type of neural network model were prepared. To this end, related dataset were collected from literature and used for developing them. Performance of MARS model was compared with MLP and the empirical formula was proposed to calculate D _L . To define the most effective parameters on D _L structure of obtained formula from MARS model and more accurate formula was evaluated. Calculation of error indices including coefficient of determination (R²) and root mean square error (RMSE) for the results of MARS model showed that MARS model with R²?=?0.98 and RMSE?=?0.89 in testing stage has suitable performance for modeling D _L . Comparing the performance of empirical formulas, ANN and MARS showed that MARS model is more accurate compared to others. Attention to the structure of developed MARS and the most accurate empirical formulas model showed that flow velocity, depth of flow (H) and shear velocity are the most influential parameters on D _L .     

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.

     

Optimization of Hydraulic-Hydrologic Complex System of Reservoirs and Connecting Tunnel

Nowadays, population growth, environmental constraints and climate change can adversely affect our water supply systems’ ability to keep up with demand. Due to lack of unsuitable distribution and dispersion of water resources, precipitation, soil resources, etc., inter-basin transfers of water could be a solution in order to balancing between supply and demand water in different areas. In this study, the optimal designing of water conveyance from basin No-1 to basin No-2 is investigated. Water is transferred between these two dams by tunnel structure. Since the water flow through the tunnel is under pressure, increasing dam height will cause the decrease of tunnel diameter for constant water conveyance efficiency. The purpose of this study is transferring 95 % of water flow between two basins after supplying the agriculture consumption and environmental needs. Therefore, the mathematical program was developed first to solve the governing equations of water balance of reservoir and hydraulic of tunnel. Then, various strategies including different diameters of tunnel and dam height were considered and finally the best strategy from economic and technical viewpoint was proposed. The results showed that dam height of 151.2 m and tunnel diameter of 3.2 m are the economic options to convey of 95 % of the water.     

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.