Journal of Water Sciences Research
Volume:4 Issue: 1, 2012

Hydraulic conductivity variations around the boreholes in uniformly graded sands for cyclic water injection pressure have been evaluated by laboratory tests. Prototype laboratory devices have been designed and constructed for this research. The cell has a capability to model the well, the boundary condition and to measure the hydraulic conductivity. While increasing water injection pressure, hydraulic conductivity suddenly increases in a special pressure value. Hydraulic conductivity is measured and compared with its initial value at the beginning of this cycle. Test procedures are conducted several times and results are compared and analyzed. In these sands with upper soil layers pressure up to 68.64 kPa, hydraulic conductivity at the end of the first cycle reaches to a 20% to 58% growth comparing to its initial amount at the beginning of the test. In the second cycle, this growth rate is up to 16% more than the first cycle. By decreasing the injection pressure at the end of each cycle, a part of the increased hydraulic conductivity due to the high injection pressure is dissipated.

Recently, particular attention has been paid to the stepped spillways due to the increasing effect of energy dissipation and the reduction of cavitations risks with the development of Roller Compacted Concrete (RCC) technique. Flow regimes on the spillways divide into three groups, namely skimming, jet and transition flow. Compared to the numerical methods, the majority of performed studies in this field have been done using experimental methods, which requires a lot of time and expenditure. This paper has attempted to train an Artificial Neural Network (ANN) which includes two layers for determination of the number of aerated steps over broad-crest stepped spillways under the jet flow regime condition using MATLAB. The best network was trained entailing one neuron on each layer. Results show that the number of aerated steps can be obtained by having the critical depth, steps height, chute length, chute angle, and total number of steps. The obtained nonlinear relationship was validated using testing data. The predicted results have been compared to experimental data. Proper conformity of outputs and experimental data has been investigated using SPSS. The P-Value of Man-Whitney Test of the training and testing data compared to experimental data are 0.89 and 0.77, respectively. Good agreement was shown between experimental data and predicted results using ANN.

DHI-Mike Software is one of best softwares describing water wave diffraction in onshore/offshore concepts. Diffraction happens due to obstacles along wave direction and has an important role in designing structures. The scope of this study is to show the relation between Porosity Coefficient and its effects on Diffraction Coefficient (Cd) by means of a suitable and verified numerical model. Porosity Coefficient is one of the breakwaters’ characteristics that describe the reflection quantity of the wave that influences on Diffraction Coefficient. This parameter determines the quantity of obstacles porosity and helps in implementing the sensitivity analysis. A calibrated Model helps as a suitable background to examine different Porosity Coefficients. Reputed "Wave Diffraction Diagrams" has been used for model calibration and will be discussed in detail in an appropriate section. It has been concluded that by increasing the Porosity Coefficient, wave height and Diffraction Coefficient increase.

This paper presents results of hydro-chemical processes controlling groundwater chemical composition, using an integrated application of hierarchical cluster analysis and factor analysis of a major ion data set of groundwater from Mighan playa aquifer. Cluster analysis classified samples into four clusters(A, B, C and D) according to their dominant chemical composition: cluster A (dominant composition: Ca-HCO3; mean TDS: 267 mg/l), cluster B (dominant composition: Ca-Mg-SO4-Cl; mean TDS: 1029 mg/l), cluster C (dominant composition: Na-Mg-SO4; mean TDS 667 mg/l) and cluster D (dominant composition Na-Mg-SO4¬-Cl; mean TDS 2998 mg/l), which were described by the first factor of factor analysis. Results of the factor analysis suggested that the spatial variation of groundwater quality is influenced by processes of carbonate minerals dissolution and mixing of saline water (Mighan playa). The calculated playawater fractions (ƒplaya) of the groundwater shows that cluster D water is almost four times more saline than cluster B water and twice and a half more saline than cluster C water, where the difference between salinities can be explained by proximity of cluster D water to the playa. In the present study, 23.8 % of the playawater samples were found to contain chloride concentrations above 250 mg/l suggesting that playawater intrusion has reached alarming levels yet. The saline/brackish groundwater is the result of the processes of evaporation (for samples close to the Mighan playa) and dissolution of SO4 and Cl evaporative salts (such as thenardite, gypsum and halite).

Predictions of suspended sediment load for Soolegan River, Iran using selected empirical equ-ations were made based on 355 data sets. Data include flow discharges from 3.11 m3/s to 43.81 m3/s, flow velocities from 0.22 m/s to 1.03 m/s, and flow depths from 0.5 to 1.03 m. Equations of Einstein (1950), Bagnold (1966), Toufalleti (1968), Brooks (1963), Chang-Simons-Richardson(1965),Lane-Kalinske (1941) are used in the evaluations. Selection was made based on the purpose of this paper, illustrating the performance of six suspended sedi-ment formulas in Soolegan River; the integrated formulae were also evaluated. Graphical comparisons of the calculated and measured transport rates are shown. The accuracy and re-liability of these formulas are verified. Results of the evaluations showed that Brooks had the best estimations while the same results for integrated formulae showed that Lane –kalinske and Bagnold estimated the suspended sediment discharges better than integrated and non-integrated ones.

Recently the stepped spillways have been used as an appropriate solution for energy dissipation. In the present study, Siahbisheh dam spillway is simulated by using Computational Fluid Dynamic (CFD), in which the Mixture method and Reynolds Stresses Model (RSM) turbulence model is used. In the first modeling series the over all steppes slope is constant. The number of the steps is increased to 80, in which the step height and length is decreased to 0.51 and 1.53 m, respectively. In another case, by reducing the number of the steps to 30, steps height and length increased to 1.36 and 3.9 m, respectively. In the next modeling series, the steppes slope was changed. By reducing the number of the steps in to 30, spillway slope increased to 330 and the height of the steps increased to 1.36 (step length was 2.1 m). Then by increasing the number of the steps to 80, spillway slope decreased to about 140and the height of the steps decreased to 0.51m (step length was 2.1 m). Results analysis and comparison of the increasing the energy dissipation to 60 steppes case shows that by decreasing the number of the steppes into 30, the energy dissipation decreases. This trend is expected to continue by increasing the steppes number into 80, but it is not. It seems that there is no fixed pattern between the number of the steppes and energy dissipation percentage. It can be said the increasing the number of the steppes and decreasing the slope up to a certain value increases the energy dissipation, and this number would be optimal under certain conditions. Finally it seems that the effect of the slope is considerable, so that the case in which larger number of steppes with a mild slope is closer to the optimum condition of maximum energy dissipation.

In order to determine hydrological behavior and water management of Sepidroud River (North of Iran-Guilan) the present study has focused on stream flow prediction by using artificial neural network. Ten years observed inflow data (2000-2009) of Sepidroud River were selected; then these data have been forecasted by using neural network. Finally, predicted results are compared to the observed data. Results showed that neural network could predict stream flow with high precision and the maximum error percentage in data prediction was about 3.