Numerical Detection of Cavitation Damage on Dam Spillway

The present paper deals with the numerical detection of cavitation damage level and location on dam spillways. At first, flow over a spillway was simulated using the computational fluid dynamics method. The flow characteristics such as pressure, velocity and depth through the spillway have been calculated for five different flow rates. Since the actual flow is turbulent, the RNG turbulence model has been used for simulation. The numerical results of flow characteristics including flow depth, velocity and pressure were compared with the available results of the hydraulic model tests. The numerical results agreed well with the experimental data, and reasonable values for the normalized root mean square error (NRMSE= 0.0476) and coefficient of determination (r2=0.8354) indicated that the numerical model is accurate. Finally occurrence of cavitation damage to the Doosti dam spillway was investigated. Based on cavitation index, five different damage levels from no damage to major damage have been considered. Results showed that the spillway may be at the risk of cavitation damage, and the serious damage can occur at ending parts of the structure

Drought Monitoring and Prediction using K-Nearest Neighbor Algorithm

Drought is a climate phenomenon which might occur in any climate condition and all regions on the earth. Effective drought management depends on the application of appropriate drought indices. Drought indices are variables which are used to detect and characterize drought conditions. In this study, it was tried to predict drought occurrence, based on the standard precipitation index (SPI), using k-nearest neighbor modeling. The model was tested by using precipitation data of Kerman, Iran. Results showed that the model gives reasonable predictions of drought situation in the region. Finally, the efficiency and precision of the model was quantified by some statistical coefficients. Appropriate values of the correlation coefficient (r=0.874), mean absolute error (MAE=0.106), root mean square error (RMSE=0.119) and coefficient of residual mass (CRM=0.0011) indicated that the present model is suitable and efficien

Cavitation Damage Prediction on Dam Spillways using Fuzzy-KNN Modeling

The present paper deals with a numerical method for prediction of cavitation damage level and location on dam spillways. A method was applied to predict the intensity of cavitation damage to spillways, using the fuzzy k-nearest neighbor algorithm. Five levels of damage intensity were considered to predict cavitation damage in the spillway of Karun-1 Dam in Iran. According to the results, the proposed model could properly predict the location and intensity of damage in comparison with the actual damage reports of past floods. According to the Pearson's correlation coefficient, mean absolute error, coefficient of residual mass, and normalized root mean square error, the fuzzy k-nearest neighbor model is efficient and suitable