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Showing 2 results for M. Gholamzadeh

A. Masjedi, M. Gholamzadeh Mahmoodi,
Volume 15, Issue 55 (spring 2011)
Abstract

Every year river flooding causes serious damage to the bridges at the time needed most. One of the most effective factors causing bridge failure is scouring around the piers in a river bend. One of the methods to decrease scouring around the bridge piers is fitting them with a coller on the piers. The collars protect the river bed against vortex flow in the vicinity of the pier base. An experiment was conducted to study lab flumes made of Plaxiglass with a 180 degree bend and 2.8 m central radius and a 0.6 m width. In this study, a 6cm diameter pier was placed with a circular collar with four different collar sizes in one position in bend with constant discharge and depth under clear-water conditions. The collar was placed at four different elevations. The soil material had a diameter of d50 = 2mm and geometric standard deviation of σg = 1.3. The results of the model study indicated that the maximum depth scouring was highly dependent on the experimental duration. It was observed that as the size of a collar plate increases, the scour decreases. So, minimum depth of scour is dependent on the 3D coller and -0.1D elevation. Circular collar results in maximum reduction in scour depth (93%) compared with no circular collar.
M. Gholamzadeh, S. Morid, M. Delavar,
Volume 15, Issue 56 (sumer 2011)
Abstract

Application of drought early warning system is an important strategy for drought management. It is more pronounced in the arid regions where dams have vital role to overcome water shortages. This papers aims to develop and apply such a system that includes three main components, which are 1) drought monitoring, 2) forecasting inflows and water demands and 3) calculation of a warning index for decision about drought management. The system is presented for the Zayanderud Dam. For this, the future six months river inflows and demands are forecasted at different probabilistic levels using the artificial neural networks and considering respected uncertainties. Also, five drought levels are indicated based on the historical records of dam’s storage and the self organizing feature map technique. Furthermore, a drought alert index (DAI) is defined using current storage of dam and the forecasted flows and demands. Finally, the different alert levels are estimated, which vary from normal to sever water scarcity. The results showed that application of the designed warning system can have effective role in the dam’s operation, rationing policy and reducing drought losses.

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