جستجوی مقالات مرتبط با کلیدواژه « sharp crested weir » در نشریات گروه « آب و خاک »

In this study the effect of orifice number and dimensions in combined structure sharp crested rectangular weir with multiple square orifice was investigated. For this propose, some experiments in different flow rate, different orifice number and dimensions were done. The results showed that by different orifice numbers and dimensions, flow discharge increased at the same upstream flow head. This increasing trend was observed in all numbers and dimensions of the investigated experiments. The analysis of the quantitative results showed that by increasing the number of orifices, the discharge rate through the combined structure of weir-orifice was increased on average 2.06 liters per second and by increasing each centimeter of orifice dimensions, the discharge was increased by 2.82 liters per second. Also by calculating the percentage of flow rate increase, it was observed that by adding the orifice number, it increases by 18.7% and by increasing the size of the orifice by one centimeter, the flow rate increases by 28.1%.

In free overfall spillways, waterfalls over the crown of the spillway almost vertically and impacts the downstream bed of the dams. Due to the high velocity and energy of the flow which impacts the erodible downstream bed, it may cause scouring close to the foundation of the dam and consequently threaten the stability of the dam.

In this study, artificial intelligence methods were used to estimate the scour depth of slope control structures with sharp-crested weir due to the complexity of the phenomenon. Three models including neural network, adaptive fuzzy neural system, and support vector machine (SVM) were used as artificial intelligence or black-box model to solve the problem..

The results showed that artificial intelligence methods are more efficient than conventional experimental methods in estimating the depth of downstream scours of slope control structures with sharp-crested weir. Using more parameters in the input of artificial intelligence models does not increase the accuracy of these models. It is because of increasing errors as a result of using more parameters in these models. In estimating the downstream scour depth of slope control structures with the sharp-crested weir in both calibration and validation stages, an adaptive fuzzy neural system model is up to 20% more reliable than the artificial neural network model and up to 8.5% than the support vector machine model.

One of the most important hydraulic structures in a dam is the spillway. The design of the ogee spillway crest is based on the lower profile of the free-flow jet passing through the sharp-crested weir. When the downstream ogee spillway profile for the design discharge conforms to the lower profile of the free-jet passing through the sharp-crested weir, the pressure on that surface of the spillway becomes zero. In this study, the design of the ogee spillway was performed initially based on both two- and three-dimensional numerical modeling and then compared to the USBR standard method. The comparison of the final numerical and analytical results showed that although the vertical two-dimensional outputs were completely in agreement with the USBR standard profile, the three-dimensional profiles were different because in this model, guide walls were not considered. According to the analysis, if the flow entering the spillway is parallel to its axis, the lower profile of the sharp-edge spillway will be in complete agreement with the standard profile. Since, the design of guide wall geometry for ogee spillways is carried out using physical modeling which iteratively revises during a high-cost trial and error procedure, this research based on the case study of the spillway of Karun-3 dam has been tried using numerical modeling. The closest geometry to the geometry of the overflow guide wall was obtained which creates the least difference in transverse velocities. In this way, the design of guide walls can be done with more accuracy and low cost in comparison to physical modeling.

Scour depth prediction downstream of control structures such as spillways and grade-control structures is one of the essential issues in the design of these structures. In the present study, the efficiency of the Gaussian Process Regression (GPR) method was evaluated to estimate the scour depth downstream of control structures. Various models were developed and the impacts of hydraulic parameters were evaluated. The results proved the high efficiency of the applied method in the research in estimating the scour depth compared to the semi-empirical equations. The best result for test series was obtained in the state of sharp crested weir with the values of CC=0.93, DC=0.90, RMSE=0.112 and MAE=0.079and in the inclined slope controlled with the values of CC=0.86, DC=0.81, RMSE=0.194 and MAE=.167. As well as, the sensitivity analysis results show that Fr1 and b/z parameters and Fr1 and Dp/h is the most important parameter affecting the scour depth simulation downstream of sharp crested weir and inclined slope controlled respectively. In fact, variations in the scour depth are influenced by geometry and flow energy in the upstream of these structures. The comparison of semi-empirical and classical relations with the results of the applied method in the research showed that the results of the intelligent GPR method are more accurate and reliable than the classical relations. So that the root mean square error decreased more than 80%.

The aim of this study is to apply different methods to investigation the discharging capacity of a sharpcrested curved plan-form weirs through the Adaptive neuro fuzzy inference system (ANFIS) and Support Vector Machines (SVM) techniques. Subsequently, For training and testing of the proposed equation, experimental data of Kumar et al, have been used and prediction of discharge coefficient through the ANFIS and SVM were compared with equations were proposed with Kumar et al and Zahiri. The result showe that proposed artificial inteligince models have sutable accuracy and also result of superior models is related to total upstream head, spillway height and the angle of curvature of axis curve which demonstrate direct relationship between discharge coefficient and hydraulic properties. Moreover, the performance of ANFIS model is a bit better than SVM technique with relatively low error and high correlation values. Determination coefficient of the proposed equation for discharge coefficient have been calculated as 0.993 for the ANFIS model with Hybrid training method and two point Gaussian membership function, Also this parameter calculated for SVM with RBF Kernel type and with having values include 3, 10 and 0.1 that is related to γ,c and ε respectively as 0.98 for testing phases.