A Fuzzy Genetic Method to Predict Air Demand Downstream of Bottom Outlet Gates

A Fuzzy Genetic Method to Predict Air Demand Downstream of Bottom Outlet Gates

With increasing the flow velocity over the surface of bottom outlet conduits and spillways of high dams, the potential for cavitation damage is also increased. Amongst various methods, aeration has been found as an effective and easy way to eliminate cavitation. Flow aeration improves the mean pressure and modifies the intensity of pressure fluctuations. So far, many works have been done and various relationships have been introduced to predict the quantity of entrained air downstream of outlet gates. However, there are uncertainties in using these expressions for various flow and geometry conditions. In recent years, the applications of artificial intelligence, such as neural networks, fuzzy logic, and genetic algorithm have attracted the attention of many investigators. They are known as powerful tools to solve engineering problems with uncertainties. Therefore, in this study, a model based on genetic algorithm was developed to express an equation for estimating the quantity of air demand downstream of gates in outlet conduits. Fuzzy logic was also adopted to control the system. The model is based on experimental information from various physical model studies. The measurements were made at Water Research Institute of Iran on several physical models of bottom outlet conduits, which are under construction or recently completed in Iran. The models mostly consists of service and emergency slide gates with fixed aerators on the top wall of the conduits. The proposed expression provides a good prediction of the quantity of required air as a function of the Reynolds and Froude numbers, the gate opening and the geometries of aerator, conduit and gate. Reasonable agreement between the predicted results and the experimental information proves the validity of the present model.