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Energy dissipation downstream of large dams is one of the most important concerns in the design procedure of dams. Flip buckets are employed whenever the velocity of flow at the downstream of the spillway is in excess of typically 20 m/s because of problems with stilling basins in terms of cavitation, abrasion and uplift. One of the most important issues in flip buckets is determining their optimal dimensions in order to increase the energy dissipation and reduce the maximum pressure on the surface of the flip bucket, simultaneously. Nowadays, the increasing computational power of computers to analyze complex problems, development of numerical modeling techniques and artificial intelligence models have caused them popular, in contrast with physical models which are often very time consuming and expensive. Hence, most of the researchers use these methods to analyze the complex engineering problems. In this research, by developing a new simulation-optimization methodology, the optimum dimensions of the flip bucket were determined based on the FLOW-3D model, artificial neural network (ANN) model and Genetic Algorithm (GA) models. The aim of determining the optimum dimensions is to calculate the radius of curvature and also the deflection angle of the flip bucket such that the maximum pressure on the surface of the bucket be minimum and the relative energy dissipation be maximum. Based on this methodology, in the first, the flip bucket of the Jareh dam was simulated for different radii and deflection angles using Flow-3D software. The calibration process was done in the basis of the experimental results which were obtained from the laboratory model. This laboratory model was built in the Water Research Institute of Tehran. Then, an artificial neural network (ANN) as a meta-model was trained using the maximum pressure on the surface of the bucket and the amount of energy dissipation after the impact of trajectory jet with the downstream channel bed. Then it was evaluated by the data that were not used during the training phase. The ability of this meta-model is to predict the values of the maximum pressure on the surface of the flip bucket and the amount of energy dissipation after the impact of trajectory jet with the downstream channel bed for different dimensions of flip bucket. Then by combining this neural network meta-model, with the genetic algorithm (GA) optimization model, the optimum dimensions of the mentioned flip bucket were determined. The optimum dimensions of flip bucket based on the mentioned objectives were found to be equal to the radius of 14 meters (0.28 m at physical model) and angle of 44.5 degrees. The results showed that despite the reduction of dimensions compared to the original size of flip bucket, the rate of energy dissipation has been increased (about 14% for the PMF flow).

Keywords: Flow-3D, Flip bucket, Genetic Algorithm, Flow-3D, Energy Dissipation, Artificial Neural Network

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