Screens are one of the recent energy dissipator structures that can be used downstream of small hydraulic structures. In this study, screens were used horizontally at the brinks of the vertical drop with downstream smooth and rough bed to investigate the energy loss of drop. Experiments were performed on two porosities of screens, a relative critical depth of 0.13–0.39 and a median size of 1.9 cm aggregates. The results showed that for a relative critical depth of more than 0.3 in a vertical drop equipped with a screen with a rough bed, the drop length with respect to smooth bed increases. Compared to applying a Type I stilling basin, a vertical drop equipped with a screen with downstream smooth and rough bed reduces the drop length by approximately 50%. Although a rough bed increases air entrainment, it has no effect on the energy loss and pool depth of a vertical drop equipped with a horizontal screen with smooth bed. The use of horizontal screens at the brinks of the vertical drop causes maximum energy loss in the downstream of drop. Equations were provided to estimate the flow parameters with a R2 value of more than 0.925 and a normalized root mean square error of less than 0.04.
In the present study, FLOW-3D software was used to simulate energy dissipation by a serrated-edge drop, downstream of this structure. For this purpose, 2, 3, and 4 serrations with two series of relative dimensions at the edge of the vertical drop, with a relative critical depth range of 0.2–0.35 were used for simulation. Then, using Artificial Neural Network (ANN), Support Vector Machine (SVM), and Gene Expression Program (GEP) methods, the accuracy of numerical models was evaluated. Results showed that increasing dimensions of the edges increased energy dissipation, and the highest and lowest energy dissipation was related to the models with 3 and 4 serrations, respectively, Compared to the edgeless state, the 4-edge model, with relative dimension of 0.1, increased energy dissipation by an average of 20%, and the 3-edge model, with relative dimension of 0.15, by an average of 69%. Results of energy dissipation prediction using ANN, SVM and GEP methods showed that although all three models have good accuracy for estimating energy dissipation, the accuracy of ANN method with RMSE of 0.0081 and R2 of 0.9938 in training phase and RMSE of 0.0125 and R2 of 0.9805 in testing phase, is higher than the other two methods.
The scouring depth caused by the water jet outputs from a dam is one of the crucial parameters for design purposes. Due to the importance of the subject, several laboratory studies have been conducted to understand this subject. Nevertheless, using soft computing techniques is a new attitude for modeling and predicting the natural process parameters. Herein, the types of soft computing techniques for estimating the scouring depth of a plunge pool caused by the symmetrical crossing jets have been explored. The parameters involved in the scouring phenomenon are densimetric Froude number, tailwater depth, vertical jet angle, horizontal crossing angles, and the distance between the crossing points of two jets and the water level. The prediction results show that the Multi-Layer Perceptron (MLP) model gives the best performance among the other models tested here. The Pearson correlation coefficient, root mean square error, and normalized root mean square error for the MLP model were 0.9527, 0.9039, and 19.36% for the test phase, respectively. Furthermore, based on the sensitivity analysis, the parameters, for instance, tailwater depth and vertical jet angle have the highest and lowest effects for predicting the scouring depth of a plunge pool, respectively.
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