qiumars roushangar

The piano key weirs (PKWѕ) are type of nonlinear weirs which can increase the discharge for a given width without the increase in head water .In piano key the length of weir crest in both upstream and downstream can be incresead and therefore can be considered to be a suitable substitution for the labyrinth weirs, with a relatively small footprint In this study, in order to evaluate the Hydraulic performance of PKWs of free & submerged flow, totally 554 experiments are conducted on 11 physical models. Results showed that in the free flow the PK₁.₄ (inlet over outlet key width; wi /w₀=1.4) in the head water ratio of 0.5≤H₀/P≤1 and the PK1.25 in H₀/P≥0.1 as compared with other weirs, has higher Hydraulic performance. In the case of constant values for the wi/w₀ ratio and changing of the floor slope from 1:1.5 to the curve (a quarter of the circle; PKC) the discharge coefficient of these weirs increased about 3 percent. In the condition of submerged flow the value of the submerged upstream head over the free flow of upstream head (H*/H₀) increased by decreasing the submerged upstream head ratio (H₀/P). For the PK1 weir and in the submergence values lower than 0.48, the downstream depth had no effect on upstream depth of weir, and H*=H₀. The modifying of PK₁ causes a decrease the modular submergence range in this weir and occurrence of the full submergence with a little delay. Modifying of PKC₁, lead to an increase in the modular submergence range and no change in the full submergence.

Ogee crested spillways having superb hydraulic properties including simplicity in design and flow passing efficacy. So far, limited research in the area of prediction and the extraction of discharge coefficient relationship is conducted. In current study two different methods for modeling the discharge coefficient of the converging ogee spillway with a curve axis by was developed and results were compared with the observed experimental values through the Genetic Expression Programming (GEP) and artificial networks (ANNs) approaches. For this purpose, the experimental data of the Germi chay ogee spillway model with varying training wall convergence angles (), was used. Based on the obtained results, applied Artificial Intelligence (AI) models have reliable performance in predicting the discharge coefficient of converging ogee spillways. Moreover, the performance of GEP model is a bit better than ANN technique with relatively low error and high correlation values. To recognize the most effective variables on the discharge coefficient, sensitivity analysis of GEP for the best model was carried out. Results showed that ratio of the design head to the critical depth (Hd/yc) and ratio of the crest length to the downstream channel width (L/Lch) are the most and least important parameters in predicting the discharge coefficient of the converging ogee spillway respectively. The best evaluation of test series were observed in GEP approach with the values of DC=0.818 and RMSE=0.089 and in ANNs approach with the values of DC=0.77 and RMSE=0.099 which demonstrates the high accuracy of predictions.