فهرست مطالب m. kouhdaragh

Weirs of the labyrinth have some advantages including the high coefficient of the irrigation of weir and the low fluctuation of water when the flow passes over the crest of the weir. In this research, the flow rate coefficient has been investigated by changing the weir geometry in terms of wall slope, arc cycle angle, and nose length change in the upstream and downstream of each cycle of the trapezoidal arc labyrinth weir. A total of 240 tests have been performed on 16 different physical models in a channel with a width of 120 cm and a narrowing of 20 cm from each wall. All models have been compared with the control model (normal labyrinth weir) (80A). The results showed that the 80B weir with an arc cycle angle of 20 degrees and without wall slope has a better performance than other weirs. Also, the weir with an arc cycle angle and a wall slope of 20 degrees in a divergent form (D20B) in the area (Ht/P) <0.31 has a better performance than other weirs with an arc cycle angle of 20 degrees, and after this area, the weir with a wall slope of 10 degrees has performed better in divergent form (D10B). In weirs with different cycles at an arc cycle angle of 20 degrees, the labyrinth weir with 5 cycles (N5) has performed better up to the point (Ht/P)=0.36. Also, at the maximum point, the difference is 13 and 17%, respectively, compared to the 4-cycle and 3-cycle weirs.

Non-linear weirs meanwhile economic advantages, have more passing flow capacity than linear weirs. These weirs have higher discharge efficiency with less free height upstream compared to linear weirs by increasing the length of the crown at a certain width. Intelligent algorithms have found a valuable place among researchers due to their great ability to discover complex and hidden relationships between effective independent parameters and dependent parameters, as well as saving money and time. In this research, the performance of support vector machine (SVM) and gene expression programming algorithm (GEP) in predicting the discharge coefficient of arched non-linear weirs was investigated using 243 laboratory data series for the first scenario and 247 laboratory data series for the second scenario. The geometric and hydraulic parameters were used in this research including the water load (HT), weir height (P), total water load ratio (HT/p), arc cycle angle (Ɵ), cycle wall angle (α), and discharge coefficient (Cd). The results of artificial intelligence showed that the combination of parameters (Cd, H_T/p, α, Ɵ) respectively in GEP and SVM algorithms in the training phase related to the first scenario (Labyrinth weir with cycle wall angle 6 degrees) were respectively equal to (R2=0.9811), (RMSE=0.02120), (DC=0.9807), and (R2=0.9896), (RMSE=0.0189), (DC=0.9871) in the second scenario (Labyrinth weir with a cycle wall angle of 12 degrees) it was equal to (R2=0.9770), (RMSE=0.0193), (RMSE=0.9768), and (R2 = 0.9908), (RMSE = 0.0128), (DC = 0.9905), which compared to other combinations has led to the most optimal output that shows the very favorable accuracy of both algorithms in predicting the coefficient the Weir discharge is arched non-linear. The results of the sensitivity analysis indicated that the effective parameter in determining the discharge coefficient of the arched non-linear Weir in GEP and in SVM is the total water load ratio parameter (HT/p). Comparing the results of this research with other researchers revealed that the evaluation indices for GEP and SVM algorithms of this research had better estimates than other researchers.