Prediction and parameter quantitative analysis of side orifice discharge coefficient based on machine learning.

In this paper, the sparrow search algorithm is used to predict the discharge coefficient (C_d) of the triangular side orifice for the first time. Dimensionless parameters influencing the size of C_d of side orifices are obtained as input values and discharge coefficient as output values of the model. The results show that the determination coefficient R² is 0.973, the root means square error RMSE is 0.0122, and the average absolute percentage error is 0.010% in the testing phase. The model has high forecast accuracy, strong generalization ability and higher accuracy than other models and traditional empirical formulas. Quantitative analysis by Sobol's method shows that the ratio W/H of top orifice height to side orifice height, F_r of upstream Froude number, and ratio B/L of channel width to a bottom edge length of side orifice are the main factors influencing the discharge capacity of triangular side orifice. The first-order sensitivity coefficient and global sensitivity coefficient are 0.23, 0.11, 0.17 and 0.41, 0.39, 0.35 respectively. [ABSTRACT FROM AUTHOR]