Simulation and prediction of hydraulic jump characteristics over expanding rough beds using FLOW-3D and soft computing techniques

In this study, hydraulic jumps over expanding beds with artificial roughness were simulated using FLOW-3D across Froude numbers ranging from 4.34 to 9.37. The simulations were conducted on both smooth and rough beds, with roughness in the form of half-spheres of 3, 4, and 5 cm in diameter, and divergence angles of 7 degrees, 14 degrees, and 90 degrees. The results showed that for maximum discharge in a sudden divergent channel, a rough bed with 5-cm diameter elements reduced flow depth by 19.77% compared to a smooth bed. Additionally, in all scenarios, the ratio of y2/y1 increased as the Froude number increased. In the second phase, soft computing models - such as Linear Regression, Support Vector Regression, Decision Tree, Random Forest, Bagging, Gradient Boosting, MLP, and Stacking - were employed to model the relationships between input parameters (Fr1, theta, D/b(1), and K-b) and outputs (y(2)/y(1) and L-j/y(1)). The R-2 coefficient value in the training stage of the Stacking model for the parameter (y(2)/y(1)) was 0.978 and in the testing stage it was 0.988, and for the parameter (L-j/y(1)) in the training and testing stages this coefficient was estimated to be 0.971 and 0.987, respectively.