Study of energy dissipation of gabion structure downstream of Ogee weir using laboratory and meta-model methods

Weirs are structures to provide the passage for excess water to flow from upstream to downstream in flood conditions. Since the special geometry of ogee weir in terms of matching the flow Trajectory with the surface of the weir, increases the efficiency of this type of weirs, so the use of terminal structures to dissipate destructive energy downstream of weir is of great importance. Increasing the water level in the Ogee weirs generally increases the contact level and head loss, but specifically in gabion weirs leads to increased permeability. One of the new methods of energy dissipation is the use of gabion structures, artificial roughness, blocks and lattice plates in the flow path as alternative solutions to using the stilling basins. Based on the results of previous research in the field of artificial intelligence, in the present study, the amount of energy consumption of the Ogee weir terminal structure (gabion structure) was predicted using the support vector machine and the effect of dimensions and grain size of the gabion structure. The amount of energy dissipation was also examined.

For this purpose, the experiments were performed in a rectangular channel located in the hydraulic laboratory of Maragheh University, 13 meters long, 120 cm wide and 80 cm high, with a metal floor and a glass wall 1 cm thick, which allows accurate observation of flow behaviors. The gabion used in this experiment was made of rebar number 6 with a width of 120 cm, a length of 10 cm (in the direction of flow) and a variable height, then it was surrounded by a metal mesh with a thickness of 1 mm and a diameter of 1 cm. To predict energy dissipation in the support vector machine, we need a series of functions based on the parameters extracted in the dimensional analysis. Models with different percentages of training and testing (65-35, 70-30, 75-25 and 80-80) and using the radial basis function (RBF), with the appropriate gamma value obtained during trial and error, were checked by a support vector machine. The following criteria were used to evaluate the obtained results and evaluate the efficiency of the models. 1- Normal root mean square error (RMSE), which no matter how close the index (RMSE) is to zero, the model has high accuracy. 2. The normal root mean square error (NRMSE) where NRMSE below 10% indicates the accuracy of the model, 10-20% indicates the suitability of the model, 20-30% the average accuracy and more than 30% indicates the weakness of the model. 3. Performance coefficient (Nash and Sutcliffe) which shows the linear correlation between the measured and predicted values and the closer the value is to one, the better the data correlation.

The results are presented in three sections: Laboratory, Soft Computing and Sensitivity Analysis. To investigate the energy dissipation of the downstream gabion structure, the Ogee weir was performed according to the various variables extracted in the dimensional analysis, including the number and width of openings, grain size, and Froude number. It can be seen that in all models, energy dissipation values are directly related to the Froude number. At the same flow rate, with decreasing Froude number, the flow depth increases and most of the flow passes through the gabion structure as a weir and the energy dissipation decreases. At shallow flow where all or most of the flow passes through the structure, most of the energy dissipation is due to the collision of the flow with the particles inside the structure. However, in higher Froude numbers, the structure is submerged under the flow and both internal flow and overflow are effective in energy dissipation, with the difference that in overgrowth structures, overflow and in fine-grained structures, internal flow is predominant.

Therefore, at a constant opening, fine-grained has the highest amount of energy dissipation. In other words, with increasing the diameter of the rock grains, the volume of the pores increases and the flow passes more easily through the rock grains and the turbulence and flow of the flow in this area decreases, so the energy dissipation decreases. It can be seen that for all models, the values predicted by the support vector machine are close to the laboratory results, but in the Froude numbers the accuracy of the support vector machine is further reduced. The reason for this can be considered as turbulence and turbulence in higher Froude numbers. Finally, it can be stated that the energy dissipation of the gabion structure in the test and training phase has acceptable compliance and overlap with laboratory values. Sensitivity analysis showed that the parameter of relative water depth after gabion has the greatest effect on the correct prediction of energy consumption due to gabion structure.