To study the characteristics of the vortex at a hydraulic intake, an experiment with a free surface vortex was conducted in a cylindrical tank. The vortex flow field was measured using particle image velocimetry (PIV) and extensive experimental data describing the vortex characteristics (radial and tangential velocity distributions, vortex core radius variation, water surface profile and circulation distribution) have been obtained and analysed. Based on the detailed experimental data, an empirical model describing the key vortex characteristics is developed. In addition, using the pressure distributions inside and outside the vortex core, a dimensionless equation for the critical submergence requiring only the Froude number and the circulation number is derived. Results using this equation are in agreement with the experimental data.

Gates and weirs are frequently used hydraulic structures employed for controlling water flow rates in irrigation and drainage networks. Therefore, accurately estimating the discharge coefficient (Cd) is important for precise flow measurement. The present study used intelligent predictive models for modeling Cd in labyrinth sluice gates. For this purpose, key dimensionless parameters and reliable experimental datasets were used. The support vector regression (SVR) model was hybridized with particle swarm optimization (PSO) and genetic algorithms (GA). The statistical metrics and graphical plots evaluated the performance of the generated models. Three commonly used statistical indicators, namely root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), were used for quantitatively evaluating the performance of the proposed models. The SVR-PSO model achieved the lowest values of RMSE (0.0287) and MAE (0.0209) and the highest value of R2 (0.9732), indicating that it was more accurate than SVR-GA (RMSE = 0.0324, MAE = 0.0257, R2 = 0.9685) and SVR (RMSE = 0.0575, MAE = 0.0468, R2 = 0.8958) on the testing data. The findings revealed that the hybrid SVR methods were more accurate than the standalone SVR model. In addition, regarding the value of the objective function criterion (OBF), the SVR-PSO (OBF = 0.0245) and SVR-GA (OBF = 0.0273) had lower OBF values and provided more precise estimates of the Cd compared to existing nonlinear regression-based formulas and existing data-driven approaches. Finally, sensitivity and SHapley Additive exPlanations (SHAP) analyses determined the relative importance of each input variable for the prediction of Cd.

Gabion weirs are environment-friendly structures widely used for irrigation and drainage network purposes. These structures' hydraulic performance is fundamentally different from solid weirs' due to their porosity and the existence of a through-flow discharge. This paper investigates the reliability and suitability of a number of Machine learning models for estimation of hydraulic performance of gabion weirs. Generally, three different Boosting ensemble models, including Gradient Boosting, XGBoost, and CatBoost, are compared to the well-known Random Forest and a Stacked Regression model, with respect to their accuracy in prediction of the discharge coefficient and through-flow discharge ratio of gabion weirs in free flow conditions. The Bayesian optimization approach is used to fine-tune model hyper-parameters automatically. Recursive feature elimination analysis is also performed to find optimum combination of features for each model. Results indicate that the CatBoost model has outperformed other models in terms of estimating the through flow discharge ratio (Q_in/Q_t) with R² = 0.982, while both XGBoost and CatBoost models have shown close performance in terms of estimating the discharge coefficient (C_d) with R² of CatBoost equal to 0.994 and R² of XGBoost equal to 0.992. Weakest results were also produced by Decision tree regressor with R² = 0.821 and 0.865 for estimation of C_d and Qin/Qt values.

Various regions are becoming increasingly vulnerable to the increased frequency of floods due to the recent changes in climate and precipitation patterns throughout the world. As a result, specific infrastructures, notably bridges, would experience significant flooding for which they were not intended and would be submerged. The flow field and shear stress distribution around tandem bridge piers under pressurized flow conditions for various bridge deck widths are examined using a series of three-dimensional (3D) simulations. It is indicated that scenarios with a deck width to pier diameter (Ld/p) ratio of 3 experience the highest levels of turbulent disturbance. In addition, maximum velocity and shear stresses occur in cases with Ld/p equal to 6. Results indicate that increasing the number of piers from 1 to 2 and 3 results in the increase of bed shear stress by 24% and 20% respectively. Finally, five machine learning algorithms, including Decision Trees (DT), Feed Forward Neural Networks (FFNN), and three Ensemble models, are implemented to estimate the flow field and the turbulent structure. Results indicated that the highest accuracy for estimation of U, and W, were obtained using AdaBoost ensemble with R2 = 0.946 and 0.951, respectively. Besides, the Random Forest algorithm outperformed AdaBoost slightly in the estimation of V and turbulent kinetic energy (TKE) with R2 = 0.894 and 0.951, respectively.

The unsteady wake downstream of a spinning sphere with a Reynolds number of 7930 (Re=UoD/ν, based on the sphere diameter and the free-stream velocity) and a range of spinning ratios (the ratio between the surface velocity and free-stream velocity, α=Vs/Uo) from 0 to 6.0 were studied using time-resolved particle image velocimetry. The Reynolds stresses and proper orthogonal decomposition results indicated that the wake exhibited distinct features in different spinning ratio regimes, and the state of the boundary layer played an important role in forming these features. In regime I (α≤0.25) where the boundary layer around the sphere was laminar, a pair of counter-rotating vortices emerged in the wake with unsteady size and position. The bottom boundary layer transitioned from laminar to turbulent as α increased from 0.25 to 0.75 (regime II). The transition caused a significant increase in the stream-wise stress as the unsteadiness becomes more coherent. The sphere was gradually covered by the turbulent boundary layer as α increased in regimes III and IV (0.75<α≤3.0). A large separation bubble was created in cases with α>3.0 (regime V); flow structures with a large stream-wise momentum separated from the bubble and was shed downstream with a strong flapping motion in the vertical direction, leading to large vertical stress behind the sphere.

Aiming at fast and accurate spatiotemporal prediction of interfacial flow fields, a novel deep learning model combining Convolutional AutoEncoder (CAE) and long short-term memory with spatial and temporal attention (LSTM-STA) is proposed and named as CAE-LSTM-STA (hybrid model of CAE and LSTM-STA) in this article. To enable a fast calculation process, CAE is first utilized to compress the high-dimensional snapshots of flow fields into the low-dimensional latent space by its encoder. Then, the latent space serves as the input feature for LSTM-STA to temporally evolve the state of the low-dimensional latent space. Finally, the low-dimensional latent space at future time steps is fed back into the decoder of CAE to derive the full-order snapshots of the flow fields. The prediction performance of the proposed model is evaluated via two representative benchmark cases, including the dam break case and the rising bubble case. The capability of CAE in dimension reduction is found to be much better than that of the widely used Proper Orthogonal Decomposition, while LSTM-STA is observed that outperforms the original LSTM in multivariant temporal prediction. These promising results indicate that the proposed CAE-LSTM-STA model is able to effectively capture and advance the spatiotemporal characteristics of the interfacial flow fields, therefore making it an advanced surrogate model for fast and precise generation of the temporally continuous interfacial flow fields.