Hydrodynamic Performance and Cavitation Analysis in Bottom Outlets of Dam Using CFD Modelling

Bottom outlets are significant structures of dams, which are responsible for controlling the flow rate, operation, or removal of reservoir sedimentation. The service gate controls the outlet flow rate, and whenever this gate is out of order, the emergency gate which is located at upstream is utilized. The cavitation phenomenon is one of the common bottom outlets’ problems due to the rapid flow transfer. The present research is a numerical study of the flow pattern in a dam’s bottom outlet for different gate openings by the use of Flow-3D software and RNG k-ε turbulence model. The investigation is carried out on the Sardab Dam, an earth dam in Isfahan (Iran). The maximum velocity for 100% opening of the gate and Howell Bunger valve is about 18 m/s in the section below the gate, and the maximum velocity for 40% opening of the gate is equal to 23.1 m/s. For 50% opening of the service and emergency gate in the valve’s upstream areas, the desired pressure values are reduced. Moreover, in the areas between the two emergency and service gates, the pressure values are reduced. The possibility of cavitation in this area can be reduced by installing aerators. The flow pattern in Sardab Dam’s bottom outlet has relatively stable and proper conditions, and there are no troublesome hydraulic phenomena such as local vortices, undesirable variations in pressure, and velocity in the tunnel, and there is no flow separation in the critical area of flow entering into the branch.

Hybrid Artificial Intelligence Methods for Predicting Air Demand in Dam Bottom Outlet

In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance. To ensure the correct and successful operation of the dam's hydraulic equipment and prevent possible damages, including gates and downstream tunnel, to build laboratory models and perform some tests are essential (the advancement of the smart sensors based on artificial intelligence is essential). One of the causes of damage to dam bottom outlets is cavitation in downstream and between the gates, which can impact on dam facilities, and air aeration can be a solution to improve it. In the present study, six dams in different provinces in Iran has been chosen to evaluate the air entrainment in the downstream tunnel experimentally. Three artificial neural networks (ANN) based machine learning (ML) algorithms are used to model and predict the air aeration in the bottom outlet. The proposed models are trained with genetic algorithms (GA), particle swarm optimization (PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely volume rate and opening percentage of the gate, are used as inputs into all bottom outlet models. The results showed that the most optimal model is ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The importance of the volume rate and opening percentage of the dams' gate parameters is more effective for suitable air aeration.

Hybrid Artificial Intelligence Methods for Predicting Air Demand in Dam Bottom Outlet

In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance. To ensure the correct and successful operation of the dam's hydraulic equipment and prevent possible damages, including gates and downstream tunnel, to build laboratory models and perform some tests are essential (the advancement of the smart sensors based on artificial intelligence is essential). One of the causes of damage to dam bottom outlets is cavitation in downstream and between the gates, which can impact on dam facilities, and air aeration can be a solution to improve it. In the present study, six dams in different provinces in Iran has been chosen to evaluate the air entrainment in the downstream tunnel experimentally. Three artificial neural networks (ANN) based machine learning (ML) algorithms are used to model and predict the air aeration in the bottom outlet. The proposed models are trained with genetic algorithms (GA), particle swarm optimization (PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely volume rate and opening percentage of the gate, are used as inputs into all bottom outlet models. The results showed that the most optimal model is ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The importance of the volume rate and opening percentage of the dams' gate parameters is more effective for suitable air aeration.

Experimental study of pressure flushing of non-cohesive sediment through slotted pipe bottom outlet

ABSTRACT Among several techniques for prevention and mitigation of reservoir sedimentation, bottom outlets arise as a means of removing sediment deposited close to the dam. Given the reduced sediment removal provided by traditional bottom outlets under pressure flushing conditions, this article proposes a new type of structure that aims to increase sediment removal in the direction parallel to the dam axis. An experimental installation was employed to evaluate its operation as a function of the variation of its diameter and flow and sediment characteristics. Through analysis of the bathymetry generated by the structure, a dimensionless relation for predicting the scour pit length was obtained, presenting good fitness to the experimental data.

Understanding Water Flows and Air Venting Features of Spillway—A Case Study

For safe spillway discharge of floods, attention is paid to the water flow. The resulting air flow inside the facility, an issue of personnel security, is sometimes disregarded. The spillway in question comprises two surface gates and two bottom outlet gates lying right below. Air passages to the outlet gates include an original gallery and a recently constructed vertical shaft. To understand water-air flow behavior, 3D CFD modelling is performed in combination with the physical model tests. The simulations are made with fully opened radial gates and at the full pool water level (FPWL). The results show that the operation of only the bottom outlets leads to an air supply amounting to ~57 m3/s, with the air flow rates 35 and 22 m3/s to the left and right outlets. The air supply to the right outlet comes from both the shaft and the gallery. The averaged air velocity in the shaft and the gallery are approximately 5 and 7 m/s. If only the surface gates are fully open, the water jet impinges upon the canal bottom, which encloses the air space leading to the bottom outlets; the air flow rate fluctuates about zero. If all the four gates are open, the total air demand is limited to ~10 m3/s, which is mainly attributable to the shear action of the meeting jets downstream. The air demand differs significantly among the flow cases. It is not the simultaneous discharge of all openings that results in the largest air demand. The flood release from only the two outlets is the most critical situation for the operation of the facility. The findings should provide reference for spillways with the same or similar layout.

3D hydro-morphodynamic models as support tools for obtaining sustainable sediment management strategies of reservoirs

<p>Reservoir sedimentation reduces not only the available storage volume of reservoirs, but may also create other serious problems, such as an increase of bed levels or accumulations of nutrients and contaminants, which affect the environment. An increase in bed levels at the head of the reservoir can reduce flood safety and increase the risk for the surrounding areas. Deposited sediments close to the dam may block hydraulic structures, such as the bottom outlets, or, in case they enter the intake, lead to possible abrasion of plant components (e.g. wear of turbines and pipes).</p><p>Prior to reservoir construction, a pre-evaluation of the sediment yield from the catchment is usually performed by using soil erosion and sediment delivery models. However, the trapping efficiency is often only obtained by empirical approaches, such as Brune’s or Churchill’s curve, which are based on the capacity of the reservoir and the mean annual inflow. This is still common practice, although 3D hydro-morphodynamic models became powerful tools to numerically study sediment transport and reservoir sedimentation prior to the construction of reservoirs as well as during its operation.</p><p>Within this study, a fully 3D hydro-morphodynamic numerical model, based on the Reynolds-averaged Navier-Stokes equations, is applied to a case study to simulate, on the one hand suspended sediment transport within a hydropower reservoir and on the other hand a reservoir flushing operation as potential management scenario, with the goal to remobilize already deposited sediments and to release these sediments from the reservoir. The modeled reservoir has a total storage capacity of around 14 million m³, whereby the water level can fluctuate due to pumped-storage operation by 40.5 m (difference between the maximum operation level and the operational outlet). At the head is the natural inflow of two creeks into the reservoir and a lateral transition tunnel is located on the orographic right side, which collects several headwater streams from adjacent catchments.</p><p>Simulations are performed for different operation modes of the reservoir. The results clearly show that through active reservoir management (variation of water levels as well as using the momentum of the discharge from the transition tunnel) the sediment motion in the reservoir can be affected to a certain extent. It is for instance possible to almost completely avoid reservoir sedimentation in front of the dam and the hydraulic structures (water intake and bottom outlets) during sediment-laden flows when simultaneously high discharges are provided from the laterally located transition tunnel. The conducted simulation results of reservoir flushing also show that the success of the flushing operation is strongly dependent on the water level. As expected, flushing with full drawdown of the water level is the most efficient method to release sediments.</p><p>Through the detailed results of the 3D hydro-morphodynamic model, it is feasible to receive a deeper knowledge of the ongoing sediment transport processes within the studied reservoir. The gained knowledge can further be used to derive sustainable and efficient management strategies for the sediment management of the reservoir.</p>

Modeling of reservoir flushing by means of existing hydrodynamic models

<p>Sediment flushing has been reported as one of the most efficient techniques for reservoir desiltation. This technique consists in opening the bottom outlets of a dam to induce an accelerated flow that mobilizes part of the sediments deposited in the reservoir. The efficacy of flushing depends much on conditions such as the hydraulic head in the reservoir, the discharge capacity of the outlets, the sediment characteristics, and the topography of the reservoir, among others. In this context, numerical models become an extraordinarily useful tool for reservoir operators, as the efficacy of flushing can be previously evaluated by means of numerical modeling. However, though there are several studies that have simulated flushing numerically, most of them are based on specific case studies whose conditions cannot be generalized. This study aims to analyze the capacity of three hydrodynamic models (HEC-RAS-1D, IBER-2D and FLOW-3D) to simulate flushing events. For that purpose, those conditions tested in laboratory for two experimental setups were implemented and simulated in these hydrodynamic models. The first experimental setup was based on a one-dimensional approach in which the width of the outlet coincided with the width of the reservoir. This experimental setup was carried out in a 12.5 m long and 0.30 m wide horizontal rectangular flume at Universidad Politécnica de Cartagena, Spain. Here, 10 pairs <em>h<sub>s</sub></em> – <em>h<sub>w</sub></em> were tested, where <em>h<sub>s</sub></em> and <em>h<sub>w</sub></em> stand for the initial sediment and water elevations, respectively. Sediments consisted of a uniform sand with <em>d<sub>50</sub></em> = 0.7 mm, bulk density <em>ρ<sub>b</sub></em> = 1650 kg/m<sup>3</sup>, and grain density <em>ρ<sub>s</sub></em> = 2650 kg/m<sup>3</sup>. In these experiments, the evolution of the water surface and bed surface, as well as the liquid and solid hydrographs, were characterized by means of videos recorded from a side of the flume. The second experimental setup consisted of 3 of the experiments documented in the PhD thesis by Lai (1994), which were conducted in a 50 m long, 2.4 m wide and 1.5 m high rectangular concrete flume at University of California at Berkeley. In this experimental setup, the reservoir was emptied through a 0.15 m wide and 0.25 m high sluice gate., which allows analyzing the influence of the width ratio between outlet and reservoir. Sediments consisted of walnut shell grit with <em>d<sub>50</sub></em> = 1.25 mm and <em>ρ<sub>s</sub></em> = 1390 kg/m<sup>3</sup>. In these experiments, liquid and solid hydrographs were characterized by means of discrete measurements of the water surface and sediment concentration at the outlet. To assess the capacity of the hydrodynamic models to simulate flushing, the hydrographs obtained from laboratory experiments are compared to those obtained numerically. Preliminary results show that the model FLOW-3D obtained the best approach to the results obtained in laboratory. The results obtained with HEC-RAS also show a good approach to the experimental results, but with comparatively high differences in magnitudes for the peaks of the liquid and solid hydrographs. The results obtained with IBER show the greatest differences with respect to the results obtained in laboratory.</p>