Reservoir Sedimentation Management: Evaluating Sediment Size and Bottom Tunnel Effects on Sediment Flushing Efficiency

Reservoir Sedimentation Management: Evaluating Sediment Size and Bottom Tunnel Effects on Sediment Flushing Efficiency | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reservoir Sedimentation Management: Evaluating Sediment Size and Bottom Tunnel Effects on Sediment Flushing Efficiency Mostafa Roshdi, Yousef Hassanzadeh, Nazila Kardan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6002097/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reservoir sediment flushing, recognized as a highly effective approach for mitigating reservoir sedimentation, involves the downstream discharge of sediment-laden flows through bottom tunnels. Alongside other critical parameters in pressure flushing, the simultaneous operation of bottom tunnels significantly contributes to the efficient removal of sediments from the reservoir, and this study evaluated the effects of the number and placement of these tunnels. The primary aim of removing sediment from reservoirs is to restore the lost volume resulting from sediment accumulation. This study endeavors to pinpoint the most optimal parameters for maximizing sediment removal from reservoirs, stressing the significance of effectively eliminating sediment from the dam reservoir during pressure flushing. To achieve these objectives, the CFD solver in Fluent is employed. While simulating bed erosion around bridge piers is not feasible within the Fluent CFD solver, a morphological model was developed and integrated with Fluent to address bed erosion beyond the flow solution. This integrated model combines the flow solution with a sediment transport model to establish the morphological model. The results demonstrated that achieving efficient pressure flushing in a reservoir with a constant water level is best accomplished in a reservoir containing sediment with a smaller average diameter, higher accumulation level, and a greater number of active bottom tunnels. Key elements include maintaining consistent discharge from each tunnel, ensuring uniform tunnel elevation, and preserving a fixed distance between them. Reservoir sedimentation Sediment flushing Non-uniform sediment Scour cone Numerical Simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Sediment deposition causes loss of storage capacity in reservoirs and lack of sediment supply to downstream channels, impairing reservoir functions and aquatic ecosystems (Morris & Fan, 1998; Fan, 2011; Schleiss et al., 2016; Huang et al., 2019b). Strategies have been developed and implemented to alleviate reservoir sedimentation (Brandt, 2000; Wang & Hu, 2009; Kondolf et al., 2014; Morris, 2020;): (1) reducing sediment inflow, such as watershed erosion control and upstream sediment trapping; (2) minimizing sediment deposition, such as turbidity current venting, sediment sluicing during floods, and sediment bypassing; and (3) removing deposited sediment, such as sediment flushing, dredging, and dry excavation. Among these strategies, hydraulic methods, including turbidity current venting, sediment sluicing, and flushing, are particularly effective and widely used (Fan & Morris, 1992; Shen & Lai, 1996; Shen, 1999). Generally, these methods involve discharging sediment-laden flows from reservoirs through the bottom tunnels at the dam. These methods will not work if bottom tunnels are not properly operating due to excess sediment deposits and debris. Pressure flushing is utilized to remove sediment deposits near the dam and keep the intakes of hydraulic structures free of sediment (Kondolf et al., 2014; Morris, 2020). Without drawdown of the reservoir level, the gates of bottom tunnels are opened to scour sediment and release the resuspended sediment out of reservoirs. Sediment removal is limited to the vicinity of the bottom tunnel intake and a localized scour hole is formed within a short period of time (Lai & Shen, 1996; Scheuerlein et al., 2004). For non-cohesive sediment, the slope of the scour hole in the equilibrium condition is approximately equal to the submerged angle of repose (Xiong, 1981; Jin, 1990). The depth of the scour hole increases with the increase in the discharge and the area (or height) of the bottom tunnel, and decreases with the increase in the sediment particle size (Fathi-Moghadam et al., 2010; Powell & Khan, 2012; Emamgholizadeh & Fathi-Moghdam, 2014; Haghjouei et al., 2021;). Figure 1 shows a schematic of deposited sediment zone and scour cone. Numerical modelling provides an effective way to predict the detailed processes of reservoir sediment flushing (Khosronejad et al., 2008; Sawadogo et al., 2019; Xu and Cao, 2024). By simulating the flow structure, sediment transport, and bed evolution, operation schemes can be proposed to improve flushing efficiency (Hung et al., 2009; Khosronejad, 2009; Ahn et al., 2013; Huang et al., 2019a; Blade Castellet et al., 2019a; Goulart et al., 2023;). However, reservoir sediment flushing may fail due to severe sedimentation and inappropriate operation, i.e., sediment particles entering the bottom tunnel cannot be transported downstream but deposited in it (Di Silvio, 1990; Morris & Fan, 1998; Fan, 2011; Xu et al., 2023; Sun et al., 2024). The effectiveness of sediment flushing operations in removing sediment from a reservoir is influenced by various factors, including sediment characteristics, reservoir morphology, hydrological conditions, and operational strategies. The limited research on the flushing of sediment from reservoirs concerning sediment uniformity and sediment size highlights a significant gap in understanding the dynamics of sediment management in dam reservoirs. The process of sediment flushing plays a crucial role in sediment transport and reservoir maintenance. However, the specific considerations related to sediment uniformity, sediment size, and number of bottom tunnels in the flushing operation have not been extensively studied. The presence of non-uniform sediment deposits behind a dam can pose challenges for sediment flushing efforts, as the distribution of sediment within the reservoir may impact the flushing efficiency and the extent of sediment removal. The non-uniform distribution of sediments in reservoirs can significantly impact sediment transport and deposition behind dams. Acknowledging the dynamic sediment movement within dam reservoirs is crucial when considering a uniform coefficient of sediment transport. Various factors such as flow velocity, sediment size, and reservoir morphology influence the distribution of bed sediments and suspended sediments as they are transported to a dam reservoir. The accumulation of bed sediments at different rates and locations can result in the formation of non-uniform sediment deposits behind the dam. The presence of sediment non-uniformity can pose challenges for the operation and maintenance of bottom tunnels in dam structures. Bottom tunnels, also known as low-level outlets or bottom outlets, are designed to release water from the lower levels of a reservoir while controlling sediment flushing and sediment transport. The number and level of bottom tunnels in a dam play a crucial role in managing sediment deposition, sediment flushing operations, and reservoir sedimentation control. In the context of sediment non-uniformity, the design and placement of bottom tunnels must consider the distribution of sediment deposits and the potential for sediment movement within the reservoir. The location and number of bottom tunnels should be strategically determined to facilitate effective sediment flushing, minimize sediment deposition risks, and maintain reservoir storage capacity over time. The primary aim of this study is to investigate the influence of sediment uniformity, the number and levels of bottom tunnels, and the deposition height on sediment flushing operations in dam reservoirs. By examining the combined effects of these parameters, the research seeks to enhance understanding of sediment management strategies and optimize the efficiency of sediment removal processes during flushing operations. The final impact of these parameters on the volume and geometry of the score cone resulting from flushing operations, as well as the percentage of reservoir sediments being removed, is under investigation. Therefore, the objectives include determining the following: The effect of the average diameter size of deposited sediment particles in the reservoir on the geometry and volume of the scour cone is being studied under the scenario of uniform particle sizing. The investigation aims to compare the impact of non-uniform particle sizing of settled sediment particles in the reservoir, where there is an equal distribution of particles forming the average diameter examined in the case of uniform particle sizing, on the geometry and volume of the scour cone. This comparison will be made against the scenario of uniform particle sizing of sediment particles. The impact of the sediment deposition level in the reservoir, on the geometry and volume of the scour cone is being studied. The influence of the number of bottom tunnels on the geometry and volume of the scour cone is being examined. The primary purpose of sediment removal from reservoirs is to replenish the lost volume resulting from sediment deposition. This study seeks to identify the most suitable parameters to maximize sediment removal from reservoirs, with the project's success hinging on the efficient removal of sediment from the dam reservoir during pressure flushing. To reach this goals, CFD solver of Fluent is applied. Simulation of the bed erosion around the bridge piers is not possible in CFD solver of Fluent. To model the bed erosion beyond the flow solution, the morphological model was produced and linked to the Fluent, in which the flow solution couples with a sediment transport model, forms the morphological model. 2. Methods and Procedures 2.1. Influenced Parameters The equilibrium of the scour cone volume, commonly developed in reservoirs post-pressure flushing, is influenced by factors including reservoir water depth (H w ), sediment depth above the intake (H s ), fluid density ( \(\:\rho\:\) ), sediment density ( \(\:\rho\:\) s ), intake diameter (D), intake water velocity (u), fluid dynamic viscosity ( \(\:\mu\:\) ), gravitational acceleration (g), mean grain diameter (d 50 ) of non-cohesive sediment, and N is the number of bottom tunnels. These parameters illustrated in Fig. 1 . The basic variable, equilibrium scour cone volume ( \(\:V\) s ) are expressed individually (Eq. [1]): \(\:{V}_{s}=f({H}_{w},{H}_{s},D,u,g,{\rho\:}_{s},\rho\:,{d}_{50},\mu\:,\:N)\) [1] Within Eq. 1, the impact of parameters Hs, d 50 , and N is investigated, while all other parameters are held constant. 2.2. Modeling of the Flushing Numerical modeling of the reservoirs, either in the stage of design or during their operation, helps designers as well as operators to have a better operational management plan for the reservoir. This can be achieved by predicting the depositional behavior of the reservoir and regulating practical guidelines for sediment management in the reservoir through a schedule of sediment flushing during the operation of the reservoir. Literature review shows that many attempts have been devoted to modeling such cases by applying one- or two-dimensional numerical models. However, neither one- nor two-dimensional models are appropriate for studying the process of pressure flushing. In two-dimensional horizontal models, the basic assumption is that the distribution of velocity and the sediment concentration along the depth are uniform. However, it has been shown that, during the process of pressure flushing and after the opening of the bottom tunnels, the velocity and sediment concentration near the outlet are much higher than above levels, so the basic assumption of those models is not reasonable. To sum up, as pressure flushing is entirely a 3-D phenomenon in a very restricted area near the outlet and its duration is also rather short (which means less computational time would be needed for its simulation), modeling this process by a 3-D code could be an advantage. In the present study, a 3-D numerical model will be applied to examine the process of pressure flushing in a simplified reservoir deposition. The main purpose of this paper is to study the capabilities of a 3-D model to simulate this phenomenon by investigating the mechanism of the pressure flushing operation and describing the behavior of the cone formation and retrograde erosion of the flushing cone under different conditions. 2.3. The hydrodynamic model The CFD solver Fluent was used to simulate the incompressible flows by solving the three-dimensional Reynolds-averaged Navier–Stokes (RANS) equations. To solve sequentially the governing hydrodynamics equations, Fluent utilizes the control volume method. Two continuity equations were solved for each phase, whereas the momentum and transport equations were simultaneously solved for both phases. The Reynolds-averaged momentum equations and the continuity equation for the i th phase are expressed as follows (Salaheldin et al. 2004): \(\:\frac{\partial\:{\alpha\:}_{q}}{\partial\:t}+{U}_{i}\frac{\partial\:{\alpha\:}_{q}}{{\partial\:x}_{i}}=0\) [2] \(\:\frac{\partial\:}{\partial\:t}\left(\rho\:{U}_{i}\right)+\frac{\partial\:}{{\partial\:x}_{j}}\left({\rho\:U}_{i}{U}_{j}\right)=-\frac{\partial\:P}{\partial\:{x}_{i}}+\frac{\partial\:{\tau\:}_{ij}}{\partial\:{x}_{j}}+\rho\:{g}_{i}\) [3] Where \(\:{\alpha\:}_{q}\) , t , U i , P , g i ,, and \(\:{\tau\:}_{ij}\) refers to the volume fraction of the q th phase in the control volume, time, velocity in the i direction, total pressure, gravitational acceleration in the i direction, averaged local density in the control volume, which was computed as \(\:\rho\:=\sum\:{\alpha\:}_{q}{\rho\:}_{q}\) , and stress tensor, respectively. In the turbulent flows, the time averaged continuity and Navier-Stokes equations were obtained from Eqs. 4 and 5 (Salaheldin et al. 2004): \(\:\frac{\partial\:\rho\:}{\partial\:t}+{\left(\rho\:\stackrel{-}{{U}_{i}}\right)}_{i}=0\) [4] \(\:\frac{\partial\:}{\partial\:t}\left(\rho\:{\stackrel{-}{U}}_{i}\right)+\left({\rho\:\stackrel{-}{U}}_{i}{\stackrel{-}{U}}_{j}\right)=-\stackrel{-}{{P}_{i}}+{\left|\mu\:\left({\stackrel{-}{U}}_{i,j}+{\stackrel{-}{U}}_{j,i}\right)-\rho\:\stackrel{-}{{U}_{i}{U}_{j}}\right|}_{j}\) =0[5] In the above equations, the overbar represents time averaging. The position vector components ( x, y, z ) are denoted as x i , while u i represents the velocity components ( u, v, w ) in the x, y , and z directions, respectively. The kinematic viscosity is represented as ν = µ/ρ , and u′ i represents the velocity fluctuating components ( u', v', w' ). The term \(\:\partial\:\stackrel{-}{\stackrel{´}{{U}_{i}}\stackrel{´}{{U}_{j}}}\) corresponds to the Reynolds stress tensor, which is modeled differently depending on the turbulence model employed (Toonder and Nieuwstadt, 1997). Here, the following models are investigated: k − ω (SST and Standard), k – ε (Standard, RNG, and Realizable), and the Reynolds stress model (RSM). The k – ω and k − ε models incorporate the Boussinesq hypothesis (Toonder and Nieuwstadt, 1997), which is not necessary for the RSM. The assessment of the Standard Wall Function (Std. WF), Enhanced Wall Treatment (EWT), and Scalable Wall Function (Scal. WF) approaches was conducted whenever applicable for the bottom tunnels ((Toonder and Nieuwstadt, 1997; El Khoury et al., 2013; Kalpakli and Orlu, 2013). For discretization, the control volume is divided into finite volume and the governing equations are solved for each cell and each variable. In Fluent software, the pressure-based solver offers four predictor-corrector algorithms (SIMPLE, SIMPLEC, PISO, COUPLE) for velocity-pressure coupling. For this study, the PISO algorithm was selected along with various turbulence models. The QUICK scheme (Leonard, 1979) replaced the central-upwind hybrid spatial discretization method, combining third-order precision with upwind stability. However, the scheme's unbounded nature may lead to over- and undershoots. A second order upwind interpolation scheme is used for momentum and turbulence equations. The convergence criterion for all equations is set at a minimum residual value of 10 − 5 . 2.4. Morphological model The CFD solver in Fluent does not allow for direct simulation of bed erosion around cylinders. Therefore, to model the bed erosion beyond the flow solution, a morphological model was developed and integrated with Fluent. This integrated model combines the flow solution with a sediment transport model to form the morphological model. The morphological model emphasizes three primary elements: (i) bed-load, (ii) sand slides, and (iii) sediment mass balance. These elements are described as follows (Hassanzadeh et al., 2020): Bed-load When developing a two-dimensional bed-load model, an extension of the bed-load equation introduced by Engelund and Fredsøe (1976) is described using a vectorial representation. This model accounts for bed-load transport occurring on a sloping bed, where U b represents the averaged transport velocity of a particle. It is important to note that at the particle's position, the fluid velocity U differs from U b . According to [31], U is equal to aU f , where U f represents the friction velocity, and a is an empirical constant typically assigned a value of a = 10 (Roulund et al., 2005). The bed-load transport rate, q b , which is measured in terms of volume per unit width and per unit time, is related to U b using the equation proposed by \(\:{q}_{b}=\frac{1}{6}\pi\:{d}^{3}\frac{{P}_{EF}}{{d}^{2}}{U}_{b}\) [6] In the mentioned equation, the parameter d represents the grain size, while P EF signifies the percentage of particles that are in motion within the bed surface layer. Both P EF and U b play pivotal roles in determining the magnitude of bed-load transport. It is worth noting that selecting the appropriate bed-load equation is crucial for accurately predicting the shape of scour holes. For instance, Dodaro et al. (2016) proposed a modification to the Einstein formula that enables an accurate prediction of the evolution of scour holes. Sand slide Experimental and numerical simulations reveal that, in certain regions, the slope of the local bed exceeds the repose angle of sediment particles during the formation of scour holes at the upstream face. This leads to shear failures occurring at these specific locations. The experimental observations conducted by Roulund et al. (2005) revealed that the collapse of the bed occurs at the upstream face of the scour hole when the bed slope, β , exceeds the angle of repose,, by a few degrees. Furthermore, they discovered that shear failure of the soil takes place just below the bed surface, resulting in the downward sliding of sediment particles towards the center of the scour hole. Following the sliding of each particle, the bed slope decreases by a few degrees below the repose angle of the sediments. Morphological scheme The equation used to calculate the mass balance of sediment at mesh points on the bed is as follows \(\:\frac{\partial\:h}{\partial\:t}=\frac{-1}{1-n}\frac{1}{A}\sum\:_{i=1}^{4}\left[\left({q}_{b,i}.{n}_{i}\right)\left|{l}_{i}\right|\right]\) [7] The variables in the equation include h for bed elevation, n for porosity, A for the projected area of a small bed-surface element, i for the assigned number to each side of the projected area ( i = 1, …, 4 ), q b,i representing the sediment-transport vector at the bed elevation ( h ) on the i th side of the projected area, n i for the normal vector at the i th side of the projected area, and | l i | for the length of the i th side of the bed element. The computational procedure involved the following steps: (I) mesh generation using Gambit, (II) flow calculation within the flow domain, (III) assessment of sediment transport as bed load, (IV) updating bed morphodynamics, (V) inspection for sand slides, and (VI) iterative repetition of steps I to V . Notably, the model presented excludes focuses on bed-load sediment transport due to flushing. 2.5. Computational domain A multi-phase computational domain containing the water flow in the reservoir and an air region at the top was simulated by the multi-phase flow model. In numerical simulations, providing an appropriate cell is very important in preparation of the computational domain. The accuracy of the results, the convergence and the computational time are the main parameters that are strongly influenced by the size and alignment of the cells (Esmaeili et al. 2011). Two mesh blocks is applied to mesh the computational domain. It should be noted that the models and their meshing have been generated in Gambit software and then their files were imported to FLUENT software. In order to mesh the models effectively, a Cartesian grid was employed, where specific numbers and sizes were assigned to cell dimensions along the X, Y, and Z Cartesian axes, each with varying dimensions. This methodology was implemented to enhance the accuracy of simulating sediment transport through the bottom tunnels of the dam by refining cell sizes in the vicinity of their intakes. As the simulation progressed towards areas such as the inlet boundary at the reservoir area, (for X-direction cell dimensions), the lateral walls of the reservoir (for Y-direction cell dimensions), and the top of the reservoir (for Z-direction cell dimensions) located further from the bottom tunnels area, the cell dimensions were adjusted to a coarser resolution. In each of the numerical models under examination, a mesh block was employed to encompass the reservoir boundary, extending up to the dam wall, for simulating the reservoir perimeter. Specifically, for each bottom tunnel to intake, a mesh was delineated from the initiation of the dam wall to the terminus of the bottom tunnel to model both the intake boundary and the sediment-laden outflow flowing through it. This outflow exits the reservoir at a specified discharge rate of 0.003 m 3 /s for each bottom tunnel and exit gate. Consequently, in the scenario of a single bottom tunnel, 2 blocks will be utilized; for 2 bottom tunnels, 3 blocks will be allocated; and in the case of three bottom tunnels, 4 blocks will be utilized within the computational domain. 2.6. Boundary Conditions Appropriate boundary conditions must be considered at computational domain boundaries depending on the flow nature. In performed simulations, at the reservoir inlet, zero transverse, v , and vertical velocities, w , were considered. The profiles for u component of velocity in reservoir inlet were selected based on the equilibrium profiles. At the bottom tunnels inlet, the flow that can be discharged from the tunnels considered as the boundary condition. This boundary also applied to the reservoir outlet boundary at the tunnels intake. At the downstream end of the bottom tunnels, for all flow variables, a correction for overall mass balance and zero diffusion flux were considered. At the sides and top surface, zero normal velocity and zero normal gradients were applied for all variables by defining a symmetric boundary condition. At the solid boundaries, the no-slip boundary condition was specified to set the velocity to zero. Wall functions are very important in tunnels around. The boundary condition for sediment concentration in the cell closest to the bed is determined by the formula given by van Rijn: \(\:{C}_{bed}=0.015\frac{{d}_{50}^{0.7}}{a}.\frac{{\left[\frac{{\tau\:}_{0}-{\tau\:}_{c}}{{\tau\:}_{c}}\right]}^{1.5}}{{\left[\frac{{\rho\:}_{s}-{\rho\:}_{w}}{{\rho\:}_{w}}.\frac{g}{{\nu\:}^{2}}\right]}^{0.1}}\) [8] where C bed is the equilibrium bed sediment concentration (volume fraction); d is the diameter of the sediment particle; a is the reference level; \(\:{\tau\:}_{\circ\:}\) is the bed shear stress; \(\:{\tau\:}_{c}\) is the critical bed shear stress for the movement of sediment particles; \(\:{\rho\:}_{s}\) is the density of the sediment; \(\:\nu\:\) is the kinematic viscosity of water; and g is acceleration due to gravity. The concentration determined by (3) could be extrapolated by the Rouse equation to calculate the values at the required level (Bhuiyan and Olsen, 2002). The initial conditions for this pressure flushing study may involve a fluid-filled region within the computational domain at the simulation's commencement, pressure distribution, and initial environmental temperature. When examining the pressure flushing of reservoir sediments, the pressure distribution initially exhibits hydrostatic characteristics within the reservoir's depth. Moreover, the definition of fluid regions encompasses the area from the reservoir bottom to the water level and fluid-filled regions within the dam's bottom tunnels as initial conditions. As a result, one, two, and three fluid-filled regions are respectively designated for one, two, and three bottom tunnels. Given that in the phenomenon of pressurized flushing, various geometric and hydraulic factors, such as model dimensions, sediment level, sediment particle characteristics, turbulence model, and discharge flow rate from the gates, play a role. After conducting numerous simulations and achieving suitable convergence of the models to ensure that significant changes in the dimensions of the scour cone do not occur thereafter, the appropriate simulation time is determined. In the present study, by analyzing the model relevant to the scenario of one bottom tunnel with coarse sediment accumulated at a depth of 0.35 m in the reservoir bottom and observing the establishment of stability conditions, a time of 200 seconds was selected for simulating each of the models. 3. Model Validation 3.1. Quantifying Discrepancies between Experimental and Numerical Velocity Profiles: The Mean Absolute Percentage Error (MAPE) To assess the discrepancies between the numerical and experimental results, the mean absolute percentage error (MAPE) is employed, defined as follows: \(\:MAPE=\frac{1}{n}\sum\:_{i=1}^{n}\left|\frac{{d}_{{s}_{exp}}\left({x}_{i},\:{y}_{i},{z}_{i}\right)-{d}_{{s}_{num}}\left({x}_{i},\:{y}_{i},{z}_{i}\right)}{{d}_{{s}_{exp}}\left({x}_{i},\:{y}_{i},{z}_{i}\right)}\right|\) [9] Here, the variable n represents the sample size of the experimental data. The term ' \(\:{d}_{{s}_{exp}}\left({x}_{i},\:{y}_{i},{z}_{i}\right)\) ' denotes the experimentally sediment eroded depth at points \(\:{x}_{i}\) , \(\:{y}_{i}\) and \(\:{z}_{i}\) in the reservoir upstream of the bottom tunnel entrance, while ' \(\:{d}_{{s}_{num}}\left({x}_{i},\:{y}_{i},{z}_{i}\right)\) ' signifies the numerically sediment eroded depth at points \(\:{x}_{i}\) , \(\:{y}_{i}\) and \(\:{z}_{i}\) in the reservoir upstream of the bottom tunnel entrance. The Mean Absolute Percentage Error (MAPE) is utilized in this context to measure the disparities between the experimental and numerical eroded depth, offering a comprehensive assessment. By employing MAPE, it enables the evaluation of how accurately the simulated eroded sediments correspond to the experimental data in terms of percentages. This analytical method is highly esteemed for its efficacy in interpreting variations in relative error (Wilcox, 2006). Thus, MAPE can offer initial insights into the numerical representation of the underlying physics in this matter. It is crucial to note that the MAPE value provided in this context does not directly correspond to the differences between numerical and exact solutions. Moreover, the MAPE assessment here encompasses various error components, including those derived from the experimental methods. Despite its limitations, MAPE remains a valuable tool for assessing the quality of the conducted simulations. 3.2. Experimental validation model To validate the results of the numerical simulations, the study by Fahtih-Moghadam et al. (2010) was used as a benchmark for performance. The experiments were conducted to investigate important parameters affecting the volume and length of the flushing cone. A wide flume 2.30 × 1.50 × 4.00 m was used at the hydraulic laboratory at Chamran University, Ahwaz, Iran, to simulate a reservoir and cone formation due to pressure flushing. The first meter of the flume was used to set up a constant head over a 0.42 m sediment layer resting on the flume bed (Fig. 1 ). The 2-inch terminal gate valve was opened as the reservoir water level reached the desired depth over the sediment layer. The outlet discharge Q was drained into the first section of a 3.5 × 1.0 × 0.8 m settling container where sediment material was deposited and clean water was measured using a 268 V-notch weir. Water was steadily circulated in the system using a pump and sump. The non-cohesive sediments used in this study were sand of density 2.65 tm − 3 . Strainers were utilized to screen uniform sediment grain sizes in three classes of fine (0.27 mm), medium (0.42 mm) and coarse (1.2 mm) with uniformity coefficient (d 84 /d 16 ) 0.5 < 1.5. Experiments were conducted with these three grain sizes, three water depths of H w = 52, 90, 120 cm, five outflow discharges 0.001 ≤ Q ≤ 0.008 m 3 s − 1 , and mean outlet velocities within 0.51 ≤ u ≤ 4.08 m s − 1 . To avoid high shear flow effects on the cone due to lowering of the water level at the test end, the flume was discharged through a 3-inch valve upstream of the sediment layer (Fig. 1 ). The water inside the cone was gradually drained through a small opening of the 2-inch gate valve. The data were then used to determine the flushing cone volume. Among the laboratory research findings, the data at a reservoir water level of 1.2 m, with sediment particles 0.42 mm diameter, and a flow rate of 4.5 lit/s were utilized as benchmarks for validating the current study. As depicted in Fig. 2 , the length of the flushing cone in the laboratory model was determined to be 0.52 m, with a calculated volume of 0.043 m 3 . 3.3. Mesh refinement strategy The mesh refinement technique aims to decrease the dimensions of control volumes across the domain. Typically, this reduction is evaluated using \(\:{y}_{fc}^{+}\) ​, which signifies the non-dimensional distance from the center of the initial mesh cell perpendicular to the wall. This non-dimensional distance is represented as [8]. \(\:{y}_{fc}^{+}=\frac{{U}_{*}{\varDelta\:y}_{fc}}{\upsilon\:}\) [10] This function is defined by the dimensional distance from the wall to the center of the first mesh cell, denoted as \(\:{\varDelta\:y}_{fc}\) , and the friction velocity, \(\:{U}_{*}=\sqrt{{\tau\:}_{w}/\rho\:}\) , where \(\:{\tau\:}_{w}\) represents the wall shear stress. Therefore, it is crucial to validate and, if necessary, adjust the mesh. The subscript ' f c ' is employed in this context to avoid confusion with the general application of y + at the wall, as y + is intrinsically zero at the wall. Within simple shear flows, such as those typical in tunnels and straight flows, a distinct mean value for \(\:{y}_{fc}^{+}\) ​ emerges due to turbulence-induced fluctuations in y + alone. Notable inhomogeneity effects, as seen in the outflow from the bottom tunnels, result in significant stress field variations, leading to a broader distribution of \(\:{\:y}_{fc}^{+}\) ​. The mean value of \(\:{y}_{fc}^{+}\) referred to as \(\:{\stackrel{-}{y}}_{fc}^{+}\) is utilized. Comprehensive details on the meshes are outlined in Table 1 , where B represents the width of the bottom tunnels. Table 1 Description of mesh characteristics within the computational domain surrounding the bottom tunnels and reservoir. Mesh Nodes [ \(\:\times\:{10}^{4}\) ] \(\:{\varDelta\:\varvec{y}}_{\varvec{f}\varvec{c}}/\varvec{B}\) [%] V sexp ( \(\:\times\:{10}^{-3}\) ) (m 3 ) V sNum ( \(\:\times\:{10}^{-3}\) ) (m 3 ) L sExp (m) L sNum (m) R1 7 10.9 43 39.1 0.52 0.490 R2 20 8.3 43 40.2 0.52 0.495 R3 150 6.9 43 40.8 0.52 0.498 R4 300 5.6 43 41.5 0.52 0.501 R5 500 4.1 43 41.9 0.52 0.507 R6 800 3.8 43 42.2 0.52 0.512 R7 1000 3.5 43 42.8 0.52 0.513 R8 1300 1.5 43 42.9 0.52 0.513 R9 1500 0.1 43 42.9 0.52 0.513 A thorough investigation was carried out that involved 9 levels of mesh refinement ( \(\:{\stackrel{-}{y}}_{fc}^{+}\:\) ≈ 0.5 to \(\:{\stackrel{-}{y}}_{fc}^{+}\:\) ≈ 35) and 14 permutations of turbulence models. This was done to provide clarity around the complex interactions between turbulence models, wall functions, and varying degrees of mesh refinement. An extensive analysis of heat maps was conducted for a range of mesh configurations labeled R1 to R9. Figure 3(a) (using a warm color scale) illustrates the \(\:{\stackrel{-}{y}}_{fc}^{+}\) , ​values calculated a posteriori, while Fig. 3(b) (using a cool color scale) presents the Mean Absolute Percentage Error (MAPE) in comparing simulation results with experimental data. The examination of results in Fig. 4 demonstrates a trend of monotonic convergence, wherein a decrease in \(\:{\stackrel{-}{y}}_{fc}^{+}\) (or an increase in mesh refinement) correlates with enhanced accuracy (lower MAPE) in the scoured hole profile . This pattern holds for all combinations of turbulence models and wall functions, except in the case of the standard wall function, where a \(\:{\stackrel{-}{y}}_{fc}^{+}\) value below 1 (observed in mesh R 9 ) results in decreased accuracy. In such scenarios, the MAPE exhibits an increase of approximately 3–4% compared to mesh R 7 ( \(\:{\stackrel{-}{y}}_{fc}^{+}\) ≈ 10). The findings indicate that additional refinement did not significantly affect the numerical results. Consequently, a mesh size of 1000×10 4 , corresponding to the P 7 mesh, was selected. The number of mesh elements in the length, width, and height directions of the reservoir will be 520×180×75 and 52×15×11 for the bottom tunnel. The best results also obtained using Scalable Wall Function (Scal. WF) and RSM turbulence model. Upon determining the optimal fluid domain cell size, wall function, and turbulence model, a comparison between the numerical and experimental outcomes for the three tests is performed and consolidated in Table 2 . Figure 4 depicts the scour cone depth for Test 1. Table 2 The volume of the flushing cone for different high water levels (H w ) and their corresponding flow discharges. Characteristics of the flow Volume of flushing cone (cm 3 ) 3D model EXP. Test 1 Q = 4.5 lit/s – Hw = 90 cm 41600 43750 Test 2 Q = 6.0 lit/s – Hw = 40 cm 46200 49147 Test 3 Q = 8.0 lit/s – Hw = 120 cm 50300 54250 Mean error (%) 5.85 Demonstrated in the results, the model effectively replicated the pressure flushing event with a minor 6% deviation in estimating the volume of the flushing cone. Given the existing uncertainties within the numerical model that may influence the outcomes, such as the utilization of empirical sediment transport equations and the necessity for enhanced bed roughness calibration, the observed variation between the simulated results and the experimental data can be considered reasonable. 3.4. Defined models The examined models comprise 18 units with specific dimensions: a reservoir length of 1 m, width of 1.4 m, a dam wall height of 0.6 m, and bottom tunnels positioned at a depth of 0.35 m from the reservoir bottom and in the middle of the dam wall. For models with more than one bottom tunnels, the distance between the tunnels is 0.1 m, and they are symmetrically arranged in the middle of the dam wall. Figure 5(a-d) illustrates the longitudinal and transverse cross-section images of the models for one, two and three bottom tunnels configurations, respectively. In all models, the reservoir water level (H w ) stands at 0.5 m above the reservoir bottom, each bottom tunnel discharges at a rate of 0.003 m 3 /s, and has a length of 0.036 m with a square cross-section of 0.45 m per side. The goal of this study is to investigate the influence of changing variables, such as reservoir sediment accumulation, average sediment particle size, sediment particle uniformity and non-uniformity, and the number of simultaneous operations of the dam's bottom tunnel, on the dimensions and volume of the cone generated during the flushing operation. This volume is equivalent to the discharged sediment volume from the reservoir. Therefore, sediment particles with three average diameters of 0.27 mm (fine particles), 0.42 mm (medium particles), and 0.81 mm (coarse particles), all with a density of 2650 kg/m 3 , were selected. These sediments were examined in three uniform grain size distributions: entirely fine, entirely medium, and entirely coarse particles, and in one non-uniform grain size distribution, which includes equal proportions of sediment from all three grain size distributions. The study investigated sediment configurations at three different sediment accumulation depths (H S ) located 0.25 m, 0.30 m, and 0.35 m from the reservoir bottom within the one bottom tunnel scenario, leading to the creation of 12 distinct models. Moreover, to explore the influence of the simultaneous activation of bottom tunnels on the scour cone, uniform medium-sized sediments were assessed under scenarios involving one bottom tunnel (3 models), two bottom tunnels (3 models), and three bottom tunnels (3 models); hence, a total of 18 models were examined to further the aims of the current study. 4. Results and Discussions The dimensions of the eroded scour cone, along with the percentage of volume emptied from the sediment in the reservoir and the relative percentage of sediments removed from the reservoir during pressure flushing operations, were compiled in Table 3 . In this table, V is the total volume of the reservoir, V s is the scour cone volume, D s is the eroded sediments volume and D R is the total volume of the sediments deposited in the reservoir. The data pertaining to both the overall reservoir volume and the sediment volume at different levels are displayed in Table 4 , quantified in cubic meters. Table 3. Initial overview of the results derived from the numerical model analysis. Table 4. Volume of the reservoir and sediments. Volumes (m 3 ) 1 The total storage capacity of the reservoir under standard water level conditions 0.70 2 The sediment volume in the reservoir at a deposition level of 0.25 m 0.35 3 The sediment volume in the reservoir at a deposition level of 0.30 m 0.42 4 The sediment volume in the reservoir at a deposition level of 0.35 m 0.49 The images representing the 2D results of transported sediments in the scour cone on the x-z plane, demonstrating the length of the scour cone in Fig. 6, and on the y-z plane aligned with the upper wall of the dam in Fig. 7, have been extracted. The estimated three-dimensional scour cone geometry is depicted in Fig. 8. 4.1. Effect of Uniform and Non-uniform distribution of Sediments As depicted in Fig. 9, the length, width, and volume of the scour cone, along with the resulting restored volume of the reservoir for various types of uniform sediment gradation (including all fine, all medium, and all coarse particles) and non-uniform sediment gradation, increase proportionally with the sediment deposition level in the reservoir. Furthermore, the width of the cone (B max ) exceeds its length (L max ). The alterations in dimensions and volume of the scour cone formation under non-uniform sediment gradation at a 0.25 m deposition level between uniform sediment gradation with medium and fine particles, and at deposition levels of 0.30 m and 0.35 m between uniform sediment gradation with medium and coarse particles (tending towards a state of medium particle gradation). With the exception of fine particles, which exhibit the highest relative sediment removal percentage at the 0.30 m level, the discharged sediment volume increases with the sediment level in the reservoir. Consequently, the model with a 0.35 m level attains the maximum percentage of restored reservoir storage volume. Notably, in the fine particle sediment scenario, the most considerable relative sediment removal occurred at the 0.35 m level. At a 0.25 m deposition level, the interaction between non-uniform sediment gradation and the resultant scour cone formation suggests that finer particles are more prone to mobilization and transport, leading to significant changes in the scour profile. This is further evidenced by the observation that, with the exception of fine particles, the discharged sediment volume tends to increase with higher sediment levels in the reservoir. This trend indicates that as the sediment level rises, the energy available for sediment entrainment and transport also increases, resulting in greater sediment removal efficiency. The finding that the model with a 0.35 m deposition level achieves the maximum percentage of restored reservoir storage volume is particularly noteworthy. It suggests that at this level, the conditions are optimal for sediment removal, possibly due to the effective interplay of hydraulic forces and sediment characteristics that facilitate the erosion of accumulated sediments. This highlights the importance of managing deposition levels to optimize reservoir capacity. Interestingly, the fine particle scenario reveals that the most considerable relative sediment removal occurs at the 0.35 m level as well. This emphasizes the unique behavior of fine sediments, which, despite their lower mass, can be highly mobile and susceptible to hydrodynamic forces. Their ability to be removed efficiently at certain deposition levels points to the necessity of considering sediment gradation when evaluating sediment management strategies in reservoirs. The observation that the maximum scour cone formation for fine particles occurs at a 0.30 m sediment level can be attributed to several interrelated factors that influence sediment dynamics and hydraulic conditions within the reservoir. Hydraulic Forces At the 0.30 m deposition level, the flow dynamics may create optimal hydraulic conditions for the mobilization of fine particles. The velocity profile and shear stresses acting on the sediment bed are likely to be sufficient to dislodge and transport fine sediments effectively. This contrasts with higher deposition levels, where increased sediment accumulation may lead to a more stable bed configuration, reducing the effectiveness of hydraulic forces in mobilizing additional sediment. Sediment Characteristics Fine particles typically have lower settling velocities compared to coarser sediments, making them more susceptible to suspension within the flow. At the 0.30 m level, the balance between sediment deposition and resuspension can be optimal, allowing for a higher rate of scour and erosion. The finer texture and lighter weight of these particles enable them to be easily entrained into the flow, resulting in significant scour cone development. E nergy Dissipation and Scour Development : As sediment levels increase, the energy available for sediment transport can also increase. However, beyond a certain point, the energy may become dissipated through increased turbulence and sediment interactions, which can stabilize the bed and inhibit further scour. The 0.30 m level may represent a threshold where the energy is sufficient to induce maximum scour without reaching a point of excessive stabilization. Interaction with Coarser Sediments When considering the gradation of sediments, the presence of coarser particles may influence the behavior of fine particles. At the 0.30 m level, the finer particles may be more effectively mobilized in the presence of coarser particles, creating a more dynamic interaction that enhances the scour process. In contrast, at higher levels, the coarser particles may dominate the bed configuration, reducing the mobility of finer grains. Scour Cone Geometry The geometry of the scour cone itself is influenced by both the sediment characteristics and the flow dynamics at different levels. At the 0.30 m level, the configuration of the scour cone may reach an optimal shape and depth that allows for maximum sediment removal, while at other levels, the geometry may not be conducive to the same degree of scour. 4.2. Impact of activating the bottom tunnels simultaneously Based on the findings in Fig. 10, there is a notable trend indicating that as the sediment deposition in the reservoir increase, the dimensions of the scour cone, the restored reservoir volume percentage, and the relative sediment removal percentage all exhibit an increase in models with varying bottom tunnels numbers. This effect is particularly accentuated in models with multiple bottom tunnels and sediment deposition levels surpassing 0.30 m. Consequently, when the sediment level reaches 0.35 m from the reservoir bottom, all sediments along the upstream wall of the dam are eroded in cases involving either two or three bottom tunnels (with the sediment scour cone width aligning with the dam wall width at 1.40 m). Moreover, across all models, the width of the sediment scour cone surpasses its length. As per Figs. 10(c) and 10(d), the most significant percentage of restored reservoir volume and the highest relative sediment removal percentage from the reservoir are evident at a sediment deposition level of 0.35 m from the reservoir bottom, specifically when utilizing three bottom tunnels. 4.3. Impact of sediment size on the development of the sediment scour cone The geometry and volume of the sediment scour cone are also impacted by the particle diameter of the sediments. Examination of Fig. 11 reveals that an increase in the average particle diameter of sediments across all sediment deposition levels correlates with a decrease in the dimensions of the sediment scour cone. Consequently, this reduction impacts the restored reservoir volume and the relative percentage of sediments removed from the reservoir. Additionally, in all the models analyzed, the width of the sediment scour cone exceeds its length. The peak restored reservoir volume is reached at a sediment deposition level of 0.35 m for fine sediments, and the greatest relative percentage of sediment discharge from the reservoir is observed at a sediment deposition level of 0.3 m for fine sediments. 4.4. Impact of the operational bottom tunnels (d 50 = 0.42 mm) at different deposition levels The research is designed to analyze the consequences of simultaneously operating reservoir bottom tunnels during hydraulic pressure flushing on sediment removal, economic considerations, water loss, especially during low water periods, and significant environmental implications. Figure 12, which showcases the evolution of the scour cone status concerning the varying number of active bottom tunnels, shows that an increase in the quantity of bottom tunnels situated below the dam will result in amplified length, width, volume, recovery percentage of reservoir volume, and sediment discharge percentage from the reservoir. The width of the scour cone exceeds its length across all models. When three bottom tunnels operate simultaneously at a sediment accumulation level of 0.35 m, the highest percentage of recovered reservoir volume attributed to sedimentation is achieved. Moreover, within this scenario, the greatest relative percentage of sediment discharged from the reservoir will occur. Furthermore, as indicated by the results, the degree of improvement in the sediment cone geometry, the recovery percentage of reservoir volume, and the relative sediment discharge percentage from the reservoir decreases for sediment levels below 0.35 m and when more than two bottom tunnels operate simultaneously. Consequently, in cases where sediment levels in the reservoir are low, utilizing two bottom tunnels for pressure flushing may yield reduced water loss in the dam reservoir, mitigate environmental harm caused by scouring operations, and potentially offer greater cost efficiency. 5. Conclusion The objective of the study was to investigate the impact of various factors, such as sediment accumulation levels, sediment particle size, uniformity in particle grading, and the operation of bottom tunnels, on the dimensions and volume of sediment cones formed during pressure flushing in reservoirs. The study aimed to identify an optimal scenario for maximizing sediment removal. Eighteen models were developed following the validation of pressure flushing simulations using FLUENT software. Results were analyzed and presented graphically, with a summary provided in seven sections. The geometry and volume of the sediment cone resulting from pressure flushing increase with higher levels of accumulated sediment in the reservoir. Conversely, the geometry and volume of the sediment scour cone decrease with an increase in the average diameter of sediment particles. In the scenario of non-uniform particle grading, where particles with average diameters of 0.27 mm, 0.42 mm, and 0.81 mm are present in equal proportions at an accumulation level of 0.25 m, the geometry and volume of the sediment scour cone demonstrate a significant relationship. This contrasts with the geometry and volume observed in the case of uniform particle grading, involving fine particles (0.27 mm) and particles with an average diameter of 0.42 mm. Additionally, at accumulation levels of 0.30 m and 0.35 m, the correlation between the geometry and volume in the uniform particle grading scenario with particles of 0.42 mm average diameter and coarse particles (average diameter 0.81 mm) closely mirrors the outcomes of uniform grading with medium-sized particles. An increase in the number of bottom tunnels is correlated with a rise in both the geometry and volume of the sediment scour cone. However, at shallower depths (less than 0.35 m from the reservoir bed), the escalation in sediment scouring intensity is less noticeable when using two or three bottom tunnels. Therefore, implementing pressure flushing with two bottom tunnels at depths below 60% of the reservoir's standard depth leads to reduced water loss and environmental impact, rendering it a more environmentally advantageous choice. In all the numerical model scenarios analyzed, the width of the sediment scour cone surpasses its length. Among the scrutinized numerical models, the model that encompassed the simultaneous operation of three bottom tunnels, an accumulation level of 0.35 m from the reservoir bed, and uniform particle grading of 0.42 mm, extracted the maximum sediment volume from the reservoir (0.7869 m 3 ). Consequently, this model achieved the highest percentage of restored reservoir volume lost due to sediment accumulation (11.24%). Moreover, it demonstrated the highest relative percentage of sediment discharge from the reservoir (6.16%), establishing it as the most successful outcome among all investigated scenarios. To achieve effective pressure flushing in a reservoir with a consistent water level, it is advisable to conduct the operation in a reservoir featuring sediment with a smaller average diameter, a higher accumulation level, and a greater number of active bottom tunnels. Maintaining a consistent discharge from each tunnel, ensuring the uniform elevation of tunnels, and keeping a fixed distance between them are crucial components. It is important to acknowledge that the findings of this study are specific to the current simulation, given the multifaceted nature of factors influencing sediment scouring in reservoirs. Further research is essential to generalize these results to other models. Declarations Author Contribution the subject of the article has been proposed by Prof. Yousef Hassanzadeh. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6002097","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414713364,"identity":"610ae401-a489-4dd0-98d9-f5a3f1a6a0a7","order_by":0,"name":"Mostafa Roshdi","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Roshdi","suffix":""},{"id":414713366,"identity":"853c5c08-18d0-44af-be5b-e43ab4358f6b","order_by":1,"name":"Yousef Hassanzadeh","email":"","orcid":"","institution":"University of 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21:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6002097/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6002097/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76273989,"identity":"1c7bde1b-8a35-465a-bc68-d44b88bfc9a4","added_by":"auto","created_at":"2025-02-14 09:28:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148235,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of deposited sediment zone and scour cone.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/2f57a308845fbb6f66101e6b.png"},{"id":76274338,"identity":"6dc68703-e17b-4658-9ed7-53c3ea36567b","added_by":"auto","created_at":"2025-02-14 09:36:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76027,"visible":true,"origin":"","legend":"\u003cp\u003eLength (L) and volume (V) of the sediment flushing cone with respect to various flow rates (Q) tested on sediment particles with average diameters (d\u003csub\u003e50\u003c/sub\u003e) (Fathi-Moghadam et al., 2010).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/3b38fcb6befd2cd1ad4727fa.png"},{"id":76273992,"identity":"8162d822-7066-4bf5-bf79-3a8d1eb86ccc","added_by":"auto","created_at":"2025-02-14 09:28:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":241225,"visible":true,"origin":"","legend":"\u003cp\u003eComputational Fluid Dynamics Simulation of Flow and sediment in reservoir and bottom tunnels with Varied Mesh Configurations, Turbulence Models, and Wall Treatments.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/3f73d66d571ab1c6cef61581.png"},{"id":76274008,"identity":"a45f4c33-182a-47f3-adc1-e4606edfc852","added_by":"auto","created_at":"2025-02-14 09:28:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":107172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eComparison of the longitudinal scour cone profile in front of the bottom tunnel \u003cstrong\u003e(b)\u003c/strong\u003e 3D view of the scour cone in Test 1 model.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/e0b5dcbc7c49ca8c0664f00f.png"},{"id":76273994,"identity":"16bde7ee-4ee4-4999-b3b4-552e43078c07","added_by":"auto","created_at":"2025-02-14 09:28:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38310,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e The longitudinal view of the dam and reservoir illustrates sediment accumulation \u003cstrong\u003e(b)\u003c/strong\u003e view of the dam wall in the one bottom tunnel \u003cstrong\u003e(c)\u003c/strong\u003etwo bottom tunnels and \u003cstrong\u003e(d)\u003c/strong\u003e three bottom tunnels model.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/e174c5b1be0f58a741c6df69.png"},{"id":76273993,"identity":"0dc1bcaf-e6e8-4953-bb00-7d992cb7220b","added_by":"auto","created_at":"2025-02-14 09:28:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":371953,"visible":true,"origin":"","legend":"\u003cp\u003eA view of the scour hole in certain models along the x-z planes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/ef34f78fe1c9ae03fe33bb72.png"},{"id":76274000,"identity":"d4330573-80d3-4291-9883-93b0612ab448","added_by":"auto","created_at":"2025-02-14 09:28:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":191865,"visible":true,"origin":"","legend":"\u003cp\u003eA 2D view of the maximum scour cone width in the y-z plane.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/acb07349471ffcde5c1475cc.png"},{"id":76273995,"identity":"e7ab68ba-a47e-47d1-8fd9-5dc16afa3d87","added_by":"auto","created_at":"2025-02-14 09:28:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":104744,"visible":true,"origin":"","legend":"\u003cp\u003e3D view of the scour cone at the end of the simulations.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/22cc7c800f04b6448e12a7d6.png"},{"id":76275513,"identity":"7f26af1d-de49-4ea7-979f-3e0a35af6f6d","added_by":"auto","created_at":"2025-02-14 09:44:26","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":84513,"visible":true,"origin":"","legend":"\u003cp\u003eThe influence of sediment deposition levels on parameters including (a) (b) cone dimensions, (c) volume, (d) storage restoration, and (e) sediment removal percentages for fine, medium, and coarse particle sediments, alongside non-uniform sediment gradation.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/51d68633aa3300de0da5492b.png"},{"id":76273998,"identity":"32082112-e1c6-423b-abd8-fbc25c4b483a","added_by":"auto","created_at":"2025-02-14 09:28:24","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":64625,"visible":true,"origin":"","legend":"\u003cp\u003eThe influence of sediment deposition level on the (a) length, (b) width, (c) volume of the sediment scour cone, (d) the percentage of restored reservoir volume, and (e) the relative percentage of sediments removed from the reservoir.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/237853cce3ff7c7dbf0e6094.png"},{"id":76274050,"identity":"d2fb719a-cd1c-42d5-a8f7-d98180f7f652","added_by":"auto","created_at":"2025-02-14 09:28:28","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":32833,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of sediment particle diameter on the (a) length of the sediment scour cone, (b) the volume of the sediment scour cone, and (c) the percentage of restored reservoir volume across diverse sediment deposition levels.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/2b5f690f031fd4b795a0446a.png"},{"id":76274033,"identity":"05e72c8c-91f1-4982-be55-22bba5a6d810","added_by":"auto","created_at":"2025-02-14 09:28:27","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":43746,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of the number of bottom tunnels on (a) the length, (b) the width, and (c) the volume of the sediment scour cone, along with (d) the percentage of recovered reservoir volume, and (e) the relative percentage of sediment flushed out of the reservoir across different sediment accumulation levels.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/bae32d3281da190d364df229.png"},{"id":94471634,"identity":"2cecec2f-22d2-40c7-9bcb-00e0190ca050","added_by":"auto","created_at":"2025-10-27 15:38:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2532982,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6002097/v1/f0636f65-a135-40f9-8271-dc205c52acbc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reservoir Sedimentation Management: Evaluating Sediment Size and Bottom Tunnel Effects on Sediment Flushing Efficiency","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSediment deposition causes loss of storage capacity in reservoirs and lack of sediment supply to downstream channels, impairing reservoir functions and aquatic ecosystems (Morris \u0026amp; Fan, 1998; Fan, 2011; Schleiss et al., 2016; Huang et al., 2019b). Strategies have been developed and implemented to alleviate reservoir sedimentation (Brandt, 2000; Wang \u0026amp; Hu, 2009; Kondolf et al., 2014; Morris, 2020;): (1) reducing sediment inflow, such as watershed erosion control and upstream sediment trapping; (2) minimizing sediment deposition, such as turbidity current venting, sediment sluicing during floods, and sediment bypassing; and (3) removing deposited sediment, such as sediment flushing, dredging, and dry excavation. Among these strategies, hydraulic methods, including turbidity current venting, sediment sluicing, and flushing, are particularly effective and widely used (Fan \u0026amp; Morris, 1992; Shen \u0026amp; Lai, 1996; Shen, 1999). Generally, these methods involve discharging sediment-laden flows from reservoirs through the bottom tunnels at the dam. These methods will not work if bottom tunnels are not properly operating due to excess sediment deposits and debris.\u003c/p\u003e \u003cp\u003ePressure flushing is utilized to remove sediment deposits near the dam and keep the intakes of hydraulic structures free of sediment (Kondolf et al., 2014; Morris, 2020). Without drawdown of the reservoir level, the gates of bottom tunnels are opened to scour sediment and release the resuspended sediment out of reservoirs. Sediment removal is limited to the vicinity of the bottom tunnel intake and a localized scour hole is formed within a short period of time (Lai \u0026amp; Shen, 1996; Scheuerlein et al., 2004). For non-cohesive sediment, the slope of the scour hole in the equilibrium condition is approximately equal to the submerged angle of repose (Xiong, 1981; Jin, 1990). The depth of the scour hole increases with the increase in the discharge and the area (or height) of the bottom tunnel, and decreases with the increase in the sediment particle size (Fathi-Moghadam et al., 2010; Powell \u0026amp; Khan, 2012; Emamgholizadeh \u0026amp; Fathi-Moghdam, 2014; Haghjouei et al., 2021;). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eshows a schematic of deposited sediment zone and scour cone.\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNumerical modelling provides an effective way to predict the detailed processes of reservoir sediment flushing (Khosronejad et al., 2008; Sawadogo et al., 2019; Xu and Cao, 2024). By simulating the flow structure, sediment transport, and bed evolution, operation schemes can be proposed to improve flushing efficiency (Hung et al., 2009; Khosronejad, 2009; Ahn et al., 2013; Huang et al., 2019a; Blade Castellet et al., 2019a; Goulart et al., 2023;). However, reservoir sediment flushing may fail due to severe sedimentation and inappropriate operation, i.e., sediment particles entering the bottom tunnel cannot be transported downstream but deposited in it (Di Silvio, 1990; Morris \u0026amp; Fan, 1998; Fan, 2011; Xu et al., 2023; Sun et al., 2024).\u003c/p\u003e \u003cp\u003eThe effectiveness of sediment flushing operations in removing sediment from a reservoir is influenced by various factors, including sediment characteristics, reservoir morphology, hydrological conditions, and operational strategies. The limited research on the flushing of sediment from reservoirs concerning sediment uniformity and sediment size highlights a significant gap in understanding the dynamics of sediment management in dam reservoirs. The process of sediment flushing plays a crucial role in sediment transport and reservoir maintenance. However, the specific considerations related to sediment uniformity, sediment size, and number of bottom tunnels in the flushing operation have not been extensively studied. The presence of non-uniform sediment deposits behind a dam can pose challenges for sediment flushing efforts, as the distribution of sediment within the reservoir may impact the flushing efficiency and the extent of sediment removal. The non-uniform distribution of sediments in reservoirs can significantly impact sediment transport and deposition behind dams. Acknowledging the dynamic sediment movement within dam reservoirs is crucial when considering a uniform coefficient of sediment transport. Various factors such as flow velocity, sediment size, and reservoir morphology influence the distribution of bed sediments and suspended sediments as they are transported to a dam reservoir. The accumulation of bed sediments at different rates and locations can result in the formation of non-uniform sediment deposits behind the dam.\u003c/p\u003e \u003cp\u003eThe presence of sediment non-uniformity can pose challenges for the operation and maintenance of bottom tunnels in dam structures. Bottom tunnels, also known as low-level outlets or bottom outlets, are designed to release water from the lower levels of a reservoir while controlling sediment flushing and sediment transport. The number and level of bottom tunnels in a dam play a crucial role in managing sediment deposition, sediment flushing operations, and reservoir sedimentation control. In the context of sediment non-uniformity, the design and placement of bottom tunnels must consider the distribution of sediment deposits and the potential for sediment movement within the reservoir. The location and number of bottom tunnels should be strategically determined to facilitate effective sediment flushing, minimize sediment deposition risks, and maintain reservoir storage capacity over time.\u003c/p\u003e \u003cp\u003eThe primary aim of this study is to investigate the influence of sediment uniformity, the number and levels of bottom tunnels, and the deposition height on sediment flushing operations in dam reservoirs. By examining the combined effects of these parameters, the research seeks to enhance understanding of sediment management strategies and optimize the efficiency of sediment removal processes during flushing operations.\u003c/p\u003e \u003cp\u003eThe final impact of these parameters on the volume and geometry of the score cone resulting from flushing operations, as well as the percentage of reservoir sediments being removed, is under investigation. Therefore, the objectives include determining the following:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe effect of the average diameter size of deposited sediment particles in the reservoir on the geometry and volume of the scour cone is being studied under the scenario of uniform particle sizing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe investigation aims to compare the impact of non-uniform particle sizing of settled sediment particles in the reservoir, where there is an equal distribution of particles forming the average diameter examined in the case of uniform particle sizing, on the geometry and volume of the scour cone. This comparison will be made against the scenario of uniform particle sizing of sediment particles.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe impact of the sediment deposition level in the reservoir, on the geometry and volume of the scour cone is being studied.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe influence of the number of bottom tunnels on the geometry and volume of the scour cone is being examined.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe primary purpose of sediment removal from reservoirs is to replenish the lost volume resulting from sediment deposition. This study seeks to identify the most suitable parameters to maximize sediment removal from reservoirs, with the project's success hinging on the efficient removal of sediment from the dam reservoir during pressure flushing. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTo reach\u003c/span\u003e this goals, CFD solver of Fluent is applied. Simulation of the bed erosion around the bridge piers is not possible in CFD solver of Fluent. To model the bed erosion beyond the flow solution, the morphological model was produced and linked to the Fluent, in which the flow solution couples with a sediment transport model, forms the morphological model.\u003c/p\u003e"},{"header":"2. Methods and Procedures","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Influenced Parameters\u003c/h2\u003e \u003cp\u003eThe equilibrium of the scour cone volume, commonly developed in reservoirs post-pressure flushing, is influenced by factors including reservoir water depth (H\u003csub\u003ew\u003c/sub\u003e), sediment depth above the intake (H\u003csub\u003es\u003c/sub\u003e), fluid density (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e), sediment density (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003es\u003c/sub\u003e), intake diameter (D), intake water velocity (u), fluid dynamic viscosity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e), gravitational acceleration (g), mean grain diameter (d\u003csub\u003e50\u003c/sub\u003e) of non-cohesive sediment, and \u003cem\u003eN\u003c/em\u003e is the number of bottom tunnels. These parameters illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The basic variable, equilibrium scour cone volume (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003es\u003c/sub\u003e) are expressed individually (Eq.\u0026nbsp;[1]):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{V}_{s}=f({H}_{w},{H}_{s},D,u,g,{\\rho\\:}_{s},\\rho\\:,{d}_{50},\\mu\\:,\\:N)\\)\u003c/span\u003e \u003c/span\u003e[1]\u003c/p\u003e \u003cp\u003eWithin Eq.\u0026nbsp;1, the impact of \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eparameters Hs, d\u003c/span\u003e\u003csub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e50\u003c/span\u003e\u003c/sub\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand N is\u003c/span\u003e investigated, while all other parameters are held constant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Modeling of the Flushing\u003c/h2\u003e \u003cp\u003eNumerical modeling of the reservoirs, either in the stage of design or during their operation, helps designers as well as operators to have a better operational management plan for the reservoir. This can be achieved by predicting the depositional behavior of the reservoir and regulating practical guidelines for sediment management in the reservoir through a schedule of sediment flushing during the operation of the reservoir. Literature review shows that many attempts have been devoted to modeling such cases by applying one- or two-dimensional numerical models. However, neither one- nor two-dimensional models are appropriate for studying the process of pressure flushing.\u003c/p\u003e \u003cp\u003eIn two-dimensional horizontal models, the basic assumption is that the distribution of velocity and the sediment concentration along the depth are uniform. However, it has been shown that, during the process of pressure flushing and after the opening of the bottom tunnels, the velocity and sediment concentration near the outlet are much higher than above levels, so the basic assumption of those models is not reasonable. To sum up, as pressure flushing is entirely a 3-D phenomenon in a very restricted area near the outlet and its duration is also rather short (which means less computational time would be needed for its simulation), modeling this process by a 3-D code could be an advantage.\u003c/p\u003e \u003cp\u003eIn the present study, a 3-D numerical model will be applied to examine the process of pressure flushing in a simplified reservoir deposition. The main purpose of this paper is to study the capabilities of a 3-D model to simulate this phenomenon by investigating the mechanism of the pressure flushing operation and describing the behavior of the cone formation and retrograde erosion of the flushing cone under different conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. The hydrodynamic model\u003c/h2\u003e \u003cp\u003eThe CFD solver Fluent was used to simulate the incompressible flows by solving the three-dimensional Reynolds-averaged Navier\u0026ndash;Stokes (RANS) equations. To solve sequentially the governing hydrodynamics equations, Fluent utilizes the control volume method. Two continuity equations were solved for each phase, whereas the momentum and transport equations were simultaneously solved for both phases. The Reynolds-averaged momentum equations and the continuity equation for the i\u003csup\u003eth\u003c/sup\u003e phase are expressed as follows (Salaheldin et al. 2004):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:{\\alpha\\:}_{q}}{\\partial\\:t}+{U}_{i}\\frac{\\partial\\:{\\alpha\\:}_{q}}{{\\partial\\:x}_{i}}=0\\)\u003c/span\u003e \u003c/span\u003e[2]\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:}{\\partial\\:t}\\left(\\rho\\:{U}_{i}\\right)+\\frac{\\partial\\:}{{\\partial\\:x}_{j}}\\left({\\rho\\:U}_{i}{U}_{j}\\right)=-\\frac{\\partial\\:P}{\\partial\\:{x}_{i}}+\\frac{\\partial\\:{\\tau\\:}_{ij}}{\\partial\\:{x}_{j}}+\\rho\\:{g}_{i}\\)\u003c/span\u003e \u003c/span\u003e[3]\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{q}\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003et\u003c/em\u003e, \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e, \u003cem\u003eg\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e,, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e refers to the volume fraction of the \u003cem\u003eq\u003c/em\u003eth phase in the control volume, time, velocity in the \u003cem\u003ei\u003c/em\u003e direction, total pressure, gravitational acceleration in the \u003cem\u003ei\u003c/em\u003e direction, averaged local density in the control volume, which was computed as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:=\\sum\\:{\\alpha\\:}_{q}{\\rho\\:}_{q}\\)\u003c/span\u003e\u003c/span\u003e, and stress tensor, respectively. In the turbulent flows, the time averaged continuity and Navier-Stokes equations were obtained from Eqs.\u0026nbsp;4 and 5 (Salaheldin et al. 2004):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:\\rho\\:}{\\partial\\:t}+{\\left(\\rho\\:\\stackrel{-}{{U}_{i}}\\right)}_{i}=0\\)\u003c/span\u003e \u003c/span\u003e[4]\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:}{\\partial\\:t}\\left(\\rho\\:{\\stackrel{-}{U}}_{i}\\right)+\\left({\\rho\\:\\stackrel{-}{U}}_{i}{\\stackrel{-}{U}}_{j}\\right)=-\\stackrel{-}{{P}_{i}}+{\\left|\\mu\\:\\left({\\stackrel{-}{U}}_{i,j}+{\\stackrel{-}{U}}_{j,i}\\right)-\\rho\\:\\stackrel{-}{{U}_{i}{U}_{j}}\\right|}_{j}\\)\u003c/span\u003e \u003c/span\u003e=0[5]\u003c/p\u003e \u003cp\u003eIn the above equations, the overbar represents time averaging. The position vector components (\u003cem\u003ex, y, z\u003c/em\u003e) are denoted as \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, while \u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the velocity components (\u003cem\u003eu, v, w\u003c/em\u003e) in the \u003cem\u003ex, y\u003c/em\u003e, and \u003cem\u003ez\u003c/em\u003e directions, respectively. The kinematic viscosity is represented as \u003cem\u003eν\u0026thinsp;=\u0026thinsp;\u0026micro;/ρ\u003c/em\u003e, and \u003cem\u003eu\u0026prime;\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e represents the velocity fluctuating components (\u003cem\u003eu', v', w'\u003c/em\u003e). The term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\partial\\:\\stackrel{-}{\\stackrel{\u0026acute;}{{U}_{i}}\\stackrel{\u0026acute;}{{U}_{j}}}\\)\u003c/span\u003e\u003c/span\u003e corresponds to the Reynolds stress tensor, which is modeled differently depending on the turbulence model employed (Toonder and Nieuwstadt, 1997). Here, the following models are investigated: k\u0026thinsp;\u0026minus;\u0026thinsp;ω (SST and Standard), k \u0026ndash; ε (Standard, RNG, and Realizable), and the Reynolds stress model (RSM). The k \u0026ndash; ω and k\u0026thinsp;\u0026minus;\u0026thinsp;ε models incorporate the Boussinesq hypothesis (Toonder and Nieuwstadt, 1997), which is not necessary for the RSM. The assessment of the Standard Wall Function (Std. WF), Enhanced Wall Treatment (EWT), and Scalable Wall Function (Scal. WF) approaches was conducted whenever applicable for the bottom tunnels ((Toonder and Nieuwstadt, 1997; El Khoury et al., 2013; Kalpakli and Orlu, 2013).\u003c/p\u003e \u003cp\u003eFor discretization, the control volume is divided into finite volume and the governing equations are solved for each cell and each variable.\u003c/p\u003e \u003cp\u003eIn Fluent software, the pressure-based solver offers four predictor-corrector algorithms (SIMPLE, SIMPLEC, PISO, COUPLE) for velocity-pressure coupling. For this study, the PISO algorithm was selected along with various turbulence models. The QUICK scheme (Leonard, 1979) replaced the central-upwind hybrid spatial discretization method, combining third-order precision with upwind stability. However, the scheme's unbounded nature may lead to over- and undershoots. A second order upwind interpolation scheme is used for momentum and turbulence equations. The convergence criterion for all equations is set at a minimum residual value of 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Morphological model\u003c/h2\u003e \u003cp\u003eThe CFD solver in Fluent does not allow for direct simulation of bed erosion around cylinders. Therefore, to model the bed erosion beyond the flow solution, a morphological model was developed and integrated with Fluent. This integrated model combines the flow solution with a sediment transport model to form the morphological model. The morphological model emphasizes three primary elements: (i) bed-load, (ii) sand slides, and (iii) sediment mass balance. These elements are described as follows (Hassanzadeh et al., 2020):\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBed-load\u003c/strong\u003e \u003cp\u003eWhen developing a two-dimensional bed-load model, an extension of the bed-load equation introduced by Engelund and Freds\u0026oslash;e (1976) is described using a vectorial representation. This model accounts for bed-load transport occurring on a sloping bed, where \u003cem\u003eU\u003c/em\u003e\u003csub\u003eb\u003c/sub\u003e represents the averaged transport velocity of a particle. It is important to note that at the particle's position, the fluid velocity \u003cem\u003eU\u003c/em\u003e differs from \u003cem\u003eU\u003c/em\u003e\u003csub\u003eb\u003c/sub\u003e. According to [31], \u003cem\u003eU\u003c/em\u003e is equal to \u003cem\u003eaU\u003c/em\u003e\u003csub\u003ef\u003c/sub\u003e, where \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003ef\u003c/em\u003e\u003c/sub\u003e represents the friction velocity, and a is an empirical constant typically assigned a value of a\u0026thinsp;=\u0026thinsp;10 (Roulund et al., 2005). The bed-load transport rate, \u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e, which is measured in terms of volume per unit width and per unit time, is related to \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e using the equation proposed by\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{q}_{b}=\\frac{1}{6}\\pi\\:{d}^{3}\\frac{{P}_{EF}}{{d}^{2}}{U}_{b}\\)\u003c/span\u003e \u003c/span\u003e[6]\u003c/p\u003e \u003cp\u003eIn the mentioned equation, the parameter \u003cem\u003ed\u003c/em\u003e represents the grain size, while \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eEF\u003c/em\u003e\u003c/sub\u003e signifies the percentage of particles that are in motion within the bed surface layer. Both \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eEF\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sub\u003e play pivotal roles in determining the magnitude of bed-load transport. It is worth noting that selecting the appropriate bed-load equation is crucial for accurately predicting the shape of scour holes. For instance, Dodaro et al. (2016) proposed a modification to the Einstein formula that enables an accurate prediction of the evolution of scour holes.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSand slide\u003c/strong\u003e \u003cp\u003eExperimental and numerical simulations reveal that, in certain regions, the slope of the local bed exceeds the repose angle of sediment particles during the formation of scour holes at the upstream face. This leads to shear failures occurring at these specific locations. The experimental observations conducted by Roulund et al. (2005) revealed that the collapse of the bed occurs at the upstream face of the scour hole when the bed slope, \u003cem\u003eβ\u003c/em\u003e, exceeds the angle of repose,, by a few degrees. Furthermore, they discovered that shear failure of the soil takes place just below the bed surface, resulting in the downward sliding of sediment particles towards the center of the scour hole. Following the sliding of each particle, the bed slope decreases by a few degrees below the repose angle of the sediments.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMorphological scheme\u003c/strong\u003e \u003cp\u003eThe equation used to calculate the mass balance of sediment at mesh points on the bed is as follows\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:h}{\\partial\\:t}=\\frac{-1}{1-n}\\frac{1}{A}\\sum\\:_{i=1}^{4}\\left[\\left({q}_{b,i}.{n}_{i}\\right)\\left|{l}_{i}\\right|\\right]\\)\u003c/span\u003e \u003c/span\u003e[7]\u003c/p\u003e \u003cp\u003eThe variables in the equation include \u003cem\u003eh\u003c/em\u003e for bed elevation, \u003cem\u003en\u003c/em\u003e for porosity, \u003cem\u003eA\u003c/em\u003e for the projected area of a small bed-surface element, \u003cem\u003ei\u003c/em\u003e for the assigned number to each side of the projected area (\u003cem\u003ei\u0026thinsp;=\u0026thinsp;1, \u0026hellip;, 4\u003c/em\u003e), \u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003eb,i\u003c/em\u003e\u003c/sub\u003e representing the sediment-transport vector at the bed elevation (\u003cem\u003eh\u003c/em\u003e) on the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e side of the projected area, \u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e for the normal vector at the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e side of the projected area, and |\u003cem\u003el\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e| for the length of the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e side of the bed element. The computational procedure involved the following steps: (I) mesh generation using Gambit, (II) flow calculation within the flow domain, (III) assessment of sediment transport as bed load, (IV) updating bed morphodynamics, (V) inspection for sand slides, and (VI) iterative repetition of steps \u003cem\u003eI\u003c/em\u003e to \u003cem\u003eV\u003c/em\u003e. Notably, the model presented excludes focuses on bed-load sediment transport due to flushing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Computational domain\u003c/h2\u003e \u003cp\u003eA multi-phase computational domain containing the water flow in the reservoir and an air region at the top was simulated by the multi-phase flow model. In numerical simulations, providing\u003c/p\u003e \u003cp\u003ean appropriate cell is very important in preparation of the computational domain. The accuracy of the results, the convergence and the computational time are the main parameters that are\u003c/p\u003e \u003cp\u003estrongly influenced by the size and alignment of the cells (Esmaeili et al. 2011). Two mesh blocks is applied to mesh the computational domain. It should be noted that the models and their meshing have been generated in Gambit software and then their files were imported to FLUENT software.\u003c/p\u003e \u003cp\u003eIn order to mesh the models effectively, a Cartesian grid was employed, where specific numbers and sizes were assigned to cell dimensions along the X, Y, and Z Cartesian axes, each with varying dimensions. This methodology was implemented to enhance the accuracy of simulating sediment transport through the bottom tunnels of the dam by refining cell sizes in the vicinity of their intakes. As the simulation progressed towards areas such as the inlet boundary at the reservoir area, (for X-direction cell dimensions), the lateral walls of the reservoir (for Y-direction cell dimensions), and the top of the reservoir (for Z-direction cell dimensions) located further from the bottom tunnels area, the cell dimensions were adjusted to a coarser resolution. In each of the numerical models under examination, a mesh block was employed to encompass the reservoir boundary, extending up to the dam wall, for simulating the reservoir perimeter. Specifically, for each bottom tunnel to intake, a mesh was delineated from the initiation of the dam wall to the terminus of the bottom tunnel to model both the intake boundary and the sediment-laden outflow flowing through it. This outflow exits the reservoir at a specified discharge rate of 0.003 m\u003csup\u003e3\u003c/sup\u003e/s for each bottom tunnel and exit gate. Consequently, in the scenario of a single bottom tunnel, 2 blocks will be utilized; for 2 bottom tunnels, 3 blocks will be allocated; and in the case of three bottom tunnels, 4 blocks will be utilized within the computational domain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Boundary Conditions\u003c/h2\u003e \u003cp\u003eAppropriate boundary conditions must be considered at computational domain boundaries depending on the flow nature. In performed simulations, at the reservoir inlet, zero transverse, \u003cem\u003ev\u003c/em\u003e, and vertical velocities, \u003cem\u003ew\u003c/em\u003e, were considered. The profiles for \u003cem\u003eu\u003c/em\u003e component of velocity in reservoir inlet were selected based on the equilibrium profiles. At the bottom tunnels inlet, the flow that can be discharged from the tunnels considered as the boundary condition. This boundary also applied to the reservoir outlet boundary at the tunnels intake. At the downstream end of the bottom tunnels, for all flow variables, a correction for overall mass balance and zero diffusion flux were considered. At the sides and top surface, zero normal velocity and zero normal gradients were applied for all variables by defining a symmetric boundary condition. At the solid boundaries, the no-slip boundary condition was specified to set the velocity to zero. Wall functions are very important in tunnels around. The boundary condition for sediment concentration in the cell closest to the bed is determined by the formula given by van Rijn:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{bed}=0.015\\frac{{d}_{50}^{0.7}}{a}.\\frac{{\\left[\\frac{{\\tau\\:}_{0}-{\\tau\\:}_{c}}{{\\tau\\:}_{c}}\\right]}^{1.5}}{{\\left[\\frac{{\\rho\\:}_{s}-{\\rho\\:}_{w}}{{\\rho\\:}_{w}}.\\frac{g}{{\\nu\\:}^{2}}\\right]}^{0.1}}\\)\u003c/span\u003e \u003c/span\u003e[8]\u003c/p\u003e \u003cp\u003ewhere C\u003csub\u003ebed\u003c/sub\u003e is the equilibrium bed sediment concentration (volume fraction); d is the diameter of the sediment particle; a is the reference level; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{\\circ\\:}\\)\u003c/span\u003e\u003c/span\u003e is the bed shear stress; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{c}\\)\u003c/span\u003e\u003c/span\u003e is the critical bed shear stress for the movement of sediment particles; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{s}\\)\u003c/span\u003e\u003c/span\u003e is the density of the sediment; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\nu\\:\\)\u003c/span\u003e\u003c/span\u003e is the kinematic viscosity of water; and g is acceleration due to gravity. The concentration determined by (3) could be extrapolated by the Rouse equation to calculate the values at the required level (Bhuiyan and Olsen, 2002).\u003c/p\u003e \u003cp\u003eThe initial conditions for this pressure flushing study may involve a fluid-filled region within the computational domain at the simulation's commencement, pressure distribution, and initial environmental temperature. When examining the pressure flushing of reservoir sediments, the pressure distribution initially exhibits hydrostatic characteristics within the reservoir's depth. Moreover, the definition of fluid regions encompasses the area from the reservoir bottom to the water level and fluid-filled regions within the dam's bottom tunnels as initial conditions. As a result, one, two, and three fluid-filled regions are respectively designated for one, two, and three bottom tunnels. Given that in the phenomenon of pressurized flushing, various geometric and hydraulic factors, such as model dimensions, sediment level, sediment particle characteristics, turbulence model, and discharge flow rate from the gates, play a role. After conducting numerous simulations and achieving suitable convergence of the models to ensure that significant changes in the dimensions of the scour cone do not occur thereafter, the appropriate simulation time is determined. In the present study, by analyzing the model relevant to the scenario of one bottom tunnel with coarse sediment accumulated at a depth of 0.35 m in the reservoir bottom and observing the establishment of stability conditions, a time of 200 seconds was selected for simulating each of the models.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Model Validation","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Quantifying Discrepancies between Experimental and Numerical Velocity Profiles: The Mean Absolute Percentage Error (MAPE)\u003c/h2\u003e \u003cp\u003eTo assess the discrepancies between the numerical and experimental results, the mean absolute percentage error (MAPE) is employed, defined as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:MAPE=\\frac{1}{n}\\sum\\:_{i=1}^{n}\\left|\\frac{{d}_{{s}_{exp}}\\left({x}_{i},\\:{y}_{i},{z}_{i}\\right)-{d}_{{s}_{num}}\\left({x}_{i},\\:{y}_{i},{z}_{i}\\right)}{{d}_{{s}_{exp}}\\left({x}_{i},\\:{y}_{i},{z}_{i}\\right)}\\right|\\)\u003c/span\u003e \u003c/span\u003e[9]\u003c/p\u003e \u003cp\u003eHere, the variable n represents the sample size of the experimental data. The term \u003cem\u003e'\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{{s}_{exp}}\\left({x}_{i},\\:{y}_{i},{z}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e' denotes the \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eexperimentally sediment eroded depth at points\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{z}_{i}\\)\u003c/span\u003e\u003c/span\u003e in the reservoir upstream of the bottom tunnel entrance, while \u003cem\u003e'\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{{s}_{num}}\\left({x}_{i},\\:{y}_{i},{z}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e' signifies the numerically \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003esediment eroded depth\u003c/span\u003e at points \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{z}_{i}\\)\u003c/span\u003e\u003c/span\u003e in the reservoir upstream of the bottom tunnel entrance. The Mean Absolute Percentage Error (MAPE) is utilized in this context to measure the disparities between the experimental and numerical eroded depth, offering a comprehensive assessment. By employing MAPE, it enables the evaluation of how accurately the simulated eroded sediments correspond to the experimental data in terms of percentages. This analytical method is highly esteemed for its efficacy in interpreting variations in relative error (Wilcox, 2006). Thus, MAPE can offer initial insights into the numerical representation of the underlying physics in this matter. It is crucial to note that the MAPE value provided in this context does not directly correspond to the differences between numerical and exact solutions. Moreover, the MAPE assessment here encompasses various error components, including those derived from the experimental methods. Despite its limitations, MAPE remains a valuable tool for assessing the quality of the conducted simulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Experimental validation model\u003c/h2\u003e \u003cp\u003eTo validate the results of the numerical simulations, the study by Fahtih-Moghadam et al. (2010) was used as a benchmark for performance. The experiments were conducted to investigate important parameters affecting the volume and length of the flushing cone. A wide flume 2.30 \u0026times; 1.50 \u0026times; 4.00 m was used at the hydraulic laboratory at Chamran University, Ahwaz, Iran, to simulate a reservoir and cone formation due to pressure flushing. The first meter of the flume was used to set up a constant head over a 0.42 m sediment layer resting on the flume bed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The 2-inch terminal gate valve was opened as the reservoir water level reached the desired depth over the sediment layer. The outlet discharge Q was drained into the first section of a 3.5 \u0026times; 1.0 \u0026times; 0.8 m settling container where sediment material was deposited and clean water was measured using a 268 V-notch weir. Water was steadily circulated in the system using a pump and sump. The non-cohesive sediments used in this study were sand of density 2.65 tm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. Strainers were utilized to screen uniform sediment grain sizes in three classes of fine (0.27 mm), medium (0.42 mm) and coarse (1.2 mm) with uniformity coefficient (d\u003csub\u003e84\u003c/sub\u003e/d\u003csub\u003e16\u003c/sub\u003e) \u003csup\u003e0.5\u003c/sup\u003e\u0026lt; 1.5. Experiments were conducted with these three grain sizes, three water depths of H\u003csub\u003ew\u003c/sub\u003e = 52, 90, 120 cm, five outflow discharges 0.001\u0026thinsp;\u0026le;\u0026thinsp;Q\u0026thinsp;\u0026le;\u0026thinsp;0.008 m\u003csup\u003e3\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and mean outlet velocities within 0.51\u0026thinsp;\u0026le;\u0026thinsp;u\u0026thinsp;\u0026le;\u0026thinsp;4.08 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. To avoid high shear flow effects on the cone due to lowering of the water level at the test end, the flume was discharged through a 3-inch valve upstream of the sediment layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The water inside the cone was gradually drained through a small opening of the 2-inch gate valve. The data were then used to determine the flushing cone volume.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAmong the laboratory research findings, the data at a reservoir water level of 1.2 m, with sediment particles 0.42 mm diameter, and a flow rate of 4.5 lit/s were utilized as benchmarks for validating the current study. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the length of the flushing cone in the laboratory model was determined to be 0.52 m, with a calculated volume of 0.043 m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Mesh refinement strategy\u003c/h2\u003e \u003cp\u003eThe mesh refinement technique aims to decrease the dimensions of control volumes across the domain. Typically, this reduction is evaluated using \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e​, which signifies the non-dimensional distance from the center of the initial mesh cell perpendicular to the wall. This non-dimensional distance is represented as [8].\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{fc}^{+}=\\frac{{U}_{*}{\\varDelta\\:y}_{fc}}{\\upsilon\\:}\\)\u003c/span\u003e \u003c/span\u003e[10]\u003c/p\u003e \u003cp\u003eThis function is defined by the dimensional distance from the wall to the center of the first mesh cell, denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:y}_{fc}\\)\u003c/span\u003e\u003c/span\u003e, and the friction velocity, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{*}=\\sqrt{{\\tau\\:}_{w}/\\rho\\:}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{w}\\)\u003c/span\u003e\u003c/span\u003e represents the wall shear stress. Therefore, it is crucial to validate and, if necessary, adjust the mesh. The subscript ' f\u003csub\u003ec\u003c/sub\u003e ' is employed in this context to avoid confusion with the general application of \u003cem\u003ey\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e at the wall, as \u003cem\u003ey\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e is intrinsically zero at the wall. Within simple shear flows, such as those typical in tunnels and straight flows, a distinct mean value for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e ​ emerges due to turbulence-induced fluctuations in \u003cem\u003ey\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e alone. Notable inhomogeneity effects, as seen in the outflow from the bottom tunnels, result in significant stress field variations, leading to a broader distribution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:y}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e​. The mean value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e referred to as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e is utilized. Comprehensive details on the meshes are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, where \u003cem\u003eB\u003c/em\u003e represents the width of the bottom tunnels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of mesh characteristics within the computational domain surrounding the bottom tunnels and reservoir.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMesh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNodes [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:{10}^{4}\\)\u003c/span\u003e\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:\\varvec{y}}_{\\varvec{f}\\varvec{c}}/\\varvec{B}\\)\u003c/span\u003e\u003c/span\u003e [%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV\u003csub\u003esexp\u003c/sub\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:{10}^{-3}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV\u003csub\u003esNum\u003c/sub\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:{10}^{-3}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003csub\u003esExp\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eL\u003csub\u003esNum\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA thorough investigation was carried out that involved 9 levels of mesh refinement (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}_{fc}^{+}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e\u0026asymp;\u003c/em\u003e0.5 to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}_{fc}^{+}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e\u0026asymp;\u003c/em\u003e35) and 14 permutations of turbulence models. This was done to provide clarity around the complex interactions between turbulence models, wall functions, and varying degrees of mesh refinement. An extensive analysis of heat maps was conducted for a range of mesh configurations labeled R1 to R9. Figure\u0026nbsp;3(a) (using a warm color scale) illustrates the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e, ​values calculated a posteriori, while Fig.\u0026nbsp;3(b) (using a cool color scale) presents the Mean Absolute Percentage Error (MAPE) in comparing simulation results with experimental data.\u003c/p\u003e\u003cp\u003eThe examination of results in Fig.\u0026nbsp;4 demonstrates a trend of monotonic convergence, wherein a decrease in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e (or an increase in mesh refinement) correlates with enhanced accuracy (lower MAPE) in the scoured \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehole profile\u003c/span\u003e. This pattern holds for all combinations of turbulence models and wall functions, except in the case of the standard wall function, where a \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e value below 1 (observed in mesh R\u003csub\u003e9\u003c/sub\u003e) results in decreased accuracy. In such scenarios, the MAPE exhibits an increase of approximately 3\u0026ndash;4% compared to mesh R\u003csub\u003e7\u003c/sub\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{-}{y}}_{fc}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u0026asymp; 10). The findings indicate that additional refinement did not significantly affect the numerical results. Consequently, a mesh size of 1000\u0026times;10\u003csup\u003e4\u003c/sup\u003e, corresponding to the P\u003csub\u003e7\u003c/sub\u003e mesh, was selected. The number of mesh elements in the length, width, and height directions of the reservoir will be 520\u0026times;180\u0026times;75 and 52\u0026times;15\u0026times;11 for the bottom tunnel. The best results also obtained using Scalable Wall Function (Scal. WF) and RSM turbulence model.\u003c/p\u003e \u003cp\u003eUpon determining the optimal fluid domain cell size, wall function, and turbulence model, a comparison between the numerical and experimental outcomes for the three tests is performed and consolidated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Figure\u0026nbsp;4 depicts the scour cone depth for Test 1.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe volume of the flushing cone for different high water levels (H\u003csub\u003ew\u003c/sub\u003e) and their corresponding flow discharges.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics of the flow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVolume of flushing cone (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3D model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEXP.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u0026thinsp;=\u0026thinsp;4.5 lit/s \u0026ndash; Hw\u0026thinsp;=\u0026thinsp;90 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u0026thinsp;=\u0026thinsp;6.0 lit/s \u0026ndash; Hw\u0026thinsp;=\u0026thinsp;40 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ\u0026thinsp;=\u0026thinsp;8.0 lit/s \u0026ndash; Hw\u0026thinsp;=\u0026thinsp;120 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean error (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eDemonstrated in the results, the model effectively replicated the pressure flushing event with a minor 6% deviation in estimating the volume of the flushing cone. Given the existing uncertainties within the numerical model that may influence the outcomes, such as the utilization of empirical sediment transport equations and the necessity for enhanced bed roughness calibration, the observed variation between the simulated results and the experimental data can be considered reasonable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Defined models\u003c/h2\u003e \u003cp\u003eThe examined models comprise 18 units with specific dimensions: a reservoir length of 1 m, width of 1.4 m, a dam wall height of 0.6 m, and bottom tunnels positioned at a depth of 0.35 m from the reservoir bottom and in the middle of the dam wall. For models with more than one bottom tunnels, the distance between the tunnels is 0.1 m, and they are symmetrically arranged in the middle of the dam wall. Figure\u0026nbsp;5(a-d) illustrates the longitudinal and transverse cross-section images of the models for one, two and three bottom tunnels configurations, respectively. In all models, the reservoir water level (H\u003csub\u003ew\u003c/sub\u003e) stands at 0.5 m above the reservoir bottom, each bottom tunnel discharges at a rate of 0.003 m\u003csup\u003e3\u003c/sup\u003e/s, and has a length of 0.036 m with a square cross-section of 0.45 m per side.\u003c/p\u003e\u003cp\u003eThe goal of this study is to investigate the influence of changing variables, such as reservoir sediment accumulation, average sediment particle size, sediment particle uniformity and non-uniformity, and the number of simultaneous operations of the dam's bottom tunnel, on the dimensions and volume of the cone generated during the flushing operation. This volume is equivalent to the discharged sediment volume from the reservoir. Therefore, sediment particles with three average diameters of 0.27 mm (fine particles), 0.42 mm (medium particles), and 0.81 mm (coarse particles), all with a density of 2650 kg/m\u003csup\u003e3\u003c/sup\u003e, were selected. These sediments were examined in three uniform grain size distributions: entirely fine, entirely medium, and entirely coarse particles, and in one non-uniform grain size distribution, which includes equal proportions of sediment from all three grain size distributions. The study investigated sediment configurations at three different sediment accumulation depths (H\u003csub\u003eS\u003c/sub\u003e) located 0.25 m, 0.30 m, and 0.35 m from the reservoir bottom within the one bottom tunnel scenario, leading to the creation of 12 distinct models. Moreover, to explore the influence of the simultaneous activation of bottom tunnels on the scour cone, uniform medium-sized sediments were assessed under scenarios involving one bottom tunnel (3 models), two bottom tunnels (3 models), and three bottom tunnels (3 models); hence, a total of 18 models were examined to further the aims of the current study.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe dimensions of the eroded scour cone, along with the percentage of volume emptied from the sediment in the reservoir and the relative percentage of sediments removed from the reservoir during pressure flushing operations, were compiled in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In this table, V is the total volume of the reservoir, V\u003csub\u003es\u003c/sub\u003e is the scour cone volume, D\u003csub\u003es\u003c/sub\u003e is the eroded sediments volume and D\u003csub\u003eR\u003c/sub\u003e is the total volume of the sediments deposited in the reservoir. The data pertaining to both the overall reservoir volume and the sediment volume at different levels are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, quantified in cubic meters.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Initial overview of the results derived from the numerical model analysis.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"594\" height=\"586\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Volume of the reservoir and sediments.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.12583%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.2914%;\"\u003e\n \u003cp\u003eVolumes (m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5828%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.12583%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.2914%;\"\u003e\n \u003cp\u003eThe total storage capacity of the reservoir under standard water level conditions\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5828%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.12583%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.2914%;\"\u003e\n \u003cp\u003eThe sediment volume in the reservoir at a deposition level of 0.25 m\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5828%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.12583%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.2914%;\"\u003e\n \u003cp\u003eThe sediment volume in the reservoir at a deposition level of 0.30 m\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5828%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.12583%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.2914%;\"\u003e\n \u003cp\u003eThe sediment volume in the reservoir at a deposition level of 0.35 m\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5828%;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe images representing the 2D results of transported sediments in the scour cone on the x-z plane, demonstrating the length of the scour cone in Fig. 6, and on the y-z plane aligned with the upper wall of the dam in Fig. 7, have been extracted. The estimated three-dimensional scour cone geometry is depicted in Fig. 8.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Effect of Uniform and Non-uniform distribution of Sediments\u003c/h2\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;9, the length, width, and volume of the scour cone, along with the resulting restored volume of the reservoir for various types of uniform sediment gradation (including all fine, all medium, and all coarse particles) and non-uniform sediment gradation, increase proportionally with the sediment deposition level in the reservoir. Furthermore, the width of the cone (B\u003csub\u003emax\u003c/sub\u003e) exceeds its length (L\u003csub\u003emax\u003c/sub\u003e). The alterations in dimensions and volume of the scour cone formation under non-uniform sediment gradation at a 0.25 m deposition level between uniform sediment gradation with medium and fine particles, and at deposition levels of 0.30 m and 0.35 m between uniform sediment gradation with medium and coarse particles (tending towards a state of medium particle gradation). With the exception of fine particles, which exhibit the highest relative sediment removal percentage at the 0.30 m level, the discharged sediment volume increases with the sediment level in the reservoir. Consequently, the model with a 0.35 m level attains the maximum percentage of restored reservoir storage volume. Notably, in the fine particle sediment scenario, the most considerable relative sediment removal occurred at the 0.35 m level.\u003c/p\u003e\u003cp\u003eAt a 0.25 m deposition level, the interaction between non-uniform sediment gradation and the resultant scour cone formation suggests that finer particles are more prone to mobilization and transport, leading to significant changes in the scour profile. This is further evidenced by the observation that, with the exception of fine particles, the discharged sediment volume tends to increase with higher sediment levels in the reservoir. This trend indicates that as the sediment level rises, the energy available for sediment entrainment and transport also increases, resulting in greater sediment removal efficiency.\u003c/p\u003e \u003cp\u003eThe finding that the model with a 0.35 m deposition level achieves the maximum percentage of restored reservoir storage volume is particularly noteworthy. It suggests that at this level, the conditions are optimal for sediment removal, possibly due to the effective interplay of hydraulic forces and sediment characteristics that facilitate the erosion of accumulated sediments. This highlights the importance of managing deposition levels to optimize reservoir capacity.\u003c/p\u003e \u003cp\u003eInterestingly, the fine particle scenario reveals that the most considerable relative sediment removal occurs at the 0.35 m level as well. This emphasizes the unique behavior of fine sediments, which, despite their lower mass, can be highly mobile and susceptible to hydrodynamic forces. Their ability to be removed efficiently at certain deposition levels points to the necessity of considering sediment gradation when evaluating sediment management strategies in reservoirs.\u003c/p\u003e \u003cp\u003eThe observation that the maximum scour cone formation for fine particles occurs at a 0.30 m sediment level can be attributed to several interrelated factors that influence sediment dynamics and hydraulic conditions within the reservoir.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHydraulic Forces\u003c/strong\u003e \u003cp\u003eAt the 0.30 m deposition level, the flow dynamics may create optimal hydraulic conditions for the mobilization of fine particles. The velocity profile and shear stresses acting on the sediment bed are likely to be sufficient to dislodge and transport fine sediments effectively. This contrasts with higher deposition levels, where increased sediment accumulation may lead to a more stable bed configuration, reducing the effectiveness of hydraulic forces in mobilizing additional sediment.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSediment Characteristics\u003c/strong\u003e \u003cp\u003eFine particles typically have lower settling velocities compared to coarser sediments, making them more susceptible to suspension within the flow. At the 0.30 m level, the balance between sediment deposition and resuspension can be optimal, allowing for a higher rate of scour and erosion. The finer texture and lighter weight of these particles enable them to be easily entrained into the flow, resulting in significant scour cone development.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eE\u003cb\u003energy Dissipation and Scour Development\u003c/b\u003e: As sediment levels increase, the energy available for sediment transport can also increase. However, beyond a certain point, the energy may become dissipated through increased turbulence and sediment interactions, which can stabilize the bed and inhibit further scour. The 0.30 m level may represent a threshold where the energy is sufficient to induce maximum scour without reaching a point of excessive stabilization.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInteraction with Coarser Sediments\u003c/strong\u003e \u003cp\u003eWhen considering the gradation of sediments, the presence of coarser particles may influence the behavior of fine particles. At the 0.30 m level, the finer particles may be more effectively mobilized in the presence of coarser particles, creating a more dynamic interaction that enhances the scour process. In contrast, at higher levels, the coarser particles may dominate the bed configuration, reducing the mobility of finer grains.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eScour Cone Geometry\u003c/strong\u003e \u003cp\u003eThe geometry of the scour cone itself is influenced by both the sediment characteristics and the flow dynamics at different levels. At the 0.30 m level, the configuration of the scour cone may reach an optimal shape and depth that allows for maximum sediment removal, while at other levels, the geometry may not be conducive to the same degree of scour.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Impact of activating the bottom tunnels simultaneously\u003c/h2\u003e \u003cp\u003eBased on the findings in Fig.\u0026nbsp;10, there is a notable trend indicating that as the sediment deposition in the reservoir increase, the dimensions of the scour cone, the restored reservoir volume percentage, and the relative sediment removal percentage all exhibit an increase in models with varying bottom tunnels numbers. This effect is particularly accentuated in models with multiple bottom tunnels and sediment deposition levels surpassing 0.30 m. Consequently, when the sediment level reaches 0.35 m from the reservoir bottom, all sediments along the upstream wall of the dam are eroded in cases involving either two or three bottom tunnels (with the sediment scour cone width aligning with the dam wall width at 1.40 m). Moreover, across all models, the width of the sediment scour cone surpasses its length. As per Figs.\u0026nbsp;10(c) and 10(d), the most significant percentage of restored reservoir volume and the highest relative sediment removal percentage from the reservoir are evident at a sediment deposition level of 0.35 m from the reservoir bottom, specifically when utilizing three bottom tunnels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Impact of sediment size on the development of the sediment scour cone\u003c/h2\u003e \u003cp\u003eThe geometry and volume of the sediment scour cone are also impacted by the particle diameter of the sediments. Examination of Fig.\u0026nbsp;11 reveals that an increase in the average particle diameter of sediments across all sediment deposition levels correlates with a decrease in the dimensions of the sediment scour cone. Consequently, this reduction impacts the restored reservoir volume and the relative percentage of sediments removed from the reservoir. Additionally, in all the models analyzed, the width of the sediment scour cone exceeds its length.\u003c/p\u003e \u003cp\u003eThe peak restored reservoir volume is reached at a sediment deposition level of 0.35 m for fine sediments, and the greatest relative percentage of sediment discharge from the reservoir is observed at a sediment deposition level of 0.3 m for fine sediments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Impact of the operational bottom tunnels (d\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.42 mm) at different deposition levels\u003c/h2\u003e \u003cp\u003eThe research is designed to analyze the consequences of simultaneously operating reservoir bottom tunnels during hydraulic pressure flushing on sediment removal, economic considerations, water loss, especially during low water periods, and significant environmental implications. Figure\u0026nbsp;12, which showcases the evolution of the scour cone status concerning the varying number of active bottom tunnels, shows that an increase in the quantity of bottom tunnels situated below the dam will result in amplified length, width, volume, recovery percentage of reservoir volume, and sediment discharge percentage from the reservoir. The width of the scour cone exceeds its length across all models.\u003c/p\u003e \u003cp\u003eWhen three bottom tunnels operate simultaneously at a sediment accumulation level of 0.35 m, the highest percentage of recovered reservoir volume attributed to sedimentation is achieved. Moreover, within this scenario, the greatest relative percentage of sediment discharged from the reservoir will occur. Furthermore, as indicated by the results, the degree of improvement in the sediment cone geometry, the recovery percentage of reservoir volume, and the relative sediment discharge percentage from the reservoir decreases for sediment levels below 0.35 m and when more than two bottom tunnels operate simultaneously. Consequently, in cases where sediment levels in the reservoir are low, utilizing two bottom tunnels for pressure flushing may yield reduced water loss in the dam reservoir, mitigate environmental harm caused by scouring operations, and potentially offer greater cost efficiency.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe objective of the study was to investigate the impact of various factors, such as sediment accumulation levels, sediment particle size, uniformity in particle grading, and the operation of bottom tunnels, on the dimensions and volume of sediment cones formed during pressure flushing in reservoirs. The study aimed to identify an optimal scenario for maximizing sediment removal. Eighteen models were developed following the validation of pressure flushing simulations using FLUENT software. Results were analyzed and presented graphically, with a summary provided in seven sections.\u003c/p\u003e \u003cp\u003eThe geometry and volume of the sediment cone resulting from pressure flushing increase with higher levels of accumulated sediment in the reservoir. Conversely, the geometry and volume of the sediment scour cone decrease with an increase in the average diameter of sediment particles.\u003c/p\u003e \u003cp\u003eIn the scenario of non-uniform particle grading, where particles with average diameters of 0.27 mm, 0.42 mm, and 0.81 mm are present in equal proportions at an accumulation level of 0.25 m, the geometry and volume of the sediment scour cone demonstrate a significant relationship. This contrasts with the geometry and volume observed in the case of uniform particle grading, involving fine particles (0.27 mm) and particles with an average diameter of 0.42 mm. Additionally, at accumulation levels of 0.30 m and 0.35 m, the correlation between the geometry and volume in the uniform particle grading scenario with particles of 0.42 mm average diameter and coarse particles (average diameter 0.81 mm) closely mirrors the outcomes of uniform grading with medium-sized particles.\u003c/p\u003e \u003cp\u003eAn increase in the number of bottom tunnels is correlated with a rise in both the geometry and volume of the sediment scour cone. However, at shallower depths (less than 0.35 m from the reservoir bed), the escalation in sediment scouring intensity is less noticeable when using two or three bottom tunnels. Therefore, implementing pressure flushing with two bottom tunnels at depths below 60% of the reservoir's standard depth leads to reduced water loss and environmental impact, rendering it a more environmentally advantageous choice.\u003c/p\u003e \u003cp\u003eIn all the numerical model scenarios analyzed, the width of the sediment scour cone surpasses its length.\u003c/p\u003e \u003cp\u003eAmong the scrutinized numerical models, the model that encompassed the simultaneous operation of three bottom tunnels, an accumulation level of 0.35 m from the reservoir bed, and uniform particle grading of 0.42 mm, extracted the maximum sediment volume from the reservoir (0.7869 m\u003csup\u003e3\u003c/sup\u003e). Consequently, this model achieved the highest percentage of restored reservoir volume lost due to sediment accumulation (11.24%). Moreover, it demonstrated the highest relative percentage of sediment discharge from the reservoir (6.16%), establishing it as the most successful outcome among all investigated scenarios.\u003c/p\u003e \u003cp\u003eTo achieve effective pressure flushing in a reservoir with a consistent water level, it is advisable to conduct the operation in a reservoir featuring sediment with a smaller average diameter, a higher accumulation level, and a greater number of active bottom tunnels. Maintaining a consistent discharge from each tunnel, ensuring the uniform elevation of tunnels, and keeping a fixed distance between them are crucial components. It is important to acknowledge that the findings of this study are specific to the current simulation, given the multifaceted nature of factors influencing sediment scouring in reservoirs. Further research is essential to generalize these results to other models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ethe subject of the article has been proposed by Prof. Yousef Hassanzadeh. Modelling and simulations has been done by Mostafa Roshdi. Dr. Kardan supervised the accuracy of the numerical results and prepared the article based of the results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhn, J., Yang, C. T., Boyd, P. M., Pridal, D. B., \u0026amp; Remus, J. I. (2013). Numerical modeling of sediment flushing from Lewis and Clark lake. International Journal of Sediment Research, 28(2), 182e193.\u003c/li\u003e\n\u003cli\u003eBrandt, S. A. (2000). A review of reservoir desiltation. International Journal of Sediment Research, 15(3), 321e342.\u003c/li\u003e\n\u003cli\u003eBhuiyan, F., \u0026amp; Olsen, N. R. B. 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Journal of Hydraulic Engineering, 149(8), 04023024.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Reservoir sedimentation, Sediment flushing, Non-uniform sediment, Scour cone, Numerical Simulation","lastPublishedDoi":"10.21203/rs.3.rs-6002097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6002097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReservoir sediment flushing, recognized as a highly effective approach for mitigating reservoir sedimentation, involves the downstream discharge of sediment-laden flows through bottom tunnels. Alongside other critical parameters in pressure flushing, the simultaneous operation of bottom tunnels significantly contributes to the efficient removal of sediments from the reservoir, and this study evaluated the effects of the number and placement of these tunnels. The primary aim of removing sediment from reservoirs is to restore the lost volume resulting from sediment accumulation. This study endeavors to pinpoint the most optimal parameters for maximizing sediment removal from reservoirs, stressing the significance of effectively eliminating sediment from the dam reservoir during pressure flushing. To achieve these objectives, the CFD solver in Fluent is employed. While simulating bed erosion around bridge piers is not feasible within the Fluent CFD solver, a morphological model was developed and integrated with Fluent to address bed erosion beyond the flow solution. This integrated model combines the flow solution with a sediment transport model to establish the morphological model. The results demonstrated that achieving efficient pressure flushing in a reservoir with a constant water level is best accomplished in a reservoir containing sediment with a smaller average diameter, higher accumulation level, and a greater number of active bottom tunnels. Key elements include maintaining consistent discharge from each tunnel, ensuring uniform tunnel elevation, and preserving a fixed distance between them.\u003c/p\u003e","manuscriptTitle":"Reservoir Sedimentation Management: Evaluating Sediment Size and Bottom Tunnel Effects on Sediment Flushing Efficiency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 09:28:16","doi":"10.21203/rs.3.rs-6002097/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59233e1a-7867-4a3f-bab0-1b588038deae","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:19:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-14 09:28:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6002097","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6002097","identity":"rs-6002097","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}