Sediment accumulation in combined sewers can induce blockage and odor problems. Among various cleaning methods, using self-cleaning device-generated flushing waves has been thought to be an effective solution. In this study, a series of numerical tests were conducted using CFD software to investigate the cleaning efficiency of deposited sediment particles based on a simplified self-cleaning device. The CFD model was validated by the experimental and numerical results in the literature. The effects of several parameters including the flushing gate height, sediment bed thickness, sediment bed length, and sediment bed position on cleaning efficiency were discussed. A relative accumulative transport rate was defined to analyze the cleaning efficiency. Results showed that the lowest height of the flushing gate had the best effects on sediment removal. The flushing waves generated from the sudden opening of the flushing gate were capable of cleaning sediment deposits in the given initial sediment bed thickness, length, and position. The required time duration for cleaning the sediment deposit completely increased about 6, 3, and 3 times when the sediment bed thickness, sediment bed length, and distance between the flushing gate and sediment bed increased 10, 4, and 7 times, respectively.

  • Both the flow hydrodynamics and sediment scour models were well validated with the experimental results.

  • The required time duration for cleaning the sediment deposit increased about 6, 3, and 3 times when the sediment bed thickness, sediment bed length, and distance between the flushing gate and sediment bed increased 10, 4, and 7 times, respectively.

Sedimentation in sewers has been recognized as a severe issue since it is related to both hydraulic problems and environmental pollution in urban drainage. The presence of sediment deposits in storm sewers can cause pipe blockage, a reduction in hydraulic capacity, resulting in low drainage efficiency (Todeschini et al. 2010; Regueiro-Picallo et al. 2018; Yang et al. 2019; Liu et al. 2021). Moreover, the pollutants attached to the sediment particles can be re-released and thus form hydrogen sulfide and methane by actions of chemical and/or biological processes, inducing water quality of receiving water bodies, odor concerns, and pipe corrosion (Fan et al. 2003; Liu et al. 2015, 2021; Chen et al. 2022).

The above issues have been studied extensively in the literature. Recently, Tang et al. (2020) considered the clay content and proposed a method for predicting sediment deposition profile. By measuring bed shear stress and flow velocity, Regueiro-Picallo et al. (2020) addressed the relationship between sewer sediment composition and its erodibility in conditions of biological transformations. As sewer overflow contains pollutants such as methane, pathogenic and fecal organisms, heavy metals (Sambito et al. 2020; Liu et al. 2021; Chen et al. 2022), effective monitoring strategies are receiving attention from a few researchers. Yaroshenko et al. (2020) reviewed the real-time monitoring methods and concluded that microwave spectroscopy and chemical materials integration is a new trend in monitoring. To identify the pollution source position, Sambito & Freni (2021) and Sambito et al. (2022) optimized the sensor location in monitoring pollutants in sewers based on Bayesian optimization approach and hydraulic information, and showed its effectiveness in a field test.

To remove the deposited sediment, self-cleaning devices in sewers are considered to be cost-effective techniques (Campisano et al. 2004; Bong et al. 2013; Safari et al. 2017; Yang et al. 2019; Montes et al. 2021; Safari & Aksoy 2021). By storing a certain amount of water in sewers, the valve of a self-cleaning device is opened when the water level attains a certain value. Then the flushing waves generated from the flushing gate wash sediment deposit in sewers. The sediment particles will continue transporting downstream till caught by the collection facilities or mechanical equipment (Campisano et al. 2019). The mechanism of this process is to increase the bed shear stress so that the sediment can be scoured and transported downstream (Bong et al. 2013; Safari et al. 2015). In recent years, this cost-effective technique has been considered as a preventive and reactive method to deal with sediment deposits.

A large number of researchers have studied the influencing factors of flushing efficiency in sewer cleaning. The factors can be mainly classified into flushing hydraulics, pipe geometry, and sediment properties (Montes et al. 2021). These three factors include parameters, such as stored water head (Guo et al. 2004), number of flushes (Bong et al. 2013), flushing duration (Campisano et al. 2004), flow velocity (Sun et al. 2022), bed shear stress (Yang et al. 2019), backwater (Jin et al. 2016), pipe slope (Zhang et al. 2011), pipe diameter (Safari et al. 2018), pipe cross-section shape (Safari & Aksoy 2021), surface roughness (Knight & Sterling 2000), sediment cohesiveness (Regueiro-Picallo et al. 2018; Tang et al. 2020), sediment thickness and width (Campisano et al. 2019; Montes et al. 2021), sediment particles median grain size (Zhang et al. 2011), gradings, porosity, and density (Campisano et al. 2019; Sun et al. 2022). By studying these factors, researchers aimed to address flushing efficiencies in sewer cleaning and to propose simple dimensionless equations for the design and operations of self-cleaning devices.

In analyzing the effects of sediment properties, the deposited sediment height and length are essential parameters in flushing efficiency. Several studies have focused on changes in deposit bed height to quantify the removal efficiency. Bong et al. (2013) conducted a series of experiments on the efficiency of flushing and found the minimum number of flushes increased by 1.5 times as the sediment bed deposit thickness doubled. Campisano et al. (2019) presented that a thinner sediment layer reduced the rate of sediment available for erosion and transport, thus resulting in a decrease in the total volume removed from the channel. Safari & Aksoy (2021) found that the rectangular cross-section channel was the most advantageous shape in flushing efficiency among the trapezoidal, circular, rectangular, U-shaped, and V-bottom-shaped cross-section channels in a scouring model. Liu et al. (2021) performed the sediment scouring and transportation process at different locations of a pipeline, finding that the scouring rate at the front section decreased with the increasing sediment thickness.

However, the above studies were at rapidly varied flow rates, in which conditions the stored volume of water in the self-cleaning device was considered small. In practice, when the valve of the self-cleaning device is opened by the asymmetric pressure induced by the accumulated water, a volume of stored water upstream of the device can be found, as the pipe slope is usually about 0.15% (Shahsavari et al. 2017). That is to say, the volume of stored water in the self-cleaning device is far larger than in those experimental tests conducted in rapidly varied flow rates. Moreover, the position of a self-cleaning device needs to be specified to take effective actions in cleaning the sediment deposit. Therefore, effects of flushing waves on sediment cleaning in the relatively constant water level tank conditions should be studied.

This paper studies the sediment transportation induced by the flushing waves, which are generated from a simplified self-cleaning device. Variable factors of flushing gate installation height, sediment bed height and length, distances between the valve and sediment bed are analyzed by discussing their flushing efficiencies on cleaning sediment deposit. The temporal bed profiles characteristics and flushing time durations required for sediment cleaning completely are obtained. The results will provide useful references for designing the self-cleaning devices, as well as hydraulic information in evaluating or monitoring pollutants in sewers.

To simulate the sediment scouring and deposition processes, a CFD model of Flow-3D (User manual 2013) is applied. The model can track the free water surface by the volume of fluid (VOF) method. It utilizes the fractional area volume obstacle representation to adapt to the grids. These methods increase computing efficiency significantly by eliminating the additional cells in capturing complex geometric regions and tracking the free water surface of these regions.

Governing equations

The model solves the three-dimensional transient Navier–Stokes equations and continuity equation. The governing equations can be expressed as follows:
(1)
(2)
(3)
(4)
where u, v, and w are the components of velocity in the x, y, and z directions, respectively; Ax, Ay, and Az are the area fraction of water flow in the x, y, and z directions, respectively; VF is the fractional volume open to flow; is the fluid density; RDIF is a turbulent diffusion term; Gx, Gy and Gz are, respectively, the components of the acceleration of gravity in the x, y, and z directions; fx, fy, and fz, are respectively, viscous acceleration in the x, y, and z directions; p is the average hydrodynamic pressure. The generalized minimum residual method (GMRES) is a fluid velocity-pressure solution scheme that introduces the intermediate velocity and pressure correlation into Poisson's equations. The GMRES method has a faster convergence speed and a high solution accuracy.

Turbulent model

The RNG turbulence model was selected in the simulations because of its high ability in predicting the velocity profiles and turbulent kinetic energy (Khodier & Tullis 2018). The empirical coefficients in the equations are modified by explicit derivation in RNG k–ε model based on the standard k–ε model (Flow-3D user manual 2013). The k and ε equations in the RNG model are as follows:
(5)
(6)
where GT is the turbulent energy generated by buoyancy; εT is the dissipation rate of the turbulent energy; PT is the turbulent flow production; kT is the specific kinetic energy related to the turbulent velocity; Dε and DkT are diffusion terms related to dissipation and turbulent kinetic energy; CDIS1, CDIS2, and CDIS3 are dimensionless diffusion coefficients of 1.44, 1.92, and 0.2, respectively.
Although the Eulerian model is widely used in CFD software, such as Fluent, the simulation duration is more time-consuming than the VOF model. The VOF model has a high quality in modeling the free surface between air and water, as well as water and sand. Firstly, all phases are assumed to be immiscible in the VOF model. Each phase fluid shares the momentum equation and is described through phase fraction. Secondly, the Eulerian model is the most complex model. Each phase is regarded as a continuum of mutual penetration and has its own continuity equation and momentum equation. Moreover, each phase is coupled through pressure and interphase model. Therefore, the water–air interface is resolved by applying the Lagrangian VOF advection method. The volumetric ratio function of the fluid is used to determine the free interface in the grids. The transport function of the volumetric ratio function (F) follows:
(7)

Sediment transport model

The sediment is assumed to be non-cohesive particles. Sediment transport is classified into bed load and suspended load. The interactions between sediment particles are not taken into consideration. The motion of each sediment particle is estimated by predicting the erosion, advection and deposition using empirical equations. Firstly, the criterion in estimating whether a sediment particle can be moved by the flow is based on the critical Shields number (θcr) and Shields number (θ), which are respectively calculated by Equations (8) and (9). When θ>θcr, the sediment particle can be moved (Soulsby 1997):
(8)
(9)
where is the dimensionless grain size; d is the sediment particle diameter; is the sediment density; g is the acceleration of gravity; νf is the kinematic viscosity of the fluid; τ is the local bed shear stress.
When the particles are in suspension, the entrainment lift velocity of (Mastbergen & Van 2003) and the settling velocity of (Soulsby 1997) of sediment are computed as:
(10)
(11)
where αi is the entrainment parameter, generally set as 0.018.
It is considered that the velocity difference between the suspended particles and the fluid-sediment mixture is mainly the settling velocity of particles. The suspended sediment concentration is calculated by solving the transport Equation (12):
(12)
where Cs is the sediment concentration; D is the diffusion rate; is the velocity of the suspended sediment; is the velocity of the fluid-sediment mixture.
Besides the suspension, a large number of sediment particles moves in form of bed load. The volumetric bed load transport rate () is calculated by Equation (13), which was proposed by Meyer-Peter & Müller (1948):
(13)
where qb is the volumetric bed-load transport rate and s is the specific weight (ρs/ρ).

Flume model and boundary condition

A 1.5-m long, 0.75-m high, and 1-m wide water tank was set upstream of the sediment bed in a 6-m long flume, as shown in Figure 1(a). The gate size of the water tank was based on the tipping gate (Bong et al. 2016). The 0.5-m high and 1-m wide tipping gate in a 1.2-m high and 0.8-m wide storm sewer was found to perform well in Bong et al. (2016). In this study, the gate opening height was set considering the downstream backwater which was unfavorable for flushing. In order to maintain a balance between the large discharge caused by rainstorm and low flow in dry season and obtain high water velocity at gate, the gate size was downsized. The gate in simulation was simplified and the size of 0.6 m × 0.25 m was chosen. In simulating the opening of the gate, the flushing gate was represented by a rectangular opening for simplicity, as seen in Figure 1(b) and 1(c). When the simulation started, water flowed from the flushing gate of the tank. The water depth in the tank was 0.7 m. To maintain a balance between the large discharge caused by a rainstorm and low flow in the dry season, a bottom elevation and a large width of gate were set. The flushing gate sized 0.6 m in width and 0.25 m in opening height had a distance of HG from the bottom. The distance between the flushing gate and the sediment bed head was LG. The length and height of the sediment bed were LS and HS, respectively. Known that the sediment particles tend to deposit in flat sewer sections (Yang et al. 2019), the slope of the flume was set to 0 so as to test conservative parameters in affecting the sediment cleaning results.
Figure 1

Sketch of the simulation setup: (a) side view, (b) schematic diagram of flushing gate, and (c) initial boundary conditions of numerical channel (unit: m).

Figure 1

Sketch of the simulation setup: (a) side view, (b) schematic diagram of flushing gate, and (c) initial boundary conditions of numerical channel (unit: m).

Close modal

The numerical model is shown in Figure 1(c). Specified pressure boundary conditions with a fixed height of the water head that equaled the initial water depth in the tank were applied to the inlet boundary while the outflow boundary was set to the out boundary. The top side of the channel was a free water surface and was specified as the atmospheric pressure. Wall boundary conditions were assigned to the bottom and the two sides along the flume. The standard wall functions were employed in the boundary conditions used for velocity and turbulence at the wall. When the simulation started, water in the tank was released and would generate flushing waves propagating downstream, and thus scoured the sediment bed.

Tests list

The sediment bed was located at the channel bottom starting at x = 2–4.5 m downstream of the flushing gate. The length of the deposition varied from 3 to 4.5 m depending on the front side and the sediment bed length. The sand bed thickness (HS) was 20–200 mm. Uniform non-cohesive sediment with a medium diameter of 0.5 mm, density of 2,650 kg/m3 and a porosity of 0.65 were used. The sediment angle of repose was 37°. The critical Shields number was 0.0304.

A total of 23 numerical tests were designed, as shown in Table 1. Tests G1–G3 were designed to study the effects of flushing gate height on scouring the sediment deposit while tests HS1–HS7, tests L1–L6, and tests LG1–LG7 were designed to study the effects of sediment bed height, sediment bed length and sediment bed position, respectively. The parameter of td is the time duration that is required for sediment particles total removal.

Table 1

Lists of numerical parameters and results

Test no.HG (m)HS (m)LS (m)LG (m)6- LS- LGtd (s)
G1 0.2 0.12 1.50 1.0 0.55 14.0 
G2 0.3 0.12 1.50 1.0 0.52 16.0 
G3 0.4 0.12 1.50 1.0 0.42 22.4 
HS1 0.2 0.02 1.50 1.0 2.18 3.0 
HS2 0.2 0.04 1.50 1.0 1.22 6.2 
HS3 0.2 0.06 1.50 1.0 0.91 10.4 
HS4 0.2 0.08 1.50 1.0 0.48 12.8 
HS5 0.2 0.12 1.50 1.0 0.55 14 
HS6 0.2 0.15 1.50 1.0 0.65 15.2 
HS7 0.2 0.20 1.50 1.0 0.81 18.2 
L1 0.2 0.12 0.50 1.0 2.56 6.0 
L2 0.2 0.12 1.00 1.0 0.64 10.8 
L3 0.2 0.12 1.25 1.0 0.58 12.2 
L4 0.2 0.12 1.50 1.0 0.55 14.0 
L5 0.2 0.12 1.75 1.0 0.62 15.8 
L6 0.2 0.12 2.00 1.0 0.83 18.6 
LG1 0.2 0.12 1.5 0.5 1.83 10.2 
LG2 0.2 0.12 1.5 1.0 0.55 14.0 
LG3 0.2 0.12 1.5 1.5 0.29 18.0 
LG4 0.2 0.12 1.5 2.0 0.21 25.0 
LG5 0.2 0.12 1.5 2.5 0.71 28.4 
LG6 0.2 0.12 1.5 3.0 0.63 29.6 
LG7 0.2 0.12 1.5 3.5 0.49 30.6 
Test no.HG (m)HS (m)LS (m)LG (m)6- LS- LGtd (s)
G1 0.2 0.12 1.50 1.0 0.55 14.0 
G2 0.3 0.12 1.50 1.0 0.52 16.0 
G3 0.4 0.12 1.50 1.0 0.42 22.4 
HS1 0.2 0.02 1.50 1.0 2.18 3.0 
HS2 0.2 0.04 1.50 1.0 1.22 6.2 
HS3 0.2 0.06 1.50 1.0 0.91 10.4 
HS4 0.2 0.08 1.50 1.0 0.48 12.8 
HS5 0.2 0.12 1.50 1.0 0.55 14 
HS6 0.2 0.15 1.50 1.0 0.65 15.2 
HS7 0.2 0.20 1.50 1.0 0.81 18.2 
L1 0.2 0.12 0.50 1.0 2.56 6.0 
L2 0.2 0.12 1.00 1.0 0.64 10.8 
L3 0.2 0.12 1.25 1.0 0.58 12.2 
L4 0.2 0.12 1.50 1.0 0.55 14.0 
L5 0.2 0.12 1.75 1.0 0.62 15.8 
L6 0.2 0.12 2.00 1.0 0.83 18.6 
LG1 0.2 0.12 1.5 0.5 1.83 10.2 
LG2 0.2 0.12 1.5 1.0 0.55 14.0 
LG3 0.2 0.12 1.5 1.5 0.29 18.0 
LG4 0.2 0.12 1.5 2.0 0.21 25.0 
LG5 0.2 0.12 1.5 2.5 0.71 28.4 
LG6 0.2 0.12 1.5 3.0 0.63 29.6 
LG7 0.2 0.12 1.5 3.5 0.49 30.6 

Validation of the model

The experimental and numerical tests results from Campisano et al. (2004) were used to verify the model. The experiment test was performed by producing flushing waves on cleaning sediment deposit. The rectangular channel was 3.9-m long, 0.15-m wide, and 0.35-m deep with a slope of 0.145%. A 1.3-m long tank was settled upstream of the sediment bed. The distance between the tank and the sand head was 0.3 m. The sand had an almost uniform size in the range of 0.425–0.600 mm. The density of sediment was 2,830 kg/m3. Two initial water levels of 0.1 m (Flushing test A) and 0.13 m (Flushing test B) were tested. No sediment particles were in test A while a 1-m long and 0.03-m high sediment bed was settled in test B.

The present results of the model are in accord with the results from Campisano et al. (2004). For test A, the deviation of curves between the present model and experimental test in Figure 2(a) was within 3 mm. In simulating sediment scour test B, the model slightly under-predicted the flow depth at t = 2‒3 s, which was the same as the numerical results from Campisano et al. (2004). This may be ascribed to the fact that the time durations for flow reaching the sediment bed in numerical simulation and the experimental test could not be maintained exactly the same. The very duration in the present model was 2‒3 s later than that in the experimental test, resulting in a slightly lower water surface line. In Figure 2(c), the sediment height of the present model was slightly higher than that of the experiment when x > 1.5 m. Nevertheless, the maximum error (RMSE) is 2 mm. To further evaluate the model accuracy, the criteria for root mean square of error was employed to distinguish the deviations with the experimental results. The RMSE obtained from Figure 2(a)–2(c) was individually 0.003, 0.006, and 0.002. Generally, the results showed that the present model had a sufficient capability in reproducing the flow depth and the sediment profile after flushing.
Figure 2

Comparisons of the model and results from Campisano et al. (2004). (a) Flow depth at x = –0.7 m in test A, (b) flow depth of test B at x = 2.0 m, and (c) sediment bed profiles along the center line at t = 20 s in test B.

Figure 2

Comparisons of the model and results from Campisano et al. (2004). (a) Flow depth at x = –0.7 m in test A, (b) flow depth of test B at x = 2.0 m, and (c) sediment bed profiles along the center line at t = 20 s in test B.

Close modal

Flushing gate height

To study the flushing gate height effects on the cleaning sediment deposit, three height values of 0.2, 0.3 and 0.4 m in tests G1‒G3 (see Table 1) were designed for testing. Figure 3(a)–3(c) showed the temporal bed profiles of tests G1‒G3. When the flushing gate opened, the high-speed flushing waves were blocked by the sediment particles, inducing a sudden erosion at the sediment bed head. The sediment bed was eroded gradually in each test, as the decreasing height of bed profiles shown in Figure 3. The sand bed profiles varied in the same trend, i.e., backward accompanied by a gradually narrow crest. At t = 5 s, the height of the sand bed (HS) increased by about 0.02 m while the sand bed surface was eroded to be lower owing to the scouring by the flow in each test. Although the locations of sand bed head varied little, HS increased dramatically with the height of the flushing gate height of HG, as seen at 10 s in Figure 1. The remaining volumes of sediment of tests G1, G2, and G3 were 22.7, 29.3, and 42.5%, respectively. In particular, HS remained to be higher than the initial sand bed height of 0.12 m, as shown in Figure 3(c). After another 5 s, the sediment deposit in test G1 had been totally removed while 0.02 and 0.08 m high sand beds had been not cleaned in tests G2 and G3, respectively. The time durations of td for completely removing sediment particles in tests G1‒G3 were about 14, 16, and 22.4 s, respectively, i.e., the flushing efficiency decreased with the increasing height of the flushing gate height.
Figure 3

Temporal sediment bed profile of tests G1–G3: (a) Test G1, (b) Test G2, and (c) Test G3.

Figure 3

Temporal sediment bed profile of tests G1–G3: (a) Test G1, (b) Test G2, and (c) Test G3.

Close modal
It is known that the relative accumulative transport rate can be defined as , in which q is the total sediment transport rate, W is sediment bed width and equals to 1 m; V is the initial sediment volume deposited in the flume. As seen in Figure 4, all the sediment particles could be removed after a period of flushing. The most efficient flushing gate height was in test G1, because its bed profile slope and time duration were the maximum and minimum, respectively, among the three tests. To study the mechanisms behind these phenomena, flow velocities at t = 5 s were taken from the profile of x = 2.6 m. The flow velocities of tests G1, G2 and G3 induced by the pressure head difference when the flushing gate opened, were 1.69, 1.40, and 1.06 m/s, respectively. Apparently, the decreased velocity led to fewer sediment particles being carried downstream, resulting in an increased scouring duration when the flushing gate height increased.
Figure 4

Relationships of relative accumulative transport rate and flushing time duration of tests G1–G3.

Figure 4

Relationships of relative accumulative transport rate and flushing time duration of tests G1–G3.

Close modal

Sediment bed thickness

According to the results from section 3.2, the most effective flushing gate position in sediment transport was 0.2 m in test G1. Therefore, sediment bed thickness (HS) ranging from 0.02 to 0.2 m were tested to estimate its effect on cleaning sediment deposit under this height. As shown in Figure 5, the time duration of td required for removing sediment particles ranged from 3 to 18.2 s in tests HS1‒HS7. It increased with the sediment bed thickness. The required time duration for totally removing the sediment deposit of td increased about 6 times when HS increased 10 times. In addition, the slope of the curve decreased with the increasing HS. The higher the sediment bed was, the less efficient the flushing waves were in cleaning the sediment depostion. The flow velocity in the vicinity of the sediment bed areas was in a range of 1.5‒2.5 m/s, which was much larger than the designed flow velocity of 0.9 m/s in a self-cleaning device in Vongvisessomjai et al. (2010).
Figure 5

Relationships of relative accumulative transport rate and flushing time duration of tests HS1–HS7.

Figure 5

Relationships of relative accumulative transport rate and flushing time duration of tests HS1–HS7.

Close modal
To study the characteristics of sediment transport, sediment bed profiles were presented at t = 5 s in Figure 6. The bed profiles along the center line varied depending on the initial sediment bed thickness (HS). When HS increased, the displacement of the sediment bed decreased. Except for test HS1, whose sediment particles had been scoured completely within 3 s, the bed profiles on the left side were parallel to each other in all the tests, indicating that the sand slope underwater was controlled by sediment properties, i.e., the angle of repose, rather than the flush. Knowing that in the process of sediment transport, the flow turbulence involved energy dissipation due to the interactions between flow and sediment particles (Shu et al. 2008; Zhao et al. 2020), sediment particles were transported downstream by the flushing waves in the scouring stage. In addition, the Froude number (Fr) values, as listed in Table 1, decreased from 2.07 to 0.55, i.e., from supercritical flow to subcritical flow, when the initial sediment bed height increased from 0.02 m (test HS1) to 0.2 m (test HS7).
Figure 6

Sediment bed profiles of tests HS1–HS7 at 5 s.

Figure 6

Sediment bed profiles of tests HS1–HS7 at 5 s.

Close modal

Sediment bed length

In studying the effects of sediment bed length on sediment deposit cleaning, the flushing gate height and the initial sediment bed height were based on the test results from the above two parts and were individually kept constant values of 0.2 and 0.12 m, since sediment transport rate did not increase dramatically when the sediment bed height exceeded 0.12 m. As listed in Table 1, the sediment bed length varied from 0.5 to 2 m in tests L1‒L6. The relationships between q* and flushing time (t) are shown in Figure 7. It can be seen that the slope decreased with the increasing sediment bed length (LS) while the time durations required for totally removing (td) increased with LS. The td ranged from 6 to 18.6 s in these six tests. The required time duration for totally removing the sediment deposit of td increased about 3 times when LS increased 4 times. The most effective of the flushing waves in cleaning the sediment deposit was in test L1, i.e., the minimum length of the sediment bed.
Figure 7

Relationships of relative accumulative transport rate and flushing time duration of tests L1–L6.

Figure 7

Relationships of relative accumulative transport rate and flushing time duration of tests L1–L6.

Close modal
To analyze the reasons for variations of sediment deposit cleaning efficiency in these tests, instant sediment bed profiles and flow velocity profiles at 5 s are presented in Figures 8 and 9, respectively. Both the scoured sediment bed height and bed displacement decreased with the increasing sediment bed length. Due to the blockage of the sediment, the flow velocity had a rapid increase under hydraulic jump in each test. At 2.5 m ≤ x ≤ 3.2 m, the flow velocity decreased with the increasing sediment bed length. The sediment bed heights, ranging from 0.135 to 0.16 m in tests L2‒L6, were larger than the initial bed height of 0.12 m. However, the sand bed height of the least long sediment bed in test L1 was far lower than the others, as the flow velocity was much larger than the other tests. In fact, the sediment deposit had been moved about 90%, as seen in Figure 7.
Figure 8

Sediment bed profiles of tests L1–L6 at t = 5 s.

Figure 8

Sediment bed profiles of tests L1–L6 at t = 5 s.

Close modal
Figure 9

Velocity distributions along center bed profiles at t = 5 s.

Figure 9

Velocity distributions along center bed profiles at t = 5 s.

Close modal

Sediment bed position

To study the flushing efficiency of different distances between the flushing gate and the sediment bed, i.e., sediment bed position, distances ranging from LG = 0.5‒3.5 m in tests LG1‒LG7, as listed in Table 1, were tested. The flushing gate height (HG), sediment bed thickness (HS) and sediment bed length (LS) were kept at 0.2, 0.12, and 1.5 m, respectively, in which LS was a moderate value in tests L1‒L6.

As shown in Figure 10, on the whole, the time duration required for totally removing the sediment deposit increased with the increasing LG. The td in tests LG1‒LG7 were 10.2, 14, 18, 25, 28.4, 29.6, and 30.6 s, respectively. Apparently, the efficiency decreased with the increasing LG. The required time duration for totally removing the sediment deposit of td increased about 3 times when LG increased 7 times. The most efficient LG for cleaning the sediment deposit was in test LG1. However, in the processes of sediment scouring, except for test LG1, the efficiency that can be evaluated by q* did not conform to this law. The value of q* in test LG2 would not be larger than in test LG3 until the flushing time was 5 s. Similar disorders could be seen in tests LG4‒LG7 in the first 10 s. These results are probably owed to the effects of backwater upstream of the sediment deposit, as Jin et al. (2016) found that the bed shear stress decreased exponentially with the increasing backwater depth. It is known that adverse pressure will slow down the flushing wave. The more extensive adverse pressure length induced by a longer backwater length will slow down the flow velocity of the flushing waves, thereby the sediment transport rate is reduced. As shown in Figure 11, the length and depth of the backwater did not have a positive or a negative correlation with the increasing LG. Taking tests LG6 and LG7 for example, backwater was not seen in either test at 3 s (Figure 11(a)), besides, the length and depth of backwater in test LG6 were individually larger than those in test LG7 at 5 s (Figure 11(b)). Backwater appeared at 7 s (Figure 11(c)) while it emerged at 9 s in test LG6 (Figure 11(d)). In contrast, both the velocity and volume of backwater in test LG7 were far larger than those in test LG6 at 9 s (Figure 11(d)) while they were opposite at 7 s (Figure 11(c)).
Figure 10

Relationships of relative accumulative transport rate and flushing time duration of tests LG1–LG7.

Figure 10

Relationships of relative accumulative transport rate and flushing time duration of tests LG1–LG7.

Close modal
Figure 11

Flow velocity distributions at the center plane: (a) t = 3 s, (b) t = 5 s, (c) t = 7 s, and (d) t = 9 s.

Figure 11

Flow velocity distributions at the center plane: (a) t = 3 s, (b) t = 5 s, (c) t = 7 s, and (d) t = 9 s.

Close modal
Figure 12(a) and 12(b) show the sediment bed profiles of tests LG1‒LG7 at t = 5 s and t = 10 s, respectively. It should be noted that the coordinates of tests LG2‒LG7 were transferred to the coordinate in test LG1 so that the sediment bed displacement could be seen clearly in the figure. The sediment beds were removed downstream with various displacements depending on the factor of LG. The displacement of the sediment bed in test LG1 was 0.5 m while the rest of the tests were 1 m. Moreover, except for the sediment bed profiles of tests LG1 and LG2, in which the sediment particles were scoured dramatically (Figure 12(b)), the profiles of tests LG3‒LG7 at t = 5 s (Figure 12(a)) or t = 10 s (Figure 12(b)) were very similar to each other. These results indicated that the flushing efficiency was not significantly reduced when the distance between the flushing gate and sediment bed was larger than 1 m (test LG2).
Figure 12

Sediment bed profiles of tests LG1–LG7: (a) t = 5 s and (b) t = 10 s.

Figure 12

Sediment bed profiles of tests LG1–LG7: (a) t = 5 s and (b) t = 10 s.

Close modal

In this paper, the CFD model of Flow-3D, which was validated by experimental and numerical data conducted by Campisano et al. (2004), was employed to study the scouring effects induced by the flushing waves based on the concept of the self-cleaning method. Influences of flushing gate height, initial sediment bed thickness and length, and sediment bed position on cleaning sediment deposit were individually investigated. The main conclusions are in the following:

  • The sand bed profiles variations were in a very similar trend in each test, i.e., a higher sand bed height was induced by the sudden erosion at the sediment bed head, then backward accompanied by a gradually narrow crest.

  • From the perspective of flushing time duration, the lower the flushing gate height was, the higher the removal efficiency. The efficiency of flushing waves in cleaning the sediment deposition decreased with the increasing bed thickness while the longer the sediment bed length was the larger the flushing time duration.

  • The required time duration for totally removing the sediment deposit increased about 6, 3, and 3 times when the sediment bed thickness, sediment bed lenth, and distance between the flushing gate and sediment bed increased 10, 4, and 7 times, respectively.

  • The flushing waves generated from the gate performed well in cleaning the sediment. All the sediment deposit could be scoured and transported downstream with a distance ranging from 1.0 to 4.5.

The authors acknowledge the support of the National Key R&D Program of China (No. 2022YFC3203200) and the Ningbo Science Foundation (Grant No. 2021J096).

H.F. conceptualized and investigated the study, prepared the methodology, did data curation, and wrote the original draft. S.D. investigated the study, prepared the methodology, supervised, wrote, reviewed, and edited the article. D.Z.Z. conceptualized the study, wrote, reviewed, and edited the article.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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