analysis of sediment transport modeling using computaitonal

Aquacultural Engineering 31 (2004) 277293

Analysis of sediment transport modeling usingcomputational fluid dynamics (CFD) for

aquaculture racewaysDania L. Huggins, Raul H. Piedrahita, Tom Rumsey

Department of Biological and Agricultural Engineering, University of California,One Shields Avenue, Davis, CA 95616 5294, USA

Received 5 December 2003; accepted 23 May 2004

Abstract

A simulation model to analyze the water flow and sediment transport in aquaculture raceways wasdeveloped using a computational fluid dynamics (CFD) software package. The simulation was usedto evaluate the efficiency of solids settling in the quiescent zone of existing trout raceways. This effi-ciency was based on the percentage of solids removed, which corresponds to the percentage of solidsintroduced into the raceway that settle in it, with settling taking place primarily in the quiescent zone.

The raceway selected for model validation was a rectangular concrete raceway 30.0 m long, 3.0 mwide, 0.9 m deep, with a slope of 0.01. The raceway included a quiescent zone of approximately 5.3 min length, which was separated from the rearing area by a screen. The water flow rate through theraceway was approximately 0.058 m3/s. Velocity measurements were recorded at 230 stations alongthe raceway using an acoustic Doppler velocimeter, for comparison with the results obtained fromthe simulations.

For the purpose of simulating sediment transport, six groups of particles were used to account for thetotal suspended solids. The sizes of the particles selected were based on an experimentally determineddistribution for solids from a similar raceway, and were 692, 532, 350, 204, 61, and 35m for Groups16, respectively. The particle density for each size was assumed to be 1150 kg/m3. Values of thepercentage of solids removed for the different particle sizes were 100.0% for the largest particles, and54.7, 0.9, and 0.1% for the three smallest particles, respectively. This methodology of analyzing theraceway sediment transport in terms of its percentage of solids removed based on CFD simulationscan also be used to examine raceway design alternatives for improving the particle removal efficiency. 2004 Published by Elsevier B.V.

Keywords: Raceway; Design; Trout; Sediment; Modeling; CFD

Corresponding author. Tel.: +1-530-752-2780; fax: +1-530-752-2640.E-mail address: [email protected] (R.H. Piedrahita).

0144-8609/$ see front matter 2004 Published by Elsevier B.V.doi:10.1016/j.aquaeng.2004.05.007

278 D.L. Huggins et al. / Aquacultural Engineering 31 (2004) 277293

1. Introduction

Given the large size of some trout rearing facilities, there is a potential for significantnutrient discharges (IWMGAO, 1999; NASS, 2001). There are strong economic, social,and regulatory pressures to reduce the release of nutrients from aquaculture operations.However, the large flows and very low constituent concentrations make it very difficult totreat effluents. A potentially effective strategy to reduce the release of solids, which containnitrogen, phosphorus, and organic matter, is to increase the proportion that settles withina raceways quiescent zone. Understanding how water and sediment particles move in thequiescent zone provides an opportunity to suggest raceway design modifications aimed atimproving particle settling and improving the quality of the effluents. Therefore, the focusof this study was to develop and validate a computation fluid dynamics (CFD) model for atrout production raceway that can be used to evaluate the efficiency of solids settling for aparticular raceway design.

CFD simulations have been used in aquaculture to describe water flow and solids removalin circular tanks (Montas et al., 2000; Veerapen et al., 2002). In their 2002 study, Veerapenand coworkers studied the factors influencing the removal efficiency of swirl separators anddouble-drain fish tanks. Rasmussen (2002) used CFD modeling to determine the mixingcharacteristics of a turbot rearing tank and the transport of particulate organic materialand the oxygen distribution in rectangular tanks. There are also some studies applyingCFD modeling to aquaculture ponds to simulate water flow velocity patterns and sedimentconditions (Peterson et al., 2000). The information obtained from pond modeling has alsobeen useful in determining the optimal arrangement of aerators in shrimp growout ponds(Peterson et al., 2001).

In the work presented here, a methodology was developed to analyze the efficiency ofsolids removal in a raceway using SSIIM (sediment simulation in water intakes with multi-block option) as the CFD software. CFD simulations were developed to analyze sedimenttransport for multiple sediment sizes, which can provide information on the distribution andflow of particles and on the proportion of the solids that settle within the quiescent zone,expressed as the percentage of solids removed.

2. Model characteristics

The selection of SSIIM for this study was based mainly on its capability to simulatesediment movement. Computing system requirements and licensing fee were some of theother characteristics considered in the selection process. SSIIM is a free CFD program thatcan be downloaded for academic use from the internet (Olsen, 2002). In addition, SSIIMsupports multiple operating systems. It can be executed in computers running under anyversion of WindowsTM, or OS2. The program is small in terms of its memory requirements(1.3 MB) and is not very resource intensive. Although SSIIM was developed about 10years ago, it continues to be updated and there are frequent releases of new versions asimprovements are made and bugs are identified and corrected.

The execution of SSIIM requires two main input files, Control and Koordina. The Controlfile contains most of the parameters needed for model execution, such as size of arrays or

D.L. Huggins et al. / Aquacultural Engineering 31 (2004) 277293 279

number of grids along the three axes, Mannings roughness coefficient of the raceway walls,complete details of inlet and outlet configurations, flow rate, sediment size, concentration,and average settling velocity. The Koordina file contains the grid geometry. Five output filesare written once the program has completed execution (Olsen, 2002) of which the Resultand Conres files are used in the sediment transport calculations. The Result file is writtenby the program when all the iterations specified in the Control file have been completed andwhen the solutions to the governing equations have converged. The convergence criterionused by SSIIM is when the residuals for all the unknown variables fall below 103 (Olsen,2002).

The Result file contains the results for the velocities Vx (in the direction of the long axis ofthe raceway), Vy (in the horizontal direction perpendicular to the long axis of the raceway),Vz (in the vertical direction), pressure, turbulent kinetic energy, and diffusivity (, epsilon).The Conres file contains the simulated values of the concentrations for the various sedimentgroups at each node created in the Koordina file (Olsen, 2002).

2.1. Model equations

The water flow velocity calculations are based on the NavierStokes equation for turbulentflow in a general 3D geometry for non-compressible and constant density flow (Olsen,1991). SSIIM uses the k turbulence model to predict the shear stresses (Versteeg andMalalasekera, 1995). The k model focuses on the mechanisms that affect the turbulentkinetic energy. More details on how the kmodel is used for calculating the turbulent shearstress are given in Olsens SSIIM manual (Olsen, 2002).

2.2. Model assumptions

SSIIM has some limitations in terms of its capabilities with different geometries, hy-draulic configurations, and the number of cells (control volumes or elements). Due to theselimitations some assumptions had to be made and these are described as follows.

2.2.1. Uniformly distributed inletAs a first assumption, the model considers that the influent flow is uniformly distributed

along the full depth of the raceway at the first upstream cross-section. This assumption wasmade because simulating influent water splashing on the surface of the water (Fig. 1), asin a real raceway, made convergence in the model slow or unreachable in some cases. Thisdifficulty in simulating a surface inlet has been reported by other authors (Bates, 2000) andit appears to be a problem not only with this particular software package, but with CFDalgorithms in general (Montas et al., 2000; Bates, 2000). Partly as a result of this assumption,validation of the model was carried out for the downstream end of the raceway, especiallyin the quiescent zone.

2.2.2. Fish presenceAs in other studies where CFD modeling has been applied for fish tank design, the pres-

ence of fish was not included in the model, due to the complexity that would be introducedand the lack of detailed information that would be needed for model development. For model

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0.22mscreen

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0.4m

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Fig. 1. Schematic diagram of a raceway (slope = 0.01).


validation purposes it would have been best to be able to measure velocities in an emptyraceway but it was difficult to combine this requirement with the planning and daily activ-ities at the farm where the raceway used to collect calibration data was located. However,this assumption does not weaken the model in the quiescent zone where particle settlinganalysis is performed, since the fish are excluded from this region (Fig. 1).

2.2.3. Screen configurationA screen separates the rearing area from the quiescent zone. In the raceway the screen

is 25.2 m from the inlet wall; however in the simulated system the screen was placed at24.9 m (Fig. 1) from the inlet wall. The screen in the raceway consisted of two wood framedsections (5 cm 10 cm (2 in. 4 in.), nominal size). Each frame had 50 PVC pipes withan outside diameter (OD) of 2.5 cm (3/4 in. nominal size), which were placed verticallyalong the whole height of the screen leaving a space of 1.2 cm between the pipes (Fig. 2).The total number of closed spaces was 100 (pipes) with a total of 98 open spaces (the firstand the last pipe in each of the 2 wood frames were very close to the frame). This exactscreen configuration was difficult to incorporate in the simulation due to the large number ofcells that would have been required and the corresponding number of calculations needed.Therefore, the pipes were replaced with 12 bars (closed space, Fig. 2) to simplify the water

Fig. 2. Divider screen between the rearing area and the quiescent zone.


flow calculations. The open spaces (between pipes) in the original system, along the width ofthe raceway, add up to 19.7% of the total width. However, due to grid size and other softwarelimitations the simulated open area was 22.5% of the total width. The number of openingsin the screen (12) was decided based on the maximum allowed number of wall statementsthat are necessary for defining all the bars in the program script. Other configurations withfewer bars were tested; however, the flow pattern was greatly influenced by the reductionof the cross-sectional area around the screen.

2.2.4. Location for sediment releaseDue to the presence of the fish in the system, sediments were released and resuspended

throughout the rearing area. Because the fish were not included in the simulation, therewas no re-suspension of the particles in the fish rearing area as is observed in the raceways.Therefore, in the simulation, the sediments were released at the water surface extending overan area of 0.75 m 3.0 m (the full width of the raceway) located 0.75 m before the screen.These assumptions probably result in an underestimation of the effect of the quiescentzone since the sediments are released at the surface (before the screen), instead of beingresuspended at mid-depth (or closer to the bottom) as they would be in a real raceway.SSIIM allows the user to specify the sediment concentration (volume fraction) for each sizegroup at the water surface. The sediment flux can be calculated at the surface by multiplyingthe concentration by the particle density and the settling velocity.

2.2.5. Sediment resuspensionDue to limitations with the software, sediment transport simulations do not account for

resuspension from the bottom of the raceway. This will result in an over-estimation of thesolids settled in the raceway. However, due to the cohesive nature of aquaculture particles,the magnitude of the over-estimation is expected to be substantially less than it would befor non-cohesive sediments.

2.3. Grid selection

A grid dependency study (Versteeg and Malalasekera, 1995) was performed to eliminateerrors due to the coarseness of the grid and also to determine the best compromise betweensimulation accuracy, numerical stability, convergence, and computational time (Table 1).The number of iterations are those required by the model to converge. The program wasexecuted on a 1.7 GHz Pentium 4 computer with 512 MB of RAM.

In this study three grids were tested. These grids were 603012 (21,600 cells), 7050 16 (56,000 cells), and 80 60 20 (96,000 cells). The first number (e.g. 60) corresponds

Table 1Characteristics of model executions for three grids

Grid numbering Time needed to converge (min) No. of iterations No. of cells60 30 12 15 1609 2160070 50 16 110 2423 5600080 60 20 240 3637 96000


to the number of grids (divisions) along the X-axis of the raceway (cross-sections), the secondnumber (e.g. 30) corresponds to the number of grids along the Y-axis of the raceway (widthof the raceway, longitudinal sections), and the third number (e.g. 12) is the number of levelsin which the control volume has been divided in the Z-direction. As the number of cellsincreased so did the number of iterations and the time needed for the model to converge(Table 1). This was due to the increase in the number of cells at which the equationswere solved. A qualitative grid analysis for the simulated velocities was made between thedifferent grid sizes (60 30 12, 70 50 16, and 80 60 20). The simulatedvelocities were very similar for grids 70 50 16 and 80 60 20, but grid 60 30 12 differed from the other two, suggesting that the low resolution grid did not result inaccurate simulations. Therefore, grid 70 50 16 was selected as the most appropriategrid for further analysis, considering that with a 80 60 20 grid it would have takenlonger for the calculations to converge (Huggins, 2003).

The grid spacing was not constant through the raceway. The cell dimensions in thefinal grid were 1.0 m in the X-direction by 0.062 m in the Y-direction in areas requiringlow resolution because of the uniformity of the flow, such as in the middle section of theraceway. In the areas requiring higher resolution (close to the screen and the outlet) thedimensions of the cells were 0.125 m 0.062 m in the X and Y-directions, respectively.

3. Methods

The model was validated against data collected at a commercial trout farm. Details ofthe raceway in which the date were collected are presented below, followed by details onthe model characteristics and simulation conditions.

3.1. Raceway characteristics

The concrete raceway used in this study (standard raceway) was 30.0 m long, 3.0 m wide,with a maximum depth of 0.9 m and a slope of 0.01. A coordinate system for the racewaywas set as X = distance along the raceway (length), Y = lateral direction, and Z = verticaldirection (Fig. 1). The rearing volume, not including the quiescent zone, was approximately48.4 m3. Each of the inlet and outlet flows were through two weirs that together add to thetotal width of the raceway (Fig. 1). The fish stocking density was 11 kg/m3 with an averagemass of 49 g per fish.

The sides of the raceways are defined as inlet wall, outlet wall, right hand side, and lefthand side for description and measurement purposes (Fig. 1). The flow rate for the standardraceway was approximately 0.058 m3/s, which was calculated using the Francis equationsfor half-contracted rectangular sharp weirs (Vennard, 1954).

3.2. Data collection using an acoustic Doppler velocimeter (ADV)

An acoustic Doppler velocimeter was used to measure velocities (Flow Tracker HandheldADV, SonTek/YSI, Inc., San Diego, CA). The instrument has a two-dimensional (2D)probe, a velocity range of(0.0015 m/s), accuracy of1% of the measured velocity, and


X

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Fig. 3. Schematic diagram for a raceway, including all the planes where velocity measurements were taken.

a resolution of 0.0001 m/s (SonTek, 2002). With the 2D probe it was possible to obtain thehorizontal components (Vx and Vy) of the water velocity vector.

The points of sampling were selected based on a preliminary analysis of velocitiespredicted through simulation. Measurements were concentrated in areas where velocitieschanged substantially as a function of their location in the raceway. Measurements weretaken at 16 planes selected along the length of the raceway (X-axis, Fig. 3) and there were12 stations for most planes, corresponding to 4 depths (Z-axis, as a fraction of the totaldepth measured from the surface 0.2 h, 0.6 h, 0.8 h, and at 3 cm from the bottom) and threelocations along the width of the tank (Y-axis) (Fig. 3).

3.3. Solids characteristics and their introduction in the system

SSIIM has the capability for particle tracking, adjusting the size and density of the inputparticles as well as the particle injection location. The particle size distribution used inthe simulation was obtained from a settling velocity distribution determined in a previousstudy by Wong and Piedrahita (2000). The diameters of the particle groups were calculatedwith Stokes law assuming a density of 1150 kg/m3 (Metcalf and Eddy, 1991; Wong, 2001;Timmons et al., 2002) (Table 2).

3.4. Sediment transport

The rate of solids exiting the raceway was calculated at the last grid along the X-axis,where the openings for the weirs are located. The rate for each particle group was calculatedas:


Table 2Sediment particle characteristics

Particle group(no.)

Settling velocity(m/s)

Particle density(kg/m3)

Particle size(m)

Inlet concentration(m3/m3)

1 0.0391 1150 692 3.2651 1072 0.0231 1150 532 5.7799 1073 0.0100 1150 350 1.3298 1074 0.0034 1150 204 2.1277 1065 0.0003 1150 61 2.0745 1056 0.0001 1150 35 3.1916 106

me,g =

ExitCellsp,gVxCgA (1)

where me,g is the mass flow rate of particles from group g exiting raceway (kg/s), p,g thedensity of the particle group g (kg/m3), Vx the water velocity in the X-direction (m/s) atcells where the weirs are located, Cg the sediment concentration of particle group g (m3/m3)obtained from the simulation results, and A the face area of the cells where the weirs arelocated and through which the sediment flux occurs.

The rate of solids settled was calculated for the cells nearest to the bottom of the racewayin a similar manner as the rate of solids exiting the raceway. The rate of solids accumulationfor each particle group was calculated as (Olsen, 1999):

ma,g =

BottomCellsp,gVs,gCgA (2)

where ma,g is the mass flow rate of particles from group g settled in the bottom of the raceway(kg/s), Vs,g = settling velocity of particle group g (m/s). The sediment calculations wereall done using ExcelTM.

Since the analysis assumes steady state conditions, the total sediment fluxes entering theraceway were calculated as the sum of the particles exiting and settling in the raceway.A check on the calculation for the influent particle flux was carried out by multiplyingthe prescribed sediment concentration at the surface times the particle density and settlingvelocity (Huggins, 2003). The percent of solids removed (PSRg) was calculated from

PSRg = 100 ma,gma,g + me,g (3)

4. Results and Discussion

Model validation was achieved through a comparison of the simulated and measuredvelocities. In all cases, comparisons were made for points about 0.5 m from the left wall,0.5 m from the right wall, and the middle of the raceway. Although velocities were measuredat 16 planes along the length of the raceway (Fig. 3), validation was not attempted forthe beginning of the raceway and there was a large difference between the simulated andmeasured velocities in this part of the raceway (Huggins, 2003). The differences were due to


the inherent turbulence around the inlet, the presence of fish in the raceway, and the need tosimplify the water inlet for the simulations. Other authors have encountered similar resultsfor areas with highly turbulent flows (Bowles, 1999; Shankar et al., 2001).

Model validation was carried out for the last 10 m of the raceway, which included thequiescent zone plus the area where the sediments were released in the simulations. Here,the measured Vx and Vy velocities agreed with the simulated velocities (Figs. 46). Themeasured and simulated velocities in the Y-direction are not shown as they were very small:in the range of 0.1 cm/s for the first 3.4 m of the raceway, 0.02 cm/s for the middle ofthe raceway (15.6 m from the inlet), and 0.07 cm/s (25.0 m from the inlet).

The data collected with the velocimeter consisted of the average and standard deviationof 3040 measurements made at approximately 1 s intervals. The sensitivity and responsespeed of the instrument was such that the velocity fluctuations caused by turbulence weredetected in the measurements giving rise to the standard deviations that are displayed aserror bars in Figs. 46. Although, the velocity fluctuations in the last section of the racewaydiminished substantially when compared to the inlet and middle sections, fluctuations werestill relatively high around the screen (Figs. 4 and 7). Fluctuations before the screen may bedue in part to the activity of the fish around the probe, and in part to the impact of the screenon the flow. Fluctuations after the screen (Fig. 7) are the result the turbulence generated bythe reduction of the cross-sectional area due to the screen (Fig. 2).

4.1. Sediment transport and sediment removal analysis

The total flow rate of sediments into the raceway was determined by specifying the inletsurface sediment concentrations and settling velocities and was 13,631 g/day (Table 3). Thetotal rate of sediments exiting the raceway was 2485 g/day and the total settling rate was11,146 g/day. The total rate of solids in corresponded to approximately 26% of the amountof feed applied (42.3 kg/day) (Timmons et al., 2002).

The percent removal for particle Groups 13 was approximately 100%. The percentremoval for the smaller particle sizes was lower at 54.7, 0.9, and 0.1% for particle sizeGroups 46, respectively (Table 3).

Contour graphs of the sediment fluxes were created in MATLAB (Fig. 8) to obtain amore detailed visualization than that available with the graphics option presented in SSIIM.The plots were done for an area near the bottom of the raceway and in the last 7 m of the

Table 3Sediment calculations for the original raceway system

Particle group(no.)

Solids entering(g/day)

Solids exiting(g/day)

Solids settled(g/day)

Solids removed(%)

1 3247 0 3247 100.02 3396 0 3396 100.03 3484 1 3483 100.04 1840 833 1007 54.75 1583 1569 14 0.96 81 81 0 0.1

Total 13631 2485 11146 81.8


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Fig. 4. Measured and simulated Vx velocities. The LEFT corresponds to a location about 0.5 m from the left sideof the raceway when facing the influent. MIDDLE is the center of the raceway, and RIGHT is 0.5 m from the rightside of the raceway. The comparison is made for points on Plane 11, 23.9 m from the inlet (Fig. 3). Water depth atthat point was 0.85 m. Mean velocity was 2.92 cm/s (calculated from flow rate divided by the cross-sectional areaat Plane 11).

raceway. The contour graphs show the rates of sedimentation (g/m2/day) for the differentparticle groups. The settling characteristics are similar for Groups 13, as the particles settlebefore reaching a distance of 28.5 m from the inlet (Fig. 8). Furthermore, a large fraction ofthe Group 13 sediments settled before the quiescent zone. This could be explained by the


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Fig. 5. Measured and simulated Vx velocities. The LEFT, MIDDLE, and RIGHT are as described for Fig. 4. Thecomparison is made for points on Plane 15, 29.7 m from the inlet (Fig. 3). Water depth at that point was 0.91 m.Mean velocity was 2.11 cm/s.

manner in which the particles are introduced into the simulated raceway, and also by the factthat no re-suspension is considered. Also, as was mentioned earlier, the turbulence createdaround the screen caused some back-flows that would allow some of the larger particles tosettle in the rearing area. Although the general settling pattern for Groups 13 is similar,the displacement of particles towards the end of the raceway increases as settling velocitydecreases.


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Fig. 6. Measured and simulated Vx velocities. The LEFT, MIDDLE, and RIGHT are as described for Fig. 4. Thecomparison is made for points on Plane 16, 29.9 m from the inlet (Fig. 3). Water depth at that point was 0.91 m.Mean velocity was 2.10 cm/s.

Differences in the sediment deposition pattern were observed for Group 4 relative toGroups 13 (Fig. 8). The lighter Group 4 particles are displaced towards the end of thequiescent zone. The sediment fluxes (g/m2/day) for Group 4 are much lower than for theprevious groups, resulting in much lower percent solids removal values. The sedimentcontours and flux rates for Group 5 are very similar to those for Group 6 as these small


Fig. 7. Top view of velocity flow patterns at the outlet area for the raceway. Each drawing has a different scale.The arrows represent velocity vectors proportional to the velocity for each view.

particles have a low settling velocity. In general, it is much harder to capture the Group 5and 6 particles than the larger particles. Settling for these particles takes place close to theright and left walls of the raceway but at a very low flux rates (Fig. 8).

4.2. Model assumptions

When using CFD modeling it is necessary to make some assumptions to simulate thedesired system. The assumptions used here, such as those of a simplified inlet structure anduniform flow distribution, and a simplified screen, were essential to obtain convergence ofthe solutions. The exclusion of re-suspension resulted in an undetermined over-estimationof percent of solids removed. However, the simulations still provide useful informationon the settling rates and patterns within a raceway, tools that can be used to analyze thepotential impact of raceway modifications on solids removal.


Fig. 8. Comparative sediment fluxes for the different particle groups. The graphs show the last 7.5 m of theraceway, which includes the quiescent zone that is 5.02 m from the outlet wall. The graphs are not to scale. Thecolor represents the sediment concentration as indicated on each of the graphs. The contour graphs were createdin MATLAB using the sediment concentration output from Conres file at Level 2.


5. Conclusion

This study investigated the use of CFD modeling to simulate water flow and sedimenttransport in aquaculture raceways. Water velocity measurements were used to successfullyvalidate the simulations. The model was used to identify the location in which particles ofdifferent densities are likely to settle and the settling rates of those particles. These prelimi-nary results are being used for the evaluation of possible raceway design modifications andtheir potential impact on solids settling in the raceway (Huggins, 2003).

Acknowledgements

The authors would like to acknowledge the support of the US Department of AgriculturesWestern Regional Aquaculture Center (WRAC), participating farms in Idaho, and Dr. NilsOlsen, the author of the CFD software used in this study.

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Analysis of sediment transport modeling using computational fluid dynamics (CFD) for aquaculture racewaysIntroductionModel characteristicsModel equationsModel assumptionsUniformly distributed inletFish presenceScreen configurationLocation for sediment releaseSediment resuspension

Grid selection

MethodsRaceway characteristicsData collection using an acoustic Doppler velocimeter (ADV)Solids characteristics and their introduction in the systemSediment transport

Results and DiscussionSediment transport and sediment removal analysisModel assumptions

ConclusionAcknowledgementsReferences