2D Modeling of Heterogeneous Dispersion in Meandering Channels

Seo, Il Won; Lee, Myung Eun; Baek, Kyong Oh

doi:10.1061/(asce)0733-9429(2008)134:2(196)

Cited by 35 publications

(33 citation statements)

References 18 publications

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“…5 shows the variation of Φ/Φ max , where Φ max is the maximum crosssectional mean shear factor, along the dimensionless longitudinal distance, x/L C , where L C is the arc length of one period of the Scurved channel. This figure reveals that Φ/Φ max varies periodically and is the highest near the apex of the bends (Seo et al, 2008). Furthermore, values of Φ/Φ max in the second bend are larger than those in the first bend.…”

Section: Application Of Transverse Velocity Equationmentioning

confidence: 85%

Prediction of transverse dispersion coefficient using vertical profile of secondary flow in meandering channels

Baek

Seo

2008

KSCE Journal of Civil Engineering

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When the concentration data are not available, the transverse dispersion coefficient should be predicted using the basic hydraulic parameters to analyze mixing characteristics. In meandering channels, where the secondary flow plays an important role in the increase of transverse mixing, the transverse dispersion coefficient must be predicted accounting for the vertical variation of the transverse velocity. In this study, based on the vertical distribution equation for the transverse velocity, the theoretical formula for the transverse dispersion coefficient is newly developed. The merits of the theoretical formula on the transverse dispersion coefficient derived in this study are that it has the finite combination of elementary functions so that the transverse dispersion coefficient can be easily obtained compared with other theoretical methods, and that it can take into consideration the proper shear effect of the secondary flow. To verify the theoretical prediction of the transverse dispersion coefficient, both flow and tracer experiments are conducted in the meandering laboratory channel. The predicted transverse dispersion coefficient from the newly proposed equation shows better agreement with the observed transverse dispersion coefficient than those from other exiting equations.

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Section: Application Of Transverse Velocity Equationmentioning

confidence: 85%

Prediction of transverse dispersion coefficient using vertical profile of secondary flow in meandering channels

Baek

Seo

2008

KSCE Journal of Civil Engineering

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“…We can calculate the transverse dispersion coefficient using the slope of the straight line which is fitted to the plot of variance (σ 2 y ) against longitudinal distance (x). The simple moment method (simple MM) by Sayre and Chang [4] can be derived from the generalized MM, Equation (2). Neglecting the transverse velocity and the tracer impinging on banks, and assuming a constant longitudinal velocity, Equation (2) becomes:…”

Section: Methodsmentioning

confidence: 99%

“…Estimated dispersion coefficients based on numerical models are sometimes unrealistic, and may not be transferable to other sites or problems [1]. Thus, reliable methods have been proposed that avoid the uncertainties associated with simulations and reduce the computational burden associated with numerical models, while facilitating the direct determination of dispersion coefficients [2].…”

Section: Introductionmentioning

confidence: 99%

Flowchart on Choosing Optimal Method of Observing Transverse Dispersion Coefficient for Solute Transport in Open Channel Flow

Baek¹

2018

Sustainability

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There are a number of methods for observing and estimating the transverse dispersion coefficient in an analysis of the solute transport in open channel flow. It may be difficult to select an optimal method to calculate dispersion coefficients from tracer data among numerous methodologies. A flowchart was proposed in this study to select an appropriate method under the transport situation of either time-variant or steady condition. When making the flowchart, the strengths and limitations of the methods were evaluated based on its derivation procedure which was conducted under specific assumptions. Additionally, application examples of these methods on experimental data were illustrated using previous works. Furthermore, the observed dispersion coefficients in a laboratory channel were validated by using transport numerical modeling, and the simulation results were compared with the experimental results from tracer tests. This flowchart may assist in choosing the better methods for determining the transverse dispersion coefficient in various river mixing situations.

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“…This expression results from the transformation of the dispersion process in the Lagrangian frame into that in the fixed Eulerian frame, using the 2D depth-averaged velocity field. Therefore, using this expression, the Lagrangian motion of tracer clouds can be considered in the fixed Cartesian coordinate (Lee and Seo, 2007;Seo et al, 2008).…”

Section: Governing Equationmentioning

confidence: 99%

2D finite element pollutant transport model for accidental mass release in rivers

Lee

Seo

2010

KSCE Journal of Civil Engineering

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A numerical model to simulate the accidental pollutant transport in rivers was developed using the two-dimensional finite element method. RAM4 is developed as the scalar transport module of the software RAMS (River Analysis and Modeling System) which can provide a graphical user interface for various river topographies and has recently been released as a beta version. The significance of RAM4 is that it is able to treat the pollutant mass itself, which is instantaneously released at a certain point of a river. The development of this ability was motivated by the inadequacy of the previous method, whereby the instantaneous mass release was expressed by defining some initial concentration at a source point. This method is not able to balance the injected mass value in accordance with the numerical time step size and the magnitude of flow velocity at this point. RAM4 was connected with the RAMS graphic user interface so that the source point and the released mass value can be controlled with a user-friendly graphical tool. The hydrodynamic module of RAMS, named RAM2, is currently undergoing beta-testing. Therefore, RAM4 was applied to the real river using the popular hydrodynamic model, RMA2, in TABS-MD. Using the obtained flow field computed by RMA2, the model has been simulated for a number of example scenarios of mass dumping accidents in the Han River of Korea in order to verify the applicability of this method in a real situation.

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2D Modeling of Heterogeneous Dispersion in Meandering Channels

Cited by 35 publications

References 18 publications

Prediction of transverse dispersion coefficient using vertical profile of secondary flow in meandering channels

Prediction of transverse dispersion coefficient using vertical profile of secondary flow in meandering channels

Flowchart on Choosing Optimal Method of Observing Transverse Dispersion Coefficient for Solute Transport in Open Channel Flow

2D finite element pollutant transport model for accidental mass release in rivers

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