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

Lee, Myung Eun; Seo, Il Won

doi:10.1007/s12205-010-0077-9

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…In the case of the PTM, the model is based on the Fickian model and the shear effects were incorporated into the dispersion coefficients. Thus, the PTM shows discrepancies against the measurements in the initial period, even though the dispersion coefficients computed using the vertical velocity profiles were reported to improve the accuracy of the simulation results compared with empirical formulas [6,16,21].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, in the 2D mixing simulations, determination of dispersion coefficients is one of the crucial concerns to conduct pollutant mixing simulations. Several studies adopted empirical formulas obtained from tracer test results or theoretical approaches to determine the dispersion coefficient for solute transport simulations [6][7][8]. The empirical formulas of dispersion coefficient are presented as follows.…”

Section: Introductionmentioning

confidence: 99%

“…The theoretical value of L a is derived as 5.93 under the assumption of an infinitely wide width of straight channel and the log distribution in the longitudinal velocity shear. However, the theoretical value shows large discrepancies from the results of field studies, which suggested the value of L a to be in the range of Several studies adopted empirical formulas obtained from tracer test results or theoretical approaches to determine the dispersion coefficient for solute transport simulations [6][7][8]. The empirical formulas of dispersion coefficient are presented as follows.…”

Section: Introductionmentioning

confidence: 99%

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Comparisons of Two Types of Particle Tracking Models Including the Effects of Vertical Velocity Shear

Park

Shin

Seong

et al. 2020

Water

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In this study, two types of particle tracking models were presented to investigate the applicability in the two-dimensional solute mixing simulations. The conventional particle tracking model, denoted as PTM, was developed based on Fick’s law, which adopted the dispersion coefficient to calculate the random displacements. The other model is the particle dispersion model (PDM), which computes the shear dispersion process by dividing into two computation procedures as the shear translation and the vertical mixing. The PTM and the PDM included the effects of vertical profiles of velocity in the computation of dispersion coefficients and the shear translation step, respectively. The main difference between the two models is whether the shear dispersion process is reproduced using Fick’s law or the direct computation method. These differences were clearly revealed by comparing with the analytic solution of the advection-dispersion equation. The concentration curve resulting from the PTM shows the Gaussian curves, which were well-fitted with the analytic solution in both initial and Taylor periods. Meanwhile, the PDM presented skewed curves in the initial period and gradually turned to the symmetric shape in the Taylor period. The inherent differences of the two particle tracking models were scrutinized against the two-dimensional tracer test results, which show the non-Fickian mixing properties. The comparisons of concentration–time curves reveal that the PDM reproduced a more accurate shape of the curves than the results by the PTM by demonstrating skewed concentration curves.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparisons of Two Types of Particle Tracking Models Including the Effects of Vertical Velocity Shear

Park

Shin

Seong

et al. 2020

Water

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“…This implies that: This delay results from the fact that the discrete time integration cannot completely follow the instant moment of mass release (Lee and Seo, 2010). In order to obtain the plots, we solved equation 5 subjected to a time periodic inlet BC (equation 8), changing the outlet BC type.…”

Section: Numerical Proceduresmentioning

confidence: 99%

Simulation of species concentration distribution in reactive flows with unsteady boundary conditions

Filho

Mangiavacchi

Pontes

2017

Braz. J. Chem. Eng.

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-The determination of species concentration profiles in reactive flows with variable inlets is a problem of practical interest to many fields such as in flow reactor transient operation and in cyclic degradable pollutants disposals in watercourses. In these cases, the inflow condition often consists of a time-dependent function, which may imply unsteady outflows, not always well represented by the usual boundary conditions (BC) used so far. A new approach, using an outlet condition in the form of a material derivative, termed Material Derivative Boundary Condition (MDBC), is introduced and a numerical model to solve convection-diffusion-reaction equations in twodimensional (2-D) incompressible flows is developed. Upon reviewing the literature, it is noted that the Finite Element Method (FEM) is rarely used in the simulation of reactive flows, in spite of its ability of consistently coping with variable BCs. The above facts are reasons to explore its use along with a semi-discrete formulation with the Galerkin Method in our simulations. Results are obtained for various conditions, in order to show features of the code, and are compared to existing solutions. Use of the MDBC is shown to provide a better approximation of the exit concentrations and use of FEM in reactive flows is further enhanced.

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“…The two-dimensional advection-dispersion equation (2D ADE) as in Equation (3) can be derived by integrating the three-dimensional transport equation for turbulent flow over the channel depth. The 2D ADE for natural streams, in which the principle flow direction changes along the channel direction, is written in the curvilinear coordinate system as (Lee and Seo, 2010). This CTM-2D is a finite element scalar transport model, and was used to simulate the contaminant dispersion that was conducted as a tracer test in meandering channels.…”

Section: Advection Dispersion Modelmentioning

confidence: 99%

Two-Dimensional Modeling of Flow and Contaminant Transport in Meandering Channels

Seo¹,

Shin²

EPiC Series in Engineering

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In this study, a depth-averaged two-dimensional hydrodynamic model and scalar transport model were used to analyze the characteristics of both velocity field and concentration field of the complex flow in meandering channels. The two-dimensional shallow water model used in this study adopted the dispersion stress method in order to induce the effect of secondary flow which is normally omitted in the depth averaging process of the shallow water equation. A new vertical profile equation for the secondary flow was applied to the momentum equations by adopting the dispersion stress method. Comparisons with the experimental results of the large meandering channels of the River Experiment Center of KICT show that the computed values of the water surface profile and velocity magnitude were in good agreement with the observed data. The results of the 2D advection-dispersion model show that the longitudinal dispersion is much larger than theoretical results by Elder (1959) in meandering channels.

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

Cited by 16 publications

References 14 publications

Comparisons of Two Types of Particle Tracking Models Including the Effects of Vertical Velocity Shear

Comparisons of Two Types of Particle Tracking Models Including the Effects of Vertical Velocity Shear

Simulation of species concentration distribution in reactive flows with unsteady boundary conditions

Two-Dimensional Modeling of Flow and Contaminant Transport in Meandering Channels

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