• Journal of Korean Society of Coastal and Ocean Engineers
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• v.15 no.2
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• pp.108-115
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• 2003

Two types of one-dimensional dispersion-correction scheme are developed to take into account the dispersion effects for the simulation of tsunami propagation using ADCIRC finite element model based on shallow-water equations The first is an implicit scheme, and the dispersion-correction is accomplished by controlling the weighting factor assigned to each spatial derivative term of different time levels. The other scheme is explicit and the dispersion is considered by adjusting the element size. The validity of the dispersion-correction scheme proposed in this study is confirmed through the comparison of numerical solutions calculated using the new schemes with analytical ones considering dispersion effect of waves.

• Journal of the Korean Institute of Gas
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• v.4 no.2 s.10
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• pp.15-19
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• 2000

This study compared and analyzed existing research on dispersion models and selected the EPA's Industrial Source Complex(ISC) model as a model suitable for the domestic petrochemical industry for 3-dimensional simulation and developed a simulation system applying. 3-dimensional algorithm with this ISC dispersion model as a basis As a result of this study, the 3D dispersion model based on ISC can help estimate a exact accident damage zone, make a emergency plan and control a ignition source.

• Journal of Environmental Science International
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• v.12 no.9
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• pp.977-986
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• 2003

In this study, the scenario for a numerical modeling of the fine grid scale air dispersion phenomena was proposed and an analysis of the special event which was occurred on September 3, 2002 was performed using by a coarse grid prognostic meteorological model, a fine grid diagnostic meteorological model and a fine grid air dispersion model. Based on the results, we found that the local circulations, like as land-sea breeze, should be seriously considered for evaluating the high PM10 concentration event and for making the reduction policy of the major air pollutant emissions in Ansan area.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1007-1015
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• 2003

In this paper we introduced a new score generating function for the rank dispersion function in a general linear model. Based on the new score function, we derived the null asymptotic theory of the rank-based hypothesis testing in a linear model. In essence we showed that several rank test statistics, which are primarily focused on our new score generating function and new dispersion function, are mainly distribution free and asymptotically converges to a chi-square distribution.

• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.289-301
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• 2001

A fractional step finite difference model for the longitudinal dispersion of nonconservative pollutants is applied to the Nakdong River to simulate the phenol spill accident which occurred on March, 1971. Prior to the dispersion calculation, the flow conditions are simulated to provide inputs to the dispersion model. An unsteady flow model based on Preissmann's four-point scheme is used for this purpose. Sensitivities of the dispersion calculation to empirical equations for dispersion coefficient and to the first-order decay coefficient are analyzed. The time to peak concentration at a downstream location is significantly different depending on the formula for the dispersion coefficient. Although the decay coefficient does not affect the shape of the temporal concentration distribution, the concentration values depend on the decay coefficient verb significantly. An optimization technique is used to calibrate the dispersion model as well as the flow model. The time to the peak concentration is simulated for major positions of water intake along the Nakdong River.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.5
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• pp.411-420
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• 1998

To estimate the dispersion coefficients in a coastal area with complex topography, several schemes using empirical equations expressed with and in lateral and vertical directions, respectively have been examined. Estimation results using these equations and meteorological data obtained from SODAR system were compared' with previously measured dispersion coefficients in other coastal areas. Validations of estimation results have been performed by comparing the measured concentrations with predicted ones empolying in Boryung coastal area. Important conclusions were drawn as follows; (1) Variations of lateral and vertical wind direction revealed different height dependency in upper and lower mixed boundary layer. (2) Because of turbulent constraint effect by large water body in a coastal region, the lateral and the vertical dispersion coefficients were smaller than those of P-G system. (3) As a result of examining the performance measure of these schemes through checking of coincidence between measured and predicted concentrations, vertical dispersion coefficients were smaller than those of P-G system, and the Cramer scheme was found to be more appropriate rather than others in the coastal area surrounding Boryung power plant.

• Water for future
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• v.16 no.4
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• pp.237-243
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• 1983

This study examines the dilution-dispersion phenomen in the main stream when a polluted branch stream flows into it. A hydraulic model was used for it. As the discharge of the main stream and the branch one were changing, the qualitative dispersion, the stream regimen, the velocity of the flow and the hydraulic properties were observed. It was found that the faster the velocity was and the greater the flow discharge ratio was, the more dilution-dispersion phenomenon occurred. And as the velocity of the flow was increasing, so was the longitudinal dispersion velocity. But the transverse dispersion velocity was relatively reduced. Therefore, it is concluded that the dispersion by the distribution of velocity is increased.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.495-506
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• 2013

This study developed the Time-split Mixing Model (TMM) which can represent the pollutant mixing process on a three-dimensional open channel through constructing the conceptual model based on Taylor's assumption (1954) that the shear flow dispersion is the result of combination of shear advection and diffusion by turbulence. The developed model splits the 2-D mixing process into longitudinal mixing and transverse mixing, and it represents the 2-D advection-dispersion by the repetitive calculation of concentration separation by the vertical non-uniformity of flow velocity and then vertical mixing by turbulent diffusion sequentially. The simulation results indicated that the proposed model explains the effect of concentration overlapping by boundary walls, and the simulated concentration was in good agreement with the analytical solution of the 2-D advection-dispersion equation in Taylor period (Chatwin, 1970). The proposed model could explain the correlation between hydraulic factors and the dispersion coefficient to provide the physical insight about the dispersion behavior. The longitudinal dispersion coefficient calculated by the TMM varied with the mixing time unlike the constant value suggested by Elder (1959), whereas the transverse dispersion coefficient was similar with the coefficient evaluated by experiments of Sayre and Chang (1968), Fischer et al. (1979).

• Journal of Korean Society for Atmospheric Environment
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• v.23 no.4
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• pp.449-456
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• 2007

Dispersion coefficient preprocessing schemes have been examined to improve plume dispersion model performance in complex coastal areas. The performances of various schemes for constructing the sigma correction order were evaluated through estimations of statistical measures, such as bias, gross error, R, FB, NMSE, within FAC2, MG, VG, IOA, UAPC and MRE. This was undertaken for the results of dispersion modeling, which applied each scheme. Environmental factors such as sampling time, surface roughness, plume rising, plume height and terrain rolling were considered in this study. Gaussian plume dispersion model was used to calculate 1 hr $SO_2$ concentration 4 km downwind from a power plant in Boryeung coastal area. Here, measured data for January to December of 2002 were obtained so that modelling results could be compared. To compare the performances between various schemes, integrated scores of statistical measures were obtained by giving weights for each measure and then summing each score. This was done because each statistical measure has its own function and criteria; as a result, no measure can be taken as a sole index indicative of the performance level for each modeling scheme. The best preprocessing scheme was discerned using the step-wise method. The most significant factor influencing the magnitude of real dispersion coefficients appeared to be sampling time. A second significant factor appeared to be surface roughness, with the rolling terrain being the least significant for elevated sources in a gently rolling terrain. The best sequence of correcting the sigma from P-G scheme was found to be the combination of (1) sampling time, (2) surface roughness, (3) plume rising, (4) plume height, and (5) terrain rolling.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2305-2314
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• 2023

The governing equations of atmospheric dispersion most often taking the form of a second-order partial differential equation (PDE). Currently, typical computational codes for predicting atmospheric dispersion use the Gaussian plume model that is an analytic solution. A Gaussian model is simple and enables rapid simulations, but it can be difficult to apply to situations with complex model parameters. Recently, a method of solving PDEs using artificial neural networks called physics-informed neural network (PINN) has been proposed. The PINN assumes the latent (hidden) solution of a PDE as an arbitrary neural network model and approximates the solution by optimizing the model. Unlike a Gaussian model, the PINN is intuitive in that it does not require special assumptions and uses the original equation without modifications. In this paper, we describe an approach to atmospheric dispersion modeling using the PINN and show its applicability through simple case studies. The results are compared with analytic and fundamental numerical methods to assess the accuracy and other features. The proposed PINN approximates the solution with reasonable accuracy. Considering that its procedure is divided into training and prediction steps, the PINN also offers the advantage of rapid simulations once the training is over.