• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.73-80
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• 1997

This study was performed to develop the dispersion modeling methodology for quantitative prediction of the hazard distance or toxic buffer distance by comparing 10-min average, 30-min average, and 1-hr average maximum ground-level concentration with $Cl_2$ regultaion concentration, IDLH and ERPG-3 concentration for hazardous toxic gas, $Cl_2$ releases from the storage tank of the chemical plant facilities. For this dispersion modeling, the source term model, dispersion model, meteorological and topographical data are incorporated into the SuperChems model, and then the effects of the atmospheric stability, wind speed, and surface roughness length changes on the maxum ground-level concentration were estimated.

• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.3
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• pp.124-132
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• 2009

The optimal type and discharging position of ocean outfall of wastewater have been determined by hydrodynamic modeling, near-field dilution modeling, and far-field dispersion modeling. Tide and tidal currents have been simulated by a finite element hydrodynamic model showing good agreements with field observations. Based on the hydrodynamic simulation results candidates of ocean outfall position were preliminary determined. Submerged single port and submerged multi-port diffuser were selected as discharging system alternatives and finally designed by considering tide, tidal currents and water depth. Initial dilution of wastewater discharged from the designed ports has been estimated by CORMIX system. A 2-dimensional random-walk dispersion model has been employed to simulate far-field dispersion of discharged wastewater.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.10a
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• pp.100-101
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• 2008

• Journal of Radiation Protection and Research
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• v.48 no.1
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• pp.28-43
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• 2023

Background: High-fidelity meteorological data is a prerequisite for the realistic simulation of atmospheric dispersion of radioactive materials near nuclear power plants (NPPs). However, many meteorological models frequently overestimate near-surface wind speeds, failing to represent local meteorological conditions near NPPs. This study presents a new high-resolution (approximately 1 km) meteorological downscaling method for modeling short-range (< 100 km) atmospheric dispersion of accidental NPP plumes. Materials and Methods: Six considerations from literature reviews have been suggested for a new dynamic downscaling method. The dynamic downscaling method is developed based on the Weather Research and Forecasting (WRF) model version 3.6.1, applying high-resolution land-use and topography data. In addition, a new subgrid-scale topographic drag parameterization has been implemented for a realistic representation of the atmospheric surface-layer momentum transfer. Finally, a year-long simulation for the Kori and Wolsong NPPs, located in southeastern coastal areas, has been made for 2016 and evaluated against operational surface meteorological measurements and the NPPs' on-site weather stations. Results and Discussion: The new dynamic downscaling method can represent multiscale atmospheric motions from the synoptic to the boundary-layer scales and produce three-dimensional local meteorological fields near the NPPs with a 1.2 km grid resolution. Comparing the year-long simulation against the measurements showed a salient improvement in simulating near-surface wind fields by reducing the root mean square error of approximately 1 m/s. Furthermore, the improved wind field simulation led to a better agreement in the Eulerian estimate of the local atmospheric dispersion. The new subgrid-scale topographic drag parameterization was essential for improved performance, suggesting the importance of the subgrid-scale momentum interactions in the atmospheric surface layer. Conclusion: A new dynamic downscaling method has been developed to produce high-resolution local meteorological fields around the Kori and Wolsong NPPs, which can be used in short-range atmospheric dispersion modeling near the NPPs.

• 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.

• Journal of Environmental Science International
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• v.11 no.9
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• pp.907-914
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• 2002

Various Eulerian-Lagrangian models for the one-dimensional longitudinal dispersion equation in nonuniform flow were studied comparatively. In the models studied, the transport equation was decoupled into two component parts by the operator-splitting approach; one part is governing advection and the other is governing dispersion. The advection equation has been solved by using the method of characteristics following fluid particles along the characteristic line and the results were interpolated onto an Eulerian grid on which the dispersion equation was solved by Crank-Nicholson type finite difference method. In the solution of the advection equation, Lagrange fifth, cubic spline, Hermite third and fifth interpolating polynomials were tested by numerical experiment and theoretical error analysis. Among these, Hermite interpolating polynomials are generally superior to Lagrange and cubic spline interpolating polynomials in reducing both dissipation and dispersion errors.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.169-175
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• 2008

A fine carbon fibers dispersion model is implemented to calculate the scattering range and ground level concentration of carbon fibers emitted at certain altitudes of atmospheric boundary layer. This carbon fibers dispersion model was composed by coupling a commonly used atmospheric dispersion model and an atmospheric boundary layer model. The atmospheric boundary layer model, applying the Monin-Obukov Similarity Rule obtained from measurement input data at ground level, was used to create the atmospheric boundary layer structure. In the atmospheric dispersion model, the Lagrangian Particle Model and the Markov Process were applied to calculate the trajectory of scattered carbon fibers relative to gravity and aerodynamic force, as well as carbon fibers specification.

• Environmental Engineering Research
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• v.14 no.2
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• pp.111-119
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• 2009

A prototype GIS-based decision support system (DSS) was developed by using a database management system (DBMS), a model management system (MMS), a knowledge-based system (KBS), a graphical user interface (GUI), and a geographical information system (GIS). The method of selecting a dispersion model or a modeling scheme, originally devised by Park and Seok, was developed using our GIS-based DSS. The performances of candidate models or modeling schemes were evaluated by using a single index(statistical score) derived by applying fuzzy inference to statistical measures between the measured and predicted concentrations. The fumigation dispersion model performed better than the models such as industrial source complex short term model(ISCST) and atmospheric dispersion model system(ADMS) for the prediction of the ground level $SO_2$ (1 hr) concentration in a coastal area. However, its coincidence level between actual and calculated values was poor. The neural network models were found to improve the accuracy of predicted ground level $SO_2$ concentration significantly, compared to the fumigation models. The GIS-based DSS may serve as a useful tool for selecting the best prediction model, even for complex terrains.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.536-539
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• 2003

This study area has been contaminated by oils. To identify contaminated ranges and to assess the possibility of contamination dispersion, monitoring wells were installed and slug test, field soil permeability test, automatic or manual measurement of groundwater table, and groundwater quality analyses in field and laboratory were performed. In addition, a groundwater modeling program was used to assess the possibility of oil contamination dispersion, based on field data and groundwater quality data. The results showed that concentration of oil contaminants in groundwater have been decreased by dispersion and adsorption.