• 한국가스학회지
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• 제1권1호
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• pp.73-80
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• 1997

본 연구는 화학장치설비중 저장탱크에서 누출된 유해독성가스인 염소의 풍하거리에 따른 10분 평균, 30분 평균 및 1시간 평균 최대 지표면 농도를 산출하여 염소가스의 법적 규제농도인 IDLH 및 ERPG-3 농도들과 비교함으로써 유해위험거리 (hazard distance) 또는 독성완충거리 (toxic buffer distance)를 정량적으로 예측하는 분산모델링 방법론을 개발하고자 수행되었다. 본 분산모델링을 위하여 누출원모델, 분산모델, 기상 및 지형자료들 이 SuperChems 모델에 입력자료로 사용되었으며, 대기의 안정도, 풍속, 표면거칠기 길이의 변화에 따른 지표면 농도의 영향이 평가되었다.

• 한국해양환경ㆍ에너지학회지
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• 제12권3호
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• pp.124-132
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• 2009

하수의 해양방류시스템의 형식과 방류위치 결정을 위해 해수유동모의, 근해역 희석률 모의 그리고 원해역 확산모의가 수행되었다. 방류 후보지점 주변의 조위와 조류는 관측조위 및 조류를 잘 재현하는 2차원 유한요소모형에 의해 수행되었으며, 계산된 조위 및 조류 모의결과에 기초하여 방류 후보지점이 결정되었다. 방류시스템으로는 단일확산관과 다공확산관이 고려되었다. 단일확산관과 다공확산관을 통한 하수 방류의 근역 확산이 CORMIX모형에 의해 검토되었으며, 원역 확산이 2차원 Random-walk 확산모형에 의해 실시되었다. 모의결과로부터 수심, 조류, 방류위치, 방류속도, 확산관 길이 등이 확산범위와 희석률에 미치는 영향이 다각도로 검토되었다.

• 한국대기환경학회:학술대회논문집
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• 한국대기환경학회 2008년도 추계학술대회 논문집
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• pp.100-101
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• 2008

• Journal of Radiation Protection and Research
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• 제48권1호
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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.

• 한국대기환경학회지
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• 제23권4호
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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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• 제55권6호
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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.

• 한국환경과학회지
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• 제11권9호
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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.

• 한국군사과학기술학회지
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• 제11권3호
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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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• 제14권2호
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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.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2003년도 추계학술발표회
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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.