• Nuclear Engineering and Technology
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• 제46권4호
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• pp.557-568
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• 2014

This paper presents the results of an analysis of wind characteristics and atmosphere dispersion modeling that are based on computational simulation and part of a preliminary study evaluating environmental radiation monitoring system (ERMS) positions within the Barakah nuclear power plant (BNPP). The return period of extreme wind speed was estimated using the Weibull distribution over the life time of the BNPP. In the annual meteorological modeling, the winds from the north and west accounted for more than 90 % of the wind directions. Seasonal effects were not represented. However, a discrepancy in the tendency between daytime and nighttime was observed. Six variations of cesium-137 ($^{137}Cs$) dispersion test were simulated under severe accident condition. The $^{137}Cs$ dispersion was strongly influenced by the direction and speed of the main wind. A virtual receptor was set and calculated for observation of the $^{137}Cs$ movement and accumulation. The results of the surface roughness effect demonstrated that the deposition of $^{137}Cs$ was affected by surface condition. The results of these studies offer useful information for developing environmental radiation monitoring systems (ERMSs) for the BNPP and can be used to assess the environmental effects of new nuclear power plant.

• 천문학회보
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• 제27권2호
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• pp.43-43
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• 2002

• 천문학회보
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• 제27권2호
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• pp.45-45
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• 2002

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.899-904
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• 2003

A new model for dense gas dispersion is formulated within the Lagrangian framework. In several accidental released situations, denser-than-air vapour clouds are formed which exhibit dispersion behavior markedly different from that observed for passive atmospheric pollutants. For relevant prediction of dense gas dispersion, the gravity and entrainment effects need to implemented. The model deals with negative buoyancy which is affected by gravity. Also, the model is subjected to entrainment. The mean downward motion of each particle was accounted for by considering the Langevin equation with buoyancy correction term.

• Journal of Radiation Protection and Research
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• 제37권3호
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• pp.116-122
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• 2012

미국 원자력규제위원회(U. S. NRC)에서 개발한 PAVAN 프로그램을 사용하여 기상자료의 통계처리방법에 따른 원자력발전소의 수명기간 중 발생할 수 있는 가상 사고시 단기 대기확산인자 값의 영향을 분석하였다. 대기확산을 평가하기 위한 기본요소는 기상자료이며, PAVAN에서는 대기안정도에 대한 풍향과 풍속의 발생빈도를 나타내는 결합빈도분포자료를 사용하여 평가한다. 고리와 월성 원자력발전소 부지에서 2006~2010년까지 5년간 관측된 기상자료를 사용하여 풍속등급의 분류방법(U. S. NRC 권고와 균등발생빈도 분류방법)과 평가에 적용되는 기상자료의 통계처리기간(1년, 2년, 3년, 4년, 5년)에 따른 결합빈도분포자료를 작성하여 단기 대기확산인자를 평가하였다. 평가결과, 두 원자력발전소 부지 모두 풍속등급의 분류에 따른 단기 대기확산인자 값의 영향은 미미한 반면 평가에 적용되는 기상자료의 분석기간은 상대적으로 중요한 영향을 나타냈는데, 최대값과 최소값의 비는 1.5배 이상의 차이를 보였다.

• 대기
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• 제29권5호
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• pp.551-566
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• 2019

Quantitative understanding of a random error that is associated with Lagrangian particle dispersion modeling is a prerequisite for backward-in-time mode simulations. This study aims to quantify the random error of the WRF-FLEXPART model and suggest an optimum number of the Lagrangian particles for backward-in-time simulations over the Seoul metropolitan area. A series of backward-in-time simulations of the WRF-FLEXPART model has conducted at two receptor points by changing the number of Lagrangian particles and the relative error, as a quantitative indicator of random error, is analyzed to determine the optimum number of the release particles. The results show that in the Seoul metropolitan area a 1-day Lagrangian transport contributes 80~90% in residence time and ~100% in atmospheric enhancement of carbon monoxide. The relative errors in both the residence time and the atmospheric concentration enhancement are larger when the particles release in the daytime than in the nighttime, and in the inland area than in the coastal area. The sensitivity simulations reveal that the relative errors decrease with increasing the number of Lagrangian particles. The use of small number of Lagrangian particles caused significant random errors, which is attributed to the random number sampling process. For the particle number of 6000, the relative error in the atmospheric concentration enhancement is estimated as -6% ± 10% with reduction of computational time to 21% ± 7% on average. This study emphasizes the importance of quantitative analyses of the random errors in interpreting backward-in-time simulations of the WRF-FLEXPART model and in determining the number of Lagrangian particles as well.

• 천문학회지
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• 제29권spc1호
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• pp.397-398
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• 1996

Large field spectrographs are severely influenced by atmospheric refraction. LAMOST is a large field multi-object spectroscopy telescope with $5^{\circ}$ field of view, f/5 focus ratio and 20m focal length. There will be 4000 fibers simultaneous on it's $\phi$1.75m focal plane. Here we discuss the atmospheric refraction effects on LAMOST in two hands. One is the effect of differential refraction across the field, another is the effect of atmospheric dispersion. According to the calculation, we find that: 1. The largest deviation from center within the field is 4.;32" during a 1.5-hour integration at $80^{\circ}$ declination. 2. The directions of deviation are complex, so the deviations can't be decreased by rotating the field. We also give out the atmospheric dispersions.

• 한국대기환경학회지
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• 제19권6호
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• pp.789-803
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• 2003

The purpose of this study is to compare the usefulness between Gaussian dispersion model and receptor model with the experimental result of the dispersion tracing of the particulate pollutants from Taean coal-fired power plants. For this purpose, the component analysis of the collected PM 10 samples was performed. In order to trace the pollution sources, factor analysis was done with the result of the component analysis. As a result of the correlativity analysis of the fifteen power plants' profiles offered by US EPA, the correlativity of No.11202 source profile showed highest rate up to 84.5%. Thus it was adopted as proper one and the contribution rate by each pollution source was calculated by Chemical Mass Balance (CMB)-8 model. The contribution rate, which was the effect rate of the power plants on each measuring point, were calculated with a range of 24∼52％ and the standard error was below 0.9 $\mu\textrm{g}$/㎥. This indicates the selection of the source profile was appropriate. Also, the concentrations of each point were calculated by the ISCST3 which is suggested by US EPA as one of the regulatory Gaussian dispersion model. The calculation result showed that the predicted concentration was 50∼58 $\mu\textrm{g}$/㎥, comparing with the measured result of 9∼65 $\mu\textrm{g}$/㎥. It was found that the concentration calculated by ISCST3 was underpredicted. It was thought that the receptor model was more favorable than the Gaussian dispersion model in estimating the effect of the particulate matter on a certain receptive point.

• 한국환경과학회지
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• 제12권3호
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• pp.265-274
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• 2003

The Pasquill-Gifford stability category is a very important scheme of the Gaussian type dispersion model defined the complex turbulence state of the atmosphere by A grade(very unstable) to F grade(very stable). But there has been made a point out that this stability category might decrease the predictability of the model because it was each covers a broad range of stability conditions, and that they were very site specific. The APSM (Air Pollution Simulation Model) was composed of the turbulent parameters, i.e. friction velocity(${\mu}$$\_$*/), convective velocity scale($\omega$$\_$*/) and Monin-Obukhov length scale(Ｌ) for the purpose of the performance increasing on the case of the unstable atmospheric conditions. And the PDF (Probability Density Function)model was used to express the vertical dispersion characteristics and the profile method was used to calculate the turbulent characteristics. And the performance assessment was validated between APSM and EPA regulatory models(TEM, ISCST), tracer experiment results. There were very good performance results simulated by APSM than that of TEM, ISCST in the short distance (<1415 m) from the source, but increase the simulation error(%) to stand off the source in others. And there were differences in comparison with the lateral dispersion coefficient($\sigma$$\_$y/) which was represent the horizontal dispersion characteristics of a air pollutant in the atmosphere. So the different calculation method of $\sigma$$\_$y/ which was extrapolated from a different tracer experiment data might decrease the simulation performance capability. In conclusion, the air pollution simulation model showed a good capability of predict the air pollution which was composed of the turbulent parameters compared with the results of TEM and ISCST for the unstable atmospheric conditions.

• Asian Journal of Atmospheric Environment
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• 제8권2호
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• pp.81-88
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• 2014

Noise-barriers on both sides of the roadway (hereafter referred to as double noise-barriers), are a common feature along roads in Korea, and these are expected to have important effects on the near-road air pollution dispersion of vehicle emissions. This study evaluated the double noise-barrier impact on near-road air pollution dispersion, using a FLUENT computational fluid dynamics (CFD) model. The realizable k-${\varepsilon}$ model in FLUENT CFD code was used to simulate vehicle air pollutant dispersion, in around 11 cases of double noise-barriers. The simulated concentration profiles and surface concentrations under no barrier cases were compared with the experimental results. The results of the simulated flows show the following three regimes in this study: isolated roughness (H/W=0.05), wake interface (H/W=0.1), and skimming flow (H/W>0.15). The results also show that the normalized average concentrations at surface (z=1 m) between the barriers increase with increasing double noise-barrier height; however, normalized average concentrations at the top position between the barriers decrease with increasing barrier height. It was found that the double noise-barrier decreases normalized average concentrations of leeward positions, ranging from 0.8 (H/W=0.1, wake interface) to 0.1 (H/W=0.5, skimming flow) times lower than that of the no barrier case, at 10 x/h downwind position; and ranging from 1.0 (H/W=0.1) to 0.4 (H/W=0.5) times lower than that of the no barrier case, at 60 x/h downwind position.