scholarly journals Atmospheric dispersion modeling for the worst-case detonation accident at the proposed Advanced Hydrotest Facility

1996 ◽  
Author(s):  
B.M., LLNL Bowen
Keyword(s):  
Atmospheric Dispersion ◽  
Dispersion Modeling ◽  
Worst Case

scholarly journals Radiation Dose Calculations for a Hypothetical Accident in Xianning Nuclear Power Plant

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Bo Cao ◽  
Junxiao Zheng ◽  
Yixue Chen
Keyword(s):  
Radiation Dose ◽  
Nuclear Power ◽  
Atmospheric Dispersion ◽  
Harmful Effect ◽  
Atmospheric Stability ◽  
Dispersion Modeling ◽  
Dose Calculations ◽  
Air Concentration ◽  
The Public ◽  
Ground Deposition

Atmospheric dispersion modeling and radiation dose calculations have been performed for a hypothetical AP1000 SGTR accident by HotSpot code 3.03. TEDE, the respiratory time-integrated air concentration, and the ground deposition are calculated for various atmospheric stability classes, Pasquill stability categories A–F with site-specific averaged meteorological conditions. The results indicate that the maximum plume centerline ground deposition value of1.2E+2 kBq/m2occurred at about 1.4 km and the maximum TEDE value of1.41E-05 Sv occurred at 1.4 km from the reactor. It is still far below the annual regulatory limits of 1 mSv for the public as set in IAEA Safety Report Series number 115. The released radionuclides might be transported to long distances but will not have any harmful effect on the public.

Estimation of Radiological Consequences at a Proposed Nuclear Power Plant Site Using Atmospheric Dispersion Code

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kwame Gyamfi ◽  
Sylvester Attakorah Birikorang ◽  
Emmanuel Ampomah-Amoako ◽  
John Justice Fletcher
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Atmospheric Dispersion ◽  
Atmospheric Stability ◽  
Assessment System ◽  
Severe Accident ◽  
Dispersion Modeling ◽  
Stability Class ◽  
Total Effective Dose Equivalent

Abstract Atmospheric dispersion modeling and radiation dose calculation have been performed for a generic 1000 MW water-water energy reactor (VVER-1000) assuming a hypothetical loss of coolant accident (LOCA). Atmospheric dispersion code, International Radiological Assessment System (InterRAS), was employed to estimate the radiological consequences of a severe accident at a proposed nuclear power plant (NPP) site. The total effective dose equivalent (TEDE) and the ground deposition were calculated for various atmospheric stability classes, A to F, with the site-specific averaged meteorological conditions. From the analysis, 3.7×10−1 Sv was estimated as the maximum TEDE corresponding to a downwind distance of 0.1 km within the dominating atmospheric stability class (class A) of the proposed site. The intervention distance for evacuation (50 mSv) and sheltering (10 mSv) were estimated for different stability classes at different distances. The intervention area for evacuation ended at 0.5 km and that for sheltering at 1.5 km. The results from the study show that designated area for public occupancy will not be affected since the estimated doses were below the annual regulatory limits of 1 mSv.

Pink Sky in the Morning, Should There Be a Warning?

2013 ◽  
Author(s):  
Stephen G. Zemba ◽  
Edmund A. C. Crouch ◽  
Michael E. Miller ◽  
Laura C. Green
Keyword(s):  
Radioactive Iodine ◽  
Large Mass ◽  
Waste To Energy ◽  
Dispersion Modeling ◽  
Short Term ◽  
Specific Knowledge ◽  
Worst Case ◽  
Air Dispersion Modeling ◽  
Energy Facility ◽  
Air Dispersion

Unexpected and unusual emissions from a large, mass-burn, waste-to-energy facility caused persistent and elevated opacity readings of the facility’s continuous opacity monitor (COM), and generated a visible pink-purple-tinted plume emanating from the exhaust stack. Non-radioactive iodine associated with medical wastes was determined to be responsible. As iodine is a known respiratory irritant, questions arose regarding potential short-term health risks to nearby residents. The rate of emission of the apparent release was estimated by two different methods, and then compared with facility-specific knowledge of waste composition. First, based on inverse, worst-case air dispersion modeling, the level of iodine emission that would be necessary to cause potential discomfort/mild irritation to people living near the facility was determined. Second, the level of iodine emission that would be necessary to account for elevations of in-stack opacity observed throughout the event was calculated. The level of iodine emissions necessary to cause mild health effects was found to be substantially greater than the actual release level as inferred from the opacity data. Moreover, based on descriptions of visual inspections of the waste stream and potential opacity interferences created by complex in-stack chemistry, it is likely that the opacity-based calculations overestimate the amount of iodine released. Accordingly, actual impacts are likely to have been smaller than those estimated herein. This paper discusses the process and procedures used to assess the health risk from this incident.

scholarly journals Inverse Dispersion for an Unknown Number of Sources: Model Selection and Uncertainty Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Eugene Yee
Keyword(s):  
Recursive Algorithm ◽  
Atmospheric Dispersion ◽  
Source Model ◽  
Bayesian Probability ◽  
Dispersion Modeling ◽  
Concentration Data ◽  
Measured Concentration ◽  
Inverse Dispersion ◽  
Unknown Number ◽  
Localized Sources

A simple recursive method is presented for performing the inverse dispersion modeling of an unknown number of (localized) sources, given a finite number of noisy concentration data acquired by an array of detectors. Bayesian probability theory is used to address the problem of selecting the source model which is most plausible in view of the given concentration dataset and all the available prior information. The recursive algorithm involves subtracting a predicted concentration signal arising from a source model consisting of N localized sources from the measured concentration data for increasing values of N and examining the resulting residual data to determine if the residuals are consistent with the estimated noise level in the concentration data. The method is illustrated by application to a real concentration dataset obtained from an atmospheric dispersion experiment involving the simultaneous release of a tracer from four sources.

scholarly journals The Mathematics of Atmospheric Dispersion Modeling

SIAM Review ◽  
2011 ◽  
Vol 53 (2) ◽  
pp. 349-372 ◽  
Author(s):  
John M. Stockie
Keyword(s):  
Atmospheric Dispersion ◽  
Dispersion Modeling

scholarly journals Atmospheric Dispersion Modeling

2012 ◽  
Vol 102 (5) ◽  
pp. 493-508 ◽  
Author(s):  
Gayle Sugiyama ◽  
John Nasstrom ◽  
Brenda Pobanz ◽  
Kevin Foster ◽  
Matthew Simpson ◽  
...  
Keyword(s):  
Atmospheric Dispersion ◽  
Dispersion Modeling
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