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Development of radiation risk assessment simulator using system dynamics methodologyopen access

Authors
Kang, Kyung MinJae, Moosung
Issue Date
Jun-2008
Publisher
TAYLOR & FRANCIS LTD
Keywords
ingestion chain model; dynamic; radioecology; system dynamics
Citation
JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY, pp.728 - 731
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF NUCLEAR SCIENCE AND TECHNOLOGY
Start Page
728
End Page
731
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/178537
DOI
10.1080/00223131.2008.10875958
ISSN
0022-3131
Abstract
The potential magnitudes of radionuclide releases under severe accident loadings and offsite consequences as well as the overall risk (the product of accident frequencies and consequences) are analyzed and evaluated quantitatively in this study. The system dynamics methodology has been applied to predict the time-dependent behaviors such as feedback and dependency as well as to model uncertain behavior of complex physical system. It is used to construct the transfer mechanisms of time dependent radioactivity concentration and to evaluate them. Dynamic variations of radio activities are simulated by considering several effects such as deposition, weathering, washout, re-suspension, root uptake, translocation, leaching, senescence, intake, and excretion of soil. The time-dependent radio-ecological model applicable to Korean specific environment has been developed in order to assess the radiological consequences following the short-term deposition of radio-nuclides during severe accidents nuclear power plant. An ingestion food chain model can estimate time dependent radioactivity concentrations in foodstuffs. And it is also shown that the system dynamics approach is useful for analyzing the phenomenon of the complex system as well as the behavior of structure values with respect to time. The output of this model (Bq ingested per Bq m(-2) deposited) may be multiplied by the deposition and a dose conversion factor (Gy Bq(-1)) to yield organ-specific doses. The model may be run deterministically to yield a single estimate or stochastic distributions by "Monte-Carlo" calculation that reflects uncertainty of parameter and model uncertainties. The results of this study may contribute to identifying the relative importance of various parameters occurred in consequence analysis, as well as to assessing risk reduction effects in accident management.
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