論文

国際誌
2021年

Simulation code for estimating external gamma-ray doses from a radioactive plume and contaminated ground using a local-scale atmospheric dispersion model.

PloS one
  • Daiki Satoh
  • ,
  • Hiromasa Nakayama
  • ,
  • Takuya Furuta
  • ,
  • Tamotsu Yoshihiro
  • ,
  • Kensaku Sakamoto

16
1
開始ページ
e0245932
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1371/journal.pone.0245932

In this study, we developed a simulation code powered by lattice dose-response functions (hereinafter SIBYL), which helps in the quick and accurate estimation of external gamma-ray doses emitted from a radioactive plume and contaminated ground. SIBYL couples with atmospheric dispersion models and calculates gamma-ray dose distributions inside a target area based on a map of activity concentrations using pre-evaluated dose-response functions. Moreover, SIBYL considers radiation shielding due to obstructions such as buildings. To examine the reliability of SIBYL, we investigated five typical cases for steady-state and unsteady-state plume dispersions by coupling the Gaussian plume model and the local-scale high-resolution atmospheric dispersion model using large eddy simulation. The results of this coupled model were compared with those of full Monte Carlo simulations using the particle and heavy-ion transport code system (PHITS). The dose-distribution maps calculated using SIBYL differed by up to 10% from those calculated using PHITS in most target locations. The exceptions were locations far from the radioactive contamination and those behind the intricate structures of building arrays. In addition, SIBYL's computation time using 96 parallel processing elements was several tens of minutes even for the most computationally expensive tasks of this study. The computation using SIBYL was approximately 100 times faster than the same calculation using PHITS under the same computation conditions. From the results of the case studies, we concluded that SIBYL can estimate a ground-level dose-distribution map within one hour with accuracy that is comparable to that of the full Monte Carlo simulation.

リンク情報
DOI
https://doi.org/10.1371/journal.pone.0245932
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/33493217
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833150
ID情報
  • DOI : 10.1371/journal.pone.0245932
  • PubMed ID : 33493217
  • PubMed Central 記事ID : PMC7833150

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