Development and uncertainty analysis of radionuclide atmospheric dispersion modeling codes based on Gaussian plume model

Bo Cao, Weijie Cui, Chao Chen and Yixue Chen

Energy, 2020, vol. 194, issue C

Abstract: It is necessary to assess the radiological consequences of radioactive leakage accident in the planning and operation of a nuclear power plant, especially an atmospheric radioactive material spill that has a rapid and broad impact on public health. Uncertainty analysis of the assessment results will help to reduce the probability of making mistakes in the emergency response after accident. The Gaussian plume model is the most widely used computational model for atmospheric diffusion assessment. Based on this model, the FORTRAN computer language is used to compile Radionuclides Atmosphere Dispersion Codes (RADC). Calculation results based on RADC are compared with HotSpot Health Physics Codes to verify its calculation accuracy. Based on the Bayesian Markov Chain Monte Carlo method, uncertainty of the Gaussian plume model is analysed, and the influence of observation error on the confidence interval is calculated. The results show that the greater the air concentration of radioactivity, the wider the confidence interval; the observation error has a great impact on the confidence interval. Meanwhile, the small observation error will cause a large change in the width of the confidence interval.

Keywords: Atmospheric dispersion; Gaussian plume model; Uncertainty analysis; Bayesian MCMC (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:194:y:2020:i:c:s0360544220300323

DOI: 10.1016/j.energy.2020.116925

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