Calculations of external irradiation from radioactive plume in the early stage of a nuclear accident

Pecha, Petr; Hofman, Radek

doi:10.1504/ijep.2012.051212

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, "Segmented Gaussian Plume Model (SGPM)" [7] and a Lagrangian puff model are integrated. While the puff model is still under development, SGPM has been already certified for emergency management use in the Czech Republic.…”

Section: Atmospheric Dispersion Modelingmentioning

confidence: 99%

Development of real-time Bayesian data assimilation system for off-site consequence assessment

Hofman¹,

Šmı́dl²,

Pecha³

2014

Progress in Nuclear Science and Technology

Self Cite

View full text Add to dashboard Cite

The task of decision support in the case of a radiation accident is to provide up-to-date information on the radiation situation, prognosis of its future evolution and possible consequences. The reliability of predictions can be significantly improved using data assimilation, which refers to a group of mathematical methods for an efficient combination of observed data with a numerical model. This paper concerns application of advanced data assimilation methods in the off-site consequence assessment when radionuclides are released into the atmosphere. Our goal is to develop a decision support system with automated real-time assessment of radiation situation that does not underestimate its uncertainty. To achieve this goal we employ particle filtering − a Bayesian approach suitable for sequential processing of a stream of measurements incoming from a monitoring network. The output of this assimilation procedure is the whole posterior probability density of a quantity of interest. This allows for reasoning in a probabilistic manner. The system is equipped with tools for this type of assessment and the focus is paid to ease of use during its development. Performance of the system is demonstrated on a simulation scenario.

show abstract

Section: Atmospheric Dispersion Modelingmentioning

confidence: 99%