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Study on source inversion technology for nuclear accidents based on gaussian puff model and ENKF
X.L. Zhang
author
Jian Guo Chen
author
Guofeng Su
author
Hongyong Yuan
author
2013
Karlsruher Institut fur Technologie
KIT; Baden-Baden
English
For nuclear power plant (NPP) accident, the assessment of the radiation consequences plays an important role in the emergency response system. However, the source characteristics which greatly influence thhe accuracy of the assessment result is poorly known or even unknown at the early phase of accident, wich can cause poorly understanding of the situation and delay the response activities. In this paper, source inversion technology in analyzing nuclear accidents based on Gaussian puff model and ensemble Kalman filter (EnKF) is proposed. The method is validated with simulated measurements and the results show that it can give reasonable estimations of the change in release rate and height simultaneously, though the first guess of release rate is 102 larger than the true value. The investigation of the influence of sharp change in source term shows that the method is robust to capture the sharp change, but there is a delay of response when the release height increases simultaneously.
Gaussian distribution
Information systems
Kalman filters
Nuclear power plants
Emergency response systems
Ensemble Kalman Filter
Gaussian puff model
Nuclear accidents
Source characteristics
Source inversion
Source term estimation
Source terms
Nuclear reactor accidents
exported from refbase (http://idl.iscram.org/show.php?record=1148), last updated on Sun, 09 Aug 2015 05:35:40 +0200
text
http://idl.iscram.org/files/zhang/2013/1148_Zhang_etal2013.pdf
X.L.Zhang_etal2013
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management
ISCRAM 2013
T. Comes
F
Fiedrich
editor
10th International ISCRAM Conference on Information Systems for Crisis Response and Management
2013
Karlsruher Institut fur Technologie
KIT; Baden-Baden
conference publication
634
639
9783923704804
2411-3387
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