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research article

On data assimilation with Monte-Carlo-calculated and statistically uncertain sensitivity coefficients

Siefman, D.
•
Hursin, M.
•
Aufiero, M.
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January 1, 2020
Annals Of Nuclear Energy

Sensitivity coefficients from Monte Carlo neutron transport codes have uncertainties that can affect nuclear data adjustments with integral experiments. This paper presents an extended version of Generalized Linear Least Squares (GLLS), called xGLLS, that accounts for these uncertainties. With very large sensitivity uncertainties, xGLLS constrains the nuclear data adjustments so that the posterior biases and uncertainties are larger than with GLLS. However, for the range of sensitivity uncertainties realistically encountered, xGLLS does not produce adjustments different from GLLS. This indicates that sensitivity uncertainties are not important compared to experimental, modeling, methodological, and nuclear data uncertainties. To balance a simulation's accuracy with its computational cost, we recommend stopping a simulation once the uncertainty of a calculated integral parameter, caused by modeling and methodologies and by the sensitivities, is an order of magnitude smaller than that caused by nuclear data. (C) 2019 Elsevier Ltd. All rights reserved.

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Type
research article
DOI
10.1016/j.anucene.2019.106951
Web of Science ID

WOS:000496898500010

Author(s)
Siefman, D.
Hursin, M.
Aufiero, M.
Bidaud, A.
Pautz, A.  
Date Issued

2020-01-01

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

Published in
Annals Of Nuclear Energy
Volume

135

Article Number

106951

Subjects

Nuclear Science & Technology

•

data assimilation

•

nuclear data

•

generalized linear least squares

•

integral experiments

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sensitivity analysis

•

monte carlo neutron transport

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LRS  
Available on Infoscience
March 3, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/166828
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