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Nuclear data uncertainty for criticality-safety: Monte Carlo vs. linear perturbation
Paul Scherrer Inst, Reactor Phys & Syst Behav Lab, Villigen, Switzerland..
Paul Scherrer Inst, Reactor Phys & Syst Behav Lab, Villigen, Switzerland..
Paul Scherrer Inst, Reactor Phys & Syst Behav Lab, Villigen, Switzerland..
Univ Florida, Gainesville, FL USA..
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2016 (English)In: Annals of Nuclear Energy, ISSN 0306-4549, E-ISSN 1873-2100, Vol. 92, 150-160 p.Article in journal (Refereed) PublishedText
Abstract [en]

This work is presenting a comparison of results for different methods of uncertainty propagation due to nuclear data for 330 criticality-safety benchmarks. Covariance information is propagated to key using either Monte Carlo methods (NUSS: based on existing nuclear data covariances, and TMC: based on reaction model parameters) or sensitivity calculations from MCNP6 coupled with nuclear data covariances. We are showing that all three methods are globally equivalent for criticality calculations considering the two first moments of a distribution (average and standard deviation), but the Monte Carlo methods lead to actual probability distributions, where the third moment (skewness) should not be ignored for safety assessments.

Place, publisher, year, edition, pages
2016. Vol. 92, 150-160 p.
Keyword [en]
Criticality benchmark, Nuclear data, Uncertainty, Sensitivity
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-294639DOI: 10.1016/j.anucene.2016.01.042ISI: 000373655600016OAI: oai:DiVA.org:uu-294639DiVA: diva2:932529
Available from: 2016-06-01 Created: 2016-05-26 Last updated: 2016-06-01Bibliographically approved

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Koning, Arjan J.
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Applied Nuclear Physics
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