Program - Brookhaven National Laboratory

In the resolved resonance region nuclear resonance parameters are mostly deduced from experimental measurements
using a least squares adjustment. Derived parameters can be mutually correlated through the
adjustment procedure as well as through common experimental or model uncertainties. In this contribution
we investigate four different methods to propagate the additional covariance caused by experimental or
model uncertainties into the evaluation of the covariance matrix of the estimated parameters: (1) including
the additional covariance into the experimental covariance matrix based on calculated or theoretical
estimates of the data; (2) include the common uncertainty affected parameter as additional experimental
data input and model parameter; (3) evaluation of the full covariance matrix by Monte Carlo sampling
of the common parameter; and (4) retroactively including the additional covariance by marginalization
proposed by Habert et al. [1]. These different methods are investigated based on simulated data and
based on experimental data resulting from transmission and capture measurements on 197 Au covering the
resolved and unresolved resonance region. Using the simulated data the impact of different components
are investigated: counting statistics, the self-shielding term in the yield expression, and the inclusion of
a thermal cross section on the deduced covariance matrix is demonstrated. The impact of the different
procedures on the covariance of the final cross section calculated is shown.
Corresponding author: Peter Schillebeeckx
[1] Habert et al., Nucl. Sci. Eng. 166, 276 – 287 (2010)
PR 134
Comparison of Two Approaches for Nuclear Data Uncertainty Propagation In MCNP(X)
for Selected Fast Spectrum Critical Benchmarks
Ting Zhu, Alexander Vasiliev, Hakim Ferroukhi, Paul Scherrer Institut. Dimitri Rochman, Nuclear
Research and Consultancy Group. William Wieselquist, Oak Ridge National Laboratory.
Nuclear data uncertainty propagation based on stochastic sampling (SS) is becoming more attractive while
leveraging the huge increase in modern computer power. Two variants of the SS approach are compared
in this study. The Total Monte Carlo (TMC) method [1] developed at the Nuclear Research Consultancy
Group (NRG) prescribes probability density functions to theoretic parameters of nuclear reaction models
and uses them to create random ENDF nuclear data files, which are translated subsequently into the
ACE-formatted nuclear data files by NJOY. At the Paul Scherrer Institut (PSI), the Nuclear data Uncertainty
Stochastic Sampling (NUSS) system is under development and based on the concept of generating
the randomly perturbed ACE-formatted nuclear data files directly from applying groupwise nuclear data
covariances onto the original pointwise ACE-formatted nuclear data [2]. Both the TMC and PSI-NUSS
methods are applied with the Monte-Carlo code MCNP(X) for the well-studied Jezebel and Godiva fast
spectrum critical benchmarks in this paper. At first, using TENDL-2012 libraries and associated covariance
matrices, uncertainty assessments for keff and spectral parameters are carried out and compared.
The propagated uncertainties due to various reactions of 239 Pu, 235 U and 238 U are crosschecked between
the two methods. Furthermore, with the PSI-NUSS method, an examination of applying different groupwise
perturbations onto continuous nuclear data and the effect of using ENDF/B-VII.1 with its covariance
library versus the SCALE6 44-group covariance library are assessed. With the latter, the results obtained
by PSI-NUSS are compared to those calculated by the Sensitivity and Uncertainty (S/U) methodology of
the SCALE6 package. Generated under different principles, the uncertainty results from the deterministic
S/U method serve as a separate verification for the two SS-based methods. The observed differences in the
propagated uncertainties of nuclear data among the considered methods and covariance files are discussed.
[1] D. Rochman, A.J. Koning, S.C. van der Marck, A. Hogenbirk and C.M. Sciolla, ”Nuclear data uncertainty
propagation: Perturbation vs. Monte Carlo” Ann. Nuc. En. 38, 942 (2011). [2] T. Zhu, A.
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