Issue |
EPJ Nuclear Sci. Technol.
Volume 4, 2018
Special Issue on 4th International Workshop on Nuclear Data Covariances, October 2–6, 2017, Aix en Provence, France – CW2017
|
|
---|---|---|
Article Number | 35 | |
Number of page(s) | 6 | |
Section | Covariance Evaluation Methodology | |
DOI | https://doi.org/10.1051/epjn/2018011 | |
Published online | 14 November 2018 |
https://doi.org/10.1051/epjn/2018011
Regular Article
Cross-observables and cross-isotopes correlations in nuclear data from integral constraints
1
CEA, DAM, DIF,
91297
Arpajon, France
2
Laboratory for Reactor Physics Systems Behaviour, Paul Scherrer Institut,
Villigen, Switzerland
* e-mail: eric.bauge@cea.fr
Received:
31
October
2017
Received in final form:
16
January
2018
Accepted:
4
May
2018
Published online: 14 November 2018
Most recent evaluated nuclear data files exhibit excellent integral performance, as shown by the very good agreement between experimental and calculated keff values over a wide range of benchmark integral experiments. However, the propagation of the uncertainties associated with those nuclear data to integral observables, generally produces calculated distribution which are much (3–5 times) wider than the experimental uncertainties. Reducing the variances of the evaluated data to achieve consistency at the integral level would lead to unreasonably narrow variances in the light of differential experimental data. One way of solving that paradox could be to allow, for different observables like fission cross-sections (σf), the prompt fission neutron spectra (χ), and the average multiplicity of fission neutrons () to be correlated in a Bayesian-like, Total Monte-Carlo approach, under constraints from integral experiments from the ICSBEP (International International Criticality Safety Benchmark Evaluation Project) benchmark compilation. Future developments will be highlighted and restrictions imposed by the current formatting of nuclear data will be discussed.
© E. Bauge and D. A. Rochman, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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