| Issue |
EPJ Web Conf.
Volume 211, 2019
WONDER-2018 – 5th International Workshop On Nuclear Data Evaluation for Reactor applications
|
|
|---|---|---|
| Article Number | 07001 | |
| Number of page(s) | 8 | |
| Section | Uncertainties And Covariance Matrices (Methodology and Impact on Reactor Calculations) | |
| DOI | https://doi.org/10.1051/epjconf/201921107001 | |
| Published online | 05 June 2019 | |
https://doi.org/10.1051/epjconf/201921107001
Influence of nuclear data parameters on integral experiment assimilation using Cook’s distance
1 CEA, DEN, Cadarache, F-13108 Saint Paul les Durance, France
2 Department of Physics and Astronomy, Uppsala University, Sweden
* Corresponding author: e-mail: cyrille.de-saint-jean@cea.fr
Published online: 5 June 2019
Nuclear data used in designing of various nuclear applications (e.g., core design of reactors) is improved by using integral experiments. To utilize the past critical experimental data to the reactor design work, a typical procedure for the nuclear data adjustment is based on the Bayesian theory (least-square technique or Monte-Carlo). In this method, the nuclear data parameters are optimized by the inclusion of the experimental information using a Bayesian inference. The selection of integral experiments is based on the availability of well-documented specifications and experimental data. Data points with large uncertainties or large residuals (outliers) may affect the accuracy of the adjustment. Hence, in the adjustment process, it is very important to study the influence of experiments as well as of the prior nuclear data on the adjusted results. In this work, the influence of each individual reaction (related to nuclear data) is analyzed using the concept of Cook’s distance. First, JEZEBEL (Pu239, Pu240 and Pu241) integral experiment is considered for data assimilation and then the transposition of results on ASTRID fast reactor concept is discussed.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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