EPJ Web Conf.
Volume 146, 2017ND 2016: International Conference on Nuclear Data for Science and Technology
|Number of page(s)||6|
|Section||Integral Experiments, Benchmarks and Data Validation|
|Published online||13 September 2017|
Methodology and issues of integral experiments selection for nuclear data validation
1 OECD-NEA, Nuclear Science Division, Boulogne-Billancourt, France
2 Institut de Radioprotection et de Sûreté Nucléaire, PSN-RES/SAG, 92262 Fontenay-aux-Roses, France
a e-mail: email@example.com
Published online: 13 September 2017
Nuclear data validation involves a large suite of Integral Experiments (IEs) for criticality, reactor physics and dosimetry applications.  Often benchmarks are taken from international Handbooks. [2, 3] Depending on the application, IEs have different degrees of usefulness in validation, and usually the use of a single benchmark is not advised; indeed, it may lead to erroneous interpretation and results.  This work aims at quantifying the importance of benchmarks used in application dependent cross section validation. The approach is based on well-known General Linear Least Squared Method (GLLSM) extended to establish biases and uncertainties for given cross sections (within a given energy interval). The statistical treatment results in a vector of weighting factors for the integral benchmarks. These factors characterize the value added by a benchmark for nuclear data validation for the given application. The methodology is illustrated by one example, selecting benchmarks for 239Pu cross section validation. The studies were performed in the framework of Subgroup 39 (Methods and approaches to provide feedback from nuclear and covariance data adjustment for improvement of nuclear data files) established at the Working Party on International Nuclear Data Evaluation Cooperation (WPEC) of the Nuclear Science Committee under the Nuclear Energy Agency (NEA/OECD).
© The Authors, published by EDP Sciences, 2017
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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