| Issue |
EPJ Web of Conf.
Volume 294, 2024
WONDER-2023 - 6th International Workshop On Nuclear Data Evaluation for Reactor applications
|
|
|---|---|---|
| Article Number | 05001 | |
| Number of page(s) | 7 | |
| Section | Uncertainties and Covariance Matrices | |
| DOI | https://doi.org/10.1051/epjconf/202429405001 | |
| Published online | 17 April 2024 | |
https://doi.org/10.1051/epjconf/202429405001
Assesment of covariance processing with GAIA for nuclear data uncertainty propagation
1 PSN-RES/SNC/LN, Institut de Radioprotection et de Sûreté Nucléaire
2 Université Paris-Saclay, CEA, Service d’Etudes des Réacteurs et de Mathématiques Appliquées, 91191, Gif-sur-Yvette, France
* Corresponding author: pierre.sole@irsn.fr
Published online: 17 April 2024
Nuclear data uncertainties are provided as covariance matrices in standard nuclear data libraries and propagating them trough neutronics simulations helps quantify the associated uncertainties on the final result. However, processing these matrices often poses challenges. Currently, the IRSN nuclear data processing code GAIA processes cross sections via several modules like DOP (Reconstruction and Doppler), TOP (URR), and SAB (TSL), but lacks the capability to process covariances. This paper introduces a new module named COP (COvariance Processing). The COP module aims to process covariance matrices comprehensively, including cross section (File 33), angular distribution (File 34), and resonance parameters (File 32). The preliminary results obtained using the COP module of GAIA in comparison with the ERRORR module of NJOY and PUFF module of AMPX are presented.
© The Authors, published by EDP Sciences, 2024
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