Methodology for an efficient statistical cross sections sampling in the unresolved resonance range

Jeannesson, Clément; Leal, L.C.; Coste–Delclaux, Mireille; Jouanne, C.

doi:10.1016/j.anucene.2021.108794

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2024

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“…GAIA already embeds a first module that performs both cross section reconstruction from resonance parameters and Doppler broadening DOP [1]. A second module produces probability tables for the unresolved resonance range TOP [2]. In our ongoing pursuit of enhanced uncertainty analysis, the COP module (COvariance Processing) within GAIA has been developed to process covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

Assesment of covariance processing with GAIA for nuclear data uncertainty propagation

Sole,

Jaiswal,

Jouanne

et al. 2024

EPJ Web of Conf.

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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.

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Section: Introductionmentioning

confidence: 99%