A Model for Fission Yield Uncertainty Propagation based on the URANIE platform - CEA - Commissariat à l’énergie atomique et aux énergies alternatives Access content directly
Conference Papers Year : 2015

A Model for Fission Yield Uncertainty Propagation based on the URANIE platform

Abstract

In the present work, we analyze how fission yields uncertainties can be propagated in a burn-up calculation. The first part of the work is dedicated to the fission yield covariances generation in CONRAD (COde for Nuclear Reaction Analysis and Data Assimilation) to be used in the neutronic code APOLLO2. Fission yield covariance files are in fact unavailable in present nuclear databases such as JEFF-3.2 and ENDF/B-VII. To propagate such uncertainties, we adopted a statistical method which has a solid theoretical base and a relatively simple implementation. Fission yields have been therefore treated as random variables to be sampled from a normal input parameter multivariate distribution, taking into account correlations. Successively, a statistical representative number of calculations are carried out with the different sampled input data. An output multivariate probability distribution for core characteristics is then inferred. Random variable sampling and statistical post-processing has been performed using URANIE, a sensitivity and uncertainty analysis platform based on ROOT. This methodology is applied on a simplified geometry, leaving further developments for more complicated layout to future works.
Fichier principal
Vignette du fichier
201500000683.pdf (591.63 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

cea-02489495 , version 1 (24-02-2020)

Identifiers

  • HAL Id : cea-02489495 , version 1

Cite

N. Terranova, M. Sumini, P. Archier, Olivier Serot, D. Bernard, et al.. A Model for Fission Yield Uncertainty Propagation based on the URANIE platform. ANS MC2015 - Joint International Conference on Mathematics and Computation: Supercomputing in Nuclear Applications (SNA) and the Monte Carlo (MC) Method, Apr 2015, Nashville, United States. ⟨cea-02489495⟩

Collections

CEA DEN
22 View
137 Download

Share

Gmail Facebook Twitter LinkedIn More