Statistical characterization of NPP transients: Application to PWR LBLOCA

•A PWR TRACE model including a Best Estimate core is used.•LBLOCA Statistical characterization using Uncertainty and Sensitivity Analysis.•15 FoMs assessed against 29 inputs in 1020 Simulations.•Nearly all inputs reveal a correlation with a FoM with 95% confidence. Uncertainty and sensitivity analys...

Full description

Saved in:
Bibliographic Details
Published in:Annals of nuclear energy 2020-09, Vol.144, p.107505, Article 107505
Main Authors: Fernández-Cosials, Kevin, Queral, Cesar, Zugazagoitia, Eneko, Magán, Javier, Sanchez-Torrijos, Jorge, Sánchez-Espinoza, Victor Hugo, Posada, José María
Format: Article
Language:English
Subjects:
LBLOCA
PWR-W
Sensitivity analysis
Sequence characterization
TRACE
Uncertainty analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A PWR TRACE model including a Best Estimate core is used.•LBLOCA Statistical characterization using Uncertainty and Sensitivity Analysis.•15 FoMs assessed against 29 inputs in 1020 Simulations.•Nearly all inputs reveal a correlation with a FoM with 95% confidence. Uncertainty and sensitivity analyses are a necessary step for best estimate calculations for licensing. In this sense, a complete statistical characterization of a sequence would be a useful tool for these assessments as it can detect behaviors and correlation of the Figures of Merit (FoM) and the input space. For this reason, the present paper presents a statistical characterization of a LBLOCA in a PWR by means of an uncertainty analysis and a sensitivity analysis using the TRACE code. Due to this characterization it is found that parameters such as the number of bursted or ballooned rods have a remarkable dispersion and that variance of most of FoMs is explained by a monotonic behavior; which is also confirmed through scatter plots and cob-webs. Finally, it is found that nearly all input parameters are correlated to at least one FoM, highlighting the importance of correctly selecting the input space.
ISSN:0306-4549
1873-2100
DOI:10.1016/j.anucene.2020.107505