Reflections on uncertainty quantification in deterministic safety analysis

Abstract of the journal article presented in:
CSARP/MCAP Meeting

June 5-7, 2023

Prepared by:
Mounia Berdaï
Canadian Nuclear Safety Commission

Abstract:

Uncertainty Quantification (UQ) in Severe Accidents (SA) aims to increase our confidence in severe accident codes’ predictions and enhance our responses to nuclear or radiological emergencies. For this purpose, Python Scripts for Uncertainty Quantification of MAAP-CANDU and MELCOR (PSUQM2) toolkit was developed to allow the coupling with MAAP-CANDU and MELCOR (severe accident codes) and performs uncertainty quantification for selected severe accident Figure Of Merit (FOM).

PSUQM2 can be used for a wide range of applications, such as assessing uncertainties around some unknown phenomena in Small Modular Reactors (SMR)s of all technologies and evaluate their safety margins.

The analysis of outcomes from UQ revealed that correlation fails to adequately describe nonlinear or nonmonotonic relationships, in fact different relationships between variables can result in similar correlation coefficients. This suggests that the relationship between data should be assessed by visual inspection through plots for example to determine the appropriate relationship, rather than relying only on correlation coefficients.

This analysis also suggests that the use of unbounded probability distribution (e.g., Normal, Lognormal, etc) should be used with care and consider the nature of the uncertain parameter.
UQ improvement will help in applying the risk-informed approach and improve decision making process.

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