A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis

Francesco Di Maio (1) , Ajit Rai (2) , Enrico Zio (3, 4, 5)

1 POLIMI - Politecnico di Milano [Milan]
2 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
3 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec
4 LGI - Laboratoire Génie Industriel - EA 2606
5 CentraleSupélec

The challenge of Risk-Informed Safety Margin Characterization (RISMC) is to develop a methodology for estimating system safety margins in the presence of stochastic and epistemic uncertainties affecting the system dynamic behavior. This is useful to support decision-making for licensing purposes. In the present work, safety margin uncertainties are handled by Order Statistics (OS) (with both Bracketing and Coverage approaches) to jointly estimate percentiles of the distributions of the safety parameter and of the time required for it to reach these percentiles values during its dynamic evolution. The novelty of the proposed approach consists in the integration of dynamic aspects (i.e., timing of events) into the definition of a dynamic safety margin for a probabilistic Quantification of Margin and Uncertainties (QMU). The system here considered for demonstration purposes is the Lead–Bismuth Eutectic- eXperimental Accelerator Driven System (LBE-XADS).

Mots clés: Order Statistics; Grace time; Risk-Informed Safety Margins; Dynamic probabilistic safety margins

Domaines: Sciences de l'ingénieur [physics]

Francesco Di Maio, Ajit Rai, Enrico Zio. A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis. Reliability Engineering and System Safety, 2016, 145, pp.9-18. ⟨10.1016/j.ress.2015.08.016⟩. ⟨hal-01786979⟩