A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis. (January 2016)

• Record Type: Journal Article
• Authors: Di Maio, Francesco; Rai, Ajit; Zio, Enrico
• Is Part Of: Reliability engineering & system safety. Volume 145(2016:Jan.)
• Page Start: 9
• Page End: 18
• Publication Date: 2016-01
• Subjects: Risk-Informed Safety Margins -- Dynamic probabilistic safety margins -- Order Statistics -- Grace time; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2015.08.016
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: 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). Highlights: We integrate dynamic aspects into the definition of a safety margins. We consider stochastic and epistemic uncertainties affecting the system dynamics. Uncertainties are handled by Order Statistics (OS). We estimate the system grace time during accidental scenarios. We apply the approach to an LBE-XADS accidental scenario.