PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Pavel Krcal Co-author(s): Ola Bäckström, Ola.Backstrom@lr.org Pengbo Wang, Pengbo.Wang@lr.org

Transparency of Dynamic Calculation Approaches
Classical fault tree and event tree models trade possibilities to express order of events, stand-by back-up systems triggered only when needed, repairs or grace delays for two important advantages: scalability of analysis and easy interpretation of models and results. The latter aspect cannot be quantified and is rarely explicitly reflected. Correctness arguments for various modeling patterns or model modifications need to find shared acceptance among modelers, system experts, reviewers and regulators. Conclusions drawn from analysis results must be supported by shared interpretations accessible to analysts, regulators, operators and owners. The concept of (mostly) independent basic events and failure propagation defined by Boolean logic offers a common ground for shared trust in the model. In many applications, the static way of modeling inherent in fault trees is sufficient for the purpose, for example for PSA Level 1 analyses. Conservatism caused by a limited handling of time and repairs stays under control and does not skew analysis results. Certain applications, on the other hand, suffer from excessive conservatism of the static analysis, such as spent fuel pool analyses. In these cases, the value of insights obtained from a safety analysis would increase with including dynamic features, such as repairs and triggering of back-up systems. We examine scalable methods for dynamic analysis and explore options for increasing transparency of results and effects of dynamic features in the model. This should enable involved parties to gain equal degree of confidence in dynamic approaches for modeling and analysis as in the static ones. We focus on repairs and a possibility to limit the demand on back-up systems only for the time when the primary system is unavailable, which also constitute the essential part of the Boolean-logic Driven Markov Processes (BDMPs). There are two methods that can efficiently quantify industrial size fault tree models with these two features included: I&AB and Bounded Repairs. Both methods first decompose the model into minimal cut sets and quantify dynamic behaviors included in these cut sets. We propose extended indicators and qualitative insights that explain the quantitative information. One goal is to estimate the impact of dynamic features and allow focused review. Quantification shall be demonstrated in a way that does not require expert knowledge about the actual algorithm. Moreover, an analyst should be able to link numerical results with assumptions on the applicability of dynamic features. We illustrate applicability of these explanations by examples from nuclear PSA analyses. Transparency of dynamic calculation approaches is fundamental for maintaining trust in probabilistic models also when they make use of dynamic features.

Transparency of Dynamic Calculation Approaches

Classical fault tree and event tree models trade possibilities to express order of events, stand-by back-up systems triggered only when needed, repairs or grace delays for two important advantages: scalability of analysis and easy interpretation of models and results. The latter aspect cannot be quantified and is rarely explicitly reflected. Correctness arguments for various modeling patterns or model modifications need to find shared acceptance among modelers, system experts, reviewers and regulators. Conclusions drawn from analysis results must be supported by shared interpretations accessible to analysts, regulators, operators and owners. The concept of (mostly) independent basic events and failure propagation defined by Boolean logic offers a common ground for shared trust in the model. In many applications, the static way of modeling inherent in fault trees is sufficient for the purpose, for example for PSA Level 1 analyses. Conservatism caused by a limited handling of time and repairs stays under control and does not skew analysis results. Certain applications, on the other hand, suffer from excessive conservatism of the static analysis, such as spent fuel pool analyses. In these cases, the value of insights obtained from a safety analysis would increase with including dynamic features, such as repairs and triggering of back-up systems. We examine scalable methods for dynamic analysis and explore options for increasing transparency of results and effects of dynamic features in the model. This should enable involved parties to gain equal degree of confidence in dynamic approaches for modeling and analysis as in the static ones. We focus on repairs and a possibility to limit the demand on back-up systems only for the time when the primary system is unavailable, which also constitute the essential part of the Boolean-logic Driven Markov Processes (BDMPs). There are two methods that can efficiently quantify industrial size fault tree models with these two features included: I&AB and Bounded Repairs. Both methods first decompose the model into minimal cut sets and quantify dynamic behaviors included in these cut sets. We propose extended indicators and qualitative insights that explain the quantitative information. One goal is to estimate the impact of dynamic features and allow focused review. Quantification shall be demonstrated in a way that does not require expert knowledge about the actual algorithm. Moreover, an analyst should be able to link numerical results with assumptions on the applicability of dynamic features. We illustrate applicability of these explanations by examples from nuclear PSA analyses. Transparency of dynamic calculation approaches is fundamental for maintaining trust in probabilistic models also when they make use of dynamic features.

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Lead Author Name: Pavel Krcal (Pavel.Krcal@lr.org)

Bio: Pavel Krcal finished his PhD in Theoretical Computer Science (Formal Verification of Real-Time Systems) at Uppsala University, Sweden, in 2009. Since then, he is working as a part of the software development team of RiskSpectrum, where he gained profound expertise in Reliability Theory and is now responsible for R&D in the area of modeling and analysis. Pavel maintains the thought leader profile of RiskSpectrum also by collaboration with universities and by scientific publications.

Country: Sweden
Company: LR RiskSpectrum
Job Title: RiskSpectrum Methods Research Lead

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Paper PA127 Preview

Author and Presentation Info

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