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

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  • Di Maio, Francesco
  • Rai, Ajit
  • Zio, Enrico

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.

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  • Di Maio, Francesco & Rai, Ajit & Zio, Enrico, 2016. "A dynamic probabilistic safety margin characterization approach in support of Integrated Deterministic and Probabilistic Safety Analysis," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 9-18.
    • Handle: RePEc:eee:reensy:v:145:y:2016:i:c:p:9-18
    DOI: 10.1016/j.ress.2015.08.016
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    1. Chris Garrett & George Apostolakis, 1999. "Context in the Risk Assessment of Digital Systems," Risk Analysis, John Wiley & Sons, vol. 19(1), pages 23-32, February.
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    3. Zio, Enrico & Di Maio, Francesco & Tong, Jiejuan, 2010. "Safety margins confidence estimation for a passive residual heat removal system," Reliability Engineering and System Safety, Elsevier, vol. 95(8), pages 828-836.
    4. Helton, Jon C., 2011. "Quantification of margins and uncertainties: Conceptual and computational basis," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 976-1013.
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    Cited by:

    1. París, C. & Queral, C. & Mula, J. & Gómez-Magán, J. & Sánchez-Perea, M. & Meléndez, E. & Gil, J., 2019. "Quantitative risk reduction by means of recovery strategies," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 13-32.
    2. Chi, Lixun & Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Bai, Hua, 2020. "Integrated Deterministic and Probabilistic Safety Analysis of Integrated Energy Systems with bi-directional conversion," Energy, Elsevier, vol. 212(C).
    3. Martorell, P. & Martón, I. & Sánchez, A.I. & Martorell, S. & Sanchez-Saez, F. & Saiz, M., 2018. "Evaluation of risk impact of completion time changes combining PSA and DSA model insight and human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 97-107.
    4. Karanki, D.R. & Dang, V.N. & MacMillan, M.T. & Podofillini, L., 2018. "A comparison of dynamic event tree methods – Case study on a chemical batch reactor," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 542-553.
    5. Kang, Dong Gu, 2020. "Comparison of statistical methods and deterministic sensitivity studies for investigation on the influence of uncertainty parameters: Application to LBLOCA," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Antonello, Federico & Buongiorno, Jacopo & Zio, Enrico, 2022. "A methodology to perform dynamic risk assessment using system theory and modeling and simulation: Application to nuclear batteries," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    7. Medeiros, C.P. & Alencar, M.H. & de Almeida, A.T., 2017. "Multidimensional risk evaluation of natural gas pipelines based on a multicriteria decision model using visualization tools and statistical tests for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 268-276.
    8. Wang, Wei & Cammi, Antonio & Di Maio, Francesco & Lorenzi, Stefano & Zio, Enrico, 2018. "A Monte Carlo-based exploration framework for identifying components vulnerable to cyber threats in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 24-37.
    9. Francesco, Di Maio & Matteo, Fumagalli & Carlo, Guerini & Federico, Perotti & Enrico, Zio, 2021. "Time-dependent reliability analysis of the reactor building of a nuclear power plant for accounting of its aging and degradation," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    10. Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
    11. Di Maio, Francesco & Picoco, Claudia & Zio, Enrico & Rychkov, Valentin, 2017. "Safety margin sensitivity analysis for model selection in nuclear power plant probabilistic safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 122-138.
    12. Raoni, Rafael & Secchi, Argimiro R., 2019. "Procedures to model and solve probabilistic dynamic system problems," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    13. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Yang, Zhaoming & Zhang, Jinjun, 2021. "Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model," Renewable Energy, Elsevier, vol. 174(C), pages 952-970.
    14. Dimaio, F. & Scapinello, O. & Zio, E. & Ciarapica, C. & Cincotta, S. & Crivellari, A. & Decarli, L. & Larosa, L., 2021. "Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Karanki, D.R. & Rahman, S. & Dang, V.N. & Zerkak, O., 2017. "Epistemic and aleatory uncertainties in integrated deterministic and probabilistic safety assessment: Tradeoff between accuracy and accident simulations," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 91-102.

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