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Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies

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  • Durga Rao, K.
  • Kushwaha, H.S.
  • Verma, A.K.
  • Srividya, A.

Abstract

There will be simplifying assumptions and idealizations in the availability models of complex processes and phenomena. These simplifications and idealizations generate uncertainties which can be classified as aleatory (arising due to randomness) and/or epistemic (due to lack of knowledge). The problem of acknowledging and treating uncertainty is vital for practical usability of reliability analysis results. The distinction of uncertainties is useful for taking the reliability/risk informed decisions with confidence and also for effective management of uncertainty. In level-1 probabilistic safety assessment (PSA) of nuclear power plants (NPP), the current practice is carrying out epistemic uncertainty analysis on the basis of a simple Monte-Carlo simulation by sampling the epistemic variables in the model. However, the aleatory uncertainty is neglected and point estimates of aleatory variables, viz., time to failure and time to repair are considered. Treatment of both types of uncertainties would require a two-phase Monte-Carlo simulation, outer loop samples epistemic variables and inner loop samples aleatory variables. A methodology based on two-phase Monte-Carlo simulation is presented for distinguishing both the kinds of uncertainty in the context of availability/reliability evaluation in level-1 PSA studies of NPP.

Suggested Citation

  • Durga Rao, K. & Kushwaha, H.S. & Verma, A.K. & Srividya, A., 2007. "Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies," Reliability Engineering and System Safety, Elsevier, vol. 92(7), pages 947-956.
    • Handle: RePEc:eee:reensy:v:92:y:2007:i:7:p:947-956
    DOI: 10.1016/j.ress.2006.07.002
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    References listed on IDEAS

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    1. Zio, Enrico & Podofillini, Luca & Zille, Valérie, 2006. "A combination of Monte Carlo simulation and cellular automata for computing the availability of complex network systems," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 181-190.
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    2. Hu, Lunhu & Kang, Rui & Pan, Xing & Zuo, Dujun, 2020. "Risk assessment of uncertain random system—Level-1 and level-2 joint propagation of uncertainty and probability in fault tree analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Zhenhao Zhang & Changchun Luo & Zhenpeng Zhao, 2020. "Application of probabilistic method in maximum tsunami height prediction considering stochastic seabed topography," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(3), pages 2511-2530, December.
    4. Villanueva, D. & Haftka, R.T. & Sankar, B.V., 2014. "Accounting for future redesign to balance performance and development costs," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 56-67.
    5. Attar, Peter J. & Vedula, Prakash, 2013. "On convergence of moments in uncertainty quantification based on direct quadrature," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 119-125.
    6. Gyun Seob Song & Man Cheol Kim, 2021. "Mathematical Formulation and Analytic Solutions for Uncertainty Analysis in Probabilistic Safety Assessment of Nuclear Power Plants," Energies, MDPI, vol. 14(4), pages 1-15, February.
    7. 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).
    8. Arul, A. John & Iyer, N. Kannan & Velusamy, K., 2009. "Adjoint operator approach to functional reliability analysis of passive fluid dynamical systems," Reliability Engineering and System Safety, Elsevier, vol. 94(12), pages 1917-1926.
    9. Sarat Sivaprasad & Cameron A. MacKenzie, 2018. "The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility," Decision Analysis, INFORMS, vol. 15(3), pages 139-153, September.
    10. Yoro, Kelvin O. & Daramola, Michael O. & Sekoai, Patrick T. & Wilson, Uwemedimo N. & Eterigho-Ikelegbe, Orevaoghene, 2021. "Update on current approaches, challenges, and prospects of modeling and simulation in renewable and sustainable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
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    12. Tu Duong Le Duy & Laurence Dieulle & Dominique Vasseur & Christophe Bérenguer & Mathieu Couplet, 2013. "An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications," Journal of Risk and Reliability, , vol. 227(5), pages 471-490, October.

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