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Enhancement of risk informed validation framework for external hazard scenario

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  • Bodda, Saran Srikanth
  • Gupta, Abhinav
  • Dinh, Nam

Abstract

In recent years, the U.S. Nuclear Regulatory Commission (USNRC) and the International Atomic Energy Agency (IAEA) have developed methodologies to assess the vulnerabilities of nuclear plants against site specific extreme hazards. In many cases, advanced simulation tools are being considered to simulate multi-physics, multi-scale phenomena and to evaluate vulnerability of nuclear facilities. The credibility of advanced simulation tools is assessed based on a formal verification, validation, and uncertainty quantification procedure. One of the key limitations in validation is the lack of relevant experimental data at system-level. This limitation leads to a decrease in the confidence of system-level risk predictions. Therefore, a robust validation framework is needed to formalize the confidence in predictive capability of advanced simulation results. This study enhances the existing risk informed validation methodology, originally proposed by Kwag et al. [1] and Bodda et al. [2], by developing additional attributes and a new set of validation indicies for a complete and wider applicability of the framework. In this manuscript, the methodology to identify the critical path that leads to the system-level failure is illustrated. The overall validation is checked for completeness and consistency by comparing the critical path for both the system-level simulation and experimental models. The applicability of the code for an intended application is represented in terms of various maturity levels and helps in the process of decision making.

Suggested Citation

  • Bodda, Saran Srikanth & Gupta, Abhinav & Dinh, Nam, 2020. "Enhancement of risk informed validation framework for external hazard scenario," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
  • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306414
    DOI: 10.1016/j.ress.2020.107140
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    References listed on IDEAS

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    1. Khakzad, Nima & Khan, Faisal & Amyotte, Paul, 2011. "Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches," Reliability Engineering and System Safety, Elsevier, vol. 96(8), pages 925-932.
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    4. Kwag, Shinyoung & Gupta, Abhinav & Dinh, Nam, 2018. "Probabilistic risk assessment based model validation method using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 380-393.
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    Cited by:

    1. Takeda, Satoshi & Kitada, Takanori, 2023. "Importance measure evaluation based on sensitivity coefficient for probabilistic risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Harleen Kaur Sandhu & Saran Srikanth Bodda & Abhinav Gupta, 2023. "A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities," Energies, MDPI, vol. 16(6), pages 1-23, March.
    3. Liu, Yang & Wang, Dewei & Sun, Xiaodong & Liu, Yang & Dinh, Nam & Hu, Rui, 2021. "Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. Robinson, Allen C. & Drake, Richard R. & Swan, M. Scot & Bennett, Nichelle L. & Smith, Thomas M. & Hooper, Russell & Laity, George R., 2021. "A software environment for effective reliability management for pulsed power design," Reliability Engineering and System Safety, Elsevier, vol. 211(C).

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