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New Risks: Issues and Management pp 423–430Cite as

Methods Used in Probabilistic Risk Assessment for Uncertainty and Sensitivity Analysis

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Part of the book series: Advances in Risk Analysis ((AIRA,volume 6))

Abstract

Probabilistic Risk Assessment (PRA) plays an important role in the nuclear reactor regulatory process, and the assessment of uncertainties associated with PRA results is widely recognized as an important part of the analysis process. However, uncertainty analysis and sensitivity analysis in the context of PRA are relatively immature fields. A review of available methods for uncertainty analysis and sensitivity analysis in the context of a PRA is presented. This review first treats methods for use with individual components of a PRA and then considers how these methods could be combined in the performance of a complete PRA. In the context of this paper, the goal of uncertainty analysis is to measure the imprecision in PRA outcomes of interest, and the goal of sensitivity analysis is to identify the major contributors to this imprecision.

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References

  1. M. Ernst, Probabilistic Risk Assessment (PRA) and Decision Making under Uncertainty, in Proceedings of the Ninth Annual Statistics Symposium on National Energy Issues, October 19–21, 1983, pp. 177–188, Report No. NUREG/CP-0053, LA-10127-C, Los Alamos National Laboratory, Los Alamos, NM (1984).

    Google Scholar 

  2. U. S. Nuclear Regulatory Commission, PRA Procedures Guide, Report No. NUREG/CR-2300, Vols. 1 and 2, Washington, DC (1983).

    Google Scholar 

  3. W. E. Vesely and D. M. Rasmuson, PRA Uncertainties and the Roles of Sensitivity and Uncertainty Analyses, in Proceedings of the Ninth Annual Statistics Symposium on National Energy Issues, October 19–21, 1983, pp. 75–102, 189, Report No. NUREG/CP-0053, LA-10127-C, Los Alamos National Laboratory, Los Alamos, NM (1984).

    Google Scholar 

  4. U. S. Nuclear Regulatory Commission, Reactor Safety Study — An Assessment of Accident Risks in U. S. Commercial Nuclear Power Plants, Report No. WASH-1400 (NUREG-75/014), Washington, DC (1975).

    Google Scholar 

  5. H. W. Lewis et al.,Risk Assessment Review Group Report to the U. S. Nuclear Regulatory Commission, Report No. NUREG/CR-0400, U. S. Nuclear Regulatory Commission, Washington, DC (1978).

    Google Scholar 

  6. J. L. Sprung et al.,Overview of the MELCOR Risk Code Development Program, in Proceedings of the International Meeting on Light Water Reactor Severe Accident Evaluation,pp. TS-10.1–1 to TS-10.1–8, American Nuclear Society, La Grange Park, IL (1983).

    Google Scholar 

  7. S. C. Hora and R. L. Iman, Bayesian Analysis of Learning in Risk Analyses, Report No. SAND84–1348, Sandia National Laboratories, Albuquerque, NM (1985).

    Google Scholar 

  8. V. Barnett, Comparative Statistical Inference, 2nd ed., Wiley, New York (1982).

    Google Scholar 

  9. G. W. Parry and P. W. Winter, Characterization and Evaluation of Uncertainty in Probabilistic Risk Analysis, Nucl. Safety 22: 28–42 (1981).

    Google Scholar 

  10. R. G. Easterling, Letter to the Editor: Bayesianism, Nucl. Safety 22:464 (1981).

    Google Scholar 

  11. Commonwealth Edison Company, Zion Probabilistic Safety Study (1981).

    Google Scholar 

  12. Power Authority of the State of New York and Consolidated Edison Company of New York, Inc., Indian Point Probabilistic Safety Study (1982).

    Google Scholar 

  13. Sandia National Laboratories, Analysis of Core Damage Frequency from Internal Events, Vols. 1–7, Report No. NUREG/CR-4550, Albuquerque, NM (1986).

    Google Scholar 

  14. G. Apostolakis, Bayesian Methods in Risk Assessments, in Advances in Nuclear Science and Technology 13, pp. 415–465, Plenum Press, New York, NY (1981).

    Google Scholar 

  15. G. Apostolakis, Data Analysis in Risk Assessments, Nucl. Eng. Des. 71: 375–381 (1982).

    Article  Google Scholar 

  16. S. Kaplan and B. J. Garrick, On the Quantitative Definition of Risk, Risk Analysis 1:11–27 (1981).

    Google Scholar 

  17. D. A. Seaver and W. G. Stillwell, Procedures for Using Expert Judgment to Estimate Human Error Probabilities in Nuclear Power Plant Operations, Report No. NUREG/CR-2743, SAND82–7054, Sandia National Laboratories, Albuquerque, NM (1983).

    Book  Google Scholar 

  18. H. F. Martz, M. C. Bryson, and R. A. Waller, Eliciting and Aggregating Subjective Judgments: Some Experimental Results, Proceedings of the 1984 Statistical Symposium on National Energy Issues, Seattle, October 1984, pp. 63–82 (1985).

    Google Scholar 

  19. S. D. Unwin, “A Fuzzy Set Theoretic Foundation for Vagueness in Uncertainty Analysis,” Risk Analysis 6 (1): 27–34 (1986).

    Article  Google Scholar 

  20. P. S. Jackson et al., Uncertainty Analysis of System Reliability and Availability Assessment, Nucl. Eng. Des. 68: 5–29 (1981).

    Article  Google Scholar 

  21. H. F. Martz et al.,A Comparison of Methods for Uncertainty Analysis of Nuclear Power Plant Safety System Fault Tree Models, Report No. NUREG/CR-3263, LA-9729-MS, Los Alamos National Laboratory, Los Alamos, NM (1983).

    Google Scholar 

  22. S. Kaplan, On the Method of Discrete Probability Distributions in Risk and Reliability Calculations–Applications to Seismic Risk Assessment, Risk Analysis 1: 189–196 (1981).

    Article  Google Scholar 

  23. S. Ahmed et al.,A Method for Propagating Uncertainty in Probabilistic Risk Assessment, Nucl. Tech. 59:238–245 (1982).

    Google Scholar 

  24. G. Apostolakis and Y. T. Lee, Methods for the Estimation of Confidence Bounds for the Top-Event Unavailability of Fault Trees, Nucl. Eng. Des. 41: 411–419 (1977).

    Article  Google Scholar 

  25. J. D. Murchland and G. G. Weber, A Moment Method for the Calculation of a Confidence Interval for the Failure Probability of a System, Proceedings of the 1972 Annual Reliability and Maintainability Symposium, pp. 565–577, Institute for Electrical and Electronic Engineers, New York, NY (1972).

    Google Scholar 

  26. F. W. Spencer and R. G. Easterling, Lower Confidence Bounds on System Reliability Using Component Data: The Maximus Methodology, Report No. SAND84–1199C, Sandia National Laboratories, Albuquerque, NM (1984).

    Google Scholar 

  27. M. D. McKay et al.,A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code, Technometrics 21:239–245 (1979).

    Google Scholar 

  28. R. L. Iman, A Matrix-Based Approach to Uncertainty and Sensitivity Analysis for Fault Trees, Risk Analysis 7 (1): 21–33 (1987).

    Article  Google Scholar 

  29. R. L. Iman and M. J. Shortencarier, A User’s Guide for the Top Event Matrix Analysis Code (TEMAC), Report No. NUREG/CR-4598, SAND86–0960, Sandia National Laboratories, Albuquerque, NM (1986).

    Google Scholar 

  30. R. L. Iman and J. C. Helton, A Comparison of Uncertainty and Sensitivity Analysis Techniques for Computer Models, Report No. NUREG/CR-3904, SAND84–1461, Sandia National Laboratories, Albuquerque, NM (1985).

    Google Scholar 

  31. D. C. Cox and P. Baybutt, Methods for Uncertainty Analysis: A Comparative Survey, Risk Analysis 1: 251–258 (1981).

    Article  Google Scholar 

  32. P. M. Frank, Introduction to System Sensitivity Theory, Academic Press, New York (1978).

    Google Scholar 

  33. R. H. Myers, Response Surface Methodology, Allyn and Bacon, Boston, MA (1985).

    Google Scholar 

  34. G. J. McRae et al.,Global Sensitivity Analysis - A Computational Implementation of the Fourier Amplitude Sensitivity Test (FAST), Comput. Chem. Eng. 5:15–25 (1982).

    Google Scholar 

  35. R. J. Meinhold and N. D. Singpurwalla, Understanding the Kalman Filter, Amer. Stat. 37:123–127 (1983).

    Google Scholar 

  36. D. G. Cacuci et al.,Adjoint Sensitivity Analysis of Extremum-Type Responses in Reactor Safety, Nucl. Sci. Eng. 83:112–135 (1983).

    Google Scholar 

  37. E. M. Oblow, An Automated Procedure for Sensitivity Analysis Using Computer Calculus, Report No. ORNL/TM-8776, Oak Ridge National Laboratory, Oak Ridge, TN (1983).

    Google Scholar 

  38. J.-T. Hwang et al.,The Green’s Function Method of Sensitivity Analysis in Chemical Kinetics, J. Chem. Phys. 69:5180–5191 (1978).

    Google Scholar 

  39. R. L. Iman and W. J. Conover, Small Sample Sensitivity Analysis Techniques for Computer Models with an Application to Risk Assessment, Communication in Statistics A9(17):1749–1842 (1980); and Rejoinder to Comments, pp. 1863–1874 (1980).

    Google Scholar 

  40. R. L. Iman et al.,An Approach to Sensitivity Analysis of Computer Models, Parts 1 and 2, J. Qual. Tech. 13:174–183 and 232–240 (1981).

    Google Scholar 

  41. J. C. Helton, R. L. Iman, J. D. Johnson, and C. D. Leigh, Uncertainty and Sensitivity Analysis of a Model for Multicomponent Aerosol Dynamics, Report No. SAND84–1307, Sandia National Laboratories, Albuquerque, NM (1985)

    Google Scholar 

  42. N. D. Cox, Comparison of Two Uncertainty Analysis Methods, Nucl. Sci. Eng. 64: 258–265 (1977).

    Google Scholar 

  43. D. H. Nguyen, The Uncertainty in Accident Consequences Calculated by Large Codes Due to Uncertainties in Input, Nucl. Tech. 49: 80–91 (1980).

    CAS  Google Scholar 

  44. D. J. Alpert et al.,A Demonstration Uncertainty/Sensitivity Analysis Using the Health and Economic Consequence Model CRAC2, Report No. SAND84–1824, Sandia National Laboratories, Albuquerque, NM (to appear).

    Google Scholar 

  45. A. Bayer et al., The German Risk Study: Accident Consequences Model and Results of the Study, Nucl. Tech. 59: 20–50 (1982).

    CAS  Google Scholar 

  46. S. Kaplan, Matrix Theory Formalism for Event Tree Analysis: Application to Nuclear-Risk Analysis, Risk Analysis 2: 9–18 (1982).

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Sandia National Laboratories, Albuquerque, NM, USA

    Ronald L. Iman

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  1. Ronald L. Iman
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Editor information

Editors and Affiliations

  1. US West Advanced Technologies, Englewood, Colorado, USA

    Louis A. Cox Jr.

  2. University of California, Berkeley, Berkeley, California, USA

    Paolo F. Ricci

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© 1990 Springer Science+Business Media New York

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Iman, R.L. (1990). Methods Used in Probabilistic Risk Assessment for Uncertainty and Sensitivity Analysis. In: Cox, L.A., Ricci, P.F. (eds) New Risks: Issues and Management. Advances in Risk Analysis, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0759-2_47

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  • DOI: https://doi.org/10.1007/978-1-4899-0759-2_47

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4899-0761-5

  • Online ISBN: 978-1-4899-0759-2

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