Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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RISK-INFORMED REGULATION: HANDLING UNCERTAINTY FOR A RATIONAL MANAGEMENT OF SAFETY

  • Zio, Enrico (Dept. of Energy, Polytechnic of Milan Via Ponzio)
  • Published : 2008.08.31
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Abstract

A risk-informed regulatory approach implies that risk insights be used as supplement of deterministic information for safety decision-making purposes. In this view, the use of risk assessment techniques is expected to lead to improved safety and a more rational allocation of the limited resources available. On the other hand, it is recognized that uncertainties affect both the deterministic safety analyses and the risk assessments. In order for the risk-informed decision making process to be effective, the adequate representation and treatment of such uncertainties is mandatory. In this paper, the risk-informed regulatory framework is considered under the focus of the uncertainty issue. Traditionally, probability theory has provided the language and mathematics for the representation and treatment of uncertainty. More recently, other mathematical structures have been introduced. In particular, the Dempster-Shafer theory of evidence is here illustrated as a generalized framework encompassing probability theory and possibility theory. The special case of probability theory is only addressed as term of comparison, given that it is a well known subject. On the other hand, the special case of possibility theory is amply illustrated. An example of the combination of probability and possibility for treating the uncertainty in the parameters of an event tree is illustrated.

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References

  1. M. Modarres, Advanced Nuclear Power Plant Regulation Using Risk-Informed and Performance-Based Methods, Reliability Engineering and System Safety, 2008, doi:10.1016/j.ress.2008.02.019
  2. IAEA, Risk informed regulation of nuclear facilities: overview of the current status, IAEA-TECDOC-1436, 2005
  3. F.R. Farmer, The Growth of Reactor Safety Criteria in the United Kingdom, Anglo-Spanish Power Symposium, Madrid, 1964
  4. Garrick, B.J. and Gekler, W.C., Reliability Analysis of Nuclear Power Plant Protective Systems, US Atomic Energy Commission, HN-190, 1967
  5. WASH-1400, Reactor Safety Study, US Nuclear Regulatory Commission, 1975
  6. U.S. Nuclear Regulatory Commission Regulatory Guide 1.174, An Approach for Using Probabilistic Risk Assessment in Risk-Informed Decisions on Plant-Specific Changes to the Current Licensing Basis, USNRC, Regulatory Guide 1.1.74, Revision 1, November 2002
  7. U.S. Nuclear Regulatory Commission Regulatory Guide 1.175, An Approach for Plant-Specific, Risk-Informed Decisionmaking: Inservice Testing, USNRC, August 1998
  8. U.S. Nuclear Regulatory Commission Regulatory Guide 1.176, An Approach for Plant-Specific, Risk-Informed Decisionmaking: Graded Quality Assurance, USNRC, August 1998
  9. U.S. Nuclear Regulatory Commission Regulatory Guide 1.177, An Approach for Plant-Specific, Risk-Informed Decisionmaking: Technical Specifications, USNRC, August 1998
  10. A.C. Kadak and T. Matsuo, The Nuclear Industry's Transition to Risk-Informed Regulation and Operation in the United States, Reliability Engineering and System Safety, Vol. 92, 2007, pp. 609-618 https://doi.org/10.1016/j.ress.2006.02.004
  11. G.E. Apostolakis, The Concept of Probability in Safety Assessments of Technological Systems, Science, 1990, pp. 1359-1364
  12. J.C. Helton, Alternative Representations of Epistemic Uncertainty, Special Issue of Reliability Engineering and System Safety, Vol. 85, 2004
  13. K.-Y Cai., System Failure Engineering and Fuzzy Methodology. An Introductory Overview, Fuzzy Sets and Systems 83, 1996, pp. 113-133 https://doi.org/10.1016/0165-0114(95)00385-1
  14. Da Ruan, J. Kacprzyk and M. Fedrizzi, Soft Computing for Risk Evaluation and Management, Physica-Verlag, 2001
  15. Soft Methods in Safety and Reliability, Special Sessions IIII, Proceedings of ESREL 2007, Stavanger, Norway, 25-27 June 2007, Volume 1
  16. L.A. Zadeh, Fuzzy Sets, Information and Control, Vol. 8, 1965, pp. 338-353 https://doi.org/10.1016/S0019-9958(65)90241-X
  17. G.J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Upper Saddle River, NJ, Prentice-Hall, 1995
  18. G. Shafer, A Mathematical Theory of Evidence, University Press, Princeton, 1976
  19. D. Dubois and H. Prade, Possibility Theory: An Approach to Computerized Processing of Uncertainty, New York, Plenum Press, 1988
  20. R.E. Moore, Methods and Applications of Interval Analysis, Philapdelphia, PA: SIAM, 1979
  21. C. Baudrit, D. Dubois and D. Guyonnet, Joint Propagation of Probabilistic and Possibilistic Information in Risk Assessment, IEEE Transactions on Fuzzy Systems, Vol. 14, 2006, pp. 593-608 https://doi.org/10.1109/TFUZZ.2006.876720
  22. P. Baraldi and E. Zio, A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis, 2008
  23. D. Dubois, Possibility Theory and Statistical Reasoning, Computational Statistics and Data Analysis, Vol. 51, 2006, pp. 47-69 https://doi.org/10.1016/j.csda.2006.04.015
  24. C. Baudrit and D. Dubois, Practical Representations of Incomplete Probabilistic Knowledge, Computational Statistics and Data Analysis, Vol. 51, 2006, pp. 86-108 https://doi.org/10.1016/j.csda.2006.02.009
  25. A.P. Dempster, Upper and Lower Probabilities Induced by a Multivalued Mapping, Ann. Mat. Stat., Vol. 38, 1967, pp. 325-339 https://doi.org/10.1214/aoms/1177698950
  26. R. Yager, On the Dempster-Shafer Framework and New Combination Rules, Information Sciences, Vol. 16, pp. 37-41
  27. K. Sentz and S. Ferson, Combination of Evidence in Dempster-Shafer Theory, SAND 2002-0835, Sandia National Laboratories, USA
  28. S. Ferson and V. Kreinovich, Representation, Propagation and Aggregation of Uncertainty, SAND Report
  29. G. de Cooman, Possibility Theory Part I : Measure- and Integral-Theoretic Groundwork; Part II: Conditional Possibility; Part III: Possibilistic Independence, Int. J. Gen. Syst., 1997, Vol. 25 (4), pp. 291-371 https://doi.org/10.1080/03081079708945160
  30. M. H. Kalos, P. A. Whitlock, Monte Carlo methods. Volume I: Basics, Wiley, 1986
  31. D. Huang, T. Chen, M. J. Wang, A fuzzy set approach for event tree analysis, Fuzzy Sets ans Systems 118, pp 153-165, 2001 https://doi.org/10.1016/S0165-0114(98)00288-7
  32. Nuclear Power Plant 2 Operating Living PRA Report (Draft). Nuclear Energy Research Center, Tao Yuan, Taiwan, 1995

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