Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Bayesian approach for prediction of primary water stress corrosion cracking in Alloy 690 steam generator tubing

  • Received : 2021.08.16
  • Accepted : 2022.04.18
  • Published : 2022.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

Alloy 690 tubing has been shown to be highly resistant to primary water stress corrosion cracking (PWSCC). Nevertheless, predicting the failure by PWSCC in Alloy 690 SG tubes is indispensable. In this work, a Bayesian-based statistical approach is proposed to predict the occurrence of failure by PWSCC in Alloy 690 SG tubing. The prior distributions of the model parameters are developed based on the prior knowledge or information regarding the parameters. Since Alloy 690 is a replacement for Alloy 600, the parameter distributions of Alloy 600 tubing are used to gain prior information about the parameters of Alloy 690 tubing. In addition to estimating the model parameters, analysis of tubing reliability is also performed. Since no PWSCC has been observed in Alloy 690 tubing, only right-censored free-failure life of the tubing are available. Apparently the inference is sensitive to the choice of prior distribution when only right-censored data exist. Thus, one must be careful in choosing the prior distributions for the model parameters. It is found that the use of non-informative prior distribution yields unsatisfactory results, and strongly informative prior distribution will greatly influence the inference, especially when it is considerably optimistic relative to the observed data.

Keywords

Acknowledgement

This work was supported by the Korea Institute of Nuclear Safety (KINS, No. 202000370001).

References

  1. H. Xu, S. Fyfitch, P. Scott, M. Foucault, R. Kilian, M. Winters, Resistance to primary water stress corrosion cracking of alloys 690, 52, and 152, in: Pressurized Water Reactors (MRP-111), EPRI, U.S. Department of Energy, Palo Alto, CA, 2004, p. 1009801.
  2. J.P. Park, C. Park, Y.-J. Oh, J.H. Kim, C.B. Bahn, Statistical analysis of parameter estimation of a probabilistic crack initiation model for Alloy 182 weld considering right-censored data and the covariate effect, Nucl. Eng. Technol. 50 (2018) 107-115. https://doi.org/10.1016/j.net.2017.09.005
  3. G. Troyer, S. Fyfitch, K. Schmitt, G. White, C. Harrington, Dissimilar metal weld PWSCC initiation model refinement for xLPR part I: a survey of alloy 82/182/132 crack initiation literature, in: The 17th International Conference on Environmental Degradation of Materials in Nuclear Power SystemsdWater Reactors, Ottawa, ON, Canada, 2015, pp. 9-13.
  4. M. Erickson, F. Ammirato, B. Brust, D. Dedhia, E. Focht, M. Kirk, C. Lange, R. Olsen, P. Scott, D. Shim, G. Steven, G. White, Models and Inputs Selected for Use in the xLPR Pilot Study, EPRI, Palo Alto, CA, 2011, p. 1022528.
  5. J.P. Park, S.C. Yoo, J.H. Kim, C.B. Bahn, Development of probabilistic primary water stress corrosion cracking initiation model for alloy 182 welds considering thermal aging and cold work effects, Nucl. Eng. Technol. 53 (2021) 1909-1923. https://doi.org/10.1016/j.net.2020.12.005
  6. J. Harris, V. Moroney, J. Gorman, Pressurized Water Reactor Generic Tube Degradation Predictions: U.S. Recirculating Steam Generators with Alloy 600TT and Alloy 690TT Tubing, EPRI, Palo Alto, CA, 2003, p. 1003589.
  7. C. Marks, J. Gorman, C. Anderson, M. Dumouchel, Steam Generator Management Program: Improvement Factors for Pressurized Water Reactor Steam Generator Tube Materials, EPRI, Palo Alto, CA, 2009, p. 1019044.
  8. M.D. Pandey, S. Datla, R.L. Tapping, Y.C. Lu, The estimation of lifetime distribution of Alloy 800 steam generator tubing, Nucl. Eng. Des. 239 (2009) 1862-1869. https://doi.org/10.1016/j.nucengdes.2009.05.027
  9. J. McCool, Using the Weibull Distribution: Reliability, Modeling, and Inference, John Wiley & Sons, Hoboken, NJ, USA, 2012.
  10. H. Rinne, The Weibull Distribution: a Handbook, CRC press, 2008.
  11. U. Genschel, W.Q. Meeker, A comparison of maximum likelihood and medianrank regression for Weibull estimation, Qual. Eng. 22 (2010) 236-255. https://doi.org/10.1080/08982112.2010.503447
  12. J. Hickling, Resistance of Alloys 690, 52 and 152 to Primary Water Stress Corrosion Cracking (MRP-237, Rev. 1): Summary of Findings from Completed and Ongoing Test Programs since 2004, EPRI, Palo Alto, CA, 2008, p. 1018130.
  13. A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari, D.B. Rubin, Bayesian Data Analysis, CRC press, 2013.
  14. Y. Liu, A.I. Abeyratne, Practical Applications of Bayesian Reliability, John Wiley & Sons, 2019.
  15. J.F. Lawless, Statistical Models and Methods for Lifetime Data, vol. 362, John Wiley & Sons, 2011.
  16. W.R. Gilks, Markov Chain Monte Carlo, Encyclopedia of Biostatistics, 2005.
  17. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equation of state calculations by fast computing machines, J. Chem. Phys. 21 (1953) 1087-1092. https://doi.org/10.1063/1.1699114
  18. A. Gelman, D.B. Rubin, Inference from iterative simulation using multiple sequences, Stat. Sci. 7 (1992) 457-511.
  19. F.P. Coolen, P. Coolen-Schrijner, M. Rahrouh, Bayesian reliability demonstration for failure-free periods, Reliab. Eng. Syst. Saf. 88 (2005) 81-91. https://doi.org/10.1016/j.ress.2004.07.015
  20. D. Sun, A note on noninformative priors for Weibull distributions, J. Stat. Plann. Inference 61 (1997) 319-338. https://doi.org/10.1016/S0378-3758(96)00155-3
  21. A. Gelman, D. Simpson, M. Betancourt, The prior can often only be understood in the context of the likelihood, Entropy 19 (2017) 555. https://doi.org/10.3390/e19100555
  22. F. Cannarile, M. Compare, S. Mattafirri, F. Carlevaro, E. Zio, Comparison of Weibayes and Markov chain Monte Carlo methods for the reliability analysis of turbine nozzle components with right censored data only, in: Safety and Reliability of Complex Engineered Systems-Proceedings of the 25th European Safety and Reliability Conference, ESREL, 2015, pp. 1937-1944.