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http://dx.doi.org/10.1016/j.net.2022.04.013

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

Falaakh, Dayu Fajrul (School of Mechanical Engineering, Pusan National University)
Bahn, Chi Bum (School of Mechanical Engineering, Pusan National University)
Publication Information
Nuclear Engineering and Technology / v.54, no.9, 2022 , pp. 3225-3234 More about this Journal
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
Alloy 600; Alloy 690; SG tubing; PWSCC; prior distribution; Bayesian approach;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 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.
2 D. Sun, A note on noninformative priors for Weibull distributions, J. Stat. Plann. Inference 61 (1997) 319-338.   DOI
3 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.
4 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.   DOI
5 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.
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 J. McCool, Using the Weibull Distribution: Reliability, Modeling, and Inference, John Wiley & Sons, Hoboken, NJ, USA, 2012.
9 W.R. Gilks, Markov Chain Monte Carlo, Encyclopedia of Biostatistics, 2005.
10 A. Gelman, D.B. Rubin, Inference from iterative simulation using multiple sequences, Stat. Sci. 7 (1992) 457-511.
11 H. Rinne, The Weibull Distribution: a Handbook, CRC press, 2008.
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 Y. Liu, A.I. Abeyratne, Practical Applications of Bayesian Reliability, John Wiley & Sons, 2019.
14 A. Gelman, D. Simpson, M. Betancourt, The prior can often only be understood in the context of the likelihood, Entropy 19 (2017) 555.   DOI
15 F.P. Coolen, P. Coolen-Schrijner, M. Rahrouh, Bayesian reliability demonstration for failure-free periods, Reliab. Eng. Syst. Saf. 88 (2005) 81-91.   DOI
16 A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari, D.B. Rubin, Bayesian Data Analysis, CRC press, 2013.
17 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.
18 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.   DOI
19 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.   DOI
20 U. Genschel, W.Q. Meeker, A comparison of maximum likelihood and medianrank regression for Weibull estimation, Qual. Eng. 22 (2010) 236-255.   DOI
21 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.   DOI
22 J.F. Lawless, Statistical Models and Methods for Lifetime Data, vol. 362, John Wiley & Sons, 2011.