과제정보
This work was supported by the Korea Institute of Nuclear Safety (KINS, No. 202000370001).
참고문헌
- 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.
- 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
- 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.
- 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.
- 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
- 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.
- 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.
- 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
- J. McCool, Using the Weibull Distribution: Reliability, Modeling, and Inference, John Wiley & Sons, Hoboken, NJ, USA, 2012.
- H. Rinne, The Weibull Distribution: a Handbook, CRC press, 2008.
- 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
- 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.
- A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari, D.B. Rubin, Bayesian Data Analysis, CRC press, 2013.
- Y. Liu, A.I. Abeyratne, Practical Applications of Bayesian Reliability, John Wiley & Sons, 2019.
- J.F. Lawless, Statistical Models and Methods for Lifetime Data, vol. 362, John Wiley & Sons, 2011.
- W.R. Gilks, Markov Chain Monte Carlo, Encyclopedia of Biostatistics, 2005.
- 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
- A. Gelman, D.B. Rubin, Inference from iterative simulation using multiple sequences, Stat. Sci. 7 (1992) 457-511.
- 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
- 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
- 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
- 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.