Bayesian model updating for the corrosion fatigue crack growth rate of Ni-base alloy X-750

• Published in: Nuclear engineering and technology Vol. 53; no. 1; pp. 304 - 313
• Main Authors: Yoon, Jae Young, Lee, Tae Hyun, Ryu, Kyung Ha, Kim, Yong Jin, Kim, Sung Hyun, Park, Jong Won
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2021; Elsevier; 한국원자력학회
• Subjects: Bayesian inference; Corrosion fatigue crack growth rate; Hydrogen embrittlement; Nickel base alloy X-750; Probabilistic modeling; 원자력공학; Probabilistic modeling; Bayesian inference; Nickel base alloy X-750; Hydrogen embrittlement; Corrosion fatigue crack growth rate
• ISSN: 1738-5733; 2234-358X
• DOI: 10.1016/j.net.2020.06.022

Nickel base Alloy X-750, which is used as fastener parts in light-water reactor (LWR), has experienced many failures by environmentally assisted cracking (EAC). In order to improve the reliability of passive components for nuclear power plants (NPP's), it is necessary to study the failure mechanism and to predict crack growth behavior by developing a probabilistic failure model. In this study, The Bayesian inference was employed to reduce the uncertainties contained in EAC modeling parameters that have been established from experiments with Alloy X-750. Corrosion fatigue crack growth rate model (FCGR) was developed by fitting into Paris’ Law of measured data from the several fatigue tests conducted either in constant load or constant ΔK mode. These parameters characterizing the corrosion fatigue crack growth behavior of X-750 were successfully updated to reduce the uncertainty in the model by using the Bayesian inference method. It is demonstrated that probabilistic failure models for passive components can be developed by updating a laboratory model with field-inspection data, when crack growth rates (CGRs) are low and multiple inspections can be made prior to the component failure.