Original Article Bayesian model updating for the corrosion fatigue crack growth rate of Ni-base alloy X-750 Jae Young Yoon a, 1 , Tae Hyun Lee b, *, 1 , Kyung Ha Ryu b , Yong Jin Kim b , Sung Hyun Kim b , Jong Won Park b a Korea Atomic Energy Research Institute,111, Daedoekdaero 989, Yuseong, Daejeon, 34057, South Korea b Korea Institute of Machinery and Materials,156, Gajeongbukro, Yuseong, Daejeon 34103, South Korea article info Article history: Received 26 March 2020 Received in revised form 11 May 2020 Accepted 15 June 2020 Available online 3 August 2020 Keywords: Nickel base alloy X-750 Corrosion fatigue crack growth rate Hydrogen embrittlement Bayesian inference Probabilistic modeling abstract 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 DK mode. These parameters characterizing the corrosion fatigue crack growth behavior of X-750 were suc- cessfully 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 in- spections can be made prior to the component failure. © 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 1. Introduction The issue of extending the lifespans of operating nuclear power plants (NPPs) beyond 60 years has been highlighted recently as the remaining lifetimes of NPPs decreases. Many material degradation modes of passive components need to be understood and pre- vented to ensure safety under long-term operation. The proactive management of materials degradation (PMMD) for light water re- actors (LWRs) is a global cooperative activity to prevent aging problems in NPPs during operation beyond the design life. It en- hances the understanding of known degradation mechanisms and the identification of potential failure processes. Advanced predic- tion capability underpinning mechanistic knowledge helps estab- lish an effective proactive materials management program (PMMD) [1 ,2]. As a part of the PMMD, a probabilistic analysis of aging degradation is called for in order to interface with risk-informed regulation and is studied and developed to make the results of in-service inspection (ISI) meaningful. The information obtained from operating experiences or experiments should be evaluated and systematized to predict the risk of structural failures. There- fore, it is necessary to quantify uncertainties in prediction and in- spection as advanced diagnostic and prognostic technologies [3]. Until now, probabilistic safety analysis has focused on active com- ponents such as pumps and valves [4]. Failure probabilities of passive components, including vessels and pipes, at NPPs have been determined from experiences with non-nuclear components. Hence, there is a large uncertainty in the probabilistic failure analysis of passive components. In order to ensure long-term safety, it is necessary to develop a probabilistic model for failures of passive components in NPPs. The paucity of failure data for passive components of NPPs makes the development of a proba- bilistic model difficult. A realistic approach would be to generate an adequate database in a laboratory measurement procedure for development of a preliminary model. Then the model could be upgraded with limited ISI data obtained from the actual compo- nents in NPPs [5]. In this study, the mechanism of hydrogen embrittlement is examined by analyzing the microstructure of AH heat treated * Corresponding author. E-mail address: [email protected] (T.H. Lee). 1 These authors contributed equally to this work. Contents lists available at ScienceDirect Nuclear Engineering and Technology journal homepage: www.elsevier.com/locate/net https://doi.org/10.1016/j.net.2020.06.022 1738-5733/© 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). Nuclear Engineering and Technology 53 (2021) 304e313
Bayesian model updating for the corrosion fatigue crack growth rate of Ni-base alloy X-750
May 28, 2023
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