Research Papers: Design and Analysis

A Probabilistic Environmentally Assisted Cracking Model for Steam Generator Tubes

[+] Author and Article Information
Tae Hyun Lee

Samsung Engineering Co., Ltd.,
415-10 Woncheon-dong,
Yeongtong-gu, Suwon,
Gyeonggi-do 443-823, South Korea
e-mail: th10.lee@samsung.com

Jae Young Yoon

Department of Energy System Engineering,
Seoul National University,
Daehak-dong, Gwanak-gu,
Seoul 151 742, South Korea
e-mail: ssoryjy0@snu.ac.kr

Hyo On Nam

Department of Material Science and Engineering,
University of Wisconsin-Madison,
333 East Campus Mall,
Madison, WI 53715
e-mail: hnam9@wisc.edu

Il Soon Hwang

Department of Energy System Engineering,
Seoul National University,
Daehak-dong, Gwanak-gu,
Seoul 151 742, South Korea
e-mail: hisline@snu.ac.kr

1Corresponding author.

Contributed by the Pressure Vessel and Piping Division of ASME for publication in the JOURNAL OF PRESSURE VESSEL TECHNOLOGY. Manuscript received January 19, 2014; final manuscript received May 7, 2014; published online October 15, 2014. Assoc. Editor: David L. Rudland.

J. Pressure Vessel Technol 137(2), 021204 (Oct 15, 2014) (7 pages) Paper No: PVT-14-1007; doi: 10.1115/1.4027641 History: Received January 19, 2014; Revised May 07, 2014

A probabilistic environmentally assisted cracking (PEAC) model was developed to describe the propagation of primary water stress corrosion cracking for Alloy 600 in roll-transition region of steam generator (SG), a severe environmentally assisted cracking problem in pressurized water reactors (PWRs). In the PEAC model, crack growth rate (CGR) and probability of failure (POF) were obtained by adopting a Bayesian inference that decreases the uncertainties of unknown parameters and their distributions in theoretical equations. The CGR is mainly dependent on three factors: probability of detection (POD), initial crack size distribution, and stress distribution. The POD, which is a logistic link was updated with Bayesian inference based on SG inspection data. The crack size distribution, which is relative to initiation time expressed by a Weibull function, was also updated with Bayesian inference using POD. The stress distribution caused by mechanical rolling is considered to be a major contributing factor along the SG tube. It based on finite element analysis is deterministic model unlike POD and initial crack distribution. According to this model, the uncertainty of hyperparameters in the CGR which are parameters of a prior distribution was reduced, and the appropriate level of confidence was achieved by utilizing the available data. Moreover, a benchmark study for the SG tube was performed to evaluate reliability of Alloy 600 SG components in nuclear power plants. The POF was estimated from the developed PEAC model and failure criteria by taking into account the effects of inspection and repair of defective tubes. The results from this study are applied to demonstrate risk reduction in PWRs by adopting risk-informed in-service inspection.

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Fig. 3

Updating of the POD model based on three different combinations of outcomes

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Fig. 2

POD model for crack depth of SG tubes [12]

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Fig. 1

Estimated crack depth using measured inspection data [12]

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Fig. 4

Schematic procedure for derivation of EICS

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Fig. 5

Residual hoop stress distribution on kiss-rolled transition of Alloy 600 SG tube calculated by FEA

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Fig. 6

Values of predicted stress intensity factor as a function of crack center position and length

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Fig. 7

Prior curve for the predicted crack growth rate as a function of initial crack length of PWSCC in SG tube [20]

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Fig. 8

Posterior curve for the predicted crack growth rate of PWSCC in SG tube updated with experimental data based on Bayesian inference [20]

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Fig. 9

The effect of EICS on SGTR probability

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Fig. 10

The effect of POD qualification on SGTR probability

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Fig. 11

The effect of ISI interval on SGTR probability




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