• Corrosion Science and Technology
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• 제11권4호
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• pp.120-128
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• 2012

The fatigue crack growth behaviors of SA508 Gr.3 Cl.2 low alloy steel in high temperature water environment were investigated. Overall, weld metal showed similar crack growth rate as that of base metal. At 0.01 Hz, fatigue crack growth rate (FCGR) was higher than that in air while the difference was smaller at 0.1 Hz. Also, FCGR showed ${\Delta}K$ dependency at 0.1 Hz only, indicating that the environmental effect was much greater at slower loading frequency of 0.01 Hz. FCGR of SA508 Gr.3 Cl.2 low alloy steel was compatible to or smaller than the ASME Sec. XI fatigue reference curves in high temperature water environment.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권3호
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• pp.271-279
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• 2018

Purpose: Probabilistic safety analysis was performed to enhance the safety and reliability of nuclear power plants because traditional deterministic approach has limitations in predicting the risk of failure by crack growth. The study introduces a probabilistic approach to establish a basis for probabilistic safety assessment of passive components. Methods: For probabilistic modeling of fatigue crack growth rate (FCGR), various FCGR tests were performed either under constant load amplitude or constant ${\Delta}K$ conditions by using heat treated X-750 at low temperature with adequate cathodic polarization. Bayesian inference was employed to update uncertainties of the FCGR model using additional information obtained from constant ${\Delta}K$ tests. Results: Four steps of Bayesian parameter updating were performed using constant ${\Delta}K$ test results. The standard deviation of the final posterior distribution was decreased by a factor of 10 comparing with that of the prior distribution. Conclusion: The method for developing a probabilistic crack growth model has been designed and demonstrated, in the paper. Alloy X-750 has been used for corrosion fatigue crack growth experiments and modeling. The uncertainties of parameters in the FCGR model were successfully reduced using the Bayesian inference whenever the updating was performed.

• Nuclear Engineering and Technology
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• 제53권1호
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• pp.304-313
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• 2021

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.

• Nuclear Engineering and Technology
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• 제49권4호
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• pp.752-759
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• 2017

The fatigue crack growth behavior of Type 347 (S347) and Type 347N (S347N) stainless steel was evaluated under the operating conditions of a pressurized water reactor (PWR). These two materials showed different fatigue crack growth rates (FCGRs) according to the changes in dissolved oxygen content and frequency. Under the simulated PWR conditions for normal operation, the FCGR of S347N was lower than that of S347 and insensitive to the changes in PWR water conditions. The higher yield strength and better corrosion resistance of the nitrogen-alloyed Type 347 stainless steel might be a main cause of slower FCGR and more stable properties against changes in environmental conditions.