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Probabilistic Approach for Predicting Degradation Characteristics of Corrosion Fatigue Crack  

Lee, Taehyun (Department of Reliability Assessment, Korea Institute of Machinery and Materials)
Yoon, Jae Young (Integrated Safety Assessment Division, Korea Atomic Energy Research Institute)
Ryu, KyungHa (Department of Reliability Assessment, Korea Institute of Machinery and Materials)
Park, Jong Won (Department of Reliability Assessment, Korea Institute of Machinery and Materials)
Publication Information
Journal of Applied Reliability / v.18, no.3, 2018 , pp. 271-279 More about this Journal
Abstract
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.
Keywords
Probabilistic Model; Corrosion Fatigue Crack Growth Rate(FCgr); Bayesian Inference; Parameter Updating; Uncertainty;
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