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- W3216532093 abstract "Accurate prediction of the distribution of maximum pit depth is necessary for life-cycle management of the steam generator (SG) tubes in a nuclear power plant. However, probabilistic modeling of the pitting corrosion data often becomes computationally prohibitive once measurement and detection issues are taken into account. This happens because the noise in degradation data turns model likelihoods into complicated functions that are difficult to evaluate. This paper presents an approach based on the approximate Bayesian computation (ABC) method which avoids the evaluation of the likelihood function with the help of forward simulation. Using the underlying model as a simulator, the ABC method generates samples from a target posterior distribution by simulating pseudo data sets and comparing them with the observed data set using a distance function and a tolerance threshold. A new ABC algorithm is proposed in this study which is based on the subset simulation method. The proposed method is applied to a real data set, and the results show that it can efficiently estimate the model parameters from noisy data and accurately predict the distribution of the maximum pit depth in SG tubes. The parameter uncertainties are further integrated for the prediction of the 95th percentile of maximum pit depth along time." @default.
- W3216532093 created "2021-12-06" @default.
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- W3216532093 creator A5051136157 @default.
- W3216532093 date "2022-01-01" @default.
- W3216532093 modified "2023-09-25" @default.
- W3216532093 title "A simulation-based Bayesian approach to predict the distribution of maximum pit depth in steam generator tubes" @default.
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- W3216532093 doi "https://doi.org/10.1016/j.nucengdes.2021.111563" @default.
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