Steam generators (SG) are a major pressure retaining component of great safety significance in nuclear power plants. Due to various manufacturing, operation and maintenance activities, as well as material interaction with the surrounding chemical environment, the SG tubes have been subject to a number of degradation modes. Among them, the under-deposit pitting corrosion at outside surfaces of the SG tubes just on top of the tubesheet support plates has had a serious impact on the integrity of the SG tubes. This paper presents an advanced probabilistic model of pitting corrosion characterizing the inherent randomness of the pitting process and measurement uncertainties of the in-service inspection (ISI) data obtained from eddy current (EC) inspections. A Bayesian method based on Markov Chain Monte Carlo (MCMC) simulation is developed for estimating the model parameters. The proposed model is able to predict the actual pit number, the actual pit depth as well as the maximum pit depth, which is the main interest of the pitting corrosion model.
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