Bayesian Modeling of Pitting Corrosion in Steam Generators

Abstract

Steam generators in nuclear power plants experienced varying degrees of under-deposit pitting corrosion. A probabilistic model to accurately predict pitting corrosion is necessary for effective life-cycle management of steam generators. This thesis 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 is developed for estimating the model parameters. The proposed model is able to estimate the number of actual pits, the actual pit depth as well as the maximum pit depth, which is the main interest of the pitting corrosion model. A MATLAB program of the Markov chain Monte Carlo technique is developed to perform the Bayesian estimations. Simulation experiments are performed to check the behavior of the Bayesian method. Results show that the MCMC algorithm is an effective way to estimate the model parameters. Also, the effectiveness and efficiency of Bayesian modeling are validated. A comprehensive case study is also presented on the in-service inspection data of pitting corrosion in a steam generator unit. The Weibull distribution is found to be an appropriate probability distribution for modeling the actual pit depth in steam generators.