Abstract
This paper presents development of an adaptive-predictive probabilistic methodology for forecasting localized-corrosion-induced pit population and pit depth distributions. Nuclear power plant (NPP) operators are required to periodically inspect components by visual and volumetric examinations to maintain integrity and ensure safety. However, as NPPs age, more frequent inspections would be needed to maintain component integrity. To define inspection schedules, a framework is needed that balances risk and cost and ensures safety. This paper presents a methodology, including a model that could be used to forecast localized-corrosion-induced damage of components based on some initial damage. For example, if a component is undergoing pitting corrosion in an environment, the model forecasts the pit population and the pit depth distribution (i.e., the fraction of pits at a particular depth) at a later time. The methodology and associated model accounts for previous inspection data, randomness of pit generation and propagation, and pit growth rate as a function of time. The model results could be used to estimate probability of component failure due to pitting corrosion and thresholds for enhanced inspection schedules.
