A bayesian probabilistic approach to improved health management of steam generator tubes

Abstract

Steam generator tube integrity is critical for the safety and operability of pressurized water reactors. Any degradation and rupture of tubes can have catastrophic consequences, e.g., release of radioactivity into the atmosphere. Given the risk significance of steam generator tube ruptures, it is necessary to periodically inspect the tubes using nondestructive evaluation methods to detect and characterize unknown existing defects. To make accurate estimates of defect size and density, it is essential that detection uncertainty and measurement errors associated with nondestructive evaluation methods are characterized properly and accounted for in the evaluation. In this paper we propose a Bayesian approach that updates prior knowledge of defect size and density with nondestructive evaluation data, accounting for detection uncertainty and measurement errors. An example application of the proposed approach is then demonstrated for estimating defect size and density in steam generator tubes using eddy current evaluation data. The proposed Bayesian probabilistic approach helps improve health management of steam generator tubes, thereby enhancing the overall safety and operability of pressurized water reactors.