Probabilistic Modelling Approaches for the Flaw Growth Rate Estimation in a Multi-Component Systems in Canada

Abstract of the technical presentation presented at:
4^th International Symposium on Probabilistic Methodologies for Nuclear Applications (ISPMNA)
November 1-3, 2022

Prepared by:
M. Pandey
Professor, NSERC-UNENE Chair
University of Waterloo

B. Wasiluk
Canadian Nuclear Safety Commission

Abstract

Nuclear power plant components are subject to in-field inspections during scheduled maintenance outages, which include considerations for minimizing inspector radiation exposure. The periodic and in-service examinations are means to detect new degradation mechanisms and monitor component condition for already known degradation processes. After the detection of a new degradation mechanism, the estimation of growth rate is required to assess the remaining component life and plan for future inspections and maintenance activities accordingly. Because of the periodic nature of the examinations, the time of degradation initiation could remain unknown, making it challenging to evaluate the degradation growth rate, at least for some time. The subject of this investigation is material degradation by a dealloying mechanism in the late life of steam generator tubing. Since this degradation mechanism manifests in considerable variation of flaw growth rates, probabilistic methods appear to be a powerful choice for fitness for service and operational assessments.

The primary objective of this work is to investigate existing probabilistic approaches and develop new models for estimating the flaw growth rate, followed by the discovery of a new degradation mechanism in a multi-component population. This investigation will include two types of probabilistic degradation growth models: random linear growth rate and stochastic gamma process models. The time of degradation initiation will be treated as a random variable. A case study will be presented using steam generator tubing inspection data in order to investigate the capabilities and features of the developed probabilistic modelling approach.

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