Bayesian regression over sparse fatigue crack growth data for nuclear piping

Tools

Lye, Adolphus and de Angelis, Marco and Patelli, Edoardo (2020) Bayesian regression over sparse fatigue crack growth data for nuclear piping. In: Modelling in Nuclear Science and Engineering Seminar 2020, 2020-11-04 - 2020-11-05, Virtual.

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Abstract

In this work, the objective is to quantify the uncertainty in crack-growth propagation with the sparse available fatigue crack growth data of a Carbon-Steel Nuclear piping. Using the Bayesian Model Updating framework, we perform a model update on the established Paris-Erdogan Crack-growth rate model with the available data and compared the results of the model updating with the uncertain bounds determined using an Interval Predictor Model (IPM). In doing so, this allows for the provision of a "Reliability Certification" on the resulting probabilistic model updating which illustrates how likely the next data would fall within the stipulated bounds.

ORCID iDs

Lye, Adolphus, de Angelis, Marco ORCID logoORCID: https://orcid.org/0000-0001-8851-023X and Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247;
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