Bayesian regression over sparse fatigue crack growth data for nuclear piping

Research output: Contribution to conferencePosterpeer-review

11 Downloads (Pure)

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.
Original languageEnglish
Number of pages1
Publication statusPublished - 4 Nov 2020
EventModelling in Nuclear Science and Engineering Seminar 2020 - Virtual, Bangor, United Kingdom
Duration: 4 Nov 20205 Nov 2020
https://www.nuclearinst.com/Events-list/Nuclear-Modelling-Conference-2020/72300

Conference

ConferenceModelling in Nuclear Science and Engineering Seminar 2020
Country/TerritoryUnited Kingdom
CityBangor
Period4/11/205/11/20
Internet address

Keywords

  • Bayesian regression
  • sparse fatigue crack growth data
  • nuclear piping
  • crack-growth propagation
  • Bayesian model updating

Fingerprint

Dive into the research topics of 'Bayesian regression over sparse fatigue crack growth data for nuclear piping'. Together they form a unique fingerprint.

Cite this