Predicting PWR Fuel Assembly CIPS Susceptibility with Convolutional Neural Networks: Performance and Uncertainty Quantification

Aidan Furlong, Farah Alsafadi, Scott Palmtag, Andrew Godfrey, Stanley Hayes, Xu Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Crud-induced power shift (CIPS) is an important operational concern in Pressurized Water Reactors (PWRs), as it creates a negative axial offset due to the deposition and precipitation of metal species and boron on fuel rod cladding.Previous studies have been primarily focused on modeling the CIPS phenomenon using mechanistic, bottom-up approaches that are computationally expensive and do not involve measurement data from operating cores.This paper describes a top-down Convolutional Neural Network (ConvNet)-based machine learning approach that has been trained with measurements from Unit 1 of the Catawba Nuclear Station in addition to calculated core design information.Using this classification method, the model obtained an overall accuracy of 92.1% with a correlation coefficient of 0.737 and has been shown to effectively predict the onset and peak of CIPS instances in a CIPS-prevalent cycle while avoiding false positive predictions in a clean cycle.Monte Carlo dropout was then applied to quantify the prediction uncertainties for selected time steps.The presented ML model can serve as a fast and accurate approach that could be of potential use in core design to assist in predicting and avoiding the CIPS phenomenon.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Physics of Reactors, PHYSOR 2024
PublisherAmerican Nuclear Society
Pages1684-1693
Number of pages10
ISBN (Electronic)9780894487972
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 International Conference on Physics of Reactors, PHYSOR 2024 - San Francisco, United States
Duration: Apr 21 2024Apr 24 2024

Publication series

NameProceedings of the International Conference on Physics of Reactors, PHYSOR 2024

Conference

Conference2024 International Conference on Physics of Reactors, PHYSOR 2024
Country/TerritoryUnited States
CitySan Francisco
Period04/21/2404/24/24

Keywords

  • Convolutional Neural Networks
  • Crud
  • Machine Learning
  • Uncertainty Quantification

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