Minimizing CRUD deposition through optimization of associated parameters

Brian Andersen, Jason Hou, Dave Kropaczek

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

Abstract

A strong correlation exists between subcooled boiling in assembly subchannels and CRUD deposition. In this work a genetic algorithm is used to optimize a 17 x 17 PWR fuel assembly to have minimized subcooled boiling, minimized peak kinf, and maximized end of cycle kinf. Optimization of these parameters act as a surrogate for the optimization of CRUD deposition in fuel assemblies due to their strong correlation. Subcooled boiling, measured by vapor void in a sub channel, and values of kinf, are calculated using VERA-CS. Due to the high computational cost of VERA-CS, artificial neural networks are used as surrogate models to VERA-CS in order for the optimization to be performed in a timely manner making design work possible. Two neural networks were trained using a training library of 1200 randomly generated assembly designs and a validation library of 100 assembly designs evaluated using VERA-CS. The combination of neural networks and genetic algorithms formed an extremely fast optimization algorithm capable of evaluating designed a set of optimized pin lattices in a matter of minutes. The optimization showed a clear reduction in vapor void in the optimization solutions. This provides a proof of principle that complex phenomena requiring coupled, Multiphysics calculations, such as CRUD deposition, may be optimized.

Original languageEnglish
Title of host publicationInternational Conference on Physics of Reactors
Subtitle of host publicationTransition to a Scalable Nuclear Future, PHYSOR 2020
EditorsMarat Margulis, Partrick Blaise
PublisherEDP Sciences - Web of Conferences
Pages2354-2361
Number of pages8
ISBN (Electronic)9781713827245
DOIs
StatePublished - 2020
Event2020 International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020 - Cambridge, United Kingdom
Duration: Mar 28 2020Apr 2 2020

Publication series

NameInternational Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020
Volume2020-March

Conference

Conference2020 International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020
Country/TerritoryUnited Kingdom
CityCambridge
Period03/28/2004/2/20

Keywords

  • CRUD
  • Genetic algorithm
  • Machine learning
  • Optimization

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