Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels

Yu chen Liu, Henry Wu, Tam Mayeshiba, Benjamin Afflerbach, Ryan Jacobs, Josh Perry, Jerit George, Josh Cordell, Jinyu Xia, Hao Yuan, Aren Lorenson, Haotian Wu, Matthew Parker, Fenil Doshi, Alexander Politowicz, Linda Xiao, Dane Morgan, Peter Wells, Nathan Almirall, Takuya YamamotoG. Robert Odette

Research output: Contribution to journalArticlepeer-review

Abstract

Irradiation increases the yield stress and embrittles light water reactor (LWR) pressure vessel steels. In this study, we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux, high fluence, extended life conditions. The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence, plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations, up to very high fluence. Notably, the machine learning model predictions for the high fluence, intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models. The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence. Similar approaches, applied to expanded databases, could be used to predict hardening in LWRs under life-extension conditions.

Original languageEnglish
Article number85
Journalnpj Computational Materials
Volume8
Issue number1
DOIs
Publication statusPublished - 2022 Dec

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Materials Science(all)
  • Mechanics of Materials
  • Computer Science Applications

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