Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels

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 condi...

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Bibliographic Details
Published in:npj computational materials 2022-04, Vol.8 (1), p.1-11, Article 85
Main Authors: Liu, Yu-chen, Wu, Henry, Mayeshiba, Tam, Afflerbach, Benjamin, Jacobs, Ryan, Perry, Josh, George, Jerit, Cordell, Josh, Xia, Jinyu, Yuan, Hao, Lorenson, Aren, Wu, Haotian, Parker, Matthew, Doshi, Fenil, Politowicz, Alexander, Xiao, Linda, Morgan, Dane, Wells, Peter, Almirall, Nathan, Yamamoto, Takuya, Odette, G. Robert
Format: Article
Language:English
Subjects:
639/301/1023/1026
639/301/1023/303
639/301/1034
Characterization and Evaluation of Materials
Chemistry and Materials Science
Computational Intelligence
Extrapolation
Fluence
Hardening
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Irradiation
Learning algorithms
Light water reactors
Machine learning
MATERIALS SCIENCE
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematical Modeling and Industrial Mathematics
mechanical properties
metals and alloys
Pressure vessels
Radiation
Structural steels
Theoretical
theory and computation
Yield stress
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Summary: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.
ISSN:2057-3960
2057-3960
DOI:10.1038/s41524-022-00760-4