TY - JOUR
T1 - Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels
AU - Liu, Yu chen
AU - Wu, Henry
AU - Mayeshiba, Tam
AU - Afflerbach, Benjamin
AU - Jacobs, Ryan
AU - Perry, Josh
AU - George, Jerit
AU - Cordell, Josh
AU - Xia, Jinyu
AU - Yuan, Hao
AU - Lorenson, Aren
AU - Wu, Haotian
AU - Parker, Matthew
AU - Doshi, Fenil
AU - Politowicz, Alexander
AU - Xiao, Linda
AU - Morgan, Dane
AU - Wells, Peter
AU - Almirall, Nathan
AU - Yamamoto, Takuya
AU - Odette, G. Robert
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85128936692
UR - https://www.scopus.com/pages/publications/85128936692#tab=citedBy
U2 - 10.1038/s41524-022-00760-4
DO - 10.1038/s41524-022-00760-4
M3 - Article
AN - SCOPUS:85128936692
SN - 2057-3960
VL - 8
JO - npj Computational Materials
JF - npj Computational Materials
IS - 1
M1 - 85
ER -