Predict ductile-to-brittle transition temperature in steel under irradiation

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.

The full fit vs. effective fluence (hardening curve) for ATR2 conditions with an optimized p value for the optimized GKRR model fit on the IVAR+ . Alloys referred in the plots were (a) CM6, (b) LG, (c) LH, (d) LI, (e) LC and (f) LD. 

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Y.-C. Liu, H. Wu, T. Mayeshiba, B. Afflerbach, R. Jacobs, J. Perry, J. George, J. Cordell, J. Xia, H. Yuan, A. Lorenson, H. Wu, M. Parker, F. Doshi, A. Politowicz, L. Xiao, D. Morgan, P. Wells, N. Almirall, T. Yamamoto and G. R. Odette, “Machine Learning Predictions of Irradiation Embrittlement in Reactor Pressure Vessel Steels”, npj Computational Materials, 8, 85 (2022) (#IF = 12.241, Rank:30/334 = 9% in MATERIALS SCIENCE, MULTIDISCIPLINARY). <Journal Pub>