Characterizing the flux effect on the irradiation embrittlement of reactor pressure vessel steels using machine learning.

In-service exposure to high-energy neutrons embrittles reactor pressure vessel (RPV) steels. An increase in the yield stress (Δσ y) results in a corresponding increase in the brittle to ductile transition temperature (ΔT c). Most existing models underpredict ΔT c at higher fluence following accelerated irradiations in test reactors. High fluence, up to 1020 n/cm2 in some cases, will be reached over extended RPV vessel operation of 80 years, or more, at low service flux. Embrittlement has been extensively studied in accelerated, higher flux test reaction irradiations. However, the use of test reactor data naturally raises the question of flux effects. This study used a machine learning approach trained on a set of hardening data, covering a wide range of flux, fluence, and steel compositions to determine the interactive effects of both irradiation and material variables on Δσ y. The analysis included machine learning-based cross-plots of the variable dependence of Δσ y for six core steels (i.e., CM6, LC, LD, LG, LH, and LI), with controlled differences in their Cu and Ni contents. A primary objective is to evaluate an effective to actual fluence (ɸt e /ɸt) ratio, as a function of flux, fluence, and steel composition. This is information critical to properly use intermediate flux-high fluence data in calibrating a low flux-high fluence embrittlement model. The predicted ɸt e /ɸt is reasonably consistent with estimates previously derived from a physics-based solute recombination trap model. [Display omitted] [ABSTRACT FROM AUTHOR]