Deciphering flux effect in irradiation hardening of steel

In-service exposure to high-energy neutrons embrittles reactor pressure vessel (RPV) steels. An increase in the yield stress (Δσy) results in a corresponding increase in the brittle to ductile transition temperature (ΔTc). Most existing models underpredict ΔTc 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 Δσy. The analysis included machine learning-based cross-plots of the variable dependence of Δσy 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 (ɸte/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 ɸte/ɸt is reasonably consistent with estimates previously derived from a physics-based solute recombination trap model.

More Info:

Y.-C. Liu*, D. Morgan, T. Yamamoto and G. R. Odette, "Characterizing the flux effect on the irradiation embrittlement of reactor pressure vessel steels using machine learning", Acta Materialia, 256, 119144 (2023) (#IF = 9.4, Rank: 3/78 = 3.8% in METALLURGY & METALLURGICAL ENGINEERING -- SCIE). <Journal Pub>