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Microstructural changes induced by helium implantation in materials lead to volumetric swelling and mechanical property changes. How these properties are linked and establishing direct relationships can be difficult due to the underlying material’s microstructure evolution. Some materials also experience a phase change due to irradiation damage making them even more complex to analyze. Here, single crystalline Si (100) was used to establish a relationship among these parameters. The swelling height as a function of implantation fluence can equally fit a linear relationship. Solely irradiation induced defects are observed at low fluence below 5.0 × 10 16 ions/cm 2 . An abrupt amorphous and crystalline mixed layer of ∼200 nm thick within a highly damaged polycrystalline matrix is observed when implantation fluence exceeds 5.0 × 10 16 ions/cm 2 , leading to the appearance of irradiation induced swelling and hardening behavior. As the fluence increases beyond 1.0 × 10 17 ions/cm 2 , the amorphous layer expands in size and the bubble size distribution takes the form of a Gaussian distribution with a maximum size of up to 6.4 nm, which causes a further increase in the height of swelling. Furthermore, irradiation induced softening appeared due to the enlarged bubble size and amorphization.
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Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels
Abstract 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.
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- PAR ID:
- 10366646
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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