G Robert Odette

Prof. Dr. G Robert Odette


Research Focus & Metrics

Academic Career Length

33Years (1994-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2001 in JOM
Verified
Journal Article
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GOG Robert Odette
G Robert Odette
GLG. E. Lucas
G. E. Lucas



9 July 2009 in JOM
Verified
Journal Article
Closed access
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GOG Robert Odette
G Robert Odette
RNR. K. Nanstad
R. K. Nanstad

3 July 2019 in Scientific Reports
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Journal Article
Open access
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NAN. Almirall
N. Almirall
PWP. B. Wells
P. B. Wells
HKHuibin Ke
Huibin Ke
PEP. Edmondson
P. Edmondson
DMDane Morgan
Dane Morgan
GOG Robert Odette
G Robert Odette
Atom probe tomography (APT) and scanning transmission electron microscopy (STEM) techniques were used to probe the long-time thermal stability of nm-scale Mn-Ni-Si precipitates (MNSPs) formed in intermediate and high Ni reactor pressure vessel steels under high fluence neutron irradiation at ≈320 °C. Post irradiation annealing (PIA) at 425 °C for up to 57 weeks was used to determine if the MNSPs are: (a) non-equilibrium solute clusters formed and sustained by radiation induced segregation (RIS); or, (b) equilibrium G or Γ2 phases, that precipitate at accelerated rates due to radiation enhanced diffusion (RED). Note the latter is consistent with both thermodynamic models and x-ray diffraction (XRD) measurements. Both the experimental and an independently calibrated cluster dynamics (CD) model results show that the stability of the MNSPs is very sensitive to the alloy Ni and, to a lesser extent, Mn content. Thus, a small fraction of the largest MNSPs in the high Ni steel persist, and begin to coarsen at long times. These results suggest that the MNSPs remain a stable phase, even at 105 °C higher than they formed at, thus are most certainly equilibrium phases at much lower service relevant temperatures of ≈290 °C.

27 April 2022 in npj Computational Materials
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Journal Article
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YLYu-Chen Liu
Yu-Chen Liu
HWHenry Wu
Henry Wu
TMTam Mayeshiba
Tam Mayeshiba
BABenjamin Afflerbach
Benjamin Afflerbach
RJRyan Jacobs
Ryan Jacobs
GOG Robert Odette
G Robert Odette
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.