Materials in Nuclear Energy Systems (MiNES) 2021: Fundamental Irradiation Damage- Session III
Program Organizers: Todd Allen, University of Michigan; Clarissa Yablinsky, Los Alamos National Laboratory; Anne Campbell, Oak Ridge National Laboratory
Tuesday 1:30 PM
November 9, 2021
Room: Allegheny
Location: Omni William Penn Hotel
Session Chair: Blas Uberuaga, Los Alamos National Laboratory
1:30 PM Invited
Point Defect Evolution under Irradiation: Finite Size Effects and Spatio-temporal Correlations: Enrique Martinez Saez1; Frederic Soisson2; Maylise Nastar2; 1Clemson University; 2DEN-Service de Recherches de Metallurgie Physique, CEA, Universite Paris-Saclay
We show a large discrepancy in the defect concentrations between standard rate theory (SRT) and the atomistic kinetic Monte Carlo (AKMC) when the average number of defects in the AKMC simulation box is close or lower than one. The reason is that AKMC naturally captures strong space and time correlations between vacancies and interstitials generated by the finite size of the periodic simulation box. These correlations strongly affect the recombination rate and the point defect concentrations but SRT fails to predict them. We introduce a Correlated Pair Theory (CPT) which fully takes into account the correlations between vacancy and interstitial pairs and predicts point defect concentrations in good agreement with AKMC simulations. Inversely, we show here that the CPT can be used to modify the elimination rates in the AKMC simulations, so as to yield point defect concentrations in agreement with SRT.
2:10 PM
Cavity Formation in Ion Irradiated Fe and Fe-Cr Ferritic Alloys: Yan-Ru Lin1; Arunodaya Bhattacharya2; Jean Henry3; Steven Zinkle1; 1University of Tennessee; 2Oak Ridge National Laboratory; 3CEA
To provide a better understanding of the formation of cavities in ferritic steels, we have performed multi-temperature (400-550°C) simultaneous dual-beam ex-situ and in-situ irradiations on a series of ultra-high purity Fe, Fe-Cr alloys (3-14 wt% Cr), and several advanced ferritic steels. Helium production rates of 0.1 and 10 appm He/dpa were selected to examine the helium synergistic effects. Transmission electron microscopy was used to characterize the microstructures in more than 50 different irradiated samples. Cavities were observed in all the irradiated samples between 400-550°C. This indicates that the narrow temperature range of observable cavities reported in prior ion irradiation studies is likely an artifact associated with the use of low ion energies. Cavity swelling as a function of the Cr content is non-monotonic and could be controlled by solute trapping of defects or formation of alpha-prime precipitates leading to increased recombination. Higher He/dpa content resulted in a higher peak swelling temperature.
2:30 PM
Explorations in Automated Cavity Detection Using an Expanded Machine Learning Training Data Domain: Matthew Lynch1; Ryan Jacobs2; Steven Chen1; Rett Graham1; Dane Morgan2; Kevin Field1; 1University of Michigan - Ann Arbor; 2University of Wisconsin - Madison
The quantification of cavities in post irradiated microscopy is key to understanding materials performance. However, manual detection is a time-consuming process. Recently, machine learning (ML) models have successfully detected and analyzed defects. These models perform at near human levels, but with increased speed and repeatability. A downfall of current models are they are mostly built around a single material/irradiation, meaning their use domain is narrow. Here, we explore the influence of expanding dataset sizes and domain for cavity features. This is accomplished via two expansions of the training data. Firstly, a 30,000+ instances experimental database has been developed to provide an expanded domain space. Secondly, using simplified physics and unirradiated micrographs, artificial data were automatically created and labeled at essentially no cost. Incremental and expanding model training using this combination revealed the correlation between training domain and test instances with model generality increasing with increased training domains.
2:50 PM
Impact of Grain Boundary and Surface Diffusion on Fission Gas Release in UO2 Nuclear Fuel Using a Phase Field Model: Md Ali Muntaha1; Michael Tonks1; Dong-Uk Kim1; 1University of Floirda
This study aims to quantify the importance of grain boundary and surface diffusion on the fission gas release mechanism in UO2 nuclear fuel. Most computational studies of fission gas bubble behavior found in the available literature do not consider faster diffusion along grain boundaries, triple junctions, and gas-bubble surfaces. Our study has investigated the importance of grain boundary and surface diffusion on fission gas release in UO2 nuclear fuel. To do that, we have added a free surface on a specific boundary on the domain to observe the gas release amount through the free surface. We have incorporated diffusion heterogeneity for providing a faster diffusion path along the grain boundary and interface. We have developed and applied a phase-field model using an open-source finite element tool MOOSE for mesoscale modeling. Our model predicts that incorporating diffusion heterogeneity changes the way microstructure evolves and changes the fission gas release rate.
3:10 PM Break