| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization and Modeling of Nuclear Fuels: Microstructure, Thermo-physical Properties
|
| Presentation Title |
N-1: 3D Reconstruction and Quantification of Oxide Nano-porosity in Zirconium Alloys |
| Author(s) |
Hongliang Zhang, Adrien Couet, Taeho Kim, William Howland |
| On-Site Speaker (Planned) |
Hongliang Zhang |
| Abstract Scope |
In the corrosion of Zirconium alloy, the oxide grows at a decreasing rate until reaching critical thickness, followed by the sudden loss of the protective property and a new cycle of oxide growth. The oxidation-induced pores in oxide may provide pathways to oxidizing species. TEM is commonly used to determine pore density and size. However, some of the small-sized pores are invisible in TEM at only one angle. Manually counting pores will also bring some artifacts. We precisely quantify the oxide porosity in corroded Zircaloy-4 as a function of exposure time and temperatures using machine-learning-based quantification. The size, spatial distribution, morphology, and interconnectivity of the pores are obtained and quantified through the 3D reconstruction. Furthermore, the chemical composition in different regions of the oxide is studied using APT and correlated to pore distribution to further our understanding of alloying elements effects on corrosion. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Nuclear Materials, |