About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
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Symposium
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Materials Informatics for Images and Multi-dimensional Datasets
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Presentation Title |
FeCrAl Alloy Design Utilizing Literature, Experiments, High Throughput Characterization, and Machine Learning |
Author(s) |
Sandipp Krishnan Ravi, Andrew Hoffman, Rajnikant Umretiya, Bojun Feng, Subhrajit Roychowdhury, Sayan Ghosh, Raul Rebak |
On-Site Speaker (Planned) |
Sandipp Krishnan Ravi |
Abstract Scope |
Iron-Chromium-Aluminum (FeCrAl) alloys are considered as lead Accident Tolerant Fuel Cladding (ATF) candidate because of their ability to form an effective passive Al film during high temperature exposure. FeCrAl alloys also exhibit good hydrothermal corrosion in light water reactor (LWR) operating conditions due to their Cr content. Though there is a significant amount of industrial data available on FeCrAl alloy behavior at high temperature environments (e.g. catalytic converter), there is a need of generating oxidation behavior data at relevant lower temperature conditions pertinent to LWR operating conditions. GE Research is conducting experiments looking at the phase stability, corrosion behavior, and mechanical properties of FeCrAl alloys with varying compositions and microstructures. A material discovery endeavor (based on alloy chemistry optimization) is undertaken through the framework of Bayesian active learning and probabilistic machine learning to develop FeCrAl alloys for LWR applications. Results from the experiments and models will be presented and discussed. |