NEUP CINR-23-28903: Predicting Pitting and Stress Corrosion Cracking of Dry Cask Storage Canisters via High Throughput Testing, Multiscale Characterization, and 3D Computer Vision based Machine Learning

We propose a US-UK collaborative research program focusing on the nucleation and growth of pits and stress corrosion cracking of stainless steel 304, a canister material used for dry cask storage of spent nuclear fuels, by leveraging multi-scale characterization techniques, 2D/3D computer vision, and machine learning approaches. The proposed study will enable the understanding and prediction of how and when pitting corrosion can nucleate, grow, and transition into stress corrosion cracking.

Additional Info

Field	Value
Awarded Institution	The Ohio State University
PI	Xiaolei Guo