Nondestructive Examination Guidance for Dry Storage Casks

Meyer, Ryan M.; Suffield, Sarah; Hirt, Evelyn H.; Suter, Jonathan D.; Lareau, J. P.; Zhuge, Jing Wei; Qiao, Hong; Moran, Traci L.; Ramuhalli, Pradeep

doi:10.2172/1333454

Cited by 2 publications

(1 citation statement)

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“…The potential for SCC at the heat af-fected zones of welded stainless-steel interim storage canisters has been identified by several agencies [1,2,3,4,5,6,7,8]. Although this concern has motivated the development of delivery systems and inspection of these canisters on a regular basis [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25], the large number of stored canisters that need to be visually inspected (currently approximately 3, 200 canisters have been installed in the U.S. and projections show that eventually over 10, 000 canisters will be required by 2050 [26]), high radiation levels, limited access (through small size vents) and space constraints (overpack-canister gap less than 5-10 centimeters) make in-situ visual inspections challenging. This necessitates the development of remotely operated systems for real-time detection of flaws including type, location, size, and density, to support future remediation activities, reduce operator errors, and optimize repair quality.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks

Papamarkou¹,

Guy²,

Kroencke³

et al. 2020

Preprint

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Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of pitting and stress corrosion cracking, with a focus on dry storage canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion cracks via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.

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