Determination of preferential binder oxidation in HTGR matrix material subjected to high temperature steam

Montoya, K.; Moczygemba, C.; Brigham, Brian A.; Spano, Tyler L.; Campbell, Anne A.; Gerczak, Tyler J.; Wood, Elizabeth Sooby

doi:10.1016/j.jnucmat.2020.152674

Cited by 5 publications

(1 citation statement)

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“…One type of off-normal reactor scenario is the ingress of steam into the reactor core, contaminating the helium coolant and subsequently exposing the fuel to a high-temperature oxidizing atmosphere. The graphite matrix material would be exposed first and preferentially oxidizes the resin prior to the graphite flakes creating oxidant pathways to reach the TRISO particles [8]- [10].…”

Section: Related Work a Related Work In Nuclear Fuel Degradation Quan...mentioning

confidence: 99%

Semantic Segmentation of Micrographs for Nuclear Fuel Analysis and Degradation Quantification

Snyder,

Montoya,

Patrick

et al. 2023

IEEE Access

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When fuel materials for high-temperature gas-cooled nuclear reactors are quantification tested, significant analysis is required to establish their stability under various proposed accident scenarios, as well as to assess degradation over time. Typically, samples are examined by lab assistants trained to capture micrograph images used to analyze the degradation of a material. Analysis of these micrographs still require manual intervention which is time-consuming and can introduce human-error. While machine learning and computer vision models would be useful to this analysis, data for training such models is limited due to physical experiment costs, including lab hours and materials. This collaborative research ( 1) establishes an open dataset of micrographs and semantic labels named Graphite-23, (2) analyzes semantic segmentation architectures against the new data, and ( 3) contributes open source code for the community to progress research in degradation analysis of materials. A U-Net architecture with various backbones demonstrates competitive performance on the proposed dataset, with an mIoU up to 0.83, establishing a clear baseline for future research in this intersection of fields.

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Section: Related Work a Related Work In Nuclear Fuel Degradation Quan...mentioning

confidence: 99%