POTENTIAL MITIGATION STRATEGIES FOR PREVENTING STRESS CORROSION CRACKING FAILURES IN HIGH-BURNUP CANDU FUEL
Bibliographic record
Abstract
Potential mitigation strategies for preventing stress corrosion cracking (SCC) failures in CANDU fuel cladding that are based on lessons learned on both domestic and international fronts are discussed in this paper. Although SCC failures have not been a major concern in CANDU reactors in recent decades, they may resurface at higher burnup for conventional fuels or with nonconventional fuels that are currently being investigated, such as MOX or thoria-based fuels. The motivation of this work is to provide the foundation for considering possible remedies for SCC failures. Three candidate remedies are discussed, namely improved fabrication methods for fuel appendages, barrier-liner cladding, and fuel doping. In support of this effort, recent advances in experimental characterization methods are described—methods that have been successfully used in non-nuclear materials that can be used to further elucidate SCC behaviour in CANDU fuel. The overall objective is to outline a path forward for characterizing material behaviour as an essential part of investigating remedies to SCC failure. This will allow increased fuel discharge burnup, maximum linear power, and plant manoeuvrability, while maintaining a high degree of reliability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".