Some Issues in Fitness for Service Assessment of Wall Thinned CANDU Feeder Pipes
Bibliographic record
Abstract
Canadian CANDU® feeder pipes experiencing pipe wall thinning due to flow accelerated corrosion (FAC) are accepted for continued service after an engineering evaluation. This evaluation is based on the assumption that FAC degradation is manageable through a comprehensive inspection program and conservative engineering evaluations. The practice of the Canadian nuclear industry is to: establish a minimum acceptable wall thickness, compare the measured thickness to predictions from the previous outage to confirm the conservatism of the predictions in a condition assessment, and predict the thickness at the next inspection and compare against the minimum acceptable value in an operational assessment. If the thickness measured during outage does not meet the pre-established thickness criteria, the feeder should be replaced, unless it is demonstrated to be fit for service through a detailed analysis. The detailed analysis usually involves more complex methodologies which are subjected to regulatory reviews. Several issues have been raised in the fitness-for-service assessments of feeder pipes relating to the definition of primary membrane stress, interpretation of minimum thickness requirements, plasticity analysis, limit load analysis and the applicability of procedures given in Code Case N-597 to Class 1 feeder pipes. This paper presents the Canadian regulatory expectations on these issues.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".