Residual Stresses in CANDU Feeder Bends: Effect of Bend Radius
Bibliographic record
Abstract
Since 1997, sections of nine feeder pipes have been removed at the Point Lepreau Generating Station (PLGS) because of cracking. All PLGS feeder cracks are axial in orientation and are located in the feeder bends. In all cases, cracks were either at the inside surface of the bend flanks (approximately 60° from the intrados symmetry plane) or at the outside surface of the bend extrados. Root cause analyses indicated that the residual stress has a significant role in these failures. In a typical feeder, there are a number of bends and welds, which are potential locations of high residual stresses. To reduce inspection scope, or to identify the highest risk components, a relative ranking of crack susceptibility is needed. This can be achieved using the residual stress data of these components. This paper compares the measured residual stress data in tight-radius and large-radius CANDU feeder bends. It was found that residual stresses are significantly higher for the bends with small bend radius (r) over diameter (D) ratios (for example, r/D = 1.5) as compared to those bends with large r/D ratios (r/D > 4). The differences in the magnitude of residual stress are consistent with the measured and calculated level of cold work in the two types of bends. It was concluded that the likelihood of cracking in large-radius bends is significantly smaller than that in tight-radius bends.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".