Development and Application of Oxide Wedging Crack Growth Model for CANDU Zr-2.5Nb Pressure Tubes
Bibliographic record
Abstract
Surface breaking and subsurface manufacturing flaws have been detected by in-service ultrasonic examination in a number of CANDU reactor Zr-2.5Nb pressure tubes. The manufacturing flaws are oriented in the circumferential direction in the Zr-2.5Nb pressure tube, and are axially aligned along the pressure tube. Metallographic examination of these flaws in an ex-service Zr-2.5Nb pressure tube revealed a series of parallel circumferentially oriented discontinuous features oriented at an angle of nominally 40 degrees relative to the axial direction in the pressure tube. Since the surface breaking flaws are exposed to the reactor coolant, it was considered prudent to evaluate potential growth of the flaws by an oxide wedging crack growth mechanism. Oxide wedging crack growth is a slow crack growth mechanism that can occur when zirconium oxide forms on the crack faces due to a corrosion reaction with the reactor coolant. An oxide wedging crack growth model was developed to predict crack growth rates and future flaw sizes as a part of the fitness-for-service evaluation of a Zr-2.5Nb pressure tube containing this type of manufacturing flaw. The model was then applied to predict crack growth from manufacturing flaws that were detected in an operating pressure tube, and the evaluation results were used as part of the justification for continued operation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".