A Statistical-Based Fatigue Crack Initiation Model for Axial Flaws in Zr-Nb Pressure Tubes
Bibliographic record
Abstract
Flaws found during in-service inspection of CANDU Zr-2.5Nb pressure tubes include fuel bundle scratches, debris fretting flaws, fuel bundle bearing pad fretting flaws, mechanical damage flaws and crevice corrosion marks. The CSA Standard N285.8 contains procedures and acceptance criteria for evaluation of the structural integrity of CANDU Zr-2.5Nb pressure tubes containing flaws. One of the requirements is to evaluate the flaws for fatigue crack initiation. There was a need to develop a statistical-based model of fatigue crack initiation at flaws for use in deterministic and probabilistic assessments of Zr-2.5Nb pressure tubes. A number of fatigue crack initiation experiments have been performed on notched specimens from irradiated and unirradiated Zr-2.5Nb pressure tube material with a range of hydrogen equivalent concentrations. These experiments were performed in an air environment and included temperature and load rise time as test parameters. The test data has been used to develop a statistical-based model of fatigue crack initiation at flaws that covers the effects of flaw root radius, load rise time and irradiation. This paper describes the development of the statistical-based model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".