Validation of Weight Function Based Process-Zone Model for Evaluation of Delayed Hydride Cracking Initiation in Zr-2.5Nb Pressure Tubes
Bibliographic record
Abstract
In-service flaws in cold-worked Zr-2.5 Nb pressure tubes in CANDU(1) reactors are susceptible to a phenomenon known as delayed hydride cracking (DHC). The material is susceptible to DHC when there is diffusion of hydrogen atoms to a service-induced flaw, precipitation of hydrides on appropriately oriented crystallographic planes in the zirconium alloy matrix material, and development of a hydrided region at the flaw tip. The hydrided region could then fracture to the extent that a crack forms and DHC is said to have initiated. Examples of in-service flaws are fuel bundle scratches, crevice corrosion marks, fuel bundle bearing pad fretting flaws, and debris fretting flaws. These flaws are volumetric in nature. Evaluation of DHC initiation from the flaw is a requirement of Canadian Standards Association (CSA) Standard N285.8. This paper describes the validation of the weight function based process-zone model for evaluation of pressure tube flaws for DHC initiation. Validation was performed by comparing the predicted threshold load levels for DHC initiation with the results from DHC initiation experiments on small notched specimens. The notches in the specimens simulate axial in-service flaws in the pressure tube. The validation was performed for both un-irradiated and pre-irradiated pressure tube material.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".