Statistical Modeling of Resistance to Crack Initiation due to Hydrided Region Overloads at Simulated Flaws in CANDU Pressure Tubes
Bibliographic record
Abstract
CANDU(1) Zr-2.5%Nb pressure tubes are susceptible to formation of hydrided regions at the locations of stress concentration, such as in-service flaws. Hydrided region overloads occur when the applied stress acting on a flaw with an existing hydrided region exceeds the stress at which the hydrided region has been formed. The overload events may potentially result in crack initiation and its subsequent growth by the mechanism of delayed hydride cracking. Therefore, evaluating the in-service flaws in the pressure tubes for crack initiation due to hydrided region overloads is required by the Canadian Nuclear Standards, and methodology is being developed to perform such evaluations. As part of this development, the resistance of pressure tube material to crack initiation due to hydrided region overloads was modeled statistically. In the proposed modeling framework, the overload resistance is expressed as a power-law function of the material resistance to initiation of delayed hydride cracking under constant loading. This approach fundamentally relies on the concept of a dual process zone introduced by E. Smith, as discussed in the paper. Both the overload crack initiation coefficient and the overload crack initiation exponent vary with the flaw geometry. The overload crack initiation coefficient also varies with the extent of stress reduction prior to hydride formation and with the number of non-ratcheting hydride formation thermal cycles. The developed model is suitable for use as a predictive model in probabilistic assessments of CANDU reactor core, and has been proposed for implementation into the scheduled revision (2015) of the Canadian Nuclear Standard CSA N285.8.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".