Crack Growth Model for the Probabilistic Assessment of Inspection Strategies for Steam Generator Tubes
Bibliographic record
Abstract
Assessment of the conditional probabilities of tube failures, leak rates, and ultimately risk of exceeding licensing dose limits as an approach used to steam generator tube fitness-for-service assessment has begun to be used increasingly in recent years throughout the nuclear power industry. One of the important topics in the assessment is the crack growth model that predicts the growth of crack size with time. The plot of the log of change in crack length per cycle, against the log of stress intensity factor range shows three distinct regions where in Regions I and III the change in crack length per cycle increases faster with stress intensity factor. On the other hand Region II shows linear variation. While effective for predicting Region II of fatigue crack growth, “Paris law” is often not properly used in making life predictions because a majority of the fatigue life of a specimen occurs in Region I. Physical models for Region I growth have not yet been successful. Recently substantial effort is made in modeling crack growth model with experimental data for Regions I and II. The present work reviews recent work in predicting the fatigue crack growth rate in Region I and II. The characteristics of such models are examined and their limitations are discussed in context with steam generator tube crack growth. Recommendations are made on crack growth models for region I and II.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".