A Risk-Informed Approach to Leak-Before-Break Assessment of Pressure Tubes in CANDU Reactors
Bibliographic record
Abstract
Abstract The leak-before-break (LBB) assessment of pressure tubes is intended to demonstrate that in the event of through-wall cracking of the tube, there will be sufficient time followed by the leak detection, for a controlled shutdown of the reactor prior to the rupture of the pressure tube. CSA Standard N285.8 (2005, “Technical Requirements for In-Service Evaluation of Zirconium Alloy Pressure Tubes in CANDU Reactors,” Canadian Standards Association) has specified deterministic and probabilistic methods for LBB assessment. Although the deterministic method is simple, the associated degree of conservatism is not quantified and it does not provide a risk-informed basis for the fitness for service assessment. On the other hand, full probabilistic methods based on simulations require excessive amount of information and computation time, making them impractical for routine LBB assessment work. This paper presents an innovative, semiprobabilistic method that bridges the gap between a simple deterministic analysis and complex simulations. In the proposed method, a deterministic criterion of CSA Standard N285.8 is calibrated to specified target probabilities of pressure tube rupture based on the concept of partial factors. This paper also highlights the conservatism associated with the current CSA Standard. The main advantage of the proposed approach is that it retains the simplicity of the deterministic method, yet it provides a practical, risk-informed basis for LBB assessment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".