Reliability Analysis for the Feeder Subject to Wall Loss
Bibliographic record
Abstract
Wall thinning is one of the most common degradation mechanisms experienced in piping system. Gradual wall thinning can cause the pipe to leak or in the worst scenario, to rupture. Wall thinning due to FAC of feeder pipe in CANDU® reactors has been identified as an active degradation mechanism, and local thinning has been observed in various locations such as elbows/bends and Grayloc. The assessment of structural integrity is important for the fitness-for-service of those feeders whose wall thickness is predicted to be lower than the required minimum wall thickness before their design life and therefore subject to costly repair or replacement. Among various probabilistic methods, the first-order reliability method (FORM) is adopted in this paper to evaluate the structural reliability of feeders subject to wall thinning, while the wall thickness, one of the key parameters in the reliability analysis, is modeled by three methods based on the wall thickness measurements. They are linear regression analysis, random thinning rate analysis and gamma process modeling. The difference and limitation of the methods for reliability analysis are addressed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".