A Probabilistic Assessment of Flow-Accelerated Corrosion Rate in Pipe Bends With Unknown Initial Thickness
Bibliographic record
Abstract
In CANDU reactors, the feeder pipe system supplies the primary coolant from the reactor core to steam generator. The flow-accelerated corrosion (FAC) of the extrados of the first bend of the outlet feeder is a life limiting factor. Therefore, accurate prediction of FAC rate is needed to determine the scope of inspection and replacement of feeder pipes during an outage. To estimate the FAC rate, the initial wall thickness of the extrados of the bend is required. However, initial wall thickness is not known precisely, because the bending of pipe during installation causes the thinning of extrados section. Another difficult is the sizing error or noise in the wall thickness measurements introduced by the inspection probes. The objective of this paper is to develop a sound probabilistic method for the FAC rate estimation considering uncertainties in the initial wall thickness of bend and the sizing error (or noise). A Bayesian model is developed and applied to predict the end of life of the outlet bend on a risk-informed basis. A practical case study is presented to illustrate the use of the proposed method.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".