Probabilistic Estimation of Flow-Accelerated Corrosion Rate at the Welded Joints of the Nuclear Piping System
Bibliographic record
Abstract
Feeder piping is an integral part of the heat transport system (HTS) that supplies the primary coolant from the reactor to the steam generator in CANDU reactors. One of the life limiting factors of the feeder pipes is the highly localized wall thinning caused by the flow-accelerated corrosion (FAC) at the welded joints of the pipes. To ensure the fitness-for-service of the piping system, periodic inspections of the pipe wall thickness and estimation of the FAC thinning rate at the welded joints are needed. A major challenge of FAC rate estimation at the welded joints is that the initial wall thickness is known precisely, since the grinding process before the welding introduces initial thinning in the pipes. Using the nominal wall thickness without considering this initial thinning is likely to overestimate the FAC rate. Another difficult is the sizing error in the wall thickness measurements, which also needs to be properly accounted. This paper develops a sound probabilistic method for the FAC estimation for the welded joints considering both the initial thinning and sizing error. Predictions regarding the lifetime of individual welded joints are also obtained from the proposed method. A practical case study of the problem in a nuclear plant is presented.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".