The Impact of Probabilistic Modelling on Predicting the Remaining Life of Pipes in Nuclear Plants
Bibliographic record
Abstract
Flow accelerated corrosion (FAC) is a serious form of degradation in primary heat transport piping system (PHTS) of the nuclear reactor. Pipes transporting hot coolant from the reactor to steam generators are particularly vulnerable to FAC degradation, such as tight radius pipe bends with high flow velocity. FAC is a life limiting factor, as excessive degradation can result in the loss of structural integrity of the pipe. To prevent this, engineering codes and regulations have specified minimum wall thickness requirements to ensure fitness for service of the piping system. Nuclear utilities have implemented periodic wall thickness inspection programs and carried out replacement of pipes prior to reaching an unsafe state. To optimize the life cycle management of PHTS, accurate prediction of time of replacement or “end of life” of pipe sections is important. Since FAC is a time-dependent process of uncertain nature, the paper presents two probabilistic models for predicting the end of life. The paper illustrates that the modeling assumptions have a significant impact on the predicted number of replacements in the piping system. A practical case study is presented using wall thickness inspection data collected from Canadian nuclear plants.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".