The Impact of Probabilistic Modeling in Life-Cycle Management of Nuclear Piping Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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, this paper presents two probabilistic models for predicting the end of life. This paper illustrates that the modeling assumptions have a significant impact on the predicted number of replacements and life-cycle management of the nuclear piping system. A practical case study is presented using wall thickness inspection data collected from Canadian nuclear plants.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it