Continuing Development of PRO-LOCA for the Prediction of Break Probabilities for Loss-of-Coolant Accidents
Bibliographic record
Abstract
As part of a possible risk-informed revision of the design-basis break size requirements for operating commercial nuclear power plants as specified in the Code of Federal Regulations (CFR), the NRC began development of a probabilistic piping fracture mechanics code called PRO-LOCA. The initial development of this code and its background was published at a prior PVP conference. Since that time, the development of the PRO-LOCA code has continued through an international group program entitled Maximizing Enhancements in Risk Informed Technology (MERIT). The MERIT program includes participation from Canada, Korea, Sweden, UK, and the US (NRC and EPRI). The PRO-LOCA code, which aides in predicting piping break frequencies as a function of break size, incorporates many enhancements in technology since some of the earlier probabilistic codes (e.g., PRAISE) were developed. These enhancements include improved crack stability analyses, leak rate models, crack initiation and growth models, and material property data. In addition, degradation mechanisms such as primary water stress corrosion cracking (PWSCC) for dissimilar welds in pressurized water reactors (PWRs) are included in the PRO-LOCA code. This paper reviews the ongoing development of the PRO-LOCA code by giving a brief description of the recent updates made to the models embedded in the code. Some of these capabilities include improvements to crack initiation and growth models, welding residual stress distribution inputs, the addition of weld overlays, past and future inspections, the addition of importance sampling, and bootstrap methods for predicting confidence limits on output. The current version of the PRO-LOCA code was used for a sensitivity analyses in order to demonstrate the effects of welding residual stress uncertainty on the probability of leak and rupture. Plans for the continuing development of the PRO-LOCA code conclude this paper.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".