A Review of Current Best Practice for Validation of Probabilistic Fracture Mechanics Codes for Assessment of Nuclear Structural Integrity
Bibliographic record
Abstract
Abstract Probabilistic fracture mechanics (PFM) based computer codes are increasingly used to evaluate integrity of structural components and systems in nuclear power plants. For example, FAVOR (Fracture Analysis of Vessels – Oak Ridge) is a PFM code for structural integrity of a nuclear reactor pressure vessel, xLPR (eXtremely Low Probability of Rupture) is a PFM code for leak-before-break evaluation of nuclear piping systems due to active corrosion damage, such as primary water stress corrosion cracking (PWSCC). These PFM based computer codes are designed to predict the probability of failure for nuclear structures. Verification and validation are essential to ensure the accuracy and credibility of a PFM based computer code. Validation is the process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the computer code. It provides evidence for how accurately the computer code simulates the real world for the system responses of interest. This conference paper provides a review of current best practice for validation of PFM codes for assessment of nuclear structural integrity. The review is based on information available in the open literature.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".