PRCI Burst Pressure Model Modernization and Performance
Bibliographic record
Abstract
Abstract The Pipeline Research Council International (PRCI) contracted development and deployment of a burst pressure prediction fracture mechanics model in 2001, embodied in the CorLAS™ V2.0 software. Since that time, many North American operators have made this model an integral part of their integrity management processes with excellent track records for both effectiveness and efficiency. Even though the PRCI burst model originally targeted pipe body stress corrosion cracking, operators have found the model performs equally well for discrete crack-like features associated with long seam welds, and highly versatile in estimating burst pressures of irregularly shaped crack-likes reported by both in-line inspection tools and field non-destructive evaluations. The question is: can application of the model evolve to address emerging threats and trends? As the pipeline industry transitions away from Charpy V-Notch energy as a low fidelity fracture toughness surrogate, Enbridge has repackaged the PRCI burst model to accept K toughness directly, and referred to as KorLAS herein to denote K input built on legacy CorLAS™ equations and empiricism. KorLAS performance over the tested range of flaw dimensions, pipe steel toughness, wall thicknesses, crack morphology, and crack shape are assessed and compared to test and forensic data. The pipeline industry has emerging technologies which are able to detect, size, and report selective seam weld corrosion (SSWC) features. The predicted burst pressures of SSWC features have historically been difficult to predict. A reasonable and prudent tailored KorLAS input method to assess SSWC fitness for service is presented. KorLAS performance at lower toughness has been difficult to validate or judge due to the scarcity of available test data. Test data now made available to the public allows tentative envelope expansion down to about the 10th percentile toughness observed in North American vintage line pipe. Although cold welds can be problematic to detect, KorLAS performance in assessment of cold welds is evaluated against available test data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".