History of Pressure Fluctuations Related to Severity of Near-Neutral pH SCC
Bibliographic record
Abstract
This paper describes a small exploratory study into the feasibility of predicting where near-neutral-pH stress-corrosion cracking (NN-pH SCC) would be more likely to occur based upon the history of pressure fluctuations in a pipeline. Such a correlation would be very valuable for prioritizing hydrostatic testing, in-line inspection (ILI), and direct assessment in addition to possibly suggesting a way to minimize the occurrence of SCC. The recent development of ILI tools that can find stress-corrosion cracks coupled with computerized SCADA and GIS systems provides an opportunity to make accurate assessments of the severity of SCC in a pipeline segment and to characterize the history of that segment. Previous work had shown the importance of strain rate and pressure fluctuations in promoting SCC. On that basis, Enbridge Pipeline, Inc. initiated a review of their system, which indicated that there appeared to be a strong correlation between SCC severity and magnitude and frequency of pressure fluctuations. Data from two other pipeline companies — one liquid and one gas — was obtained to see if the correlation was more broadly valid. It was determined that the correlation utilized by Enbridge was biased towards fatigue-type considerations. This suggests that the cracking on pipelines that demonstrate this correlation may involve a corrosion-fatigue mechanism in addition to, or instead of, traditional environment-sensitive SCC. Analysis of data from the other two companies was ambiguous, primarily because the available SCADA data covered only 1.25 years of recent operation. It also is possible that some effects of pressure fluctuations might have been masked by environmental effects and steel susceptibility. Consideration of such factors in addition to the inclusion of information on coating, pipe manufacturer, and geological features, would be expected to produce an even higher level of predictability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".