Near-Neutral pH Stress Corrosion Crack Test Results and Potential Application to Crack Growth Retardation and Hydrogen Sensitivity in Pipeline Steels
Bibliographic record
Abstract
Abstract Over a five-year period, a series of fatigue crack growth experiments have been conducted in air and in a bubbling CO2 environment to induce diffusible hydrogen ingress. While the main objective of the hydrogen-charging research targeted characterization of near-neutral stress corrosion crack growth, many of the findings can inform emerging hydrogen transport crack threat management. Summarizing and highlighting these findings and trends could benefit the pipeline industry and accelerate research toward international net zero carbon objectives. Crack growth retardation in X52 and X65 were found to be readily achieved with an overload cycle, both in air and with hydrogen charging. Subsequent underload cycles were shown to cause resumption of crack advancement: such underload cycles are typical of pipeline operations. Grade X65 pipe steel samples with hydrogen charging were found to be sensitive to underload cycles with significantly higher da/dN growth rates. Crack retardation models currently available were evaluated using the AFGROW crack growth modeling software to determine and compare model performance to test results. Potential implications of these findings to mainline pipe integrity management are considered in light of crack threat management methods, specifically periodic hydrostatic test methodologies, including emerging hydrogen transport considerations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".