Depressurization-Induced Crack Growth Enhancement for Pipeline Steels Exposed to Near-Neutral pH Environments
Bibliographic record
Abstract
Pressure fluctuations are recognized as the driving force for the crack growth of pipeline steels in near-neutral pH environments; however, the crack growth mechanisms are still not fully understood. Difficulty in understanding the crack growth mechanisms is present due to two dilemmas between laboratory testing and field findings: high frequency study in the laboratory versus low frequency pressure fluctuations in the field; constant amplitude cyclic laboratory tests versus random pressure fluctuations in the actual spectra. To bridge the dilemmas, the crack growth behavior of X60 pipeline steel was investigated in near-neutral pH solution at frequencies as low as 1×10−5 Hz under variable amplitude cyclic loading. Special attention was given to the loading scheme consisting of minor cycles with R ratios (minimum stress/maximum stress) as high as 0.9 and underloads with a relatively lower R ratio of 0.5. It was found that the constant amplitude crack growth rate in near-neutral pH solution in the frequency region below 1×10−3 Hz decreases with decreasing loading frequency, and it reaches a constant value at very low frequencies. This crack growth rate-frequency relation is opposite of that found in the high loading-frequency regime, where crack growth rate was found to increase with decreasing loading frequency. Crack growth rate was observed to increase by a factor of up to 10 when the underload plus minor cycle loading scheme, as mentioned previously, was applied. Based on the findings obtained from the investigation, recommendations of pressure control were also made to minimize the crack growth during pipeline operation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".