High pH Crack Growth Sensitivity to Underload-Type of Pressure Fluctuations
Bibliographic record
Abstract
High pH Stress Corrosion Cracking (HpHSCC) is a significant threat to the buried pipelines, which are protected through simultaneous coating and cathodic protection strategies. In the past decades, extensive research has been devoted to assessing the influence of environmental and metallurgical factors on the susceptibility to HpHSCC. With reference to mechanical factors, previous studies employed either slow strain rate or constant amplitude testing methods. However, the pressure fluctuation data extracted from pipeline operations has indicated that pipelines experience highly variable amplitude loading conditions during their service. Accordingly, an important consideration in managing HpHSCC is load interaction. Statistics show a higher probability of HpHSCC failures within the 30 km downstream from pump/compressor stations where the pipeline steels experience elevated service temperatures, with incipient higher susceptibility to HpHSCC. However, the pipeline sections within the 30 km downstream from pump/compressor stations also experience the underload-type of pressure fluctuations that feature a maximum pressure close to the design limit, frequent and large amplitudes of depressurization, resulting in low stress ratio, R (minimum stress/maximum stress), and many smaller pressure fluctuations (minor cycles) with R ratio closer to +1.0. It has been well characterized that the underload-minor-cycle-type of pressure fluctuations has the significant acceleration effect on crack growth rates in near-neutral pH (NNpH) environments. However, the effect of the underload-type of pressure schemes on HpHSCC crack growth has not been well developed. In this research work, a cathodically protected X65 steel specimen in the developed high pH solution, composed of 1N Na2CO3 and 1N NaHCO3, was subjected to different loading conditions. These loading waveforms simulate underload cycles (R = 0.5), minor cycles (R = 0.9) and variable amplitudes consisting of both underload and minor cycles, respectively. The HpHSCC test results showed that the highest and lowest crack growth rates were obtained in high and low R ratio constant amplitude loading conditions, respectively. Furthermore, an intermediate crack growth rate was obtained under variable amplitude loading condition. These results indicate that the underload cycles retard crack growth rate in high pH environments.
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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 | 0.001 |
| 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 | 0.001 |
| 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".