Modeling of hydrogen atom distribution at corrosion defect on existing pipelines repurposed for hydrogen transport under pressure fluctuations
Bibliographic record
Abstract
While repurposing the existing pipelines for hydrogen transport contribute to accelerated development of the full-scale hydrogen economy, the suitability of the aged pipelines should be assessed on their hydrogen embrittlement (HE) susceptibility in high-pressure gaseous hydrogen environments. In this work, a three-dimensional mechanics-hydrogen diffusion coupling finite element model was developed to determine the distribution of hydrogen (H) atoms at corrosion defect on pipelines under pressure fluctuations. Parametric effects, including corrosion defect dimensions (i.e., width, length, and depth) and pressure fluctuating parameters (i.e., cyclic load ratio and loading frequency), were determined. A high stress concentration exists in the longitudinal edge of the defect, while the stress level in the circumferential edge is low. The defect center is associated with the greatest stress and stress variation amplitude. H atoms tend to concentrate at the corrosion defect, especially the defect center, representing the most vulnerable site to initiate hydrogen-induced cracks. Most H atoms reside at the lattice sites, rather than the traps, indicating a limited capacity of the traps to host H atoms as compared to the crystalline lattice sites. With the increase in defect depth and length, both the stress level and stress variation amplitude at the corrosion defect are apparently elevated. More H atoms accumulate at the defect center, increasing the susceptibility to HE. As a comparison, the HE susceptibility of the corroded pipelines decreases with increased corrosion width. As the cyclic load ratio decreases, less H atoms accumulate at the corrosion defect. A decreasing cyclic loading frequency results in decreased stress variations but an increased H atom concentration at the corrosion center. The results provide a base to control pipeline HE by properly adjusting the operating pressure.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".