Numerical Analysis of Hydrogen Diffusion and Distribution at Corrosion Defect on Aged Pipelines for Hydrogen Service
Bibliographic record
Abstract
Abstract Using existing natural gas pipelines to transport hydrogen blended natural gas is an important way to achieve efficient hydrogen transportation. However, the existing pipelines may contain corrosion defects, which can affect the diffusion and distribution behavior of hydrogen atoms, further resulting in hydrogen embrittlement and pipeline failure. This study applied the numerical analysis method to simulate the hydrogen atom redistribution in interstitial lattice sites and in trapping sites of X52 steel pipeline with corrosion defect by coupling stress field and hydrogen diffusion field. It was found that under applied tensile strain, the hydrogen atoms accumulated at corrosion defect due to local stress concentration. The maximum lattice concentration was located on the outer pipe wall, and the maximum trapped concentration occurred at the defect center. And both of them elevated with tensile strain and initial concentration increasing, although there was a difference of several orders of magnitude between them. The increase of temperature reduced the maximum concentration. Besides, the maximum hydrogen concentration in trapping sites also increased with the higher binding energy. The proposed model can be used to determine the hydrogen concentration and distribution behavior at corrosion defect for HE prediction of corroded pipeline.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".