Finite element modeling of the hydrogen atom distribution on dented pipeline for hydrogen transport under cyclic loading
Bibliographic record
Abstract
Abstract Repurposing existing natural gas pipelines for hydrogen transport requires an accurate assessment of the distribution of hydrogen (H) atoms at surface defects such as dents under frequent pressure fluctuations encountered on gas pipelines. In this work, a 3-dimensional finite element-based model was developed to determine the stress/strain and H atom concentrations at an unconstrained dent on an X52 steel pipe experiencing denting, spring-back and cyclic loading processes. As expected, a stress/strain concentration generates at the dent center, while the cyclic loading reduces the stress level and shifts the stress concentration zone from the dent center along the circumferential direction. As the dent depth increases, the maximum H atom concentration is further shifted from the dent center to the side. A coincident relationship between the maximum H atom concentration, von Mises stress, hydrostatic stress and plastic strain does not exist. Pressure fluctuations decrease both the stress and H atom concentrations, providing a beneficial effect on reduced risk of the dented pipelines to hydrogen embrittlement in high-pressure hydrogen gas environments. Further analysis shows that the indenter size has little influence on the H distribution in the dent area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".