Experimental Study on Hydrogen-Induced Crack Propagation of X80 Steel Under Alternating Pressure Fluctuations
Bibliographic record
Abstract
When hydrogen is transported in a pipeline, the fatigue loading in the pipeline will enhance hydrogen accumulation and diffusion, thus increasing the risk of hydrogen-induced fracture. In this study, specimens are subjected to cyclic loading within an autoclave, where hydrogen gas pressure is varied to examine its impact on fatigue crack growth. The influence of hydrogen pressure and stress variations on the fatigue crack growth rate is investigated. The findings show that as hydrogen pressure increases, the crack growth rate also rises, and at 3 MPa hydrogen pressure the rate is elevated by one order of magnitude compared to that in air, reaching 10−2 mm/cycle. In hydrogen, the fatigue crack propagation rate decreases with increasing loading frequency. When the frequency is 0.02 Hz, the crack propagation rate reaches a maximum of 10−2 mm/cycle, whereas at 0.5 Hz, the fatigue crack propagation rate is generally below 10−3 mm/cycle. With the maximum stress held constant during cyclic loading, the fatigue crack growth rate increases as the stress range widens, and when the stress ratio reaches 0.5, the crack propagation rate can increase to a maximum of 10−1 mm/cycle. Based on these experimental results, a predictive model is proposed to estimate the crack growth rate under different hydrogen pressures and loading conditions, and the average relative errors of predictive values and experimental data are limited below 10%.
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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".