Hydrogen uptake and embrittlement behavior in pipeline steels: Insights from slow strain rate testing and synchrotron micro-CT imaging
Bibliographic record
Abstract
• Synchrotron micro-CT enhanced visualization of internal cracks undetectable in 2D analysis. • Steel microstructure influenced hydrogen diffusion and trapping, affecting crack initiation and severity. • Simultaneous hydrogen ingress and stress resulted in more severe embrittlement. • Hydrogen retention, not just crack initiation, critically drives embrittlement and failure mode. Hydrogen embrittlement (HE) presents a major challenge to the integrity of steel pipelines, often leading to premature failure. Traditional methods using two-dimensional (2D) analysis of damaged structures, often overlook critical features related to failure. Hence, this study investigates the hydrogen embrittlement susceptibility of two pipeline steels, X60 and X65, using a combination of mechanical testing, hydrogen diffusion and trapping studies, microstructural characterization, and synchrotron micro-computed tomography (micro-CT) imaging. The results highlight the critical role of hydrogen trapping and retention in HE, with steel microstructure significantly affecting hydrogen uptake and diffusion as well as crack nucleation and propagation. Synchrotron micro-CT imaging provided more accurate crack pattern assessments than traditional 2D methods, revealing potential misinterpretations from 2D cross-sectional analysis. This study concludes that simultaneous hydrogen ingress and mechanical loading is more damaging than pre-charging with high hydrogen concentrations, and that hydrogen retention capacity plays a greater role in embrittlement behavior than crack initiation. The failure mechanism of the hydrogen-charged steels shifted from being plasticity-based to decohesion-driven, based on the hydrogen content and retention in the steel, which is in line with the unified HELP+HEDE model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".