Fracture Toughness Assessment of Pipeline Steels Under Hydrogen Exposure for Blended Gas Applications
Bibliographic record
Abstract
Hydrogen embrittlement (HE) is a critical concern for pipeline steels, particularly as the energy sector explores the feasibility of blending hydrogen with natural gas to reduce carbon emissions. Various mechanical testing methods assess HE, with fracture toughness testing offering a quantitative measure of defect impacts on structural safety, particularly for cracks arising during manufacturing, fabrication, or in-service conditions. This study focuses on assessing the fracture toughness of two pipeline steels from an existing natural gas network under varying hydrogen concentrations using double cantilever beam (DCB) fracture tests. A vintage API X52 steel with a ferritic–pearlitic microstructure and a modern API X65 steel with polygonal ferrite and elongated pearlite colonies were selected to represent old and new pipeline materials. Electrochemical hydrogen charging was employed to simulate hydrogen exposure, with the charging parameters derived from hydrogen permeation tests. The results highlight the differing impacts of hydrogen on the fracture toughness and crack growth in vintage and modern pipeline steels. These findings are essential for ensuring the safety and integrity of pipelines carrying hydrogen–natural gas blends.
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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 | 0.000 | 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".