Hydrogen pipelines and embrittlement in gaseous environments: An up-to-date review
Bibliographic record
Abstract
Pipelines represent the most economical and efficient means for transporting hydrogen in large volumes across vast distances, contributing to accelerated realization of hydrogen economy. Nowadays, the development of hydrogen pipeline projects, including repurposing existing pipelines for hydrogen service, has become a global interest, especially in those major energy-producing and energy-consuming countries. However, steel pipelines are susceptible to hydrogen embrittlement (HE) in high-pressure hydrogen gas environments, potentially leading to pipeline failures. In this review, we establish a comprehensive knowledge base for comprehending, testing, and evaluating the gaseous HE in pipelines by a thorough examination of relevant research work. In addition to an overview of some major hydrogen pipeline projects in the world, the article consists of four integral parts essential to gaseous HE studies, namely, methods for exposure of steels to high-pressure hydrogen gas; measurements of the quantity of H atoms inside the steels; stress-strain behavior of pipeline steels under high-pressure hydrogen gas exposure; and fracture and fatigue testing of pre-cracked steels within gaseous environments. Further research into gaseous HE in pipelines focuses on developing standardized, quantitative, and consistent methods to assess and define the susceptibility of pipelines to gaseous HE. • Examined the testing methods for hydrogen atom entry into steels in gaseous environments. • Reviewed the mechanical properties and fracture/fatigue behavior of steels under exposure to hydrogen gas. • Analyzed the gaps in gaseous hydrogen embrittlement problem. • Proposed methods for further research in gaseous hydrogen embrittlement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".