Enhancing hydrogen pipeline safety: Perspectives on resilience against hydrogen-induced degradation
Bibliographic record
Abstract
ABSTRACT Transport of hydrogen in blended form in existing natural gas pipelines contributes to the accelerated realization of a full-scale hydrogen economy. However, steel pipelines are considered susceptible to hydrogen (H)-induced degradation (HID) in high-pressure gaseous hydrogen environments. This work develops a critical review of the up-to-date progress in pipeline HID research between 1994 and 2024, defining the spatiotemporal distributions of research publications, featured researchers and their affiliations, and thematic topics. Pipeline HID research encompasses five themes: (i) hydrogen (H) atom generation, (ii) adsorption and absorption of H atoms, (iii) diffusion and trapping of H atoms in steels, (iv) HID mechanisms, and (v) hydrogen-induced pipeline failures and integrity management. Specific disturbance factors for resilience assessment on hydrogen pipelines are identified, and the evolution of research methods and test approaches is reviewed. There is a rapid increase in pipeline HID research, with the number of relevant publications from single digital to over 60 per year. The publications are mainly from Asia (47.7%), Europe (23.1%), and North America (20.6%), where China (53) contributes the most literature on pipeline HID in Asia. Of the top sixteen institutions contributing to HID research, there are five located in China. Currently, the primary knowledge gap lies in reliable assessment and effective control of HID-related risks to pipelines for timely decision-making to maintain pipeline safety. Resilience is one of the new paradigms to assess the vulnerability, robustness, reliability, and recovery ability of pipelines when facing disturbance. This critical review also covers the engineering applications of resilience, along with its principles, performance metrics, models, and solution methods. The concept of pipeline resilience is proposed considering the HID risk. The pathway for effective assessment, optimization, and improvement of the hydrogen pipeline resilience is discussed.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".