Évaluation des risques liés à l’hydrogène et analyse des dangers pour les applications ferroviaires
Bibliographic record
Abstract
The integration of environmentally friendly fuel options, such as hydrogen, in mass transportation is a pivotal research area for fostering a sustainable hydrogen economy. Hydrogen Fuel Cell (HFC) technology represents a major zero-emission alternative for powering rail transport. However, the large-scale deployment of HFC trains faces several technological and non-technological barriers, including safety concerns related to hydrogen storage and handling. The project, titled Hydrogen Hazard Assessment and Risk Management for Rail Applications, aimed to support the demonstration and pilot deployment of hydrogen technologies in the rail sector while ensuring safety compliance. Key activities included a literature review on hydrogen locomotives and risk assessment methods, industry engagement to identify system configurations, and a comprehensive evaluation of hazards associated with hydrogen-containing systems. Both semi-quantitative and quantitative risk assessments (QRA) were conducted, focusing on failure modes in locomotive systems. The analyses revealed that most failure modes posed low risk, while two medium-risk scenarios were identified related to onboard hydrogen storage tanks. Recommendations for countermeasures, such as impact protection and enhanced pressure relief systems, were proposed. The QRA case study utilized CNL’s toolkit to model outcomes of hydrogen leaks, demonstrating the importance of leak detection, maintenance, and reliable safety systems. Additionally, a review of relevant regulations and standards, including the Canadian Hydrogen Installation Code (CHIC), provided a framework for risk reduction strategies. The study emphasized integrating engineering and administrative measures to ensure safety. Overall, this project establishes a foundation for the safe adoption of hydrogen in the rail sector and contributes to the broader goals of decarbonizing transportation.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".