Establishing Localized Fire Test Methods and Progressing Safety Standards for FCVs and Hydrogen Vehicles
Bibliographic record
Abstract
The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 11 years. In the past couple of years, significant attention has been directed toward a revision to the standard for vehicular hydrogen systems, SAE J2579(1). In addition to streamlining test methodologies for verification of Compressed Hydrogen Storage Systems (CHSSs) as discussed last year,(2) the working group has been considering the effect of vehicle fires, with the major focus on a small or localized fire that could damage the container in the CHSS and allow a burst before the Pressure Relief Device (PRD) can activate and safely vent the compressed hydrogen stored from the container. Even though there have been no incidents of localized fire causing failure of CHSS in FCVs or hydrogen vehicles, a few incidents of localized fire and subsequent container burst have occurred in Compressed Natural Gas (CNG) vehicles.(3,4) Given the potential severity of such an event, the Safety Working Group decided to address this potential failure mode by developing a new systems level test to demonstrate the performance of the CHSS in the specified fire conditions. Test conditions for the proposed test are based on information derived from actual vehicle fire tests that were conducted both by the Japanese Automobile Research Institute (JARI) and US automobile manufacturers, and the test set-up is loosely based on preliminary work done by Transport Canada and the National Highway Traffic Safety Administration (NHTSA) in the US. The new test method provides the opportunity to verify localized fire resistance of the CHSS and is expected to be published in the next revision of SAE J2579 in 2011. Concurrent with SAE efforts, the United Nations Economic Commission for Europe (ECE) is developing a Global Technical Regulation (GTR) for FCVs. The published SAE standards are being considered along with ISO and other regional standards as the basis for possible requirements. As technical issues are identified at the GTR meetings, these issues are discussed at the SAE Safety Working Group meetings to see if there are opportunities for improvement of SAE documents to ensure that SAE standards remain relevant during this period of rapid technical growth and initial commercialization.
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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".