Enhancing safety in hydrogen refuelling stations: Computational analysis of hydrogen explosion hazards
Bibliographic record
Abstract
Abstract This paper aims to enhance the understanding of hydrogen explosions in hydrogen refuelling stations and evaluate associated risk factors using computational fluid dynamics simulations. The model is first validated against the measured data for hydrogen dispersion and explosion. Different scenarios are then modelled to understand the ignition timing and location. The study estimates acceptable distances to minimize asset damage and human injury from explosion incidents. It has been found that higher wind speeds lead to faster and more extensive dispersion of the hydrogen gas released during a leak. In addition, since strong wind can act as a powerful driving force for the shock wave, the impact of the explosion is found to be less. Interestingly, moving the source of ignition to regions with higher hydrogen concentration has a marginal impact on overpressure and temperature; however, the blockage ratio can significantly amplify the overpressure. It is found that cases with high blockage, including storage room, and cases with large volumes of flammable cloud, including leakage from compressor towards the ground, have the highest hazards. The findings will provide valuable insights into fire and explosion prevention in various areas of hydrogen refuelling stations and contribute to safer hydrogen infrastructure construction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".