The Effects of Ventilation and Preburn Time on Water Mist Extinguishing of Diesel Fuel Pool Fires
Bibliographic record
Abstract
The goal of the National Institute for Occupational Safety and Health (NIOSH) Pittsburgh Research Laboratory Fire Fighting and Prevention Program is to reduce the number of fires and fire-related injuries in the mining industry. As part of this effort, water mist is being evaluated for the suppression of underground mine fires, such as fires in diesel fuel storage areas. In this study a series of large-scale fire tests was conducted to investigate the effects of ventilation and preburn time on water mist extinguishing of three diesel fuel pool fires with heat release rates of 230 kW, 1, and 3MW. The experiments were conducted in a simulated underground coal mine diesel fuel storage area under three ventilation conditions: no ventilation, natural ventilation, and forced ventilation and with two preburn times for the no ventilation condition: 30 s and 1 min. Without ventilation the 230kW fire was the most difficult to extinguish; with natural ventilation the 1MW fire took the longest time to extinguish; and with forced ventilation the 3MW fire was the most challenging one. With the 30-s preburn time, the extinguishing time was nearly the same for the 230kW fire as with the 1-min preburn time, while it increased for both 1 and 3MW fires, with the 1MW fire being the most difficult to extinguish. The extinguishing mechanisms including fuel surface cooling, flame cooling, and oxygen depletion and displacement are discussed. The critical water flow rate is estimated for the fires extinguished by the surface cooling mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".