Fire suppression performance of water mist under ventilation and cycling discharge conditions
Bibliographic record
Abstract
When gaseous agents are used to extinguish fires, ventilation systems in the compartment must be shut down, otherwise the fire protection system can be expected to fail. Recent research [1] showed that water mist fire suppression systems were able to extinguish fires effectively with a definable degree of ventilation, such as with open doors or vents in a compartment, while gaseous agents could not work effectively under such ventilation conditions. In order to systematically investigate the fire suppression performance of water mist systems under ventilation conditions, a series of full-scale fire tests were carried out by the National Research Council of Canada [2]. The fire scenarios used in the tests included small and large pool fires, spray fires and wood crib fires. These fires were placed in different locations within the compartment and some fires were shielded from the direct hit of water mist. The ventilation conditions in the compartment included non-ventilation (door closed), natural ventilation (door opened) and forced ventilation (door opened and an exhaust fan running). Two types of water mist systems (single-fluid and twin-fluid) were used in the tests. Also, the use of cycling discharge mode for the improvement of extinguishing performance of water mist system for ventilated fires was studied [3]. This paper presents the extinguishing performance of two water mist systems under natural and forced ventilation, and the improvement of fire suppression performance by using cycling discharge mode.
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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".