A Review of Water Mist Fire Suppression Technology: Part II-Application Studies
Bibliographic record
Abstract
The progress on the research and application of water mist technology in fire suppression has been substantial over the last decade. This paper, following our previous review on water mist fundamental studies, reviews recent water mist applications for: the extinguishment of Class B spray and pool fires in machinery spaces, gas turbine enclosures, combat vehicles, and flammable liquid storage rooms; the extinguishment of ClassA fires in residential occupancies, marine accommodations and public spaces, heritage buildings and libraries; the extinguishment of Class C fires in electronic equipment and computer rooms; and the protection of aircraft onboard cabin and cargo compartments. Some new applications, such as the use of water mist for the extinguishment of Class K fires in commercial cooking areas; and the use of water mist as a possible total-ship protection method, as well as the use of water mist for the protection of heavy goods vehicle shuttle trains, are also reviewed. Up-to-date development of corresponding test and design criteria for the installation of water mist fire protection systems and for the evaluation of the capabilities and limitations of watermist for fire suppression in some application areas, such as machinery spaces, ship’s cabins and corridors, and turbine enclosures, are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".