Review of three dimensional water fog techniques for firefighting
Bibliographic record
Abstract
This report provides a review of research into, and application of, a three dimensional (3D) water fog technique for firefighting. The impact of water fog characteristics associated with properties of the nozzle (e.g., droplet size, momentum, flow rate, spray angle and pattern) and discharge techniques (e.g., discharge angle, and discharge duration related to the bursts) on performance of the 3D water fog technique are discussed. Potential use of numerical computer studies to help understand and improve this technique is also reviewed and discussed. The 3D water fog technique is not designed to replace the direct fire attack but rather to complement existing forms of fire attack in an effort to increase the safety and effectiveness of fire fighting teams. Compared to the traditional straight-stream attack, the 3D water fog technique has advantages in controlling steadily growing fires where the space can still be entered, but where the seat of the fire cannot be attacked directly. It has also been used for offensive attack to control flashover. However, there is not sufficient research to evaluate its capabilities in other fire scenarios, such as reducing the likelihood of backdraft, and in controlling fire threats in low visibility scenarios. Research on the effects of the nozzle type, application techniques and fire conditions on the performance of the 3D water fog tactic is also very limited. Further research efforts, including both experimental and numerical studies, can help firefighters understand how to most effectively use this technique and also help to improve its performance in firefighting.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".