Overview of recent progress in fire suppression technology
Bibliographic record
Abstract
In recent years, with the halon phase-out, there has been a major thrust towards finding new advanced fire suppression systems. Some of the newly developed fire suppression systems include halocarbon and inert gaseous agents, water mist systems, compressed-air-foam systems, and aerosol and gas generators. Halocarbon agents are chemicals similar to halon except that its molecular structure was modified to reduce or eliminate the chlorine and bromine atoms that are responsible for ozone depletion. They can extinguish fires at their design concentration, however, they produce Thermal Decomposition Products (TDP) including hydrogen fluoride (HF) at much higher levels than halon. Inert gas agents extinguish fire by oxygen depletion. They have zero ODP and no Global Warming potential, and they are not subject to thermal decomposition when used in extinguishing fires. However, they require high-pressure storage cylinders which has implications for space and weight. Fire suppression by water mist is mainly by a physical mechanism. Water mist fire suppression systems have demonstrated a number of advantages, such as good fire suppression capability, no environmental impact and no toxicity. However, water mist does not behave like a total flooding agent, thus the fire suppression effectiveness of water mist depends on the potential size of the fire, properties of the combustibles, and the degree of obstruction, as well as the water mist characteristics. Aerosol systems produce micron size dry chemical particles and gas products, and extinguish fires by removing and recombining flame propagation radicals and by absorbing heat. Gas generators produce a large quantity of inert gases by combustion of solid propellants, and extinguish fires by oxygen depletion. All of the recently developed fire suppression systems extinguish fires at their design conditions, however, no one system can be chosen as the best system for all applications. Some perform better than others in a particular application. All have some limitations and concerns that have to be dealt with in extinguishing fires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.035 | 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 teacher head, 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".