A SIMULATED STUDY ON THE EFFECT OF WATER TEMPERATURE ON COOLING EFFICIENCY OF WATER MIST FIRE EXTINGUISHERS
Bibliographic record
Abstract
In this paper, the effect of initial water temperature on the cooling performance of a water mist fire suppression system has been investigated. A zero-dimensional analytical model was first developed to study the thermal and dynamic behavior of a single water droplet. The developed model was validated against available experimental data in the literature. The developed model was further extended to simulate the transient heating, evaporation, break-up, and liquid penetration length of a hollow-cone water spray. Results indicated that increasing the initial temperature of the water spray before injection resulted in a decrease in the initial size distribution of the spray droplets. It was further found that the evaporation time of the injected droplets decreased by about 11% and the cooling power of the pre-heated water mist system enhanced by 12% in exchange for increasing the initial temperature of the water spray by 10˚C. It was concluded that pre-heating the sprayed water droplets would likely improve the cooling efficiency of the water mist system. It could be also inferred that at equal cooling power, less water was consumed by the pre-heated spray compared to conventional water mist systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".