The Effects of Wind on Liquid Fuelled Pool Fires
Bibliographic record
Abstract
Macroscopic fire parameters such as fuel regression rate, flame height and flame tilt are critical to the development of detailed fire models and empirical tools for hazard analysis [1–3]. As a result, these characteristics have been investigated by many researchers using various measurement methods in studies of liquid fuelled pool fires of different diameters and fuel types, under a range of crosswind conditions. In investigations related to transportation accidents, fire scenarios have been complicated further through interactions between the fire and upwind or downwind objects [1,2]. Of particular interest is the determination of fuel regression rate, an important parameter but one that is generally difficult to characterize accurately. Many techniques have been reported for measurement of fuel regression rate. These include load cells [2,4,5], differential pressure systems [2,5–7], sight glass and float-type level meters [6–8] and thermocouple rakes [1]. In general, load cells have been employed most successfully for measurements in smaller scale fires [2,4], while researchers have turned to differential pressure and thermocouple type systems for measurements in fires above 5 m diameter [2,6,7]. All the techniques have been used with varying levels of success to measure fuel regression rate under quiescent conditions. Under crosswind conditions and in cases with an object present, however, inherent wandering of the fire plume and dynamic wind loading on the pool can be of additional concern as they affect the accuracy and repeatability of the measurements [1,2,6,7]. In several excellent reviews, available results have been summarized and used to derive empirical correlations relating overall fire characteristics to fire diameter, fuel type and/or wind velocity [3,9–11].
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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) | 0.000 | 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".