CFD study of smoke movement during the early stage of tunnel fires: comparison with field tests
Bibliographic record
Abstract
Temperature and smoke spread in the early stage of a fire were modeled, using computational fluid dynamic techniques, and compared with data obtained from field tests conducted in an operating roadway tunnel in the City of Montreal, Canada. Fire characteristics, including temperatures and smoke spread over the tunnel were measured during these tests. Two types of fire scenarios were simulated: gasoline pool fires under vehicles and gasoline pool fires behind vehicles. The estimated fire size used in the simulations was 650 kW. The initial and boundary conditions of each simulation were set to mimic the conditions of the corresponding test. Comparisons were made to temperature and smoke optical density measurements. In general, favorable comparisons between the numerical predictions and the experimental data were observed. The ceiling temperature downstream of the fire decreased with an increase in the distance from the fire source, which is also the case for smoke optical density. The ceiling temperatures produced by the fire behind the vehicle were higher than those produced by the fire under the vehicle. The temperature variation along the central cross section of the tunnel shows that the highest ceiling temperature occurs 3~5 m downstream of the fire because the plume was tilted by the airflow inside the tunnel. Fire location had a significant impact on ceiling temperature development in the tunnel. The airflow conditions at the fire location significantly affect smoke and temperature distributions in the tunnel which will also affect the performance of detection systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".