Some effects on natural ventilation system for subway tunnel fires
Bibliographic record
Abstract
A series of experiments, using a 1:15 model tunnel, were designed in accordance with the Froude conservation approach to investigate the influential parameters on temperature distributions in tunnel fires with natural ventilation. The effect of parameters such as fire size, shaft length and height were investigated. A natural ventilation system was achieved by introducing four vertical shafts spaced at 4 m in the ceiling of the tunnel. In the study reported in this paper, propane gas, located on the floor of the tunnel, was used to simulate the fire source which produced heat release rates in the range of 3.61kW to 12.2 kW. The smoke temperature distributions along the tunnel ceiling and in the vertical direction along the tunnel length were measured using K-type thermocouples trees. Based on the fire plume theory, a dimensionless temperature was defined and it was found that the fire size did not have a major effect on the dimensionless temperature. Despite the fact that the shaft sizes did not significantly affect the distribution of the temperature in the near-field of the fire, they did influence the temperature values with the temperatures dropping with the increase of shaft size. Based on the one-dimensional theory and rational assumptions, two formulas were developed to predict the ceiling longitudinal temperature distribution in tunnel fires with natural ventilation.
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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) | 0.000 | 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".