Influence of Ambient Pressure over Natural Smoke Ventilation in Shaft Tunnel Fire
Bibliographic record
Abstract
This paper aims to disclose the influence of different ambient pressures over the smoke flow and smoke ventilation in the shaft tunnel. For this purpose, numerical simulations were carried out on the shaft tunnel, using Fire Dynamics Simulator (FDS), a computational fluid dynamics (CFD) software package. The results show that: Under the same fire power, the smoke of tunnel fire spread faster and faster, with the falling ambient pressure. The longitudinal temperature curves of tunnel fire have basically the same features, at different ambient pressures. With the increase of ambient pressure, the longitudinal smoke temperature on the roof of the shaft tunnel gradually declines. The declining rate is positively correlated with the proximity to the fire source. Compared with that in small power fire, the roof temperature difference between low pressure and normal pressure is large in large power fire. The CO concentration in the shaft falls with the rise of ambient pressure. The smoke ventilation efficiency is better in the low-pressure environment, which facilitates the ventilation of fire smoke. The increase of fire power enhances the horizontal inertial force of smoke, and reduces the probability of plug holing. In addition, the ambient pressure has a small effect on the shaft smoke ventilation in small power fire.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".