The influence of cross‐sectional aspect ratio on ceiling temperature profile and mass flow rate of ceiling jet in tunnel fires
Bibliographic record
Abstract
Abstract In this paper, the influence of tunnel cross‐sectional aspect ratio on the ceiling temperature profile and mass flow rate (MFR) of ceiling jet is studied theoretically and numerically, and 13 tunnel cross sections with different aspect ratios () are considered. A total of 26 full‐scale numerical simulation cases are conducted using Fire Dynamics Simulator, and small‐scale experiments are used to verify the accuracy of the simulations. Results show that the maximum ceiling temperature is more sensitive to the tunnel height and decreases with increasing aspect ratio, which can be divided into two regions, <1 and ≥1. When ≥1, the maximum ceiling temperature varies more linearly. The initial locations of the one‐dimensional spread for the tunnel with different tunnel cross‐sectional aspect ratios are similar, which are concentrated at 15–20 m from the fire source when taking the MFR increase rate of 0.001 as the criterion. By introducing the sectional coefficient, the MFR model and temperature attenuation model of ceiling jet are developed for the tunnels with <1 and ≥1, respectively. The results of this paper could provide definite reference value for the smoke control in tunnel fires.
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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 | 0.000 | 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".