Smoke Spread in Underground Metro Station
Bibliographic record
Abstract
In the event of subway train fire smoke is the most fatal factor because smoke spreads in direction coincide with passenger’s evacuation path. It reduces visibility and can cause fatalities by asphyxiation. This research presents a numerical study to investigate the effect of exhausting smoke by single point extraction and exhausting smoke by multi- point extraction on passengers’ life safety. Also, effect of adding smoke barriers at stairs entrance on passengers’ life safety is studied. Fire Dynamics Simulator (FDS) software version 5.5.3 is utilized to simulate 6 case studies in 150 m long, 20 m wide and 13 m height domain with a subway car fire source simulated as a fire with unsteady heat release rate of 35 MW resulted from burning Heptane as a fuel. Results show that exhausting smoke by multipoint extraction system in underground subway station gives better performance than single point extraction system. By increasing the distance between vents in multipoint extraction system, tenable conditions improves at human level. Smoke barrier addition to ventilation system has a great effect on the efficiency of smoke extraction and improving tenable conditions at human level.
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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".