The effects of different turbulence models on the fire plume characteristics of train fires in tunnels
Bibliographic record
Abstract
Abstract To investigate the effects of different turbulence models on the fire plume characteristics of train fires in tunnels, we employed five turbulence models: (1) one single-equation model: Spalart–Allmaras (S–A); and (2) four two-equation models: k−ε, k−ω, improved delayed detached eddy simulation (IDDES) based on SST k−ω and large eddy simulation (LES). These models were adopted for the numerical simulation of train fire plumes in tunnels, and their outcomes were compared with those of experiments conducted on a reduced-scale train fire model in a laboratory setting. These findings highlight the substantial impact of turbulence model selection on the simulation of fire plumes resulting from train fires in tunnels. When a train fire occurs within a tunnel, it is observed that the longitudinal distributions of temperature, pressure, velocity and soot density on the tunnel ceiling exhibit asymmetry. Among the selected turbulence models, the LES model consistently provided predictions that closely aligned with the experimental data for both fire plume morphologies and tunnel ceiling temperatures. The findings will help address the current gap in turbulence model applicability studies in fire simulations and offer important references for high-precision fire dynamics simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".