Assessment of the capabilities of FireFOAM to model large-scale fires in a well-confined and mechanically ventilated multi-compartment structure
Bibliographic record
Abstract
This article presents a large eddy simulation study of a pool fire in a well-confined and mechanically ventilated multi-room configuration. The capabilities of FireFOAM are assessed by comparing the numerical results to a well-documented set of experimental data available from Propagation d’un Incendie pour des Scénarios Multi-locaux Elémentaires. The eddy dissipation concept, finite volume discrete ordinate method, and one k-equation model are used for combustion, thermal radiation, and sub-grid scale closure, respectively. The main boundary conditions are imposed based on the experimental profiles. A detailed comparison is made with available experimental data. Good agreement between the large eddy simulation results and experimental values is achieved for temperatures, velocity, CO 2 volume concentrations, and pressures for most compartments. There are some noticeable underpredictions of temperature in the outlet room. Overall, FireFOAM is shown to have good predictive capabilities for the present confined large-scale fire scenario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".