An evaluation of fire-plume properties simulated with the Fire Dynamics Simulator (FDS) and the Clark coupled wildfire model
Bibliographic record
Abstract
Before using a fluid dynamics physically based wildfire model to study wildfire, validation is necessary and model results need to be systematically and objectively analyzed and compared to real fires, which requires suitable data sets. Observational data from the Meteotron experiment are used to evaluate the fire-plume properties simulated by two fluid dynamics numerical wildfire models, the Fire Dynamics Simulator (FDS) and the Clark coupled atmosphere–fire model. Comparisons based on classical plume theory between numerical model and experimental Meteotron results show that plume theory, because of its simplifying assumptions, is a fair but restricted rendition of important plume-averaged properties. The study indicates that the FDS, an explicit and computationally demanding model, produces good agreement with the Meteotron results even at a relatively coarse horizontal grid size of 4 m for the FDS, while the coupled atmosphere–fire model, a less explicit and less computationally demanding model, can produce good agreement, but that the agreement is sensitive to surface vertical-grid sizes and the method by which the energy released from the fire is put into the atmosphere.
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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) | 0.000 | 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 | 0.001 | 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".