Numerical prediction of size, mass, temperature and trajectory of cylindrical wind-driven firebrands
Bibliographic record
Abstract
Mathematical models and numerical solution procedures for predicting the trajectory, oscillation, possible rotation, and mass and size time-evolution of cylindrical wind-driven firebrands are described and discussed. Two test problems and the results, used for validating the mathematical models, are presented. In one, experimental measurements of non-burning cylindrical particles falling in still air are compared to numerical predictions and in the other, predictions of time-evolution of mass and size of stationary burning particles in air flows are compared with experimental results reported in the literature. Results yielded by the proposed models for a demonstration problem involving cylindrical wind-driven firebrands, with the same initial volume, mass and position, but different initial aspect ratios and distinct initial orientations relative to the wind velocity, are then presented. These results show the following: the horizontal distance travelled by the firebrand from release to landing locations is an increasing function of its initial aspect ratio; and the initial orientation of the firebrand, and its subsequent oscillations including possible rotation, have a significant influence on its trajectory, thus it is important to account for them in mathematical models formulated for predicting the spread of fires by spotting.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".