Influences of free-stream turbulence and char-layer porosity on the drag on windborne firebrands
Bibliographic record
Abstract
A better understanding of the mechanisms that cause or affect the propagation of forest fires is essential for their effective mitigation and control. One such mechanism is spotting, in which burning leaves, branches, and other debris, termed firebrands, are transported away from the main fire by the prevailing wind and ignite new fires. Spotting has been investigated in several experimental and numerical studies. However, the influences of free-stream turbulence and char-layer porosity on the trajectories of windborne firebrands have not been accounted for in these past studies. In this work, wind tunnel experiments were conducted to quantify the effects of free-stream turbulence intensity and char-layer porosity on the drag on stationary non-burning cylinders. A wind tunnel was set up to conduct the experiments, and the flow in its test section was characterized. A combination of passive and active grids was used to generate turbulence intensities of 1.7%, 2.7%, and 12.4% (with no grid installed, the background turbulence intensity was 0.4%). The porous char-layer was mimicked by wrapping wire meshes around the cylinders, with three values of pores per inch (10, 20, and 40) and wrapped to three different thicknesses (1/16", 1/8", and 1/4"). The drag on one smooth cylinder and nine cylinders with porous outer layers (1" diameter, 11.75" length exposed to air flow) was measured for Reynolds number in the range 7000 to 17000, at the aforementioned four turbulence intensities. The results showed that i) the free-stream turbulence intensity and pores per inch of the wire meshes affect the way in which the drag coefficient changes with Reynolds number; ii) the drag coefficient increases with free-stream turbulence intensity when it is relatively low (0.4%-2.7%), then decreases at high turbulence intensity (12.4%), and this decrease is more pronounced as the number of pores per inch is increased; iii) the drag coefficient increases with the thickness of the porous layer, and asymptotes to an effectively constant value after a critical thickness of about 1/8"; and iv) the drag coefficient exhibits a non-monotonic dependence on the number of pores per inch of the wire mesh. These results demonstrate the importance of accounting for the free-stream turbulence intensity and the char-layer (porosity and depth) when modelling the drag on windborne firebrands. It is hoped that this work will eventually lead to a better understanding of the spread of forest fires by spotting, allow more accurate predictions of it, and thus enable better forest-fire prevention and suppression strategies.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".