Mathematical model and sensor development for measuring energy transfer from wildland fires
Bibliographic record
Abstract
Current practices for measuring high heat flux in scenarios such as wildland forest fires use expensive, thermopile-based sensors, coupled with mathematical models based on a semi-infinite-length scale. Although these sensors are acceptable for experimental testing in laboratories, high error rates or the need for water cooling limits their applications in field experiments. Therefore, a one-dimensional, finite-length scale, transient-heat conduction model was developed and combined with an inexpensive, thermocouple-based rectangular sensor, to create a rapidly deployable, non-cooled sensor for testing in field environments. The proposed model was developed using concepts from heat conduction and with transient temperature boundary conditions, to avoid complicated radiation and convection conditions. Constant heat flux and tree-burning tests were respectively conducted using a mass loss cone calorimeter and a propane-fired radiant panel to validate the proposed analytical model and sensor as well as test the sensor in a simulated forest fire setting. The sensor was mounted directly beside a commercial Schmidt–Boelter gauge to provide data for comparison. The proposed heat flux measurement method provided results similar to those obtained from the commercial heat flux gauge to within one standard deviation. This suggests that the use of a finite-length scale model, coupled with an inexpensive thermocouple-based sensor, is effective in estimating the intense heat loads from wildland fires.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".