Ignition time and heat release rate in upward flame spread: From bench to intermediate scale
Bibliographic record
Abstract
This research assesses the effect of two common assumptions in simplified flame spread models: (i) that the pyrolysis front propagates in one dimension and (ii) that ignition time ( t ign ) and heat release rate per unit area (HRRPUA) values measured at bench scale can be utilised in simplified flame spread models. To that aim, the study introduces a methodology to measure time to ignition, time-dependent burning area, heat release rate per unit area, and ignition temperature for concurrent upward flame spread at intermediate-scale, while examining the feasibility of using these metrics to predict flame spread scenarios. The methodology employs a narrow-spectrum illumination source and thermocouple arrays to precisely track the pyrolysis front progression during flame spread experiment. Measurements were obtained for Poly(methyl methacrylate), and they can serve as reference points for researchers aiming to develop or refine testing protocols or perform sensitivity analyses for assumptions relevant to time to ignition and heat release rate models. Measurements were compared with cone calorimeter data from standard and modified testing protocols in the literature. Post-ignition removal of external heating reduced HRRPUA values, aligning them more closely with intermediate-scale experiments, while spatial heat flux distribution changed the time to ignition toward the values that were measured in intermediate-scale. The impact of the solid ignition parameters on the flame spread rate was evaluated individually using a simplified flame spread model. The proposed methodology provides a means to refine extrapolation methods for solid ignition parameters.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".