Effects of variations in incident heat flux when using cone calorimeter test data for prediction of full‐scale heat release rates of polyurethane foam
Bibliographic record
Abstract
Summary The development of methods to predict full‐scale fire behaviour using small‐scale test data is of great interest to the fire community. This study evaluated the ability of one model, originally developed during the European Combustion Behaviour of Upholstered Furniture (CBUF) project, to predict heat release rates. Polyurethane foam specimens were tested in the furniture calorimeter using both centre and edge ignition locations. Input data were obtained using cone calorimeter tests and infrared video‐based flame area measurements. Two particular issues were investigated: how variations in incident heat flux in cone calorimeter tests impact heat release rate predictions, and the ability of the model to predict results for different foam thicknesses. Heat release rate predictions showed good agreement with experimental results, particularly during the growth phase of the fire. The model was more successful in predicting results for edge ignition tests than for centre ignition tests and in predicting results for thinner foams. Results indicated that because of sensitivity of the burning behaviour to foam specimen geometry and ignition location, a single incident heat flux could not be specified for generating input for the CBUF model. Potential methods to determine appropriate cone calorimeter input for various geometries and ignition locations are discussed. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".