Effects of incident heat flux on heat release rates and temperatures in cone calorimeter tests of polyurethane foam
Bibliographic record
Abstract
Abstract There is great interest in developing methods to predict full‐scale fire performance of mattresses and upholstered furniture for design and regulatory purposes using cone calorimeter and other small‐scale test results. One method used in the past is a model developed during the European Combustion Behavior of Upholstered Furniture (CBUF) project. To support the further development of this model, cone calorimeter tests of polyurethane (PU) foam specimens 5–10 cm thick were conducted using incident heat fluxes between 5 and 35 kW/m 2 . Temperatures were measured using thermocouples located on the surface and at four depths within 10 cm thick foam specimens to determine the effects of heat flux on heat transfer and foam degradation. Peak and average heat release rate (HRR) values for a particular thickness of foam increased with an increase in heat flux. An increase in heat flux decreased the times to reach the two peaks in the HRR curve, which represent the collapse of foam and burning of liquid products, as well as burning duration. Heat flux had a larger effect on the second HRR peak than the first peak. Significant temperature gradients were initially confined to the top portion of the foam. A surface temperature of 150–200°C was shown to be indicative of the onset of ignition, while a temperature of 150°C at a particular location was indicative of when temperatures began to more rapidly increase at deeper locations within the foam. Infrared video records were also used to examine three‐dimensional burning behavior of the foam.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".