Experimental and Numerical Investigation of Fire Behaviour in Polyurethane Foams
Bibliographic record
Abstract
Due to the complex and varying nature of a flame and its products, the scaling of fire behaviour has been a challenge in the area of fire science. The use of small-scale test data to interpret full-scale fire behaviour is an area of ongoing research with potential savings for manufacturers required by code to test products for large-scale fire behaviour. Polyurethane foam was selected as the sample material for the research due to its widespread application in home and office furniture and its potential to act as a fuel source in fires due to a high hydrocarbon content. The heart of the problem lies with predicting how much heat is released by the fire and the rate at which flame spreads across the material. This research builds on previous University of Saskatchewan research and seeks to provide a method to predict full-scale flame spread across a material. Additionally, methodologies such as the Combustion Behavior of Upholstered Furniture (CBUF) Model applied for full-scale heat release rate (HRR) predictions and Alpert’s correlation employed in predicting compartment temperatures are also evaluated. Small-scale cone calorimeter tests which serve as input to the CBUF model were conducted for foam thickness of 2.5, 7.5 and 10 cm at incident heat fluxes of 5, 10, 15, 20, 35 and 50 kW/m2. Separate small-scale tests were conducted on foams instrumented with thermocouples to measure temperatures on the surface and at depth. A numerical model was proposed to predict the surface temperatures and estimate the time to ignition of the small-scale foam specimens. Full-scale compartment fire tests were conducted for centre and edge ignition at the University of Waterloo Live Fire Facility. Compartment temperatures and flame areas were measured. A model was developed to predict flame spread based on the data collected from previous University of Saskatchewan furniture calorimeter test. The results of the flame spread model showed promise in predicting the area spread rates. The model, however, did not capture some of the edge effects that occurred due to the flame reaching the foam boundaries. The area spread model was used within the CBUF model which satisfactorily predicted the full-scale HRR. The HRR predictions were then applied to a modified version of Alpert’s correlation which predicted ceiling jet temperatures accounting for the spread of flame. Predictions of ceiling jet temperatures made using Alpert’s correlation was improved by considering flame spread.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".