Scalability of cone calorimeter test results for the prediction of full scale fire behavior of polyurethane foam
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The ignition and subsequent burning of polyurethane foam based mattresses poses a significant danger to life and safety in North American homes. The development of fire models which can predict the full scale fire behavior of these mattresses using bench scale data would assist manufacturers and regulators to manage this danger in a cost effective manner. This thesis builds on previous work by the University of Saskatchewan and University of Waterloo fire research groups and focuses on the evaluation of one such scaling model, which was originally developed during the Combustion Behavior of Upholstered Furniture (CBUF) project. The evaluation of the CBUF model conducted in this thesis isolates the heat release rate (HRR) density sub-model and explores the effects of 1) cone calorimeter incident heat flux setting, 2) specimen thickness and 3) ignition location on the predictive capability of the CBUF model. To provide input for the CBUF model cone and furniture calorimeter tests were conducted. Cone calorimeter tests were conducted on foam specimen thicknesses of 2.5, 5.0, 7.5 and 10.0 cm at incident heat flux settings of 25, 35, 50 and 75 kW/m2. Furniture calorimeter tests were conducted on foam specimen thicknesses of 2.5, 5.0, 7.5 and 10.0 cm in both edge and center ignition configuration. Flame area spread rates were measured from infrared video of the furniture calorimeter tests using an automated algorithm. It was found that HRR curves predicted by the CBUF model showed good agreement with experimental results. Experimental results from tests of thinner foams were predicted with greater success than results from thicker foams, and results from edge ignition tests were predicted with greater success than results of center ignition tests. The results of this study indicated that specimen thickness and ignition location need to be considered when selecting an appropriate incident heat flux setting for producing input data for the CBUF model.
DegreeMaster of Science (M.Sc.)
SupervisorTorvi, David A.
CommitteeBergstrom, Donald J.; Sumner, David
Copyright DateAugust 2014