Dushyant M. Chaudhari

Poly (methyl methacrylate) (PMMA) has long been a benchmark material of study in the fire research and fire modeling community due to its simple and predictable thermal decomposition behavior. Two common PMMA manufacturing processes, casting and extruding, produce polymers with slightly different properties and characteristics, primarily a result of differences in molecular mass. A dichotomy has been observed between study and application; while cast PMMA is more commonly studied and modeled, it is extruded PMMA that has wider use and application. This study investigates the differences between cast and extruded PMMA on a property-specific level, and whether the differences justify unique pyrolysis models for each. Experimental results show notable variation in milligram-scale tests (TGA, DSC and MCC), but only slight variation in gram-scale tests (Controlled Atmosphere Pyrolysis Apparatus II or CAPA II). Comprehensive pyrolysis models are developed for each PMMA type with the help of hill climbing optimization algorithms to automate inverse analysis. A sensitivity analysis is performed on the fully parameterized models to examine the effect of prominent parameters on the model’s ability to predict mass loss rate from CAPA II tests. It is determined that the two materials are sufficiently similar to be represented with a generic model.

A new experimental setup was developed to study turbulent, buoyancy-driven flame spread on a corner wall. Two 146 \texttimes 50 \texttimes 0.58 cm panels of cast black poly(methyl methacrylate) (PMMA), whose pyrolysis properties were fully characterized in a separate study, were ignited using a triangular propane burner. The corner wall assembly was placed into a well-ventilated enclosure and heat release rate (HRR) and flame heat flux to the corner wall were measured in 7 repeated experiments. The HRR measurement was designed to achieve a fast response time (13 s) and was correlated with the flame heat flux, which was obtained using water-cooled heat flux gauges positioned in 28 locations distributed over the PMMA surface. Simultaneously, a monochromatic, 900 nm, video of the spreading flame was recorded and analyzed to determine the evolution of soot radiation intensity and flame geometry. Using collected data, an empirical model relating spatially resolved heat feedback from the flame to the solid fuel surface and HRR was developed and integrated with a detailed pyrolysis model. Simulations were conducted in an uncoupled and coupled mode. The uncoupled simulations, where the flame heat feedback was effectively prescribed to match the experimental measurements, revealed the importance of the knowledge of the split between the convective and radiative portions of the flame heat flux. The coupled simulations, where the flame heat feedback was computed from the simulated HRR, revealed that the coupling amplifies even relatively small uncertainties in the model parameters to produce large errors in the HRR predictions.

Controlled fire experiments using a gas burner were previously conducted in a purpose-built, two-story, moderately air-tight residential structure to understand the effect of a heating, ventilating, and air conditioning (HVAC) system and door positions on the fire-induced environment. Temperatures, gas concentrations (oxygen, water vapor, carbon dioxide), and differential pressures were monitored throughout the structure. HVAC status (off vs. on) and stairwell door position of the fire room (open vs. closed) were varied for the experiments analyzed in this paper. In this study, Fire Dynamics Simulator (FDS) v. 6.7.9 was used to simulate these experiments. Experimental data quantifying the air tightness of the building and cold flowrates through HVAC vents were determined to be important to optimize leakages and HVAC loss coefficients for the simulation setup. Pressure development in the structure was predicted correctly to be higher on the first floor and lower in the basement, but the magnitude of steady-state pressure was underpredicted. The measured and predicted steady-state temperature distributions were statistically different for the cases with and without the HVAC on, regardless of the door position. FDS predicted gas transport through the HVAC duct network, and under-predicted temperature rise and water vapor content by about 9% and 10%, respectively, and over-predicted volumetric oxygen and carbon dioxide content by about 21% and 6%, respectively. Temperature rise prediction in the closed room, where the gas transport primarily occurred via the HVAC duct network, improved after including heat loss from the HVAC duct to the ambient.