FEMTC 2014

A Partially-Stirred Batch Reactor Model for Under-Ventilated Fire Dynamics

Craig Weinschenk - National Institute of Standards and Technology


When fire fighters arrive at a burning structure, the state of fire progression is usually under-ventilated, meaning the pyrolyzed fuel has consumed most, if not all, of the available oxygen inside the structure. These conditions are extremely hazardous for building occupants, who may be exposed to lethal concentrations of carbon monoxide (CO) and smokeproduced by fuel-rich combustion. Carbon monoxide inhalation remains one of the leading causes of fire fatalities. Modeling these scenarios is therefore of great interest to the fire community.

Under fuel-lean conditions, basic compartment fire dynamics is dominated by fast, heat-releasing reactions. These reactions cause thermal expansion of the surrounding gas mixture, thus generating buoyant plumes that entrain and efficiently mix the surrounding air with unburned fuel and hot combustion products. This mixture readily reacts, releases heat, and radiates back to the fuel source to complete the cycle. In these scenarios, a simple “mixed is burnt” approximation is often sufficient to model combustion chemistry. However, the quantitative prediction of CO presents a challenge to conventional fire models, because fuel-rich CO chemistry is relatively slow and highly temperature dependent.

As a step toward improved prediction of CO concentrations in under-ventilated compartment fires (which are low-Mach turbulent reacting flows), this talk presents a framework for transport, mixing, and reaction of chemical species in large-eddy simulation (LES). A partially-stirred batch reactor (PaSR) is adopted as a simple yet flexible model to treat a spectrum of complexity in the chemical reaction network, from mixture-fraction-based state relations to detailed chemical kinetics. Each computational cell is modeled as a PaSR. The PaSR model is implemented in a low-Mach LES solver called the Fire Dynamics Simulator (FDS). Verification and validation of this model within FDS will also be presented.



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