Abstract

Fire models are routinely used in life safety design projects and are being used more often in fire and arson investigations as well as reconstructions of firefighter line-of-duty deaths (LODDs) and injuries. In all of these applications, the fire heat release rate (HRR), location of a fire in a compartment, gas-phase soot concentration, and solid-phase soot accumulation are important parameters that govern the evolution of thermal conditions within the fire compartment. These input parameters can be a large source of uncertainty in fire models, especially in scenarios in which experimental data or detailed information on fire behavior are not available, such as fire investigations and LODD reconstructions. Various methods have been reported in literature to determine the size and location of a fire in a compartment using ceiling-mounted detectors [1–4]. A previous study by the authors developed an inverse fire modeling technique to determine the time-varying HRR of fire in a compartment using measured thermocouple data [5]. The work presented in this paper extends the inverse HRR methodology by developing a technique to determine the location of a fire using wall-mounted heat flux sensors or a surrogate such as degradation characteristics of enclosure boundaries that can be collected during post-fire assessments. Additionally, the presence of soot modifies the radiative transfer field in the hot gas layer (gas phase) as well as radiative heat transfer to surfaces (condensed phase). As a detailed history of compartment conditions becomes less available, there is a need for an inversion methodology to accurately recover governing input parameters such as fire size, fire location, and fire burning properties while maintaining an adequate level of accuracy. As an intermediate step using measured fire test data, we can begin to construct an approach to use rich data to invert for fire intensity, fire location, and fire properties such as the amount of soot produced by the fire.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call