Spotting ignition by lofted firebrands is a significant mechanism of fire spread, as observed in many large-scale fires. The role of firebrands in fire propagation and the important parameters involved in spot fire development are studied. Historical large-scale fires, including wind-driven urban and wildland conflagrations and post-earthquake fires are given as examples. In addition, research on firebrand behaviour is reviewed. The phenomenon of spotting fires comprises three sequential mechanisms: generation, transport and ignition of recipient fuel. In order to understand these mechanisms, many experiments have been performed, such as measuring drag on firebrands, analysing the flow fields of flame and plume structures, collecting firebrands from burning materials, houses and wildfires, and observing firebrand burning characteristics in wind tunnels under the terminal velocity condition and ignition characteristics of fuel beds. The knowledge obtained from the experiments was used to develop firebrand models. Since Tarifa developed a firebrand model based on the terminal velocity approximation, many firebrand transport models have been developed to predict maximum spot fire distance. Combustion models of a firebrand were developed empirically and the maximum spot fire distance was found at the burnout limit. Recommendations for future research and development are provided.

This paper is one of a series on brand lofting and propagation. Here, spherical brand propagation in a constant ambient wind is addressed. Maximum propagation distances are calculated for wooden brands with diameters up to 0.18 m, which are lofted above axisymmetric pool fires with heat release rates, Qo, between 1 MW and 3 GW. Winds of 1.8 m/s ≤ Uw ≤ 92 m/s are considered. A maximum propagation distance equation is developed as a function of Qo, Uw and wood type, or β. Cedar brands (β = 1), lofted by fires with Qo = 1 MW, 50 MW and 1 GW, travel a maximum of 49 m, 290 m and 1100 m, respectively, in 10 m/s winds before landing at burn out. Brands between a "collapse" diameter, dcol = 0.49 Q0 0.269 β0.782, and a maximum loftable diameter, do,max = 0.454 β Q0 0.04, propagate the same maximum distance, since the larger brands move slower and therefore have more time to combust. Hence, only brands with 0 ≤ d ≤ d col need be studied for given Qo, Uw and β.

Based on energy conservation and detailed heat transfer mechanisms, a simple physical model for fire spread is presented for the limit of one-dimensional steady-state contiguous spread of a line fire in a thermally-thin uniform porous fuel bed. The solution for the fire spread rate is found as an eigenvalue from this model with appropriate boundary conditions through a fourth order Runge-Kutta method. Three experiments on fire spread are compared to the model simulations and good agreement is demonstrated. The comparisons with wind tunnel experiments on white birch fuel beds show that the physics in this model successfully evaluates wind and slope effects on the fire spread rate. The grassland fuel experiments with various fuel characteristics also compare well to the simulations. Limited comparison with data on fire spread in shrubs, obtained in China, also shows good agreement. These comparisons suggest that this model may serve as the basis for an improved operational model.

In this study, a series of sensitivity analyses were conducted to evaluate a computational fluid dynamic (CFD) model, Fire Dynamics Simulator (FDS) version 4.0, for tunnel fire simulations. A tunnel fire test with a fire size on the order of a 100 MW with forced, time-varying longitudinal ventilation was chosen from the Memorial Tunnel Ventilation Test Program (MTVTP) after considering recent tunnel fire accidents and the use of CFD models in practice. A careful study of grid size and parameters used in the Large Eddy Simulation (LES) turbulence model-turbulent Prandtl number, turbulent Schmidt number, and Smagorinsky constant-was conducted. More detailed analyses were performed to refine the smoke layer prediction of FDS, especially on backflow (i.e., a reversed smoke flow near the ceiling). Also, energy conservation was checked for this scenario in FDS. A simple guideline is given for smoke layer simulations using FDS for similar tunnel fire scenarios.

Abstract