Academic literature on the topic 'Spotfires, spotting, firebrand transport'

Firebrands of ribbon bark eucalypt are notorious for igniting spotfires many kilometres ahead of a bushfire. However, no research to date has demonstrated that this bark type can sustain combustion at its terminal velocity for the travel time required. Fifty samples of shed bark of Eucalyptus viminalis of three distinct morphologies were ignited at one end and burned tethered in a vertical wind tunnel at air velocities approximating their terminal velocity. Mean terminal velocity and burnout time for ‘flat plates’, ‘simple cylinders’ and ‘internally convoluted cylinders’ were 5.4 m s–1 and 251 s; 5.2 m s–1 and 122 s; and 5.8 m s–1 and 429 s. The corresponding maximum burnout times were 785 s, 353 s and 1304 s. One internally convoluted cylinder flamed continuously and consumed its length of 368 mm in 271 s. The maximum burnout time for the internally convoluted cylinders is commensurate with a potential spotting distance exceeding 20 km given a mean wind speed during transport of 60 km h–1. This is the first study in which combustion times exceeding a few minutes have been recorded for this bark morphology, and thus provides some corroboration of the notoriety for long-distance spotting.

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.

Spotting can start fires up to tens of kilometres ahead of the primary fire front, causing rapid spread and placing immense pressure on suppression resources. Here, we investigate the dynamics of the buoyant plume generated by the fire and its ability to transport firebrands. We couple large-eddy simulations of bushfire plumes with a firebrand transport model to assess the effects of turbulent plume dynamics on firebrand trajectories. We show that plume dynamics have a marked effect on the maximum spotting distance and determine the amount of lateral and longitudinal spread in firebrand landing position. In-plume turbulence causes much of this spread and can increase the maximum spotting distance by a factor of more than 2 over that in a plume without turbulence in our experiments. The substantial impact of plume dynamics on the spotting process implies that fire spread models should include parametrisations of turbulent plume dynamics to improve their accuracy and physical realism.

Abstract. Fire spotting is often responsible for dangerous flare-ups in wildfires and causes secondary ignitions isolated from the primary fire zone, which lead to perilous situations. The main aim of the present research is to provide a versatile probabilistic model for fire spotting that is suitable for implementation as a post-processing scheme at each time step in any of the existing operational large-scale wildfire propagation models, without calling for any major changes in the original framework. In particular, a complete physical parameterisation of fire spotting is presented and the corresponding updated model RandomFront 2.3 is implemented in a coupled fire–atmosphere model: WRF-SFIRE. A test case is simulated and discussed. Moreover, the results from different simulations with a simple model based on the level set method, namely LSFire+, highlight the response of the parameterisation to varying fire intensities, wind conditions and different firebrand radii. The contribution of the firebrands to increasing the fire perimeter varies according to different concurrent conditions, and the simulations show results in agreement with the physical processes. Among the many rigorous approaches available in the literature to model firebrand transport and distribution, the approach presented here proves to be simple yet versatile for application to operational large-scale fire spread models.

Firebrand spotting is a potential threat to people and infrastructure, which is difficult to predict and becomes more significant when the size of a fire and intensity increases. To conduct realistic physics-based modeling with firebrand transport, the firebrand generation data such as numbers, size, and shape of the firebrands are needed. Broadly, the firebrand generation depends on atmospheric conditions, wind velocity and vegetation species. However, there is no experimental study that has considered all these factors although they are available separately in some experimental studies. Moreover, the experimental studies have firebrand collection data, not generation data. In this study, we have conducted a series of physics-based simulations on a trial-and-error basis to reproduce the experimental collection data, which is called an inverse analysis. Once the generation data was determined from the simulation, we applied the interpolation technique to calibrate the effects of wind velocity, relative humidity, and vegetation species. First, we simulated Douglas-fir (Pseudotsuga menziesii) tree-burning and quantified firebrand generation against the tree burning experiment conducted at the National Institute of Standards and Technology (NIST). Then, we applied the same technique to a prescribed forest fire experiment conducted in the Pinelands National Reserve (PNR) of New Jersey, the USA. The simulations were conducted with the experimental data of fuel load, humidity, temperature, and wind velocity to ensure that the field conditions are replicated in the experiments. The firebrand generation rate was found to be 3.22 pcs/MW/s (pcs-number of firebrands pieces) from the single tree burning and 4.18 pcs/MW/s in the forest fire model. This finding was complemented with the effects of wind, vegetation type, and fuel moisture content to quantify the firebrand generation rate.

AbstractFirebrand travel and ignition of spot fires is a major concern in the Wildland-Urban Interface and in wildfire operations overall. Firebrands allow for the efficient breaching across fuel-free barriers such as roads, rivers and constructed fuel breaks. Existing observation-based knowledge on medium-distance firebrand travel is often based on single tree experiments that do not replicate the intensity and convective updraft of a continuous crown fire. Recent advances in acoustic analysis, specifically pattern detection, has enabled the quantification of the rate at which firebrands are observed in the audio recordings of in-fire cameras housed within fire-proof steel boxes that have been deployed on experimental fires. The audio pattern being detected is the sound created by a flying firebrand hitting the steel box of the camera. This technique allows for the number of firebrands per second to be quantified and can be related to the fire's location at that same time interval (using a detailed rate of spread reconstruction) in order to determine the firebrand travel distance. A proof of concept is given for an experimental crown fire that shows the viability of this technique. When related to the fire's location, key areas of medium-distance spotting are observed that correspond to regions of peak fire intensity. Trends on the number of firebrands landing per square metre as the fire approaches are readily quantified using low-cost instrumentation.

The process of spotting whereby burning firebrands are transported by convection and wind to ignite new fires ahead of the source fire is significant both economically and in terms of exposure of fire crews to dangerous situations. Spotting behaviour recorded in Australia is the worst in the world in terms of spotfire distance and concentration and this has been attributed to features of eucalypt bark types. This thesis is the first comprehensive firebrand investigation of any bark. It briefly examines selected firebrand characteristics of Eucalyptus diversicolor, E. marginata and E. bicostata and examines in detail the aerodynamic and combustion characteristics and fuel bed ignition potential of Eucalyptus obliqua...
Mayne Nickless Limited, CSIRO Division of Forestry and Forest Products

Wildfire propagation is a non-linear and multiscale system in which there are involved multiple physical and chemical processes. One critical mechanism in the spread of wildfires is the so-called fire-spotting: a random phenomenon which occurs when embers are transported over large distances by the wind, causing the start of new spotting ignitions which jeopardize fire fighting actions. Due to its nature, fire-spotting is usually modeled as a probabilistic process. Three principal processes are involved during the fire-spotting: firebrands generation, transport and landing, and spot ignition. In this work, the physical parametrization of fire-spotting RandomFront (Trucchia et al. 2019) has been implemented into the operational wildfire spread simulator PROPAGATOR (Trucchia et al. 2020), that is based on a cellular automata approach. In the RandomFront parametrization the downwind landing distribution of firebrands is modeled by the means of a lognormal distribution, which is parameterized taking into account the physics involved in the phenomenon. The considered physical parameters are wind field, fire-line intensity, fuel density, firebrand radius, maximum loftable height, as well as factors related to atmospheric stability and flame geometry (Trucchia et al. 2019; Egorova et al. 2020,2022). We have reproduced the evolution of a wildfire occured in Italy, in which the fire-spotting effects played a critical role in its spread, to test how RandomFront is able to reproduce it accurately. In addition, we have already implemented some established fire-spotting empirical parametrizations for cellular automata-based wildfire models to compare also the performance between the three firebrand landings models. The results show that the RandomFront parametrization on the one hand reproduces the main spotting effects given by the available literature parametrizations (Alexandridis et al. 2011; Perryman et al. 2013), while, on the other hand, generates a variety of fire-spotting situations as well as long range fluctuations of the burning probability. The physical parametrization allows for complex patterns of fire spreading in this operational simulator context.