@article {bnh-8376, title = {Determining firebrand generation rate using physics-based modelling from experimental studies through inverse analysis}, journal = {Fire}, volume = {5}, year = {2022}, month = {01/2022}, abstract = {

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.

}, keywords = {FDS, Firebrands, physics-based modeling, wildland fire, Wildland-urban interface}, doi = {https://doi.org/10.3390/fire5010006}, url = {https://www.mdpi.com/2571-6255/5/1/6}, author = {Amila Wickramasinghe and Nazmul Khan and Khalid Moinuddin} } @article {bnh-8373, title = {A review of firebrand studies on generation and transport}, journal = {Fire Safety Journal}, volume = {134}, year = {2022}, month = {09/2022}, abstract = {

Firebrands play a vital role in the propagation of fire by starting new fires called spotfires, ahead of the fire front during wildfire progression. Firebrands are a harbinger of damage to infrastructure; their effects particularly pose a threat to people living within the wildland-urban-interface, they can hamper the suppression of wildfire and block evacuation routes for communities and emergency services. Short-range firebrands which travel along with the wind, with little or no lofting, are particularly crucial in increasing fire front propagation and damaging structures situated close to the wildland-urban interface. In the Daylesford fire of 1962 in Australia, massive short-range spotting (the process of spot fire ignition and merging of spots caused by firebrands) occurred in the eucalyptus forest and increased the rate of fire spread by roughly three times more than that computed using an operational fire model. Similarly, long-range firebrands can be transported by the fire plume and ambient wind and can ignite new fire up to 30{\textendash}40\ km from the source of fire as observed in the 2009 Black Saturday fire in Australia.

A large amount of experimental research has been conducted to quantify the effects of firebrands, to develop empirical models and to benchmark results for\ Computational Fluid Dynamic\ (CFD) based fire model validations. In recent years, some CFD models have been studied primarily for their validation purposes. These studies have been reviewed here. To perform useful\ parametric studies\ of firebrand transport using CFD models as well as further development of CFD models, more targeted studies need to be conducted.

}, keywords = {firebrand generator, Firebrand spotting, Firebrand transport, wildfires}, doi = {https://doi.org/10.1016/j.firesaf.2022.103674}, url = {https://www.sciencedirect.com/science/article/pii/S0379711222001515}, author = {Rahul Wadhwani and Catherine Sullivan and Amila Wickramasinghe and Matthew Kyng and Nazmul Khan and Khalid Moinuddin} } @article {bnh-7996, title = {Fire spread across different fuel types: research and utilisation {\textendash} final project report}, number = {668}, year = {2021}, month = {05/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

It is crucial for emergency and disaster management organisations to predict of the rate of spread and intensity of bushfires for operational planning, community warnings and the deployment of their resources. Currently, this is achieved by simulation using simplified operational models that have the useful attribute of providing results on time scales commensurate with those required by emergency managers. However, when Cruz \& Alexander [1] reviewed the performance of the operational fire models used by fire and emergency service analysts on seven vegetation types found in Australia, they found that on an average most of the fire models have an error of 20{\textendash}80\% in estimating the rate of fire spread. These differences in prediction are due to the assumptions and limitations of these models. Therefore, it is essential that these simplified operational tools be refined so that they can better predict fire behaviour. Additionally, a more physically based firebrand model needs to be included in operational models to predict firebrand distribution and subsequent spotting, which lead to an increased rate of fire spread (ROS). Currently, no such model exists. With an increased population in the rural{\textendash}urban interface (or wildland{\textendash}urban interface, WUI), it is also important to understand the vulnerability of houses from radiant heat and firebrand flux in order to minimise such vulnerability.

In this project, we tested two established reliable physics-based models: Fire Dynamics Simulator (FDS) and FIRESTAR3D to simulate bushfire scenarios in three broad areas:

(1)\  sub-canopy wind flow,

(2)\  firebrand transport, and

(3)\  propagation of grass and forest fires.

We have made significant inroads into providing usable outputs as well understanding various aspects of bushfire behaviour. The following are particular highlights:

This project was also established to create a capability and capacity in Australia to conduct research and understand physical-based wildfire modelling approaches. There are several international groups developing these models, and it is imperative that Australia can interact and work alongside these researchers to translate the findings to the Australian context.

Overall, we have achieved our goal of obtaining greater insight into bushfire physics and we are now utilising those insights to parameterise various phenomena for operational models.

This report explains these issues in more detail.

[1] Meaning WRF will vary for the geographical location and driving wind velocity and direction

}, keywords = {Fire, fuel, research, spread, types, utilisation}, issn = {668}, author = {Nazmul Khan and Amila Wickramasinghe and Mahmood Rashid and Khalid Moinuddin} } @article {bnh-6819, title = {Physics-based simulation of firebrand and heat flux on structures in the context of AS3959}, number = {560}, year = {2020}, month = {04/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Firebrand is known as one of the most dangerous airborne components of wildfires having the potential to ignite structures in Wildland-Urban Interface (WUI). Quantifying the firebrand and heat flux on structures is essential to determine the wildfire risks and prepare strategic plans to mitigate the hazard. We endeavor to use a physics-based model, Fire Dynamic Simulator (FDS) to map firebrand and heat flux to determine the vulnerability of structures in WUI. In this study, we have validated FDS{\textquoteright} tree burning and firebrand transporting sub-models against the experiment conducted in no wind condition at the National Institute of Standards and Technology (NIST). The experimental data of firebands were processed to use as inputs in the numerical simulation and the grid convergence was appraised in terms of mass loss rate (MLR). The intial velocity and direction of firebrands, the number of generations were determined as model inputs by a reverse analysis through comparing firebrand distribution with the experiment. As the FDS{\textquoteright} sub-models were validated, we attempted to quantify the heat flux and firebrand risk on a structure at three different driving wind velocities. Increasing wind speed showed more firebrands transported towards the structure but none of them landed on the house because of the low height of the tree and insufficiency of fire-induced buoyancy to lift them enough to carry a longer distance by the wind field. Similarly, heat flux computed on the structure is well below Australian building standard AS3959{\textquoteright}s bushfire attack level (BAL). it is due to the low heat release by single tree burning instead of a 100m wide fire line assumed in the standard. In future study, simulations will be conducted with a cluster of taller trees (100m wide) to quantify the heat flux and firebrand hazard on structures to apprise AS3959.

}, keywords = {firebrand, heat flux, physics simulation, structures}, issn = {560}, author = {Amila Wickramasinghe and Nazmul Khan and Khalid Moinuddin} }