@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} }