@article {bnh-8157, title = {Numerical study on effect of relative humidity (and fuel moisture) on modes of grassfire propagation}, journal = {Fire Safety Journal}, year = {2021}, month = {08/2021}, abstract = {

Relative humidity of air is directly related to fuel moisture. Fuel moisture is often considered as the index of flammability in the context of bushfire. Variation of relative humidity and fuel moisture is considered to have a significant effect on the rate of spread of grassfire propagation and fire intensity. In this study, four sets of grassfire simulations have been conducted: three sets with 210 mm high grass and another set with 175 mm high grass. For all sets, the ambient temperature was kept constant while relative humidity and fuel moisture were varied, with fuel moisture deduced from the McArthur MKV GFDI model. With 210 mm grass heights, driving wind velocities were varied. Lower relative humidity (and fuel moisture) was observed to lead to higher fire intensity and a faster rate of spread, which are intuitively expected. Byram number analysis showed that relative humidity (and fuel moisture) can lead to change in the fire propagation mode (wind-driven vs buoyancy-driven), but the greater factor is the wind velocity.

}, keywords = {Byram number, grassfire, Mode of fire propagation, Physics-based modelling, relative humidity}, doi = {https://doi.org/10.1016/j.firesaf.2021.103422}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0379711221001636}, author = {Khalid Moinuddin and Nazmul Khan and Duncan Sutherland} } @mastersthesis {bnh-6825, title = {Development of an interface using penalisation method for improving computational performance of bushfire simulation tools}, volume = {Master of Engineering}, year = {2019}, month = {05/2019}, pages = {350}, school = {Victoria University}, address = {Melbourne}, abstract = {

Wind is the dominant environmental factor affecting wildland fire intensity and spread. Previously, fire analysts and managers have relied on local measurements and site-specific forecasts to determine how winds influence fire. The advancements in computer hardware, increased availability of electronic topographical and experimental data, and advances in numerical methods for computing winds, have led to the development of new tools capable of simulating wind flow. Several numerical models have been developed for fire prediction and forecasting. Modelling wind in physics-based models like Fire Dynamics Simulator (FDS) has been shown to produce promising results, but at an inordinate cost. Because of the high computational expense, physics-based models are not suitable for operational use. Little research has been conducted to improve the computational speed of these models. The current study intends to decrease the computational cost of physics-based fire simulations and improve physics-based models by including more complicated driving winds.\ 

Physics-based wildfire simulations are driven by inlet boundary conditions which model the atmospheric boundary layer. Various inlet conditions, such as the 1/7-powerlaw or the log law models with artificial turbulence (e.g. the synthetic eddy method, [SEM]) can be used as an inlet to generate a statistically steady wind field for a fire simulation. The power-law inlet is the default inlet condition used in FDS where the wind develops turbulence as it sweeps through the domain, and is often used with wall-of-wind\ type methods. The log-law inlet generates a log wind profile similar to Atmospheric Boundary Layer (ABL). Development of techniques for imposing inlet conditions and initial conditions for flow simulations have been topics of interest for the past few decades. CUrrent inlet and initial conditions requires time in a scale order of 100s of CPU hours, for generating an appropriate condition to start a fire simulation, hence resulting in increased computational expense. A novel nesting method has been implemented, which involves two regions: penalisation and blending, named as the PenaBlending method. The initial conditions of the fire simulations in FDS are set to the initial condition prescribed by an external model or simulation. This is achieved by a one-way coupling method. External wind data, for which u,v,w can vary in space and time, can be obtained. The precursor data can be generated either from any reduced wind model such as Windninha, which gives terrain\ modified wind data, or by using analytical methods such as generating logarithmic windfield using Matlab.\ These external data can be introduced into the FDS domain\ through a penalization region at the inlet/outlet. A blending region has also been\ implemented near the specified inlet/outlet which allows a smooth mixing of a precursor\ wind field to that in the simulation domain. This new inlet condition allows complicated\ terrain modified temporally and spatially varying wind fields, obtained from precursor\ simulations or any other models, to be implemented relatively easily in the FDS domain. To test the implementation of this method, a flat terrain is considered in the current study.\ However, this method could also be used for complicated terrain structures, as a part of\ future studies. The PenaBlending method provides appropriate flow conditions with reduced\ computational effort (up to  80\%), to start a fire simulation, and, hence, reduces\ the computational expense of physics-based models.

The results obtained using the PenaBlending method have been compared with that obtained using the existing inlet conditions of FDS, like the SEM method, wall-of-wind method and mean-forcing methods, using the 1/7 power-law or log-law inlets. To test these three methods, a set of fire simulations have been conducted and tested against the PenaBlending method. It was found that the results of the PenaBlending methods agree well with that of existing methods, with small variations for both the wind and fire cases.\ 

FDS 6.6.0 (the version used in this study) requires a very fine grid to obtain grid convergence. This is not feasible in the case of a large-scale simulation because of very high computation cost. FDS 6.2.0, with a reaction-rate-limiter combustion model, needs less fine grids to obtain grid convergence. Therefore, this combustion model is re-introduced into FDS 6.6.0, providing an option\ \ of choosing between two different combustion models,\ as a part of this study. For all the simulations, the reaction-rate-limiter combustion\ model has been used. The simulations are carried out in a neutral-atmospheric stability\ condition. However, the PenaBlending method can apply any general driving wind, and\ the effect of atmospheric stability, could be included, as part of future studies. The PenaBlending\ method could be extended in conjunction with Monin-Obukhov Similarity\ Theory (introduced in FDS 6.6.0) to model fire in various atmospheric stability conditions.

}, keywords = {computational fluid dynamics, Fire, fire dynamics simulator, Physics-based modelling, Wildfire, wind}, url = {http://vuir.vu.edu.au/40023/}, author = {Sesa Singha Roy} } @article {bnh-6045, title = {Modelling of tree fires and fires transitioning from the forest floor to the canopy with a physics-based model}, journal = {Mathematics and Computers in Simulation}, year = {2019}, month = {06/2019}, abstract = {

Wildland fires can take different forms such as surface fire or an elevated crown fire or combination of both. Crown fires are normally originated from surface fires spreading either along the bark of the tree trunks or direct flame contact to low branches with leaves and needles. In the past, surface fire (grassfire) spread simulations were conducted using physics-based models with fidelity. Here, we firstly seek numerically converged results for the burning of a single tree. Previously, numerical convergence for such physics-based fire simulations has been elusive. Subsequently, the linear and Arrhenius thermal degradation sub-models are appraised. For both thermal degradation sub-models grid convergence of the mass-loss rate is achieved with a 50\ mm grid. The grid converged simulations also agree with experimental results of a single burning Douglas fir tree. A fire in a modelled tree plantation is then simulated using the linear thermal degradation sub-model. The aim of this part is twofold: one to demonstrate a good modelling practice; and secondly to assess the model capability to simulate transitioning from a forest floor fire to a crown fire leading to a quasi-steady rate of spread. The Kolmogorov{\textendash}Smirnov test is used to rigorously demonstrate that the simulations on different grid and domain sizes have converged {\textemdash}\ that is, the results have become independent of the numerical parameters imposed upon the simulation.

A physics-based model reproduces many observed features of surface fire to forest fire transition. The crown fire propagates with a quasi-steady rate-of-spread after an initial development period. Analysis of the volumetric heat release rate shows that a surface fire propagates under the crown fire and supplies energy to support the burning of the crown. Overall many features are qualitatively in agreement with other tree and crown fire studies.

}, keywords = {Crown fire, Fire transition, Physics-based modelling, Rate of spread, Tree fire}, doi = {https://doi.org/10.1016/j.matcom.2019.05.018}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0378475419301995}, author = {Khalid Moinuddin and Duncan Sutherland} } @article {bnh-6046, title = {Simulation study of grass fire using a physics-based model: striving towards numerical rigour and the effect of grass height on the rate of spread}, journal = {International Journal of Wildland Fire}, volume = {27}, year = {2018}, month = {11/2018}, pages = {800-814}, abstract = {

Grid-independent rate of spread results from a physics-based simulation are presented. Previously, such a numerical benchmark has been elusive owing to computational restrictions. The grid-converged results are used to systematically construct correlations between the rate of spread (RoS) and both wind speed and grass height, separately. The RoS obtained from the physics-based model is found to be linear with wind speed in the parameter range considered. When wind speed is varied, the physics-based model predicts faster RoS than the Mk III and V (McArthur) models (Noble\ et al.\ 1980) but slower than the CSIRO model (Cheney\ et al.\ 1998). When the grass height is varied keeping the bulk density constant, the fire front changes from a boundary layer flame mode to plume flame mode as the grass height increases. Once the fires are in plume mode, a higher grass height results in a larger heat release rate of the fire but a slower RoS.

}, keywords = {atmospheric boundary layer, operational model, Physics-based modelling, wildland fire, wind speed}, doi = {https://doi.org/10.1071/WF17126}, url = {http://www.publish.csiro.au/wf/WF17126}, author = {Khalid Moinuddin and Duncan Sutherland and William Mell} }