Wildfire modeling
Using computational science, wildfire modeling involves the statistical analysis of past fire events to predict spotting risks and front behavior.
Early attempts to determine wildfire behavior assumed terrain and vegetation uniformity.
However, the exact behavior of a wildfire's front is dependent on a variety of factors, including wind speed and slope steepness.
Fire can accelerate in narrow canyons and it can be slowed down or stopped by barriers such as creeks and roads.
Forest-fire models have been developed since 1940 to the present, but a lot of chemical and thermodynamic questions related to fire behaviour are still to be resolved.
Many semi-empirical fire spread equations, as in those published by the USDA Forest Service,[7] Forestry Canada,[8] Nobel, Bary, and Gill,[9] and Cheney, Gould, and Catchpole[10] for Australasian fuel complexes have been developed for quick estimation of fundamental parameters of interest such as fire spread rate, flame length, and fireline intensity of surface fires at a point for specific fuel complexes, assuming a representative point-location wind and terrain slope.
Based on the work by Fons's in 1946,[11] and Emmons in 1963,[12] the quasi-steady equilibrium spread rate calculated for a surface fire on flat ground in no-wind conditions was calibrated using data of piles of sticks burned in a flame chamber/wind tunnel to represent other wind and slope conditions for the fuel complexes tested.
[15] Although more sophisticated applications use a three-dimensional numerical weather prediction system to provide inputs such as wind velocity to one of the fire growth models listed above, the input was passive and the feedback of the fire upon the atmospheric wind and humidity are not accounted for.
A simplified physically based two-dimensional fire spread models based upon conservation laws that use radiation as the dominant heat transfer mechanism and convection, which represents the effect of wind and slope, lead to reaction–diffusion systems of partial differential equations.
The cost of added physical complexity is a corresponding increase in computational cost, so much so that a full three-dimensional explicit treatment of combustion in wildland fuels by direct numerical simulation (DNS) at scales relevant for atmospheric modeling does not exist, is beyond current supercomputers, and does not currently make sense to do because of the limited skill of weather models at spatial resolution under 1 km.
And, although FIRETEC and WFDS carry prognostic conservation equations for the reacting fuel and oxygen concentrations, the computational grid cannot be fine enough to resolve the reaction rate-limiting mixing of fuel and oxygen, so approximations must be made concerning the subgrid-scale temperature distribution or the combustion reaction rates themselves.
Grishin's work is based on the fundamental laws of physics, conservation and theoretical justifications are provided.
The simplified two-dimensional model of running crown forest fire was developed in Belarusian State University by Barovik D.V.
Also, although they have reached a certain degree of realism when simulating specific natural fires, they must yet address issues such as identifying what specific, relevant operational information they could provide beyond current tools, how the simulation time could fit the operational time frame for decisions (therefore, the simulation must run substantially faster than real time), what temporal and spatial resolution must be used by the model, and how they estimate the inherent uncertainty in numerical weather prediction in their forecast.
