A review of a new generation of wildfire–atmosphere modeling

Abstract

One of the first significant developments in wildfire modeling research was to introduce heat flux as wildfire line intensity (kW·m -1 ). This idea could be adapted to using weather station measurements, topography, and fuel properties to estimate rate of fire spread, shape, and intensity. This review will present, in an accessible manner, the next evolution in wildfire models. The new generation models use mechanistic combustion models and large-eddy simulation (LES) to define the flaming combustion and the mechanism of rate of spread. These wildfire models are then coupled to a computational fluid dynamics (CFD) or mesoscale weather model. In other words, wildfire models become weather and climate models with add-in fuel and terrain models. These coupled models can use existing fire and weather physics or developed noncoupled models with a coupling mechanism. These models are tailored for specific spatial and temporal scales.Résumé : Un des premiers développements en recherche sur la modélisation des feux de forêt a été d'introduire le flux de chaleur en tant qu'intensité de la ligne de feu (kW·m -1 ). Cette idée pouvait être adaptée pour utiliser les mesures des stations météorologiques, la topographie et les propriétés des combustibles pour estimer la forme, l'intensité et le taux de propagation du feu. Cette synthèse présente de façon accessible la prochaine évolution dans les modèles de feux de forêt. La nouvelle génération de modèles utilise des modèles de combustion mécanistes et la simulation de grands écoulements tourbillonnants (LES) pour définir la combustion accompagnée de flammes et le mécanisme du taux de propagation. Ces modèles de feux de forêt sont ensuite couplés à un modèle de dynamique des fluides numérique (CFD) ou un modèle météorologique méso-échelle. En d'autres mots, les modèles de feux de forêt deviennent des modèles météorologiques et climatiques avec des modèles complémentaires de combustibles et de terrain. Ces modèles couplés peuvent utiliser un feu en cours et la physique météorologique ou des modèles non couplés avec un mécanisme de couplage. Ces modèles sont adaptés à des échelles spatiales et temporelles spécifiques. [Traduit par la Rédaction]Mots-clés : feu de forêt, combustible, comportement du feu, modèles de simulation WRF-FIRE, FIRETEC, WFDS, CAWFE, ForeFire/ Meso-NH, ARPS/DEVS-FIRE, différence finie, volume fini.

Machine learning–accelerated computational fluid dynamics

“…Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization. machine learning | turbulence | computational physics | nonlinear partial differential equations S imulation of complex physical systems described by nonlinear partial differential equations (PDEs) is central to engineering and physical science, with applications ranging from weather (1,2) and climate (3,4) and engineering design of vehicles or engines (5) to wildfires (6) and plasma physics (7). Despite a direct link between the equations of motion and the basic laws of physics, it is impossible to carry out direct numerical simulations at the scale required for these important problems.…”

“…Simulation of complex physical systems described by nonlinear partial differential equations (PDEs) is central to engineering and physical science, with applications ranging from weather ( 1 , 2 ) and climate ( 3 , 4 ) and engineering design of vehicles or engines ( 5 ) to wildfires ( 6 ) and plasma physics ( 7 ). Despite a direct link between the equations of motion and the basic laws of physics, it is impossible to carry out direct numerical simulations at the scale required for these important problems.…”

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review

“…He studied the rate of fire movement, predicted flame length, energy, and ecological impact, and concluded that Canadian models, including RedAPP and CanFIRE provide more accurate predictions than BehavePlus. Similarly, many tools exist for fire, and smoke models, which are crucial in decision making and planning to tackle the forest or a wild-land fire [74][75][76][77]. A lot of research has been done in the last two decades, especially after the increase of computation power during the last few years [77][78][79][80].…”

“…Similarly, many tools exist for fire, and smoke models, which are crucial in decision making and planning to tackle the forest or a wild-land fire [74][75][76][77]. A lot of research has been done in the last two decades, especially after the increase of computation power during the last few years [77][78][79][80]. Satellite active fire data such as Visible Infrared Imager Radiometer Suite (VIIRS) [81], moderate-resolution imaging spectro-radiometer (MODIS) [82] etc., can be vention and fighting [62,63].…”

A Multi-Fidelity Framework for Wildland Fire Behavior Simulations over Complex Terrain

“…In the past few decades, wildland and wildland-urban interface (WUI) fires have received substantial attention due to their destructive nature and extensive cost to lives and property [1][2][3]. A large body of literature can be found on the subject, as seen in various review articles [2,4,5]. Due to the complex nature of fire, limited computing resources, and the needs of planners and first responders, most models of wildfires have historically relied on simplified field-tested rules and correlations.…”