4.5 Article Proceedings Paper

Real-time wildland fire spread modeling using tabulated flame properties

Journal

FIRE SAFETY JOURNAL
Volume 91, Issue -, Pages 872-881

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.firesaf.2017.03.006

Keywords

Wildfires; Tabulation; Genetic algorithm; Real-time simulation; Front-tracking method

Funding

  1. Commission for Higher Education in Thailand
  2. Thailand Research Fund
  3. National Science and Technology Development Agency in Thailand
  4. French Ministry of Foreign and European Affairs
  5. French Ministry of Education and Research
  6. Hubert Curien Partnerships SIAM [27571PF]
  7. NOVELTIS
  8. ANRT (CIFRE/ANRT) [566/2013]

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This paper is an extension of previous papers [1,2] on a raster-based fire spread model which combines a network model to represent vegetation distribution on land and a physical model of the heat transfer from burning to unburnt vegetation items, and takes into account local conditions of wind, topography, and vegetation. The physical model, still based on the unsteady energy conservation in every fuel element and detailed local and non-local heat transfer mechanisms (radiation from the flaming zone and embers; surface convection, and radiative cooling from the heated fuel element to the environment), now includes wind-driven convection through the fuel bed. To address the challenge of real-time fire spread simulations, the model is also extended in two ways. First, the Monte Carlo method is used in conjunction with a genetic algorithm to create a database of radiation view factors from the flame to the fuel surface for a wide variety of flame properties and environment conditions. Second, the front-tracking method, drafted in [2], is extended to polydisperse networks and implemented in the new version of the model, called SWIFFT. Finally, the SWIFFT model is validated against data from different fire scenarios, showing it is capable of capturing the trends observed in experiments in terms of rate of spread, and area and shape of the burn, with reduced computational resources.

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