Modeling the spreading of large-scale wildland fires

The present work is devoted to the development of a hybrid model for predicting the rate of spread of wildland fires at a large scale, taking into account the local heterogeneities related to vegetation, topography, and meteorological conditions. Some methods for generating amorphous network, representative of real vegetation landscapes, are proposed. Mechanisms of heat transfer from the flame front to the virgin fuel are modeled: radiative preheating from the flame and embers, convective preheating from hot gases, radiative heat losses and piloted ignition of the receptive vegetation item. Flame radiation is calculated by combining the solid flame model with the Monte Carlo method and by taking into account its attenuation by the atmospheric layer between the flame and the receptive vegetation. The model is applied to simple configurations where the fire spreads on a flat or inclined terrain, with or without a constant wind. Model results are in good agreement with literature data. A sensitivity study is conducted to identify the most influential parameters of the model. Eventually, the model is validated by comparing predicted fire patterns with those obtained from a prescribed burning in Australia and from a historical fire that occurred in Corsica in 2009, showing a very good agreement in terms of fire patterns, rate of spread, and burned area.

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Additional Info

Field Value
Source https://theses.hal.science/tel-00931806
Author Mohamed, Drissi
Maintainer CCSD
Last Updated May 7, 2026, 09:35 (UTC)
Created May 7, 2026, 09:35 (UTC)
Identifier tel-00931806
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Laboratoire « Sciences pour l’Environnement » (UMR CNRS 6134 SPE) (SPE) ; Centre National de la Recherche Scientifique (CNRS)-Università di Corsica Pasquale Paoli [Université de Corse Pascal Paoli]
creator Mohamed, Drissi
date 2013-02-08T00:00:00
harvest_object_id b54823d0-fcaf-4bb7-8f04-21bb5eebcb5c
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-08-12T00:00:00
set_spec type:THESE