To fight against forest fires, simple and improved models are more searched out due to the fact they are more easily understandable by the users. This actual model is part of the fire propagation models within a network. It is simple and easy to implement. However, it depends on several parameters that are difficult to measure or estimate precisely beforehand. The prediction by this model is therefore insufficient. A deterministic optimization method is introduced to calibrate its parameters. The optimized model was tested on several laboratory experiments and on two large-scale experimental fires. The comparison of the model results with those of the experiment shows a very significant improvement in its prediction with the optimal parameters.
Dans la lutte contre les feux de forêt, les modèles simples et améliorés sont plus recherchés car plus aisément compréhensibles par les utilisateurs. Le présent modèle fait partie des modèles de propagation de feu à l’intérieur d’un réseau. Il est simple et facile à mettre en œuvre. Cependant, il dépend de plusieurs paramètres difficiles à mesurer ou à estimer avec précision au préalable. La prédiction par ce modèle est de ce fait insuffisante. Par conséquent, une méthode déterministe d’optimisation est introduite pour calibrer ses paramètres. Le modèle optimisé a été testé sur plusieurs feux de laboratoires et sur deux feux expérimentaux à grande échelle. La comparaison des résultats du modèle avec ceux de l’expérience montre une amélioration très significative de sa prédiction avec les paramètres optimaux.
Revised:
Accepted:
Published online:
M. H. Tchiekre 1; A. D. V. Brou 2; J. K. Adou 1

@article{CRMECA_2020__348_8-9_759_0, author = {M. H. Tchiekre and A. D. V. Brou and J. K. Adou}, title = {Deterministic optimization techniques to calibrate parameters in a wildland fire propagation model}, journal = {Comptes Rendus. M\'ecanique}, pages = {759--768}, publisher = {Acad\'emie des sciences, Paris}, volume = {348}, number = {8-9}, year = {2020}, doi = {10.5802/crmeca.58}, language = {en}, }
TY - JOUR AU - M. H. Tchiekre AU - A. D. V. Brou AU - J. K. Adou TI - Deterministic optimization techniques to calibrate parameters in a wildland fire propagation model JO - Comptes Rendus. Mécanique PY - 2020 SP - 759 EP - 768 VL - 348 IS - 8-9 PB - Académie des sciences, Paris DO - 10.5802/crmeca.58 LA - en ID - CRMECA_2020__348_8-9_759_0 ER -
%0 Journal Article %A M. H. Tchiekre %A A. D. V. Brou %A J. K. Adou %T Deterministic optimization techniques to calibrate parameters in a wildland fire propagation model %J Comptes Rendus. Mécanique %D 2020 %P 759-768 %V 348 %N 8-9 %I Académie des sciences, Paris %R 10.5802/crmeca.58 %G en %F CRMECA_2020__348_8-9_759_0
M. H. Tchiekre; A. D. V. Brou; J. K. Adou. Deterministic optimization techniques to calibrate parameters in a wildland fire propagation model. Comptes Rendus. Mécanique, Volume 348 (2020) no. 8-9, pp. 759-768. doi : 10.5802/crmeca.58. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.58/
[1] Firespread through fuel beds: Modeling of wind-aided fires and induced hydrodynamics, Phys. Fluids, Volume 12 (2000), pp. 1762-1782 | DOI | Zbl
[2] Numerical simulations of grass fires using a coupled atmosphere-fire model: Basic fire behavior and dependence on wind speed, J. Geophys. Res., Volume 110 (2005), D13107
[3] A physics-based approach to modelling grassland fires, Int. J. Wildland Fire, Volume 16 (2007), pp. 1-22 | DOI
[4] A simple physical model for forest fire spread rate, Fire Saf Sci., Volume 8 (2005), pp. 851-862 | DOI
[5] Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour, Ecol. Model., Volume 210 (2008), pp. 71-84 | DOI
[6] Simulating wildfire patterns using a small-world network model, Ecol Model., Volume 221 (2010), pp. 1463-1471 | DOI
[7] Wildland fire behaviour case studies and fuel models for landscape-scale fire modeling, J. Combust., Volume 2011 (2011), pp. 1-12 | DOI
[8] A comparison of level set and marker methods for the simulation of wildland fire front propagation, Int. J. Wildland Fire, Volume 25 (2016), pp. 229-241 | DOI
[9] Calibrating Rothermel’s fuel models by genetic algorithms, Adv. Forest Fire Res. (2014), pp. 102-106 (doi:10.14195/978-989-26-0884-6_10)
[10] Time aware genetic algorithm for forest fire propagation prediction: exploiting multi-core platforms: Time aware genetic algorithm for forest fire propagation prediction: exploiting multi-core platforms, Concurr. Comput. Pract. Exp., Volume 29 (2017), e3837 | DOI
[11] Modeling wildland fire propagation using a semi-physical network model, Case Stud. Fire Saf., Volume 4 (2015), pp. 11-18 | DOI
[12] Heat Transfer, Addison-Wesley, Reading, MA, 1984
[13] Modeling thermally thick pyrolysis of wood, Biomass Bioenergy, Volume 22 (2002), pp. 41-53 | DOI
[14] Heat transfer and kinetics in the pyrolysis of shrinking biomass particle, Chem. Eng. Sci., Volume 59 (2004), pp. 1999-2012 | DOI
[15] Modeling of beech wood pellet pyrolysis under concentrated solar radiation, Renew. Energy, Volume 99 (2016), pp. 721-729 | DOI
[16] Scilab, 2019 (http://www.scilab.org)
[17] The influence of fuel, weather and fire shape variables on fire-spread in grasslands, Int. J. Wildland Fire, Volume 3 (1993), pp. 31-44 | DOI
[18] Prediction of fire spread in grasslands, Int. J. Wildland Fire, Volume 8 (1998), pp. 1-13 | DOI
[19] A qualitative comparison of fire spread models incorporating wind and slope effects, For. Sci., Volume 43 (1997), pp. 170-180
[20] Effects of wind velocity and slope on fire behavior, Fire Saf. Sci. - Proc. Fourth Int. Symp., Volume 4 (1994), pp. 1041-1051 | DOI
Cited by Sources:
Comments - Policy