Effect of two-way coupling on the calculation of forest fire spread: model development
A. M. G. Lopes A B , L. M. Ribeiro A , D. X. Viegas A and J. R. Raposo A
+ Author Affiliations
- Author Affiliations
A ADAI (Associação para o Desenvolvimento da Aerodinâmica Indistrial), DEM (Department of Mechanical Engineering), University of Coimbra, PT-3030 Coimbra, Portugal.
B Corresponding author. Email: antonio.gameiro@dem.uc.pt
International Journal of Wildland Fire 26(9) 829-843 https://doi.org/10.1071/WF16045
Submitted: 19 March 2016 Accepted: 31 May 2017 Published: 17 August 2017
Abstract
The present work addresses the problem of how wind should be taken into account in fire spread simulations. The study was based on the software system FireStation, which incorporates a surface fire spread model and a solver for the fluid flow (Navier–Stokes) equations. The standard procedure takes the wind field computed from a single simulation in the absence of fire, but this may not be the best option, especially for large fires. The two-way coupling method, however, considers the buoyancy effects caused by the fire heat release. Fire rate of spread is computed with the semi-empirical Rothermel model, which takes as input local terrain slope, fuels properties and wind speed and direction. Wind field is obtained by solving the mass, momentum and energy equations. Effects of turbulence on the mean flow field are taken into account with the k – ϵ turbulence model. The calculation procedure consists of an interchange between the fire spread model and the wind model through a dynamic interaction. The present work describes the first part of this research, presenting the underlying models and a qualitative sensitivity analysis. It is shown that the update frequency for the dynamic interaction markedly influences the total calculation time. The best strategy for updating the wind field during the fire progression is presented. The dependence of results on mesh size is also described.
Additional keywords: fire spread simulation, fire–wind interaction, surface fire, wind simulation.
References
Albini F (1976) Estimating wildfire behavior and effects. USDA Forest Service, Intermountain Forest and Range Experimental Station, General Technical Report GTR-INT-30. (Ogden, UT, USA)Albini FA (1996) Iterative solution of the radiation transport equations governing spread of fire in wildland fuel. Fizika Goreniya I Zvryva 32, 71–82.
Albini FA, Baughman RG (1979) Estimating windspeeds for predicting wildland fire behavior. USDA Forest Service, Intermountain Forest and Range Experimental Station, Research Paper INT-221. (Ogden, UT, USA)
Alexander ME (1985) Estimating the length-to-breadth ratio of elliptical forest fire patterns. In ‘Proceedings of the Eighth Conference on Fire and Forest Meteorology’, 29 April–2 May 1985, Detroit, MI, USA, pp. 287–304. (Society of American Foresters: Bethesda, MD, USA)
Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. USDA Forest Service, Intermountain Forest and Range Experimental Station, General Technical Report INT-122. (Ogden, UT, USA)
Anderson HE (1983) Predicting wind-driven fire size and shape. USDA Forest Service, Intermountain Forest and Range Experimental Station, Research Paper INT-305. (Ogden, UT, USA)
Andrews PL, Bevins CD, Seli RC (2008) BehavePlus fire modeling system, version 4.0: user’s guide. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-106. (Ogden, UT, USA).
Castro FA, Palma JMLM, Silva Lopes A (2003) Simulation of the Askervein flow. Part 1: Reynolds averaged Navier–Stokes equations (k – ϵ turbulence model). Boundary-Layer Meteorology 107, 501–530.
| Simulation of the Askervein flow. Part 1: Reynolds averaged Navier–Stokes equations (k – ϵ turbulence model).Crossref | GoogleScholarGoogle Scholar |
Clark TL, Jenkins MA, Coen J, Packham D (1996a) A coupled atmosphere–fire model: convective feedback on fire line dynamics. Journal of Applied Meteorology 35, 875–901.
| A coupled atmosphere–fire model: convective feedback on fire line dynamics.Crossref | GoogleScholarGoogle Scholar |
Clark TL, Jenkins MA, Coen J, Packham D (1996b) A coupled atmosphere–fire model: role of the convective Froude number and dynamic fingering at the fire line. International Journal of Wildland Fire 6, 177–190.
| A coupled atmosphere–fire model: role of the convective Froude number and dynamic fingering at the fire line.Crossref | GoogleScholarGoogle Scholar |
Clark TL, Coen J, Latham D (2004) Description of a coupled atmosphere–fire model. International Journal of Wildland Fire 13, 49–64.
| Description of a coupled atmosphere–fire model.Crossref | GoogleScholarGoogle Scholar |
Coen JL (2013) Modeling wildland fires: a description of the Coupled Atmosphere–Wildland Fire Environment model (CAWFE). NCAR Technical Note NCAR/TN-500þSTR. Available at http://nldr.library.ucar.edu/repository/collections/TECH-NOTE-000-000-000-866 [Verified 12 July 2017]
Coen JL, Riggan PJ (2014) Simulation and thermal imaging of the 2006 Esperanza Wildfire in southern California: application of a coupled weather–wildland fire model. International Journal of Wildland Fire 23, 755–770.
| Simulation and thermal imaging of the 2006 Esperanza Wildfire in southern California: application of a coupled weather–wildland fire model.Crossref | GoogleScholarGoogle Scholar |
Coen JL, Cameron M, Michalakes J, Patton EG, Riggan PJ, Kara MY (2013) WRF-Fire: coupled weather–wildland fire modeling with the weather research and forecasting model. Journal of Applied Meteorology and Climatology 52, 16–38.
| WRF-Fire: coupled weather–wildland fire modeling with the weather research and forecasting model.Crossref | GoogleScholarGoogle Scholar |
Finney MA (1998) FARSITE: Fire Area Simulator – model development and evaluation. USDA Forest Service, Rocky Mountain Station, Research Paper RMRS-RP-4. (Ogden, UT, USA)
Flassak T, Moussiopoulos N (1988) Direct solution of the Helmholtz equation using Fourier analysis on scalar and vector computers. Environmental Software 3, 12–16.
| Direct solution of the Helmholtz equation using Fourier analysis on scalar and vector computers.Crossref | GoogleScholarGoogle Scholar |
Forthofer J, Shannon K, Butler B (2009) Simulating diurnally driven slope winds with WindNinja. In ‘Proceedings of 8th Symposium on Fire and Forest Meteorological Society’, 13–15 October 2009, Kalispell, MT, USA. (Eds TJ Brown, BE Potter) (American Meteorological Society: Boston, MA, USA)
Forthofer JM, Forthofer AB, Butler BW, Wagenbrenner NS (2014) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements. International Journal of Wildland Fire 23, 969–981.
| A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. Model formulation and comparison against measurements.Crossref | GoogleScholarGoogle Scholar |
Green DG, Gill AM, Noble IR (1983) Fire shapes and the adequacy of fire-spread models. Ecological Modelling 20, 33–45.
| Fire shapes and the adequacy of fire-spread models.Crossref | GoogleScholarGoogle Scholar |
Grishin AM (1994) ‘Forest Fire Physics’. (Publishing House of TSU: Tomsk, Russia)
Henkes RAWM, van der Flugt FF, Hoogendoorn CJ (1991) Natural convection flow in a square cavity calculated with low-Reynolds-number turbulence models. International Journal of Heat and Mass Transfer 34, 377–388.
| Natural convection flow in a square cavity calculated with low-Reynolds-number turbulence models.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK3MXksFanu78%3D&md5=36637193a9c47cfc5d2fe0cb28bb2e5eCAS |
Kourtz PH, O’Regan WG (1971) A model for a small forest fire ... to simulate burned and burning areas for use in a detection model. Forest Science 17, 163–169.
Launder BE, Spalding DB (1972) ‘Mathematical Models of Turbulence.’ (Academic Press: New York, NY, USA)
Launder BE, Spalding DB (1974) The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering 3, 269–289.
| The numerical computation of turbulent flows.Crossref | GoogleScholarGoogle Scholar |
Linn R, Cunningham P (2005) Numerical simulations of grass fires using a coupled atmosphere–fire model: basic fire behavior and dependence on wind speed. Journal of Geophysical Research 110, D13107
| Numerical simulations of grass fires using a coupled atmosphere–fire model: basic fire behavior and dependence on wind speed.Crossref | GoogleScholarGoogle Scholar |
Linn R, Reisner J, Colman J, Winterkamp J (2002) Studying wildfire behavior using FIRETEC. International Journal of Wildland Fire 11, 233–246.
| Studying wildfire behavior using FIRETEC.Crossref | GoogleScholarGoogle Scholar |
Lopes AMG (2003) WindStation – a software for the simulation of atmospheric flows over complex topography. Environmental Modelling & Software 18, 81–96.
| WindStation – a software for the simulation of atmospheric flows over complex topography.Crossref | GoogleScholarGoogle Scholar |
Lopes AMG, Sousa ACM, Viegas DX (1995) Numerical simulation of turbulent flow and fire propagation in complex topography. Numerical Heat Transfer Part A 27, 229–253.
| Numerical simulation of turbulent flow and fire propagation in complex topography.Crossref | GoogleScholarGoogle Scholar |
Lopes AMG, Cruz MG, Viegas DX (2002) FireStation – an integrated software system for the numerical simulation of wind field and fire spread on complex topography. Environmental Modelling & Software 17, 269–285.
| FireStation – an integrated software system for the numerical simulation of wind field and fire spread on complex topography.Crossref | GoogleScholarGoogle Scholar |
Marino E, Dupuy JL, Pimont F, Guijarro M, Hernando C, Linn R (2012) Fuel bulk density and fuel moisture content effects on fire rate of spread: a comparison between FIRETEC model predictions and experimental results in shrub fuels. Journal of Fire Sciences 30, 277–299.
| Fuel bulk density and fuel moisture content effects on fire rate of spread: a comparison between FIRETEC model predictions and experimental results in shrub fuels.Crossref | GoogleScholarGoogle Scholar |
Markatos NC, Malin MR, Cox G (1982) Mathematical modelling of buoyancy-induced smoke flow in enclosures. International Journal of Heat and Mass Transfer 25, 63–75.
| Mathematical modelling of buoyancy-induced smoke flow in enclosures.Crossref | GoogleScholarGoogle Scholar |
Mell W, Jenkins MA, Gould J, Cheney P (2007) A physics based approach to modeling grassland fires. International Journal of Wildland Fire 16, 1–22.
| A physics based approach to modeling grassland fires.Crossref | GoogleScholarGoogle Scholar |
Panofsky HA, Dutton JA (1984) ‘Atmospheric Turbulence Models and Methods for Engineering Applications.’ (Wiley: New York, NY, USA)
Patankar SV (1980) ‘Numerical Heat Transfer and Fluid Flow.’ (Hemisphere Publishing Corporation: Washington, DC, USA)
Plate EJ (1971) ‘Aerodynamic Characteristics of Atmospheric Boundary Layers.’ (US Atomic Energy Commission: USA, National Technical Information Service, US Department of Commerce: Springfield, VA, USA)
Plate EJ (1982) ‘Engineering Meteorology.’ (Elsevier Scientific Publishing Company: New York, NY, USA)
Raithby GD, Stubley GD (1987) The Askervein Hill Project: a finite control volume prediction of three-dimensional flows over the hill. Boundary-Layer Meteorology 39, 247–267.
| The Askervein Hill Project: a finite control volume prediction of three-dimensional flows over the hill.Crossref | GoogleScholarGoogle Scholar |
Rhie CM, Chow WL (1983) Numerical study of the turbulent flow past an airfoil with trailing edge separation. AIAA Journal 21, 1525–1532.
| Numerical study of the turbulent flow past an airfoil with trailing edge separation.Crossref | GoogleScholarGoogle Scholar |
Richards GD (1990) An elliptical growth model of forest fire fronts and its numerical solution. International Journal for Numerical Methods in Engineering 30, 1163–1179.
| An elliptical growth model of forest fire fronts and its numerical solution.Crossref | GoogleScholarGoogle Scholar |
Ross DG, Smith IN, Manins PC, Fox DG (1988) Diagnostic wind field modeling for complex terrain: model development and testing. Journal of Applied Meteorology 27, 785–796.
| Diagnostic wind field modeling for complex terrain: model development and testing.Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-115 (Ogden, UT, USA).
Rothermel RC (1983) How to predict the spread and intensity of forest and range fires. USDA Forest Service, Intermountain Forest and Range Experiment Station General Technical Report INT-143. (Ogden, UT, USA).
Séro-Guillaume O, Margerit J (2002) Modelling forest fires. Part I: A complete set of equations derived by extended irreversible thermodynamics. International Journal of Heat and Mass Transfer 45, 1705–1722.
| Modelling forest fires. Part I: A complete set of equations derived by extended irreversible thermodynamics.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL (2009a) Wildland surface fire spread modelling, 1990–2007. 1. Physical and quasi-physical models. International Journal of Wildland Fire 18, 349–368.
| Wildland surface fire spread modelling, 1990–2007. 1. Physical and quasi-physical models.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL (2009b) Wildland surface fire spread modelling, 1990–2007. 3. Simulation and mathematical analogue models. International Journal of Wildland Fire 18, 387–403.
| Wildland surface fire spread modelling, 1990–2007. 3. Simulation and mathematical analogue models.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL (2009c) Wildland surface fire spread modelling, 1990–2007. 2. Empirical and quasi-empirical models. International Journal of Wildland Fire 18, 369–386.
| Wildland surface fire spread modelling, 1990–2007. 2. Empirical and quasi-empirical models.Crossref | GoogleScholarGoogle Scholar |
Van Doormaal JP, Raithby GD (1984) Enhancements of the simple method for predicting incompressible fluid flows. Numerical Heat Transfer 7, 147–163.
| Enhancements of the simple method for predicting incompressible fluid flows.Crossref | GoogleScholarGoogle Scholar |
Viegas DX (2004a) A mathematical model for forest fires blow-up. Combustion Science and Technology 177, 27–51.
| A mathematical model for forest fires blow-up.Crossref | GoogleScholarGoogle Scholar |
Viegas DX (2004b) Slope and wind effects on fire propagation. International Journal of Wildland Fire 13, 143–156.
| Slope and wind effects on fire propagation.Crossref | GoogleScholarGoogle Scholar |
Viegas DX (2006) Parametric study of an eruptive fire behaviour model. International Journal of Wildland Fire 15, 169–177.
| Parametric study of an eruptive fire behaviour model.Crossref | GoogleScholarGoogle Scholar |
Wagenbrenner NS, Forthofer JM, Lamb BK, Shannon KS, Butler BW (2016) Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja. Atmospheric Chemistry and Physics 16, 5229–5241.
| Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28Xht1Oqsr%2FN&md5=dd00d0b7446a308471c34eedc64a15dfCAS |
White FM (2011) ‘Fluid Mechanics’, 7th edn. (McGraw-Hill Book Company: New York, NY, USA)
