Modelling and simulation of wildland fire in the framework of the level set method

Mentrelli, Andrea; Pagnini, Gianni

doi:10.1007/s11587-016-0272-1

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…A mathematical model to represent the random effects associated with the wildland fires has been developed by Pagnini and co-authors (Pagnini and Massidda, 2012b, a;Pagnini, 2013Pagnini andMentrelli, 2016, 2014;Kaur et al, 2015Mentrelli and Pagnini, 2016). This formulation describes the motion of the fire line as a composition of the drifting part and the fluctuating part.…”

Section: Model Formulationmentioning

confidence: 99%

RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models – implementation in WRF-SFIRE and response analysis with LSFire+

Trucchia¹,

Egorova²,

Butenko³

et al. 2019

Geosci. Model Dev.

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Abstract. Fire spotting is often responsible for dangerous flare-ups in wildfires and causes secondary ignitions isolated from the primary fire zone, which lead to perilous situations. The main aim of the present research is to provide a versatile probabilistic model for fire spotting that is suitable for implementation as a post-processing scheme at each time step in any of the existing operational large-scale wildfire propagation models, without calling for any major changes in the original framework. In particular, a complete physical parameterisation of fire spotting is presented and the corresponding updated model RandomFront 2.3 is implemented in a coupled fire–atmosphere model: WRF-SFIRE. A test case is simulated and discussed. Moreover, the results from different simulations with a simple model based on the level set method, namely LSFire+, highlight the response of the parameterisation to varying fire intensities, wind conditions and different firebrand radii. The contribution of the firebrands to increasing the fire perimeter varies according to different concurrent conditions, and the simulations show results in agreement with the physical processes. Among the many rigorous approaches available in the literature to model firebrand transport and distribution, the approach presented here proves to be simple yet versatile for application to operational large-scale fire spread models.

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Section: Model Formulationmentioning

confidence: 99%

RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models – implementation in WRF-SFIRE and response analysis with LSFire+

Trucchia¹,

Egorova²,

Butenko³

et al. 2019

Geosci. Model Dev.

Self Cite

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“…Based on the knowledge of fire spread rate, the evolution of the fire front can be described by means of different techniques, which can be split into two main categories depending on the implementation, i.e., raster and vector. Examples of raster techniques are those based on cellular automata [6,7,8,9,10], while the most popular vector approaches are level set methods [11,12,13]. In raster implementations, the fire evolution only depends on the interaction among contiguous cells, which can be denoted as burnt, burning, or not burning.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Fire Propagation Models Using Level Set Methods

Alessandri,

Bagnerini,

Gaggero

et al. 2020

Preprint

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The availability of wildland fire propagation models with parameters estimated in an accurate way starting from measurements of fire fronts is crucial to predict the evolution of fire and allocate resources for firefighting. Thus, we propose an approach to estimate the parameters of a wildland fire propagation model combining an empirical fire spread rate and level set methods to describe the evolution of the fire front over time and space. After validating the model, the estimation of parameters in the spread rate is performed by using fire front shapes measured at different time instants as well as wind velocity and direction, landscape elevation, and vegetation distribution. Parameter estimation is performed by solving an optimization problem, where the objective function to be minimized is the symmetric difference between predicted and measured fronts. Numerical results obtained by the application of the proposed method are reported in two simulated scenarios and in an application case study using real data of the 2002 Troy fire in Southern California, thus showing the effectiveness of the proposed approach.

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