Estamos realizando la búsqueda. Por favor, espere...
1436
37
173
30460
4471
2648
361
403
Abstract: Many nonlinear phenomena, whose numerical simulation is not straightforward, depend on a set of parameters in a way which is not easy to predict beforehand. Wildland fires in presence of strong winds fall into this category, also due to the occurrence of firespotting. We present a global sensitivity analysis of a new sub-model for turbulence and fire-spotting included in a wildfire spread model based on a stochastic representation of the fireline. To limit the number of model evaluations, fast surrogate models based on generalized Polynomial Chaos (gPC) and Gaussian Process are used to identify the key parameters affecting topology and size of burnt area. This study investigates the application of these surrogates to compute Sobol' sensitivity indices in an idealized test case. The performances of the surrogates for varying size and type of training sets as well as for varying parameterization and choice of algorithms have been compared. In particular, different types of truncation and projection strategies are tested for gPC surrogates. The best performance was achieved using a gPC strategy based on a sparse least-angle regression (LAR) and a low-discrepancy Halton's sequence. Still, the LAR-based gPC surrogate tends to filter out the information coming from parameters with large length-scale, which is not the case of the cleaning-based gPC surrogate. The wind is known to drive the fire propagation. The results show that it is a more general leading factor that governs the generation of secondary fires. Using a sparse surrogate is thus a promising strategy to analyze new models and its dependency on input parameters in wildfire applications.
Fuente: Communications in Nonlinear Science and Numerical Simulation, 2019, 73, 120-145
Editorial: Elsevier
Fecha de publicación: 15/07/2019
Nº de páginas: 76
Tipo de publicación: Artículo de Revista
DOI: 10.1016/j.cnsns.2019.02.002
ISSN: 1007-5704
Proyecto español: MTM2016-76016-R
Url de la publicación: https://doi.org/10.1016/j.cnsns.2019.02.002
Google Scholar
Citas
Repositorio UCrea Leer publicación
TRUCCHIA, ANDREA
VERA EGOROVA EGOROVA
PAGNINI, GIANNI
ROCHOUX, MÉLANIE C.
Volver