Mathematics for environmental problems: from modelling to applications

[EN]In this paper we summarize some aspects of the simplified physical wild fire spread model developed by the authors and of the Geographical Information System tool specially developed to provide a real usable wild fire spread tool based on this model.Conserjería de Educación de la Junta de Castilla y León; FEDER; Fundación General de la Universidad de Salamanc

Space-Time adaptive algorithm for the mixed parabolic problem

Se presenta una estimación a posteriori del error para el problema parabólico lineal, y se diseña el correspondiente algoritmo de adaptación de malla y paso de tiempo. Para la discretización espacial se utiliza el elemento de Raviart-Thomas de menor orden y para la integración temporal la aproximación de Galerkin discontinua con paso variable. Se aplican los métodos numéricos desarrollados a varios problemas significativos que muestran la eficiencia del algoritmo desarrollado.In this paper we present an a-posteriori error estimator for the mixed formulation of a linear parabolic problem, used for designing an efficient adaptive algorithm. Our space-time discretization consists of lowest order Raviart-Thomas finite element over graded meshes and discontinuous Galerkin method with variable time step. Finally, several examples show that the proposed method is efficient and reliable

Space-Time adaptive algorithmfor the mixed parabolic problem

Se presenta una estimación a posteriori del error para el problema parabólico lineal, y se diseña el correspondiente algoritmo de adaptación de malla y paso de tiempo. Para la discretización espacial se utiliza el elemento de Raviart-Thomas de menor orden y para la integración temporal la aproximación de Galerkin discontinua con paso variable. Se aplican los métodos numéricos desarrollados a variosproblemas significativos que muestran la eficiencia del algoritmodesarrollado.In this paper we present an a-posteriori error estimator for the mixed formulation of a linear parabolic problem, used for designing an efficient adaptive algorithm. Our space-time discretization consists of lowest order Raviart-Thomas finite element over graded meshes and discontinuous Galerkin method with variable time step. Finally, several examples show that the proposed method is efficient and reliable

A downscale wind forecasting method based on WRF-HDMW coupling

[EN]In this paper, we propose a wind prediction strategy based on a mesoscale-microscale coupling technique. We will use the Weather Research and Forecast (WRF) [3] prediction as input data for the High Wind Defintion Model (HWDM) [2] to yield the forecast on a wind farm.Junta de Castilla y león; Fondos FEDE

A GIS-based fire spread simulator integrating a simplified physical wildland fire model and a wind field model

[EN]This article discusses the integration of two models, namely, the Physical Forest Fire Spread (PhFFS) and the High Definition Wind Model (HDWM), into a Geographical Information System-based interface. The resulting tool automates data acquisition, preprocesses spatial data, launches the aforementioned models and displays the corresponding results in a unique environment. Our implementation uses the Python language and Esri’s ArcPy library to extend the functionality of ArcMap 10.4. The PhFFS is a simplified 2D physical wildland fire spread model based on conservation equations, with convection and radiation as heat transfer mechanisms. It also includes some 3D effects. The HDWM arises from an asymptotic approximation of the Navier–Stokes equations, and provides a 3D wind velocity field in an air layer above the terrain surface. Both models can be run in standalone or coupled mode. Finally, the simulation of a real fire in Galicia (Spain) confirms that the tool developed is efficient and fully operational.Junta de Castilla y León; Fundación General de la Universidad de Salamanc

Neptuno ++: An Adaptive Finite Element Toolbox for Numerical Simulation of Environmental Problems

[EN]In this talk, we show some of the main features of Neptuno++, through several examples. Neptuno++ is a finite element toolbox mainly developed by L. Ferragut at SINUMCC (Group of Numerical Simulation and Scientific Computation) and implemented in C++.Junta de Castilla y León; Fondos FEDE

GIS-integrated environmental models

[EN]In this paper, we present the integration of the mathematical models Physical Forest Fire Spread (PhFFS) and High Definition Wind Model (HDWF), developed by the authors, into a GIS-based interface in order to supply to the end-user a func- tional and efficient tool. The resulting tool automates data acquisition, pre-processes spatial data, launches the aforementioned models, and displays the corresponding results in a unique environment. Our implementation uses the Python language and Esri’s ArcPy library to extend the functionality of ArcMap 10.4. The PhFFS is a simplified 2D physical wildland fire spread model based on conservation equations, with convection and radiation as heat transfer mechanisms. It also includes some 3D effects. The HDWF arises from an asymptotic approximation of the Navier-Stokes equations, and provides a 3D wind velocity field in an air layer above the terrain surface. Both models can be run in standalone or coupled mode. Finally, we confirm that the developed tool is efficient and fully operational presenting some examples of its successful application.Departamento de Educación de la Junta de Castilla y León; Fondos FEDER; Fundación General de la Universidad de Salamanc

PhyFire & HDWind: from the initial ideas to the current tool

[EN]We present a historical review of PhyFire and HDWind, both models developed by the research group on Numerical Simula- tion and Scientific Computation founded by L. Ferragut at the University of Salamanca.Junta de Castilla y León; Fondos FEDE

Parallel implementation of a simplified semi-physical wildland re spread model using OpenMP

[EN]We present a parallel 2D version of a simplified semi-physical wildland fire spread model based on conservation equations, with convection and radiation as the main heat transfer mechanisms. This version includes some 3D effects. The OpenMP framework allows distributing the prediction operations among the available threads in a multicore architecture, thereby reducing the computational time and obtaining the prediction results much more quickly. The results from the experiments using data from a real fire in Galicia (Spain) confirm the benefits of using the parallel version.Junta of Castilla y Leó

Diseño de un curso SPOC sobre modelización matemática

Memoria ID-029. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2019-2020