Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/45239
Título: Applications of genetic algorithms in realistic wind field simulations
Autores/as: Montenegro, Rafa 
Montero, G. 
Rodriguez, E. 
Escobar, J. M. 
González-Yuste, J. M.
Clasificación UNESCO: 12 Matemáticas
Palabras clave: Genetic Algorithm
Control Point
Wind Velocity
Turbulence Intensity
Planetary Boundary Layer
Fecha de publicación: 2008
Proyectos: Simulacion Numerica de Campos de Viento Orientados A Procesos Atmofericos. 
Publicación seriada: Studies in Computational Intelligence 
Resumen: Mass consistent models have been widely use in 3-D wind modelling by finite element method. We have used a method for constructing tetrahedral meshes which are simultaneously adapted to the terrain orography and the roughness length by using a refinement/derefinement process in a 2-D mesh corresponding to the terrain surface, following the technique proposed in [14,15,18]. In this 2-D mesh we include a local refinement around several points which are previously defined by the user. Besides, we develop a technique for adapting the mesh to any contour that has an important role in the simulation, like shorelines or roughness length contours [3,4], and we refine the mesh locally for improving the numerical solution with the procedure proposed in [6]. This wind model introduces new aspects on that proposed in [16, 19, 20]. The characterization of the atmospheric stability is carried out by means of the experimental measures of the intensities of turbulence. On the other hand, since several measures are often available at a same vertical line, we have constructed a least square optimization of such measures for developing a vertical profile of wind velocities from an optimum friction velocity. Besides, the main parameters governing the model are estimated using genetic algorithms with a parallel implementation [12,20,26]. In order to test the model, some numerical experiments are presented, comparing the results with realistic measures.
URI: http://hdl.handle.net/10553/45239
ISBN: 978-3-540-77474-7
978-3-540-77475-4
ISSN: 1860-949X
DOI: 10.1007/978-3-540-77475-4_11
Fuente: Studies in Computational Intelligence [ISSN 1860-949X], v. 102, p. 165-182
Colección:Artículos
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