Genetic algorithms for an improved parameter estimation with local refinement of tetrahedral meshes in a wind model

Montero, G.; Rodríguez, Eduardo; Montenegro, R.; Escobar, J. M.; González-Yuste, J. M.

doi:10.1016/j.advengsoft.2004.03.011

Cited by 42 publications

(54 citation statements)

References 33 publications

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“…The untangling and smoothing procedure introduced by Escobar et al [20] has been applied in the generation of this mesh, which contains 15 3085 tetrahedra and 28 387 nodes. The wind velocity field has been simulated by Montero et al [21]. Both the mesh and the wind field have been used in this paper with the authors' kind permission.…”

Section: Application: Air Pollutionmentioning

confidence: 99%

Numerical performance of incomplete factorizations for 3D transient convection–diffusion problems

Ferran

Sandoval

2007

Advances in Engineering Software

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Many environmental processes can be modelled as transient convection-diffusion-reaction problems. This is the case, for instance, of the operation of activated-carbon filters. For industrial applications there is a growing demand for 3D simulations, so efficient linear solvers are a major concern. We have compared the numerical performance of two families of incomplete Cholesky factorizations as preconditioners of conjugate gradient iterations: drop-tolerance and prescribed-memory strategies. Numerical examples show that the former are computationally more efficient, but the latter may be preferable due to their predictable memory requirements.

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Section: Application: Air Pollutionmentioning

confidence: 99%

Numerical performance of incomplete factorizations for 3D transient convection–diffusion problems

Ferran

Sandoval

2007

Advances in Engineering Software

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“…In [26,27], we presented a new 3-D finite element model that uses adaptive unstructured meshes of tetrahedrons with elements of small size where it is necessary but maintaining greater elements where such level of discretization is not required. The resulting 3-D mesh were constructed by using a refinement/derefinement process to adapt a 2-D mesh to the terrain surface, a vertical spacing function to locate nodes in the air and a Delaunay triangulation algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

“…These parameters are generally approximated using empirical criteria. Our 3-D wind model includes a tool for the parameter estimation based on genetic algorithms [17,27,33].…”

Section: Introductionmentioning

confidence: 99%

Comparison between 2.5-D and 3-D realistic models for wind field adjustment

Ferragut

Montenegro

Montero

et al. 2010

Journal of Wind Engineering and Industrial Aerodynamics

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In previous works, many authors have widely used mass consistent models for wind field simulation by the finite element method. On one hand, we have developed a 3-D mass consistent model by using tetrahedral meshes which are simultaneously adapted to complex orography and to terrain roughness length. In addition, we have included a local refinement strategy around several measurement or control points, significant contours, as for example shorelines, or numerical solution singularities. On the other hand, we have developed a 2.5-D model for simulating the wind velocity in a 3-D domain in terms of the terrain elevation, the surface temperature and the meteorological wind, which is consider as an averaged wind on vertical boundaries. Using the meteorological wind as datum, the 2.5-D model provides a 3-D local wind modified by topography and thermal gradients on the surface by solving only a 2-D optimal control problem where the boundary condition is the control. In this case, the finite element discretization consists on a triangular mesh adapted to the terrain topography and roughness length. In both models, the wind field adjusts to several wind speed measurements at several points in the 3-D domain and eventually to an average wind flux on the boundary.In this paper we introduce several advances in the 2.5-D and 3-D wind models and we compare their results on a region located in the Province of Lugo (Spain) with realistic data that have been provided by the company Desarrollos Eólicos S.A. (DESA). In order to obtain the best adjustment of models results to the measurements, the main parameters governing the models are estimated by using genetic algorithms with a parallel implementation.

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“…Marzban (1995Marzban ( , 1997Marzban ( , 1998 and Hsieh (2003) have used artificial neural networks to improve model forecasts of tornadoes, wind predictions, precipitation and rare events. Montero et al (2005) have used genetic algorithms to improve local parameters for wind models. Artificial neural networks are machine learning classifiers which map a set of input attributes into a boolean or multivalued output attribute class.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of atmospheric emissions using machine learning

Cervone

Franzese

Ezber

et al. 2008

Nat. Hazards Earth Syst. Sci.

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Abstract. Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions.First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find complex nonlinear relationships between the meteorological input conditions and each cluster of clouds. The patterns discovered are provided in the form of probabilistic measures of contamination, thus suitable for result interpretation and dissemination.The learned patterns can be used for quick assessment of the areas at risk and of the fate of potentially hazardous contaminants released in the atmosphere.

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Genetic algorithms for an improved parameter estimation with local refinement of tetrahedral meshes in a wind model

Cited by 42 publications

References 33 publications

Numerical performance of incomplete factorizations for 3D transient convection–diffusion problems

Numerical performance of incomplete factorizations for 3D transient convection–diffusion problems

Comparison between 2.5-D and 3-D realistic models for wind field adjustment

Risk assessment of atmospheric emissions using machine learning

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