A 3-D diagnostic model for wind field adjustment

Montero, G.; Montenegro, R.; Escobar, J. M.

doi:10.1016/s0167-6105(98)00022-1

Cited by 34 publications

(48 citation statements)

References 7 publications

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“…This model [23] is based on the continuity equation for an incompressible flow where the air density is constant in the domain Ω, and no-flow-through conditions on S ∪ A (terrain and top) are considered…”

Section: Mass Consistent Model In 3-dmentioning

confidence: 99%

“…We have considered a logarithmic profile [13] in the surface layer, which takes into account the previous horizontal interpolation [23], as well as the effect of roughness and the air stability (neutral, stable or unstable atmosphere, according to the Pasquill stability class) on the wind intensity and direction. Above the surface layer, a linear interpolation is carried out using the geostrophic wind.…”

Section: Interpolated Windmentioning

confidence: 99%

“…This would finally lead us to larger linear systems of equations and higher computational cost for solving them. Our first 3-D models (see [23,24]) had some of these limitations since the meshes used for defining the terrain surface were uniform.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Mass Consistent Model In 3-dmentioning

confidence: 99%

Section: Interpolated Windmentioning

confidence: 99%

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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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“…No geostrophic winds are considered. The profile is given by the logaritmic profile for the surface layer while it is extrapolated for the mixed layer as is done in [8].…”

Section: Wind Parameters and Simulation Regionsmentioning

confidence: 99%

A wind speed and fluctuation simulator for characterizing the wind lidar correlation method

Tomás

Sicard

Masjuan

et al. 2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-Aerosol distribution in the atmosphere is used as a wind-tracer by lidars since it is drifted by the wind and respond to its changes. Two methods are used: the correlation and the Doppler method. This first one is easier and cheaper to implement than the latter. It makes it competitive for retrieving wind speed profiles or estimating wind turbulence. However, its accuracy decreases significantly as the distance from the optical radiation source increases, hence the need to characterize the method by means of wind field profile simulations and validate it by comparing the retrieval of real wind velocities with that of cooperative instruments such as radiosoundings. In this paper, the bases of a 2D lidar signal simulator are presented. The relationship between wind fields and the aerosol concentration dynamics, and the way they relate to lidar signal returns is explained. The first results of the application of the correlation method for the retrieval of wind velocities from real data at the UPC are presented and compared to radiosoundings measurements.

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“…In addition, along the plume trajectory, we apply a local refinement algorithm [5] based on the 8-subtetrahedron subdivision. This tetrahedral mesh is used in a mass consistent model for wind field adjustment [6]. To obtain the observed wind, horizontal interpolation of the station measures is carried out.…”

Section: Introductionmentioning

confidence: 99%

Velocity Field Modelling for Pollutant Plume Using 3-D Adaptive Finite Element Method

Montero

Montenegro

Escobar

et al. 2004

Computational Science - ICCS 2004

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Abstract. Air pollution models usually start from the computation of the velocity field of a fluid. In this paper, we present a model for computing that field based on the contribution of the observed wind flow and the vertical buoyancy or momentum plume rise defined by a Gaussian plume model. This initial velocity field is adjusted to verify incompressibility and impermeability conditions by using a mass consistent model. In this environmental modelling that occur in a three-dimensional domain defined over complex terrain, a mesh generator capable of adapting itself to the topographical data and to the numerical solution is essential. Here, the unstructured tetrahedral meshes are generated by combining the use of a refinement/derefinement algorithm for two-dimensional domains and a tetrahedral mesh generator based on Delaunay triangulation. Occasionally in this process, low quality or even inverted elements may appear. A simultaneous untangling and smoothing procedure allows to optimise the resulting meshes. Once we have constructed the adapted mesh in accordance with the geometrical characteristics of our domain, we use an adaptive local refinement in the plume trajectory. Finally, this model is applied in a test problem.

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A 3-D diagnostic model for wind field adjustment

Cited by 34 publications

References 7 publications

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

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

A wind speed and fluctuation simulator for characterizing the wind lidar correlation method

Velocity Field Modelling for Pollutant Plume Using 3-D Adaptive Finite Element Method

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