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Open AccessJournal ArticleDOI

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

TLDR
In this paper, a 2.5D and 3D model for wind field simulation by the finite element method is presented, which is based on a triangular mesh adapted to the terrain topography and roughness length.
About
This article is published in Journal of Wind Engineering and Industrial Aerodynamics.The article was published on 2010-10-01 and is currently open access. It has received 25 citations till now. The article focuses on the topics: Roughness length & Wind profile power law.

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Citations
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Journal ArticleDOI

Adaptive finite element simulation of stack pollutant emissions over complex terrains

TL;DR: In this article, a three-dimensional finite element model for the pollutant dispersion is presented, where the first stage consists on the construction of an adaptive tetrahedral mesh of a rectangular region bounded in its lower part by the terrain and in its upper part by a horizontal plane.
Journal ArticleDOI

Wind Forecasting Based on the HARMONIE Model and Adaptive Finite Elements

TL;DR: A new method for wind field forecasting over complex terrain using the predictions of the HARMONIE meso-scale model as the input data for an adaptive finite element mass-consistent wind model with a minimal user intervention.
Patent

Method and system for estimating economic losses from wind storms

Sean Maher
TL;DR: In this paper, the authors present systems and methods for estimating economic losses from wind storms, including estimating roughness length of an area surrounding a structure, estimating local wind speed at a structure and estimating wind pressure on a structure.
Journal ArticleDOI

A multimesh adaptive scheme for air quality modeling with the finite element method

TL;DR: In this paper, a multimesh adaptive scheme for convection-diffusion-reaction problems for a large number of components is presented, where the evaluation of the nonreactive part for each component and the reaction at each node constitute independent tasks.
Journal ArticleDOI

Spatial persistence in wind analysis

TL;DR: An easy-to-implement model, inspired in the ‘persistence’ model used in wind forecasting, that can be used as reference in wind spatial studies is proposed, allowing the comparison among different studies.
References
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Book ChapterDOI

Parameter Estimation in a Three-Dimensional Wind Field Model Using Genetic Algorithms

TL;DR: The main goal of this work is the estimation of these parameters using genetic algorithms, such that the wind velocities observed at the measurement station are regenerated as much as possible by the model.
Journal Article

Generación automática de mallas de tetraedros adaptadas a orografías irregulares

TL;DR: In this paper, a codigo capaz de generating a malla of tetraedros is defined, a partir de una distribucion "optima" of nodos in el dominio de la Isla de La Palma.
Book ChapterDOI

Applications of Genetic Algorithms in Realistic Wind Field Simulations

TL;DR: 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 surface, following the technique proposed in [14, 15, 18].
Related Papers (5)
Frequently Asked Questions (14)
Q1. What have the authors contributed in "Comparison between 2.5-d and 3-d realistic models for wind field adjustment " ?

In this paper the authors introduce several advances in the 2. 5-D and 3-D wind models and the authors 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 ). 

However, though such differences are small, further research is needed in order to construct new wind profiles that exactly satisfy all the available measures of wind velocities. However, further considerations should be taken into account in future works for a better performance of the models. For example, a finer map of roughness, a more sophisticated interpolation of wind velocities, a better approximation of the friction coefficient and a greater number of measurement stations well distributed over the studied region will help to reduce the errors of the models. 

In order to obtain an accurate windfield in zones with very steep slopes, the mesh should be adapted to the contour lines, since a change in the direction of edges in the mesh may strongly affect the computed wind. 

Wind models are interesting tools to the study of several problems related to the atmosphere, such as, the effect of wind on structures, pollutant transport [26], fire spreading [19], wind farm location, etc. 

It is evident that, in order to avoid spurious solutions, more than 20 repetitions for parameter setting of each hour should be done. 

The periodic updating of the main parameters of the models has proved to be fundamental for reducing the errors of the computed wind. 

Vi ∥ ∥ ∥ ∥ 2+ α2∫∂ω v2 (20)where ρǫ,i is a suitable smoothing function given for example byρǫ,i(x) = 1ǫ2 ρ( x − xi ǫ )ρ(x) = {Me− 1 1−||x||2 for ||x|| < 10 for ||x|| ≥ 1for a small ǫ and M such that ∫ρǫ,i(x)dx = 1The optimal control problem to be solved is posed as follow: Find u ∈ V such thatJ(u) = inf v∈V J(v) (21)The solution u of the optimal control problem (21) is characterized by J ′(u) = 0.Using the general optimal control theory [15], and introducing the adjoin state, then the problem (21) is characterized by the following three equations relating p , q and u:• ∫ω a∇p(u) · ∇ϕ+1α∫∂ω qϕ = −∫ω b∇t̂ · ∇ϕ ∀ϕ (22)• ∫ω a∇q(u) · ∇ψ− N ∑i=1∫ω ρǫ,i(m∇p(u) + n∇t̂− Vi)m · ∇ψ = 0 ∀ψ (23)•u = − 1α q on ∂ω (24)There exist a unique solution of the problem (9). 

Once the authors have interpolated the height and the roughness length in the nodes of these refined two-dimensional mesh, the authors use the derefinement algorithm [9,31] described in section 5.1 with εh = 10m and εr = 0.01m, keeping in any case the nodes located inside the six circles. 

The relative simplicity of diagnostic models makes them attractive from the practical point of view, since they do not require many input data and may be easily used. 

The use of their refinement/derefinement process in the 2-D mesh corresponding to the terrain surface allows us to obtain meshes that are accurately adapted to different functions as well as are locally refined around several points. 

The intensity of turbulence i is defined as the square root of the sum of variances σ21 , σ 2 2, σ 2 3 , of the three components of the velocity U 0 1 , U 0 2 , U 0 3 ,respectively, divided by the average wind velocity that has been measured,i =√σ21 + σ 2 2 + σ 2 3||U0|| (51)However, only measures of speed variations are often available but not of the wind direction. 

The authors have used a technique for constructing tetrahedral meshes which are simultaneously adapted to the terrain orography and the roughness length. 

For ε → 1, the importance of the horizontal distance from each point to the measurement stations is greater, while ε → 0 signifies more importance of the height difference between each point and the measurement stations. 

In addition, since several measures are often available at the same vertical line, the authors have constructed a least square adjustment of such measures for developing a vertical profile of wind velocities from an optimum friction velocity.