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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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Journal ArticleDOI

Adaptive strategies using standard and mixed finite elements for wind field adjustment

TL;DR: The problem of obtaining an incompressible wind field that adjusts to an experimental one is solved by two different adaptive finite element methods, using linear interpolation of a potential function and a mixed finite element method.
Journal ArticleDOI

A convection model for fire spread simulation

TL;DR: A convection model which can be coupled with fire propagation models in order to take into account the wind and the slope which are two of the most relevant factors affecting surface fire spread is presented.
Book ChapterDOI

Tetrahedral Mesh Generation for Environmental Problems over Complex Terrains

TL;DR: A code for generating a tetrahedral mesh from an "optimal" node distribution in the domain based on Delaunay triangulation and a procedure to optimise the resulting mesh is developed.
Journal ArticleDOI

3-D modelling of wind field adjustment using finite differences in a terrain conformal coordinate system

TL;DR: In this paper, a mass consistent model is developed in order to obtain a wind field which adjusts to an initial one, where horizontal interpolation is considered at the level of the measure stations over the terrain and vertical wind profiles are built according to atmospheric stability conditions, terrain roughness, geostrophic wind, atmospheric stratification.
Journal ArticleDOI

Application of a nonlinear evolution model to fire propagation

TL;DR: In this article, a 2D model based on a convection-diffusion model for temperature and a mass-consistent model for wind field simulation is proposed. And a numerical solution of the former model is presented using a finite difference method together with the study of stability.
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.