Plugin WindLab

Plugin WindLab
Description
This plugin implement various WindLab features. Plugin version: 1.0 Last modified: 2024-04-15 LabRPS version: All Author: Koffi Daniel
Author
Koffi Daniel
Download
None
Features
Cholesky Decomposition, Horizontal Uniform Distribution, Vertical Uniform Distribution, Uniform Distribution, Grid Points, General Distribution, Import Simulation Points from File, Power Law Profile, Logarithmic Law Profile, Deaves and Harris Profile, Sine Modulation Function, Three Parameter Modulation Function, Exponential Modulation Function, Davenport Coherence Function, Krenk Coherence Function, Davenport Along Wind Spectrum, Harris Along Wind Spectrum, Kaimal Along Wind Spectrum, Kaimal Across Wind Spectrum, Kaimal Vertical Wind Spectrum, Simiu Along Wind Spectrum, Simiu Across Wind Spectrum, Simiu Vertical Wind Spectrum, von Karman Along Wind Spectrum, Uniform Random Phases, Uniform Random Phases Import, Double Index Frequency Discretization, Single Index Frequency Discretization, Zerva Frequency Discretization
Plugin Version
1.0
Date last modified
2024-04-15
LabRPS Version(s)
All
Default shortcut
None
See also
None

You can find the source code of this plugin on the following Github repository: Get the code here!. This plugin is one of the official plugins provided by LabRPS. It provides very useful features (tools) for the simulation of random wind velocity. Plugins are very easy to create in LabRPS, therefore, anyone can develop plugin for any random phenomenon in LabRPS. Go to this page to see how to create new plugin for LabRPS. You can get quick assistance from LabRPS community by sending your concern to the community forum.

Cholesky Decomposition

This feature performs the Cholesky decomposition of a positive Hermitian power spectrum matrix and returns the lower triangular matrix (L) of the decomposition. The Cholesky decomposition is a numerical method used to decompose a positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose. Specifically, for a matrix A, the decomposition is given by:

[math]\displaystyle{ \mathbf{A} = \mathbf{L L}^{*}, }[/math]

[math]\displaystyle{ L_{j,j} = \sqrt{ A_{j,j} - \sum_{k=1}^{j-1} L_{j,k}^*L_{j,k} }, }[/math]

[math]\displaystyle{ L_{i,j} = \frac{1}{L_{j,j}} \left( A_{i,j} - \sum_{k=1}^{j-1} L_{j,k}^* L_{i,k} \right) \quad \text{for } i\gt j. }[/math]

where [math]\displaystyle{ L }[/math] is a lower triangular matrix with real and positive diagonal entries, and [math]\displaystyle{ L^* }[/math] denotes the conjugate transpose of [math]\displaystyle{ L }[/math].

The feature is optimized for performance and can handle large matrices efficiently using [math]\displaystyle{ O(n^3) }[/math] computational complexity in the worst case. It checks if the input matrix is indeed positive-definite and Hermitian before performing the decomposition and raises an error if the matrix does not meet these conditions. The feature belong to the PSD Decomposition Method feature group.

Scripting

The feature can be used from the python console as follows:

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation
  
 featureType = "Cholesky Decomposition"
 featureGroup = "Spectrum Decomposition Method"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 decomposedPSD = WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not decomposedPSD :
     LabRPS.Console.PrintError("Error on creating the spectrum decomposition method.\n")
     # abord the computation
    
 # get the active simulation points feature and compute the simulation points coordinates
 simPoints = sim.computeLocationCoordinateMatrixP3()

 # use a vector to represent a simulation point based on its coordinates
 v1 = vec(simPoints[0][1], simPoints[0][2], simPoints[0][3])
 v2 = vec(simPoints[1][1], simPoints[1][2], simPoints[1][3])
 v3 = vec(simPoints[2][1], simPoints[2][2], simPoints[2][3])

 # This feature is used to decompose power spectrum matrices which may vary in time. Let's assume that
 # the active power spectrun density function in this example is stationary. Meanning it is not varying in time.
 #Then, we use time instant of 0 second.
 time = 0.0

 # compute the decomposed cross spectrum between points 1 and 3, at time instant of 0 second and for all frequency
 # increments. Note that when the following code is run, WindLab will try to identify the active frequency distribution, 
 # the active power spectrum feature, the active coherence function feature and others. If WindLab fails to find any 
 # of these dependency features, the computation will fails and specific error messages will be sent to the report view.
 # The following function is only one of its functions. There are some other two.
 psd13 = sim.computeDecomposedCrossSpectrumVectorF(v1, v3, time)

 # psd13 can be converted to numpy vector and be used for some other purposes.
 arr = numpy.asarray(psd13)

Horizontal Uniform Distribution

This feature provides an efficient method to distribute random wind simulation points uniformly in space. It allows users to generate a set of points within a 3D spatial domain, ensuring that the points are evenly distributed along a horizontal line that is parallel to one of the coordinate system axis. This uniform distribution is critical in certain simulation methods. For [math]\displaystyle{ n }[/math] simulation points [math]\displaystyle{ (P_1,P_2,P_3,...,P_n) }[/math], the distance [math]\displaystyle{ d_{jk} }[/math] between points [math]\displaystyle{ P_j }[/math] and [math]\displaystyle{ P_k }[/math] must be given by the following formula:

[math]\displaystyle{ d_{jk} = s\times|j-k| }[/math]

where [math]\displaystyle{ s }[/math] is the even space between any two adjacent points.

Properties

• DataFirstPoint: This is a point in 3D space representing the first point the distribution will start from.
• DataSpacing: This is the even space between any two adjacent points in the distribution.

Scripting

import WindLab
import GeneralToolsGui
import WindLabObjects
from LabRPS import Vector as vec
import numpy

def compute():
    installResuslt = WindLab.installPlugin("WindLabPlugin")

    doc = App.ActiveDocument
    if not doc:
       doc = App.newDocument()

    # get an existing WindLab simulation called "Simulation"
    sim = WindLabObjects.makeSimulation(doc, "Simulation")
  
    # check if the simulation does really exist
    if not sim:
       LabRPS.Console.PrintError("The simulation does not exist.\n")
       return None
  
    featureType = "Horizontal Distribution"
    featureGroup = "Location Distribution"
  
    # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
    # case you don't understand the next line)
    simPoints= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
    # check if the created feature is good
    if not simPoints:
       LabRPS.Console.PrintError("Error on creating the uniform points feature.\n")
       return None

    # let's set the first point of the distribution (x =0, y = 0, z = 80m)
    simPoints.FirstPoint = vec(0,0,80000)

    # let's set the spacing (s = 10m)
    simPoints.Spacing = '10m'

    # compute the simulation points coordinates. WindLab will internally use the "unifSimPoints" feature.
    simPoints = sim.computeLocationCoordinateMatrixP3()

    # now you can convert the coordinate matrix to numpy array and use it for any other purposes
    arr = numpy.asarray(simPoints)

    GeneralToolsGui.GeneralToolsPyTool.showArray(sim.getSimulationData().numberOfSpatialPosition, 4, simPoints, False)

compute()

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Vertical Uniform Distribution

This feature provides an efficient method to distribute random wind simulation points uniformly in space. It allows users to generate a set of points within a 3D spatial domain, ensuring that the points are evenly distributed along a vertical line. This uniform distribution is critical in certain simulation methods. For [math]\displaystyle{ n }[/math] simulation points [math]\displaystyle{ (P_1,P_2,P_3,...,P_n) }[/math], the distance [math]\displaystyle{ d_{jk} }[/math] between points [math]\displaystyle{ P_j }[/math] and [math]\displaystyle{ P_k }[/math] must be given by the following formula:

[math]\displaystyle{ d_{jk} = s\times|j-k| }[/math]

where [math]\displaystyle{ s }[/math] is the even space between any two adjacent points.

Properties

• DataLowestPoint: This is a point in 3D space representing the lowest point the distribution will start from. In many applications, this point should not be lower than 10 meters.
• DataSpacing: This is the even space between any two adjacent points in the distribution.

Scripting

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation
  
 featureType = "VerticalDistribution"
 featureGroup = "Location Distribution"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 unifSimPoints= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not unifSimPoints:
     LabRPS.Console.PrintError("Error on creating the uniform points feature.\n")
     # abord the computation
 # let's set the first point of the distribution (x =0, y = 0, z = 30m)
 simPoints.LowestPoint = vec(0,0,30000)

 # let's set the spacing (s = 10m)
 simPoints.Spacing = '10m'

 # compute the simulation points coordinates. WindLab will internally use the "unifSimPoints" feature.
 simPoints = sim.computeLocationCoordinateMatrixP3()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(simPoints)

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Uniform Distribution

This feature may be seen as a general form of the horizontal and the vertical distribution features. It allows users to generate a set of points within a 3D spatial domain, ensuring that the points are evenly distributed along a line parallel to one of the coordinate system axis. For [math]\displaystyle{ n }[/math] simulation points [math]\displaystyle{ (P_1,P_2,P_3,...,P_n) }[/math], the distance [math]\displaystyle{ d_{jk} }[/math] between points [math]\displaystyle{ P_j }[/math] and [math]\displaystyle{ P_k }[/math] must be given by the following formula:

[math]\displaystyle{ d_{jk} = s\times|j-k| }[/math]

where [math]\displaystyle{ s }[/math] is the even space between any two adjacent points.

Properties

• DataFirstPoint: This is a point in 3D space representing the first point the distribution will start from. This should be the lowest point in case the distribution is parallel to the vertical axis and should not be lower than 10 meters in many application.
• DataSpacing: This is the even space between any two adjacent points in the distribution.
• DataDirection: Its value can be X, Y or Z. This is the axis the points distribution is parallel to.

Scripting

This feature can be used to produce vertical distribution as we did before in the Vertical Distribution feature.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "UniformDistribution"
 featureGroup = "Location Distribution"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 unifSimPoints= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not unifSimPoints:
     LabRPS.Console.PrintError("Error on creating the uniform points feature.\n")
     # abord the computation

 # set the direction of the distribution to be vertical
  simPoints.Direction= 'Z'

 # let's set the first point of the distribution (x =0, y = 0, z = 30m)
 simPoints.FirstPoint = vec(0,0,30000) # This is the lowest point in this case (Direction = 'Z')

 # let's set the spacing (s = 10m)
 simPoints.Spacing = '10m'

 # compute the simulation points coordinates. WindLab will internally use the "unifSimPoints" feature.
 simPoints = sim.computeLocationCoordinateMatrixP3()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(simPoints)

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Grid Points

This feature allows users to generate a set of grid points within a 3D spatial domain, ensuring that the points are evenly distributed in a plane parallel to one of the coordinate system planes (XY Plane, YZ Plane, XZ Plane).

Properties

• DataSpacing1: This is the points spacing along one of the axis forming the plane.
• DataSpacing2: This is the points spacing along the second axis.
• DataLength1: This is the length within points are distributed along one of the axis forming the plane.
• DataLength2: This is the length within points are distributed along the second axis.
• DataCenterPoint: This is the center of the grid around which the points are generated. It is a 3D point.
• DataNumberOfPoints: This is the resulting total number of points in the grid. This is a read only property for internal use. User cannot change its value directly.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Grid Points"
 featureGroup = "Location Distribution"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 unifSimPoints= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not unifSimPoints:
     LabRPS.Console.PrintError("Error on creating the uniform points feature.\n")
     # abord the computation

 # set the plan the points grid is parallel to
  simPoints.LocationPlan= 'YZ Plane'

 # let's set the center point of the distribution (x =0, y = 0, z = 0)
 simPoints.CenterPoint = vec(0,0,0) 

 # let's set the spacing1 (s1 = 10m)
 simPoints.Spacing1 = '10m'

 # let's set the spacing2 (s2 = 10m)
 simPoints.Spacing2 = '10m'

 # let's set the length1 (l1 = 200m)
 simPoints.Length1= '200m'

 # let's set the length2 (l2 = 200m)
 simPoints.Length2= '200m'

 # compute the simulation points coordinates. WindLab will internally use the "unifSimPoints" feature.
 simPoints = sim.computeLocationCoordinateMatrixP3()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(simPoints)

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General Distribution

When the simulation points distribution is more general and does not follow any of the previous uniform distribution, this feature can be used. This feature allows users to input simulation points one by one using their coordinates based on the vector dialog shown below:

Properties

• DataLocations: This is a list holding the simulation points

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "General Distribution"
 featureGroup = "Location Distribution"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 genSimPoints= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not genSimPoints:
     LabRPS.Console.PrintError("Error on creating the uniform points feature.\n")
     # abord the computation

 # create the simulation points by their coordinates
 v1 = vec(0, 0, 35000)
 v2 = vec(0, 0, 40000)
 v3 = vec(0, 0, 140000)

 # add the points to the locations
 loc.Locations = [v1, v2, v3]

 # compute the simulation points coordinates. WindLab will internally use the "genSimPoints" feature
 simPoints = sim.computeLocationCoordinateMatrixP3()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(simPoints)

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Import Simulation Points from File

When the simulation points are stored in a file, this feature can be used. The feature allows users to import simulation points coordinates from file. Note that, for now only tab separated text file is supported and the file is expected to have number of rows and number of columns which are number of simulation points and four, respectively.

Properties

• DataFilePath: This is the path to the file.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Import Simulation Points from File"
 featureGroup = "Location Distribution"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 simPoints= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not simPoints:
     LabRPS.Console.PrintError("Error on creating the simulation points feature.\n")
     # abord the computation

 # set the direction of the distribution to be vertical
 simPoints.FilePath = "myfolderpath/myfile.txt"

 # compute the simulation points coordinates. WindLab will internally use the "simPoints" feature.
 importedSimPoints = sim.computeLocationCoordinateMatrixP3()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(importedSimPoints )

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Power Law Profile

This feature is designed to compute the wind speed at a given height based on the power law mean wind profile, which is commonly used to model the variation of wind speed with height in the atmospheric boundary layer. This model is essential in fields such as wind energy, structural engineering, and environmental science. The power law formula that governs the relationship between wind speed and height is expressed as:

[math]\displaystyle{ U(z) = U(z_0)\times\left( \frac{{z-\phi}}{z_0} \right)^\alpha }[/math]

where:

• [math]\displaystyle{ U(z) }[/math] is the wind speed at height [math]\displaystyle{ z }[/math],
• [math]\displaystyle{ U(z_0) }[/math] is the reference wind speed at a known reference height [math]\displaystyle{ z_0 }[/math],
• [math]\displaystyle{ \alpha }[/math] is the power law exponent, a dimensionless constant that varies depending on terrain and atmospheric conditions,
• [math]\displaystyle{ \phi }[/math] is the zero plan displacement,
• [math]\displaystyle{ z }[/math] is the height at which the wind speed is to be calculated.

Properties

• DataReferenceHeight: This is a reference height.
• DataReferenceSpeed: This is the reference wind speed at the reference height.
• DataDimensionlessPower: This is the power law exponent.
• DataZeroPlanDisplacement: This is the zero plan displacement.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Power Law Profile"
 featureGroup = "Mean Wind Profile"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 meanSpeed= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not meanSpeed:
     LabRPS.Console.PrintError("Error on creating the uniform points feature.\n")
     # abord the computation

 meanSpeed.ReferenceHeight = '10.00 m'
 meanSpeed.ReferenceSpeed = '30.00 m/s'
 meanSpeed.DimensionlessPower = 0.12
 meanSpeed.ZeroPlanDisplacement = '0.0 m'

 # In WindLab, mean wind velocity can vary with time. In case the user desires a time dependent mean wind speed,  
 # a modulation function can be used for this purpose. The feature account for this. When the Stationarity property of the parent 
 # simulation of this feature is false, the feature identify the active modulation function and use it to produce non-stationary 
 # mean wind speed. But for this example we shall use time instant of 0 second.
 time = 0.0

 # compute the mean wind speeds at time instant of 0 second and for all simulation points
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 meanValues = sim.computeMeanWindSpeedVectorP(time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(meanValues )

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Logarithmic Law Profile

This feature is designed to compute the wind speed at a given height based on the logarithmic law mean wind profile. The logarithmic law is commonly used to model the variation of wind speed with height in the atmospheric boundary layer, especially in cases where the wind profile is influenced by surface roughness, such as over flat terrain, forests, or urban environments. The logarithmic law for wind speed variation is given by the following equation:

[math]\displaystyle{ U(z) =\left( \frac{u_*}{k} \right)\times\ln{\left( \frac{{z-\phi}}{z_0} \right)} }[/math]

where:

• [math]\displaystyle{ U(z) }[/math] is the wind speed at height [math]\displaystyle{ z }[/math],
• [math]\displaystyle{ u_* }[/math] is the friction velocity, which is a measure of the turbulence intensity,
• [math]\displaystyle{ k }[/math] is the von Kármán constant (approximately 0.4),
• [math]\displaystyle{ \phi }[/math] is the zero plan displacement,
• [math]\displaystyle{ z_0 }[/math] is the roughness length, which characterizes the roughness of the surface,
• [math]\displaystyle{ z }[/math] is the height at which the wind speed is to be calculated.

Properties

• DataTerrainRoughness: This is the terrain roughness value.
• DataShearVelocity: This is the shear velocity of the flow.
• DatavonKarmanConstant: This is the von karman constant.
• DataZeroPlanDisplacement: This is the zero plan displacement value.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Logarithmic Law Profile"
 featureGroup = "Mean Wind Profile"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 meanSpeed= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not meanSpeed:
     LabRPS.Console.PrintError("Error on creating the uniform points feature.\n")
     # abord the computation

 meanSpeed.TerrainRoughness = '0.001266 m' 
 meanSpeed.ShearVelocity = '1.76 m/s' 

 # In WindLab, mean wind velocity can vary with time. In case the user desires a time dependent mean wind speed,  
 # a modulation function can be used for this purpose. The feature account for this. When the Stationarity property of the parent 
 # simulation of this feature is false, the feature identify the active modulation function and use it to produce non-stationary 
 # mean wind speed. But for this example we shall use time instant of 0 second.
 time = 0.0

 # compute the mean wind speeds at time instant of 0 second and for all simulation points
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 meanValues = sim.computeMeanWindSpeedVectorP(time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(meanValues )

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Deaves and Harris Profile

This feature is designed to compute the wind speed at a given height based on the logarithmic law mean wind profile. The logarithmic law is commonly used to model the variation of wind speed with height in the atmospheric boundary layer, especially in cases where the wind profile is influenced by surface roughness, such as over flat terrain, forests, or urban environments. The logarithmic law for wind speed variation is given by the following equation:

[math]\displaystyle{ U(z) =\left( \frac{u_*}{k} \right)\times\left [\ln{\left( \frac{{z-z_d}}{z_0} \right)} + 5.75\left( \frac{{z-z_d}}{h} \right) - 1.88\left( \frac{{z-z_d}}{h} \right)^2 - 1.33\left( \frac{{z-z_d}}{h} \right)^3 + 0.25\left( \frac{{z-z_d}}{h} \right)^4\right ] }[/math]

where:

• [math]\displaystyle{ U(z) }[/math] is the wind speed at height [math]\displaystyle{ z }[/math],
• [math]\displaystyle{ u_* }[/math] is the friction velocity, which is a measure of the turbulence intensity,
• [math]\displaystyle{ k }[/math] is the von Kármán constant (approximately 0.4),
• [math]\displaystyle{ z_d }[/math] is the zero plan displacement,
• [math]\displaystyle{ z_0 }[/math] is the roughness length, which characterizes the roughness of the surface,
• [math]\displaystyle{ h }[/math] is the gradient height, defined as the height where atmospheric flow is free from surface stresses and becomes geostrophic,
• [math]\displaystyle{ z }[/math] is the height at which the wind speed is to be calculated.

Properties

• DataTerrainRoughness: The terrain roughness length.
• DataShearVelocity: The shear velocity of the flow.
• DataZeroPlanDisplacement: The zero plan displacement.
• DataLatitude: The latitude.
• DataEarthAngularVelocity: The earth angular velocity.
• DataBetta: The coefficient beta.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Deaves and Harris Profile"
 featureGroup = "Mean Wind Profile"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 meanSpeed= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not meanSpeed:
     LabRPS.Console.PrintError("Error on creating the uniform points feature.\n")
     # abord the computation

 meanSpeed.TerrainRoughness = '0.001266 m' 
 meanSpeed.ShearVelocity = '1.76 m/s' 

 # In WindLab, mean wind velocity can vary with time. In case the user desires a time dependent mean wind speed,  
 # a modulation function can be used for this purpose. The feature account for this. When the Stationarity property of the parent 
 # simulation of this feature is false, the feature identify the active modulation function and use it to produce non-stationary 
 # mean wind speed. But for this example we shall use time instant of 0 second.
 time = 0.0

 # compute the mean wind speeds at time instant of 0 second and for all simulation points
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 meanValues = sim.computeMeanWindSpeedVectorP(time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(meanValues )

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Sine Modulation Function

This feature represents a uniform modulation function used to achieve stationarity in the simulation of random wind velocity. Uniformity here means the modulation function is not function of frequency. It is modeled as a sine wave, described by the following equation:

[math]\displaystyle{ A(t) = sin\left( \frac{\pi\times t}{T} \right) }[/math]

where:

• [math]\displaystyle{ A(t) }[/math] is the modulation function value at time [math]\displaystyle{ t }[/math],
• [math]\displaystyle{ T }[/math] is the pulse duration,
• [math]\displaystyle{ t }[/math] is the time at which the modulation function is computed.

Properties

• DataPulseDuration: The pusle duration.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Sine Wave Modulation Function"
 featureGroup = "Uniform Modulation Function"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 modulation= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not modulation:
     LabRPS.Console.PrintError("Error on creating the modulation function feature.\n")
     # abord the computation

 modulation.PulseDuration= '150 m/s'
 
 # set a time instant of 2 seconds
 time = 2

 # compute the modulation value at time instant of 2 second and for all simulation points
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # If WindLab fails to find this dependency feature, the computation will fails and specific error messages will be sent to the report view.
 modulations= sim.computeModulationVectorP(time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(modulations)

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Three Parameter Modulation Function

This feature represents a uniform modulation function used to achieve stationarity in the simulation of random wind velocity. Uniformity here means the modulation function is not function of frequency. It is modeled as an exponential function, described by the following equation:

[math]\displaystyle{ A(t) = \alpha t^\beta e^{-\lambda t} }[/math]

where:

• [math]\displaystyle{ A(t) }[/math] is the modulation function value at time [math]\displaystyle{ t }[/math],
• [math]\displaystyle{ \alpha }[/math] is the alpha coefficient,
• [math]\displaystyle{ \beta }[/math] is the beta coefficient,
• [math]\displaystyle{ \lambda }[/math] is the lambda coefficient,
• [math]\displaystyle{ t }[/math] is the time at which the modulation function is computed.

Properties

• DataAlpha: The alpha coefficient.
• DataBetta: The beta coefficient.
• DataLambda: The lambda coefficient.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Three Parameter Modulation Function"
 featureGroup = "Uniform Modulation Function"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 modulation= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not modulation:
     LabRPS.Console.PrintError("Error on creating the modulation function feature.\n")
     # abord the computation

 modulation.Alpha = 4.98
 modulation.Betta = 3.00
 modulation.Lambda = 0.003

 # set a time instant of 2 seconds
 time = 2

 # compute the modulation value at time instant of 2 second and for all simulation points
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # If WindLab fails to find this dependency feature, the computation will fails and specific error messages will be sent to the report view.
 modulations= sim.computeModulationVectorP(time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(modulations)

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Exponential Modulation Function

This feature represents a uniform modulation function used to achieve stationarity in the simulation of random wind velocity. Uniformity here means the modulation function is not function of frequency. It is modeled as an exponential function, described by the following equation:

[math]\displaystyle{ A(t) = exp\left[-\frac{1}{2}\left( \frac{t-t_{m} }{t_{l}} \right)^2\right] }[/math]

where:

• [math]\displaystyle{ A(t) }[/math] is the modulation function value at time [math]\displaystyle{ t }[/math],
• [math]\displaystyle{ t_{m} }[/math] is the time when the modulation function reaches its maximum,
• [math]\displaystyle{ t_{l} }[/math] is the storm length,
• [math]\displaystyle{ t }[/math] is the time at which the modulation function is computed.

Properties

• DataTimeOfMax: The time when the modulation function reaches its maximum.
• DataStormLength: The storm length.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Exponential Modulation Function"
 featureGroup = "Uniform Modulation Function"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 modulation= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not modulation:
     LabRPS.Console.PrintError("Error on creating the modulation function feature.\n")
     # abord the computation

 modulation.TimeOfMax = '300 s'
 modulation.StormLength = '60 s'

 # set a time instant of 2 seconds
 time = 2

 # compute the modulation value at time instant of 2 second and for all simulation points
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # If WindLab fails to find this dependency feature, the computation will fails and specific error messages will be sent to the report view.
 modulations= sim.computeModulationVectorP(time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(modulations)

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Davenport Coherence Function

The purpose of this feature is to model the spatial correlation of wind velocities, which is crucial in many engineering applications, especially in the context of wind turbine design, structural analysis, and environmental studies. The Davenport Coherence Function quantifies the correlation between the wind velocity at two points, in space (at different locations). It is derived from the theory of wind turbulence and helps in simulating the correlated behavior of wind speeds over a geographical area. The mathematical form of the Davenport Coherence Function, for two points [math]\displaystyle{ P_{j}\left( x_{j},y_{j},z_{j} \right) }[/math] and [math]\displaystyle{ P_{k}\left( x_{k},y_{k},z_{k} \right) }[/math] is expressed as:

[math]\displaystyle{ \gamma(\omega) = exp\left[-\frac{\omega}{2 \pi}\left( \frac{\sqrt{C_{x}^2 \left( x_{j}-x_{k} \right)^2 + C_{y}^2 \left( y_{j}-y_{k} \right)^2 + C_{z}^2 \left( z_{j}-z_{k} \right)^2}}{\frac{U_{j}\left( z_{j} \right)+U_{k}\left( z_{k} \right)}{2}} \right)\right] }[/math]

where:

• [math]\displaystyle{ \gamma(\omega) }[/math] is the coherence function for two points [math]\displaystyle{ P_{j}\left( x_{j},y_{j},z_{j} \right) }[/math] and [math]\displaystyle{ P_{k}\left( x_{k},y_{k},z_{k} \right) }[/math] value for frequency [math]\displaystyle{ \omega }[/math],
• [math]\displaystyle{ C_{x} }[/math] is the decay coefficient along x,
• [math]\displaystyle{ C_{y} }[/math] is the decay coefficient along y,
• [math]\displaystyle{ C_{z} }[/math] is the decay coefficient along z,
• [math]\displaystyle{ U_{j}\left( z_{j} \right) }[/math] is the mean wind speed at altitude [math]\displaystyle{ z_{j} }[/math],
• [math]\displaystyle{ U_{k}\left( z_{k} \right) }[/math] is the mean wind speed at altitude [math]\displaystyle{ z_{k} }[/math],
• [math]\displaystyle{ \omega }[/math] is the frequency for which the coherence function is computed.

Properties

• DataExponentialDecayCx: The decay coefficient along x.
• DataExponentialDecayCy: The decay coefficient along y
• DataExponentialDecayCz: The decay coefficient along z

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Davenport Coherence Function"
 featureGroup = "Coherence Function"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 coherence = WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not coherence:
     LabRPS.Console.PrintError("Error on creating the coherence function feature.\n")
     # abord the computation

 coherence.ExponentialDecayCx = 10.0
 coherence.ExponentialDecayCy = 7.0
 coherence.ExponentialDecayCz = 6.0

 # In WindLab, coherence function can vary with time. In case the user desires a time dependent coherence function,  
 # a modulation function can be used for this purpose. The feature account for this. When the Stationarity property of the parent 
 # simulation of this feature is false, the feature identify the active modulation function and use it to produce non-stationary 
 # coherence function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 coherences = sim.computeCrossCoherenceMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(coherences)

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Krenk Coherence Function

Documentation coming soon.

Davenport Along Wind Spectrum

The Davenport spectrum is widely used in wind engineering to model the power spectral density (PSD) of wind velocity in its main direction. The Davenport Power Spectrum or Davenport Wind Spectrum, can be mathematically expressed as:

[math]\displaystyle{ \frac{nS(n)}{u_{*}^2} = k\frac{x^2}{\left(1 + x^2 \right)^{4/3}} }[/math]

where:

• [math]\displaystyle{ x = \frac{1200n}{U_{10}} }[/math],
• [math]\displaystyle{ S(n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ u_{*} }[/math] is the shear velocity of the flow,
• [math]\displaystyle{ k }[/math] is the von Karman constant which is equal to 4.0,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U_{10} }[/math] is the mean wind speed at height of 10 meters.

Properties

• DataMeanWindSpeed10: The mean wind speed at height of 10 meters
• DatashearVelocity: The shear velocity of the flow

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Davenport Along Wind Spectrum"
 featureGroup = "Along Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.MeanWindSpeed10= '30.0 m/s'
 spectrum.shearVelocity= '1.76 m/s'

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeXCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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Harris Along Wind Spectrum

The Harris spectrum is widely used in wind engineering to model the power spectral density (PSD) of wind velocity in its main direction. The Harris Power Spectrum or Harris Wind Spectrum, can be mathematically expressed as:

[math]\displaystyle{ \frac{nS(n)}{u_{*}^2} = k\frac{x^2}{\left(2 + x^2 \right)^{5/6}} }[/math]

where:

• [math]\displaystyle{ x = \frac{1800n}{U_{10}} }[/math],
• [math]\displaystyle{ S(n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ u_{*} }[/math] is the shear velocity of the flow,
• [math]\displaystyle{ k }[/math] is the von Karman constant which is equal to 4.0,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U_{10} }[/math] is the mean wind speed at height of 10 meters.

Properties

• DataMeanWindSpeed10: The mean wind speed at height of 10 meters
• DatashearVelocity: The shear velocity of the flow

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Harris Along Wind Spectrum"
 featureGroup = "Along Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.MeanWindSpeed10= '30.0 m/s'
 spectrum.shearVelocity= '1.76 m/s'

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeXCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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Kaimal Along Wind Spectrum

The Kaimal Spectrum is a frequency-domain model that describes the distribution of energy in turbulent wind velocity across different frequencies. It is specifically designed for modeling atmospheric turbulence. The Kaimal model provides a power spectral density (PSD) function that approximates the energy contained in the wind velocity as a function of frequency. Mathematically, the Kaimal along wind spectrum is given by:

[math]\displaystyle{ \frac{nS(z,n)}{u_{*}^2} = \frac{200f}{\left(1 + 50f \right)^{5/3}} }[/math]

where:

• [math]\displaystyle{ f = \frac{nz}{U(z)} }[/math],
• [math]\displaystyle{ S(z,n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ u_{*} }[/math] is the shear velocity of the flow,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U(z) }[/math] is the mean wind speed at height [math]\displaystyle{ z }[/math].

Properties

• DatashearVelocity: The shear velocity of the flow
• DataConstant1: A constant equal to 200.0 by default
• DataConstant2: A constant equal to 50.0 by default

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Kaimal Along Wind Spectrum"
 featureGroup = "Along Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.shearVelocity= '1.76 m/s'
 spectrum.Constant1 = 200.0
 spectrum.Constant2 = 50.0

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeXCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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Kaimal Across Wind Spectrum

The Kaimal Spectrum is a frequency-domain model that describes the distribution of energy in turbulent wind velocity across different frequencies. It is specifically designed for modeling atmospheric turbulence. The Kaimal model provides a power spectral density (PSD) function that approximates the energy contained in the wind velocity as a function of frequency. Mathematically, the Kaimal across wind spectrum is given by:

[math]\displaystyle{ \frac{nS(z,n)}{u_{*}^2} = \frac{15f}{\left(1 + 9.5f \right)^{5/3}} }[/math]

where:

• [math]\displaystyle{ f = \frac{nz}{U(z)} }[/math],
• [math]\displaystyle{ S(z,n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ u_{*} }[/math] is the shear velocity of the flow,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U(z) }[/math] is the mean wind speed at height [math]\displaystyle{ z }[/math].

Properties

• DatashearVelocity: The shear velocity of the flow
• DataConstant1: A constant equal to 15.0 by default
• DataConstant2: A constant equal to 9.5 by default

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Kaimal Across Wind Spectrum"
 featureGroup = "Across Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.shearVelocity= '1.76 m/s'
 spectrum.Constant1 = 15.0
 spectrum.Constant2 = 9.5

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeYCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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Kaimal Vertical Wind Spectrum

The Kaimal Spectrum is a frequency-domain model that describes the distribution of energy in turbulent wind velocity across different frequencies. It is specifically designed for modeling atmospheric turbulence. The Kaimal model provides a power spectral density (PSD) function that approximates the energy contained in the wind velocity as a function of frequency. Mathematically, the Kaimal vertical spectrum is given by:

[math]\displaystyle{ \frac{nS(z,n)}{u_{*}^2} = \frac{3.36f}{1 + 10f^{5/3}} }[/math]

where:

• [math]\displaystyle{ f = \frac{nz}{U(z)} }[/math],
• [math]\displaystyle{ S(z,n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ u_{*} }[/math] is the shear velocity of the flow,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U(z) }[/math] is the mean wind speed at height [math]\displaystyle{ z }[/math].

Properties

• DatashearVelocity: The shear velocity of the flow
• DataConstant1: A constant equal to 3.36 by default
• DataConstant2: A constant equal to 10.0 by default

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Kaimal Vertical Wind Spectrum"
 featureGroup = "Vertical Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.shearVelocity= '1.76 m/s'
 spectrum.Constant1 = 3.36
 spectrum.Constant2 = 10.0

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeZCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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Simiu Along Wind Spectrum

The Simiu Spectrum is a frequency-domain model that describes the distribution of energy in turbulent wind velocity across different frequencies. It is specifically designed for modeling atmospheric turbulence. The Simiu model provides a power spectral density (PSD) function that approximates the energy contained in the wind velocity as a function of frequency. Mathematically, the Simiu along wind spectrum is given by:

[math]\displaystyle{ \frac{nS(z,n)}{u_{*}^2} = \frac{105f}{\left(1 + 33f \right)^{5/3}} }[/math]

where:

• [math]\displaystyle{ f = \frac{nz}{U(z)} }[/math],
• [math]\displaystyle{ S(z,n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ u_{*} }[/math] is the shear velocity of the flow,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U(z) }[/math] is the mean wind speed at height [math]\displaystyle{ z }[/math].

Properties

• DatashearVelocity: The shear velocity of the flow
• DataConstant1: A constant equal to 105.0 by default
• DataConstant2: A constant equal to 33.0 by default

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Simiu Along Wind Spectrum"
 featureGroup = "Along Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.shearVelocity= '1.76 m/s'
 spectrum.Constant1 = 105.0
 spectrum.Constant2 = 33.0

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeXCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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Simiu Across Wind Spectrum

The Simiu Spectrum is a frequency-domain model that describes the distribution of energy in turbulent wind velocity across different frequencies. It is specifically designed for modeling atmospheric turbulence. The Simiu model provides a power spectral density (PSD) function that approximates the energy contained in the wind velocity as a function of frequency. Mathematically, the Simiu across wind spectrum is given by:

[math]\displaystyle{ \frac{nS(z,n)}{u_{*}^2} = \frac{17f}{\left(1 + 9.5f \right)^{5/3}} }[/math]

where:

• [math]\displaystyle{ f = \frac{nz}{U(z)} }[/math],
• [math]\displaystyle{ S(z,n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ u_{*} }[/math] is the shear velocity of the flow,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U(z) }[/math] is the mean wind speed at height [math]\displaystyle{ z }[/math].

Properties

• DatashearVelocity: The shear velocity of the flow
• DataConstant1: A constant equal to 17.0 by default
• DataConstant2: A constant equal to 9.5 by default

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Simiu Across Wind Spectrum"
 featureGroup = "Across Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.shearVelocity= '1.76 m/s'
 spectrum.Constant1 = 17.0
 spectrum.Constant2 = 9.5

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeYCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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Simiu Vertical Wind Spectrum

The Simiu Spectrum is a frequency-domain model that describes the distribution of energy in turbulent wind velocity across different frequencies. It is specifically designed for modeling atmospheric turbulence. The Simiu model provides a power spectral density (PSD) function that approximates the energy contained in the wind velocity as a function of frequency. Mathematically, the Simiu vertical spectrum is given by:

[math]\displaystyle{ \frac{nS(z,n)}{u_{*}^2} = \frac{2f}{1 + 5.3f^{5/3}} }[/math]

where:

• [math]\displaystyle{ f = \frac{nz}{U(z)} }[/math],
• [math]\displaystyle{ S(z,n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ u_{*} }[/math] is the shear velocity of the flow,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U(z) }[/math] is the mean wind speed at height [math]\displaystyle{ z }[/math].

Properties

• DatashearVelocity: The shear velocity of the flow
• DataConstant1: A constant equal to 2 by default
• DataConstant2: A constant equal to 5.3 by default

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Simiu Vertical Wind Spectrum"
 featureGroup = "Vertical Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.shearVelocity= '1.76 m/s'
 spectrum.Constant1 = 2
 spectrum.Constant2 = 5.3

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeZCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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von Karman Along Wind Spectrum

The von Karman Spectrum is a frequency-domain model that describes the distribution of energy in turbulent wind velocity across different frequencies. It is specifically designed for modeling atmospheric turbulence. The von Karman model provides a power spectral density (PSD) function that approximates the energy contained in the wind velocity as a function of frequency. Mathematically, the von Karman along wind spectrum is given by:

[math]\displaystyle{ \frac{nS(z,n)}{\sigma_{u}^2} = \frac{4f}{\left(1 + 70.8f^2 \right)^{5/6}} }[/math]

where:

• [math]\displaystyle{ f = \frac{nL_u}{U(z)} }[/math],
• [math]\displaystyle{ S(z,n) }[/math] is the power spectral density value for frequency value [math]\displaystyle{ n }[/math],
• [math]\displaystyle{ \sigma_{u} }[/math] is the standard deviation,
• [math]\displaystyle{ n }[/math] is the frequency for which the power spectral density is computed.
• [math]\displaystyle{ U(z) }[/math] is the mean wind speed at height [math]\displaystyle{ z }[/math].

Properties

• DataStandardDeviation: The standard deviation
• DataIntegralLengthScale: The integral length scale
• DataConstant1: A constant equal to 4.0 by default
• DataConstant2: A constant equal to 70.8.0 by default

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "von Karman Along Wind Spectrum"
 featureGroup = "Along Wind Spectrum"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 spectrum= WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not spectrum:
     LabRPS.Console.PrintError("Error on creating the spectrum feature.\n")
     # abord the computation

 spectrum.StandardDeviation = '1 m/s'
 spectrum.IntegralLengthScale = '80 m'
 spectrum.Constant1 = 4.0
 spectrum.Constant2 = 70.8.0

 # In WindLab, power spectral density function can vary with time. In case the user desires a time dependent spectrum function,  
 # a modulation function can be used for this purpose if the feature is stationary and allows uniform modulation. 
 # The feature account for this. When the Stationarity property of the parent simulation of this feature is false, 
 # the feature identify the active modulation function and use it to produce non-stationary 
 # spectrum function by transforming the the mean wind speeds into time dependent mean wind speeds. But for this example we shall use time instant of 0 second.
 time = 0.0
 frequency = 0.25

 # compute the coherence matrix at time instant of 0 second and frequency of 0.24 rad/s
 # Note that when the following code is run, WindLab will try to identify the active locations distribution,
 # it will also try to identity the active modulation function in case the parent simulation is non-stationary. 
 # If WindLab fails to find these dependency features, the computation will fails and specific error messages will be sent to the report view.
 spectrumMatrix = sim.computeXCrossSpectrumMatrixPP(frequency, time)

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(spectrumMatrix)

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Uniform Random Phases

In the simulation of random wind velocity, one common technique is to represent the wind velocity as a sum of multiple sinusoidal components in the frequency domain. Each of these components is associated with a random phase, which determines the position of the sine wave relative to time. By assigning uniform random phases to each frequency component, the resulting time series will have random characteristics, while still adhering to the desired spectral properties, such as a specific power spectral density (PSD) or turbulence spectrum. The feature generate [math]\displaystyle{ n }[/math] sequences [math]\displaystyle{ (\phi_{1l}, \phi_{2l}, \phi_{3l},..., \phi_{nl}; l = 1, 2, 3, ..., N) }[/math] of independent random phase angles uniformly distributed over the interval [math]\displaystyle{ [0, 2\pi] }[/math] by default.

Properties

• DataMinimumValue: The minimum value that can be generated
• DataMaximumValue: The maximum value that can be generated

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Uniform Random Phases"
 featureGroup = "Randomness Provider"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 randomness = WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not randomness:
     LabRPS.Console.PrintError("Error on creating the randomness feature.\n")
     # abord the computation

 randomness.MinimumValue = 0
 randomness.MaximumValue = 6.28

 # generate random phase angle
 randomnessMatrix = sim.generateRandomMatrixFP()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(randomnessMatrix)

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Uniform Random Phases Import

When the simulation numbers are stored in a file, this feature can be used. The feature allows users to import random numbers from file. Note that, for now only tab separated text file is supported and the file is expected to have number of rows and number of columns which are number of frequency increments and number of simulation points, respectively.

Properties

• DataFilePath: This is the path to the file.

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Uniform Random Phases Import"
 featureGroup = "Randomness Provider"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 randomness = WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not randomness:
     LabRPS.Console.PrintError("Error on creating the randomness feature.\n")
     # abord the computation

 randomness.FilePath = "myfolderpath/myfile.txt"

 # generate random phase angle
  randomnessMatrix = sim.generateRandomMatrixFP()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(randomnessMatrix)

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Single Index Frequency Discretization

This feature allows to discretize continuous frequency according to the following formula:

[math]\displaystyle{ \omega_{l} = l\Delta\omega; \quad l = 1, 2, 3, ..., N }[/math].

where:

• [math]\displaystyle{ \Delta\omega }[/math] is the frequency increment,
• [math]\displaystyle{ N }[/math] is the number of frequency increments,

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Single Index Frequency Discretization"
 featureGroup = "Frequency Distribution"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 frequency = WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not frequency:
     LabRPS.Console.PrintError("Error on creating the frequency feature.\n")
     # abord the computation

 # compute the freqency distribution
 frequencyMatrix = sim.computeFrequenciesMatrixFP()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(frequencyMatrix)

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Double Index Frequency Discretization

This feature allows to discretize continuous frequency according to the following formula:

[math]\displaystyle{ \omega_{ml} = (l-1)\Delta\omega + \frac{m}{n}\Delta\omega; \quad m = 1, 2, 3, ..., n; \quad l = 1, 2, 3, ..., N }[/math].

where:

• [math]\displaystyle{ \Delta\omega }[/math] is the frequency increment,
• [math]\displaystyle{ n }[/math] is the number of simulation points,
• [math]\displaystyle{ N }[/math] is the number of frequency increments,

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Double Index Frequency Discretization"
 featureGroup = "Frequency Distribution"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 frequency = WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not frequency:
     LabRPS.Console.PrintError("Error on creating the frequency feature.\n")
     # abord the computation

 # compute the freqency distribution
 frequencyMatrix = sim.computeFrequenciesMatrixFP()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(frequencyMatrix)

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Zerva Frequency Discretization

This feature allow to discretize continuous frequency according to the following formula:

[math]\displaystyle{ \omega_{l} = (1+\frac{l}{2})\Delta\omega;\quad l = 1, 2, 3, ..., N }[/math],

where:

• [math]\displaystyle{ \Delta\omega }[/math] is the frequency increment,
• [math]\displaystyle{ N }[/math] is the number of frequency increments,

Scripting

The following script shows how this feature can be created and used.

import WindLab
import WindLabObjects
from LabRPS import Vector as vec
import numpy

 # get an existing WindLab simulation called "Simulation"
 sim = WindLab.getSimulation("Simulation")
    
 # check if the simulation does really exist
 if not sim:
     LabRPS.Console.PrintError("The simulation does not exist.\n")
     # abord the computation 
  
 featureType = "Zerva Frequency Discretization"
 featureGroup = "Frequency Distribution"
  
 # create the feature and add it to the existing simulation (you may refer to the WindLab Workbench page in 
 # case you don't understand the next line)
 frequency = WindLabObjects.makeFeature("MyNewFeature", sim.Name, featureType, featureGroup)
    
 # check if the created feature is good
 if not frequency:
     LabRPS.Console.PrintError("Error on creating the frequency feature.\n")
     # abord the computation

 # compute the freqency distribution
  frequencyMatrix = sim.computeFrequenciesMatrixFP()

 # now you can convert the coordinate matrix to numpy array and use it for any other purposes
 arr = numpy.asarray(frequencyMatrix)

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