comparison of ansys cfx and wasp for...

Technical University of Denmark

Department of Mechanical Engineering

Comparison of Ansys CFX and WAsP for Sarfannguaq

Marco PianigianiMaster ThesisFebruary 2009

MEK-FM-EP-2009-02

DTUMekanik

Fluid Mechanics

Comparison of Ansys CFX and WAsP for Sarfannguaq Marco Pianigiani ‐ s081505 Danmarks Tekniske Universitet

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TABLE OF CONTENTS

Introduction .................................................................................................................... 3

1 Description of Sarfannguaq site ............................................................................. 5

2 Wind measurements .............................................................................................. 7

3 WAsP simulations .................................................................................................. 9

3.1 WAsP results: 330°N wind direction ................................................................. 14

4 CFD Simulations ................................................................................................... 16

4.1 Turbulence model study: Askervein ................................................................. 16

4.1.1 Simulation parameters ........................................................................... 18

4.1.2 Results .................................................................................................... 21

4.2 Roughness study: Sarfannguaq ......................................................................... 25

4.2.1 Simulation parameters ........................................................................... 25

4.2.2 Results simulations A,B and C (330°N wind direction) ........................... 32

4.2.3 Roughness comparisons ......................................................................... 37

4.3 Simulation D (300°N wind direction, k‐ε, 0.03m roughness) ........................... 40

4.4 Simulation E (120°N wind direction, k‐ε, 0.03m roughness) ............................ 42

4.5 Simulation F (90°N wind direction, k‐ε, 0.03m roughness) .............................. 45

5 Comparison WAsP vs CFD .................................................................................... 48

5.1 330°N wind direction ........................................................................................ 48

5.2 300°N wind direction ........................................................................................ 53

5.3 120°N wind direction ........................................................................................ 56


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5.4 90°N wind direction .......................................................................................... 60

5.5 WAsP “prediction error” and site ruggedness .................................................. 65

Conclusion .................................................................................................................... 70

A. Appendix: WAsP wind speed profiles. .................................................................. 72

B. Appendix: CFD wind speed profiles 330°N direction. ............................................ 75

C. Appendix: Roughness comparisons. ..................................................................... 77

D. Appendix: Comparison WAsP vs CFD .................................................................... 78

E. Appendix: Wind roses and distribution. ............................................................... 87

F. Appendix: Comparison WAsP vs CFD (300°N wind direction) ............................... 88

References .................................................................................................................... 89


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Introduction

Environmental issues, increasing oil prices and huge distribution cost for energy have initiated an

investigation of local energy resources for the small settlements in Greenland. Because of this,

wind resource measurements has been recorded since 2003 in several locations all over

Greenland. The goal of these measurements was to assess the wind resource for those locations

in order to evaluate the possibilities of supplying renewable wind energy for covering part of the

demand.

This paper deals with one of these locations, Sarfannguaq, a small village located 43 km east of

Sisimiut. For this location wind measurements are available for five years, from August 2003 until

March 2008.

The software used in previous studies for the estimation of the wind resource in Sarfannguaq is

WAsP (the Wind Atlas Analysis and Application Program, developed at Risø National Laboratory,

Roskilde, Denmark, in the late 1980s). WAsP is a software for predicting/estimating wind climates,

wind resources and power productions from wind turbines. The predictions are based on wind

data measured at stations in the same region.

WAsP model for flow over orography is simple, very quick, and accurate in flat to mildly

undulating terrain. However, it performs poorly when applied to complex orography: slopes

steeper than about 30% lead to flows that violate its basic assumptions, especially in terms of

flow separation, and thus discrepancies arise between the predicted and actual flow

perturbations, most notably the speed‐up.

In Sarfannguit the terrain orography is complex and WAsP may be used for estimating the wind

resource and choose the best location for wind turbine, taking into account that it may perform

poorly. Some methods to take into account prediction errors have been developed in the past

years. One of these methods was introduced in a previous study on a Portuguese wind farm by

Mortensen and al.[1]. In that study a general relation between the ruggedness of the terrain and

the prediction error has be found for that area. In order to find a relation and using it to correct

the WAsP estimations, Mortensen and al. used five met masts and wind speed cross‐predictions.

Plotting the prediction errors versus the orographic performance indicators (∆RIX, defined as the

difference in percentage between the predicted and reference site ruggedness) for 20 cross‐

predictions and 5 self‐predictions, they noted that these points approximately lie on a logarithmic

curve.


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Unfortunately, only one met mast is available in the area in question and therefore the same

error estimation method cannot be applied. However, the WAsP model reliability in the area can

be improved by CFD simulations and comparisons between the CFD and WAsP wind speed

estimations.

This report starts with a brief presentation of the location of study, Greenland and Sarfannguaq,

then the wind data are introduced and a climate analysis from these data using WAsP Climate

Analyst is made for the area surrounding Sarfannguaq (area of about 100 Km2). From the obtained

climate data wind speed estimations are made from WAsP in some points of the domain, which

will be used for creating fictitious wind atlases. From these wind atlases new WAsP simulations

are made and wind speed profiles are extracted for the comparisons. In fact, the main issue in

comparing CFD and WAsP results is that WAsP works with all wind direction at the same time,

whereas a CDF simulation only deal with one wind direction for each simulation. In fact, the main

issue in comparing CFD and WAsP results is that the latter works with all wind directions at the

same time, whereas CFD only deals with one wind direction per simulation. This issue is treated in

this report by making WAsP work with only one wind direction each time. The method is

illustrated later. Not all directions are studied, however: only the four main wind directions are

analyzed in depth. After obtaining the WAsP estimations, the study goes on with CFD simulations,

using wind data estimations from WAsP for the boundary surfaces as inputs. This is done in order

to have the boundary conditions as similar as possible in both kinds of simulations. After that, the

results from WAsP and CFD simulations, made with Ansys CFX, are compared. Comparisons are

made for wind speed profiles at different locations in the domain and for different wind

directions.

The aim of this paper is to compare the results from CFD and from WAsP simulations, taking into

account the known problematic of WAsP in predicting wind resources in complex terrain, the

complexity and the computational expense (requiring several hours on supercomputers for

completion) of realizing a CFD simulation and the uncertainties for both of them. Moreover, the

possibility of using CFD simulations in site poor of wind measured data, for improving the

knowledge about WAsP prediction error‐ruggedness relation has been evaluated.


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1 Description of Sarfannguaq site

Greenland is the biggest island in the world. It stretches from Nunap Isua (Kap Farvel) in the

south at 59°46. N lat to 83°40. N lat. The polar circle crosses the country at 66°33. N lat. The

country covers an area of 2,175,600 km2. Only about fifteen percent of the country is free of ice;

the rest is covered by the world’s second largest ice sheet: the inland ice. Greenland is located in

the Arctic, which means that the average

temperature in the summer is never

over 10°C. The permafrost makes only

the top layers of soil thaw in the

summer. Furthermore, the country has

little rainfall and no forests but only little

brush and bushes (maximum man height

in south Greenland). The country can be

divided into subarctic, low‐Arctic and

high‐Arctic climate zones. The lowest

precipitation levels are in North

Greenland, where there is arctic desert

in some areas. South Greenland receives

more precipitation, and is fertile enough

for limited agriculture. Several systems

of sea currents meet in Greenlandic

waters. They influence the temperature

and salt content of the sea. In

Greenland, the different transportation

means are mainly planes, ships, dog

sledges and snow scooters.

Sarfannguaq (also Sarfannguit/Sarfánguaq) is a settlement in the Municipality of Sisimiut (100 km

north from the polar circle, it counts 5968 inhabitants). Sarfannguaq (meaning "the little current

point") lies 43 km east of Sisimiut at the foot of the Amerloq Fjord and is situated on an island

close to the mainland. The settlement was established in 1843 as a cod fishing station and in 1850

became a trading station equipped with a dwelling house, staff house and blubber house.

Figure 1‐1 Map of Greenland.


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In the below figure a red circle points Sarfannguaq and a yellow rectangle points Sisimiut.

Figure 1‐2 Stellite image of Sarfannguaq.

The image below is a view of Sarfannguaq, the picture shows the locations of the harbour, the

actual power plant (diesel generator) and the meteorological mast.

Figure 1‐3 View of the Sarfannguaq area.

Harbour Power Plant

Mast 10m


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2 Wind measurements

Wind data is measured by a meteorological station located on a local hill (about 76m a.s.l.) above

the settlement with free inflow from all directions, except for the western, which is dominate by

peaks. All other directions are dominated by a mixture of water and lower peaks.

The meteorological data in terms of wind speed and wind direction at 10 m height, are recorded

as 10 minute statistics with a standard NRG‐system data‐logger. The measurement campaign was

initiated in July 2003.

Figure 2‐1 Meteorological mast seen from the sea towards N.

There are wind measurements available for five years, from August 2003 until March 2008, but

some of them are missing and because of this the statistical quality is not very good. In fact only

124,304 records are available, which means 863 full days. The table below shows the time

distribution of the measurements.

Count: 124,304 21,163 29,638 19,039 43,915 10,549

Start time: 01/08/2003 01/08/2003 01/01/2004 15/08/2006 01/01/2007 01/01/2008

End time: 22/03/2008 01/01/2004 05/08/2004 01/01/2007 01/01/2008 22/03/2008

Table 2‐1 Wind data time distribution.

The wind data has been downloaded from [2].

This data is studied using WAsP Climate Analysis, a program which performs analyses on time‐

series of meteorological data.


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The images below show the wind rose and the fitted wind speed distribution at the met mast.

Figure 2‐1 Wind rose and distribution

The predominant directions at the mast are 90°N, 120°N, 300°N and 330°N, the speed distribution

is characterized by low values (the mean velocity is 6 m/s).

The table below shows the distribution parameters for all wind directions, the highest wind speed

belongs to the 330°N sector (7.42m/s) and the highest frequency is for the direction 300°N

(21.7%). The lowest mean speed is for the directions 300°N (2.04m/s) and the lowest frequency is

for the direction 0°N (1.4%). In general the less windy sectors are these between 0°N and 60°N

and between 150°N and 270°N.

0 30 60 90 120 150 180 210 240 270 300 330 All

A 2.7 2.2 5.1 7.4 8.3 4.7 1.7 2.7 5.0 4.7 6.7 8.3 6.8

k 1.03 1.28 1.28 2.25 2.47 1.51 0.82 1.09 1.88 1.29 1.97 2.90 1.97

U 2.66 2.04 4.72 6.54 7.33 4.24 1.85 2.63 4.46 4.38 5.90 7.42 6.01

E 64 18 225 293 384 125 41 54 111 180 244 359 258

f 1.4 1.6 5.0 13.9 15.8 6.8 1.5 1.6 6.1 7.8 21.7 16.7 100

Table 2‐2 Wind distribution parameter for all the directions


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3 WAsP simulations

WAsP is a software for predicting/estimating wind climates, wind resources and power

productions from wind turbines. The software is based on the wind atlas methodology, which was

used initially for creation of the European Wind Atlas. WAsP generates what is called an

observational wind atlas. The observed wind climates are thus representative for specific

locations and heights above ground level, so in order to be able to predict the wind climate at a

given wind turbine or wind farm site the observed wind climates must be transformed into

generalised regional wind climates. The observed wind climates contain the wind speed and

direction distributions derived from long‐term time‐series of wind speed and direction

measurements at the meteorological stations. Inputting detailed descriptions of terrain elevation,

land‐use and the presence of sheltering obstacles around each meteorological station, the

observed wind climate is transformed into what would have been measured at the location of the

station if the surroundings were completely flat, featureless and with a homogeneous surface and

the measurements had been taken at 10, 25, 50, 100 and 200 m a.g.l. Through this procedure, the

observed wind climate is freed from the influence of local topography to become regionally

representative. The results in an observational wind atlas are given in the form of detailed

statistics of the generalized wind speed and direction distributions for the locations of the

meteorological stations. These data sets may then be used as inputs to the application process,

whereby the same models are used in reverse to transform the regional wind climate to the

predicted wind climate at any specific site and height.

As it is known, WAsP performs poorly when applied to complex orography. This case study is

referred to a complex terrain, in the table below is the ruggedness index in the area surrounding

the met mast within a radius of 400m. The average for all the direction is 21.1%, which means

that the 21.1% of the terrain surrounding the mast is steeper than 33% (slope 0.3).

Table 3‐1 WAsP site effects.


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The table above shows the roughness, obstacles and orography effects at the met mast for all

directions. The reported differences are referred to the regional wind climate, calculated by

removing all the local topography effects and then calculating the wind atlas.

Note that the WAsP domain is an area of about 100 km2, whereas the CFD domain is an area of

about 1.5 km2. This is because we want to investigate and compare the wind speed profiles in

details only the area surrounding the mast, where obtained results from both programs are as

reliable as possible (for WAsP makes sense working with a high resolution map within an area

around the mast of radius 100 times the height of the mast, after that distance the more the

distance, the less the reliability of the results).

The maps below show the location of the met mast along with the orography (first one) and

roughness effects (second one) on the wind rose evaluated by WAsP. In fact, WAsP calculates a

regional wind climate, using the measured time‐series data, and then it applies that to the site to

predict wind resources. In the images below, the green colour covers an increase of the mean

wind speed, the red colour covers a decrease of the mean wind speed. As shown all sectors are

affected by a speed‐up due to the orography and some of them also by the roughness effects.

Figure 3‐1 Met mast location and orography effects on the wind rose.


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Figure 3‐2 Met mast location and roughness effects on the wind rose.

The image below shows the energy density rose. As shown, the wind main directions are equal to

the energy density main directions, only with a few differences. The rose indicates the percentage

of energy density for each direction and the other image the energy density distribution.

Figure 3‐3 Energy production rose and distribution.


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In order to compare the results from WAsP with those from Ansys CFX, different simulations for

different wind directions are made. In fact the main issue in comparing the two programs is that

WAsP works all wind directions at the same time, whereas in a CFD simulation only one flow

direction can be chosen each time. Hence the study is split into parts, each of them referring to

only one wind direction. Four WAsP simulations are made to overcome this problem. The

simulations are made only for the main wind directions and for each of them the wind input data

(time‐series) used are not the measurements from the met mast, but fictitious values instead, in

order to consider only one direction each time. In fact, for having the most similar boundary

condition in WAsP and in Ansys CFX, only one wind direction for each simulation is used as input.

That is done creating a fictitious observed wind climate (using the fictitious time‐series data) and

setting a fictitious met mast at the border of the domain for calculating a regional wind climate

for each of the directions.

Here, the method is explained thoroughly:

1) For each of the four directions of interest, use the WAsP model to obtain the mean wind

speed at 10m in the furthest point in that direction, constrained to the CFD domain. The

image below shows the predicted wind roses at 10m for all the furthest point within the

domain and for all the directions. From each wind rose we take the mean wind speed for

the particular direction. For example for the 300°N direction the furthest point within the

domain is the point marked with number 11, from that wind rose the wind mean speed

for 300°N direction is taken.

Figure 3‐4 Predicted wind roses.

11 12

4

5


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2) Create four observed wind climate files based on the previously obtained mean wind

speed at 10m. The observed wind climate are made using fictitious wind measurement, in

fact the wind data input is formed by a series with only one direction and one wind speed.

The image below shows the fictitious observed wind climate for the 330°N direction.

Figure 3‐5 Fictitious wind atlas for 330°N wind direction.

3) Run four simulations, each based in one of the four wind atlases, that created from the

fictitious observed wind climate for each direction. The met mast is located at the point of

the domain where the wind mean speed is extracted. These simulation will be compared

with the CFD results. The four cross predictions (estimating the wind speed at the original

met mast using the fictitious met masts and WAsP simulations) show an underestimation

of the mean wind speed between 3% and 18%.

Also the wind speed for the boundary conditions of the CFD simulations are extracted from these

four WAsP simulations.

Here the wind speed profiles for the 330°N direction calculated by WAsP are shown as an

example. The same working method is applied to the other directions and the results are shown

in the appendix A.


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3.1 WAsP results: 330°N wind direction The direction analysed in this section is 330°N and it is the only one shown and explained in the

report. The WAsP wind profiles are obtained along the line passing by the met mast with

direction 330°N. Sections are taken every 50m close to the mast (up to 250m from it, both

upstream and downstream), whereas at larger distance from the mast there are sections every

100 or 150m (100m is for this direction and 150m for the other three directions). The height of

the profiles is 500m, even if WAsP results for speeds are not fully reliable at heights above 100m.

Because of this the comparison with the CFD risults will take into account only this athmospheric

layer.

The WAsP wind speed profiles are extracted using the utility script “turbine site transect”, which

provide wind speed information along a selected direction, for a selected height above the

ground, for a selected spacial gap and for a total lenght of the transect.

The images below show the wind speed upwind (first image) and downwind (second image) the

mast.

Figure 3‐6 WAsP wind speed profiles upwind the mast.

Note the first two profiles are very similar to each other and they are located above the sea, the

third one is located just after the high roughness area of the Sarfannguaq village.

0

50

100

150

200

250

300

350

400

450

500

0 2 4 6 8 10 12

(m)

Wind speed (m/s)

Wind speed profiles upwind the mast (330°N)

WAsP upstream 450m

WAsP upstream 350m

WAsP upstream 250m

WAsP upstream 200m

WAsP upstream 150m

WAsP upstream 100m

WAsP upstream 50m

WAsP mast


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Figure 3‐7 WAsP wind speed profiles downwind the mast.

Note the highest speed‐up is located at the mast site at about 10m above the ground level.

Moreover at the site 250m downwind the mast WAsP shows a profile quite different from the

other ones, at this site there is a roughness and slope change (it is the site where the wind

approaches the sea after the downhill slope). The WAsP simulations for the other directions show

a similar behaviour wherever there is a roughness and slope change on the surface.

0

50

100

150

200

250

300

350

400

450

500

0 2 4 6 8 10 12

(m)

Wind speed (m/s)

Wind speed profiles downwind the mast (330°N)

WAsP mast

WAsP downstream 50m

WAsP downstream 100m








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4 CFD Simulations

The goal of this study is to compare the results obtained by WAsP and Ansys CFX for the four main

directions. In a CFD simulation the information on boundary conditions is necessary and some

parameters have to be set . The information we have about Sarfannguaq are contour‐line maps,

some pictures and a orthophoto. The other parameters required by the CFD simulations of

Sarfannguaq are the turbulence model and the roughness values of the area. Two introductive

studies are made for choosing turbulence model and roughness.

Regarding the roughness of the area, a brief analysis is done for one wind direction, studying the

influence of this parameter on the wind speed profile. Simulations are made for the 330°N

direction for three different roughness values. These simulations are illustrated in the relative

section.

Regarding the turbulence, choosing a turbulence model is quite complicated in a case study like

this, where there is measured data in only one location and hence comparisons between different

turbulence models may not be done for other points of the domain. This issue may be dealt with

using a sensitivity analysis, but in this study the objective of the CFD simulations is obtaining quite

sure results for comparing them with the WAsP estimations. Hence we decided to use as

reference a previous CFD study regarding Askervein. The wind behavior at this location has been

monitored and studied in depth in the past years and the general conditions (complex terrain

formed by an isolated hill) are quite similar to Sarfannguaq. Because of this, CFD simulations are

made, for comparing the results with the measurements and for choosing a turbulence model.

4.1 Turbulence model study: Askervein Askervein is on the west coast of the island of South Uist in the Outer Hebrides of Scotland, it is a

smooth hill with an interesting topography: the hill has a quite simple geometrical shape and

resembles an ellipsoid. The hill has minor and major axis of 1,000 and 2,000 meters respectively,

the height of the top point is 126 m.

Askervein is the most complete field experiment site to date; with 50 towers deployed, out of 50

towers 27 were equipped with three component turbulence sensors.


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Figure 4‐1 Contour map showing the topography of Askervein Hill.

Askervein hill has been extensively measured in‐situ in the past years, providing the ideal frame

work for the validation of computational models.

A previous Msc. project about Askervein hill is made by Naeem Memon and Venkata Ratnam

Kondreddi [4]. The objective of that study was to simulate the flow over the complex terrain by

using different turbulence models. These simulations have been compared with the field

experimental results from [5] and also with the results from simulations conducted by Karl

J.Eidsvik [6]. Three turbulence models have been selected for that purpose: K‐ ε model, K‐ω model

and Shear Stress Turbulence (SST) model.

The objective of this turbulence model study is to repeat some of the simulations (using Ansys CFX

11) conducted in [4] (using Ansys CFX 5.7.1) and to analyze the results obtained in order to choose

a turbulence model for the Sarfannguaq simulations. In particular, the simulations are repeated

using three turbulence models (k‐ ε, K‐ω and SST) and to compare the results to the measured

data and to the ones found by Eidsvik in [6].


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4.1.1 Simulation parameters

In order to repeat the simulation made in [4], the same inlet, boundary conditions and meshing

parameters were used.

Because of the big dimension of the domain, the simulation is realized with a scaled model (scale

factor 1:10). In order to have the same Reynolds number every length is divided (in each

direction) by a factor of 10 and every velocity is multiplied by the same factor.

The Reynolds‐Averaged‐Navier‐Stokes (RANS) equations and the continuity equation are used,

the turbulence models are the standard k‐ε, K‐ω and the SST.

4.1.1.1 Geometry and Mesh

The topographical data of Askervein (*.dat file which consist of a grid of the xyz coordinates of the

hill with 30 m of resolution) comes from [4] . Naeem Memon and Venkata Ratnam Kondreddi

received the data from Niels.N. Sørensen of Risø [7], that data was originally extracted from a full‐

scale field experiment. Topographical data are available for a rectangular terrain of almost 2,600 x

3,300m.

This data is a *.dat file and it has been imported in Rhinoceros 4.0 in order to obtain a, so called,

3‐D points cloud. After that, using a special plug‐in, called Point Cloud (utility developed by

Synecode), all the points of the grid are interpolated and the smooth surface of Askervein is

created.

After obtaining a smooth surface, it has been extruded in z‐direction to create a solid (the height

of extruding is 1,000m) and then the solid is again exported like *.iges file and subsequently

imported in Gambit (meshing software for Fluent).

Hence the solid is meshed using Gambit, which is able to realize a more accurate mesh than CFX‐

Mesh. The mesh is realized following the advice from the preview report.

Near the wall there are 35 inflated layers, the first layer is 0.049m, the expansion factor is 1.5. We

set in the vertical edge the spacing between each center cell to 4m, at the bottom we realized a

grid of 150 x 150 elements (each element is about 2.2 x 1.73 m).


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At the end we have a meshed solid formed by 1,270,000 hexahedral elements. The mesh quality is

good (minimum orthogonality angle = 45.6° , maximum aspect ratio = 90,393 , maximum

expansion factor = 5.7), the high value of the parameter maximum aspect ratio is due to the

boundary layers near the wall. In fact in this zone of the domain the cells are very thin and the xy

surface is the same of all the other cells (about 22 x 17.3 m).

Figure 4‐2 Meshed solid.

4.1.1.2 Boundary Condition: Inlet

The wind direction taken equals to 210° , measured with respect the North , approximately along

the line A. Wind speed at the reference site was 8.9 m/sec at 10m above the ground, whereas the

roughness length (z0) was 0.03m for the entire terrain.


20

Figure 4‐3 Diagram Representing Velocity vectors

· cos

· sin

The theoretical velocity profile is shown below.

Figure 4‐4 Theoretical Velocity Profile at inlet.

ln

Where

U* = Frictional velocity=0.61m/s

k= Von Karman coefficient =0.40

z = Height above ground

z0=0.03m


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4.1.1.3 Other Boundary conditions

The inlet was specified according to the profile discussed above: we consider as inlet both sides of

the domain in directions x and y (in each side we set Vsx and Vsy). The outlet was considered fully

developed (in Ansys CFX at the outlet is set a static pressure) and the terrain of the site was

modeled according to the rough wall version of logarithmic law of wall; symmetry condition was

applied for the top boundary of the domain.

Figure 4‐5 Boundary conditions.

4.1.2 Results

Data is visualized and analysed for the first time in Ansys CFX‐Post; and is subsequently exported

for further elaborations in Excel.

The first analysis regards the wind speed in two planes. The first plane is parallel to the surface of

the terrain at 10 m height. The second one is the vertical plane including the line A. The speed is

represented with stream lines in the first plane and with coloured arrows in the second one.


22

In the figure below is shown the velocity in the horizontal plane. Note the two recirculation

regions at the lee side of the hill and the increasing of the speed at the top of the hill.

Figure 4‐6 Stream lines on the plane parallel to the terrain.

The values of velocity and the values of the length are scaled, in order to read the real values on

the figure, you have to divide the velocity by 10 and multiply the length by 10.

The figure below shows the wind speed in the vertical plane including the line A. It shows the

speed increase at the top of the hill and the speed decrease in the lee side and, as well as the

direction change in the recirculation region. In fact, the figure shows the different arrows

direction beyond the hill, close to the ground level.

The recirculation region is described by this two planes in the horizontal and vertical direction.


23

Figure 4‐7 Velocity vector in the vertical plane.

The following analysis regards the wind speed along the line A at 10 m. In the following page the

results obtained from the simulations are compared with a few measured data and with the

results from Karl J.Eidsvik [6]. The compared property is the speed‐up, defined like:

The results from Eidsvik [6] are used as reference for the zone between wind speed measured

points.

In the figure below are showed the different speed‐up for each turbulence model, for the Karl

J.Eidsvik’s results from [6] and for the measured data.

It shows that the three turbulence models have very similar behavior until 100m upwind the hill

top, after that point relevant differences are present.


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Figure 4‐8 Comparison of speed‐up values along line A.

The k‐ε model is in good agreement with experimental data both on upwind and downwind the

hill top. It has very good behavior all over the line A. It may have some discrepancies in the zone

not covered by measurements after the last measurement taken at 400m downwind the hill top.

The SST model is predicting reasonable results upwind, but probably at the top and in the lee side

the results are far from the real behavior. The agreement is good until 100m upwind the hill top,

then this turbulence model underestimates at the hill top and it seems to overestimate the

recirculation region and underestimate the wind speed in the lee side.

The K‐ω turbulence model follows the measured data until 100m downwind the hill top and then

it underestimates the recirculation zone and it overestimates the wind speed. After 700m

downstream the hill top this turbulence model produces result in good accordance with those

from Eidsvik [6].

Considering the results from these simulations and taking into account the dimension of the

Sarfannguaq domain, the k‐ε turbulence model is chosen for the following simulations.

‐1

‐0.8

‐0.6

‐0.4

‐0.2

0

0.2

0.4

0.6

0.8

1

‐1000 ‐800 ‐600 ‐400 ‐200 0 200 400 600 800 1000

Speed up

Dist long line A

EXP

Eidsvik

SST

K‐epsilon

k‐omega


25

4.2 Roughness study: Sarfannguaq After choosing a turbulence model, the roughness issue is taken into account.

The 330°N wind direction is chosen for the study regarding roughness because it has the second

frequency value (16.7%), the highest mean wind speed (7.42 m/s) and it is the most affected by

the high roughness of the village of Sarfannguaq.

Different roughness area are located at the bottom of the domain, the sea roughness is taken

0.00001m according to the result obtained studying the logarithmic wind speed profile from

WAsP wind atlas. In WAsP, roughness for the water surface is set to 0m roughness, but studying

the profiles obtained from the wind atlas it is possible to fit them into a logarithmic function.

Following this function the wind speed becomes zero at 0.00001m. The roughness on the area of

the village of Sarfannguaq is taken 0.3m (shelter belts) in accordance to the report made by

Mortensen [9] and consulting the roughness classes of the European Wind Atlas [10]. The other

terrain roughness is taken 0.03m (farmland with very few buildings) according to the report above

mentioned and looking at the orthophoto of the area.

4.2.1 Simulation parameters

Because of the domain’s large dimension, the simulation is created with a scaled model (scale

factor 1:10). In order to have the same Reynolds number every length is divided (in each

direction) by a factor of 10 and every velocity is multiplied by the same factor.

The Reynolds‐Averaged‐Navier‐Stokes (RANS) equations and the continuity equation are used, the

turbulence model is the standard k‐ε, as chosen from the Askervein simulation.

4.2.1.1 Geometry

The geometry of the simulation is the terrain and the sea surface surrounding Sarfannguaq. The

geometry is formed by two hills and a little island between the two hills. The highest point of the

domain measures 118 m.

The figure below is an orthophoto of the area in question.


26

Figure 4‐9 Orthophoto of the area.

The data is obtained from the Arctic Technology Centre at DTU. This data consists in maps of the

area (dimension about 1520 x 910m). The first map is a *.dwg file and is shown below. The

resolution is 0.5m contour‐line.

Figure 4‐10 Map *.dwg of the area.


27

The second map is in *.tab format (MapInfo).

Using MapInfo Professional 8.5 the original map is elaborated and a new map formed by points is

obtained. For doing this, points are extracted from the polylines representing the contour‐line of

the map.

This new map is elaborated again in order to obtain an equispaced grid of points. In fact points

obtained by polylines have a such special distribution, they are not usable for obtaining a smooth

surface. This new grid is made interpolating the original points and creating a thematic map.

The figure below shows the thematic map.

Figure 4‐11 Thematic elevation map of Sarfannguaq.

Using the thematic map and the MapInfo’s utility Vertical Mapper, finally, a grid of points is

obtained. This grid is exported in *.txt file and subsequently imported in Rhinoceros 4.0.

Using Rhinoceros 4.0 the 3‐D point cloud is drawn. After that, using the previously introduced

utility Point Cloud (Synecode), a smooth surface is created.

The figure below shows the 3‐D points cloud.


28

Figure 4‐12 3‐D point cloud.

After obtaining a smooth surface, it is necessary to split it into many faces for setting the different

roughness values in Ansys CFX. Then whole the surface is extruded towards the z‐direction to

obtain a solid (the height of extruding is 500m). Before exporting this solid like *.iges file, it has to

be scaled because Ansys CFX has a dimension limitation. The maximum dimension for a domain is

a cube of 1000m each edge and centered in the origin.

Figure 4‐13 The solid obtained from Rhinoceros.


29

4.2.1.2 Mesh

The *.iges file is imported in Gambit for being meshed.

The mesh characteristics are the following : the bottom surface mesh is made using a quad‐pave

function, which creates about 14,000 elements; near the bottom there are 12 inflated vertical

layers, the first layer is 0.005m high, the expansion factor is 1.2; in the vertical edge the cell height

is 1.8m and a rate factor of 1.08 is applied. In order to obtain a volume mesh a hex\wedge setting

with Cooper function is used.

At the end, a meshed solid formed by 589,000 hexahedral elements is obtained. The mesh quality

is good (minimum orthogonality angle = 17° , maximum aspect ratio = 50.3 , maximum expansion

factor = 9.7), the low value for the minimum orthogonality angle is due to the bottom surface

mesh. In fact, it is not possible to make a rectangular grid because there are faces with irregular

shape and, because of this, the pave option is used (quadrilateral and triangular elements).

Figure 4‐14 Mesh of the domain.


30

4.2.1.3 Boundary condition : Inlet

The wind direction for these simulations regarding the choice of roughness is 330°N. For the other

simulations (D, E and F), wind directions take into account change among 90°N, 120°N, and 300°N,

so each simulation has a different inflow.

The inlet velocity for the CFD simulations is obtained from WAsP using the fictitious observed

wind climate. The method for obtaining the wind predictions for the four main directions has

already been illustrated in the WAsP section. Here the successive step to the previous three ones

for obtaining the wind speed all over the inlet boundary faces is described:

4) Extract from the WAsP model the mean wind speed at every points of the boundary for

the simulated direction. That is done by using the utility script “turbine site vertical slice” ,

which creates a vertical grid of points for a selected direction starting from a selected

point. In this case we set two corner points in the domain (north‐west and south‐east)

and we used the 90°N and 180°N slice directions for the 330°N and 300°N wind directions

and the 0°N and 270°N slice directions for the 90°N and 120°N wind directions. The

resolution of the grid is 10m. The grid can be imported in SAGA, subsequently exported as

*.xyz file, it can then be open in Excel and elaborated on creating the inlet file for Ansys

CFX.

The image below shows the domain with the inlet condition for the 330°N wind direction. The

image concerns the velocity in the u‐direction.

Figure 4‐15 Inlet 330°N wind direction.


31

4.2.1.4 Boundary condition : Sea and land surfaces

The bottom boundary condition is set as a wall, as mentioned above. In this section the various

roughness zones are shown. The first image concerns the high roughness zone, where there is the

village of Sarfannguaq. Here the roughness is 0.3m. In the simulations the used value is 0.03m

because of the scale question.

Figure 4‐16 High roughness zone.

The other terrain zone is shown in the image below. In that area the roughness changes for the

different simulations. The main value is 0.03m, but 0.01m and 0.05m are used. In the simulations

the used values are 0.003m, 0.001m and 0.005m because of the scale question.

Figure 4‐17 Low roughness zone.


32

The left part of the bottom surface is sea, in this area the roughness is 0.00001m. In the

simulations the used values are 0.000001m.

Figure 4‐18 Sea zone.

4.2.1.5 Other Boundary Conditions

The other boundary conditions applied are the outlet in front of the inlet condition and the

opening at the top boundary of the domain. In case of a 90°wind direction the symmetry

condition is applied to the lateral faces perpendicular to the flow.

4.2.2 Results simulations A,B and C (330°N wind direction)

In order to choose a roughness for all the wind directions, three simulations are made for the

330°N wind direction, the simulation A (0.01m roughness), the simulation B (0.03m roughness)

and the simulation C (0.05m roughness). The image below shows the domain and some speed

profiles along the line crossing the mast and towards the wind direction for the simulation B.

All three simulations have a very similar wind speed profile. Because of this, only the figures

regarding the CFD simulation A are shown. All the other wind profiles are in the appendix B.


33

Figure 4‐19 View of the domain with some wind speed profile from Ansys CFX.

In the figure below the wind intensity and direction are shown on a surface at 5m a.g.l. This is the

wind direction in the sea zone between the two hills, the air flow along the coastline and a lot of

recirculation zones in whole the domain.

Figure 4‐20 Wind speed distribution at 5m high.


34

The first figure regards the wind speed profiles approaching the met mast and the second one the

area downwind the mast.

Figure 4‐21 CFD wind speed profiles upwind the mast (330°N, k‐ε,roughness 0.03m).

In the figures below the profiles are shown from 0m a.g.l. to the top of the domain, 500m a.s.l.

The wind speed is calculated for the 330°N direction using the x‐component (u) and y‐component

(v), because in some areas of the domain there is a strong wind perpendicular to the 330°N

direction, as will be shown later.

The image shows that the first two wind profiles, located above the sea, have a profile very similar

to the one of the inlet. The third profile is located just after the village area (with high roughness)

and it is also affected by the morphology (speed‐up). The highest wind speed is reached at the

mast site.

0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9 10 11

(m)

wind speed (m/s)

Wind speed profiles upstream the mast (330°N, k‐ε, 0.03m)

CFX upstream 450m

CFX upstream 350m

CFX upstream 250m

CFX upstream 200m

CFX upstream 150m

CFX upstream 100m

CFX upstream 50m

CFX mast


35

The figure below shows the wind speed profiles downwind the met mast. Note in the section

250m downwind the mast there is fluid separation and a negative wind speed. Here, there is very

steep terrain after which the flow reaches the sea, resulting in a recirculation zone.

Figure 4‐22 CFD wind speed profiles downwind the mast (330°N, k‐ε,roughness 0.03m).

Moreover, starting from the section 350m downwind the mast the profiles change in shape at the

lowest part, because of speed component perpendicular to the main wind direction. In fact,

where the flow reaches the sea and all over the sea zone, there is a wind speed component

coming from direction north (this air flow comes from the gorge between the two hills and it is

present from the sea level up to 50m) as well as a strong air flow along the coastline. These affect

the speed profiles in direction and intensity.

As we see, the wind direction changes a lot in the different zones of the domain at the height of

5m. Because of this a study has been made in order to show the relation between the height and

the wind direction for all the section we are investigating.

0

50

100

150

200

250

300

350

400

450

500

‐1 0 1 2 3 4 5 6 7 8 9 10 11 12

(m)

wind speed (m/s)

Wind speed profiles downwind the mast (330°N, k‐ε, 0.03m)

CFX mast

CFX downstream 50m

CFX downstream 100m

CFX downstream 150m

CFX downstream 200m

CFX downstream 250m

CFX downstream 350m

CFX downstream 450m

CFX downstream 550m


36

Figure 4‐23 Wind direction vs height in the sections upwind the mast (330°N, k‐ε,roughness 0.03m).

The figure shows the wind direction changes only for the first 20m, because after that height they

are not as relevant as they are close to the ground.

The figure above shows slight variations in wind direction when the wind approaches land. In fact,

the first two sections are taken at the sea zone and the flow turns to the left as it tends to follow

the coastline. Whereas the other sections are affected by the local morphology of the terrain.

Anyway, after a few meters the wind direction tends to stick to the initial direction and the

maximal variation all over the height is 40°.

The figure below shows big variations in wind direction, on the lee side the maximum variation is

100°. A strange behavior is present at the section taken 100m downwind the mast, this is a

recirculation zone and the direction is affected by that. In the zone between 150m and 250m

downwind the mast the direction is strongly affected by the wind speed component coming along

the coast line. The most affected section is the closest to the coast line, that is 250m downwind

the mast. The following sections are slightly affected by the coast line and by the north wind

speed components.

0

2

4

6

8

10

12

14

16

18

20

‐80 ‐70 ‐60 ‐50 ‐40 ‐30 ‐20 ‐10 0

(m)

angle (°)

Wind direction upwind the mast (330°N, k‐ε, 0.03m)

upstream 450m

upstream 350m

upstream 250m

upstream 200m

upstream 150m

upstream 100m

upstream 50m

mast


37

Figure 4‐24 Wind direction vs height in the sections downwind the mast (330°N, k‐ε,roughness 0.03m).

4.2.3 Roughness comparisons

In this section two roughness comparisons are shown. As above mentioned, simulations are made

using different roughness values only for a part of the domain. In fact, for the sea and for the

village of Sarfannguaq the roughness is fixed. For the other land surface the roughness change

among the values of 0.01m, 0.03m and 0.05m. The comparisons are made taking the 0.03m

roughness as reference, because is the most probable for that kind of soil, characterized by stones

and grass.

The figure below shows the comparison between 0.01m and 0.03m for some of the speed

profiles. The results are in percentage and for the first 10m a.g.l, that is the part of the wind

profile affected by the different roughness effects. The figure shows that approaching the mast

the difference between the speed profiles, calculated with different roughness values, rises. The

0

2

4

6

8

10

12

14

16

18

20

‐90 ‐60 ‐30 0 30 60 90

(m)

angle (°)

Wind direction downwind the mast (330°N, k‐ε, 0.03m)

mast

downstream 50m

downstream 100m

downstream 150m

downstream 200m

downstream 250m

downstream 350m

downstream 450m

downstream 550m


38

first two sites have very similar wind speed profile, the biggest percentage difference is 0.2%.

From the third one to the 100m downwind the mast the difference rises up to 23%.

Figure 4‐25 Roughness comparison (0.01m vs 0.03m).

In the appendix C there is the graphic regarding the roughness comparison of the other sections

downwind the mast. For these sections the height influenced by the different roughness is bigger

and it reaches 50m in the section 250m downwind the mast. This is because at the lee side, at the

height normally affected by the roughness (5‐10m), the difference of level with the previous sites

has to be taken into account. For the wind profile above the sea the speed difference is less than

2%.

The figures below show the comparison between 0.05m and 0.03m for some of the wind speed

profiles. These graphics are very similar to the previous ones in shape, but not in value. In this

case the wind velocity is lower than the one in reference, hence positive percentage values

become negative. Another difference is that values are slightly smaller than in the previous

comparison of roughly 30‐50%.

0

1

2

3

4

5

6

7

8

9

10

‐5 0 5 10 15 20 25

(m)

∆V/V0.03(%)

Roughness comparison (0.01m vs 0.03m)

upstream 450m

upstream 350m

upstream 250m

upstream 200m

upstream 150m

upstream 100m

upstream 50m

mast

downstream 50m

downstream 100m


39

Figure 4‐26 Roughness comparison (0.05m vs 0.03m) .

Even for this comparison the graphic regarding the other sections downwind the mast is in the

appendix C.

Regarding the roughness, it is possible to conclude that it influences only the first few meters of

the wind speed profile (after 5m the difference is less than 7% in the worst case). Downwind the

mast the influence of the roughness is bigger and even the difference of level has to be taken into

account at the lee side.

Taking into account that the hub height of a suitable turbine for Sarfannguaq is about 10m or

more, setting a roughness of 0.03m, the uncertainty about wind speed for this height is less than

±3%, in the area around the mast. Because of this, the intermediate roughness is applied for all

the other simulations.

0

1

2

3

4

5

6

7

8

9

10

‐15 ‐10 ‐5 0 5

(m)

∆V/V0.03(%)


upstream 450m

upstream 350m

upstream 250m

upstream 200m

upstream 150m

upstream 100m

upstream 50m

mast

downstream 50m

downstream 100m


40

4.3 Simulation D (300°N wind direction, kε, 0.03m roughness) This simulation is made using the parameters chosen from the previous simulations (Askervein for

the turbulence model and Sarfannguaq 330°N wind direction for the roughness).

The image below shows the domain and some speed profiles along the line crossing the mast and

towards the wind direction.

Figure 4‐27 View of the domain with some wind speed profiles from Ansys CFX (330°N, k‐ε,roughness 0.03m).

Even for this direction, there are recirculation zones in the domain and the main wind direction in

the gorge is 180°N. The figure shows the wind speed profile behavior over the sea area, where it

is strongly affected by the flow from north and by the presence of a steep reef in the right corner.

In this area the ruggedness index is the highest of all the domain and the wind direction is almost

perpendicular to the hill. Because of this the wind profiles in this area of the domain are strongly

affected by the morphology.

The image below shows the wind speed and direction on a surface 5m above the ground level.

The image shows the particular wind speed distribution over the sea. Along the line passing by the

mast and with direction 300°N, in the area over the sea, there is a confluence of different air

fluxes. The first one coming from the north and the second one coming from the east. The image

also shows the high wind speed flowing on the steep slope of the hill in the right side of the

domain. This helps to understand the wind speed profiles shown later.


41

Figure 4‐28 Wind speed distribution at 5m high a.g.l. (330°N, k‐ε,roughness 0.03m).

The figures below show the wind speed profiles upwind and downwind the mast.

It is interesting to look at the wind speed profiles on the sea zone (from the 250m upwind the

mast). All these profiles are strongly affected by the air flux coming from the gorge between the

two hills.


0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9 10

(m)

wind speed (m/s)

Wind speed profiles upstream the mast (300°N, k‐ε, 0.03m)

CFX upstream 700m

CFX upstream 550m

CFX upstream 400m

CFX upstream 250m

CFX upstream 200m

CFX upstream 150m

CFX upstream 100m

CFX upstream 50m

CFX mast


42


4.4 Simulation E (120°N wind direction, kε, 0.03m roughness) This simulation is made using the parameters chosen from the previous simulations (Askervein for


The image below shows the domain and some speed profiles along the line crossing the mast and

towards the wind direction. The direction is the same of the previous simulation, but this case it

flows towards north‐west.

0

50

100

150

200

250

300

350

400

450

500

‐1 0 1 2 3 4 5 6 7 8 9 10 11 12

(m)

wind speed (m/s)

Wind speed profiles downstream the mast (300°N, k‐ε, 0.03m)

CFX mast

CFX downstream 50m

CFX downstream 100m

CFX downstream 150m

CFX downstream 200m

CFX downstream 250m

CFX downstream 400m

CFX downstream 550m

CFX downstream 700m

CFD downstream 850m

CFD downstream 1000m


43

Figure4‐31 View of the domain with some wind speed profile from Ansys CFX (120°N, k‐ε,roughness 0.03m).

Even for this direction, there are recirculation zones and coastline air fluxes in the northern part

of the domain. It shows the wind distribution over the sea area behind the hill. Here the wind

profile is slightly affected by the wind flux flowing from the west side. In the whole left part of the

domain, for the examined direction, the wind profiles don’t have anomalies. The biggest wind

speed is registered at the met mast and in the zone approaching it.

The image below shows the wind speed distribution on a surface 5m above the ground level. The

image shows the wind distribution over the sea in the north part of the domain, dominated by

towards coming air flux.


44

Figure4‐32 Wind speed distribution at 5m high a.g.l. (120°N, k‐ε,roughness 0.03m).

The figures below show the wind speed profiles upwind and downwind the mast.

It is interesting to look at the wind speed profiles on the sea zone (from the 550m downwind the

mast). All these profiles are affected by the air flux flowing along the coastline.


0

50

100

150

200

250

300

350

400

450

500

‐1 0 1 2 3 4 5 6 7 8 9 10 11 12

(m)

wind speed (m/s)

Wind speed profiles upwind the mast (120°N, k‐ε, 0.03m)

CFX mast

CFX upstream 50m

CFX upstream 100m

CFX upstream 150m

CFX upstream 200m

CFX upstream 250m

CFX upstream 400m

CFX upstream 550m

CFX upstream 700m

CFD upstream 850m

CFD upstream 1000m


45

Figure 4‐34 CFD wind speed profiles downwind the mast (120°N, k‐ε,roughness 0.03m)

4.5 Simulation F (90°N wind direction, kε, 0.03m roughness) This simulation is made using the parameters chosen from the previous simulations (Askervein for


The image below shows the domain and some speed profiles along the line crossing the mast

towards the wind direction. For this direction the wind profiles are affected in the area over the

sea, in the gorge between the two hills. In fact, in all the other cases there is an uphill slope

followed by a downhill slope, whereas in this case there is only a steep downhill slope, followed

by 300m of flat see surface and then a uphill slope until reaching the mast. Beyond the mast the

terrain is very complex and downhill slopes are followed by uphill slopes. Moreover the mast

location is not the highest of the line, passing by it and with 270°N direction.

0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9 10

(m)

wind speed (m/s)


CFX downstream 700m

CFX downstream 550m

CFX downstream 400m

CFX downstream 250m

CFX downstream 200m

CFX downstream 150m

CFX downstream 100m

CFX downstream 50m

CFX mast


46

Figure 4‐35 View of the domain with some wind speed profile from Ansys CFX (90°N, k‐ε,roughness 0.03m).

The image below shows the wind distribution at 5m a.g.l. Note the negative speed in the gorge

and recirculation areas all over the sea between the two hills.

Figure 4‐36 Wind speed distribution at 5m high a.g.l. (90°N, k‐ε,roughness 0.03m).

The figures below show the wind speed profile obtained from the CFX simulation. Note that the

negative wind speed reaches the height of 35m in the section taken 400m upwind the mast.


47



0

50

100

150

200

250

300

350

400

450

500

‐2 0 2 4 6 8 10

(m)

Wind speed (m/s)


CFX upstream 850m

CFX upstream 700m

CFX upstream 550m

CFX upstream 400m

CFX upstream 250m

CFX upstream 200m

CFX upstream 150m

CFX upstream 100m

CFX upstream 50m

CFX mast

0

50

100

150

200

250

300

350

400

0 2 4 6 8 10

(m)

Wind speed (m/s)


CFX mast

CFX downstream 50m

CFX downstream 100m

CFX downstream 150m

CFX downstream 200m

CFX downstream 250

CFX downstream 400

CFX downstream 550


48

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10

(m)

Wind speed (m/s)

Comparison WAsP vs CFD (330°N)

WAsP upstream 150m

CFX upstream 150m

5 Comparison WAsP vs CFD

In this chapter the comparison between WAsP and Ansys CFX is illustrated. The comparisons are

made using the wind speed profiles obtained by the simulations shown above and separately for

the four main wind directions for all sites. The speed profiles are compared in the height between

the ground level and 100m a.g.l. (WAsP is based on surface layer theory, so usually the results are

very reliable only the first 100m).

5.1 330°N wind direction The first comparison is about the 330°N wind direction. The images below show some wind speed

profiles from WAsP (red) and from Ansys CFX (blue). Here only the most interesting sections are

shown, see appendix D for the other wind speed profile comparisons.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP upstream 250m

CFX upstream 250m


49

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10

(m)

Wind speed (m/s)



CFX downstream 100m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 450m

Figure 5‐1WasP vs CFX speed profiles for the 330°N wind direction.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP mast

CFX mast

0

10

20

30

40

50

60

70

80

90

100

‐1 1 3 5 7

(m)

Wind speed (m/s)



CFX downstream 250m


50

The figures above show the qualitative difference between the two softwares, in particular

upwind the mast, WAsP underestimates the wind speed and the profile is characterized by a

sharp speed‐up at 7‐10m high. At the mast the profiles are slightly different, the speed‐up from

WAsP is at 17m and at that height the difference from CFD are minimal. Downwind the mast

there are more differences, due to the fluid separation. In fact, for example in the section 100m

downwind the mast is at 53m high a.s.l., about 25m lower than the mast (slope of 0.25). WAsP

considers that the stream wises follow the terrain and where this doesn’t happen the results are

wrong. In the following pages numerical results are illustrated in order to show the percentage

differences between CFD an WAsP wind speed profiles. Moreover, notice the anomaly in wind

speed for the lowest part of the profile at the 250m downwind section, here the flow is

approaching the sea and there are roughness and slope changes. WAsP at this section doesn’t

show good results in the estimation of the wind speed close to the ground. As seen in the figures

regarding the other directions, WAsP shows the same kind of error where there is, at the same

position, a roughness and a slope change. In the last figure the CFD wind profile is affected by the

air flux coming from the gorge and it doesn’t make sense to compare the result in this area of the

domain. Anyway it is important to have a look the images below. They represent the wind roses

and distributions at 20m high at the section 250m and 450m downwind the mast, where the CFD

simulation predicts the presence of a coastline air flux and an air flux from the gorge. As shown,

even WAsP predicts these air fluxes.

Figure 5‐2 WAsP Wind rose and wind distribution at 20m high at 250m downwind the mast (330°N).


51

Figure 5‐3 WAsP Wind rose and wind distribution at 20m high at 450m downwind the mast (330°N).

The images below show the wind speed differences between WAsP and Ansys CFX in percentage.

Figure 5‐4 Wind speed profile comparison upwind the mast (330°N).

0

10

20

30

40

50

60

70

80

90

100

‐50 ‐40 ‐30 ‐20 ‐10 0 10 20

(m)

∆V/Vk‐ε(%)

Comparison WAsP vs CFD upwind the mast (330°N)

upstream 350m

upstream 250m

upstream 200m

upstream 150m

upstream 100m

upstream 50m

mast


52

Figure 5‐5 Wind speed profile comparison downwind the mast (330°N).

Regarding the 330°N direction, the figures show that usually WAsP underestimates the wind

speed less than 10%.

Upwind the mast the results are quite good and only in the first part of the profile, between the

terrain and 4‐7m high, the errors may reach 20‐25% (the part of the profile between 0m and 1m

a.g.l. is ignored, here the errors may reach 50%). Depending on the sites, the lowest difference is

located between 7m and 20m a.g.l. Generally, above this level WAsP tends to underestimate the

wind speed with a value of roughly of 7‐10%.

Downwind the mast the issue of fluid separation is more relevant and, starting where the

ruggedness overcomes the value of 15‐20%, generally WAsP overestimates the wind speed in the

lowest part of the profile and underestimates it in the highest part, as upwind the mast. In the

area between 100m and 250m downwind the mast, WAsP overestimates the velocity for the

height of fluid separation, where the wind speed slows down. That height reaches 25‐30m for the

section 250m downwind. The last three sections are disregarded because of the influence of the

air flux coming from the gorge.

0

10

20

30

40

50

60

70

80

90

100

‐20 0 20 40 60 80 100 120 140

(m)

∆V/Vk‐ε(%)

Comparison WAsP vs CFD downwind the mast (330°N)

mast

downstream 50m

downstream 100m

downstream 150m

downstream 200m

downstream 350m

downstream 450m

downstream 550m


53

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 100m

CFX upstream 100m

0

10

20

30

40

50

60

70

80

90

100

‐2 3 8

(m)

Wind speed (m/s)



CFX downstream 100m

5.2 300°N wind direction The second comparison is about the 300°N wind direction. The images below show some wind

speed profiles from WAsP (red) and from Ansys CFX (blue). Here only the most interesting

sections are shown, see appendix D for the other wind speed profiles.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 400m

CFX upstream 400m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP mast

CFX mast


54

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 1000m

Figure 5‐6 WasP vs CFX speed profiles for the 300°N wind direction.

The figures above show the qualitative difference between the two softwares, in particular

upwind the mast, WAsP underestimates the wind speed. At the mast, the profiles are slightly

different, the speed‐up from WAsP is at 20m and at that height the difference with CFD are

minimal. Downwind the mast there are more differences, due to the fluid separation and to the

known air flux coming from the gorge. For example the section 100m downwind the mast is

located at 47m a.s.l., about 30m lower than the mast (slope of 0.3, that value is usually

considered the critical slope). In the figure regarding the 400m downwind section, note that the

profile is strongly affected by the air flux coming from the gorge. Even WAsP, as shown previously

and here confirmed, predicts that air flux. Appendix E includes wind roses and distributions at the

height of 20m at the section 400m and 700m downwind the mast, where the CFD simulation

predicts the presence of an air flux from the gorge. This simulation shows results very similar to

the previous one. The images below show the difference, in percentage, between the speed

profiles obtained from the two softwares. The first image is referred to the profiles upwind the

mast. In the first two sections the difference is very low ( a few percent) after 10m of height, but

in the other sections the it tends to reach an underestimation of 10% at 100m. In the lowest part

of the profile, disregarding the first few meters, the difference can be more than 30%. Generally

the underestimation is less than 15% above 20m.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 400m


55


Regarding the sections downwind the mast the differences are quite huge for most of them. In

the figure below only some of them are shown, the complete figure is in the appendix F. The

section 50m downwind the mast shows a behavior similar to the previous ones, with an

underestimation of about 10%. The sections between that one and the 700m downwind are not

taken into account to have a clear figure. The last three sections show a slight affection by the air

flux coming from the gorge and from the presence of the reef. In fact, the wind is pulled by the

presence of the very steep slope of the hill to turn to the right and to rise. In this area of the

domain the vertical component of the wind speed increases its value and, consequently, it makes

the horizontal component slow down. Because of this change in direction of the wind, WAsP and

Ansys CFX cannot be compared. Anyway, it is possible to show qualitatively that WAsP predicts

this direction change looking at the wind rose and distribution, but it is not possible to make a

numeric comparison.

0

10

20

30

40

50

60

70

80

90

100

‐40 ‐30 ‐20 ‐10 0 10 20

(m)

∆V/Vk‐ε(%)

Comparison WAsP vs CFD upstream the mast (300°N)

upstream 700m

upstream 550m

upstream 400m

upstream 250m

upstream 200m

upstream 150m

upstream 100m

upstream 50m

mast


56


5.3 120°N wind direction The third comparison concerns the 120°N wind direction. The images in the following pages show

some wind speed profiles from WAsP (red) and from Ansys CFX (blue). Here only the most

interesting sections are shown, see appendix for the other wind speed profiles.

Note this wind direction is the opposite to the previous one, but in this case the wind speed

profile over the sea zone between the two hills is not affected by any south‐going wind

component. Anyway, a towards west component affects in the first sections, due to the steep

reef. The influence of this reef is present in all the sections until 400m upwind the mast. In the

first image the wind profile is modified for the first 20m. In the second image note the wind

behavior (in the lowest part of the profile, close to the ground) predicted by WAsP for the section

where a roughness and slope change is present.

0

10

20

30

40

50

60

70

80

90

100

‐50 ‐30 ‐10 10 30 50

(m)

∆V/Vk‐ε(%)


mast

downstream 50m

downstream 700m

downstream 850m

downstream 1000m


57

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 550m

CFX upstream 550m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 100m

CFX upstream 100m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 250m

CFX upstream 250m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP mast

CFX mast


58


In the area surrounding the mast the WAsP estimation, for the first part of the profiles, is always

confirmed by the CFD simulation. In fact in the sections 100m upwind and downwind the mast the

wind speeds are very close to each other up to 20‐30m. Above this level the CFD simulation

predicts a higher velocity than WAsP. The mast site shows a speed‐up at about 10m high and a

vertical profile above that. In the last image the wind speed is affected by a coastline air flux

coming from the east, that modifies the shape in the lowest part. The stream wises tend to turn

to the left, pulled by the flux and that decreases the velocity component in the analyzed direction.

Note that even in this case WAsP underestimates the wind speed for most of the sections.

The images below confirm the general WAsP underestimation of about 10% in the highest part of

the speed profiles and a non‐homogeneous behavior in the lowest part. There, disregarding the

first few meters, the difference may reach more than 30% depending on the section. For example

the error for the section taken 1000m and 850m upstream the wind is more than 30% close to the

sea level and after 5m it becomes roughly 2% for all the profile height left.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 100m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 550m


59


Downwind the mast the wind behavior is very similar for the first 100m of distance, after that the

effects of fluid separation become relevant and the differences increase in the lowest part of the

domain, where WAsP doesn’t predict the recirculation zone. The last three sections are affected

by the coastline air flux and the comparison doesn’t make sense. Also in this case a qualitative

comparison is made by looking at the WAsP wind roses and distributions at those places and the

conclusion is the same as found for the other simulations: WAsP predicts an increase in the mean

wind speed coming from the direction that Ansys CFX considers the wind direction.

0

10

20

30

40

50

60

70

80

90

100

‐40 ‐30 ‐20 ‐10 0 10 20 30 40

(m)

∆V/Vk‐ε(%)


mast

upstream 50m

upstream 100m

upstream 150m

upstream 200m

upstream 250m

upstream 400m

upstream 550m

upstream 700m

upstream 850m

upstream 1000m


60


5.4 90°N wind direction The fourth comparison regards the 90°N wind direction. The images below show some wind

speed profiles from WAsP (red) and from Ansys CFX (blue). Here only the most interesting

sections are shown, see the appendix D for the other wind speed profiles.

Note that in this case WAsP overestimates the wind speed for most of the sections at the

analysed height. For the first shown section WAsP predicts a speed‐up, even though it is the top

point of an escarpment 80m high. The successive image refers to a section taken at 40m a.s.l. and

is strongly affected by the fluid separation along the slope of the hill. The wind slows down

reaching negative velocity values.

0

10

20

30

40

50

60

70

80

90

100

‐20 ‐10 0 10 20 30 40 50

(m)

∆V/Vk‐ε(%)


downstream 700m

downstream 550m

downstream 400m

downstream 250m

downstream 200m

downstream 150m

downstream 100m

downstream 50m

mast


61

0

10

20

30

40

50

60

70

80

90

100

‐2 0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 550m

CFX upstream 550m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 700m

CFX upstream 700m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 200m

CFX upstream 200m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP mast

CFX mast


62


In the third image, the fluid separation after the escarpment still affects the wind profile (note

that for this direction the width of the sea is less than 250m and the escarpment 200m long and

80m high). This section is located 30m a.s.l. and WAsP predicts a speed‐up very close to the

terrain. Focusing on the mast site, the figure shows that the profile is still affected for the first

50m by the fluid separation and turbulence generated by the escarpment. WAsP predicts a speed‐

up at the height of 10m. The last two sections are characterized by similar profiles even if at 150m

downwind the mast is located in a little valley (62m a.s.l.) and the last one in one of the highest

points of whole the area (90m a.s.l.).

The image below, referring to the per cent wind speed difference between WAsP and Ansys CFX,

shows a similar behavior to the previous ones. The per cent difference for the first section is

almost zero above 20m, after that section the difference increases until the 250m upstream

section (the per cent difference for this profile is 20% at 100m). In the successive sections the

difference decreases gradually and at the mast it is less than 20% at 25m. For this wind direction

the case is different from the other ones because a downhill slope is followed by a uphill slope. As

seen, that for the other cases WAsP generally underestimates, whereas here it overestimates.

Note that the underestimation tends to a 10% difference, whereas the overestimations to 0%.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 150m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 550


63


Regarding the downwind sections, the WAsP predictions are better than in the upwind zone.

Anyway, the differences are always quite high and only above 30m the values are less than 20%

(only above 50m the values are less than 10%). The differences decrease leaving the mast, even if

for the first few meters of the profile an underestimation is present (it can reach values higher

than 60%). For the last section the difference is always less than 15%.

0

10

20

30

40

50

60

70

80

90

100

‐40 ‐20 0 20 40 60 80 100

(m)

∆V/Vk‐ε(%)


upstream 850m

upstream 700m

upstream 550m

upstream 400m

upstream 250m

upstream 200m

upstream 150m

upstream 100m

upstream 50m

mast


64


0

10

20

30

40

50

60

70

80

90

100

‐30 ‐20 ‐10 0 10 20 30 40 50

(m)

∆V/Vk‐ε(%)


mast

downstream 50m

downstream 100m

downstream 150m

downstream 200m

downstream 250m

downstream 400m

downstream 550m


65

5.5 WAsP “prediction error” and site ruggedness This section presents the analysis of the relation between WAsP wind speed “prediction error”

and difference in extent of steep slopes (RIX values in %) for various wind directions. Mortensen

and al. [1] found a linear correspondence between the orographic performance indicators, ∆RIX,

and a function of the prediction‐error for the site in Portugal. In this section an analysis is made,

considering the CFD results as measured wind data and the WAsP‐CFD differences as prediction‐

errors, in order to find a correspondence with the Mortensen’s study[1]. Following this approach,

in the figure below the wind speed prediction‐errors and the orographic performance indicators

are shown in percentage. The wind speed prediction error is calculated as ln(UWAsP/UCFD), where

UWAsP is the wind speed from WAsP and UCFD the speed estimated from the CFD simulations. The

prediction errors are calculated averaging all height of the profiles up to 100m.

The figure below regards only the met mast site and the prediction errors for the four studied

wind directions. Only the mast location is here analyzed because, in order to compare the results

obtained with the other ones from various authors, the site characteristics should be as similar as

possible. For example, in the site in Portugal the cross‐predictions are among five masts and they

are located on the top of hills or mountains, hence the site of the met mast is here analyzed. The

parameters used for calculating the ruggedness are: calculation radius R = 600m, critical slope θc =

0.3 and number of radii N = 72.

Figure 5‐15 WAsP wind speed prediction error vs difference in ruggedness indices (all directions).

y = ‐2.4532x + 0.1175R² = 0.8617

‐0.15

‐0.1

‐0.05

0

0.05

0.1

0.15

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

Ln(U

WAsP/U

CFD)

dRIX


66

This figure says that increasing the ∆RIX, WAsP underestimates the wind speed, whereas

decreasing the ∆RIX, it overestimates the wind speed. This goes against that one found by

Mortensen in [1]. In the Portugal study the interpolation line pass by the origin of axes with

inclination 1.5.

Actually, the site of Sarfannguaq and the applied method have characteristics different from the

previous one studied by Mortensen [1]. The first difference is in the calculation of the speed error,

in the Portugal study it was calculated at a determinate height, whereas in this one it is averaged

for a height of 100m. The second difference is the presence of the sea, in fact this completely flat

surface all around the site modifies a lot the RIX and, consequently, the ∆RIX and this fact have to

be taken into account. Moreover, the fact that the analysis comprises only four points should also

be considered and, last but not least, the input in WAsP is only one wind direction each time.

Anyway, in order to find a relation between the ruggedness and the prediction error a deeper

analysis is made. The figure below shows the relation between the prediction error and the ∆RIX

calculated only for the wind direction and not all directions. As shown, the results are more

similar to the ones obtained by other authors (for all ∆RIX values, calculated with different

combinations radius‐slope, the inclination coefficient is between 1.4 and 1.7), but some

discrepancies are still present. In the figures, the parameters for calculating the ∆RIX are the

following: calculation radius R = 700m, critical slope θc = 0.3 and number of radii N = 72.

Figure 5‐16 WAsP wind speed prediction error vs difference in ruggedness indices (only wind direction).

R² = 0.9665

‐0.15

‐0.1

‐0.05

0

0.05

0.1

0.15

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln(U

WAsP/U

CFD)

dRIX


67

The table below shows the correlation coefficients calculated for various radii and costant θc =

0.3.

Radius 300m 400m 500m 600m 700m 750m 800m 900m 1000m 2000m

All directions

0.0083 0.7419 0.844 0.8617 0.8121 0.8086 0.6884 0.2671 0.163 0.0435

Wind direction

0.3301 0.2534 0.6372 0.8886 0.9665 0.9195 0.8792 0.9029 0.8816 0.5362

Table 5‐1 Correlation coefficient calculated for various radii.

Note all the trend line, calculated considering ∆RIX relative to all directions, always has negative

inclination, whereas the trend line calculated considering ∆RIX relative only to the wind direction,

always has positive inclination.

The table below shows the correlation coefficients for various critical slopes calculated for a

radius of 700m, for ∆RIX relative to all directions and relative only to the wind direction.

Critical slope 0.2 0.25 0.3 0.35 0.4 0.45

All directions 0.7201 0.6747 0.8121 0.9061 0.8964 0.9109

Wind direction 0.959 0.9883 0.9665 0.9608 0.9168 0.8534

Table 5‐2 Correlation coefficient calculated for various critical slopes.

The tables show that the correlation coefficients are quite strongly related to the radius and to

the critical slope. This means that the strong relation between errors and ruggedness, found for

the sites in Portugal, doesn’t give the same good results. It may be due to the different orographic

condition, to the different application method (average of errors for a height of 100m and not

error at defined height and short distance between points), to the uncertainties of the CFD

simulation and to the little number of data. Anyway, these results have be consider satisfactory,

taking into account the peculiarity of this analysis. Note the best results are obtained for a radius

of 700m, this distance is the mean distance between the original mast and the fictitious ones.

An additional analysis has been done in order to consider the wind speed prediction‐error for

various heights. In fact, the previous figure and tables refers to average of prediction‐error. In this

additional study we want to check if the relation between ∆RIX and prediction‐error is also valid

for different point of the wind speed profile.

The table below shows the correlation coefficients calculated for radius R = 700m, critical slope θc

= 0.25 and number of radii N = 72.


68

Height 10m 20m 30m 40m 50m 60m 70m 80m 90m 100m

All 0.6916 0.801 0.8206 0.7936 0.7306 0.6861 0.6468 0.5895 0.528 0.4755

Direction 0.9921 0.9395 0.9505 0.9615 0.9779 0.9954 0.9931 0.9774 0.9581 0.9353

Figure 5‐17 Correlation coefficient calculated for various heights.

The table shows the strong relation between ∆RIX calculated for the wind direction and the

prediction‐error for all heights, the correlation coefficient are always higher than 0.9353.

This analysis confirms the results from the previous one, and a correspondence, with the study

made by Mortensen [1], is present and this may confirm the good quality of the CFD results and

the WAsP prediction error‐ruggedness relation.

The last prediction error‐ruggedness analysis introduced regards most of the sites taken into

account in this report for comparing WAsP and Ansys CFX results. The analysis applies the same

method of the first one, but using the highest number of site available. Some of the sites are not

available because of strong speed component perpendicular to the main wind direction, which

affects the shape of the profile, or because the profiles from WAsP and Ansys CFX cross each

other and, in this case, doesn’t make sense averaging the prediction error for all height. The total

number of sites is 71 (including the met mast four times), for the 330°N wind direction there are

16 sites (7 are not used for the analysis), for the 300°N wind direction there are 19 sites (9 are not

available, they are the last six downstream the mast), for the 120°N wind direction there are 19

sites (11 are not available), for the 90°N wind direction there are 17 sites (6 are not available). At

the end, 33 sites are excluded from the analysis. The met mast is counted 4 times, one for each

direction. The parameters used for calculating ∆RIX are: radius R = 1000m, critical slope θc = 0.3

and number of radii N = 72.

The figure below shows the result of the analysis, the correlation coefficient doesn’t have a high

value, but considering the prediction‐errors are averaged all over the height, the peculiar site

characteristic and the CFD uncertainties, the results have to be considered satisfactory.

Other correlation coefficients have been calculated taking into account the prediction‐errors at

defined height and good results are obtained between 30m and 50m (correlation coefficient

higher than 0.8), with a maximum at 40m (correlation coefficient 0.9). Whereas, low correlation

coefficients are at the other height, the minimum is 0.54 at 100m. The peculiarity of this analysis

is that it takes into account sites upwind and downwind the hill top, and, for example, the


69

prediction error‐ruggedness study by Mortensen [1] is made using wind speed measurements

(met masts are not located in such sites).

Correlation coefficients are estimated using various ∆RIX calculating parameters, for a radius of

2000m it is 0.2, whereas for a radius of 750m it is 0.76.

5‐18 WAsP wind speed prediction error vs difference in ruggedness indices (most of the sites).

The results can be considered satisfactory and they may suggest the possibility to using CFD

simulations, in combination with WAsP, on sites poor of wind data, even in complex terrain,

where WAsP shows poorly performance.

R² = 0.8201

‐0.25

‐0.2

‐0.15

‐0.1

‐0.05

0

0.05

0.1

0.15

‐0.05 0 0.05 0.1 0.15 0.2 0.25

Ln(UW

AsP/U

CFD)

dRIX


70

Conclusion

Environmental issues, increasing oil prices and energy consumption demand renewable energy

resources from all over the world, Greenland included, where other issues has to be taken into

account for the small settlements.

Wind is one of the most competitive renewable energy resources and the electricity produced

using this source, increases every day. The first, and probably the most important step in

producing wind energy , is the resource assessment. Wind resource assessment is carried out by

using measured wind data and specific software programs. In order to have reliable results, it is

important have a lot of wind measurements and a good software. This paper deals with one of

the software used to estimate the wind energy resource all over the world, the WAsP. The

problems of this program are known and one of them is the poor performance in a complex

terrain. Hence, the necessity of improving the knowledge about the WAsP behavior in complex

terrain and the necessity to work on sites poor of wind data, urge to analyze case studies

thoroughly, in order to find or confirm relations between known parameters and to obtain

correction methods to estimate the wind resource correctly.

This paper deals with a particular case study, a complex terrain surrounded by the sea, and a

particular method is used in order approach the problem. In fact, CFD simulations are used to

compare the wind speed profiles with the WAsP ones in a site located in Greenland, where a plan

for installing one or more wind turbines was made some years ago.

The comparison between the two software programs shows satisfactory results and the most

interesting results come out by analyzing the relation between wind speed differences and

ruggedness of the area.

Regarding the comparison between WAsP and Ansys CFX, the results show the known WAsP

difficulty in predicting fluid separation, due to the simple computational model used by this

software. The biggest differences are located in the recirculation zones, and in most of the sites

analyzed discrepancies are relevant, the common wind speed difference is 10%, that is not an

acceptable value in wind resource assessing. This result agrees with other studies made on WAsP

in complex terrain.

Regarding the relation between wind speed differences and ruggedness of the area, interesting

results are obtained. In fact, a relation found by Mortensen [1] is here confirmed for the met mast

site and a quite similar relation has been found for most of the sites in the area surrounding the

mast. The correspondence with the Mortensen’s study [1], confirms that study and the possibility


71

of using CFD simulations in site poor of wind measuring station. These may be help WAsP in

evaluating more accurately wind resource in complex terrain.

Moreover, the results for the sites all around the met mast confirm the relation between wind

speed differences and ruggedness, even for different locations from the common hill top. In these

zones, the correlation coefficient shows a weaker relation than that one previously found, but the

relation seems to still exist.


72

A. Appendix: WAsP wind speed profiles.

Figure A‐1 WAsP Wind speed profiles upwind the mast (300°N wind direction).

Figure A‐2 WAsP Wind speed profile downwind the mast (300°N wind direction).

0

50

100

150

200

250

300

350

400

450

500

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP upstream 700m

WAsP upstream 550m

WAsP upstream 400m

WAsP upstream 250m

WAsP upstream 200m

WAsP upstream 150m

WAsP upstream 100m

WAsP upstream 50m

WAsP mast

0

50

100

150

200

250

300

350

400

450

500

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP mast

WAsP downstream 50m











73

Figure A‐3 WAsP wind speed profiles upwind the mast (120°N wind direction).

Figure A‐4 WAsP wind speed profiles downwind the mast (120°N wind direction).

0

50

100

150

200

250

300

350

400

450

500

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP upstream 1000m

WAsP upstream 850m

WAsP upstream 700m

WAsP upstream 550m

WAsP upstream 400m

WAsP upstream 250m

WAsP upstream 200m

WAsP upstream 150m

WAsP upstream 100m

WAsP upstream 50m

WAsP mast

0

50

100

150

200

250

300

350

400

450

500

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP mast

WAsP downstream 50m









74

Figure A‐5 WAsP wind speed profiles upwind the mast (90°N wind direction).

Figure A‐6 WAsP wind speed profiles downwind the mast (90°N wind direction).

0

50

100

150

200

250

300

350

400

450

500

0 2 4 6 8 10 12

(m)

Wind speed (m/s)


WAsP upstream 850m

WAsP upstream 700m

WAsP upstream 550m

WAsP upstream 400m

WAsP upstream 250m

WAsP upstream 200m

WAsP upstream 150m

WAsP upstream 100m

WAsP upstream 50m

WAsP mast

0

50

100

150

200

250

300

350

400

450

500

0 2 4 6 8 10 12

(m)

Wind speed (m/s)

Wind speed profiles (90°N)

WAsP mast

WAsP downstream 50m








75

B. Appendix: CFD wind speed profiles 330°N direction.

Figure B‐1 CFD wind speed profiles upwind the mast (330°N, k‐ε,roughness 0.01m).

Figure B‐2 CFD wind speed profiles downwind the mast (330°N, k‐ε,roughness 0.01m).

0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9 10 11

(m)

wind speed (m/s)


CFX upstream 450m

CFX upstream 350m

CFX upstream 250m

CFX upstream 200m

CFX upstream 150m

CFX upstream 100m

CFX upstream 50m

CFX mast

0

50

100

150

200

250

300

350

400

450

500

‐1 0 1 2 3 4 5 6 7 8 9 10 11 12

(m)

wind speed (m/s)

Wind speed profiles downwind the mast(330°N, k‐ε, 0.01m)

CFX mast

CFX downstream 50m

CFX downstream 100m

CFX downstream 150m

CFX downstream 200m

CFX downstream 250m

CFX downstream 350m

CFX downstream 450m

CFX downstream 550m


76

Figure B‐3 CFD wind speed profiles upwind the mast (330°N, k‐ε,roughness 0.05m).

Figure B‐4 CFD wind speed profiles downwind the mast (330°N, k‐ε,roughness 0.05m).

0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9 10 11

(m)

wind speed (m/s)


CFX upstream 450m

CFX upstream 350m

CFX upstream 250m

CFX upstream 200m

CFX upstream 150m

CFX upstream 100m

CFX upstream 50m

CFX mast

0

50

100

150

200

250

300

350

400

450

500

‐1 0 1 2 3 4 5 6 7 8 9 10 11 12

(m)

wind speed (m/s)


CFX mast

CFX downstream 50m

CFX downstream 100m

CFX downstream 150m

CFX downstream 200m

CFX downstream 250m

CFX downstream 350m

CFX downstream 450m

CFX downstream 550m


77

C. Appendix: Roughness comparisons.

Figure C‐1 Roughness comparison (0.01m vs 0.03m) .

Figure C‐2 Roughness comparison (0.05m vs 0.03m) .

0

5

10

15

20

25

30

35

40

45

50

‐10 ‐5 0 5 10 15 20 25 30 35

(m)

∆V/V0.03(%)


downstream 150m

downstream 200m

downstream 250m

downstream 350m

downstream 450m

downstream 550m

0

5

10

15

20

25

30

35

40

45

50

‐25 ‐20 ‐15 ‐10 ‐5 0 5 10

(m)

∆V/V0.03(%)


downstream 150m

downstream 200m

downstream 250m

downstream 350m

downstream 450m

downstream 550m


78

D. Appendix: Comparison WAsP vs CFD

1. 330° N wind direction

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)

Comparison WAsP vs CFD(330°N)

WAsP upstream 450m

CFX upstream 450m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 200m

CFX upstream 200m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP upstream 50m

CFX upstream 50m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8(m

)Wind speed (m/s)


WAsP upstream 350m

CFX upstream 350m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP upstream 100m

CFX upstream 100m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10

(m)

Wind speed (m/s)


WAsP downstream 50m

CFX downstream 50m


79

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 550m

Figure D‐1 WasP vs CFX speed profiles for the 330°N wind direction.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 150m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 350m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 200m


80


0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 700m

CFX upstream 700m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 250m

CFX upstream 250m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 150m

CFX upstream 150m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 50m

CFX upstream 50m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 550m

CFX upstream 550m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 200m

CFX upstream 200m


81

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP downstream 50m

CFX downstream 50m

0

10

20

30

40

50

60

70

80

90

100

‐2 0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 150m

0

10

20

30

40

50

60

70

80

90

100

‐2 0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 200m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 550m

0

10

20

30

40

50

60

70

80

90

100

‐2 0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 250m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 700m


82

Figure D‐2 WasP vs CFX speed profiles for the 300°N wind direction.


0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 850m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 1000m

CFX upstream 1000m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 700m

CFX upstream 700m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 850m

CFX upstream 850m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 400m

CFX upstream 400m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 400m

CFX upstream 400m


83

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 200m

CFX upstream 200m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 50m

CFX upstream 50m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 150m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 150m

CFX upstream 150m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP downstream 50m

CFX downstream 50m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 200m


84

Figure D‐3 2 WasP vs CFX speed profiles for the 120°N wind direction.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 250m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 700m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 400m


85


0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 850m

CFX upstream 850m

0

10

20

30

40

50

60

70

80

90

100

‐2 0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 250m

CFX upstream 250m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 100m

CFX upstream 100m

0

10

20

30

40

50

60

70

80

90

100

‐2 0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 400m

CFX upstream 400m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 150m

CFX upstream 150m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP upstream 50m

CFX upstream 50m


86

Figure D‐4 2 WasP vs CFX speed profiles for the 90°N wind direction.

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)


WAsP downstream 50m

CFX downstream 50m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 200m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 400

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 100m

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8

(m)

Wind speed (m/s)



CFX downstream 250m


87

E. Appendix: Wind roses and distribution.

Figure E‐1 WAsP Wind rose and wind distribution at 20m high at 400m downwind the mast.

Figure E‐2 WAsP Wind rose and wind distribution at 20m high at 700m downwind the mast.


88

F. Appendix: Comparison WAsP vs CFD (300°N wind direction)

Figure F‐1 Wind speed profile comparison downwind the mast (300°N).

0

10

20

30

40

50

60

70

80

90

100

‐100 0 100 200 300 400

(m)

∆V/Vk‐ε(%)


mast

downstream 50m

downstream 100m

downstream 150m

downstream 200m

downstream 250m

downstream 400m

downstream 550m

downstream 700m

downstream 850m

downstream 1000m


89

References

1. Niels G. Mortensen, Anthony J. Bowen and Ioannis Antoniou. Improving WAsP predictions

in (too) complex terrain.

2. Kurt S. Hansen. Database of wind characteristic. Website. http://www.winddata.com/

3. Kurt S. Hansen, Martin O.L. Hansen & Poul Linnert. Operational experience with 6

meteorological stations installed in Sisimiut and Umanak.

4. Memon and Venkata Ratnam Kondreddi. Wind Resource Assessment in Complex Terrain

Using CFD. Msc. thesis.

5. P.A. Taylor and H.W. Teunissen. ASKERVEIN ’82: Report on the September/ October 1982

Experiments to Study Boundary Layer Flow over Askervein, South Uist. Technical report

MSRB‐83‐8, Atmos. Environ Service, Downsview, Ontario, 1983.

6. Karl.J.Eidsvik. A System for Wind Power Estimation in Mountainous Terrain. Prediction of

Askervein Hill Data. Wind Energy 2005;8:237‐249.

7. Niels.N. Sørensen . General purpose Flow solver Applied to Flow Over Hills. Risø‐R‐827(EN). 1995.

8. Fluent Inc. Gambit help, version 2.4.6. 1988‐2009.

9. Niels G. Mortensen and Lars Landberg. Vindenergi i udvalgte byer i Grønland:

Qasigiannguit Sisimiut og Narsaq. Risø‐I‐718(DA). September 1993.

10. Risø National Laboratory. European Wind Atlas. 1989.

11. Risø National Laboratory, Wasp help. WAsP9. 1987‐2009.

12. ANSYS Europe Ltd. Ansys CFX help, version 11. 1996‐2007.


90

13. MapInfo Corporation. MapInfo Professional 8.5 help. 1985‐2006.

14. SAGA User Group Association. SAGA help, version 2.0.3. 2005‐2008.

15. H. K. Versteeg and W. Malalasekera. An introduction to Computational Fluid Dynamics.

Second Edition. 2007.

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