International Congress Wind Engineering-ICWE15

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
Experimental and Numerical Evaluation of Micrositing in Complex Areas:
Speed up Effect Analysis
Mattuella, J.M.L.1
, Loredo-Souza, A.M.1
,Vecina,T.D.J.2
Petry, A.P.2
1
Department of Civil Engineering, Laboratório de Aerodinâmica das Construções -LAC
2
Department of Mechanic Engineering
1,2
Universidade Federal do Rio Grande do Sul-UFRGS, Porto Alegre, Brazil,
jussara.mattuella@ufrgs.br, acir@ufrgs.br, promec@ufrgs.br, adrianep@mecanica.ufrgs.br,
1 ABSTRACT
Wind mapping is essential in various wind energy and wind engineering applications. For wind energy assessment purposes,
micrositing in complex areas represents challenging projects in the identification of intercurrent phenomena. Complex
topography such as ridges, hills and cliffs affects the airflow and direction, leading to deceleration or acceleration of the wind in
a short distance with growth of the turbulence intensity [1] [2] On the other hand, the wind velocity on the crest of the hills is
higher than on the plain area, since the wind increases with height and on the ridges, which allows a wide exposure to the
predominant wind from all directions. In addition to these factors, speed up effect occurs on the crest of topographic forms [2].
Such places may have favorable wind potential to install micrositings provided that the turbulence intensity determined by
topography is assessed [3]. In order to identify these special conditions, physical modeling in wind tunnel[4] and Computational
Fluid Dynamics-C.F.D are possible complementary tools [5]. This research presents the results of measurements and modeling
of speed-up effects for the mean horizontal velocity and the turbulence intensity profiles above the crest of eight symmetrical
hill models with slopes of 25o, 32o, 52o and 68o for two law exponents, corresponding to p=0.11 and p=0.23 [6]and compared
with those in the undisturbed (no-hill) boundary layer and those downwind of the hill. It focuses on comparing both methods to
analyze the speed up factor on the crest of the hills and to show how the incremental velocity may be decisive to install
micrositing on complex terrains.
KEY WORDS: Computational fluid dynamics, Complex terrains, Numerical simulation, Speed-up effect, Wind-
tunnel experiment, Wind-tunnel modeling
2 1.INTRODUCTION
Areas with diverse levels of complexity concerning topography, roughness and surrounding elements are increasingly common
in wind power projects. Complex micrositing may cause, at the same time, speed-up effect on the top of the hills, as well as flow
separation and recirculation downwind of the hill. While the wakes determined by topography and other turbines may represent
a challenging undertaking of the airflow, the speed up effect may present greater wind power across the flat areas, which
determine that such places can be references to install micrositings.
Terrain geometries can determine different flow patterns. Flow on complex terrain varies unpredictably, depending on daily and
seasonal variations on the thermal stability of flowing air masses. Geophysical phenomena such as thermal stratification and
Earth's rotation can add to the complexity [7]. The study about the development of the turbulent boundary layer is fundamental
for analyzing the micrositing, especially in areas characterized by variable topography. When the topographic characteristic is
sufficiently abrupt around 30 to 40%, a strong adverse pressure gradient occurs, which causes the deceleration at the base
windward. This fact gives rise to the local pressure adverse gradient. Thus, in order for a flow separation to take place, two
conditions are critical: the average velocity and its gradient must be zero, simultaneously, at the same point. These critical points
are called "separation points," and determine the instability of the flow and the start of the turbulence process. The different
wind profiles caused by turbulence intensity, the increase in the wind speed at the top of the hill, the extent of the recirculation
wake, and the reattachment length of the airflow are the key aspects for micrositing in complex areas.
Determinant variables in a project such as mean wind speed, extreme wind speed, turbulence intensity, and wake turbulence
remain not sufficiently defined by the traditional methods of wind energy assessment such as anemometric towers. In complex

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
areas, mainly in sloppy terrain, such measurement is not enough to analyze the assessment of the local wind resource, and
classical linear models fail to predict the extrapolation of the wind [3]. Anemometric towers provide a punctual measurement
with limited representativity in the surrounding. In order to have a comprehensive understanding of the intercurrent phenomena
of the airflow on the complex area, it is necessary to add a tool appropriate to airflow modeling, such as wind tunnel
experiments and C.F.D numerical analysis, where most of airflow parameters are possible to estimate[4] [5]. Hence, current
research in wind energy consists of field measurement in anemometric towers as a basic methodology for data measurement of
wind parameters, being numerical simulations and wind-tunnel experiment complementary tools, which are essential
methodologies to validate the wind flow and turbulence intensity profile in a micrositing layout.
2. STUDY ON FLOW OVER COMPLEX TERRAIN-Models and methodologies
The foundation for the study of atmospheric boundary layer flow over complex terrain was laid in the early 1970s. In 1975, an
analytical model for a two-dimensional flow over a low and isolated hill was developed [7]. The dimensions of the hill were
such that “d<<L”, where d is the height of elevation and L is the characteristic length of the hill in the direction of the flow. The
surface roughness zo was considered uniform and without separation of the airflow. The results obtained testified that the wind
speed and the cutting forces were proportional to the size, shape and roughness of the hill for slopes where “d/L <<1” and that
within these limits it would be possible to employ the equations of fluid movement [8].Such results remain valid until the
present day and have been confirmed by different methods. Basically, the flow over an isolated hill can be described by the
increment in wind speed at the crest of the hill, called the speed-up effect, and the associated deceleration of the flow on the
leeward side of hill, with the beginning of the formation of turbulence area and airflow detachment. Flow recirculation occurs at
the foot of the hill determining the so- called wind wake [8]. In the investigation of complex terrain, numerical models were
more extensively employed in the 70s. The results of that period suggested that the most relevant changes in the turbulence
intensity characteristics occur in the wake area downwind of the hill, where the transfer of energy to higher frequencies is more
evident [9]. In the 80s, the assessment of the flow behavior on large hills such as Blashaval, Askervein and others confirmed the
thesis of 1975 [8]. Britter, Hunt and Richards (1981) showed that the speed up effect at the crest of the hills was due to both the
slope and the surface roughness [10].
Kim et al. (1997) sought to validate mathematical models for airflow behavior forecast on hills with performing experiments in
tunnel and numerical simulations on complex terrain. Such experiments improved the understanding of the wake recirculation in
the downwind. Comparisons obtained by experimental results with numerical simulations matched both the average speed
values and pressure distribution [11].
In the 20th Century, Kim and Patel, H. showed the characterization of the airflow phenomenon, especially according to its
detachment and reattachment over topographic models in 2D and 3D analysis. The results obtained indicated that Reynolds
Stress increased rapidly in regions of the boundary layer with a strong adverse pressure gradient. Such increase remained even
when the flow reached the reattachment point, when turbulence decreased at the same time, and when the mean velocity profiles
started to recompose [12].
In the 21st century, Kastner and Rotach presented wind tunnel measurements compared with Laser-Doppler velocimetry in
2003. In this study, a detailed model of an urban landscape has been re-constructed in the wind tunnel and the flow structure
inside and above the urban canopy was investigated. Vertical profiles of all three velocity components were measured with a
Laser-Doppler velocimeter, and an analysis of the measured mean flow and turbulence profiles was carried out. The results
showed that the mean wind profiles above the urban structure follow a logarithmic wind law. Inside the roughness sub layer, a
local scaling approach results in good agreement between measured and predicted mean wind profiles [13].
Several methods are accessible to model the wind in the atmospheric boundary layer (ABL) both at meso and at micro scale
levels. Micro scale models focus on the atmospheric processes at the lowest part of the ABL and do not exceed the length of
one kilometer. In this case the Earth’s rotation is not taken into consideration and the flow is commonly considered neutral. In
this scale the experiments are developed in a wind tunnel and constructed through numerical methods to predict wind fields in
the surface layer [12].
The numerical methods, based on mass-conservation and Navier-Stokes equations, Computational Fluid Dynamics (CFD)
models, such simulate de ABL modeling turbulent flow with the use of Reynolds Averaged Navier Stokes (RANS)
or Large Eddy Simulation (LES) [14].
Research in tunnels. play an important role on the definition and validation of micrositings, especially in the study of

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
the wind velocity profiles, turbulence intensity profiles and wakes, both coming from the topography, as well as
from the other wind turbines. Modelling and evaluation of the wind velocity, the turbulence intensity profiles on
complex micrositings for wind power assessment using numerical and experimental methodology compared has
been recently reported by Mattuella, Loredo-Souza and Petry [5].
2.1 Speed up effect
Speed up effect is an increment in velocity that often occurs on the top and near the top upwind and downwind of topographic
features, such as escarpments and ridges. This happens because the wind is compressed upwind, and when it reaches the top of
the hill, it expands in the low pressure area on the lee side of the hill. The velocity over the topography is written according to
Eq 01.
),()(),( zxUzUozxU  Eq.(01)
where
)(xUo is the mean velocity of the incoming flow upstream the terrain feature.
The speed-up effect may be quantified by the fractional speed-up ratio, which is defined as the fraction of the change in the
velocity in relation to approaching undisturbed velocity at each height and as follows Eq. 02 [15].
)(
),(
)(
)(),(
),(
zUo
zxU
zUo
zUozxU
zxS




Eq.(02)
3.THE EXPERIMENTAL SET-UP
The present experiments were conducted at Joaquim Blessmann Atmospheric Boundary Layer Wind Tunnel located at
Laboratório de Aerodinâmica das Construções(Construction Aerodynamic Laboratory) of Universidade Federal do Rio Grande
do Sul(Federal University of Rio Grande do Sul), Brazil. The LAC Wind Tunnel is a closed return low speed wind tunnel,
specifically projected for the dynamic and static studies on civil construction models. Its design allows the simulation of the
natural winds main characteristics. It has a length/height ratio on the main test section greater than 10 and dimensions of 1.30 m
x 0.90 m x 9.32 m (width x height x length), as shown in Figure 01. The maximum wind speed in this chamber, with soft and
uniform flow is 42 m s^-1 (150 km h^-1). The propeller is driven by a 100HP electric motor and the wind speed is controlled by
a frequency inverter. The data acquisition is performed using a Dantec Dynamics anemometer, System 90 Streamline N S. The
system uses a constant temperature anemometer as reference. The hot wire probe and a calibrator are automatically integrated to
the computer. The frame also has an input for a temperature sensor, which is designed to measure the flow temperature (fast
fluctuations).The signal registration was done by the probe and the effective data acquisition was performed with the use of the
Stream Ware application software, from same brand. The frequency was 2 kHz, and the acquisition period was 64s. The data
acquisition program is from Dantec Dynamic, and it also enables its own calibration and data accumulation. Mano Air 500
equipment measures the pressure difference between the piezometric rings, which are situated at the entrance of the tunnel and
measure the average temperature inside it at an interval of 65s [5].
The Boundary Layer Wind Tunnel Prof. Joaquim Blessman – floor plan is shown in Figure 01.
Figure 1. Boundary Layer Wind Tunnel Prof. Joaquim Blessmann – Floor Plan

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
3.1 Experimental model
The experimental program performed at the cited tunnel focused on the boundary layer behavior at the crest of the symmetrical
hill models with different slopes for two types of terrain, according to NBR standard, Category I - plain and Categories III/IV -
moderated roughness. For this examination were employed eight hills models, (four 2D and four 3D) in a reduced 1:1000 scale.
The models were constructed in layers in order to minimize the effects of the Reynolds number. This technique is used in
several laboratories around the world. The experimental measurements were realized with 20 (twenty) height measurements. For
each height a total of 131,072 wind speed values were collected. Figure 2 (a,b,c,d)shows the 2D/3D models inside the test
chamber [16].
2D(a) 3D(b) 2D(a) 3D(b)
(a)Model A – 25º slope (b)Model B – 32º slope
2D(a) 3D(b) 2D(a) 3D(b)
(c)Model C – 52º slope (d)Model D – 64º slope
Figure. 2. Experimental Models 25o
, 32o
, 52o
and 64o
in 2D/3D inside the wind tunnel chamber.
In order to estimate the surface flow over hilly terrain, velocity profiles were measured at the crest of eight models, as shown in
Tables 1 and 2. In each point, both profiles p=0.11 and p=0.23 were recorded at twenty (20) heights of measurement: 10, 15, 20,
25, 30, 40, 50, 70, 100, 130, 160, 200, 250, 300, 350, 400, 450, 500, 550 and 600 (cm). For each height of a given point,
131,072 values were collected. The experimental measurement data obtained were individually analyzed and compared
regarding the behavior of the wind and turbulence intensity profiles at the crest of the hill. Afterwards, they were confronted in
order to establish the correlations among them for two different types of terrain analyzed and according to the use of a bi or
tridimensional models. Tables 1 and 2 show the width, length and height of the model hills evaluated, as well as the
measurement point coordinates at the crest of the model hills. The bidimensional models have the same length values
corresponding to l=1290. All the models have the same height correlating to h=150 mm.
Table 1. 2D Model Dimensions.
Model
width-
w(mm)
Measurements points coordinates
(mm)
Floor plain and cutting plane
A 784.98 l=392.49;c=685;h=150
B 561.74 l=280.87;c=685;h=150
C 280.86 l=140.43;c=685; h=150
D 140.44 l=70.22;c=685; h=150
Table 2. 3D Model Dimensions.
Model
width-
w(mm)
Measurements points coordinates
(mm)
Floor and cutting plan
A 800 w=400; h=150
B 600 w=300; h=150
C 300 w=150; h=150
D 150 w=75; h=150

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
4. NUMERICAL METHODOLOGY
The numerical simulation in this paper uses the method of finite volume to solve the Reynolds Averaged Navier-Stokes
equations, two turbulence models are used, the k-ε and the k-ω SST [16]. Both simulations are performed through the ANSYS-
Fluent 13.0 software. The working fluid is air, as an ideal gas at 25 °C. The method of solution used is Pressure Velocity
Coupling SIMPLE and the convergence criterion is set at 1x10-5.
The simulations are developed through the computational representation of the same geometries of the experimental analysis in
Prof. Joaquim Blessmann wind tunnel [8], as shown in Figure 3 and Figure 4.
(a) (b)
Figure 3: Computational Model 2D.
(a) (b)
Figure 4. Computational Model 3D.
The computational domain is discretized in finite volumes. Ansys ICEM-13.0 software was used to generate the mesh, which
will then be used for the simulations and for obtaining the results. The discretization is done using hexahedral finite volumes
with refinement near the surface. For each case, two-dimensional and tree-dimensional hills, four meshes are created varying the
number of finite volumes, in order to evaluate their quality, for both cases the mesh adopted have about 1,500,000 of volumes,
Figure 6 represents the discretized computational domain.
Figure 5 – Computational domain, 2D and 3D model meshes
As boundary condition of inlet, it is used the velocity profile with the same characteristics of the experiments in the wind tunnel
to reproduce results closer to the experimental data. According to experimental data, the bottom of the tunnel elements have
such roughness that the power law expressed with p = 0.23 and the upper part where no roughness elements is used a value of p
= 0.11 for the velocity profile. The input profile (Eq. 03 and Eq. 04) is created in C language as User Define Function (UDF).
The tunnel walls are made of wood, so the prescribed roughness set for wood is 0.0009 m, and the ground uses a wall, no-slip,
boundary condition.
( )
|( ) | Eq. (03)

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
( )
|( ) | Eq. (04)
Table 3 shows the CFD cases analyzed, for 2D model steady state flow is solved with the two turbulence models, for
3D model five case are presented, two cases for steady state and three cases for transient analysis. At 3D model Case
V is simulated with prescribed roughness at the model surface.
Table 3 – CFD analyzed cases.
Case Regime esc. Modelo Turb.
I 2D Steady k-ε
II k-ω SST
III
3D
Steady
k-ε
IV k-ω SST
V k-ω c/ rug.
VI Transient k-ε
VII k-ω SST
5. RESULTS AND DISCUSSION
The experimental profiles were compared regarding the behavior of the wind profile at the crest of the hill in order to establish
the correlations among them for Categories I and III-IV, related to the power law exponents p=0.11 and p=0.23 according to
Brazilian Standard Code NBR 6123[6].in 2D/3D. To calculate the mean velocity, it was considered the value of the basic wind
velocity (vo m/s), and the normalization of the roughness factor according to NBR 6123[5]. The velocity was normalized by Δpa
(mmH2O). The profiles of mean stream-wise velocity over the crest of the models are plotted in Fig 6 and show the comparison
among the experimental profile measurements at the crest of the hills models, for each model separately.
(a) Model A - 25º (b) Model B - 32º
(a) Model C - 52º (b) Model D - 64º
Figure 6. Comparative study of experimental wind profiles among the models for p=0.11 and p=0.23 in 2D/3D.

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In this scenery one can analyze and compare the speed-up effect, which occurs in such points. According to the graphs one can
see that the most significant difference in measurements performed on the 2D models comparatively to 3D models mainly
occurs from Model C on and for both to p=0.11 and p=0.23. One can also see that the best speed up configuration occurs in
Models D for p=0.23.
Next, the profiles of mean stream-wise velocity over the crest of the models are plotted in comparison separately p=0.11 and
p=0.23 in Fig. 7(a) and (b).
(a) p=0.11-2D/3D (b) p=0.23-2D/3D
Figure 7 Comparative study of experimental wind profiles among the models in 2D/3D for p=0.11 and p=0.23.
One can see in Figures 7 (a)(b) that models in 2D may lead to a greater difference in the wind velocity profiles from Model C
on.
In the sequence, the measured profiles above the crest of the hill were contrasted with the undisturbed stream flow for
p=0.11/p=0.23 with focus on the speed-up modeling and how it may vary under different conditions (slopes, roughness and
2D/3D). Figure 8(a,b,c,d) presents the comparison study for each model.
(a)Model A-25º (b)Model B-32º
(c)Model C-52º (d)Model D-64º
Fig. 8 Comparative study of experimental wind profiles with the reference profiles for p=0.11 and p=0.23, both in 2D/3D
models.

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
According to Figure 8, in all the models for two types of potential law and for 2D/3D we can identify that the mean horizontal
velocity profiles at the crest increased in comparison with reference profiles on a plain terrain. Such incremental velocity on the
crest of hills is named speed up effect. It is possible to verify that the 3D measurements show more intense speed up effect,
being more evident in Models C and D, for both p=0.11 and p=0.23.
The speed up factor varied from 7.14% to 14.28% for p=0.11 and from 20% to 25% for p=0.23, being the best definition in
models C and D and in 3D.
The flow at the crest of the hills is subjected to the influence of an adverse pressure gradient, which occurs due to the airflow
detachment at the crest of the hill and determines the turbulence intensity. It is necessary to consider these parameters to define
the installation of the micrositing at the crest of the hills. Turbulence intensity is calculated according to Eq. 05, where σu is
standard deviation of the wind speed and U(z) is the mean wind speed profile.
)(
)(
zU
zI u
u


Eq.(05)
Figure 9(a,b,c,d) presents the comparison among the turbulence intensity profiles at the crest of the models with profile 1-
reference profile on plain terrain. Model B also compares with profile 2-downwind profile, which is plotted 200 m horizontally
at the crest for p=0.11/p=0.23.
(a) Model A-25º (b) Model B-32º
(c)Model C-52º (d)Model D-64º
Figure 9 Comparative study of turbulence intensity profiles for p=0.11 and p=0.23, both in 2D/3D.
The turbulence intensity analysis show that the values at 100 m height varied from lu=0.02 to 0.05 for p=0.11, and from lu=0.07
to 0.10 for p=0.23 according to the slope and models 2D/3D. The figures show a decrease in the turbulence intensity at the crest
of the hill compared to the profile 1-reference profile. The reason for this decrease is the increase in mean velocity [15]. The
maximum changes of the turbulence intensity were found to occur in the ridge leeside.
Model B comparison shows that the highest value of turbulence intensity occurs downwind of the hills, which reinforces the
speed up thesis.

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
Numerical results for the velocity profile on the top of the hill, for the seven different cases are, compared with
experimental data, as shown in Figure 10. In the simulation of flow over 2D geometry with turbulence models
k-ε and k-ω SST results are almost the same for the profiles, the mean difference between the first model results and
experimental values are 5.07% and 6.38% for the last one. For the 3D hill, results for the velocity profile obtained
with turbulence models k-ε, k-ω SST and k-ω SST with roughness prescribed at the surface of the model are
presented, and the mean difference between experimental data and numerical values are 3.51% for the first model,
3.44% for the second one and 2.3% for the k-ω SST with roughness prescribed. It is possible to verify that the best
results are obtained for the simulations with the k-ω SST with roughness prescribed at the surface of the model.
Figure 10. Numerical and experimental velocity profiles over the point at top of the 2D and 3D models.
Numerical results are suitable to analyze the flow topology. Figure 11 shows the velocity field at a plane over the
center line of the domain for both cases of the 2D model, Case I and Case II, defined in Table 3. It is possible to
identify in the images the speed-up over the top at the hill and the wake region after the model, with slow velocity.
At the velocity field is possible to observe differences of the results obtained with the two turbulence models,
especially the length of the recirculation region of the wake.
(a) (b)
Figure 11. Velocity field for the 2D model, at the medium plane obtained with k-ε (a) and k-ω SST (b) turbulence
model.
The velocity vectors in the region near the wake for cases I and II are presented at Figure 13. The velocities obtained
for Case II are smaller than for Case I.

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14th International Conference on Wind Engineering – Porto Alegre, Brazil – June 21-26, 2015
(a) (b)
Figure 12. Velocity vectors for the 2D model, at the medium plane, near wake, obtained with k-ε (a) and k-ω SST
(b) turbulence model.
Figure 13 shows the velocity field at a plane over the center line of the domain for both cases of the 3D model, Case
V, and Figure 15 shows numerical results for Cases VI and VII. Results for Cases III and IV (steady state) are
almost the same as transient Cases VI and VII, respectively, so the velocity fields of those cases are not shown. As
in 2D model simulation it is possible to identify the speed-up over the top at the hill and the wake region after the
model. However, the use of different turbulence models results in a more significant difference at the velocity field
for the 3D model, specially on the height and length of the wake, Figure 14(a) and 15(b).
Figure 13. Velocity field for the 3D model, at the medium plane obtained with k-ω SST turbulence model and
prescribed roughness at the model surface.
(a) (b)
Figure 14. Velocity field for the 3D model, at the medium plane obtained with k-ε (a) and k-ω SST (b) turbulence
model.

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The roughness length imposed in Case V (Figure 14 b) results in relevant differences at the wake topology when
compared to the similar Case VII (Figure 14 b) with smooth surface.
The velocity vectors in the region near the wake for Cases VI and VII are presented in Figure 15 and Figure 16. The
velocities obtained for Case VII produce a wake with oscillating behavior.
Figure 15 – Velocity vectors near the wake, 3D hill, k-ε turbulence model.
Figure 16 – Velocity vectors near the wake, 3D hill, k-ω SST turbulence model.
6. CONCLUSIONS
Analyzing the construction of the profiles regarding wind energy purposes, measurements in 2D models may lead to increases in
the values in the order of 30% higher than 3D, determining an overestimated outcome of wind potential and, consequently
leading to a lower generation of energy than the in the expected in power plant. This assertion applies to both categories.
In all the models for two types of power law profiles and for 2D/3D models we can identify the mean horizontal velocity
profiles at the crest increased in comparison with reference profiles, as well as the speed up effect on the crest of hills. It is
possible to verify that it is more evident above 30o
for both p=0.11 and p=0.23 in 3D models (Figures 6 and 7).
The speed up effect varies proportionally with the slope of the hill upwind and at the crest. Considering the hub height of the
machine at 100 m from the crest of the hills, such phenomenon may determine an increase in the wind velocity up to 14% in
comparison with the same height on a plain terrain, for p=0.11 and up to 25% for p=0.23 (Figure 8).The speed factor values in
3D were lower than 2D for all the models and varied according to the slope and the roughness. Deducting the turbulence
intensity values, it may be concluded that it is possible to reach up to 11% for p=0.11 and 15% for p=0.23 increase in wind
velocity profiles at 100 m above the crest of the hills in the examined conditions when compared with the same on plain terrain.
In this case, it is possible to conclude that the wind power can increase up to around 50% according to the slope, the shape of the

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hill, the roughness factor, and the neighbor elements (Figure 9). Flow above the hills shows to be very advantage for wind
turbines, with increased mean velocities and reduced turbulence intensity, compared to the incoming flow.
In the 2D model, there is a strong similarity between the numerical results obtained by both turbulence models. In relation to the
experimental data, the average difference of the velocity profile found in a place was no more than 6%. Regarding the 3D
model, the analyses of the numerical simulations over velocity fields allow to note differences in flow behavior, especially
downstream of the obstacle. Comparing the numerical results, it is noted that k-ω SST model results show a better agreement
with the experimental velocity profile data (obtained in vertical section at the top of the hill) that were provided by model k-ε.
For this reason, the roughness geometry was used exclusively with the first model. Despite the good agreement, it was found
that the numerical velocity profile in the region delimited between 75 and 200 mm vertically to the top of the hill is greater than
that of experimental model However, even between 350 and 450 mm the opposite occurs, and the calculated speed data are
smaller than those in experimental results.
This investigation proves that the optimization of a wind farm lay-out can be better obtained in an experimental and CFD
previous micrositing study, as to provide more detailed information regarding turbulence areas mapping and modeling power
losses in order to certify these points to install wind farm projects.
ACKNOLEGEMENTS
The authors gratefully thank the assistance of FAPERGS of Rio Grande do Sul, Brazil.
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