1. LAMINAR FLOW AND TURBULENCE MODELING TO
TRACE WAKE PATTERN AROUND BUILDING
CLUSTERS FOR DOMESTIC SCALE WIND TURBINE
SITING
Govindarajan A Chittaranjan, Member IEEE
Abstract— Some of the primary concerns in integrating
domestic scale renewable energy systems in city
buildings is siting optimization and size scaling of
domestic wind turbine models on building roof tops
discounting the zone of wake influence.
This is a Case study scenario of a regional wind
resources distributions and predominant wind directions
computation for CFD modeling to identify laminar and
turbulent flow regions around building clusters which is
crucial in tracing and discounting the wake and non
potential zones which are not suitable for wind turbine
installations. This study identifies potential locations for
installing renewable energy systems for optimized
energy conversion in future studies. This study also
briefly summarizes the energy yield of a 50 kW wind
turbine and evaluates the energy loss if the turbine’s real
availability decreased due to improper turbine site
location.
Keywords—CFD simulation, WasP, Reynold’s Number,
Strouhal’s Number, vortice shedding.
I. INTRODUCTION
This research is aimed at simulating the wind flow to
trace the wake around tall and medium sized building
clusters of a small section in a commercial zone of a busy
downtown in order to study the wake size and less energy
potential zones, the aim is to propose at a latter stage an
optimized wind turbine layout on the roof tops to capture and
convert maximum energy at domestic scale. The CFD1
simulation software used is Yamada Soft’s A2C. Due to
limitations2
in the evaluation version of this software certain
modifications and assumptions were made to guide the study
in a potentially right direction, hence accuracy of the
computations should not be considered at this stage. This
study is aimed at outlining the basic methodology and the
process flow that is required for such a project. The sample
size is 41 deg N latitude to 42 degree N latitude and 72 deg
W to 73 deg W longitude, with topographic region data of
New York State to simulate near reality. The samples are a
1
Computational Fluid Dynamics
2
GIS module not available for Topography map creation
Cell size cannot be altered, hence samples cannot be placed next to each
other in less than 2 km
Different simulations [at different wind speeds, directions, heights cannot
be saved in the same project
Data Export is not allowed.
50 meter to 20 meter tall building clustered together at a
major intersection. The predominant wind is primarily from
the west direction. Consider this as a major city in North
America and the most reliable source of wind data is
recorded at the regions International airport. The study uses
an actual 2008 wind data from such a source location for the
analysis. Annual mean wind speed recorded at 32 m
elevation on a 10 m met tower is estimated at 3.8 m/s using
the WasP3
software.
The region under study is in the northern hemisphere with a
false Northing of 0, and a roughness class of 3 meters at an
elevation of 39 meters. The coriolis4
force of the region is
45.9 N with favorable geostrophic5
wind. This is a good
zone for wind energy harvest.
However the region under study is part of a city location
with an estimated wind shear of 0.3 and a high roughness
class of 3-56
. The mean annual wind speeds of the sample
region which falls in UTM 18 is 3.5 m/s to 4.5 m/s range
and hence the measured data was extrapolated horizontally
to suit the sample simulation.
II. METHODOLOGY
The area for simulation is selected using Google Earth and
exported to A2C simulation software for mapping the wind
flow over the terrain. A wind shear exponent of 0.3 is used
for estimating the wind shear at 50 meter height extrapolated
from the 10m met data. WasP was used to estimate the mean
annual wind speeds and the direction distributions.
III. WIND DATA ANALYSIS
One year data from the regions airport is obtained the same
was verified and adjusted suitably for missing and
erroneous wind data.
WasP software was used to obtain the wind resource
distribution and the mean wind speed [3.9 m/s] from the 10
m Meteorology data monitoring mast
3
WasP – Wind Atlas Analysis and Application Program
4
F = 2ω sin φ
5
Vg = pressure gradient / F x ρ
6
The roughness of the city is replaced by displacement height :
H – Z0 / k= Z – Z0
Where, k is the surface drag coefficient.

2. Figure 1: Wind Distributions and Direction
Wind Rose in Figure 1 depicts the annual wind directions
indicating the predominant wind from the west with a scale
factor of 4.4 m/s. The weibull distribution indicates a
perfect fit of the wind speeds in 1 meter bins.
Annual average wind speed of 4.4 m/s measured at 10 m
Metrological Wind mast is vertically extrapolated to 50m
hub heights by the power law equation
V2 = V1 x [ Z2 /Z1]α
= 4.4 x (50/10)0.3
= 7.1 m/s
As we know the Gust factor is Peak wind / Mean wind
speed, the maximum gust that this region can experience
during the sample period at 50 m = 1.2 x 7.1 = 8.52 m/s
[using gust factor = 1.2]. The design life period of wind
turbines are 20 years hence assuming a return period of 50
years the probability of not exceeding 8.52 m/s in 20 year
design life is almost 100%7
by the equation: 1-(1/R)L 8
The turbulence intensity9
by estimation is 0.43 indicating
high turbulence around the clusters at 50m heights.
Assuming the along wind direction is perpendicular to the
buildings the length L for the 5 collective buildings are
taken as 100 meters then the Reynolds Number at an annual
average air density of 1.253 is estimated at 5.93 x 1010 10
at
7
This may not be true in actual practice
8
R is the return period and L is Design life period
9
TI = σ V / Vmean; σ V = 3.09; Vmean = 7.1 m/s
10
Indicates High Turbulence at a distance D from the leading edge
7.1 m/s at 50m height using Reynolds equation: Re = (ρ x V
x L) / μ where ρ is the air density and μ is the air viscosity.
This indicates an air turbulence of 1.6; μ = 1.5x10-5
kg/m s
at annual average of 70
C
As these buildings are located near a water front the surface
drag forces are low indicating low wind shear from the
predominant west winds. The Drag force11
on the leeward
side are 1.6 x 10-34
Nm this is expected to fluctuate as Re > 2
x 105
Figure 2: Cluster view from South of south east
IV. WIND MAP MODEL OF SIMULATION AREA
The simulation area is bound by 41 deg N to 42 deg N and
72 deg W to 73 deg W where in the tall towers are of 50
meter high and the medium sized buildings wider on x-axis
are around 20 meter high, the sample region is of the size
100 m x 100 m12
.
A wind flow simulation is limited to 6 m/s13
in 3 D around
the buildings as the simulation outputs are limited by the
evaluation software A2C. The vortices could be visualized
but not so distinctly although earlier estimates and
simulation indicates turbulence. Figure 3 depicts the
westerly laminar wind flow simulation and a turbulent wake
on the east justifying the earlier estimation of a turbulent
Reynolds’s Number.
The wake area indicated in blue in Figure 3 is quite large on
the leeward side of the 20-metre high buildings, indicating
high roughness class influence on wind shear. However at
50 m height the gradient wind drops from 6 m/s to 5 m/s
indicating not so significantly wind shear at heights above
50 meters.
11
Drag Force FD = 0.5 x CD x ρ x A x V2
12
Hence the D = 100 for Reynolds’s Number estimation
13
due to the limitations of the A2C evaluation edition

3. Figure 3: Y-axis wake simulation from west wind
Figure 4 depicts a 3-D top view of the wake simulation from
the west wind; the marker indicates the spot of simulated
wind speed indicated on the scale.
Figure 4: 3-D View of wake simulation from west wind
Due to the building cluster of an area of 10 km2
and
adjacent terrain with smooth hills [Figure 5] in the near
vicinity the angle of separation [Figure 4] is quite wide
leading to windward wind speed of 4.5 m/s [greenish
yellow] dropping down to less than 3 m/s [blue] in the wake
region
The Wake on the eastern side depicted in Figure 4 is
approximately fourteen times the tallest building in the
cluster with a length of almost similar size; this abnormal
breath is due to the contributions of the terrain topography
and is typical in a city centre.
Figure 5: 3-D wake simulation from east wind
Vortices are possibly in the zone tainted in aquamarine with
a shedding frequency14
of 8 x 10-3
Hz at a shedding period15
of 117 seconds with a vortex separation16
of 833 m. This
explains the reason as to why the simulation visualizes a
wide wake zone and a wind turbine should not be installed
here on a building sheltered by a still taller building on the
windward side.
The time varying drag force [FD (t)]17
acting on the leeward
side of the building on the western side with a dimension of
50 x 50 m is 160 kilo Newton.
V. ENERGY YIELD AND WIND TURBINES
A 50 kW horizontal axis wind turbine18
is chosen to
estimate the annual energy yield19
at 50 m hub height. This
turbine was chosen on the basis of attaining commercial
feasibility at domestic scale for roof top installations.
Higher capacity turbines have been installed on city
towers20
earlier hence this should not pose a major
challenge at least in future structures. Table 1 depicts the
annual energy yield of a 50 kW HAWT. Note the 18.64 %
decrease from arbitrary 2% turbine non-availability, hence it
is absolutely necessary to locate these turbines at an
optimum location to increase the “real availabilities” as
much as possible, this can be effectively done by avoiding
the wake region and less potential zones
14
Ns = Strouhal Number S x U / D [ S = 0.12; D = 100 m] at 7.1 m/s wind
speed
15
T = 1/NS
16
S = U x T
17
FD (t) = ½ x ρ x
18
Power Curve is depicted in Figure 6
19
AEP = [½ x ρ x A x V3
x 0.59] x 8760 x .98 [as this is a medium sized
turbine a Betz limit of 0.59 is used]
20
World Trade Tower in Bahrain has 3 x 225 kW Norwind turbines
installed at 3 levels

4. Figure 6: Power Curve of 50 kW Turbine
The energy yield estimations were based on the monthly
average wind data which was used in the AEP equation
depicted the in foot note 19.
An annual energy yield at 0.98% turbine real availability
yields 33547 kWh caused by falling wind speeds due to
marginal wake region siting
Unfortunately no model exists to predict the energy yield
with 100% accuracy. By experience it was observed that a
marginal wake region could contribute to 1% to 20% fall in
a wind speeds a more accurate study is required at a latter
stage. Accurate predictions can be obtained only by
measuring the wind data at each turbine’s proposed site.
Table 1: Annual Energy Yield of 50 kW wind turbines at
50 meter hub-height
Figure 7 is a month-wise graphically representation
depicting the energy loss between the 100% availability and
98% availability. The graph infers there could be higher
losses during winter when there are higher wind speeds due
to increase in air density with fall in seasonal temperature,
hence diligent micro siting of these turbines are crucial for
commercial feasibility of a domestic scale system.
21
18.64% loss
Figure 7: EY of a 50 kW Turbine at 100% Vs 98%
Availability
VI. INFERENCE AND CONSLUSION
• The Extrapolated wind speeds at 50 m height is 7.1
m/s although the simulation was based on these
data to the software limitation the simulation
display was only up to a maximum of 6 m/s.
• The estimation of high Reynolds number and drag
forces indicating high turbulence at 50 meter levels
and below. High vortex shedding frequency and
time period and the wide wake confirms this.
• The wake separation angle is wide typical of large
building clusters and small hills on the terrain in
such cases.
• Annual Energy Yield for a 50 kW turbine at 50m
high on top of the building was computed to
emphasis on significant energy loss if installed
without diligently considering the wake and
turbulence. It should be noted that there is 18.64 %
decrease in energy conversion from 2% turbine
non-availability, this non availability could be due
to improper siting of these wind turbines hence
appropriate placement of the turbines [optimized
lay out] is a crucial element.
• Wind turbines at 60 meter and above are preferred
for optimum energy but permitting issues might
pose constraints for city building roof top
installation
• The wind loads on building structures may not be
adequate for embedded turbine technology; the
Gross Energy Yields at 50m
0
1000
2000
3000
4000
5000
6000
7000
8000
Jan-
08
Feb-
08
Mar-
08
Apr-
08
May-
08
Jun-
08
Jul-08 Aug-
08
Sep-
08
Oct-
08
Nov-
08
Dec-
08
Month
MaximumEnergyYields[kWh]
0
1000
2000
3000
4000
5000
6000
7000
8000
EnergyYieldfor50kWTurbine
[kWh]
Maximun Energy at
50m at 100% turbine
availability
50kW Turbine
Description
Energy Yield at
50m [kWh]
Energy Yield of 50 kW Wind Turbine at 100% Real
Availability
41235
Energy Yield of a 50 kW Wind Turbine at 98 %
Turbine’s Real Availability
33547
21

5. same appears as technically unfeasible at the
present stage using the existing technology.
• However further detailed study on wind loads on
the windward side, the drag coefficients on the
leeward side, vortices and its shedding frequency
in line with emerging turbine technologies
including shrouded Windjets and transversal axis
turbines are suggested along with embedded wind
technologies.
VII. REFERENCES
BOOKS
[1] Emil Simiu and Robert H. Scanlan on “Wind Effects on Structures –
Fundamental and Applications to Design” [III rd Edition]
[2] John Wabha, David Brinker, Mark Malouf and John Erichson on
“New Standards for Broadcast Structures” ANSI/EIA/TIA-222-G by
[3] Boundary Layer Theory - pdf exercise study [unidentified]
WEB REFERENCES
[1] http://www.engineeringtoolbox.com/air-absolute-kinematic-viscosity-
d_601.html
[2] http://www.efunda.com/formulae/fluids/calc_reynolds.cfm#calc
[3] www.wasp.dk
[4] www.ysasoft.com
[5] www.atlas.nrcan.gc.ca
[6] www.ec.gc.ca
[7] www.earth.google.com
VIII. BIOGRAPHIES
Govindarajan A Chittaranjan (M80255874) is from
Chennai, India graduated from Middlesex University, UK is
currently pursuing his PhD program in Canada.
He is a wind energy analyst and operations & maintenance
professional with detailed and long-term experience in the
installation, operation and maintenance of wind farms and
industrial instrumentation. He has been in industrial
instrumentation sector and Unit Head for Operations and
Maintenance of Wind Farms for over 21 years. He has a
sound understanding of both the technical as well as
commercial side of wind farm and industrial automation. He
has adequate knowledge & experiences in current Mega-
Watt class wind turbines, balance of plant equipments,
Marketing & Sales. He has done detailed study on Wind
project resource and risk assessments, wind farm losses,
crane investigations and other special projects.
.