1. Savonius Vertical Axis Wind Turbine Power Production Assessment through
Wind Tunnel Simulations
Horn Rev offshore wind farm in Denmark
Researcher: Obaida Mohammad
Advisor: Dr. Andre Mazzoleni
NC State University

2. Abstract
Many constraints exist that restrict widespread deployment of renewable wind energy
such as land requirements. Land usage demands conflict with farm land requirements,
and impose environmental impacts such as endangerment of wildlife in local habitats.
Horizontal Axis Wind Turbines (henceforth, HAWTs) require vast amounts of land for
turbine spacing to reduce the effect of wake turbulence that leads to decreased
efficiencies from adjacent turbines. Current wind farm technology accesses higher
quality wind resources by increasing tower height and rotor diameter to counter power
density losses due to space requirements from limiting wake trace effects. However,
this solution also associates higher manufacturing costs, land usage due to spacing
requirements, and environmental impacts as well.
Vertical Axis Wind Turbines (henceforth, VAWTs), have the capability to increase the
power density associated with wind farming. Through our study, we have concluded that
interaction amongst VAWTs is beneficial to power production. This is most likely a result
of wake vortices that are shed from VAWT blades are significantly less disruptive
downstream compared to the wake generated from HAWTs. On the contrary, a
substantial improvement in individual turbine performance was observed in simulations
of clusters of intuitively spaced turbines compared to isolated turbine simulations. For
this reason, VAWTs can help in generating greater power per unit land area on a wind
farm. An important realization must be made that careful turbine configuration is
essential for developing optimum power output. Examination and assessment allow us
to make educated deployment scenarios of VAWT configurations. The VAWT models
were used to simulate a small idealized wind farm analysis. The highlights of the
knowledge acquired through the simulations of 8 savonius VAWT models with real time
power generation measurement in a closed circuit wind tunnel include the importance of
counter-rotating turbines for more efficient configurations, and the potential to reduce
land usage by 33%-60%. Taking into consideration all the factors of our experiment, we
were able to run simulations that yielded 10% to 100% greater power coefficient
potentials. The future of wind farming with the possibility of integrating existing HAWT
farms with small clusters of VAWTs within the land gaps present between large HAWTs
is a potential way to take advantage of current land usage and harvest more power.
However, the idea of integrated use would require further research, not covered in this
evaluation, to assess the aerodynamic interaction between VAWTs at hub heights of
10-20 meters and existing HAWTs at 80-100 meter hub heights on an existing land
footprint.
Introduction
Wind has a vastly crippling effect called the capacity factor which is a result of the
intermittence and variability of wind power. According to the U.S. Department of Energy
(DOE), the increasing development of wind technology has allowed capacity factors to
reach a higher maximum in 2013(~53%)1
for specific projects in locations with abundant
wind resources, but the increase in new wind sites with less quality wind resources
hasn’t allowed the average capacity factor to increase significantly since 2005.

3. Intermittence of the wind resource is the ultimate limitation of power generation, and is
the concept that defines the capacity factor. For those who are interested, an
engineering economics analysis is included in the appendix of this report that compares
financial feasibility of wind energy compared to other forms of clean energy and carbon
intensive energy as well. Since this intermittence exists we must take advantage of
periods of higher quality and abundance of wind. One way to begin taking advantage is
by implementing wind farms with more efficient configurations to yield higher power
density. This form of “smart farming” is a leap ahead, and if followed by technological
enhancements to VAWTs, such as efficiency (coefficient of power) and reduced
structural fatigue, these advances can propel us into innovative power generation
techniques.
The disadvantage associated with HAWTs is their increase in footprint as the rotor
diameter grows to capture a larger area and essentially more power. VAWTs, on the
other hand, do not necessarily increase in footprint as the swept area of the rotor grows
because this can be achieved by increasing the height of the blade.
The power coefficient2
of a wind turbine can be determined by the following formula:
𝐶 𝑝 =
𝑃
1
2
𝜌𝐴𝑈3
The power coefficient represents the fraction of kinetic energy passing through the rotor
blades that is converted to electrical power where P is the electric power generated, ρ is
the density of air, A is the rotor swept area, and U is the free stream velocity. For the
sake of our study, all turbines experienced the same velocity U, air density ρ, and have
the same swept area A. Since this is true, we can assume that the power coefficient is
proportional to the electrical power generated. The power coefficient is a measure of
efficiency of a wind turbine. VAWTs typically have lower efficiencies compared to that of
HAWTs. HAWTs have been able to achieve theoretical efficiency limits of roughly
~59%2
. This is why some skeptics may consider higher power density clusters of
VAWTs to be farfetched and unachievable, although bringing innovation to the current
wind farming standard. The results are phenomenal and include simpler, smaller
designs, lower manufacturing costs, and ultimately more power generation on the same
area of land. The possibility for lower operation and maintenance costs becomes
achievable with VAWTs as well because the key components in VAWTs are usually
much lower to the ground and that makes maintenance of equipment more accessible.
Methodology
Current HAWT wind farming technology ideally spaces turbines 3-5 diameters laterally
and 6-10 diameters downstream2
. A recent study also confirmed that a specific large
HAWT required 15-20 diameters in downstream spacing to recover wake effects5,6
.
Observations were made that when the VAWT models were spaced at 2 diameters
laterally and 4 diameters downwind, the performance is recovered considerably

4. compared to denser VAWT spacing. Such a change would yield a 33%-60% increase in
land usage efficiency in both lateral and downstream directions.
In this analysis the wind turbine models were placed in a closed circuit wind tunnel in
order to conduct the experiment under constant conditions. The test section of the wind
tunnel where the models were placed during simulations is characterized by fully
developed flow and all tests were conducted at a wind velocity of 10 m/s. The test
section dimensions are displayed in figure 1 below. All simulations were also given a
substantial timeframe to reach steady state conditions before any data acquisition
occurred. To acquire the amount of power produced by each turbine during the
analysis, a 100Ω resistor was placed in each wiring scheme and the AC voltage drop
across the resistor was measured. Power is then easily calculated through Ohm’s Law
relationships. P=V2
/R is the formula that was used to calculate power. The heat
dissipated in the resistor is equivalent to the amount of work that each wind turbine is
doing, and consequently the measure of power produced.
Figure 1: Wind tunnel test section dimensions. The significance of the test section
dimensions is the physical constraint that is imposed on the degrees of freedom of
multiple turbine configurations.
The effects of the wind tunnel walls, and discrepancies due to physical turbine design
inconsistency were also taken into consideration by simulating each individual turbine in
isolation in the wind tunnel. The isolated simulations were first conducted in the center

5. of the wind tunnel followed by a separate testing in each turbines individual location for
configuration F. The structural design consistency of each turbine varied due to minute
design differences. Therefore, conducting isolated turbine testing informed us on ideal
capacity power generation for each turbine. Understanding the effects of design
inconsistency and physical wind tunnel constraints provide a thorough understanding of
the overall performance of the turbines due to their aerodynamic interactions amongst
each other. For further clarification, turbines produce greater power when the rotating
magnets are closer to the stationary coil wires. Due to design constraints, the rotating
magnet plate on each turbine was not identically spaced to each corresponding
turbine’s set of coils which delivers various amounts of power strictly due to the magnet
to coil spacing.
Voltage Readings (Volts) Power Calculations (Watts)
Resistance
(Ohms)
In Position
(Config. F)
Center of
Tunnel
%Change In Position
(Config. F)
Center of
Tunnel
%Change
Turbine1 99.7 1.03 1.06 2.91 0.0106 0.0113 5.91
Turbine2 98.9 0.86 0.856 -0.47 0.0075 0.0074 -0.93
Turbine3 99.1 0.725 0.72 -0.69 0.0053 0.0052 -1.37
Turbine4 99.2 0.725 0.68 -6.21 0.0053 0.0047 -12.03
Turbine5 99.6 0.66 0.7 6.06 0.0044 0.0049 12.49
Turbine6 99.9 0.75 0.76 1.33 0.0056 0.0058 2.68
Turbine7 99.1 0.425 0.45 5.88 0.0018 0.0020 12.11
Turbine8 99.2 0.94 0.89 -5.32 0.0089 0.0080 -10.36
Table 1: Voltage measurements acquired through real time simulation and
corresponding power calculations for each turbine in the center of the wind tunnel and in
actual positions for configuration F. The percent change columns show the percent
change in power production and voltage readings for each turbine from the center to the
actual position in configuration F.
As shown in table 1 above, it was observed that the difference between any given
individual turbine performance from the center of the wind tunnel to its actual location in
configuration F did not exceed 6% for voltage readings which corresponded to 12%
power production fluctuations. This observation stands true for turbines that are located
in the vicinity of the wind tunnel test section walls as well, in fact turbines situated near
the walls showed the most drastic difference in performance. Turbines situated closer to
the center in configurations involving multiple turbines saw changes in performance less
than 0.5% in voltage readings and approximately 1% power production compared to
simulations conducted in the center of the wind tunnel test section.
Various configurations were simulated to assess the effects of upstream wind turbines
on downstream power potential with various spacing arrangements. Figure 2 below
portrays the configurations that were evaluated during this study.

6. Figure 2: Eight configurations that were simulated in the wind tunnel. Note each turbine
is numbered and the individual turbine configurations are not presented here. The red
arrows represent counter-clockwise rotating turbines and black arrows represent
clockwise rotating turbines. The spacing is in terms of diameters where 2D represents 2
diameter spacing. All measurements are made at the center of rotation at the turbine
shafts.
For the sake of clarification, take configuration F as an example. The individual turbine
simulation for turbine 1 was conducted in the center of the wind tunnel first. Then a
separate simulation was conducted with turbine 1 located in its position as shown in
configuration F above without the presence of any other turbines in the wind tunnel.
This pattern was followed for all turbines.

7. Results and Discussion
Power
Generated
(Watts)
Config A Config B Config C Config D Config E Config F Config G Config H
Turbine 1 .01234 .01206 .01234 0.01435 0.01527 0.01456 .01661 .01382
Turbine 2 .00644 .00662 .00719 0.01073 0.01075 0.00991 .00961 .00967
Turbine 3 .00029 .00759 .00776 0.00587 0.00595 0.00577 .00667 .00659
Turbine 4 n/a n/a n/a 0.00530 0.00546 0.00527 .00525 .00530
Turbine 5 n/a n/a n/a 0.00349 0.00422 0.00984 .00510 .00528
Turbine 6 n/a n/a n/a 0.00323 0.00459 0.00950 .01200 .01249
Turbine 7 n/a n/a n/a 0.00043 0.00081 0.00023 .00033 .00056
Turbine 8 n/a n/a n/a 0.00555 0.00652 0.00183 .00336 .00233
Total Power .01907 .02627 .02729 0.04895 0.05356 0.05691 .05894 .05604
(Watts)
Individual
Power Sum
0.0239 0.0239 0.0239 .0493 .0493 .0493 .05283 .05283
(%) % Change -20.2 9.92 14.2 -0.71 8.64 15.4 11.6 6.1
(m2
) Total Area .081 .108 .134 .69 .82 .54 .54 .952
(W/m2
)
Power
Density .235 .243 .204 0.071 0.065 0.105 .109 .059
Table 2: The power generated by each turbine for configurations A-H, and the
correlating power density attributed to each configuration. The total power is the sum of
the power generated for each configuration. The individual power sum is the cumulative
sum for each individual turbine in isolation. The percent change highlights the change in
the total power for isolated turbines compared to configuration results. The power
density is based on the total power generated for each configuration.
Table 2 listed above presents the power generation for each turbine for configurations
A-H and the associated power density for each configuration. The following results
displayed in figure 3 are the normalized power coefficient values for each configuration
in comparison to the individual turbine power coefficient which is determined through
the isolated turbine simulations. Displaying the results in this manner allows a deeper
understanding of the benefit or interference of each configuration’s spacing on overall
performance for each setup.
Figure 3 displays the results obtained through the wind tunnel simulations. As shown
below, configurations A-C included testing for turbines 1-3 only. Configurations D-H
included test simulations for all 8 savonius VAWT models. Although it could be argued
that turbines 5 and 6 were in proximity of the wind tunnel walls for configurations F-H
and could have experienced the 12% wall effect, it is still apparent that three turbine
clusters enhance power production immensely. Turbines 5 and 6 experience a
tremendous increase in power productivity for configurations F-H, and once again
although the wall effects could be contributing to that slightly, the readings for these two
turbines in configuration H are greater than any other turbine for any other configuration
simulated. Configurations B and C confirm that two upstream counter-rotating turbines
improve the downstream turbine performance, and this can be said with confidence in
the cases of configurations B and C because they are situated in the center where the

8. wall effects are negligible. Figure 3 shows that in configurations B and C, turbine 3
delivers a normalized coefficient of power of 1.49 compared to their isolated simulation
results. This is approaching a 50% enhanced power production. Noteworthy is the
benefit that the upstream counter-rotating turbines experience as well. Configurations B
and C portray a greater collective power generation for turbines 1, 2, and 3 compared to
their isolated simulations. Turbines 1 and 2 experienced a 10% enhancement and a
10% reduction in performance, respectively, for configurations B and C, whereas turbine
3 experienced a 46% enhancement for configuration B and a 49% enhancement for
configuration C. The significance behind these results is that the channeled air flow
coming between two upstream counter-rotating turbines has an increased kinetic
energy potential which should be taken advantage of and harvested with a downstream
turbine.
Figure 3: Cp
norm
, Normalized power coefficient for each turbine for the corresponding
configuration in comparison to individual turbine capacity. A Cp value of 1.0 corresponds
to the power coefficient associated with each turbines isolated simulation performance.
Some data values have been labeled here to give a general idea of the range of results.
0.00
0.50
1.00
1.50
2.00
2.50
Turbine
1
Turbine
2
Turbine
3
Turbine
4
Turbine
5
Turbine
6
Turbine
7
Turbine
8
1.09
0.87
0.05
1.10
0.97
1.48
1.36
1.45
1.14 1.17
0.86
0.79
0.40
0.82
1.29
1.34
1.10 1.13
2.00
1.64
0.11
0.23
1.12
1.31 1.31
1.25
1.18
1.38
0.24
0.29
Cp
norm
Normalized Cp
for all Simulations
Config. A
Config. B
Config. C
Config. D
Config. E
Config. F
Config. G
Config. H

9. Normalized Power Coefficient Cp
norm
Config. A Config B Config C Config D Config E Config F Config G Config H
Turbine 1 1.09 1.07 1.10 1.27 1.36 1.29 1.34 1.12
Turbine 2 0.87 0.89 0.97 1.45 1.45 1.34 1.30 1.31
Turbine 3 0.05 1.45 1.48 1.12 1.14 1.10 1.33 1.31
Turbine 4 n/a n/a n/a 1.14 1.17 1.13 1.24 1.25
Turbine 5 n/a n/a n/a 0.71 0.86 2.00 1.14 1.18
Turbine 6 n/a n/a n/a 0.56 0.79 1.64 1.33 1.38
Turbine 7 n/a n/a n/a 0.21 0.40 0.11 0.14 0.24
Turbine 8 n/a n/a n/a 0.70 0.82 0.23 0.42 0.29
Figure 4: Numerical values for Cp
norm
as shown graphically in figure 3 above.
This study was not a configuration optimization experiment. The configurations
analyzed shown in figure 2 were chosen through trial and error of several other
configurations which generated unsatisfactory results and were weaved out.
Configuration F displayed a 15% increase in overall power generation compared to the
cumulative power generation of the individual turbine simulations. Turbines 7 and 8
suffered a drastic drop in performance for configuration F and all configurations, mostly
due to lack of space. However, the outcome was still improved performance in the
collective performance for configuration F and several other configurations as well. The
15% increase in power in configuration F is a clear indication that interaction amongst
the VAWTs has progressive effects that should be taken advantage of in industrial,
commercial, and residential applications of wind energy. That being said, further
investigation into more optimal configurations could lead to even further improvement to
power generation. The concept grasped from our experimental methods is that
interaction between VAWTs enhances generation potential.
The turbine models used to conduct this analysis are by no means manufactured to be
high quality energy harvesting machinery. The turbines were purchased from an
educational website which sells renewable energy educational products belonging to a
company named Picoturbine4
. The highest power density performance of these models
was found to be approximately 0.243W/m2
in configuration B, which had the best power
density by any configuration simulated. This power density value should not be taken
too seriously because it can’t be compared to any industrial wind farming values. The
power density is a ratio of accumulative power over footprint of land area occupied by
any given configuration. Similar studies have been conducted in natural wind conditions
on open desert land by Dabiri et al5
(2011) at Cal Tech in Pasadena, California. In their
analysis, six 10m tall x 1.2m diameter VAWTs were used. The Cal Tech turbines were a
modified version of a commercially available Windspire Energy Inc. model. Dabiri et al
were able to conclude that their modified commercial models, using the concept of
counter-rotating VAWTs, generated power densities ranging from 21-47 W/m2
at wind
speeds above cut in rated speed and 6-30 W/m2
overall during the length of their field
simulations. These power densities are a substantial improvement compared to the 2-3
W/m2
defining current wind farming standards. ArborWind3
, a member of the American
Wind Energy Association (AWEA), manufactures a commercially available VAWT which
has a rated power output of 60kW at a rated wind speed of 10m/s. At 10m/s our turbine

10. models performed with a rated capacity of approximately 5mW which is 6 orders of
magnitude smaller than Arborwind’s VAWT. Through the enhanced VAWT model
performance observed in this study it is apparent that applying the concept of VAWT
clusters to a large scale wind turbine design suggests the potential to revolutionize the
future of wind farming. Furthermore, implementation of cluster farming will allow VAWTs
to perform with improved quality energy production, greatly supplementing for the
individual turbine efficiency offset.
Several limitations existed that took a toll on the nature of the study conducted and the
results acquired. The wind tunnel dimensions were the first constraint that limited the
number of wind turbines and the spacing throughout the configurations. For example,
due to the lack of downstream space in the wind tunnel, we were unable to assess the
downstream spacing of three turbine clusters to assess interactions between clusters. In
the case of configuration A-C, it would be beneficial to determine how far downstream
another 3 turbine cluster could be located to generate enhanced power production.
Also, the structural consistency of the wind turbines was a limitation to this study
because each turbine had to be assessed individually before generating normalized
results that could be compared to other turbines.
Future Work
This study could be elaborated on with more rigid wind turbine models and taken
another step further if the experiment is conducted in natural wind conditions at a
location where dimensional spacing is not a constraint. Such efforts to take this work
further would allow for a wide variety of configurations and spacing setups and would
generate results in real world conditions. Natural behavior in real world scenarios
always provides more in depth insight since the walls of the wind tunnel wouldn’t be a
factor. Obstacles such as trees or buildings would become something to account for if
present, which would make the results that much more relatable and applicable to
industrial, commercial, and residential uses. Also, with the degree of freedom of no
space constraints, configuration optimization can be assessed.

11. References
1. http://emp.lbl.gov/sites/all/files/2013_Wind_Technologies_Market_Report_Final3.pdf Last accessed
12/3/14
2. http://scitation.aip.org/content/aip/journal/jrse/3/4/10.1063/1.3608170 Last accessed 12/3/14
3. http://www.arborwind.com/aw-turbines/pt-180-specs/ Last accessed 1/4/15
4. http://www.picoturbine.com/stem-4-pack-of-savonius-wind-turbines/ last accessed 11/10/14
5. http://scitation.aip.org/content/aip/journal/jrse/3/4/10.1063/1.3608170 last accessed 12/10/14
6. http://institute.lanl.gov/ei/_docs/Annual_Workshops/Wind_Workshop_2011/WinBlade_Linn.pdf Last
accessed 12/12/14
7. http://www.eia.gov/forecasts/aeo/assumptions/pdf/electricity.pdf Last accessed 12/3/14
8. http://www.ipcc.ch/pdf/special-reports/srccs/srccs_wholereport.pdf Last accessed 12/1/14
9. http://www.eia.gov/naturalgas/weekly/ Last accessed 12/3/14

12. Appendix
Economic Assessment
Wind technology was compared with another nearly carbonless option,
Combined Cycle Gas Turbine (CCGT) power plants with Carbon Capture and Storage
(CCS) to understand the economic challenges associated with both technologies. CCS
has become an innovative way to continue the use of fossil fuel resources without the
harmful effects of direct emissions of greenhouse gases into the atmosphere. The most
recent values for costs associated7
, capacity factors1,8
, and efficiency1,8
were obtained
and several calculations were made to assess the viability of both options. Table 1
below lists the data used for each technology for Net Present Value (NPV), cost per unit
product, and mitigation cost calculations.
Wind CCGT w/CCS CCGT w/o CCS
Capital Cost ($/kW)7
2206 2085 915
Capacity Factor1,8
33 95 95
Variable O&M ($/kWh)7
0 .00678 .0036
Fixed O&M ($/kW-yr)7
39.55 31.79 13.17
Fuel Cost ($/GJ)9
0 4.25 4.25
Thermal Efficiency8
n/a 42 58
Lifetime (yrs) 20 20 20
Table A1: Values used for economic analysis calculations.
The values listed in table A1 are current, and were obtained from reliable sources such
as the Intergovernmental Panel on Climate Change (2014)8
and the U.S. Energy
Information Administration (2014)7
. The capacity factor for wind technology was chosen
as an average of the capacity factors of the entire US market. The increasing

13. development of wind technology has allowed capacity factors to reach a higher
maximum in 2013(~53%), but the increase in new wind sites with less quality wind
resources has restricted the average capacity factor from increasing significantly for the
past decade. The United States has an abundant amount of undeveloped high quality
wind resource locations1
, but some factors contributing to this trend include the
increased availability of turbines designed for low wind speeds, transmission availability,
and policy influence to meet in state or in region requirements to access cash grants
and other credits. According to the report on carbon capture and storage/sequestration
(CCS) by the Intergovernmental Panel on Climate Change (2013), a reference natural
gas combined cycle (NGCC) plant without CCS operates at a net lower heating value
(LHV) efficiency of 58%8
. A NGCC power plant with CCS will see an increase in the
amount of fuel required by the plant to operate by 11-22%8
. This hinders the overall
plant efficiency and must be accounted for. The thermal efficiency used in the
calculations accounts for the energy penalty that is associated with CCS and is
assumed to be 42%. This value assumes an average penalty of the range of increased
fuel required to operate a plant with CCS. Also, although there are plants and pre-
combustion capture methods that are expected to reach 100% carbon capture, some
methods do not capture the carbon entirely. According to table 3.13 from the IPCC’s
“Carbon Dioxide Capture and Storage” report8
post-combustion capture methods can
capture roughly 86% of carbon from the fuel and was also accounted for in the
calculations. Noteworthy is how the cost of fuel has gone down in recent years due to
the increased natural gas reserves9
as a result of hydraulic fracturing techniques. In
general, with all variation accounted for, CCS is estimated to increase electricity costs

14. from .01-.05 $/kWh3 depending on the fuel, location, national circumstances, and
technology deployed. Further assumptions include an annual discount rate of 15%, an
electricity purchase price of $.11/kWh, no salvage value, and wind tax credit from the
American Taxpayers Relief Act (ATRA) of 2012 in the amount of $.023/kWh5. The
discount rate was chosen based on S&P 500 typical yields between 8%-11%. Being
that wind energy is currently a volatile market with uncertainties for the future,
companies operating in this industry will be more likely to use a higher rate of return to
reduce risk and attract investors. NPV calculations yielded results for wind at $-
463.09/kW, $1196.70/kW for CCGT w/CCS, and $3213.09/kW for CCGT. The wind
NPV calculation does not account for the tax credit presented for operations that began
by the end of 2013. Accounting for the $.023/kWh tax credit associated with wind yields
an NPV of $-44.38/kW, a significant difference for investors. The cost per unit product
calculation allows us to visualize the cost of one unit of electricity production and is
needed to make mitigation cost calculations. For wind the results were $.14/kWh, as for
CCGT w/CCS the cost per unit product was $.09/kWh. This is another direct
consequence of the capacity factor associated with wind. The effect the capacity factor
inflicts is truly restricting to wide deployment. On the other hand, mitigation cost was
also calculated for wind vs reference plant CCGT and CCGT w/CCS vs reference plant
CCGT. The results for wind were a mitigation cost of $1020/ton C avoided when
replacing CCGT with wind and $542/ton C avoided when replacing CCGT with CCGT
w/CCS. According to economic analysis, each technology has its own associated
advantages and disadvantages. CCS is a new technology that doesn’t have the depth
of experience that wind energy research has. There are uncertainties involved with real

15. world implementation such as the associated risks involved with long term storage of
carbon. These include health risks, and hazards that require sophisticated monitoring
techniques. Secondary storage system methods could potentially ensure safety and
proper storage with higher confidence levels, and may also increase the cost of
implementation.

16. Hand Calculations: