wind feasibility and optimization study in east tennessee
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Wind Power Feasibility and Optimization Study inEast Tennessee
In submission for the Chancellors Honors Program Senior Thesis project
Megan SchuttAerospace Engineering
Mechanical, Aerospace, and Biomedical Engineering Department
University of Tennessee, Knoxville, Tennessee
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Wind Power Feasibility and Optimization Study in
East Tennessee
Megan Schutt
Aerospace EngineeringMechanical, Aerospace, and Biomedical Engineering Department
University of Tennessee, Knoxville, Tennessee
Wind energys application in the East Tennessee and West North Carolina
area was addressed, as well as regions with similar topography and
climatology. A Geographic Information System (GIS) study was developed
using data from the Wind Energy Resource Atlas. These regions relatively
small wind energy sources are optimized through a tailored airfoil and the
investigation of less invasive turbine technologies. In this way, smaller and
more efficient farms can be constructed, lessening the impact on wildlife and
the surrounding community.
Nomenclature
= density
P = power
= mass flow rate
v = velocity
A = rotor area
dE/dt = kinetic energy change
L = lift force
D = drag forceCL = coefficient of lift
CD = coefficient of drag
CP = coefficient of pressure
L/D = lift to drag ratio
= angle of attack
1. IntroductionIn order to lessen dependence on fossil fuels, an
alternative energy supply study remains
necessary. Especially in regions overcome withcurrent land abuse through such resource
gleaning as mountaintop removal, traditional
coal mining and hydraulic fracturing,
unconventional methods may need to be
optimized. For wind farms to become feasible in
mountainous regions such as those in the East
Tennessee and Western North Carolina
Appalachian region, different power
maximization technologies may be
advantageous. This study will attempt to
localize regions prime for wind farm
development through Geography Information
Systems (GIS) as well as to create a solution for
wind turbine technology suitable for such areas
with customized turbine type selection and
unique airfoil design.
2. Theory2.1Geographic Study
First, ideal locations must be scouted according
to average wind speed and temporal variability,
and practical topography. A sound criticism of
wind farms as an anchor energy resource is the
inconsistency in availability. This cannot be
completely alleviated without large gains in
energy storage capabilities, but an expectedsupply can be denoted with analysis of data in
regards to historical patterns throughout
different parts of the year. In fact, some studies
have shown that diurnal wind cycling is actually
in sync with power demand, with higher wind
speeds during the early evening, and tapering
off until the early hours of the morning.1Using
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the Wind Resource Energy Atlas from the US
Department of Energy, wind categories can be
mapped for any state in the country.2 In Table
1, the categories are listed in terms of both
average wind speed, and resultant wind power
density available. In addition, Figure 1 shows a
graphical approximation of wind resource
capability according to geographical features.
Figure 1: NREL: RREDC
Wind
Power
Class*
10 m (33 ft) 50 m (164 ft)
Wind Power Density
(W/m2)
Speed(b)
m/s (mph)Wind Power Density
(W/m2)
Speed(b)
m/s (mph)
10 0 0
100 4.4 (9.8) 200 5.6 (12.5)2
150 5.1 (11.5) 300 6.4 (14.3)
3
200 5.6 (12.5) 400 7.0 (15.7)
4
250 6.0 (13.4) 500 7.5 (16.8)
5
300 6.4 (14.3) 600 8.0 (17.9)
6
400 7.0 (15.7) 800 8.8 (19.7)
71000 9.4 (21.1) 2000 11.9 (26.6)
Table 1: NREL Wind Atlas Categories with associated power densities and wind speeds at heights of 10 m and 50 m
Maximum power from a wind turbine, or any
turbine for that matter, can be estimated using
the mass flow rate across the given rotor area.
Area can be controlled by increasing or
decreasing the rotor diameter, but locations
must be investigated to maximize available
wind, as power is most sensitive to changes in
velocity. Ideally, wind speed would be taken at
each potential turbine point, and a power
distribution function would be modeled off of
these individual values.3
While average wind speed is a good
resource for determining the ultimate power
output available and technology best suited in a
particular area, a more detailed analysis is
necessary to define the expected supply on aday-to-day basis. In addition, directional
information is important to ensure a laminar,
one directional flow that is most useful to
horizontal-axis wind turbines (HAWTs). In
locations where wind speed is low and/or
direction is most variable, vertical-axis wind
turbines (VAWTs) would perhaps be utilized.4
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NREL also offers a theory that exposed
mountain ridges and summits offer more
promising wind resources than perhaps even
average wind speed as a result of the relative
topography and orientation to prevailing winds.
A Venturi speed-up effect happens as the
wind goes over these ridges and the flows are
compressed.5 This instance has already been
utilized by TVA with the one wind energy
generation plant in East Tennessee on Buffalo
Mountain (see Figure 2 below).
Figure 2: Buffalo Mountain Wind Farm, picture courtesy
of American Council on Renewable Energy.
GIS has long been used along with
various data mining sources to graphically
analyze the wind available in particular regionsacross the world.6,7 The main benefit behind
this method is to utilize existing infrastructure
and historical data. After any necessary
modifications in format are made, the same
data used for the sciences of meteorology and
environmental studies can be employed in
modern studies of energy implementation such
as these.
2.2Airfoil DesignGeographic wind availability may indicate
potential for wind energy generation, but the
technology for sourcing this energy can severely
curtail ultimate power generation if not
developed in an optimum way. Improvements
in airfoil design can be a method in which
efficiency gains maximize the power generation
of wind farms.
In design of airfoils for use in HAWTs,
the NACA 63-4XX and NACA 63-6XX series have
already been developed. There have also been
improvements in by the DU xx-W-xxx series, the
S8xx series, and the Riso-A1-xxx series. General
design goals include high maximum lift to drag
ratio, high maximum lift, and insensitivity to
leading edge roughness,8 as HAWTs are
generally lift based mechanisms. VAWTs, such
as Darrieus and Savonius Rotor designs, are
generally drag based9, so their airfoil design
characteristics will have different goals.
Lift and L/D maximization goals
contribute toward increasing power generation,
in the same way that the thrust available in
aircraft engines increases available power,
albeit with a dependency on velocity. The other
design goal of insensitivity to roughness isnecessary, especially in wind turbine usage, to
eliminate effects of particulate matter on
aerodynamic performance. Roughness can lead
to quicker transition of laminar to turbulent
flow if the airfoil is not sufficiently insensitive.
In order to increase insensitivity to
roughness, several techniques can be
employed, that can both negatively and
positively affect the other design goals of lift
and lift/drag maximization. One means of
theoretically affecting the roughness sensitivityis optimizing the maximum thickness, upper
surface thickness, and transition point
thickness10
(see Figure 3).
Figure 3: Wind turbine airfoil study by Delft University of
Technology
Coefficient of lift is approximated by a polar
analysis of the design airfoil, and is wont to be
maximized in order to produce maximum lift,
but simultaneous tests must be done to take
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into account the total drag on the airfoil, and is
represented by the lift to drag ratio. Many
leading edge roughness design constraints
reduce drag while also reducing lift, so
numerical analysis is often utilized to optimize
all of these parameters.11
3. Assessment Methodology3.1Geographic Study
The GIS data and shapefiles used in this study
are wind speed categories from the nation-wide
from the NREL with direct application to wind
energy availability. The map developed from
this data can give a general idea of which areas
have the most potential for wind power
generation.Further investigation into wind
direction, variability, and other factors like land
management and environmental concerns, is
the next step in location determination. Wind
roses are one source that tracks cardinal
direction as well as speed in those directions.
The wind roses utilized for this study were for
Knoxville, Tennessee. Data was collected
monthly for the year 1961 and 2012 to account
for long term changes in the cyclical pattern. A
sample distributed by hour was taken throughthe months of July and December. This method
allowed a diurnal cycle to be established to the
differences between seasonal patterns to be
investigated.
3.2Airfoil DesignIn designing a unique airfoil for use in this
atypical environment, several factors needed to
be examined. Along with various resources of
study and theory, the program Xfoil developed
by MIT was used as the main design analysis
tool. The design concepts observed were:
delineating an optimum airfoil thickness,
creating an ideal camber line, and maximizing
the lift to drag ratio for peak power generation.
Polars were evaluated for different basis airfoils
of NACA design, with several graphical
parameters changed. The effects of the changes
on the lift to drag ratio and maximum lift were
examined, and the best combination of these
constituents was evaluated.
4. Results and Discussion4.1 Geographic Study
The GIS results from the NREL data can be
found in Figure 4a-c below. From these figures,
it is easy to see that premium wind energy sites
are located along areas of elevation, near the
Cumberland Plateau and Smoky Mountains. For
further elaboration, see the topographical map
of the area in Appendix A. The regions marked
here as upper-division classes (found mostly
along the mountain ridges at the
Tennessee/North Carolina border) have beenidentified as suitable for wind energy
development. According to the NREL wind
energy assessment, the locations found in
mountainous regions are indicative of such
areas as exposed hilltops and ridge crests.
However, as these numbers are based on mean
wind values, they may not indicate high
variability in wind resource in accordance with
local terrain differences. It is probably best to
use this location evaluation as a guideline, and
for exact spots to be scouted and assessedbefore development is undertaken.
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Figure 4a: The GIS data of Tennessee where white
indicates NREL Wind Power Class 1 and black indicatesWind Power Class 7.
Figure 4b: Inset of East Tennessee where white indicatesNREL Wind Power Class 1 and black indicates Wind
Power Class 7.
In addition to average wind speed as
categorized by the NREL study, wind roses for
Knoxville, TN were studied to examine the
temporal variability of the wind resource in this
area. Data from the Western Regional Climate
Center was gathered from the wind roses
available over a period of time. In addition to
annual, seasonal patterns, a diurnal distribution
was also analyzed
In Figure 5, it is easy to see the pattern
of wind behavior over the course of a year.
While average wind speed stays around the
same, the maximum values taper off in the
summer months and gain speed in the winter
months. This is not ideal, as in electricity
demand is typically higher in the summer, and
lower in the winter when households generally
use natural gas or other heating methods to
heat water and spaces. The mean behavior isimportant to note in regards to expectations of
power generation and costs, but the variability
in maximum wind speeds can offer insight into
available power at peak demand.
Figure 4c: Inset of Mid-East Tennessee where white
indicates NREL Wind Power Class 1 and black indicates
Wind Power Class 7.
Figure 5: Average and maximum wind speed data for
Knoxville, TN in 2012.
It is also interesting to note the change
in wind behavior over the course of time in the
long term. The same data available for the year
1961 can be found in figure 6a, and the change
during these years is found in 6b. While the
trends have remained similar, the average
speeds have dropped about 1 m/s across the
board. This may not have much effect on the
feasibility of wind energy at present, but it
should definitely be monitored to examine
whether this trend continues. The effects could
range from eliminating wind energy as a
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possible energy source to other more serious
climatological consequences.
Figure 6a: Average and maximum wind speed data for
Knoxville, TN in 1961.
Figure 6b: Change in average wind speed from 1961 to
2012.
Regarding the daily pattern, Figure 7
shows the hourly variation in two months of the
year 2012. This pattern is more advantageous
for usage in the electricity grid with the highest
wind speeds available in the late afternoon and
evening hours, with the smallest wind speeds
occurring in the dead hours of the morning.
Even across seasons, from the two months
shown (July and December) the patterns
remained similar, suggesting that they were not
significantly affected by seasons. The onenoticeable difference was that the range
throughout the day was lower in summer than
in winter, which is in accordance with the lower
mean wind speed in winter months.
Nevertheless, the diurnal trend present
regardless of seasonal variation matches the
electricity demand. Power need is generally
highest in late afternoon, when children get out
of school and workers come home from their
jobs, and lowest during the day, when no one is
home. These findings can perhaps establish
some argument to the criticism of sporadic
availability in wind as a power source.
Figure 7: Average Daily Wind Speed Variation in July and
December of 2012.
4.2 Airfoil Design
The Xfoil analysis of airfoils designed was
undertaken using the direct analysis method of
the program. There is also an inverse analysis
method, but this study sought to see whatincremental geometrical changes to particular
airfoils did to their aerodynamic performance.
The basis airfoil used in these assessments was
the NACA 6XXXXX series, the airfoil developed
for use in HAWTs, while various other airfoils
were used for comparison purposes.
The first design constraint for analysis
was the maximum thickness, and its effect on
lift, and lift to drag ratio. Five foils (see
Appendix B) based off of the NACA 6XXXXX
series with differing thickness, ranging from21% to 25%, were analyzed for the aerodynamic
performances (Figure 8).
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Figure 8: L/D for six different airfoils ranging in thickness from 0.12 to 0.25
As is obvious from Figure 8, the
maximum thickness of the airfoil significantly
affects the lift to drag ratio. This relationship
was outlined before, in stating that various
design components would be beneficial to
decreasing sensitivity to roughness, but
detrimental to both maximum lift and lift to
drag ratio. It is, however, possible to optimize
the effect, while keeping both positive aspects.It is perhaps hard to tell in these figures, but for
the smaller thicknesses, the L/D relationships
are very close, almost overlapping. As the
thicknesses increase, the changes become more
drastic, indicating that there is a point where
the benefit gained in insensitivity to roughness
through increasing thickness is surpassed by the
negative effects in the lift to drag ratio. It is
more apparent, as seen in Figure 9 and 10 that
the maximum lift and maximum lift to drag ratio
are adversely affected by the increase inthickness. While L/D remains fairly constant
until a thickness value of about 0.18, where it
drops off steeply, the lift behavior is more
extreme, but consistent with the previous
findings.
Figure 9: Maximum lift to drag ratio of the six airfoils of
varying thickness found in Appendix B.
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Figure 10: Maximum lift coefficient of the six airfoils of
varying thickness found in Appendix B.
In this series of airfoils, this point of contention
was found to be around 18%, although otherstudies have found the best thickness to be
about 21%.
The next design constraint analyzed was
the behavior of the camber line, or more
specifically, the high point of the camber line. A
series of airfoils was developed with increasing
camber positions, a 610XX, 620XX, 630XX, and
640XX series were all compared with respect to
aerodynamic properties at stall. In improving
sensitivity to surface roughness, the goal is to
make the laminar to turbulent behavior of the
airflow as close to the leading edge as possible,
at angles of attack post-CLmax. In Appendix C, the
flow behavior across the airfoils at an of 15 is
shown. At a higher asymmetry across the airfoil,
it is shown that the transition of laminar to
turbulent flow is moved closer to the leading
edge. Therefore, increasing the camber line can
be said to increase insensitivity to roughness.
However, this design point is again contrary to
increasing L/D, so an optimization of both
components would be necessary. A numerical
analysis is ideal, but a general value of 10%
camber has been accepted (NACA 62021).
Finally a design implementation isnecessary to maximize lift available. An angling
of the trailing edge of the airfoil (as seen in
Appendix D) was found to significantly increase
the lift across angles of attack, when all other
things were equal, see Figure 9.
Figure 11: This chart shows the increase in lift as a result of a trailing edge modification on the NACA 62021 series airfoil.
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From the pressure vector graphs found in
Appendix C, it can be inferred that the trailing
edge modification significantly increased the
magnitude of the vector across the top of the
airfoil, and mostly decreased CP across the
bottom, leading to an increase in maximum lift.
The increase under the new trailing edge did
not apparently affect the lift generated,
especially at low angles of attack.
The resultant airfoil takes into account
the previous conclusions in its design, seen in
Figure 12. Based off of the NACA 62018, the
optimum camber and thickness are called
inherently in NACA form, but the tail rotation is
graphically customized.
Figure 12: Graph of tailored airfoil
The airfoils polar assessment can be found in
Figure 13, resulting in a maximum lift coefficient
at about 1.74, at an angle of attack of around10.2. This is higher than any of the individual
test cases.
Figure 13: Aerodynamic Performance
Figure 14: CPV of tailored airfoil.
Figure 14 shows a pressure vector display of the
airfoil at maximum lift ( = 10.2). There is
clearly an indication of positive lift, the pressure
vectors along the top of the airfoil, while drag,
the components along the bottom, has been
relatively minimized.
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4.3 Environmental Assessment
While wind energy developments may be less
destructive than fossil fuels in terms of
emissions and other spectrums of
environmental friendliness, but they are not
without fault. Factors that influence the
intrusiveness of wind farms include noise
pollution, effects on wildlife and habitat, as well
as impacts on land and seascapes in regards to
tourism and community value.
Though studies such as those outlined
in Table 2 have shown that bird, bat, and other
avian fatalities are next to negligible and more
obvious in the short term compared to other
human and natural factors,12
it is important to
keep these impacts in mind and to minimize
them as much as possible. This can be managedthrough conscious siting practices, including
thorough habitat and migratory avoidance, and
operation procedures like ceasing processes in
bad weather. For Eastern Tennessee on
exposed, rocky ridges in particular the focus of
this study, special attention must be paid to
migratory bat population. During migratory
season (late summer) bat mortality rate has
been shown to climb as high as 20 bats per
turbine per year13
at the currently active Buffalo
Mountain Wind Farm. If migratory patternsremain similar, however, energy production
could be planned around bat population
movement, as summer nights have been shown
above to be some of the least wind power
dense periods throughout the year. Turbine
type is also a significant aspect of aviary
mortality. For example, lattice type tower
structures provide roosting prospects for local
wildlife, attracting rather than discouraging
birdlife from interaction with the wind turbines.
Further, VAWTs have been shown to be safer
than prop-type turbines in bird mortality rates,
for their more easily avoidable structural
pattern.
Table 2: From Saidur, et al, this table outlines the relative
danger that wind turbines pose to wildlife as compared
to other human-related causes.
The next most common concern with respect to
wind is noise pollution, possibly resulting in
decreased property values and creating
unpleasant environments for residential areas.
An obvious solution to this problem is to site
wind farms at a clear distance from areas that
might be affected. In this studys focus area,
this can be achieved easily, as most regions ofEast Tennessee and the surrounding
Appalachian range are sparsely populated.
While higher infrastructure prices would be one
negative consequence of this solution, this is
already normal with most energy generating
methods. Coal, natural gas, and crude oil are all
mined and refined at considerable distances
from their demand locations. A second, and
perhaps more fitting option to consider, is the
utilization of VAWTs, like the Darrieus and H-
Rotor type of turbine. These create less noise,
14
and if chosen at a small size, would be less
environmentally impactful to wildlife. VAWTs
are not typically implemented because of their
low density in power generation, but because of
the smaller wind speed availability in this area
would perhaps be a good tradeoff in terms of
environmental impact.
5. ConclusionIn covering a wind feasibility study in this region
taking into account geographic and
meteorological patterns, unique airfoil design,
and environmental assessment, this study has
been slightly exhaustive on certain points.
Nevertheless, certain topics could be furthered.
The goals elaborated in the airfoil design would
best be suited to a numerical analysis, with
weights placed on factors most important to
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6. Appendix A
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8. Appendix CNACA 61021
NACA 62021
NACA 63021
NACA 64021
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9. Appendix DNACA 62021
NACA 62021B
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NACA 62021C
NACA 62021D
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