Wind energy applications, ams short course, august 1, 2010, keystone, co

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
Boundary Layer Turbulence and Turbine
Interactions with a Historical Perspective
AMS Short Course:
Wind Energy Applications,
Supported by Atmospheric
Boundary Layer Theory,
Observations, and Modeling
Keystone, Colorado
Neil D. Kelley
National Wind Technology Center
August 1, 2010
Innovation for Our Energy Future

Outline
2
• Background
• Lecture objective
• Collecting turbulence-turbine interaction data
• Interpreting the results
• Understanding the impact of turbulence on turbine
structural components
• The role of the stable boundary layer
• Conclusions
• For more information
• A discussion question

Background
3
• Wind energy technology was resurrected in the U.S. in the
early 1970s
• After initially being established at the National Science
Foundation, the Federal Wind Program was located in
what became the U.S. Department of Energy
• The Federal Wind Program had four major components:
utility-scale turbine development, small turbine
development, vertical axis turbine development, and
resource assessment
• The utility-scale program was managed by NASA for the
U.S.DOE with prototype turbines built by several
contractors between 1975 and 1985.

200 kW
600 kW
2000 kW
2500 kW
3200 kW
4000 kW
Capacity Evolution of Federal Wind Program Turbines
1975-1985

Hamilton-
Standard
BoeingBoeingGeneral
Electric
Westinghouse Boeing
Rotor Diameter and Hub Height Evolution
latest
generation
turbine
hub height
range

California Experience
6
Tehachapi Pass
Altamont Pass
San Gorgonio Pass
(Palm Springs)

The Turbine Operating Situation in the mid 1980’s
7
In California:
• Significant number
of equipment
failures
• Poor performance
due in part to the
high density of
turbines
In Hawaii:
• High maintenance costs and
poor availability for
Westinghouse turbines on
Oahu
• Poor performance of wind
farms on the Island of Hawaii

Hawaiian Experience
8
• 15 Westinghouse 600 kW Turbines 1985-1996
• DOE/NASA 3.2 MW Boeing MOD-5B Prototype
1987-1993
• Installed on complex uphill terrain at Kuhuku
Point with predominantly upslope, onshore flow
but occasionally experienced downslope flows
(Kona Winds)
• Chronic underproduction relative to projections
for both turbine designs
• Significant numbers of faults and failures
occurred during the nighttime hours
particularly on the Westinghouse turbines.
• Serious loading issues with the MOD-5B during
Kona Winds required the turbine to be locked
out because of excessive vibrations generated
within the turbine structure
Oahu
Westinghouse 600 kW
MOD-5B

Hawaiian Experience – cont’d
9
• 81 Jacobs 17.5 and 20 kW
turbines installed downwind of
a mountain pass on the Kahua
Ranch 1985-
• Wind technicians reported in
1986 a significant number of
failures that occurred
exclusively at night
• At some locations turbines
could not be successfully
maintained downwind of local
terrain features and were
abandoned
Hawaii

Results . . .
10
• None of the large, multi-megawatt turbine prototypes reached
full production status
• Post analysis revealed that the structural fatigue damage to
these machines far exceeded the original design estimates in
virtually all cases
• These excessive loads were attributed to atmospheric
turbulence
• In the late 1980’s and early 1990’s the industry concentrated on
the development wind farms employing large numbers of
turbines in the 25 to 200 kW range

The Payoff in California . . .
11
Year
1985 1990 1995 2000 2005
RegionalCapacityfactor(%)
0
10
20
30
40
50
60
Altamont
Tehachapi
San Gorgonio
Year
1985 1990 1995 2000 2005
RegionalCapacityfactor(%)
0
10
20
30
40
50
60
Altamont
Tehachapi
San Gorgonio
Source: California Energy Commission
Annual Average
Q2 & Q3 Average (Wind Season)
Range of Current
Capacity Factors
In the U.S.
There have been incremental
improvements in the California
wind farm Capacity Factor
performance in the early 1990s
and again beginning in about
2000. This has been largely the
result of installation of more
reliable and efficient turbines.

Today
12
• The U.S. has the greatest installed wind energy capacity in
the world
• New turbine designs are now reaching the capacities of the
1970-1980 prototypes once again and are beginning to
surpass them
• New turbines are being designed to capture energy from
lower wind resource sites which increases their rotor
diameters and hub heights
• The new machines are being constructed of lighter and
stronger materials in order to reduce the cost of energy but
they are also more dynamically active.

Current Evolution of U.S. Commercial Wind Technology
13

However There is a Down Side . . .
• The aggregate performance of currently operating wind U.S. wind farms has
been estimated to be in the neighborhood of 10% below project design
estimates
• Maintenance and operations (M&O) costs are seen as approaching equivalency
with the production tax credit
• Both are major contributors to a continuance of a higher than the targeted Cost
of Energy (COE)
10% Wind Farm Power
Underproduction & Possible Sources
Source: American Wind Energy Association
$
High Maintenance & Repair Costs Contribution to M&O
Expected annual M&R costs over a 20 year turbine lifetime
Courtesy: Matthias Henke, Lahmeyer International
presented at Windpower 2008
used with permission

An Interpretation . . .
15
$
Turbines, as designed, are not
compatible with their operating
environments
This incompatibility manifests
itself as increasing cumulative
costs as the turbines age
• We believe atmospheric turbulence continues to
play a major role in this incompatibility
• The larger and more flexible turbines being
designed and installed today when coupled with a
much different atmospheric operating environment
at these heights are being challenged
• We will now overview our research into the effects
of turbulence on wind turbines conducted over the
past 20 years

Lecture Objective
16
To provide a summary and overview of the
results of research into the effects of boundary
layer turbulence on wind turbines in order to
inform boundary layer meteorologists about how
wind energy technology is dependent on their
knowledge and understanding.

Research Approach
17
Make simultaneous, detailed measurements of both the turbulent
inflow and the corresponding turbine response!
Interpret the results in terms of how various turbulent fluid
dynamics parameters influence the response of the turbine (loads,
fatigue, etc.)
Let the turbine tell us what it does not like!
Develop the ability to include these important characteristics in
numerical inflow simulations used as inputs to the turbine design
codes
Adjust the turbulent inflow simulation to reflect site-specific
characteristics or at least general site characteristics; i.e.,
complex vs homogeneous terrain, mountainous vs Great Plains,
etc.

Data Sources
18
We have had two source of measurements of both
the detailed characteristics of the turbulent inflow
and the resulting dynamic response of a wind
turbine
• Deep within a 41-row wind farm in San Gorgonio Pass,
California that contained nearly 1000 turbines in 1989-90
• The National Wind Technology Center Test Site south of
Boulder, Colorado in 1999-2000

Innovation for Our Energy Future19
San Gorgonio Pass California
• Large, 41-row wind farm located downwind of the
San Gorgonio Pass near Palm Springs
• Wind farm had good production on the upwind
(west) side and along the boundaries but
degraded steadily with each increasing row
downstream as the cost of turbine maintenance
increased
• Frequent turbine faults occurred during period
from near local sunset to midnight
• Significant amount of damage to turbine
components including blades and yaw drives

San Gorgonio Regional Terrain
20
Pacific Ocean
Salton
Sea
wind farms
(152 m, 500 ft)
(−65 m, −220 ft)
(793 m, 2600 ft)
Los Angeles
Basin
Mohave Desert
Sonoran
Desert
San Bernardino Mountains

Wind Farm Nearby Topography
21
Palm Springs
Mt. Jacinto
(
downwind
tower
(76 m, 200 ft)
upwind
tower
(107 m, 250 ft)
row 37
San Gorgonio Pass
nocturnal
canyon flow
(3166 m, 10834 ft)

Side-by-Side Turbine Testing
at Row 37
7D row-to-row spacing
Gathering Data in a Wind Farm Environment
SeaWest 41-row San Gorgonio Wind Farm in 1989 & 1990 – A legacy site

Analyzing San Gorgonio Wind Farm Turbine
Turbulence-Turbine Responses
23
• Two, 65 kW side-by-side turbines were available that were
identical except for different rotor aerodynamic designs
• Location was deep within the wind farm with turbines 7
rotor diameters upstream
• Very turbulent wake conditions produced elevated turbine
dynamic responses that allowed better correlation with
turbulent scaling parameters
• Provided initial analyses of turbulence-turbine
interactions that could be extended and refined using data
from the NWTC experiment

December 1999 to May 2000
24
Testing at the NWTC

Gathering Response Data in the Natural Flow of a High
Turbulence Site
25
NWTC
(1841 m – 6040 ft)
NWTC
Great Plains
Terrain Profile Near NWTC in Direction of Prevailing Wind
ection
Denver
Boulder
•Strong downslope
winds (Chinooks)
from the 13,000
foot Front Range
Mountains that
occur during the
fall, winter, and
spring months
•The winds have a
distinct pulsating
characteristic that
contain strong,
turbulent bursts

Measurements at the NWTC
26
• Measurements were made with the naturally-
occurring wind flows, no upstream turbine wakes
• Data was taken in flows that originated over the
Front Range of the Rocky Mountains to the West
• Objective was to compare the turbine response to
natural turbulent flows with those measured in the
multi-row wind farm

3-axis sonic anemometers/thermometers
Details of Inflow Turbulence
Dynamics Measured By
Planar Array of Sonic
Anemometers
Measured the Resulting
Dynamic Responses
of the ART Turbine
Using An Upwind Inflow Array and a 600 kW Turbine
80-m mean wind speed, V80 (m/s)
80-mturbulence
intensity,I80
rated wind
speed range
The NWTC is a Very Turbulent Site!
Turbulence intensity Standard deviation
Nov 1999-April 2000

What We Have Found From Testing at Both
Sites
• In a wake environment deep
within a very large wind farm
• In very energetic natural
turbulent flow downwind of a
major mountain range

Turbulence and Wind Turbines
29
• Turbulence in the turbine inflow has a significant
influence on the power performance efficiency and
the lifetime of turbine components
• The primary source of degraded performance and
component reliability are the unsteady aerodynamic
effects created by turbulent flow over the turbine
rotor blades
• These unsteady effects create dynamic loads on the
rotor blades that in turn excite a range of vibrational
frequencies associated with the turbine structure that
must be dissipated by the turbine structure

Turbulence-Induced Dynamic Loads
30
• The fluctuating structural loads created by turbulent
flow across the turbine rotor blades are one of the
most important sources of cyclic stresses in the
mechanical components of the turbine
• These cyclic stresses cumulatively induce
component fatigue damage that continues to
increase until failure
• We will now look at what we have found in our
research that relates turbulent flow properties to
fatigue damage accumulation.

Alternating stress cycles/hour
Source: Jackson, K. L., July 1992, “Estimation of Fatigue Life Using Field Test Data,”
Oral presentation to the NREL Wind Energy Program Subcontractor Review Meeting,
Golden, CO.
An Example of the Relationship Between Applied Cyclic
Stresses and Cumulative Fatigue Damage
High Fatigue
Damage
Turbine Steel Low-Speed Shaft
Predictedalternatingstress(kNm)
Stress amplitude versus
frequency of occurrence
Predictedcumulativedamage(%)
Cumulative Fatigue Damage
A few large stress cycles
are more damaging than many
smaller ones!

Load Cycle Frequency Distributions
32
In analyzing turbulence-induced alternating stress or
load cycles in wind turbines we found:
• Small amplitude, often occurring load cycles
were normally or Gaussian distributed
• Less frequent and more damaging high
amplitude cycles were exponentially
distributed

N
cycles
per
hour
Characteristic alternating load cycle magnitude, Mp-p
Fewer cycles
but more intense:
Exponentially
Distributed
More cycles
but lower
intensity:
Gaussian
distributed
High Fatigue Damage
Region
Observed Blade Root Loading Cycle Distributions
What does this say about the nature
of the turbulence excitation?

Example of Distribution of Alternating Blade Root Out-
of-Plane Loading Cycles From An Actual Turbine Blade
34
OBSERVED RAINFLOW SPECTRA FOR AWT-26/P2 TURBINE
(Tehachapi Pass, California)
P-P root flapwise bending moment, kNm
0 25 50 75 100 125
Cycles/hr
10-1
100
101
102
103
104
exponential fit
Observed Turbulent Load Cycle Spectra for
AWT-26/P2 Turbine
(Tehachapi Pass, California)

N
cycles
per
hour
increased
fatigue
damage
decreased
fatigue
damage
Slope of Loading Distribution Determines Level of
Fatigue Damage

Turbine Response
Dynamic Load
Statistical
Distribution
Model
Dominant Inflow
Turbulence Scaling
Parameter(s)
Percent
Variance
Explained#
Blade root out-of-plane bending Exponential , Ri 89
Low-speed shaft torque Exponential , Ri 78
Low-speed shaft bending Exponential , Ri 94
Yaw drive torque Exponential , Ri 87
Tower top torque Exponential , 88
Tower axial bending Exponential σH 78
Nacelle inplane thrust Exponential , Ri 77
Tower inplane thrust Exponential 69
Blade root inplane bending Extreme value 86
1/2
(| ' '|)u w
1/2
(| ' '|)u w
1/2
(| ' '|)u w
1/2
(| ' '|)u w
1/2
(| ' '|)u w
1/2
(| ' '|)u w
HU
1/2 1/2 1/2
(| ' '|) ,(| ' '|) ,(| ' '|)u w u v v w
1/2 1/2
(| ' '|) , (| ' '|)u w v w
#includes both turbines, values greater for turbine equipped with NREL blades
Multivariate ANOVA Analysis Results of San Gorgonio Wind Farm
Turbine Response Variables and Turbulence Scaling Parameters

N
cycles
per
hour
N = βoe−β
1
M
p-p
Rotor Blade Root Out-of-Plane Larger Amplitude Loads
Scale with Turbine Layer Dynamic Stability and Hub u*
β1 = f(Ri, u*hub)

Hub local shear stress, u* (m/s)
1 1
2 2
exp exp 1p p
o
M M
N
γ γ
γ
γ γ
−
  −   −
= − − − +     
      
Rotor Blade Root In-Plane High Amplitude Loads Scale
with Turbine Layer Dynamic Stability and Hub u*
• Blade root in-plane (edgewise) cyclic load distributions have two peaks:
• a lower amplitude one due to the once/revolution gravity load
• a higher amplitude one due to turbulence
• Gumbel Extreme Value Distribution Describes High Blade Root In-Plane Loads

Gradient Richardson number, Ri
Blade Root Out-of-Plane Load Cycle Exponential
Distribution Slope Parameter β1 vs Turbine Layer Stability
INFLOW TURBULENCE
SCALING VARIABLES
TURBINE
DYNAMIC
RESPONSE
VARIABLE
M-O Stability Parameter, z/L

Blade Root In-Plane (Edgewise) Load Cycle Extreme Value
Distribution Shape Parameter γ2 vs Turbine Layer Dynamic
Stability

Normalizedcrosscovariance(uiuj)/ij
Peakbladerootflapbendingmoment(kNm)
Turbulence Vertical Component is a Key Player in Turbine
Dynamic Response
Large peak loads
tend to be associated
with the vertical wind
component

Micon 65 Turbine Root Flap Moment Fatigue Damage Loads
as a Function of Hub Local u* and Turbine Layer Ri
6
8
10
12
14
16
18
20
22
24
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-0.4
-0.3
-0.2
-0.1
0.0
0.1
Damageequivalentload(kNm)
Hub local u*
value (m/s)
Turbine layer Ri
6
8
10
12
14
16
18
20
22
24
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-0.4
-0.3
-0.2
-0.1
0.0
0.1
Hub local u *
value (m
/s)
Turbine layer Ri
6
8
10
12
14
16
18
20
22
24
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-0.4
-0.3
-0.2
-0.1
0.0
0.1
Hub local u *
value (m/s)
Turbine layer Ri
6
8
10
12
14
16
18
20
22
24
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-0.4
-0.3
-0.2
-0.1
0.0
0.1
Hub local u *
value (m
/s)
Turbine layer Ri
Peak Value from
Three Blades
Three Blade
Average Value
AeroStar Rotor NREL Rotor
Unstable
Stable

What are the details of the turbulent wind
field and turbine blade to produce these
responses?

NREL blade
Turbine Blade Response Due to Turbulence-Induced
Unsteady Aerodynamic Response Stress Cycles!
Organized or Coherent Turbulence is a Major
Contributor to Turbine Fatigue Damage
Inflow turbulence characteristics
Coherent turbulent structures
Turbine Dynamic Responses

Turbulent Structures That Induce Turbine Dynamic
Responses Can be Smaller than the Rotor Disk
Their Intensity is a Function of the Dynamic Stability of
the Rotor Layer
Ri =+0.034
more intense peak
loads generated
within single blade
rotation
Ri = +0.007
blades encountered
turbulent structures at
the same location
during three consecutive
rotor rotations
Peak Blade Root Out-of-Plane Bending Loads Generated within Rotor Rotations

Here we compare results from both the San Gorgonio Wind
Farm and the NWTC Measurements to see if there are any
systematic differences
46
Are There Certain Times of Day and BL
Conditions when Greater Fatigue Damage
Occurs?

Diurnal Variations in High Blade Structural Loads
San Gorgonio Wind Farm Micon 65 Turbines at Row 37
Time-of-Day Distribution of Occurences of High Blade Loads
Local standard time (h)
2 4 6 8 10 12 14 16 18 20 22 24
Probability(%)
0
2
4
6
8
10
12
14
sunrise sunrset
0 2 4 6 8 10 12 14 16 18 20 22 24
Probability(%)
0
2
4
6
8
OctMay Oct May
NWTC ART Turbine
Time-of-Day Distribution of Occurences of High Blade Loads
too
turbulent
for
turbine to
operate
winds below
turbine
cut-in
wind speed
Peak Blade Loads
Occur At Same Point
In Diurnal Cycle

Mean Wind Speeds Associated With High Fatigue Loads
Distributions of Hub-height Mean Wind Speeds Associated with
High Values (P95) of Rotor Blade Root Fatigue Loads
Hub mean wind speed (m/s)
8 10 12 14 16 18
Probability(%)
0
10
20
30
40
rated wind speed
San Gorgonio Micon 65 Turbine
Hub mean wind speed (m/s)
8 10 12 14 16 18
Probability(%)
0
5
10
15
20
25
30
rated wind speed
NWTC ART Turbine
Conclusion: Highest Blade Root Fatigue Damage Occurs Near Rated Wind Speed!

-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10
Probability(%)
0
10
20
30
40
50
60
-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04
Probability(%)
0
5
10
15
20
25
unstable
conditions
stable
conditions
stable
conditions
unstable
conditions
Ri
Atmospheric Stability Probability Associated with High
Levels (P95) of Turbine Blade Loading
San Gorgonio Micon 65 kW Turbine NWTC ART 600 kW Turbine
• Highest fatigue loading occurs in weakly stable flow conditions
• Much greater probability of encountering high loading at Row 37 in the
California wind farm likely due to influence of upstream turbine wakes

NWTC Diurnal Variation of Turbine Layer Stability
Diurnal Variation of Turbine Layer Ri During Turbine Operation
0 2 4 6 8 10 12 14 16 18 20 22 24
Ri
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
Ric
critical upper limit
significant turbine response upper limit
 P05-P95
Ri = +0.1
Ri = +0.05
Significant probability of stability in critical range!

Need a Way to Correlate Organized Turbulent
Structures and Turbine Component Fatigue
• Need single numbers that represent
– Level of turbine component fatigue damage
– Intensity of turbulent energy associated with coherent structures
• Damage Equivalent Load (DEL)
– a measure of the equivalent fatigue damage caused by each load taking into
account the fatigue properties of the material
where DEL = (Σ Ni Li
m / Neq )1/m
where Ni is the number of cycles for load Li , m is dependent on the material
(steel = 3 and composite = 10 is usually used), and
Neq is the equivalent number of cycles within a 10-minute period (at a 1 Hz
reference frequency it is 1200)
– It describes the level of fatigue damage with one number
• Coherent TKE (CTKE or Coh TKE)
– Defined as the partition of turbulent kinetic energy that is coherent as
CTKE = 1/2[ (u’w’)2 + (u’v’)2 + (v’w’)2]1/2; CTKE of isotropic turbulence = 0

Conclusions from Measurements from San Gorgonio
Pass Wind Farm and at the NWTC
52
Similar load sensitivities to vertical
stability (Ri) and vertical wind motions
were found at both locations
We found that the turbine loads were
also responsive to the new inflow
scaling parameter, Coherent Turbulent
Kinetic Energy (CTKE) with greater
levels of fatigue damage occurring
with high values of this variable
In both locations, the peak damage
equivalent load occurred at a slightly
stable value of Ri in the vicinity of
+0.02
Clearly, based on both sets of
measurements, coherent or organized
turbulence played a major role in
causing increased fatigue damage on
wind turbine rotors
San Gorgonio
Micon 65/13
NWTC 600 kW ART

Overall Interpretation of the Field Measurements
53
The greatest fatigue damage occurs during the nighttime hours when
the atmospheric boundary layer up to the maximum height of the
turbine rotor is just slightly stable (0 < Ri < +0.05)
Significant vertical wind shear was often also present
Both of these conditions are prerequisites for Kelvin-Helmholtz
Instability (KHI)
The presence of KHI can be responsible for generating atmospheric
motions called KH billows or waves which in turn generate coherent
turbulence as they breakdown or decay

Let’s look at these details but first we need to discuss a
analytical tool that is necessary to for us to identify the
mechanisms involved
54
How does turbulent energy in the turbine
inflow contribute to the fatigue damage of
structural components?

Power
Spectrum
55
Conventional Power Spectrum of Blade Flapwise
Load Time History
Frequency (Hz)
0.1 1 10
Rootflapload(kNm)2
/Hz
10-5
10-4
10-3
10-2
10-1
100
101
102
103
1-P
Zero-mean flapwise loads
Time (s)
0 10 20 30 40 50 60
kNm
-15
-10
-5
0
5
10
15
20
Time Series Representation
•Excellent frequency
resolution or localization
(0.1 Hz)
•Very poor time resolution
or localization (60 secs)
Frequency Domain Representation
Power
Spectrum
But what is
the spectral
distribution for
these transient
event peaks?

Use of Continuous Wavelet Transform to Examine Stress Energy
Distribution of Turbulence-Induced Transient Loads
Wind Turbine Blade Root Out-of-Plane Time-Varying Load
data sample number (time)
min - dynamic stress energy - max
1-P (0.93 Hz)
0.4
0.5
0.7
0.6
0.8
1.0
1.2
1.5
3.0
5.0
10.0
2.0
Scales
Wavelet Scalogram

Time Series and Wavelet Analyses Presentations
Time
Histories
Continuous Wavelet Transform
Coefficients of
Root Flapwise-Bending Signal
Discrete Wavelet Transform
Detail Frequency Bands of
Root Flapwise-Bending Signal
(Multi-resolution Analysis)
Time
Hub-height horizontal wind speed
Hub-height Reynolds stresses
Root flapwise-bending load

Example of Typical Conditions Seen During Daytime and
Nighttime Hours for Flows into the NWTC ART Turbine
0 100 200 300 400 500 600
0
10
20
m/s
0 100 200 300 400 500 600
-50
0
50
100
(m/s)2
u'w'
u'v'
v'w'
0 100 200 300 400 500 600
0
50
100
150
(m/s)2
0 100 200 300 400 500 600
-0.2
0
0.2
mm
Y
Z
0 100 200 300 400 500 600
-2
0
2
deg/s
Pitch
Yaw
0 100 200 300 400 500 600
-200
0
200
kNm
Time (seconds)
(a) Hub-Height Wind Speed
(b) Reynolds Stresses
(c) Turbulence Kinetic Energy
(d) IMU Displacement
(e) IMU Angular Rate
(f) Blade Root Flap Bending Moment
Hub-height wind speed
Reynolds stresses
Turbulence K.E.
IMU Displacement
IMU Angular Rate
Blade flapwise bending
Nocturnal boundary layer
Pitch
Yaw
Time (seconds)
0 100 200 300 400 500 600
0
10
20
m/s
0 100 200 300 400 500 600
-50
0
50
100
(m/s)2
u'w'
u'v'
v'w'
0 100 200 300 400 500 600
0
50
100
150
(m/s)2
0 100 200 300 400 500 600
-0.2
0
0.2
mm
Y
Z
0 100 200 300 400 500 600
-2
0
2
deg/s
Pitch
Yaw
0 100 200 300 400 500 600
-200
0
200
kNm
Time (seconds)
(a) Hub-Height Wind Speed
(b) Reynolds Stresses
(c) Turbulence Kinetic Energy
(d) IMU Displacement
(e) IMU Angular Rate
(f) Blade Root Flap Bending Moment
Hub-height wind speed
Reynolds stresses
IMU Displacement
Turbulence K.E.
IMU Angular Rate
Blade flapwise bending
Daytime boundary layer
Pitch
Yaw
Time (seconds)
intense coherent turbulent event
560 kNm cycle

Upwind array
inflow CTKE
m
2
/s
2
0
20
40
60
80
100
120
0
20
40
60
80
100
120
rotor top (58m)
rotor hub (37m)
rotor left (37m)
rotor right (37m)
rotor bottom (15m)
IMU velocity components
0 2 4 6 8 10 12
mm/s
-20
-10
0
10
20
-20
-10
0
10
20
Time (s)
492 494 496 498 500 502 504
vertical (Z)
side-to-side (Y)
fore-aft (X)
zero-mean
root flap
bending
moment
kNm
-400
-300
-200
-100
0
100
200
300
400
-400
-300
-200
-100
0
100
200
300
400
Blade 1
Blade 2
Response to Intense Coherent Inflow Event on ART
Turbine
59
Intense coherent structure
encountered at center of
rotor disk (80 m2/s2)
Significant blade root out-of-plane
bending excursions (~ 500 kNm)
response
Upwind Planar Array
Sonic Measurements
Out-of-Plane
Blade Root
Loads
High frequency resonant response
in lateral and vertical directions
of low-speed shaft forward
support bearing
Orthogonal Velocity
Measurements at Head
of Low-Speed Shaft

400 450 500 550 600
-1000
0
1000
(m/s)3
400 450 500 550 600
-1000
0
1000
(m/s)3
400 450 500 550 600
-1000
0
1000
(m/s)3
Time (seconds)
58 m
37 m
15 m
TKE Vertical Flux During This Coherent Event
58-m level (rotor top)
37-m level (hub)
15-m level (rotor bottom)
VerticalTKEflux(m/s)3
Time (seconds)
environment
more stable
(increased turbulence damping)
environment
less stable
available
turbulent
kinetic
energy
turbulence generation
Downward Transport of Turbulent Kinetic Energy

Corresponding Day and Night Example Flapwise
Load Cycle Counting Spectra
0 100 200 300 400 500 600
10
-4
10
-3
10
-2
10
-1
10
0
10
1
Peak-to-peak Amplitude (kNm)
Cycles/second
Nocturnal Boundary Layer
Daytime Boundary Layer
560 kNm cycle
Peak-to-peak load amplitude (kNm)
560 kNm cycle
Cycles/second
result of
rotor
encountering
coherent
event
produces a
“rare event”

Let’s Use a Version of the Wavelet Analysis
Tool to See What the Impact of
Encountering A Coherent Turbulent
Structure Has on the Turbine Drive train

ART Turbine Rotor/Drive Train Time Series Parameters
Associated with Intense Coherent Event
Blade 1 root zero-mean inplane bending load
Bearing Fore-aft
velocity
Bearing Side-Side
velocity
Bearing Vertical
velocity
Low-Speed Shaft
torque
Low-Speed Shaft Forward Support Bearing
Time Series Data
Measured by an Inertial Measurement Unit (IMU)
Mounted on Top of Bearing and Aligned with Low-Speed Shaft

Turbulence-induced KE Flux from ART Rotor into Low-
Speed Shaft Associated with Coherent Event – cont’d
64
Blade in-plane response
Bearing response
KE flux into bearing
Co-Scalograms
Scalograms
Scalograms

Conclusion
65
• The encountering of a coherent turbulent structure
simultaneously excites many vibrational (modal)
frequencies in the turbine blade as it passes through it
• The KE energy associated with each frequency sums
coherently creating a highly energetic burst
• This burst is applied to the structure as an impulse
which can be more damaging than cyclic loading
because of the energy density is greater
• Thus conditions that produce coherent turbulent
structures such as KH instability can be hard on wind
turbine structures and decrease component life if
frequently encountered

The Stable BL Is Hard on Wind Turbines
• Buoyancy plays a major role in shaping the impact of
coherent turbulent structures in the stable BL and the
subsequent impact on wind turbine components
• KH instability is a major player in the generation of coherent
turbulent structures in the nocturnal BL when much of the
fatigue damage to wind turbine structural components takes
place
Height
Time
wind
turbines
Coherent turbulent structures observed in stable BL by NOAA/ESRL HRDL Lidar in Southeast Colorado
during NREL/NOAA Lamar Low-Level Jet Project, September 2003.
Coherent
Structures

Buoyancy Damping Is A Major Player . . .
67
PeakFlapwiseStressCycle(kNm)
0
100
200
300
400
500
600
TurblinelayerRi
0.001
0.01
0.1
1
TL Ri vs TL Lb/D
Turbine layer lb/D
0.1 1 10
HubPeakCTKE(m2
/s2
)
1
10
100
Turbine layer Ri
0.0001 0.001 0.01 0.1 1
TurbineLayerlb/D
0.1
1
10
Buoyancy Damping
Limits Coherent Structure
Size & Intensity and
Reduces Induced Stress
Cycle Magnitude
lb= buoyancy length scale,
D = rotor diameter
/b w BVl Nσ=
Length Scale = Rotor Disk Diameter
Cyclic stress level
Turblne Layer Stability
Hub-level CTKE
moderate
buoyancy
damping
high
buoyancy
damping
low
buoyancy
damping

Turbine layer Ri
0.0001 0.001 0.01 0.1 1
TurbineLayerlb/D
0.1
1
10
The Damping Present Influences the Nature of the
Transient Loads Seen on Wind Turbines
high
buoyancy
damping
Ri =+0.034Ri = +0.007
low
buoyancy
damping
moderate
buoyancy
damping
Upwind array
inflow CTKE
m
2
/s
2
0
20
40
60
80
100
120
0
20
40
60
80
100
120
rotor top (58m)
rotor hub (37m)
rotor left (37m)
rotor right (37m)
rotor bottom (15m)
IMU velocity components
0 2 4 6 8 10 12
mm/s
-20
-10
0
10
20
-20
-10
0
10
20
Time (s)
492 494 496 498 500 502 504
vertical (Z)
side-to-side (Y)
fore-aft (X)
zero-mean
root flap
bending
moment
kNm
-400
-300
-200
-100
0
100
200
300
400
-400
-300
-200
-100
0
100
200
300
400
Blade 1
Blade 2
Ri = +0.015

Conclusions
69
• Spatiotemporal turbulent structures exhibit strong transient
features which in turn induce complex transient loads in wind
turbine structures
• The encountering of patches of coherent turbulence by wind
turbine blades can cause amplification of high frequency
structural modes and perhaps increased local dynamic stresses
in turbine components that are not being adequately modeled
with the inflow simulations used by turbine designers
• Current wind turbine engineering design practice employs
turbulence inflow simulations that are based on neutral,
homogeneous flows that do not reflect the diabatic
heterogeneity that is particularly present in the SBL as we
discussed today
• We believe this disconnect is a major contributor to the
observed wind farm production underperformance and
cumulative maintenance and repair costs

Conclusions – cont’d
70
• Physics-based CFD simulations have the capability of
providing accurate and realistic inflows but 1000s of
simulations are often needed in the turbine design process
and their computational cost makes them feasible for only
a small class of specific problems
• Purely Fourier-based stochastic inflow simulation
techniques cannot adequately reproduce the transient,
spatiotemporal velocity field associated with coherent
turbulent structures
• The NREL TurbSim stochastic inflow simulator has been
designed to provide such a capability for both general and
site specific environments

For more information. . .
71
• Kelley, N. D., 1993, “The identification of inflow fluid dynamics parameters that can
be used to scale fatigue loading spectra of wind turbine structural components,”
NREL/TP-442-6008
• Kelley, N. D., 1994, “Turbulence descriptors for scaling fatigue loading spectra of
wind turbine structural components,” NREL/TP-442-7035
• Kelley, N. D., 1999, “A case for including atmospheric thermodynamic variables in
wind turbine fatigue loading parameter identification,” NREL/CP-500-26829.
• Kelley, N. D., Osgood, R. M., Bialasiewicz, J. T., and Jakubowski, A., 2000, “Using
wavelet analysis to assess turbulence-rotor interactions,” Wind Energy, 3(3), 121-
134.
• Kelley, N., Hand, M., Larwood, S., and McKenna, E.,2002, “The NREL Large-Scale
Turbine Inflow and Response Experiment – Preliminary Results,” NREL/CP-500-
30917
• Kelley, N. D., Jonkman, B. J., and Scott, G. N., 2005, “The impact of coherent
turbulence on wind turbine aeroelastic response and its simulation,” NREL/CP-500-
38074.
• Kelley, N. D., Jonkman, B. J., 2007, “Overview of the TurbSim Stochastic Inflow
Turbulence Simulator Version 1.21,” NREL/TP-500-41137.

A Discussion Question . . .
72
Given a familiarity of the information presented in
this lecture . . .
How would a boundary layer meteorologist
develop a systematic approach to assessing the
turbulence operating environment of candidate
wind energy resource sites in order to insure
compatibility with both the turbine designs being
proposed and the operational protocol?
How can this be communicated to the developer,
turbine supplier, and wind farm operator?

Wind energy applications, ams short course, august 1, 2010, keystone, co

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