wind energy applications, ams short course, august 1, 2010, keystone, co
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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
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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
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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.
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200 kW
600 kW
2000 kW
2500 kW
3200 kW
4000 kW
Capacity Evolution of Federal Wind Program Turbines 1975-1985
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Hamilton- Standard
Boeing Boeing General Electric
Westinghouse Boeing
Rotor Diameter and Hub Height Evolution
latest generation turbine hub height range
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California Experience
6
Tehachapi Pass
Altamont Pass
San Gorgonio Pass (Palm Springs)
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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
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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
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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
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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
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The Payoff in California . . .
11
Year1985 1990 1995 2000 2005
Reg
iona
l Cap
acity
fact
or (%
)
0
10
20
30
40
50
60
AltamontTehachapiSan Gorgonio
Year1985 1990 1995 2000 2005
Reg
iona
l Cap
acity
fact
or (%
)
0
10
20
30
40
50
60
AltamontTehachapiSan 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.
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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.
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Current Evolution of U.S. Commercial Wind Technology
13
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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
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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
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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.
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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.
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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
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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
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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
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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)
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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
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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
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December 1999 to May 2000
24
Testing at the NWTC
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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
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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
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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-m
turb
ulen
ce
inte
nsity
,I 80
rated wind speed range
The NWTC is a Very Turbulent Site!
Turbulence intensity Standard deviation
Nov 1999-April 2000
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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
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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
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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.
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Alternating stress cycles/hourSource: 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 Pr
edic
ted
alte
rnat
ing
stre
ss (k
Nm
)
Stress amplitude versus frequency of occurrence
Predicted cumulative dam
age (%)
Cumulative Fatigue Damage
A few large stress cycles are more damaging than many smaller ones!
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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
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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?
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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
Cyc
les/
hr
10-1
100
101
102
103
104
exponential fit
Observed Turbulent Load Cycle Spectra for AWT-26/P2 Turbine
(Tehachapi Pass, California)
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N cycles per hour
increased fatigue damage
decreased fatigue damage
Characteristic alternating load cycle magnitude, Mp-p
Slope of Loading Distribution Determines Level of Fatigue Damage
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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 w1/2(| ' ' |)u w1/2(| ' ' |)u w1/2(| ' ' |)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
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N cycles per hour
Characteristic alternating load cycle magnitude, Mp-p
N = βoe−β1M
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)
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Hub local shear stress, u* (m/s)
1 1
2 2
exp exp 1p po
M MNγ γγ
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
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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
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Gradient Richardson number, Ri
Blade Root In-Plane (Edgewise) Load Cycle Extreme Value Distribution Shape Parameter γ2 vs Turbine Layer Dynamic Stability
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Gradient Richardson number, Ri
Nor
mal
ized
cro
ss c
ovar
ianc
e (u
iuj)/ i j
Peak blade root flap bending mom
ent (kNm
)
Turbulence Vertical Component is a Key Player in Turbine Dynamic Response
Large peak loads tend to be associated with the vertical wind component
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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
0.20.4
0.60.8
1.01.2
1.41.6
1.8
-0.4-0.3
-0.2-0.1
0.00.1
Dam
age
equi
vale
nt lo
ad (k
Nm
)
Hub local u * value (m
/s)Turbine layer Ri
6
8
10
12
14
16
18
20
22
24
0.20.4
0.60.8
1.01.2
1.41.6
1.8
-0.4-0.3
-0.2-0.1
0.00.1
Dam
age
equi
vale
nt lo
ad (k
Nm
)
Hub local u * value (m
/s)
Turbine layer Ri
6
8
10
12
14
16
18
20
22
24
0.20.4
0.60.8
1.01.2
1.41.6
1.8
-0.4-0.3
-0.2-0.1
0.00.1
Dam
age
equi
vale
nt lo
ad (k
Nm
)
Hub local u * value (m
/s)
Turbine layer Ri
6
8
10
12
14
16
18
20
22
24
0.20.4
0.60.8
1.01.2
1.41.6
1.8
-0.4-0.3
-0.2-0.1
0.00.1
Dam
age
equi
vale
nt lo
ad (k
Nm
)
Hub local u * value (m
/s)
Turbine layer Ri
Peak Value from Three Blades
Three Blade Average Value
AeroStar Rotor NREL Rotor
Unstable
Stable
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What are the details of the turbulent wind field and turbine blade to produce these
responses?
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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
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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
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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?
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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
Prob
abili
ty (%
)
0
2
4
6
8
10
12
14sunrise sunrset
Local standard time (h)
0 2 4 6 8 10 12 14 16 18 20 22 24
Prob
abili
ty (%
)
0
2
4
6
8OctMay Oct May
NWTC ART TurbineTime-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
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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
Prob
abili
ty (%
)
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
Prob
abili
ty (%
)
0
5
10
15
20
25
30
rated wind speed
NWTC ART Turbine
Conclusion: Highest Blade Root Fatigue Damage Occurs Near Rated Wind Speed!
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-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10
Prob
abili
ty (%
)
0
10
20
30
40
50
60
-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04
Prob
abili
ty (%
)
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
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NWTC Diurnal Variation of Turbine Layer Stability
Diurnal Variation of Turbine Layer Ri During Turbine Operation
Local standard time (h)
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 limitsignificant turbine response upper limit
P05-P95 Ri = +0.1
Ri = +0.05
Significant probability of stability in critical range!
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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
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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
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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
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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?
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Power Spectrum
55
Conventional Power Spectrum of Blade Flapwise Load Time History
Frequency (Hz)0.1 1 10
Roo
t fla
p lo
ad (k
Nm
)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
-15-10-505
101520
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?
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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
0.81.01.21.5
3.05.0
10.0
2.0
Scal
e s
Wavelet Scalogram
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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
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Example of Typical Conditions Seen During Daytime and Nighttime Hours for Flows into the NWTC ART Turbine
0 100 200 300 400 500 6000
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 6000
50
100
150
(m/s
)2
0 100 200 300 400 500 600
-0.2
0
0.2
mm
YZ
0 100 200 300 400 500 600
-2
0
2
deg/
s
PitchYaw
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 6000
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 6000
50
100
150
(m/s
)2
0 100 200 300 400 500 600
-0.2
0
0.2
mm
YZ
0 100 200 300 400 500 600
-2
0
2
deg/
s
PitchYaw
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
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Upwind arrayinflow CTKE
m2 /s
2
0
20
40
60
80
100
120
0
20
40
60
80
100
120rotor 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-meanroot flapbendingmoment
kNm
-400
-300
-200
-100
0
100
200
300
400
-400
-300
-200
-100
0
100
200
300
400
Blade 1Blade 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
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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) Vert
ical
TK
E flu
x (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
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Corresponding Day and Night Example Flapwise Load Cycle Counting Spectra
0 100 200 300 400 500 60010
-4
10-3
10-2
10-1
100
101
Peak-to-peak Amplitude (kNm)
Cyc
les/
seco
ndNocturnal Boundary LayerDaytime Boundary Layer
560 kNm cycle
Peak-to-peak load amplitude (kNm)
560 kNm cycle
Cyc
les/
seco
nd
result of rotor encountering coherent event produces a “rare event”
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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
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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
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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
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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
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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
Hei
ght
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
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Buoyancy Damping Is A Major Player . . .
67
Peak
Fla
pwis
e St
ress
Cyc
le (k
Nm)
0
100
200
300
400
500
600
Turb
line
laye
r Ri
0.001
0.01
0.1
1
TL Ri vs TL Lb/D
Turbine layer lb/D0.1 1 10
Hub
Peak
CTK
E (m
2 /s2 )
1
10
100
Turbine layer Ri0.0001 0.001 0.01 0.1 1
Turb
ine
Laye
r lb
/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
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Turbine layer Ri0.0001 0.001 0.01 0.1 1
Turb
ine
Laye
r lb
/D
0.1
1
10
The Damping Present Influences the Nature of the Transient Loads Seen on Wind Turbines
high buoyancy damping
Ri =+0.034 Ri = +0.007
low buoyancy damping
moderate buoyancy damping
Upwind arrayinflow CTKE
m2 /s
2
0
20
40
60
80
100
120
0
20
40
60
80
100
120rotor 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-meanroot flapbendingmoment
kNm
-400
-300
-200
-100
0
100
200
300
400
-400
-300
-200
-100
0
100
200
300
400
Blade 1Blade 2
Ri = +0.015
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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
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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
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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.
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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?
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