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NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated...
Innovation for Our Energy Future
Seminar Objective
• To provide a very brief overview
of NREL research into the impact
of ...
Innovation for Our Energy Future
Outline
3
• Background
• Evolution of Inflow Stochastic Turbulence Simulators
• Research ...
Innovation for Our Energy Future4
Background
Innovation for Our Energy Future
MOD-0A
200 kW
WWG-0600
600 kW
MOD-1
2000 kW
MOD-2
2500 kW
MOD-5B
3200 kW
WTS-4
4000 kW
Ca...
Innovation for Our Energy Future
Hamilton-
Standard
BoeingBoeingGeneral
Electric
Westinghouse Boeing
Rotor Diameter and Hu...
Innovation for Our Energy Future
Results . . .
7
• None of the large, multi-megawatt turbine prototypes reached
full produ...
Innovation for Our Energy Future
The Turbine Operating Situation in the mid 1980’s
8
In California:
• Significant number
o...
Innovation for Our Energy Future
Hawaiian Experience
9
• 15 Westinghouse 600 kW Turbines 1985-1996
• DOE/NASA 3.2 MW Boein...
Innovation for Our Energy Future
Hawaiian Experience – cont’d
10
• 81 Jacobs 17.5 and 20 kW
turbines installed in mountain...
Innovation for Our Energy Future
Today
11
• The U.S. has the greatest installed wind energy capacity in
the world
• New tu...
Innovation for Our Energy Future
However There is a Down Side . . .
• The aggregate performance of currently operating win...
Innovation for Our Energy Future
An Interpretation . . .
13
$
Turbines, as designed, are not
compatible with their operati...
Innovation for Our Energy Future
Research Goals 1989-Present
14
• Develop a physical understanding the role
atmospheric tu...
Innovation for Our Energy Future
Evolution of Stochastic Turbulence Inflow Simulators
15
SNLWIND
Paul Veers
1988
SNLWIND-3...
Innovation for Our Energy Future
Research Approach
16
• Make simultaneous, detailed measurements of both the
turbulent inf...
Innovation for Our Energy Future
Data Sources
17
We have had two sources of measurements of both
the detailed characterist...
Innovation for Our Energy Future18
San Gorgonio Pass California
• Large, 41-row wind farm located downwind of
the San Gorg...
Innovation for Our Energy Future
San Gorgonio Wind Farm
19
Palm Springs
Mt. Jacinto
(
downwind
tower
(76 m, 200 ft)
upwind...
Innovation for Our Energy Future
Micon 65/13 Test Turbines
20
Original
Equipment
AeroStar Rotor
Rotor with NREL
Thin Airfo...
Innovation for Our Energy Future
The National Wind Technology Center
21
NWTC
(1841 m – 6040 ft)
NWTC
Great Plains
Terrain ...
Innovation for Our Energy Future
Measurements at the NWTC
22
• Measurements were made with the naturally-
occurring wind f...
Innovation for Our Energy Future
3-axis sonic anemometers/thermometers
Details of Inflow Turbulence
Dynamics Measured By
P...
Innovation for Our Energy Future24
Correlating Turbulence Scaling Parameters
with Turbine Dynamic Response
Innovation for Our Energy Future
Defining Turbulence-Turbine Dynamics Scaling
Parameters
25
• We chose the primary paramet...
Innovation for Our Energy Future
Definition of variables
26
u = streamwise wind component (along turbine main shaft)
v = c...
Innovation for Our Energy Future
Turbulent K.E. Budget
27
( ' ') ( ' ') ( ' )
T
T
E u g
u w w T w E
t z zT
ε
 ∂ ∂ ∂
=− +...
Innovation for Our Energy Future
Candidate Turbine Response Turbulence Local Scaling
Parameters
28
*' 'u w u=
/u z∂ ∂
, , ...
Innovation for Our Energy Future
Concept of Atmospheric Stability
29
• Static Stability
• Dynamic Stability
Innovation for Our Energy Future
Schematically
cold, dense air
warm,
less
dense
air
IT IS STABLE
But if . . .
IT IS UNSTAB...
Innovation for Our Energy Future
Static Stability and Atmospheric Buoyancy
Height
Temperature
Parcel has
positive buoyancy...
Innovation for Our Energy Future
Buoyancy Creates Dynamic Stability or Instability
Time
An example of dynamic instability
...
Innovation for Our Energy Future
Turbulence-Induced Turbine Dynamic Loads
33
• The fluctuating structural loads created by...
Innovation for Our Energy Future
Turbine Response
Dynamic Load
Statistical
Distribution
Model
Dominant Inflow
Turbulence S...
Innovation for Our Energy Future
Micon 65/13 rotor dynamic response with scaling
parameters
35
RiTL
-0.10 -0.05 0.00 0.05 ...
Innovation for Our Energy Future
Initial Simulation Attempts Inadequate
36
• Simulated San Gorgonio
turbulent inflow into ...
Innovation for Our Energy Future
Comparing Micon and NWTC ART Turbine Responses Sensitivities
to Richardson Number Stabili...
Innovation for Our Energy Future
Turbine Blade Response Due to Turbulence-Induced
Unsteady Aerodynamic Response Stress Cyc...
Innovation for Our Energy Future
Strong Correlation with Peak Coherent Turbulent
Kinetic Energy
39
RiTL
-0.3 -0.2 -0.1 0.0...
Innovation for Our Energy Future
Upwind array
inflow CTKE
m
2
/s
2
0
20
40
60
80
100
120
0
20
40
60
80
100
120
rotor top (...
Innovation for Our Energy Future
Comparing Micon 65 & ART Responses
41
San Gorgonio Micon 65s NWTC ART
Richardson number s...
Innovation for Our Energy Future
Role of Vertical Transport of Coherent Turbulent
Kinetic Energy in Turbine Dynamic Respon...
Innovation for Our Energy Future
Conclusions from Measurements from San Gorgonio
Pass Wind Farm and at the NWTC
43
• Simil...
Innovation for Our Energy Future44
The Impact of a Coherent Turbulent
Structure on a Turbine Drivetrain
Innovation for Our Energy Future
ART Turbine Rotor/Drive Train Time Series Parameters
Associated with Intense Inflow Coher...
Innovation for Our Energy Future
Turbulence-induced KE Flux from ART Rotor into Low-
Speed Shaft Associated with Coherent ...
Innovation for Our Energy Future
Conclusions
47
• The encountering of a coherent turbulent structure
simultaneously excite...
Innovation for Our Energy Future
Conclusions – cont’d
48
• Spatiotemporal turbulent structures exhibit strong transient
fe...
Innovation for Our Energy Future
Conclusions – cont’d
49
• Physics-based CFD simulations have the capability of
providing ...
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Nwtc seminar overview of the impact of turbulence on turbine dynamics, september 14, 2011

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Overview presentation on the impact of atmospheric turbulence on the dynamic response of wind turbines derived from 20 years of research at the National Renewable Energy Laboratory.

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Nwtc seminar overview of the impact of turbulence on turbine dynamics, september 14, 2011

  1. 1. 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. Overview of the Impact of Turbulence on Turbine Dynamics NWTC Seminar Neil D. Kelley September 14, 2011 Innovation for Our Energy Future
  2. 2. Innovation for Our Energy Future Seminar Objective • To provide a very brief overview of NREL research into the impact of atmospheric turbulence and its simulation conducted between 1989 and 2011. • The material for this series of two lectures is contained within the report on the right which is currently in final editing.
  3. 3. Innovation for Our Energy Future Outline 3 • Background • Evolution of Inflow Stochastic Turbulence Simulators • Research Approach • Data Sources • Defining Turbulence and Turbine Response Scaling Parameters • Concept of Atmospheric Stability • Correlating Turbulence Scaling Parameters with Turbine Dynamic Response • Impact of Turbulent Coherent Structures on Turbine Drivetrain • Conclusions
  4. 4. Innovation for Our Energy Future4 Background
  5. 5. Innovation for Our Energy Future MOD-0A 200 kW WWG-0600 600 kW MOD-1 2000 kW MOD-2 2500 kW MOD-5B 3200 kW WTS-4 4000 kW Capacity Evolution of Federal Wind Program Horizontal Axis Turbines 1975-1985
  6. 6. Innovation for Our Energy Future Hamilton- Standard BoeingBoeingGeneral Electric Westinghouse Boeing Rotor Diameter and Hub Height Evolution latest generation turbine hub height range
  7. 7. Innovation for Our Energy Future Results . . . 7 • 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
  8. 8. Innovation for Our Energy Future The Turbine Operating Situation in the mid 1980’s 8 In California: • Significant number of equipment failures • Poor performance due to the installed 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
  9. 9. Innovation for Our Energy Future Hawaiian Experience 9 • 15 Westinghouse 600 kW Turbines 1985-1996 • DOE/NASA 3.2 MW Boeing MOD-5B Prototype 1987-1993 • Installed on 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 MOD-5B during Kona Winds required turbine lock out because of excessive vibrations Oahu
  10. 10. Innovation for Our Energy Future Hawaiian Experience – cont’d 10 • 81 Jacobs 17.5 and 20 kW turbines installed in 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
  11. 11. Innovation for Our Energy Future Today 11 • The U.S. has the greatest installed wind energy capacity in the world • New turbine designs are now reaching or surpassing the capacities of the earlier prototypes • 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.
  12. 12. Innovation for Our Energy Future 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 (Example: Gearbox failures have reached epidemic proportions) • M&O costs are major contributors to a continuance of a higher than targeted COE 10% Wind Farm Power Underproduction & Possible Sources Source: American Wind Energy Association; G. Poulos, V-Bar $ 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
  13. 13. Innovation for Our Energy Future An Interpretation . . . 13 $ 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
  14. 14. Innovation for Our Energy Future Research Goals 1989-Present 14 • Develop a physical understanding the role atmospheric turbulence plays in the dynamic response of wind turbines and its relationship to fatigue accumulation • Describe the atmospheric dynamics responsible for creating the inflow turbulent conditions most damaging to wind turbines • Develop a numerical simulation of such conditions that can be used to drive turbine dynamic design codes in order to engineer ameliorating design solutions
  15. 15. Innovation for Our Energy Future Evolution of Stochastic Turbulence Inflow Simulators 15 SNLWIND Paul Veers 1988 SNLWIND-3D Neil Kelley 1992, 1996 TurbSim Neil Kelley Bonnie Jonkman 2005
  16. 16. Innovation for Our Energy Future Research Approach 16 • 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 turbines tell us what they do not 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.
  17. 17. Innovation for Our Energy Future Data Sources 17 We have had two sources of measurements of both the detailed characteristics of the turbulent inflow and the resulting dynamic response of wind turbines • Field campaign with Developer SeaWest deep within a 41- row wind farm in San Gorgonio Pass, California that contained nearly 1000 turbines in 1989-1990 • LIST Project field campaign at the National Wind Technology Center in 1999-2000 Great Plains turbine operating environment only • Lamar Low-Level Jet Project in 2002-2003 with Enron Wind (will be discussed in 2nd lecture)
  18. 18. Innovation for Our Energy Future18 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
  19. 19. Innovation for Our Energy Future San Gorgonio Wind Farm 19 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)
  20. 20. Innovation for Our Energy Future Micon 65/13 Test Turbines 20 Original Equipment AeroStar Rotor Rotor with NREL Thin Airfoil Blade Design
  21. 21. Innovation for Our Energy Future The National Wind Technology Center 21 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
  22. 22. Innovation for Our Energy Future Measurements at the NWTC 22 • 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
  23. 23. Innovation for Our Energy Future 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 Planar 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 CART2 ART
  24. 24. Innovation for Our Energy Future24 Correlating Turbulence Scaling Parameters with Turbine Dynamic Response
  25. 25. Innovation for Our Energy Future Defining Turbulence-Turbine Dynamics Scaling Parameters 25 • We chose the primary parameters to correlate with turbine dynamics that influence the creation and destruction of turbulent kinetic energy (K.E. or ET) in the atmospheric boundary layer flows that wind turbines operate in • Using the following variables, the turbulent K.E. budget equation that relates these parameters to the local rate of change of K.E. (ET ) within the atmospheric layer in which turbine rotors reside can be expressed as . . .
  26. 26. Innovation for Our Energy Future Definition of variables 26 u = streamwise wind component (along turbine main shaft) v = crosswind or lateral wind component w = vertical wind component T = temperature t = time z = height above the ground surface Overbar = mean Primed quantities have mean removed
  27. 27. Innovation for Our Energy Future Turbulent K.E. Budget 27 ( ' ') ( ' ') ( ' ) T T E u g u w w T w E t z zT ε  ∂ ∂ ∂ =− + − −  ∂ ∂ ∂  mechanical shear stress production buoyant production/ damping vertical flux (transport) viscous dissipation rate local rate of change in turbulent K.E. T iso cohE E E= + total turbulent K.E. isotropic contribution 2 2 2 1/2 1/ 2[( ' ') ( ' ') ( ' ') ]cohE u w u v v w= + + instantaneous coherent kinetic energy coherent contribution
  28. 28. Innovation for Our Energy Future Candidate Turbine Response Turbulence Local Scaling Parameters 28 *' 'u w u= /u z∂ ∂ , , /u u uu I uσ σ= , ', ww w σ ( )( )' 'g T w T ( )( ) ( ) 2 / / / g T T z u z ∂ ∂ ∂ ∂ turbulence generated/damped by buoyancy turbulence generated by shear= ( )( ) 2 / ' ' ( ' ') ( / ) g T w T u w u z∂ ∂ = turbulence generated/damped by buoyancy turbulence generated by shear Rate of gradient Richardson number, Ri = = Mean shearing stress or friction velocity (measure of turbulence level) important parameters in turbulence K.E. budget Measures of Dynamic Stability = flux Richardson number, Rif + = stable − = unstable 0 = neutral
  29. 29. Innovation for Our Energy Future Concept of Atmospheric Stability 29 • Static Stability • Dynamic Stability
  30. 30. Innovation for Our Energy Future Schematically cold, dense air warm, less dense air IT IS STABLE But if . . . IT IS UNSTABLE
  31. 31. Innovation for Our Energy Future Static Stability and Atmospheric Buoyancy Height Temperature Parcel has positive buoyancy and will continue to rise Parcel has no net buoyancy and will remain at this height Parcel has negative buoyancy and will return to its original level It is Unstable It is Neutral It is Stable If we vertically displace the air parcels below .. . Height Height Temperature Temperature warm air cold air constant temperature with height (isothermal) cold air warm air
  32. 32. Innovation for Our Energy Future Buoyancy Creates Dynamic Stability or Instability Time An example of dynamic instability Height warm air cold air The right combination of temperature stratification and wind shear can produce an oscillatory or resonant response in the vertical wind field.
  33. 33. Innovation for Our Energy Future Turbulence-Induced Turbine Dynamic Loads 33 • The fluctuating structural loads created by the varying velocity of turbulent flow across the turbine rotor blades are the primary source 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 found in our research that relates turbulent flow properties to fatigue damage accumulation.
  34. 34. Innovation for Our Energy Future 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 Analysis Results of San Gorgonio Micon 65/13 Turbine Response Variables and Turbulence Scaling Parameters
  35. 35. Innovation for Our Energy Future Micon 65/13 rotor dynamic response with scaling parameters 35 RiTL -0.10 -0.05 0.00 0.05 0.10 3-bladeaveragedFBMDEL(kNm) 13 14 15 16 17 18 19 NREL rotor AeroStar rotor DEL = damage equivalent (fatigue) load Remembering u* = ' 'u w RiTL -0.3 -0.2 -0.1 0.0 0.1 0.2 Hublocalu*(ms-1 ) 1.6 1.8 2.0 2.2 2.4 2.6 2.8 8 10 12 14 16 FBM DEL (kNm) NREL rotor neutral stability Ri = 0 peak dynamic response +0.01 < Ri < +0.025 decaying dynamic response Ri > + 0.05 unstable stable Conclusion: Peak turbulent dynamic response occurs in flow conditions that are highly sheared and weakly stable!
  36. 36. Innovation for Our Energy Future Initial Simulation Attempts Inadequate 36 • Simulated San Gorgonio turbulent inflow into Micon 65 turbine with SNLWIND-3D • Reproduced body of cyclic load distribution • Failed to create the largest observed load cycles • RESULT: Simulated fatigue damage was well below observed
  37. 37. Innovation for Our Energy Future Comparing Micon and NWTC ART Turbine Responses Sensitivities to Richardson Number Stability Parameter 37 Flow Deep within A Multi-row Wind Farm Natural Turbulent Inflow to ART Turbine
  38. 38. Innovation for Our Energy Future Turbine Blade Response Due to Turbulence-Induced Unsteady Aerodynamic Response Stress Cycles! NREL blade Found Organized or Coherent Turbulent Structures Were The Source of the Damaging and Under Predicted Cyclic Loads Inflow turbulence characteristics coherent structure
  39. 39. Innovation for Our Energy Future Strong Correlation with Peak Coherent Turbulent Kinetic Energy 39 RiTL -0.3 -0.2 -0.1 0.0 0.1 0.2HubPeakEcoh(m2 s-2 ) 20 30 40 50 60 6 8 10 12 14 16 18 20 22 24 26 kNm NREL rotor 2 2 2 1/2 1/ 2[( ' ') ( ' ') ( ' ') ]cohE u w u v v w= + +
  40. 40. Innovation for Our Energy Future 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 Measured on NWTC ART Turbine 40 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 Into Low-Speed Shaft
  41. 41. Innovation for Our Energy Future Comparing Micon 65 & ART Responses 41 San Gorgonio Micon 65s NWTC ART Richardson number stability parameter critical stability range Hub peak Ecoh Root flapwise bending damage equivalent load (DEL) Hub vertical velocity standard deviation σw
  42. 42. Innovation for Our Energy Future Role of Vertical Transport of Coherent Turbulent Kinetic Energy in Turbine Dynamic Response 42 Vertical Transport (Flux) of Coherent Kinetic Energy, w’Ecoh Peak [w’Ecoh ] w’Ecoh San Gorgonio Row 37 NWTC ART Peak[w’Ecoh]FlapBMDEL(kNm)FlapBMDEL(kNm) w’Ecoh Wind farm flow has a negative mean downward flux of Ecoh not seen at the NWTC Peaks in downward Ecoh flux are only associated with negative means in wind farm
  43. 43. Innovation for Our Energy Future Conclusions from Measurements from San Gorgonio Pass Wind Farm and at the NWTC 43 • 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 (Ecoh or CTKE) with greater levels of fatigue damage occurring with high values and vertical fluxes 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
  44. 44. Innovation for Our Energy Future44 The Impact of a Coherent Turbulent Structure on a Turbine Drivetrain
  45. 45. Innovation for Our Energy Future ART Turbine Rotor/Drive Train Time Series Parameters Associated with Intense Inflow 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
  46. 46. Innovation for Our Energy Future Turbulence-induced KE Flux from ART Rotor into Low- Speed Shaft Associated with Coherent Event – cont’d 46 Blade in-plane response Bearing response KE flux into bearing Co-Scalograms Scalograms Scalograms
  47. 47. Innovation for Our Energy Future Conclusions 47 • The encountering of a coherent turbulent structure simultaneously excites many vibrational (modal) frequencies in the turbine blade as it passes through • 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 can be hard on wind turbine structures and decrease component life if frequently encountered. The atmospheric processes that produce such conditions will be discussed in the next lecture.
  48. 48. Innovation for Our Energy Future Conclusions – cont’d 48 • 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 stable boundary layer 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
  49. 49. Innovation for Our Energy Future Conclusions – cont’d 49 • 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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