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

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Overview of boundary layer meteorology and wind energy technology presented at AMS Short Course, Aug 1, 2010 at Keystone, Colorado.

Overview of boundary layer meteorology and wind energy technology presented at AMS Short Course, Aug 1, 2010 at Keystone, Colorado.

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  • 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. 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
  • 2. 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
  • 3. Innovation for Our Energy Future 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.
  • 4. Innovation for Our Energy Future 200 kW 600 kW 2000 kW 2500 kW 3200 kW 4000 kW Capacity Evolution of Federal Wind Program Turbines 1975-1985
  • 5. Innovation for Our Energy Future Hamilton- Standard BoeingBoeingGeneral Electric Westinghouse Boeing Rotor Diameter and Hub Height Evolution latest generation turbine hub height range
  • 6. Innovation for Our Energy Future California Experience 6 Tehachapi Pass Altamont Pass San Gorgonio Pass (Palm Springs)
  • 7. Innovation for Our Energy Future 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
  • 8. Innovation for Our Energy Future 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
  • 9. Innovation for Our Energy Future 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
  • 10. Innovation for Our Energy Future 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
  • 11. Innovation for Our Energy Future 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.
  • 12. Innovation for Our Energy Future 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.
  • 13. Innovation for Our Energy Future Current Evolution of U.S. Commercial Wind Technology 13
  • 14. 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 • 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
  • 15. Innovation for Our Energy Future 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
  • 16. Innovation for Our Energy Future 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.
  • 17. Innovation for Our Energy Future 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.
  • 18. Innovation for Our Energy Future 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
  • 19. 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
  • 20. Innovation for Our Energy Future 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
  • 21. Innovation for Our Energy Future 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)
  • 22. Innovation for Our Energy Future 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
  • 23. Innovation for Our Energy Future 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
  • 24. Innovation for Our Energy Future December 1999 to May 2000 24 Testing at the NWTC
  • 25. Innovation for Our Energy Future 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
  • 26. Innovation for Our Energy Future 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
  • 27. 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 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
  • 28. Innovation for Our Energy Future28 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
  • 29. Innovation for Our Energy Future 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
  • 30. Innovation for Our Energy Future 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.
  • 31. Innovation for Our Energy Future 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!
  • 32. Innovation for Our Energy Future 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
  • 33. Innovation for Our Energy Future 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?
  • 34. Innovation for Our Energy Future 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)
  • 35. Innovation for Our Energy Future 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
  • 36. 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 ANOVA Analysis Results of San Gorgonio Wind Farm Turbine Response Variables and Turbulence Scaling Parameters
  • 37. Innovation for Our Energy Future N cycles per hour Characteristic alternating load cycle magnitude, Mp-p 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)
  • 38. Innovation for Our Energy Future 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
  • 39. Innovation for Our Energy Future 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
  • 40. Innovation for Our Energy Future Gradient Richardson number, Ri Blade Root In-Plane (Edgewise) Load Cycle Extreme Value Distribution Shape Parameter γ2 vs Turbine Layer Dynamic Stability
  • 41. Innovation for Our Energy Future Gradient Richardson number, Ri 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
  • 42. Innovation for Our Energy Future 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 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 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 Damageequivalentload(kNm) Hub local u * value (m /s) Turbine layer Ri Peak Value from Three Blades Three Blade Average Value AeroStar Rotor NREL Rotor Unstable Stable
  • 43. Innovation for Our Energy Future43 What are the details of the turbulent wind field and turbine blade to produce these responses?
  • 44. Innovation for Our Energy Future 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
  • 45. Innovation for Our Energy Future 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
  • 46. Innovation for Our Energy Future 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?
  • 47. Innovation for Our Energy Future 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 Local standard time (h) 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
  • 48. Innovation for Our Energy Future 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!
  • 49. Innovation for Our Energy Future -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
  • 50. Innovation for Our Energy Future 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 limit significant turbine response upper limit  P05-P95 Ri = +0.1 Ri = +0.05 Significant probability of stability in critical range!
  • 51. Innovation for Our Energy Future51 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
  • 52. Innovation for Our Energy Future 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
  • 53. Innovation for Our Energy Future 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
  • 54. Innovation for Our Energy Future 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?
  • 55. Innovation for Our Energy Future 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?
  • 56. Innovation for Our Energy Future56 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
  • 57. Innovation for Our Energy Future 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
  • 58. Innovation for Our Energy Future 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
  • 59. 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 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
  • 60. Innovation for Our Energy Future 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
  • 61. Innovation for Our Energy Future 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”
  • 62. Innovation for Our Energy Future62 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
  • 63. Innovation for Our Energy Future 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
  • 64. Innovation for Our Energy Future 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
  • 65. Innovation for Our Energy Future 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
  • 66. Innovation for Our Energy Future 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
  • 67. Innovation for Our Energy Future 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
  • 68. Innovation for Our Energy Future 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
  • 69. Innovation for Our Energy Future 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
  • 70. Innovation for Our Energy Future 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
  • 71. Innovation for Our Energy Future 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.
  • 72. Innovation for Our Energy Future 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?