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

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Boundary Layer Turbulence and Turbine Interactions with a Historical Perspective

AMS Short Course:

Wind Energy Applications, Supported by Atmospheric Boundary Layer Theory, Observations, and Modeling Keystone, Colorado

Neil D. Kelley National Wind Technology Center

August 1, 2010

Innovation for Our Energy Future


Outline

2

• Background • Lecture objective • Collecting turbulence-turbine interaction data • Interpreting the results • Understanding the impact of turbulence on turbine

structural components • The role of the stable boundary layer • Conclusions • For more information • A discussion question


Background

3

• Wind energy technology was resurrected in the U.S. in the early 1970s

• After initially being established at the National Science Foundation, the Federal Wind Program was located in what became the U.S. Department of Energy

• The Federal Wind Program had four major components: utility-scale turbine development, small turbine development, vertical axis turbine development, and resource assessment

• The utility-scale program was managed by NASA for the U.S.DOE with prototype turbines built by several contractors between 1975 and 1985.


200 kW

600 kW

2000 kW

2500 kW

3200 kW

4000 kW

Capacity Evolution of Federal Wind Program Turbines 1975-1985


Hamilton- Standard

Boeing Boeing General Electric

Westinghouse Boeing

Rotor Diameter and Hub Height Evolution

latest generation turbine hub height range


California Experience

6

Tehachapi Pass

Altamont Pass

San Gorgonio Pass (Palm Springs)


The Turbine Operating Situation in the mid 1980’s

7

In California: • Significant number

of equipment failures

• Poor performance due in part to the high density of turbines

In Hawaii: • High maintenance costs and

poor availability for Westinghouse turbines on Oahu

• Poor performance of wind farms on the Island of Hawaii


Hawaiian Experience

8

• 15 Westinghouse 600 kW Turbines 1985-1996

• DOE/NASA 3.2 MW Boeing MOD-5B Prototype 1987-1993

• Installed on complex uphill terrain at Kuhuku Point with predominantly upslope, onshore flow but occasionally experienced downslope flows (Kona Winds)

• Chronic underproduction relative to projections for both turbine designs

• Significant numbers of faults and failures occurred during the nighttime hours particularly on the Westinghouse turbines.

• Serious loading issues with the MOD-5B during Kona Winds required the turbine to be locked out because of excessive vibrations generated within the turbine structure

Oahu

Westinghouse 600 kW

MOD-5B


Hawaiian Experience – cont’d

9

• 81 Jacobs 17.5 and 20 kW turbines installed downwind of a mountain pass on the Kahua Ranch 1985-

• Wind technicians reported in 1986 a significant number of failures that occurred exclusively at night

• At some locations turbines could not be successfully maintained downwind of local terrain features and were abandoned

Hawaii


Results . . .

10

• None of the large, multi-megawatt turbine prototypes reached full production status

• Post analysis revealed that the structural fatigue damage to these machines far exceeded the original design estimates in virtually all cases

• These excessive loads were attributed to atmospheric turbulence

• In the late 1980’s and early 1990’s the industry concentrated on the development wind farms employing large numbers of turbines in the 25 to 200 kW range


The Payoff in California . . .

11

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.


Today

12

• The U.S. has the greatest installed wind energy capacity in the world

• New turbine designs are now reaching the capacities of the 1970-1980 prototypes once again and are beginning to surpass them

• New turbines are being designed to capture energy from lower wind resource sites which increases their rotor diameters and hub heights

• The new machines are being constructed of lighter and stronger materials in order to reduce the cost of energy but they are also more dynamically active.


Current Evolution of U.S. Commercial Wind Technology

13


However There is a Down Side . . .

• The aggregate performance of currently operating wind U.S. wind farms has been estimated to be in the neighborhood of 10% below project design estimates

• Maintenance and operations (M&O) costs are seen as approaching equivalency with the production tax credit

• Both are major contributors to a continuance of a higher than the targeted Cost of Energy (COE)

10% Wind Farm Power Underproduction & Possible Sources

Source: American Wind Energy Association

$

High Maintenance & Repair Costs Contribution to M&O

Expected annual M&R costs over a 20 year turbine lifetime

Courtesy: Matthias Henke, Lahmeyer International presented at Windpower 2008

used with permission

Presenter

Presentation Notes

AWEA Estimates of sources of underproduction Data from Lahmeyer Int’l indicates accumulative damage is occurring and certainly not the “bucket” curve model


An Interpretation . . .

15

$

Turbines, as designed, are not compatible with their operating environments This incompatibility manifests itself as increasing cumulative costs as the turbines age

• We believe atmospheric turbulence continues to play a major role in this incompatibility

• The larger and more flexible turbines being designed and installed today when coupled with a much different atmospheric operating environment at these heights are being challenged

• We will now overview our research into the effects of turbulence on wind turbines conducted over the past 20 years


Lecture Objective

16

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


Research Approach

17

Make simultaneous, detailed measurements of both the turbulent inflow and the corresponding turbine response!

Interpret the results in terms of how various turbulent fluid dynamics parameters influence the response of the turbine (loads, fatigue, etc.)

Let the turbine tell us what it does not like!

Develop the ability to include these important characteristics in numerical inflow simulations used as inputs to the turbine design codes

Adjust the turbulent inflow simulation to reflect site-specific characteristics or at least general site characteristics; i.e., complex vs homogeneous terrain, mountainous vs Great Plains, etc.


Data Sources

18

We have had two source of measurements of both the detailed characteristics of the turbulent inflow and the resulting dynamic response of a wind turbine

• Deep within a 41-row wind farm in San Gorgonio Pass, California that contained nearly 1000 turbines in 1989-90

• The National Wind Technology Center Test Site south of Boulder, Colorado in 1999-2000

Innovation for Our Energy Future 19

San Gorgonio Pass California

• Large, 41-row wind farm located downwind of the San Gorgonio Pass near Palm Springs

• Wind farm had good production on the upwind (west) side and along the boundaries but degraded steadily with each increasing row downstream as the cost of turbine maintenance increased

• Frequent turbine faults occurred during period from near local sunset to midnight

• Significant amount of damage to turbine components including blades and yaw drives


San Gorgonio Regional Terrain

20

Pacific Ocean Salton

Sea

wind farms (152 m, 500 ft)

(−65 m, −220 ft)

(793 m, 2600 ft) Los Angeles

Basin

Mohave Desert

Sonoran Desert

San Bernardino Mountains


Wind Farm Nearby Topography

21

Palm Springs

Mt. Jacinto (

downwind tower

(76 m, 200 ft)

upwind tower

(107 m, 250 ft)

row 37

San Gorgonio Pass

nocturnal canyon flow

(3166 m, 10834 ft)


Side-by-Side Turbine Testing at Row 37

7D row-to-row spacing

Gathering Data in a Wind Farm Environment SeaWest 41-row San Gorgonio Wind Farm in 1989 & 1990 – A legacy site

Presenter

Presentation Notes

Very dense wind farm with 41 rows with a 7D row-row spacing and included almost 1000 turbines� High damage and low production of turbines in center and downwind� Today much of the farm has been either repowered with larger machines but there are a few clusters of the originally installed turbine-types with the same row-row spacing. Large stretches of the original site have had the turbines removed.� Strong vertical motions were frequently seen at Row 37 during the nighttime hours particularly during the day-night transition period


Analyzing San Gorgonio Wind Farm Turbine Turbulence-Turbine Responses

23

• Two, 65 kW side-by-side turbines were available that were identical except for different rotor aerodynamic designs

• Location was deep within the wind farm with turbines 7 rotor diameters upstream

• Very turbulent wake conditions produced elevated turbine dynamic responses that allowed better correlation with turbulent scaling parameters

• Provided initial analyses of turbulence-turbine interactions that could be extended and refined using data from the NWTC experiment


December 1999 to May 2000

24

Testing at the NWTC


Gathering Response Data in the Natural Flow of a High Turbulence Site

25

NWTC (1841 m – 6040 ft)

NWTC

Great Plains

Terrain Profile Near NWTC in Direction of Prevailing Wind ection

Denver

Boulder

• Strong downslope winds (Chinooks) from the 13,000 foot Front Range Mountains that occur during the fall, winter, and spring months

• The winds have a distinct pulsating characteristic that contain strong, turbulent bursts

Presenter

Presentation Notes

Except perhaps at certain turbine test sites along Row 1 at the west side of the property, the NWTC is technically not a complex terrain site using the IEC definition.� Upwind fetch of the eastern most sites (Row 4) is flat with low vegetation and a slight west to east downward slope� Measurements showed the existence of strong vertical motions during downslope winds somewhat similar to those seen within the San Gor wind farm in Calif.


Measurements at the NWTC

26

• Measurements were made with the naturally-occurring wind flows, no upstream turbine wakes

• Data was taken in flows that originated over the Front Range of the Rocky Mountains to the West

• Objective was to compare the turbine response to natural turbulent flows with those measured in the multi-row wind farm


3-axis sonic anemometers/thermometers

Details of Inflow Turbulence Dynamics Measured By Planar Array of Sonic Anemometers

Measured the Resulting Dynamic Responses of the ART Turbine

Using An Upwind Inflow Array and a 600 kW Turbine

80-m mean wind speed, V80 (m/s)

80-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

Presenter

Presentation Notes

Detailed analyses of the significant turbine response episodes revealed the existence of intense, spatially organized coherent structures smaller than the rotor disk that were being brought down from above by strong vertical gusts � These episodes translate into the distribution of TI and Sigma statistics being shown� It is unlikely that either of the IEC NTM models would be able to reproduce such events


What We Have Found From Testing at Both Sites

• In a wake environment deep within a very large wind farm

• In very energetic natural turbulent flow downwind of a major mountain range


Turbulence and Wind Turbines

29

• Turbulence in the turbine inflow has a significant influence on the power performance efficiency and the lifetime of turbine components

• The primary source of degraded performance and component reliability are the unsteady aerodynamic effects created by turbulent flow over the turbine rotor blades

• These unsteady effects create dynamic loads on the rotor blades that in turn excite a range of vibrational frequencies associated with the turbine structure that must be dissipated by the turbine structure


Turbulence-Induced Dynamic Loads

30

• The fluctuating structural loads created by turbulent flow across the turbine rotor blades are one of the most important sources of cyclic stresses in the mechanical components of the turbine

• These cyclic stresses cumulatively induce component fatigue damage that continues to increase until failure

• We will now look at what we have found in our research that relates turbulent flow properties to fatigue damage accumulation.


Alternating stress cycles/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!


Load Cycle Frequency Distributions

32

In analyzing turbulence-induced alternating stress or load cycles in wind turbines we found:

• Small amplitude, often occurring load cycles were normally or Gaussian distributed

• Less frequent and more damaging high amplitude cycles were exponentially distributed


N cycles per hour

Characteristic alternating load cycle magnitude, Mp-p

Fewer cycles but more intense: Exponentially Distributed

More cycles but lower intensity: Gaussian distributed

High Fatigue Damage Region

Observed Blade Root Loading Cycle Distributions

What does this say about the nature of the turbulence excitation?

Presenter

Presentation Notes


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)


N cycles per hour

increased fatigue damage

decreased fatigue damage


Slope of Loading Distribution Determines Level of Fatigue Damage


Turbine Response Dynamic Load

Statistical Distribution Model

Dominant Inflow Turbulence Scaling Parameter(s)

Percent Variance Explained#

Blade root out-of-plane bending Exponential , Ri 89

Low-speed shaft torque Exponential , Ri 78

Low-speed shaft bending Exponential , Ri 94

Yaw drive torque Exponential , Ri 87

Tower top torque Exponential , 88

Tower axial bending Exponential σH 78

Nacelle inplane thrust Exponential , Ri 77

Tower inplane thrust Exponential 69

Blade root inplane bending Extreme value 86

1/2(| ' ' |)u 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


N cycles per hour


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)


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


Gradient Richardson number, Ri

Blade Root Out-of-Plane Load Cycle Exponential Distribution Slope Parameter β1 vs Turbine Layer Stability

INFLOW TURBULENCE SCALING VARIABLES

TURBINE DYNAMIC RESPONSE VARIABLE

M-O Stability Parameter, z/L



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



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


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

)


/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

)


/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

)


/s)

Turbine layer Ri

Peak Value from Three Blades

Three Blade Average Value

AeroStar Rotor NREL Rotor

Unstable

Stable


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

responses?


NREL blade

Turbine Blade Response Due to Turbulence-Induced Unsteady Aerodynamic Response Stress Cycles!

Organized or Coherent Turbulence is a Major Contributor to Turbine Fatigue Damage

Inflow turbulence characteristics

Coherent turbulent structures

Turbine Dynamic Responses


Turbulent Structures That Induce Turbine Dynamic Responses Can be Smaller than the Rotor Disk Their Intensity is a Function of the Dynamic Stability of the Rotor Layer

Ri =+0.034

more intense peak loads generated within single blade rotation

Ri = +0.007

blades encountered turbulent structures at the same location during three consecutive rotor rotations

Peak Blade Root Out-of-Plane Bending Loads Generated within Rotor Rotations


Here we compare results from both the San Gorgonio Wind Farm and the NWTC Measurements to see if there are any

systematic differences

46

Are There Certain Times of Day and BL Conditions when Greater Fatigue Damage

Occurs?


Diurnal Variations in High Blade Structural Loads

San Gorgonio Wind Farm Micon 65 Turbines at Row 37 Time-of-Day Distribution of Occurences of High Blade Loads

Local standard time (h)2 4 6 8 10 12 14 16 18 20 22 24

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


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!


-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


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!


Need a Way to Correlate Organized Turbulent Structures and Turbine Component Fatigue

• Need single numbers that represent – Level of turbine component fatigue damage – Intensity of turbulent energy associated with coherent structures

• Damage Equivalent Load (DEL)

– a measure of the equivalent fatigue damage caused by each load taking into account the fatigue properties of the material where DEL = (Σ Ni Li

m / Neq )1/m where Ni is the number of cycles for load Li , m is dependent on the material (steel = 3 and composite = 10 is usually used), and Neq is the equivalent number of cycles within a 10-minute period (at a 1 Hz reference frequency it is 1200)

– It describes the level of fatigue damage with one number • Coherent TKE (CTKE or Coh TKE)

– Defined as the partition of turbulent kinetic energy that is coherent as CTKE = 1/2[ (u’w’)2 + (u’v’)2 + (v’w’)2]1/2; CTKE of isotropic turbulence = 0


Conclusions from Measurements from San Gorgonio Pass Wind Farm and at the NWTC

52

Similar load sensitivities to vertical stability (Ri) and vertical wind motions were found at both locations

We found that the turbine loads were also responsive to the new inflow scaling parameter, Coherent Turbulent Kinetic Energy (CTKE) with greater levels of fatigue damage occurring with high values of this variable

In both locations, the peak damage equivalent load occurred at a slightly stable value of Ri in the vicinity of +0.02

Clearly, based on both sets of measurements, coherent or organized turbulence played a major role in causing increased fatigue damage on wind turbine rotors

San Gorgonio Micon 65/13

NWTC 600 kW ART


Overall Interpretation of the Field Measurements

53

The greatest fatigue damage occurs during the nighttime hours when the atmospheric boundary layer up to the maximum height of the turbine rotor is just slightly stable (0 < Ri < +0.05)

Significant vertical wind shear was often also present

Both of these conditions are prerequisites for Kelvin-Helmholtz Instability (KHI)

The presence of KHI can be responsible for generating atmospheric motions called KH billows or waves which in turn generate coherent turbulence as they breakdown or decay


Let’s look at these details but first we need to discuss a analytical tool that is necessary to for us to identify the

mechanisms involved

54

How does turbulent energy in the turbine inflow contribute to the fatigue damage of

structural components?


Power Spectrum

55

Conventional Power Spectrum of Blade Flapwise Load Time History

Frequency (Hz)0.1 1 10

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?


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


Time Series and Wavelet Analyses Presentations

Time Histories

Continuous Wavelet Transform Coefficients of

Root Flapwise-Bending Signal

Discrete Wavelet Transform Detail Frequency Bands of

Root Flapwise-Bending Signal (Multi-resolution Analysis)

Time

Hub-height horizontal wind speed

Hub-height Reynolds stresses

Root flapwise-bending load


Example of Typical Conditions Seen During Daytime and Nighttime Hours for Flows into the NWTC ART Turbine

0 100 200 300 400 500 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


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


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


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”


Let’s Use a Version of the Wavelet Analysis Tool to See What the Impact of

Encountering A Coherent Turbulent Structure Has on the Turbine Drive train


ART Turbine Rotor/Drive Train Time Series Parameters Associated with Intense Coherent Event

Blade 1 root zero-mean inplane bending load

Bearing Fore-aft velocity

Bearing Side-Side velocity

Bearing Vertical velocity

Low-Speed Shaft torque

Low-Speed Shaft Forward Support Bearing Time Series Data

Measured by an Inertial Measurement Unit (IMU) Mounted on Top of Bearing and Aligned with Low-Speed Shaft


Turbulence-induced KE Flux from ART Rotor into Low-Speed Shaft Associated with Coherent Event – cont’d

64

Blade in-plane response

Bearing response

KE flux into bearing

Co-Scalograms

Scalograms

Scalograms


Conclusion

65

• The encountering of a coherent turbulent structure simultaneously excites many vibrational (modal) frequencies in the turbine blade as it passes through it

• The KE energy associated with each frequency sums coherently creating a highly energetic burst

• This burst is applied to the structure as an impulse which can be more damaging than cyclic loading because of the energy density is greater

• Thus conditions that produce coherent turbulent structures such as KH instability can be hard on wind turbine structures and decrease component life if frequently encountered


The Stable BL Is Hard on Wind Turbines • Buoyancy plays a major role in shaping the impact of

coherent turbulent structures in the stable BL and the subsequent impact on wind turbine components

• KH instability is a major player in the generation of coherent turbulent structures in the nocturnal BL when much of the fatigue damage to wind turbine structural components takes place

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


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


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


Conclusions

69

• Spatiotemporal turbulent structures exhibit strong transient features which in turn induce complex transient loads in wind turbine structures

• The encountering of patches of coherent turbulence by wind turbine blades can cause amplification of high frequency structural modes and perhaps increased local dynamic stresses in turbine components that are not being adequately modeled with the inflow simulations used by turbine designers

• Current wind turbine engineering design practice employs turbulence inflow simulations that are based on neutral, homogeneous flows that do not reflect the diabatic heterogeneity that is particularly present in the SBL as we discussed today

• We believe this disconnect is a major contributor to the observed wind farm production underperformance and cumulative maintenance and repair costs


Conclusions – cont’d

70

• Physics-based CFD simulations have the capability of providing accurate and realistic inflows but 1000s of simulations are often needed in the turbine design process and their computational cost makes them feasible for only a small class of specific problems

• Purely Fourier-based stochastic inflow simulation techniques cannot adequately reproduce the transient, spatiotemporal velocity field associated with coherent turbulent structures

• The NREL TurbSim stochastic inflow simulator has been designed to provide such a capability for both general and site specific environments


For more information. . .

71

• Kelley, N. D., 1993, “The identification of inflow fluid dynamics parameters that can be used to scale fatigue loading spectra of wind turbine structural components,” NREL/TP-442-6008

• Kelley, N. D., 1994, “Turbulence descriptors for scaling fatigue loading spectra of wind turbine structural components,” NREL/TP-442-7035

• Kelley, N. D., 1999, “A case for including atmospheric thermodynamic variables in wind turbine fatigue loading parameter identification,” NREL/CP-500-26829.

• Kelley, N. D., Osgood, R. M., Bialasiewicz, J. T., and Jakubowski, A., 2000, “Using wavelet analysis to assess turbulence-rotor interactions,” Wind Energy, 3(3), 121-134.

• Kelley, N., Hand, M., Larwood, S., and McKenna, E.,2002, “The NREL Large-Scale Turbine Inflow and Response Experiment – Preliminary Results,” NREL/CP-500-30917

• Kelley, N. D., Jonkman, B. J., and Scott, G. N., 2005, “The impact of coherent turbulence on wind turbine aeroelastic response and its simulation,” NREL/CP-500-38074.

• Kelley, N. D., Jonkman, B. J., 2007, “Overview of the TurbSim Stochastic Inflow Turbulence Simulator Version 1.21,” NREL/TP-500-41137.


A Discussion Question . . .

72

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