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

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


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.


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

Innovation for Our Energy Future 4

Background


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


Hamilton- Standard

Boeing Boeing General Electric

Westinghouse Boeing

Rotor Diameter and Hub Height Evolution

latest generation turbine hub height range


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


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


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


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


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.


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

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 . . .

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


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


Evolution of Stochastic Turbulence Inflow Simulators

15

SNLWIND Paul Veers

1988

SNLWIND-3D Neil Kelley 1992, 1996

TurbSim Neil Kelley

Bonnie Jonkman 2005


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.


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)


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 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)


Micon 65/13 Test Turbines

20

Original Equipment

AeroStar Rotor

Rotor with NREL Thin Airfoil

Blade Design


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

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

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


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-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 CART2

ART

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


Correlating Turbulence Scaling Parameters with Turbine Dynamic Response


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 . . .


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


Turbulent K.E. Budget

27

( ' ') ( ' ') ( ' )TT

E u gu w w T w Et 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 1/21/ 2[( ' ') ( ' ') ( ' ') ]cohE u w u v v w= + + instantaneous coherent kinetic energy

coherent contribution


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


Concept of Atmospheric Stability

29

• Static Stability • Dynamic Stability


Schematically

cold, dense air

warm, less

dense air

IT IS STABLE

But if . . .

IT IS UNSTABLE


Static Stability and Atmospheric Buoyancy He

ight

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 .. .

Heig

ht

Heig

ht

Temperature Temperature warm air cold air

constant temperature with height (isothermal)

cold air warm air


Buoyancy Creates Dynamic Stability or Instability

Time

An example of dynamic instability

Heig

ht

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.


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.


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 Analysis Results of San Gorgonio Micon 65/13 Turbine Response Variables and Turbulence Scaling Parameters


Micon 65/13 rotor dynamic response with scaling parameters

35

RiTL

-0.10 -0.05 0.00 0.05 0.10

3-bl

ade

aver

aged

FB

M D

EL

(kN

m)

13

14

15

16

17

18

19

NREL rotorAeroStar rotor

DEL = damage equivalent (fatigue) load

Remembering u* = ' 'u w

RiTL

-0.3 -0.2 -0.1 0.0 0.1 0.2

Hub

loca

l u* (

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!


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


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


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


Strong Correlation with Peak Coherent Turbulent Kinetic Energy

39

RiTL

-0.3 -0.2 -0.1 0.0 0.1 0.2H

ub P

eak

Eco

h (m

2 s-2)

20

30

40

50

60

6 8 10 12 14 16 18 20 22 24 26

kNm

NREL rotor

2 2 2 1/21/ 2[( ' ') ( ' ') ( ' ') ]cohE u w u v v w= + +


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 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


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


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’E

coh

] Fl

ap B

M D

EL (k

Nm

) Fl

ap B

M D

EL (k

Nm

)

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


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


The Impact of a Coherent Turbulent Structure on a Turbine Drivetrain


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


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


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.


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


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