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 Future4
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
BoeingBoeingGeneral
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

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

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-mturbulence
intensity,I80
rated wind
speed range
The NWTC is a Very Turbulent Site!
Turbulence intensity Standard deviation
Nov 1999-April 2000 CART2
ART

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
( ' ') ( ' ') ( ' )
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

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
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

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.

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 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

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!

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 0.2HubPeakEcoh(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= + +

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

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’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

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

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