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303-275-3000 • www.nrel.gov

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337 

J.M. Jonkman 

National Renewable Energy Laboratory

A. Craig Hansen 

Windward Engineering, LLC 

To be presented at the 43rd AIAA Aerospace Sciences 

Meeting and Expedition 

Reno, Nevada 

January 10–13, 2005 

December 2004 • NREL/CP-500-39589 

Development and Validation of 

an Aeroelastic Model of a Small 

Furling Wind Turbine

Preprint 
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Development and Validation of an Aeroelastic Model of a 

Small Furling Wind Turbine 

Jason M. Jonkman*

National Renewable Energy Laboratory (NREL), Golden, Colorado, 80401-3393

and 

Dr. A. Craig Hansen†

Windward Engineering LLC, Salt Lake City, Utah, 84117

Small wind turbines often use some form of furling (yawing and/or tilting out of the 

wind) to protect against excessive power generation and rotor speeds in high winds. Though 

the apparatus used to passively furl a rotor is straightforward, the modeling of furling 

behavior is inherently complex, because the behavior depends on the rotor tilt angle and 

thrust offset, furling arrangement and mass balance and numerous other properties. Until 

recently, ADAMS® with AeroDyn has been the only simulator capable of modeling this 

complex aeroelastic furling problem. The need for a simulator with furling capability that is 

simpler to use and more readily available led NREL to upgrade their FAST aeroelastic 

simulator. The upgrade’s features include a lateral offset and skew angle of the rotor shaft, 

rotor- and tail-furling degrees-of-freedom, up- and down-furl stops and tail fin 

aerodynamics and inertia. To verify and validate these new features, response comparisons

were made between FAST, ADAMS and test data from the Small Wind Research Turbine.

The verification study demonstrated the correct implementation of FAST’s furling 

dynamics. During validation, the model tends to predict mean rotor speeds higher than 

measured in spite of the fact that the mean furl motion and rotor thrust are predicted quite 

accurately. This work has culminated with an enhanced version of FAST that should prove 

to be a valuable asset to designers of small wind turbines. 

I. Introduction 

MALL distributed-scale wind turbines rated 50kW or less often use some form of furling to protect the turbine

against excessive power generation and rotor speeds in high winds. Furling is a passive form of control in which

the rotor yaws and/or tilts out of the wind to limit the aerodynamic torque and thrust loading. 

S 

Furling control is impractical in large utility-scale wind turbines because of the enormous gyroscopic loads that

would ensue. Instead, large wind turbines usually employ active controls to regulate power, limit loads and improve

stability. These include active control of the blade pitch, generator torque and nacelle yaw. These means of control

are unreasonable for small wind turbines due to the large costs involved. 

Furling behavior is achieved through the use of a lateral rotor thrust offset, a tail fin and a furling hinge. In high

winds, the rotor thrust and aerodynamic moments tend to yaw the rotor out of the wind. The furling hinge allows the 

tail to remain nominally aligned with the wind while the rotor yaws out of the wind. 

Several furling hinge configurations have been used successfully in small commercial wind turbines. In one

configuration, the tail boom is connected to the tower-top through the yaw bearing and the furling hinge is placed 

between the tail boom and the generator housing, which in turn connects to the rotor through the rotor bearing. This 

 *

 Engineer II-Structural Systems & Dynamics, National Wind Technology Center (NWTC), 1617 Cole Boulevard,

AIAA Professional Member. 

†

 Partner, 2225 East Murray-Holladay Road #201A. 

This material is declared a work of the U.S. Government and is not subject to copyright protection in the United

States. 

1
configuration, which is employed in Southwest 

Windpower‡’s Whisper H40 wind turbine (Fig. 1), is 

referred to as rotor-furling in this paper. In another

configuration, the generator housing is connected to the

tower-top through the yaw bearing and the furling hinge 

is placed between the generator housing and the tail 

boom. This configuration, which is employed in Bergey 

Windpower§

’s Excel wind turbine (Fig. 2), is referred to

as tail-furling in this paper. Other furling configurations

are possible, though less common, and are not discussed at length in this paper.

Figure 1. Whisper H40 by Southwest Windpower. 

Though the apparatus used to passively furl a rotor is simple and straightforward, 

furling behavior is inherently complex because it is affected by the unsteady 

aerodynamics of a rotor in a highly skewed flow; rotor tilt angle and lateral thrust 

offset; furling hinge configuration and mass balance; tail size, location and lift and 

drag characteristics; blade pitch; alternator torque characteristics and numerous other

properties. This complexity makes optimization difficult and provides incentive to use 

a comprehensive aeroelastic turbine simulator in the design process.

Over the past decade, the U.S. Department of Energy’s (DOE’s) National 

Renewable Energy Laboratory (NREL)

** has sponsored the development, verification

and validation of comprehensive aeroelastic simulators capable of predicting both the 

extreme and fatigue loads of horizontal-axis wind turbines (HAWTs). These 

simulation tools, also known as design codes, are used by industry, academia and 

government entities for wind turbine design, certification and research. 

In general, these design codes enable a user to (1) define an aerodynamic and

structural model of a wind turbine given the turbine geometry and aerodynamic and 

mechanical properties of its members and (2) simulate the wind turbine’s aerodynamic

and structural response by imposing complex, virtual, wind-inflow conditions. Outputs

of the simulations include time-series data on the aerodynamic loads, as well as loads 

and deflections of the structural members of the wind turbine. Post-processing codes are then used to analyze these 

data. 

Figure 2. Bergey Excel by 

Bergey Windpower. 

FAST (Fatigue, Aerodynamics, Structures, and Turbulence) and ADAMS® (Automatic Dynamic Analysis of

Mechanical Systems) (“ADAMS” is used to imply “ADAMS®” throughout this paper)2,3 are two of the primary 

design codes used by the U.S. wind industry and the two most promoted by NREL’s National Wind Technology 

Center (NWTC)††. FAST is a relatively simple, structural-response, HAWT-specific code written and distributed by

the NWTC. The code is based on previous developments done at Oregon State University and the University of

Utah. The more complex ADAMS code is a commercially available, general purpose, multibody-dynamics code 

from MSC.Software Corporation‡‡ that is adaptable for modeling wind turbines. Both FAST and ADAMS use the 

AeroDyn aerodynamic subroutine package developed by Windward Engineering LLC§§ for calculating aerodynamic 

forces. 

Until recently, ADAMS with AeroDyn has been the only code capable of modeling the complex aeroelastic 

furling problem. This and the complexity and cost involved with using ADAMS, are some of the reasons why

design refinement of small furling wind turbines has historically depended almost entirely on trial-and-error testing.

For years, designers of small wind turbines have asked the NWTC to develop a simpler-to-use and more-readily

available wind turbine simulator with furling capability. In fact, code development was a recommended action of the 

NWTC Furling Workshop held in July of 2000***. 

To address these requests, the NWTC has recently upgraded their FAST code to include furling effects. This

paper presents the results of this effort and shows comparisons of the new FAST model predictions to ADAMS

 ‡

 Website: http://www.windenergy.com/. §

 Website: http://www.bergey.com/. ** Website: http://www.nrel.gov/. †† Website: http://www.nrel.gov/wind/. ‡‡ Website: http://www.mscsoftware.com/. §§ Website: http://www.windwardengineering.com/. *** Website: http://wind.nrel.gov/Furling/workshop.html. 

2
model predictions and, more importantly, to test data taken from the NWTC’s recent testing campaign of the Small 

Wind Research Turbine (SWRT). 

II. Overview of FAST 

Before describing FAST’s new furling capability, it is constructive to step back and outline the general class of

modeling techniques employed in FAST. For a more detailed description of these methodologies, see the FAST

User’s Guide1

. 

FAST models the blades and tower as individual flexible elements using a modal representation. The flexibility

characteristics of these members are determined by specifying distributed stiffness and mass properties along the 

span of the members and by prescribing their mode shapes through equivalent polynomial coefficients. FAST has 

two flapwise and one edgewise bending mode per blade and two fore-aft and two side-to-side bending modes in the

tower. Torsional flexibility in the drivetrain is modeled using an equivalent linear spring and damper model in the

low-speed shaft. The nacelle and hub are modeled in FAST as rigid bodies with appropriate mass and inertia terms. 

Time marching of the equations of motion is performed using a constant-time-step, Adams-Bashforth-AdamsMoulton, predictor-corrector integration scheme. FAST has a limited number of degrees-of-freedom (DOFs) but can

model most common wind turbine configurations and control scenarios. These DOFs can be enabled or locked

through switches, permitting the user to easily increase or decrease the fidelity of the model. 

FAST can extract linearized representations of the complete nonlinear aeroelastic wind turbine model. This 

analysis capability is useful for developing state matrices of a wind turbine “plant” to aid in controls design and 

analysis. It is also useful for determining the full system modes of an operating or stationary HAWT through the use 

of a simple eigenanalysis. 

Another feature available in FAST is the ADAMS preprocessor. The FAST-to-ADAMS preprocessor uses the 

configuration information available in the FAST input files to construct an ADAMS dataset (model) of the complete

aeroelastic wind turbine. 

Both FAST and ADAMS use the AeroDyn aerodynamic subroutine package for computing aerodynamic forces

4

. 

This aerodynamic package models rotor aerodynamics using the classic, equilibrium-based, bladeelement/momentum (BEM) theory or by using a generalized dynamic inflow model, both of which include the 

effects of axial and tangential induction. The BEM model uses tip and hub losses as characterized by Prandtl. 

Dynamic-stall behavior can be characterized using the optional Beddoes-Leishman dynamic stall model. More 

details can be found in the AeroDyn theory manual. 

III. Furling Capability Upgrades 

The recent upgrades to FAST feature a lateral thrust offset and skew angle of the rotor shaft from the yaw axis, 

rotor- and tail-furling DOFs, up- and down-furl stops and tail fin aerodynamics and inertia. 

The location and orientation of the new furling DOFs are user-specified, making the simulator flexible enough to

model many furling wind turbine 

configurations. For example, to 

model a wind turbine with tailfurling, the new tail-furl DOF 

should be enabled and the rotor-furl

DOF locked. The angular motion of

the tail boom and fin through the

tail-furl DOF then takes place about

a tail-furl axis defined by input

parameters TFrlPntxn, TFrlPntyn, 

TFrlPntzn, TFrlSkew and TFrlTilt. 

Inputs TFrlPntxn, TFrlPntyn and 

TFrlPntzn locate an arbitrary point

on the tail-furl axis relative to the 

tower-top. Inputs TFrlSkew and 

TFrlTilt then define the angular 

orientation of the tail-furl axis 

passing through this point. See Fig.

3 for a schematic. A similar set of

input parameters is used to define

zn

yn

xn

Tail-furl

axis

Arbitrary point on

rotor-furl axis 

Arbitrary point on

tail-furl axis 

Rotor-furl

axis

Yaw bearing

C.M.

Rotor-furl

Tail-furl 

Wind

Figure 3. Layout of furl axes in FAST. 

3
the rotor-furl DOF for a turbine with 

rotor-furling (such a turbine should 

have the tail-furl DOF locked). The 

rotor-furl DOF can alternatively be 

used to model torsional flexibility in

the gearbox mounting if the rotor-furl

axis is aligned with the rotor shaft axis. 

The structural dynamic equations

of motion of the full system now

include all of the inertia forces 

(gyroscopic, Coriolis, etc.) that result

from furl motion. These equations do

not require that the furl motions remain

small because small angle assumptions 

were not used in their derivations. A

FAST user does not need to worry

about the complexity of the equations

of motion because they are built within

the code; a user need only be

concerned with defining the

configuration of the system properly. Figure 4 and Fig. 5 illustrate the input parameters that define the configuration 

of the rotor-furling and tail-furling components, respectively. As implied by these figures, the generator housing is 

modeled as a rigid body with a lumped point mass and a moment of inertia specified about the rotor-furl axis. The 

tail is modeled as a rigid body with lumped tail boom and tail fin point masses and a moment of inertia specified 

about the tail-furl axis. 

zn

yn

xn

Rotor shaft 

axis

C.M. of structure that

furls with the rotor [not

including rotor]

Rotor 

rotation

Wind

Yaw bearing 

C.M.

Figure 4. Rotor-furling mass and configuration properties in FAST. 

The furling hinges can be ideal with no friction. A standard model is also available that includes a linear spring, 

linear damper and Coulomb damper, as well as up- and down-stop springs and up- and down-stop dampers. FAST

models the stop springs with a linear function of furl deflection. The furl stops start at a specified angle and work as

a linear spring based on the deflection past the stop angle. The furl dampers are linear functions of the furl rate and 

start at the specified up-stop and down-stop angles. These dampers are bidirectional, resisting motion equally in both

directions once they pass the stop angle. Hooks for interfacing user-defined furl springs and dampers are also 

available. 

The AeroDyn rotor aerodynamics, which already accounted for skewed flow, were not modified for the addition

of furling capability. However, a simple tail fin aerodynamics model has been implemented in FAST. Hooks for 

interfacing user-defined models are also available. By accessing information from AeroDyn, the simple model 

computes the relative velocity of the 

wind-inflow and its angle of attack 

relative to the tail fin chordline and 

uses an AeroDyn airfoil table chosen

by the user to determine the lift and 

drag forces acting at the tail fin 

center-of-pressure. To account for the

velocity deficit in the rotor wake, the 

wind velocity at the tail fin center-ofpressure is decreased by the average 

rotor induced velocity in the direction 

of the rotor shaft. The chordline and 

plane of the tail fin may be skewed, 

tilted and banked relative to the tail

boom as shown in Fig. 5. 

zn

yn

xn

Tail fin

chordline

Tail boom

C.M.

Tail fin 

C.M.

Tail fin 

C.P.

Yaw bearing 

C.M.

Wind

Tail fin x

Tail fin z

Tail fin y

Figure 5. Tail-furling mass and configuration properties in FAST.

The linearization features of

FAST have been extended so that the

resulting linearized models can now 

contain furling DOF “states,” which

produce furling modes. 

4
The FAST-to-ADAMS preprocessor has also been

upgraded so that FAST can create an ADAMS dataset 

of a furling wind turbine by making use of the input

parameters that specify the furling configuration.

Graphic elements of the tail boom and tail fin are 

included in the ADAMS dataset.

IV. SWRT Test and Simulation Models 

To verify the correct implementation of the newly

added furling dynamics, response predictions from

FAST were compared to those of ADAMS using

models of the SWRT. For validation of the SWRT 

model and of FAST’s furling predictions, comparisons 

were made to test data. 

The real SWRT (Fig. 6) is a highly instrumented

and extensively modified Bergey Excel 10 kW system.

The rotor furls horizontally out of the wind by means 

of a tail-furling hinge, which includes up- and downfurl stops and a furl damper. The SWRT testing 

campaign has provided detailed measurements of

furling behavior and loads data that were previously

unavailable. All tests were run in the field over a 

course of one year at the NWTC. More details on the turbine and test methods are provided in a companion paper at 

this conference5

. 

Figure 6. SWRT at the NWTC. 

The FAST model of the SWRT was developed using geometric, aerodynamic and mass properties measured at

the NWTC or provided by Bergey Windpower. The model includes blade flexibility, a variable speed generator with

a torque-speed look-up table, tail-furling with nonlinear user-defined springs and dampers and free yaw. 

V. Verification of Furling Dynamics 

It is advantageous to verify FAST’s furling dynamics 

against ADAMS because the dynamics in ADAMS are not

defined by the user and are well verified. FAST’s dynamic 

response predictions for turbines without furling have been

well verified against ADAMS in a previous study. 

For our recent comparisons to ADAMS, we used FAST

v5.10d-jmj, AeroDyn v12.56 and ADAMS 2003.0.0. We 

generated our ADAMS dataset (Fig. 7) using the FAST-toADAMS preprocessing capability of FAST. We used

SNwind v1.22 to generate full-field turbulent wind files for 

some of the test cases and IECWind v4.21 to generate 

extreme wind conditions for others. 

Because of the space limitations for this paper, we

cannot describe the verification tests in any detail; we will

only describe the results. 

In general, the comparisons of response predictions

between the codes show excellent agreement. In most 

output channels, the FAST and ADAMS response 

predictions agree so well that the ADAMS curve cannot be 

distinguished from the FAST curve. For example, Fig. 8 

shows predictions of the tail-furl and yaw angle responses 

to a one-year extreme operating gust in IEC category A 

conditions. The slight variations that do exist are attributed 

to the higher-fidelity blade flexibility model in ADAMS 

hat accounts for blade torsion and higher-order bending

odes. Comparisons like these demonstrate that FAST’s 

t

m Figure 7. ADAMS SWRT model generated by 

FAST. 

5
furling dynamics are implemented properly. 

VI. Comparison of Model and Test 

Results 

The SWRT tests provide a unique 

opportunity to validate the aeroelastic

models. Three configurations of the SWRT

rotor have been tested with both an inverter 

and a fixed resistance electrical load on the

alternator. Two configurations, labeled A and 

B, use SH3052 blades. These configurations

differ only in the offset distance between the 

rotor centerline and the yaw axis. The 

distance is 0.106 m in configuration A and 

0.083 m (a 22% reduction) in configuration

B. Test results from these two configurations

will be used in this paper. Configuration C

uses SH3055 blades. Validation using data

from this configuration will be the subject of 

future work. 

-20

-15

-10

-5

0

5

10

0 5 10 15 20

Time, s

Tail-Furl and Yaw Angles, deg

ADAMS

FAST

Tail-Furl

Yaw

Figure 8. Tail-furl and yaw angle response during a one-year 

extreme operating gust. 

All model results shown in this section were generated with FAST v5.10d-jmj. Two types of comparisons will be 

shown. Direct comparison of time series is useful in that it provides a highly detailed view of a small amount of

data. Comparisons of summary statistics are valuable because they provide a view of a large quantity of data, albeit

in much less detail. 

Time series comparisons are made by driving the FAST model with measured wind data. In these cases, we had 

measurements of the hub-height horizontal and vertical wind speeds and the wind direction. We did not have 

measurements of instantaneous horizontal wind shear. Horizontal wind shear is known to be an important driver of

yaw and furling motions, but it is difficult to measure. Driving a model with such “hub-height” wind data misses

small-scale turbulence effects, but captures the large-scale, slow variations in wind characteristics. 

Comparisons of statistics are accomplished by running the FAST model with simulated turbulent wind

conditions generated by SNWind v1.22. We used an 8 x 8 grid of points across the rotor disc and generated three 

components of time-varying wind at each grid point. In the present work, we generated turbulence with statistics that 

match the IEC category A conditions10. It 

would be preferable from a technical point

of view to generate turbulence with

statistics that match each of the test 

records. Unfortunately, the cost and time

required to generate 186 turbulence files

that match three components of turbulence

intensity and length scale of the 

measurements made this option

impractical. 

Figures 9 and 10 compare measured

and predicted rotor speed and furl angle for 

10-minutes of operation of configuration A

with an inverter load. The mean wind 

speed during this test was 14.1 m/s. Two 

sets of predictions are shown. The first

predictions were made with no “tuning” of 

model inputs to improve the match (though

measured turbine properties such as blade

natural frequencies and masses were used

to develop the model). The second 

predictions were made after the airfoil data 

tables were modified to improve

0 100 200 300 400 500 600

150

200

250

300

350

400

450

500

550

600

 Rotor Speed, RPM

 Time, s

 Test

 Original prediction

 "Tuned" prediction

Figure 9. Time series of rotor speed. Configuration A with 

inverter load. 

6
agreement between measured and predicted power 

coefficients. The lift coefficients were reduced and the 

drag increased to reduce the power output of the rotor.

This tuning process is highly uncertain and could be

the subject of another paper. There is not enough space

in this paper to provide more details on the tuning that

was done. 

Figures 9 and 10 show that the model correctly

predicts the general behavior of the rotor but

significantly over-predicts the rotor speed. The rotor

speed prediction improved after the airfoil tables were 

modified, but the predictions remain higher than the 

data. At times, the furl angle is accurately predicted,

but there are many other times when the furl angle is

over- or under-predicted. These observations are

consistent with prior experience modeling and testing

a Whisper H40 furling turbine. 

Though not shown in any of the following figures,

the FAST model significantly under-predicts root edge

bending moments in high rotor speeds, while rotor

torque is predicted reasonably well. It was discovered that the cause of this discrepancy is chordwise mass offsets

combined with increasing centrifugal forces in high rotor speeds. (The real SWRT has chordwise mass offsets that

the FAST model does not account for.) The chordwise mass offset generates no centrifugal moment about the axis

of rotation; hence it has no effect on the rotor torque. It does, however, generate an edgewise moment at any blade 

station that is not on the rotor axis. See Ref. 5 for more information on this phenomenon. 

0 100 200 300 400 500 600

0

10

20

30

40

 Furl Angle, deg

 Time, s

 Test

 Original prediction

 "Tuned" prediction

Figure 10. Time series of tail-furl angle. 

Configuration A with inverter load. 

Figures 11 through 15 compare statistics for 186 10-minute test records and 40 10-minute simulations. All of

these results are for configuration A with an inverter

load. All of these and subsequent predictions use the 

modified airfoil tables. Figure 11 compares the 

measured turbulence intensities with the values 

generated by SNWind and used in the FAST

predictions. There is much more variation in

measured turbulence than in the turbulence used in 

the FAST code. This inevitably produces more

variations in the measured turbine response than in

the predicted response. 

The trends are quite interesting. Mean rotor 

speed is over-predicted in winds above 12 m/s, but

maximum rotor speed is under-predicted except in

winds above approximately 14 m/s. Mean furl angle 

and rotor thrust predictions are in very good 

agreement with the data. Although there is quite a bit

of scatter in the peak values, the model and test

results are similar for furl angle. The peak rotor 

thrust predictions are consistently lower than the data. Mean yaw error is under-predicted by approximately 5° to

10° as shown in Fig. 15. Maximum yaw error, due to the rapid fluctuations in wind direction, contains tremendous

scatter and is not shown. 

0

5

10

15

20

25

30

35

40

45

6 8 10 12 14 16 18

Mean Windspeed, m/s

Turbulence Intensity, %

Test

IEC Category A

Figure 11. Measured and IEC “A” turbulence 

intensities. Configuration A with inverter load.

The results seem to indicate that the lift forces on the blades are predicted with reasonable accuracy, hence the

good prediction of rotor thrust and furl angle. An error in drag prediction on the blades could account for the error in

rotor speed prediction without causing errors in the thrust prediction. It is also possible that compressibility effects

are becoming important at the high rotor speeds of these tests. The Mach number at the blade tip is approximately

0.45 at 500 RPM and 0.62 at 700 RPM. Unfortunately, there are too many variables involved in the furling 

prediction to allow us to determine, with confidence, what the dominant source of error might be. 

7
-10

0

10

20

30

40

50

60

70

80

6 8 10 12 14 16 18

Mean Windspeed, m/s

Furl Angle, deg 

Test mean

Test max

FAST Mean

FAST Max

Figure 13. Statistics of tail-furl angle for the same

tests and predictions of Fig. 12. 

0

100

200

300

400

500

600

6 8 10 12 14 16 18

Mean Windspeed, m/s

Rotor Speed, RPM

Test mean

Test max

FAST Mean

FAST Max

Figure 12. 10-minute mean and maximum values 

of rotor speed. Configuration A with inverter load. 

0

1

2

3

4

5

6

6 8 10 12 14 16 18

Mean Windspeed, m/s

Rotor Thrust, kN

Test mean

Test max

FAST Mean

FAST Max

Figure 14. Statistics of rotor thrust for the same

tests and predictions of Fig. 12. 

0

5

10

15

20

25

30

6 8 10 12 14 16 18

Mean Windspeed, m/s

Yaw Error, deg

Test mean

FAST Mean

Figure 15. Mean yaw error for the same tests

and predictions of Fig. 12. 

Figures 16 through 19 show similar results for configuration B with an inverter load. There are 47 test data sets 

and 40 simulations represented in these plots. The trends are very similar to those shown for configuration A though, 

as expected, the reduced thrust offset results in less furling. However, the reduced rotor offset does not significantly

change the rotor speeds. Note that the mean furl angle is accurately predicted but, unlike configuration A, the peak

furl angles are generally over-predicted. 

Finally, time series comparisons were done for configuration B operating with a resistor load. This load changes 

the alternator torque-speed curve and makes it more consistent (the inverter was constantly changing the load on the 

alternator, introducing a random element in the torque-speed curve that our models do not account for). The average

wind during this 10-minute test was 17.6 m/s, which produced considerable furl motion.

Figures 20 through 23 show some of the results with more detail but point to the same general conclusions. 

Rotor speed predictions are consistently too high while furl, thrust and yaw predictions are quite accurate. 

Figure 24 presents the furl and thrust results of Fig. 21 and Fig. 22 in a different format. The results were 

smoothed using a 1-second moving average to reduce the scatter in the plot. The model and data trends are quite

different when viewed in this manner. Note that, while the model peak thrust is higher, there is less furling. This 

indicates a possible problem in the model inputs, perhaps in the nonlinear furl damper, which provides little

resistance to furling but high resistance to unfurling. This behavior is included in the model, but there is some

uncertainty regarding the characteristics. The lack of instantaneous wind shears in the model winds is another likely

contributor to this discrepancy. Of course, it may also indicate a more fundamental problem with the aerodynamic

model of the rotor or tail fin. As mentioned before, there are too many variables to be confident of finding the major

sources of model errors. 

8
0

100

200

300

400

500

600

6 8 10 12 14 16 18 20

Mean Windspeed, m/s

Rotor Speed, RPM

Test Max

Test Mean

FAST Mean

FAST Max

Figure 16. 10-minute mean and maximum values 

of rotor speed. Configuration B with inverter load. 

0

10

20

30

40

50

60

70

80

6 8 10 12 14 16 18 20

Mean Windspeed, m/s

Furl Angle, deg

Test Max

Test Mean

FAST Mean

FAST Max

Figure 17. Statistics of tail-furl angle for the same

tests and predictions of Fig. 16. 

0

5

10

15

20

25

6 8 10 12 14 16 18 20

Mean Windspeed, m/s

Yaw Error, deg

Test Mean

FAST Mean

Figure 19. Statistics of yaw error for the same

tests and predictions of Fig 16. 

0

1

2

3

4

5

6

6 8 10 12 14 16 18 20

Mean Windspeed, m/s

Rotor Thrust, kN

Test Max

Test Mean

FAST Mean

FAST Max

Figure 18. Statistics of rotor thrust for the same 

tests and predictions of Fig. 16. 

VII. Conclusion 

The need for a wind turbine simulator with furling capability that is simpler to use and more readily available led 

NREL to upgrade their FAST aeroelastic simulator. The features in the upgrade include a lateral offset and skew

angle of the rotor shaft, rotor- and tail-furling DOFs, up- and down-furl stops and tail fin aerodynamics and inertia. 

A verification study demonstrated the correct implementation of the newly added furling dynamics by 

comparing response predictions from FAST and ADAMS. 

As in previous code validation efforts

13,14, we see areas of broad agreement between model and test results. We

also see important areas of disagreement. The model tends to predict mean rotor speeds higher than measured in

spite of the fact that the mean furl motion and rotor thrust are predicted quite accurately. Peak values show larger 

discrepancies than mean values. This is expected in the very turbulent winds of the NWTC test site, but it probably 

also indicates that nonlinearities in the system are not modeled with sufficient accuracy. Examples of important 

nonlinearities in the SWRT turbine are the mechanical furl damper and, of course, the unsteady aerodynamics of a 

rotor with a skewed and turbulent inflow. 

Experience with design and research projects has shown us that computer models can be very helpful during the 

design and optimization of furling behavior. They have successfully pointed out counter-intuitive design changes 

that have shortened the design/test/iterate cycle. The current and earlier projects have also clearly pointed out the 

need to improve our understanding of furling behavior and its representation in our computer models. 

Future Work 

The NWTC is planning to incorporate additional DOFs and functionality to FAST in the near future. We plan to

add a torsion DOF to the modal representation of the tower and to extend the modal representation of the blades to

include mass and elastic offsets, torsion DOFs and coupled mode shape properties. In addition, we plan to introduce 

9
0 100 200 300 400 500 600

-5

0

5

10

15

20

25

30

35

40

45

50

55

Furl Angle, deg

Time, s

 Test

 FAST

Figure 21. Tail-furl angle during the same test

shown in Fig. 20. 

a variable step size integrator into FAST to eradicate the

numerical difficulties involved in modeling rapidly changing

rotor speeds and/or highly nonlinear user-defined springs 

and dampers. Both of these code enhancements should help

improve the accuracy of dynamic response predictions of

furling wind turbines. 

0 100 200 300 400 500 600

200

300

400

500

600

700

Rotor Speed, RPM

Time, s

 Test

 FAST

Figure 20. Rotor speed during one 10-minute

test of Configuration B with resistor load. 

0 100 200 300 400 500 600

0

1

2

3

4

5

6

7

Rotor Thrust, kN

Time, s

 Test

 FAST

Figure 22. Rotor thrust during the same test

shown in Fig. 20. 

0 100 200 300 400 500 600

-55

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10

15

20

Yaw Angle, deg

Time, s

 Test

 FAST

Figure 23. Rotor yaw angle during the same test

shown in Fig. 20. 

Additional code enhancements not relating to furling

behavior are planned for FAST. The most important include 

plans to add six support-platform motion DOFs (three 

translation, three rotation), foundation flexibility and

earthquake loading models and wave loading dynamics.

Two orthogonal drivetrain shaft-bending DOFs will also be

added so that users can predict the important dynamics 

associated with the whirl of the shaft. 

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5

0

10

20

30

40

50 Test

 FAST

Furl Angle, deg

Thrust, kN

~1 sec averages

Figure 24. Scatterplots of 1-second average tailfurl angle versus rotor thrust for the same dataset 

of Fig. 20. 

As the code becomes more complex, the NWTC will 

complete additional verification studies. One such study is

already in the works as FAST and ADAMS are being tested

10
by Germanischer Lloyd AG††† (GL) in partnership with the NWTC. 

We also plan to continue to exploit the data from the SWRT experiment to further validate and improve furling 

dynamics predictions. We have plans to continue the SWRT model validation using data from configuration C and

to inspect and test output channels that have yet to be examined. 

Acknowledgments 

The authors would like to thank everyone who helped with this work, including the small wind turbine design

community, for their invaluable feedback related to code functionality and usability considerations (especially the 

staff of Bergey Windpower, Southwest Windpower, Global Energy Concepts LLC, Windward Engineering LLC and

NREL); Jason Cotrell of NREL, for helping create turbine layout schematics in SolidWorks; Jeff Minnema of 

Windward Engineering LLC, for assembling the first furling model in FAST and alpha-testing the new features;

Dave Corbus, Dan Prascher and Mark Meadors of NREL, for providing us with SWRT data and Kathleen O’Dell of

NREL, for making this paper much more readable. 

This work was performed in support of the U.S. DOE by NREL under contract number DE-AC36-99-GO10337

and by Windward Engineering LLC under NREL subcontract number LAM-3-33216-01.

References 1

Jonkman, J. M., and Buhl, M. L., Jr., “FAST User's Guide,” NREL/EL-500-29798, Golden, CO: National Renewable 

Energy Laboratory, October 2004. 2

Laino, David J., and Hansen, A. Craig, “User's Guide to the Computer Software Routines AeroDyn Interface for ADAMS®,”

Salt Lake City, UT: Windward Engineering LLC, Prepared for the National Renewable Energy Laboratory under Subcontract 

No. TCX-9-29209-01, September 2001. 3

Elliott, A. S., “Analyzing Rotor Dynamics with a General-Purpose Code,” Mechanical Engineering, Vol. 112, No. 12, 

December 1990, pp. 21–25. 4

Laino, David J., and Hansen, A. Craig, “User's Guide to the Wind Turbine Dynamics Aerodynamics Computer Software 

AeroDyn,” Salt Lake City, UT: Windward Engineering LLC, Prepared for the National Renewable Energy Laboratory under 

Subcontract No. TCX-9-29209-01, December 2002. 5

Corbus, D., and Prascher, D., “Analysis and Comparison of Test Results from the Small Wind Research Turbine Test

Project,” Collection of the 2005 ASME Wind Energy Symposium Technical Papers Presented at the 43rd AIAA Aerospace

Sciences Meeting and Exhibit, 10–13 January 2005, Reno, NV, Washington, D.C.: American Institute of Aeronautics and

Astronautics, January 2005, NREL/CP-500-36891. 6

Moriarty, Patrick J., and Hansen, A. Craig, “AeroDyn Theory Manual,” NREL/EL-500-36881, Golden, CO: National 

Renewable Energy Laboratory, October 2004. 7

Jonkman, J. M., and Buhl, M. L., Jr., “New Developments for the NWTC’s FAST Aeroelastic HAWT Simulator,” 

Collection of the 2004 ASME Wind Energy Symposium Technical Papers Presented at the 42nd AIAA Aerospace Sciences 

Meeting and Exhibit, 5–8 January 2004, Reno, NV, Washington, D.C.: American Institute of Aeronautics and Astronautics, 

January 2004, pp. 181–191; NREL/CP-500-35077. 8

Buhl, M. L., Jr., “SNwind User's Guide,” NREL/EL-500-30121, Golden, CO: National Renewable Energy Laboratory, June 

2003. 9

IECWind, Software Package, Ver 4.21, Golden, CO: National Renewable Energy Laboratory, November 2002. 10IEC 61400-1, Wind Turbine Generator Systems – Part 1: Safety Requirements, 2nd ed., International Electrotechnical 

Commission (IEC), 1999. 11Hansen, A. C., “Yaw Dynamics of Horizontal Axis Wind Turbines, Final Report,” NREL/TP-442-4822, Golden, CO: 

National Renewable Energy Laboratory, May 1992. 12Davis, Dean A., Hansen, A. Craig, and Laino, David J., “Estimates of Maximum Design Loads for a Whisper H40 Using 

Extrapolation Methods,” Proceedings of the Windpower 2003 Conference, 18-21 May 2003, Austin, TX, Washington, D.C.: 

American Wind Energy Association, session 4C, 2003. 13Eggers, A. J. Jr., Chaney, K., Holly, W. E., Ashley, Holt, Green, H. James, and Sencenbaugh, Jim, “Modeling of Yawing 

and Furling Behavior of Small Wind Turbines,” Collection of the 2000 ASME Wind Energy Symposium Technical Papers 

Presented at the 38th AIAA Aerospace Sciences Meeting and Exhibit, 10–13 January 2000, Reno, NV, Washington, D.C.:

American Institute of Aeronautics and Astronautics, January 2000, pp. 1–11. 14Schreck, Scott (Editor), “Special Issue: Analysis and Modeling of the NREL Full-Scale Wind Tunnel Experiment,” Wind

Energy, Vol. 5, No. 2-3, April-September 2002. 

 ††† Website: http://www.gl-group.com/start.htm. 

11
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Development and Validation of an Aeroelastic Model of a Small 

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14. ABSTRACT (Maximum 200 Words)

Small wind turbines often use some form of furling (yawing and/or tilting out of the wind) to protect against excessive 

power generation and rotor speeds in high winds.The verification study demonstrated the correct implementation of 

FAST’s furling dynamics. During validation, the model tends to predict mean rotor speeds higher than measured in 

spite of the fact that the mean furl motion and rotor thrust are predicted quite accurately. This work has culminated 

with an enhanced version of FAST that should prove to be a valuable asset to designers of small wind turbines. 

15. SUBJECT TERMS

small wind turbine; modeling; furling; aeroelastic furling 

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