A national laboratory of the U.S. Department of Energy 

Office of Energy Efficiency & Renewable Energy 

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Conference Paper Effect of Blade Torsion on 

NREL/CP-500-39000 Modeling Results for the Small January 2006 

Wind Research Turbine (SWRT) 

Preprint 

D. Corbus 

National Renewable Energy Laboratory 

A.C. Hansen and J. Minnema 

Windward Engineering LLC 

To be presented at the 2006 ASME Wind Energy Symposium 

Reno, Nevada 

January 8–10, 2006 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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Effect of Blade Torsion on Modeling Results for the Small 

Wind Research Turbine (SWRT) 

Dave Corbus 

National Wind Technology Center 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 (USA) 

A. Craig Hansen, Jeff Minnema 

Windward Engineering LLC 

2225 East Murray Holladay Road #201A 

Salt Lake City, Utah 84117 (USA) 

The Small Wind Research Turbine (SWRT) testing project was initiated with the goal of 

better characterizing both small wind turbine loads and dynamic behavior. The main 

purpose of the testing was to produce high quality data sets for model development and 

validation and to help the wind industry further their understanding of small wind turbine 

behavior, including furling. Previous papers have discussed modeling results for the SWRT 

using the FAST aeroelastic simulator. This paper summarizes modeling results from both 

the FAST and ADAMS aeroelastic simulators. The ADAMS model allows for blade 

torsional stiffness to be modeled, which is shown to significantly improve the agreement 

between model and test results for rotor speed and explain variances in previous 

comparisons between the SWRT FAST modeling results and the SWRT test data. The 

ADAMS model results also show that the aerodynamic pitching moment impacts the blade 

torsion more than the inertial forces. 

I. Introduction 

mall wind turbines provide unique challenges to researchers trying to model their performance because they 

have higher RPMs than large wind turbines and often operate with large yaw errors and high yaw rates. 

Furthermore, many small wind turbines incorporate furling, whereby the rotor either tilts and/or yaws out of the 

wind to protect itself from overspeed during very high winds, and this can add further complications to modeling 

their performance. Because of these operating conditions, modeling the performance of small wind turbines is 

particularly challenging. 

S 

 The Small Wind Research Turbine (SWRT) project was initiated with the goal of providing reliable data for a 

small furling wind turbine for model development, furthering the understanding of small wind turbine aerodynamics 

and furling, and model validation. The test was the most extensive loads test ever conducted for a small wind 

turbine 1

, and included measurements of turbine furling and thrust, as well as many other loads measurements. 

 The SWRT was modeled using the FAST 2,3 (Fatigue, Aerodynamics, Structures, and Turbulence) code with the 

AeroDyn aerodynamic subroutine package developed by Windward Engineering LLC 4

 to calculate aerodynamic 

forces. FAST, which was developed and is distributed by the National Renewable Energy Laboratory’s (NREL’s) 

National Wind Technology Center (NWTC), is the primary aeroelastic simulator used by the U.S. wind industry to 

model small wind turbines. The recent upgrades to FAST allow users to simulate lateral offset and skew angle of 

the rotor shaft from the yaw axis, rotor-furling and tail-furling degrees-of-freedom (DOFs), up- and down-furl stops, 

and tail inertia and tail fin aerodynamic effects 5

. Because the location and orientation of the furling DOFs are userspecified, the simulator is flexible enough to model virtually any furling wind turbine configuration. FAST’s builtin features allow for the determination of full-system modes, including furling, of an operating or stationary turbine. 

1
 Extensive modeling of the SWRT turbine, using FAST, showed that rotor RPM was always over predicted, 

whereas the predicted and measured thrust were well correlated. This error in the rotor RPM was found for different 

airfoils and increased with wind speed 6

. Various approaches were tried to understand why the FAST model 

predicted rotor speeds much higher than measured in high winds. In low winds, the predictions were quite good. 

Both the airfoil tables and the alternator torque-speed curve were adjusted in various ways. Unrealistically large 

changes were required to improve the agreement in high winds, but then the accuracy in low winds was 

compromised. The reason for the discrepancy was finally discovered to be the elastic twist of the blade, which 

increases with rotor speed (or wind speed). 

Subsequent modeling with ADAMS® (Automatic Dynamic Analysis of Mechanical Systems, “ADAMS” is used 

to imply “ADAMS®” throughout this paper) 7

, allowed blade torsion to be modeled. ADAMS is a general purpose, 

multibody-dynamics code from the MSC.Software Corporation for calculating multi-body dynamic forces that is 

adaptable for modeling wind turbines. FAST cannot currently model blade torsion. 

The approach to modeling the SWRT consisted of the following: (1) upgrading the FAST model to include 

furling; (2) validating the FAST model by constructing an ADAMS model of the SWRT and comparing the two 

results for furling cases; (3) running simulations with the FAST model and comparing them to the SWRT test data; 

and finally, (4) including torsional blade stiffness in the ADAMs model and re-running the simulations performed in 

step (3) in ADAMs. This last step, which is the topic of this paper, was necessary because after lengthy data 

analysis and comparisons between FAST SWRT simulations and test data, it was determined that the “live twist” of 

the blade was having a significant impact on rotor performance. Torsional stiffness cannot currently be modeled in 

FAST, so the ADAMS model was used for the final simulations and the torsional stiffness of the blade was input 

into the model. 

The ADAMS modeling with blade torsion as an input resulted in a significantly improved correlation between 

modeled and measured rotor RPM as discussed in this paper and revealed that blade torsion, referred to herein as 

“live twist”, can have a very significant effect in wind turbine modeling. 

II. Test Turbine Description 

The SWRT is a modified Bergey EXCEL 10-kW turbine that furls horizontally out of the wind. The tail boom 

attaches with a hinge and bumper arrangement to the rotor/generator/main-frame assembly, and there is a lateral 

offset between the yaw axis and the rotor axis. The axis of the hinge pivot joint is inclined laterally at a small angle 

to the vertical yaw axis and produces a gravity restoring moment. A main-frame stop keeps the tail boom from 

furling more than about 68 degrees. As the wind speed increases, so do the thrust and aerodynamic normal force on 

the nominally aligned vertical tail at the end of the tail boom. Furling occurs when the rotor moments exceed the 

gravity restoring moment. The furl damper provides very little resistance to furling but a high resistance to unfurling 

1

. 

 The Bergey EXCEL is a three-bladed upwind turbine with a rated output of 10 kW at 13.0 m/s. The EXCEL 

uses a permanent magnet alternator to produce three-phase variable frequency output at a nominal 240-volts. The 

three-phase output is then rectified to DC power and converted to single-phase 240-volt 60-hz AC power by the 

Gridtek inverter. The turbine blades are constant chord and made from pultruded fiberglass, and the direction of 

rotation is counter-clockwise. The experimental data in this paper are from Configuration C of the SWRT, which 

has constant chord blades with the SH3055 airfoil, a swept area of 35.3 m2

, and a rotor diameter of 6.7 m. The 

turbine nacelle and shaft have an eight-degree tilt, and the SWRT is installed on a Rohn SSV, 24.4 m. (80 ft.), 

freestanding lattice tower. 

 The SWRT was modified in several ways to allow for the installation of test instrumentation. One significant 

modification was the turbine main shaft was shortened approximately 0.18 m. (7 in.) to allow for installation of a 

0.18 m (7 in.) load fixture in-line with the non-rotating shaft to measure shaft loads. It should be noted that because 

the SWRT was modified, the test results in this report are not necessarily representative of the Bergey EXCEL 

turbine. 

III. Modeling Approach 

To supply inputs for aeroelastic models of the SWRT, the turbine tail assembly and mainframe were weighed 

and centers of gravity (Cgs) were determined. Tests were also conducted to calculate the inertia about the tail axis 

and the yaw axis. Tail damper properties were measured and all turbine geometries noted. A modal test was 

conducted for a blade to determine mode shapes for flap and edge. This data, as well as all turbine related 

parameters, including airfoil data, was input into the FAST and ADAMS models of the SWRT. To measure the 

blade torsion, 182.3 N-m of torque was applied about the 0.25 chord of the blade. This was applied 2.74 m from the 

2
outboard edge of the rootpad (twisting 2.74 m of blade) and the measured angular deflection was 4.1 degrees or .022 

degrees/N-m over 2.74 m of blade. Based on this data the torsional stiffness of the SH3055 blade is 6500 Nm. The 

c.g. offset is 0.017 m (towards the trailing edge) from the quarter chord. 

The FAST and ADAMs models of the SWRT were developed using geometric, aerodynamic, and mass 

properties for the SWRT. The models include blade flexibility, a variable-speed generator with a torque-speed lookup table, tail furling with nonlinear user-defined springs and dampers, and free yaw. 

Modeling data was compared to experimental data taken for the SWRT turbine 1. Data were sampled at 160 hz, 

and a 40-hz, 6-pole low-pass Butterworth filter was used to filter the data. Records of 10 minutes in length were 

stored. 

IV. SWRT Modeling 

A. Blade Torsion 

Because the FAST program cannot model blade elastic torsion motion, an ADAMS model was created from the 

FAST model that includes the blade torsion degrees of freedom (DOF). The torsional stiffness of the blades was 

measured and input into the ADAMS model. The aerodynamic pitching moment was added to the airfoil tables so 

the ADAMS model includes both aerodynamic and inertial causes of blade twisting. There were no other changes to 

the model. Therefore, the only substantial reason for differences between the FAST and ADAMS predictions is live 

blade twist. (Previous work has shown that FAST and ADAMS predictions are nearly identical when blade torsion 

stiffness is very high 5

). 

 ADAMS and Fast predict blade motions differently. ADAMS uses 6 DOF per blade element, and there are 15 

elements per blade for a total of 90 DOF per blade. Fast has 3 DOF per blade ( 2 flapwise and 1 edgewise) plus a 

polynomial mode shape for each DOF. ADAMS also requires blade torsional stiffness values, chordwise center of 

gravity (Cg) offsets, and the option of using the center of mass (Cm) values in the airfoil tables. Fast does not allow 

for any of these. 

B. Modeling Results for Summary Statistics 

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

data. Comparisons of 10-minute average, maximum, and minimum summary statistics are valuable because they 

provide a view of a large quantity of data, albeit in much less detail. The statistics were compared by running the 

ADAMS model with simulated turbulent wind conditions generated by SNWind v1.22. An 8 x 8 grid of points was 

used across the rotor disc and three components of time-varying wind were generated at each grid point. The 

turbulence was generated with statistics that match the IEC Category-A conditions. Site-specific turbulent inputs 

were not used in this analysis. Figure 1 shows a plot of the actual turbulence intensity from the SWRT tests and the 

IEC Category A turbulence. 

 Figures 2-7 show the results of the summary statistics for SWRT Configuration C using the ADAMS model. 

These results were generated using the original ‘untuned’ airfoil and alternator data. They therefore represent the 

accuracy of predictions that would be done during a design if airfoil and alternator properties were available from 

wind tunnel or bench testing and blade elastic properties were known from Finite Element Analysis (FEA) or 

testing. 

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 1. IEC Category A and 

test turbine turbulence intensities 

3
0

100

200

300

400

500

600

4 6 8 10 12 14 16 18 20

Mean Wind Speed, m/s

Rotor RPM

Max RPM Data Mean RPM Data Max RPM ADAMS Mean RPM ADAMS

0

2

4

6

8

10

12

14

4 6 8 10 12 14 16 18 20

Mean Wind Speed, m/s

Electrical Power, kW

Max Power Data Mean Power Data Min Power Data Max Power ADAMS Mean Power ADAMS Min Power ADAMS

Figure 5. 10-Minute mean, maximum, and 

minimum values of electric power for 

Configuration C. 

Figure 2. 10-Minute mean and maximum 

 values of rotor RPM for Configuration C. 

-10

0

10

20

30

40

50

60

70

80

4 6 8 10 12 14 16 18 20

Mean Wind Speed, m/s

Furl Angle, Degrees

Max Furl Data Mean Furl Data Max Furl ADAMS Mean Furl ADAMS

Figure 3. 10-Minute mean and maximum 

values of tail furl for Configuration C. 

-8

-6

-4

-2

0

2

4

6

8

10

4 8 12 16 20

Mean Windspeed, m/s

Thrust - kN

Max Thrust Data Mean Thrust Data

Min Thrust Data Max Thrust ADAMS

Mean Thrust ADAMS Min Thrust ADAMS

Figure 4. 10-Minute mean, minimum, and 

maximum values of thrust for Configuration C.

-60

-50

-40

-30

-20

-10

0

4 6 8 10 12 14 16 18 20

Mean Wind Speed, m/s

Yaw Error, Degrees

Mean Yaw Error Data

Figure 6. 10-Minute mean values of yaw 

error for Configuration C. 

-4

-3

-2

-1

0

1

2

3

4 6 8 10 12 14 16 18 20 Mean Windspeed, m/s

Blade 1 Root Flap Bending

Moment, kN-m

Max Flap Mom. B1 Data Mean Flap Mom. B1 Data

Min Flap Mom. B1 Data Max Flap Mom. B1 ADAMS

Mean Flap Mom. B1 ADAMS Min Flap Mom B1 ADAMS

Figure 7. 10-Minute mean, maximum, and 

minimum values of Blade 1 flap bending for 

Configuration C. 

4
5

 

C. Time Series Modeling Results 

Time series comparisons are made by driving the ADAMS model with measured wind data. Measurements of 

the hub-height horizontal and vertical wind speeds and the wind direction are used for the comparisons. 

Measurements of instantaneous horizontal wind shear were not available. Horizontal wind shear is known to be an 

important driver of yaw and furling motions 8

, but it is difficult to measure. Driving a model with “hub-height” 

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

Figure 8 is a plot of blade elastic twist versus rotor RPM. The twist can be up to 4 deg. at higher RPMs. Figure 9 

is a time series plot of blade elastic twist for the SWRT that shows the large amount of live twist during one 10-

minute time series file with an average wind speed of 16.2 m/s. Figures 10-13 show the same time series file for 

RPM, furl, thrust, and yaw error. The FAST modeling results without torsional stiffness are also shown in addition 

to the ADAMS results with torsional stiffness. The agreement between the ADAMS model data and test data is 

good, whereas the FAST model severely over predicts rotor RPM, and furl and yaw error agreement are also poor. 

The results demonstrate the importance of blade live twist to modeling, especially for the RPM. 

Figure 8. Elastic twist versus rotor RPM for 

one 10-minute test of Configuration C. Figure 10. Rotor RPM versus time for same 10-

minute test. 

Figure 9. Elastic twist of blade tip and rotor RPM 

versus time for same 10-minute test (negative 

twist is toward feather). 

Figure 11. Rotor furl versus time for same 10-

minute test. 
6

D. Effect of Aerodynamic Pitching Moment and Wake Model Algorithm on ADAMS Results 

Modeled sensitivity analyses with ADAMS show the importance of the aerodynamic pitching moment to the 

blade live twist. Figure 14 shows a plot of rotor speed versus time for the same 10-minute data series shown 

previously with the aerodynamic pitching moment turned both on (i.e. Cm) and off (i.e. No Cm). The data show 

aerodynamic forces have a large impact on “live” blade twist. Live blade twist has a big impact on RPM and hence 

power. Figure 15 shows a plot of electric power output versus time for the same 10-minute data set. 

 Figures 14 and 15 also show the effect of changing the dynamic inflow option in ADAMS. When the value is 

DYNIN 4

, a generalized dynamic wake (GDW) inflow model is used to calculate the induction factor. A value of 

EQUIL 4 assumes that the wake is always in equilibrium with the forces on a blade element (the “quasi-steady” or 

equilibrium wake assumption). For EQUIL, the BEM theory with skewed wake and tip loss corrections is used. The 

GDW model was developed as an expanded version of the Pitt and Peters model. It uses a series solution to describe 

the induced velocity field in the rotor plane, which includes Legendre functions in the radial direction and 

trigonometric functions in the tangential direction. The current GDW model in AeroDyn employs 0P, 1P, 2P and 3P 

terms 4

. The dynamic inflow effect is often insignificant, so results in most cases should not differ from the BEM 

results. The exception is cases with rapid changes in blade angle-of-attack, where the dynamic inflow effect can be 

significant, as is the case with the SWRT turbine. Yaw oscillation resulting from the GDW model has also been 

observed while modeling a small, upwind, tail-vane turbine, though the reasons for this are not yet known 4

. 

E. Comparison of Rotor RPM between ADAMS and SWRT Data 

Comparisons between predicted rotor RPM for the ADAMS model and measured rotor RPM from the SWRT 

data show some interesting trends depending on if the GDW or BEM wake model is used. Figure 16 shows 

summary statistics for rotor RPM versus wind speed for the ADAMS model results with the two different wake 

models and the SWRT data for Configuration C. The aerodynamic pitching moment is turned on in these ADAMS 

plots. 

Figure 12. Rotor thrust versus time for same 10-

minute test. 

 

Figure 13. Yaw error versus time for same 10-

minute test. 
0 100 200 300 400 500 600

100

200

300

400

500

600

700

800

Rotor RPM

Time (sec)

 Dynin_NoCm

 Dynin_Cm

 Equil_NoCm

 Equil_Cm

Figure 14. 10-Minute mean and maximum 

values of rotor RPM for Configuration C. 

0 100 200 300 400 500 600

-2

0

2

4

6

8

10

12

14

16

Generator Power (kW)

 Dynin_NoCm

 Dynin_Cm

 Equil_NoCm

 Equil_Cm

Time (sec)

Figure 15. 10-Minute mean and maximum 

values of rotor RPM for Configuration C. 

7
100

150

200

250

300

350

400

4 6 8 10 12 14 16 18 20

Mean Wind Speed, m/s

Mean Rotor RPM

Mean RPM Test Data ADAMS with Equil ADAMS with Dynin

 Figure 16. Rotor RPM versus wind speed for ADAMS with two different 

wake models and SWRT data. 

V. Conclusion 

The SWRT test has provided the small wind turbine and research community with a unique set of data for 

validating aerodynamic models for a turbine that operates at a significant yaw error, has high yaw rates, is in and out 

of stall quickly, and operates at higher RPMs. Analysis and comparison of FAST and ADAMS model simulations 

and test data for the SWRT has resulted in a better understanding of small wind turbine modeling, as well as a FAST 

furling model that can be used by engineers to better design small furling wind turbines. However, comparison of 

FAST and ADAMS results has shown the importance of including blade torsion in the SWRT model, which can 

only be done with the ADAMS model at this time. The SWRT blade is quite soft in torsion; hence it is doubtful that 

live twist will be as important for many other turbines. It was shown with the ADAMS model that the aerodynamic 

pitch moment has more of an effect on blade torsion than do inertial forces. Future near-term changes to the FAST 

model will include adding the ability to model blade torsion. 

Acknowledgments 

The authors would like to thank Mark Meadors of NREL for his technical help in conducting the SWRT testing, 

Jason Jonkman of NREL for his help in upgrading the FAST model to include the effects of furling, and Kathy 

O’Dell of NREL for her help in making this document more readable. We would also like to thank Todd Hanley of 

Bergey Windpower for his continuous help during both the testing and modeling phase of the project. 

References 

1 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. 

8
2

 Jonkman, J.M. and Buhl, M.L. (March 2004). “FAST User’s Guide”. NREL/EL-500-29798. Golden, CO: National 

Renewable Energy Laboratory, 105 pp. 

3

 Jonkman, J.M. and Buhl, M.L. (January 2004). “New Developments for the NWTC's FAST Aeroelastic HAWT Simulator,” 

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

and Exhibit, 5-8 January 2004, Reno, NV. Washington, D.C.: American Institute of Aeronautics and Astronautics, pp. 181-191; 

NREL/CP-500-35077. 

4

 David J. and Hansen, A. Craig. (September 2001). 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, 24 pp. 

5 Jonkman, J.M. and Hansen, A.C., “Development and Validation of an Aeroelastic Model of a Small Furling Wind Turbine,” 

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-500-36776. 

6 Corbus, D. and Hansen, C. (2005). "Test Results from the Small Wind Research Turbine (SWRT) Test Project." Prepared for 

the WindPower 2005 Conference Proceedings, May 2005. NREL/CP-500-38224. Golden, CO: National Renewable Energy 

Laboratory, 14 pp. 

7 Laino, David J. and Hansen, A. Craig. (September 2001). 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, 24 pp. 

8 Hansen, A.C. (May 1992). Yaw Dynamics of Horizontal Axis Wind Turbines, Final Report. NREL/TP-442-4822. Golden, 

CO: National Renewable Energy Laboratory. 

9
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Effect of Blade Torsion on Modeling Results for the Small Wind 

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14. ABSTRACT (Maximum 200 Words)

The Small Wind Research Turbine (SWRT) testing project was initiated with the goal of better characterizing both 

small wind turbine loads and dynamic behavior. The main purpose of the testing was to produce high quality data 

sets for model development and validation and to help the wind industry further their understanding of small wind 

turbine behavior, including furling. Previous papers have discussed modeling results for the SWRT using the FAST 

aeroelastic simulator. This paper summarizes modeling results from both the FAST and ADAMS aeroelastic 

simulators. The ADAMS model allows for blade torsional stiffness to be modeled, which is shown to significantly 

improve the agreement between model and test results for rotor speed and explain variances in previous 

comparisons between the SWRT FAST modeling results and the SWRT test data. The ADAMS model results also 

show that the aerodynamic pitching moment impacts the blade torsion more than the inertial forces. 

15. SUBJECT TERMS

small wind research turbine; furling; FAST; ADAMS 

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