NREL/TP-442-4807

NREL Combined Experimental Final 

Report—Phase II 

C.P. Butterfield 

W.P. Musial 

G.N. Scott 

D.A. Simms 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

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

Managed by Midwest Research Institute 

for the U.S. Department of Energy 

under contract No. DE-AC36-83CH10093 

August 1992 
NREL/TP-442-4807

NREL Combined Experimental Final 

Report—Phase II 

C.P. Butterfield 

W.P. Musial 

G.N. Scott 

D.A. Simms 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

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

Managed by Midwest Research Institute 

for the U.S. Department of Energy 

under contract No. DE-AC36-83CH10093 

Prepared under Task No. AS575440 

August 1992
NOTICE 

This report was prepared as an account of work sponsored by an agency of the United States 

government. Neither the United States government nor any agency thereof, nor any of their employees, 

makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, 

completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents 

that its use would not infringe privately owned rights. Reference herein to any specific commercial 

product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily 

constitute or imply its endorsement, recommendation, or favoring by the United States government or any 

agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect 

those of the United States government or any agency thereof. 

 
Acknowledgments 

The authors wish to acknowledge the Department of Energy for the support of this work through contract 

number DE-ACO2-83CH10093. Other conbibut01~ include Al Eggers, Craig Hansen, Bob Akins, Bob 

Thnsher, Dan Reda (Sandia National Laborratories), Mike Jerks, Dave Jeger, Bob KeIIer and Diane PipanLog= 
1.0 Table of Contents 

1.1 list of Nomenclature ........................................ 1.2 ListofFigures ............................................ 1.3 List of Tables ............................................. 

3.0 Quasi-Steady Aerodynamic Measurements .............................. 

3.1 Inttoduction .............................................. 3.2 DataProcessing ........................................... 3.3 Results ................................................. 3.3.1 Force Coefficients .................................... 3.3.2 Airfoil Pressure Distributions ............................ 3.3.3 Spanwise Pressure Distributions .......................... 3.4 Conc~,usions .............................................. 

4.0 Unsteady Aerodynamic Loads ....................................... 

4.1 Introciuction .............................................. 4.2 Datawon ........................................... 4.2.1 Testcases ......................................... 4.22 Pressure Coefficient Normdlization ........................ 4.3 Azimuth-Avemged Results .................................... 4.4 Dynamic Stall Results ....................................... 4.5 Transient Pressure Distributions ................................ 4.6 Conclusions .............................................. 

5.0 Flow Visdhtion Tests ........................................... 

5.1 LiquidCrystdlTests ........................................ 5.2 Correlations of Pressure Distribution with Flow Pattern ............... 5.2.1 Intraduction ........................................ 5.22 Flow Visualization Test Setup ........................... 5.2.3 DataPn>cessing ..................................... 5.2.4 fressure Distributions ................................. 5.3 Conclusions ............................................... 

6.1 Introduction .............................................. 6.2 Air Load Estimating Too1 (AWEST) ............................. 6.3 Blade-LoadDataBase ....................................... 6.4 Conclusions .............................................. 
7.0 Futurework ................................................... 

8.0 Conclusio~ .................................................... 

9.0 Refe~~lces ..................................................... 

10.0 Appendices .................................................... 
1 .I list of Nomenclature 
1.2 List of Figures 

Figure 3.2. 

Figure 3.2. 

Figure 3.3. 

3.4. 

Figure 3.5. 

Figure 3.6. 

Figure 3.7. 

Figure 3.8. 

Figure 3.9. 

Figure 3.10. 

Figure 3.11. 

Figure 3.12. 

Figure 3.13. 

Figure 3.14. 

Figure 3.15. 

Figure 3.16. 

Figure 3.17. 

Figure 3.18. 

Figure 4.1. 

Figure 4.2. 

Aidoil pressure distribution integration ........................... 

Relationships of force coefficients to airfoil sections .................. 

Normal force comparisons at 80% blade span ....................... 

Normal force comparisons throughout blade span .................... 

Tangent coefficient comparisons throughout blade span ................ 

Torque and thrust coefficient comparisons for rotating-blade 

and wind-tunnel data ........................................ 

Rotating blade normal and tangent force coefficient comparisons 

withwind-tuxulelW ....................................... 

Life coefficient comparisons throughout blade span ................... 

Pressure drag coefficient comparisons throughout blade span ............ 

Pressure distributions complirison at 63% blade span and 2.5 deg AOA ..... 

Pressure distribution comparisons at 63% blade span and 9.5 &g AOA ..... 

Pressure distributions at 63% blade span for post-stall AOAs ............ 

Post-stall pressure distributions at 80% blade span ................... 

Post-stall pressure distritlmtions at 30% blade span .................... 

Bin averaged spanwise pressure coefficients for the 4% chord 

for 8-14 ds wind speeds .................................... 

Bin averaged spanwise pressure coefficients for the 4% chord 

for 145-192 mls wind speeds ................................. 

Bin averaged spanwise pressure coefficients for the 36% chord 

for 8.8-145 ds wind speeds .................................. 

Bin averaged spanwise psure coefficients for the 36% chord 

for 145-19.2 m/s wind speeds ................................. 

Dynamic-pressure variations during yaw .......................... 

Effects of timevarying dynamic-pressure normalization. ............... 
Figure 4.3. Azimuth-averaged AOA and lift coefficient at 80% span 

for30°yawcase ........................................... 

Figure 4.4. 

Figure 4.5. 

Figure 4.6. 

Figure 4.7. 

Figure 4.8. 

Figure 4.9. 

Figure 4.10. 

Figure 4.11. 

Figure 4.12. 

Figure 4.13. 

Figure 4.14. 

Figure 4.15. 

Figure 4.16. 

Figure 4.17. 

Figurtt4.18. 

Azimuth-averaged AOA and lift coefficient at 63% span 

for3Ooyawcase ........................................... 

Azimuth-averaged AOA and lift coefficient at 47% span 

for30°yawcase ........................................... 

Azimuth-averaged AOA and lift coefficient at 30% span 

for30°yawcase ........................................... 

Azimuth-averaged AOA and lift coefficient at the 80% span 

fornoyawcase ........................................... 

Azimuth-averaged AOA and lift coefficient at 63% span 

fornoyawcase ........................................... 

Azimuth-averaged AOA and lift meflicient at 47% span 

fornoyawcase ........................................... 

Azimuth-averaged AOA and lift coeflicient at 30% span 

fornoyawcase ........................................... 

Dyaamic stall at 6396 span oornpared to wind-tunnel dynamic stall ........ 

Dynamic stall at 47% span .................................... 

DyPamicstallat30%sp .................................... 

Drag dynamic stall at 80% span ................................ 

Drag dynamic stall at 63% span ................................ 

Dragdynamicstallat47%span ................................ 

.Dragdynamicstallat3O%span ................................ 

Figure 4.20. Chordwise dhce-pressure distribution vs. azimuth angle. V = 14.7 dsec, 

0" yaw, 30% span. One cycle of data ........................... 

Figure 4.21. Measured and geometric local AOA vs. azimuth angle ................ 

Figure 4.22. Themetical geometdc AOA vs. azimuth angle. 30' yaw, V = 16.7 mlsec ... 

vii 
Figure 4.23. 

Figure 4.24. 

Figure 4.25. 

Figure 4.26 

Figure 4.27 

Figure 4.28. 

Figure 4.29. 

Figure 4.30. 

Figure 4.31. 

Figure 5.1. 

5.2. 

Figure 5.3. 

Figure 5.4. 

FQure 5.5. 

Figure 5.6. 

Figure 5.7. 

Figure 5.8. 

Figure 6.1. 

Figure 6.2. 

Figure 6.3. 

Chordwise surface-pressure distribution vs. azimuth angle. 

30' yaw, V = 14 mlsec, 30% span. Bin-averaged data ............... 

Chordwise surface-pressure distribution vs. azimuth angle. 

30' yaw, V = 16.7 m/sec, 30% span. One cycle of data ............... 

Chordwise smfh-pressure distti,bution vs. azimuth angle. 

-33' yaw, V= 13.3 dsec, 30% span. One cycleof data .............. 

Chordwise Sutface-presswe distribution vs. azimuth angle. -33' yaw, 

V = 13.3 m/w, 63% span. One cycle of data ..................... 

Spanwise pressure distribution vs. azimuth angle. 7.7 m/s wind speed, 

4% chord, tipvelocity normalization, bin-averaged data ............... 

Spanwise pressure distribution vs. azimuth angle. 18.7 mls wind speed, 

4% chord, tipvelocity nonnahtioa, bin-averaged dara ............... 

Spanwise pressure diebution vs. azimuth angle. 18 m/s wind speed, 

4% chord, tipvelocity normalization, bin-avemged data, -26.S0 yaw. ...... 

Spanwise pressure distribution vs. azimuth angle. 14 m/s wind speed, 

4% chord, tipvelocity normalization, bin-averaged data, 30' yaw. ........ 

Spanwise pressure distribution vs. azimuth angle. 16.7 m/s wind speed, 

4% chord, tipvelocity normalization, bin-averaged data, 30' yaw. ........ 

Wind turbine with video cameras ............................... 

Videosignalpath .......................................... 

View of blade at low AOA ................................... 

View of black at medium AOA ................................ 

View of blade at high AOA ................................... 

Comparison of wind-tu~~nel and rotating-blade pressures ............... 

Comparison of lift coefficients from wind tunnel and wind turbine ........ 

Comparison of pressure drag coefficients fhm wind mane1 

andwindturbine ........................................... 

Sensitivity to input bending moment ............................. 

Sensitiviy to precone angle ................................... 

Sensitivity to blade mass distribution ............................. 
Figure 6.4. Blade airload estimates ...................................... 

Figure 6.5. ALEST analysis procedure .................................... 

Figure 6.6. Comparison of ??? ......................................... 
13 List of Tables 

Table 4.1. 

Table 5.1. 

Table 6.1. 

Table 6.2. 

Table 6.3. 

....................................................... 

AnglesofAuackProbe ...................................... 

Case Study Pafameter Statistics ................................ 

Input Bending Moment Channels ............................... 

Blade Mass Distribution ...................................... 
Introduction 

Wind turbine operating evrience has shown that current analysis techniques are inadequate when used 

to predict peak power and loads on a fixed-pitch wind turbine. Butterfield et al. (1992), Vitema and 

Corrigan (1981), and Tangler (1983) all show evidence of higher-than-predicted power levels on stallcontrolled wind turbines. Because performance and loads are the most important design idomation 

needed to achieve more reliable and inexpensive wind turbines, it is important to understand the cause of 

the discrepancy. The primary question is how the performance of the airfoil in the wind tunnel differs 

from the performance on an operating horizontal-axis wind turbine (HAW. The National Renewable 

Energy Laboratory (NREL) conducted a comprehensive test program, the Combined Experiment, that 

focused on answering this question and understanding the basic fluid mechanics of rotating HAWT stall 

aerodynamics in both steady and unsteady flow regimes. 

The basic approach was to instrument a wind-turbine rotor, using an airfoil that was well documented by 

wind-tunnel tests, and measure operating pressure distributions on the rotating blade. Using the integrated 

values of the pressure data, airfoil performance coefficients were obtained, and comparisons were made 

between the rotating-blade data and the wind-tunnel data Care was taken to minimize the aerodynamic 

and geometric difrences between the rotating-blade and the wind-tunnel models. Blades and wind-tunnel 

models were made in the same molds, and the same instruments were used for both the rotating-blade and 

the wind-tunnel tests. 

This is the second of two reports describing the Combined Experiment frogram and results. This Phase II 

report concentrates on the results derived from aerodynamic pressure measurements, wind-turbine load 

measurements, and flow-visualizafion studies. Average and unsteady aerodynamic measurements are 

presented for yawed and unyawed operation. These reports disseminate basic aerodynamic data for code 

validation and wind turbine-design information, and will hopefully assist other researchers using the 

Combined Experiment data These reports provide a comprehensive description of results to date and a 

description of how the experiment operates. 

The Combined Experiment was planned and carried out over a period of 4 years. It was the most 

comprehensive wind-turbine test program ever attempted. The test program was divided into two phases, 

the first beginning in the spring of 1987. Many configurations of instrumentation were considered during 

both phases of testing. The most significant anbguration change between Phase I and Phase In was the 

extent of the pressure measurements. The inshnunentecl blade for Phase I had only one spanwise radial 

station of pressure taps (32 taps) at 80% radius, while the Phase II blade had four radial stations of 

pressure taps at radial positions ranging tiom 30% radius to 808, as well as six intermediate radial 

stations of taps located between the primary stations. Appendix A describes the test blade layout with 

pressure tap locaiions and sWn gage locations. A more detailed description of this and the other 

instrumentation changes that were made between Phases I and II is given by Butterfield et al. (1992) iri 

the Phase I repa 

The Phase I report also covers the test set-up, instrumentation, wind-tunnel tests, and airfoil-roughness 

testing, However, most of the data came from the Phase II data sets and are presented in this report. This 

report covers: 

Bin-averaged aerodynamic coefficients data integrated from pressure distributions 

Unsteady aerodynamicdata 

Flow-visualization-test results 

Bin-averaged blade-load data. 
3.0 

3.1 Introduction 

Quasi-Steady Aerodynamic Measurements 

The aircraft industry has historically avoided operating in aerodynamic stall. As a result there has been 

relatively little effort expended to investigate steady stall on poststall airfoil performance. Unlike airfoils 

on conventional straight-wing aircraft and helicopters, wind turbines must be designed to operate in stall 

and to withstand thc forces associated with this operating condition. In mockrate to high winds, a fixedpitch wind turbine continuously experiences stall over large portions of its blades. Peak power production 

and blade loads occur during high-wild operating conditions, when stall dominates the applied airloads. 

Therefore, it is critically important for wind turbine designen to mdemad and predict stall behavior. 

This section presents the results of the quasi-steady airfoil measurements from the Combined Experiment's 

rotor. Because of the untwisted geometry of the rotor's blades, the root sections stan at relatively low wind 

speeds and operate in stall most of the time. This allowed researchers to observe the progression of stdl 

over the blade for a range of wind speeds pader normal operating conditions. 

The objective of this part of the analysis was to Qcument the average airfoil performance on the rotating 

wind turbine blade and compare it to wind-tunnel results. This section discusses some of these 

comparisons aui reviews the data-processing techniques. Detailed descriptions of the data-processing 

proceduns are included in Butterfield et al. (1992). Appendix B contains the complete dog of results 

from the bin-averaging data analyses, including angle of attack (AOA) and local total pressure (dynamic 

pressure) measurements correlated with wind speed. This section discusses only the heglilights of the 

comparisons. 

32 Data Processing 

After the raw data were recorded, calibration coefficients were applied to the data During testing, 

pressure instnunentation calibrations were pafonned every 5 minutes. Tbis enabled calibration coefficients 

to be updated fnquently enough to reduce the total measurement uncertainty to less than 3% of the local 

dynamic pressure. The procednre was laborious bat ensured accurate engineering data for later processing. 

The reference pressures for each traasducer located in the blade were tmufimd from the hub to the 

transducer through a tube as described in humfield et al. (1991). Ihe effects of centrifugal fbrce on air 

in the tube were corrected per Equalion 3.1, which is very similar to the procedun described by Hurst and 

Owen (1988), Equation 3.2. Hunt's equation assumed that the transducer was located at the axis of 

rotation and that a long tube was run from the transducer down the blade to the surface pressure tap. 

Equation 32 includes compressibi'fity effeds that are negligible and not included in Equation 3.1. 

In this test program, two centrifugal-force corrections were needed. 'Lhe first corrected the reference tube 

pressures fkom the axis of rotation to the transducer, and the second corrected the pressures in the tubes 

leading from the transducer to the blade surf&ce. 
where 

P, = reference pressure at transducer 

P = atmosphericpressure 

Pse = actual surface pressure 

P,, = measured surface pressure 

o = rotor speed 

K = gas constant 

T = temperature 

r =~ustorefemceportorsurfacepressuntap 

P = air density. 

To obtain mnI3alized pressure coefficients (C& dimensional pressure data were divided by local dynamic 

pressure as shown in Equation 3.3. Dyoamic pressure was established in two ways. First, atmospheric 

pressure was subtracted fiom measured total pressure to get a local, measured dynamic pressure (%& 

using Equation 3.4. The second method derived the local value of dynamic pressure (Qda) by using 

Equation 35 and the disk-averaged wind speed (measured fEom the vertical-plane array of anemometers 

one diameter upwind of the rotor), the rotor angular speed, and the radius to the pressure tap. 

where 

Qmarr = measured dynamic pressure 

Qa, = berived dynamic pressure 

V = disk-averaged wind speed 

= normalized pressure coefficient 

= measured total pnssun. 

Both methods gave similar results t3m-t agreed with wind-tunnel data at low AOAs. At high AOAs (greater 

than 25 deg) on the 30% blade-span pressure distribution, the %w method gave values of pressure 

coefficient (Cd greater than 1 at the won point, lhis indicates that the value of Q, measured at the 

total-pressure probe, was lower than the leading-edge won pressure. The won pressure should 
always be equal to the dynamic pressure. Because of this problem, all the pressure data pesented in this 

section were normalized by the calculated dynamic pressure (&). 

All the pressure data were digitized at a 520-Hz sample rate. Data were later block-averaged by a factor 

of 52 to obtain a final 10-Hz sample rate. 'Ihese data were then soaed into bins using the measured angle 

of attack as the independent variable. Analog filters were used to eliminate biasing of the data These 

fliltas were four-pole Butterworth type, set at a 100-Hz roll-off frequency. 

3.3 Results 

3.3.1 Force Coefficients 

One of the main objectives of the data processing was to transfm the large number of measured pressure 

distributions into a smaller number of performance coefficients to make them more useful for application 

in aerodynamic analysis and code validation. Expression in terms of performance coefficients also makes 

it possible to easily quantify the comparisoas with wid-ml data 'Ihe raw pressure distribution data, 

which provide a more fundamental picture of aerodynamic performance charactcrstics, are presented in 

Section 3.3.2. 

The pressure distributions for both rotating-blade and wind-tunnel data were integrated to compute normal 

force coefficients (CN) and the tangent force coefficients (G). These are the fundamental coefficients that 

are commonly used to describe Moil perfbrmance fiom pressure measurements because thdi derivation 

is independent of the AOA measurements. They represent the forces acting tangent and parallel to the 

airfoil chord, iespectively. The integration procedure is shown in figure 3.1, where the pressures are first 

projected onto the chord line and integrated to deterxnine the C, values, and then projected onto an axis 

orthogonal to the chord and integrated to compute the C, values. This procedure is described in detail by 

Pope (1966). Equations 3.6 and 3.7 give the integration procedure used to determine C, and C, 

Figure 3.1. Airfoil pressure distribution integration 

All other airfoil performance coefficients, such as q, C, G,,, and &, can be computed using the 

C, and C, values in conjanction with their reference angles. Figure 3.2 shows the relationship of each 

of the force coefficients to an airfoil section on a rotating wind mine blade. For the Combined 

Experiment rotor, the pitch angle ($) is the same over the entire blade span because the blades are 

untwisted 

Figure 36. Relationship of force coefficients to airfoil sections 


The CN and cwes are presented in Figures 3.3,3.4, and 3 5. Some general trends prevail throughout 

the data Fa it is clear from all of the data that effects of rotation on airfoil performance are strictly a 

stall and poststall phenomenon. At low AOAs the correlation with wind-tunnel data is good in each case. 

The discrepancies generally begin to take place at the normal static wind-tunnel stall angle. Second, the 

rotating-blade coefficients can usually be characterized by gradual stall behavior whereas the wind-tunnel 

coefficients show abmpt stall. This gradual stan characteristic tends to cause all of the rotating blade force 

coefficients to be higher than those in the wind tunnel at high AOAs. Another trend that was consistently 

observed was that the rotathggblade effects become mon pronounced at inboard blade stations, 

particularly at the 30% station. 

Figure 3.3. Normal force comparisons at 80% blade span 

Figure 3.4. Normal force comparisons throughout blade span 

Figure 3.5. Tangent coefficient comparisons throughout blade span 

Figure 3.3 shows normal force coefficients (CN) for both the Colorado State University (CSU) wind tunnel 

and the 80% blade span on the wind turbine. (A complete catalog of the CSU wind-tunnel test results is 

presented in Butterfield et al. (1992).) Beyond 15 deg, staI1 causes normal force coefficients to fall 

ab~fly for the wind-tunnel data, whereas the rotating-blade data drop very graddy with increasing 

AOA. This is consistent with results from Phase I testing as described by Butterfield et al. (1992). 

Figure 3.4 shows he CN versus AOA cums at the 30%, 47%,63%, and 8046 blade span locations. All 

curves agree at low AOk At the normal stall angle, the two mid-span stations (47% and 63%) show an 

inaease of 10% in CN(-] and a levelling of values for higher AOAs The 80% station CN(-] agrees 

with the wind-tunnel &ta but shows the same flattening at higher AOAs. At the 30% span the results are 

very different 'Ihe magnitude of CN continues to increase with increasing AOA. A maxinun value of 

2.0 was recorded .at an AOA of 30 deg, which is more than twice as large as the maximum due 

measured in the wind t.llnnel. 

Tangent force coefficients are compand in Figure 3.5. Wind-tunnel data again show good agreement with 

rotating-blade data for low AOAs. The wind-tunnel data stall sharply, whereas the rotating-blade data 

show a more gradd drop-off after stall. This discrepancy can have a large effect on rotor torque because 

for relatively small blade pitch angles the tangent forces are closely aligned with the plane of rotation. 

'The CN end values can be related to wind turbine performance parameters such as Gqne and b, 

by Equations 3.8 and 3.9. 

By these equations, these coefficients are geometrically related to the CN and CT values by the pitch angle 

of the airfoil. For a s&llamtrolled wind turbine, the pitch is fixed, and this angle will not change. For 

the Combined Experiment rotor, which has no twist in the blade, tfiis angle is a constant for the entire 

span for all wind speeds. 

Figure 3.6 shows the C,orQPe and Ls versus AOA cwes for the 63% blade span. These awes are 

compared to the wind-tunnel data The effect of rotation on wind turbine performance can be most easily 

seen from these data because the force coefficients are aligned with the in-plane and out-of-plane forces, ' rotor torque andthruk After stall is reach& there is a tremendous discrepancy between both the torque 



and the thrust coefficients at 63% span. Torque coeficients are more than three times the win&tunnel 

results, which would translate into higher power and increased service loads on the gearbox and drivetrain. 

Similarly, the thrust coefficients are up to twice the wind-tunnel values. These loads are transmitted 

directly into the blades and wind turbine supporting structure. In terms of wind turbine design, 

discrepancies such as these would be unacceptable for predicting the structural fatigue life of key wind 

turbine components. 

Figure 3.6. Torque and thrust coefficient comparisons for rotating-blade and wind-tunnel data 

Figure 3.7 shows a comparison of normal and tangent coefficients for the rotating-blade and wind-tunnel 

data cases. The discrepancy near stall is obvious. 

Figure 3.7. Rotating blade normal-and tangent force coefficient comparisons with wind-tunnel data 

Aerodynamic performance is also evaluated by the lift and drag coefficients, C1 and Cd, respectively. At 

low AOAs, CI is approximately equal to CN. As the AOA increases the deviations are greater. These 

relationships are given in equations (3.10) and (3.1 1). These measurements give a more intuitive picture 

of the airfoil performance, because most airfoils are documented by measuring lift and drag. 

Lift coefficients, shown in Figure 3.8, have characteristics very similar to those of the normal-force 

coefficients shown in Figure 3.4. In a side-by-side comparison, the two curves are difficult to distinguish. 

Figure 3.8. Lift coefficient comparisons throughout blade span 

Pressure-drag data are shown in Figure 3.9. Below stall, the wind turbine blade data show the usual 

agreement with the wind-tunnel data. Beyond stall, the wind turbine drag values are greater than windtunnel values. This is a surprising result, when one considers that the tangent forces were greater than 

the wind-tunnel data. However, Equation (3.1 1) shows that the drag term can be dominated by CN if 

AOAs are large enough. Because CN is typically large compared to Cp this is not particularly surprising. 

Figure 3.9. Pressure drag coefficient comparisons throughout blade span 

By understanding the effect of blade rotation and stall on these coefficients, improved methods for 

predicting wind turbine performance may result. 

3.3.2 Airfoil Pressure Distributions 

This section describes highlights of the bin-averaged pressure distribution results. The complete catalog 

of the pressure distributions is presented in Appendices C and D. These pressure distributions are 

correlated with two different measured parameters to provide two independent means for validating the 

data measurements, and to provide a separate perspective from which the data can be examined. First, in 

Appendix C measured AOA was used to correlate the pressure data This method gives a convenient 

format for comparing the behavior of the four spanwise stations at the same AOA. However, since the 

AOA measurements are made directly on the rotor, they are subject to the effects of wake-induced 

velocities that could have influenced the data 




In Appendix D, measured wind speed is used to correlate the data In this appendix, two different data 

sets were used to obtain the complete range of wind speeds. The windwindspeed correlation is presented as 

a means of relating the pressure measurements to a measured quantity that is not influenced by rotorinduced effects. These plots also provide a means for comparing the behavior of each of the spanwise 

distributions at the same point in time. 

In the body of this report, only the pressure distribution comparisons based on AOA are discussed. 

Figure 3.10 shows one such comparison at 63% span and a low AOA of 25 &g. The good correlation 

at this low angle helps to conSrm the integrity of the data-acquisition system and data-reduction 

techniques. The close agreement indicates that the airfoil is behaving in a two-dimensional manner for 

this attached flow condition. 

Figure 3.10. Pressure distributions comparison at 63?& blade span and 25 dog AOA 

Figure 3.11 shows the same 63% span station at 9.5 deg AOA. At this angle the suction peab on the 

pressure distribution are fully developed. These dara show good correlation with the wind-tunnel suction 

peaks as well as between the high-pressure-tide (bottom) pressure coefficients. The flat regions behind 

60% chord on the low-pressure side of the pressure distributions indicate that flow separation has occnared 

in both the wind tunnel and on the rotating airfoil. The pressure gradient that begins about mid-chord 

indicates the point of flow attachment. The wind-hubhe data show this at&achment point at 65% chord 

whereas the windwindtunnel data show attachment at 55% chord. The Merence in integrated area under the 

pressure distribution curves is 5% to 10% higher for the wind turbine for this range of AOA. 

Figure 8.1 1. Pressure distribution comparisons at 63% blade span and 9.5 ckg AOA 

This discrepancy was consistenfly observed between the two data sets. As the AOA rises and stall 

separation progresses forward from the trailing edge, the point of sepQlrPion is generally further aft in the 

wind-turbine data at the same AOA. 

Figure 3.12 shows a wind-tunnel pressure distribution at 185 &g AOA and a family of wind-turbine 

cwes for the 63% span ranging f&m 185 deg to 215 deg AOA. ?he wind--1 data are flat across 

the entire low-pressure side of the airfoil, indicating leading-edge separation. In contrast, the wind-turbine 

data clearly show that the suction peaks and pnsslln gradients an sustained to AOAs higher than 

23.5 deg. 

Figure 3.12. Pressure distmibutions at 63?k blade span for post-stall AOAs 

Figure 3.13 shows data for similar AOAs at the 80% span. The suction peak appears to be more 

pronounced at @is stirtion, and the pressure gradient following the peak is well defined. In both the 80% 

and the 63% cases, the existence of suction peaks and higher negative pressures along the low-pressure 

side of the airfoil results in the higher-than-expected normal force coefficients shown in 3.3 and 

3.4. 

Figurn 3.13. Post4all pressure distributions at Wr6 blade span 

Results from the 47% span pressure measurements are very similar to those for the 63% span. These 

results are shown in their complete form in Appendix C. 

Figure 3.14 shows results from the 3096 sprrn measurements. At this inboard location the same behavior 

is continued, with good corzelation at low angles (Appendix C) and delayed staIl at high angles. In this 




case the low-pressureside pressure coefficients are more negative than at the outboard stations. The larger 

area under the rotating-blade pressure awe results in higher values of CN and CL at high AOAs. 

Figure 3.14. Post-stall pressure distributions at 30% blade span 

The existence of suction pealcs and pressure gradients on the low-pressure side of the high-AOA wind 

turbine pressure distributions leads the authors to the conclusion that stall was delayed on the rotating 

airfoils. This is reasonable because these characteristics are commody used to identify attached flow in 

wind-tunnel pressure messurements. Conversely, their disappearance is used to determine stall. Therefore, 

it would follow that the pressure gradients such as those shown in Figure 3.14 indicate attached flow in 

spite of the high AOAs. However, by using the flow-visualization studies that were conducted 

concurreatly with the pressore measurements, it was dernomtnkd that the flow is actually separated over 

most of the airfoil &ace. 'Ihis was accomplished using video data of tnRs time-correlated with pressure 

distributions. These flow-visualization measaremeats are discussed in &tail in Section 5.2, and by Scott --- 

et al. (1991). They have led to the surprising conclusion that the pressure gradients measured on the wind 1. 

turbine exist in the presence of separated flow. 9 

The higher suction pressures on the wind turbine explain why the inkgrated force coefficients are higher 

but do not explain what is causing the pressure distributions to be consistently different from wind-tunnel 

data measured on the same Moil. There are some possible explanations, but they are very speculative. 

Om possibility is that the chordwise pressure gradients may be caused by spanwise flow in the separation 

region. Another is that vortiuty in the qmaiion bubble may be influencing the surface pressures. Future 

tests and data analysis will be focused on ihe answers to these questions. 

3.3.3 Spamise Pressure Distributions 

This section pnsents the results &om presswe measurements that were made along the blade span 

Pressure taps were located on the low-pressure surface at both the 4% chord and the 36% chord locations 

at 10 spanwise locations. The taps at each of the primary stations were supplemented by two additional 

pressure taps in between primary stations. me 4% chordwise position was chosen because it is close to 

the location of the suction peak on the airfoil. The 36% chordwise position gave another cross-section 

of the pressure distributions at a point behind the pressure peak, but on a significant part of the pressure 

distributions. The blade layout and pressun-tap locations are described in Appendix A and in more detail 

in Butterfield et al. (1992). 

In this section the pltssure coefficients (Cp) were normalized by the local dynamic Fessure as described 

in Equation 3.11 of Section 3.2. These spanwise distributions an bin-averaged and are presented as a 

function of wind speed. No cornpatisons with predictions are made. 

These two rows of pressure taps made it possible to measure spanwise pressure gradients in addition to 

the chordwise distributions shown in Section 3.3.2. The pressure measurements that were made between 

the chordwise distributions provide the resolution necessary for examining the spanwise pressure gradients. 

Figures 3.15 through 3.18 show the spanwise pressure distributions for the 4% and the 36% chordwise 

stations. Each plot shows a family of spanwise pressure distributions for a range of wind speeds. 

Figure 3.15. Bikaveraged spanwise pressure coefficients for the 4% chord for 8-14 m/s wind 

speeds 

Figure 3.16. Birr-averaged spanwise pressure coefficients forthe 4% chord for 14.5192 mls wind 

speeds 



Figure 3.17. Bin-averaged spanwise pressure coefficients for the 36% chord for 8.8-I45 mls wind 

speeds 

Figure 3.18. Bin-averaged spanwise pressure coefficients for the 36?h chord for 14.5-192 m/s 

wind speeds 

The first curve in Figure 3.15 shows pressure coefficients decreasing fkom the 30% span to the 80% span 

at the 4% chord and 8 mls wind speed At this low wind speed the inboard stations are operating at high 

AOA and the outboard stations are operating at low AOA. Because of this AOA distribution, the suction 

pressures at 4% chord are high inboard and steadily decrease toward outboard stations, as expected. 

However, as wind speed and mean AOA increase, conventional theory would predict stall separation at 

inboard stations to result in suction pressures less than 1 because of leading-edge stall, and outboard 

suction pressures to dramatically increase where the flow becomes reattached. However, for the high-windspeed cases, shown in figure 3.16, inboard suction pressures are significantly greater than expected, 

reaching values over 2.5. This is not surprising because the data from Section 3.3.2 showed that the 

pressure peaks were sustained at high AOAs for the inboard station. 

Further outboard, near the 47% span, these mes exhibit irregular behavior, with a precipitous drop 

followed by a rise near midspan. As wind speed continues to increase, a second drop in pressures 

develops at the 63% span, followed by a rise near the far out-board stations. 

Figures 3.15 and 3.16 show high inboard suction pressure and unexpected drops in the spanwise pressure 

coefficients at 47% and 63% span, but for high winds only. The cause of this behavior is unknown. 

Figures 3.17 and 3.18 show similar trends in the 36% chord spmwise distributions. Again, it is expected 

that pressure coefficients would imease at outboard stations and decrease at inboard Ststtions in high 

winds. This trend can be seen, but the erratic curves are unexpected, Local stall separation could explain 

a single dip in spanwise pressure coefficient curve. Only one such decrease would be expected, and its 

location should progress outboard as wind speed increases. 

Similar irregularities can be seen in the unsteady instantaneous spanwise pressure distributions presented 

in Section 4.6. In the unsteady case, one would expect to see a spanwise progression of stall boundaries, 

yet unexpected dips in the curves are evident again. It is possible that instrumentation error could cause 

inconsistent behavior, but the dips only appear at high wind speeds. If the instrumentation were the cause, 

then the dips would be expected to exist at all wind speeds. Future tests that focus on the spanwise 

pressure distributions could help determine the source of this odd behavior. 

3.4 Conclusions 

The data show-the averaged behavior of rotating airfoils located at 30%,47%, 638, and 80% spanwise 

blade stations on a stall-conrolled wind turbine operating over a range of conditions. Several conclusions 

can be drawn from these data: 

Rotating airfoils behave as they would in the wind tunnel for angles of attack below stall. 

Wind-turbine airfoils operating in quasi-steady conditions show a delay in stall due to a combination 

of suction-peak persistence and high negative pressures on the suction side of the airfoil. 

t Delayed stall observed on rotating airfoils has resulted in high nod forces and high tangent forces 

I in the poststall region that are uncorrelated with wind-tunnel stall measurements. 

i 


?be high suction-pressure pks and pressure gradients measured on the wind turbine during delayed 

stall at high AOAs exist in the presence of separated flow. 

The performance deviations on wind-turbine airfoil force coefficients caused by delayed stall are more 

pronounced at inboard blade stations. While outboard stations experienced only a delayed stall effect, 

maximum values of the inboard force coefficients were increased to over twice the values measured 

in the wind hrnne1. Some of the behavior of spanwise pressures was erratic and inexplicable. 

The airfoil behavior on a fixed-pitch wind-turbine blade is modified significantly emu&, by stall effects 

that wind-tunnel stall data may not be accurate for &sign purposes. 

The causes of this behavior are not uoderstood yet, and the current Combined Experiment test 

configuration is not equipped to identify the causes. It is not known what effects blade plaoform (twist 

and taper) will have on the ahfoil performaace. Future work must be coacted to understand the 

complexities of this problem. 
4.0 Unsteady Aerodynamic Loads 

4.1 Introduction 

Wind turbines commonly operate in steady stall as a means of regulating peak power and loads. It was 

shown in Chapter 3.0 of lhis report and previously by others (Butterfield et al., 1991; Musial and 

Butterfield, 1990; and Vitema and Comgan, 1981) that airfoils do not stall on rotating wings as they do 

in wind-tunnel tests under quasi-steady conditions. The helicopter industIy has ignored steady stall beme 

helicopters avoid operating conditions that would result in large-scale steady stall. However, they have 

invested significant research time into understanding dynamic stall, a condition that helicopters cannot 

avoid. 

Wid turbines are also subjected to dynamic loading. Wind shear and turbulence cause time-varying 

inflow that results in unsteady airloads. Tower shadow, wakes from upwind turbines, and yaw emrs also 

introduce unsteady inflow to wind-turbine rotors. Wind-turbine designers must predict these loads in order 

to ade~uately design blades, hubs, and the remaining support structure to achieve a 30-year life. Structural 

analysts have not been able to predict mean or dynamic loads accurately enough to confidently determine 

the fatigue life of major wind-turbine components. 

Part of the problem is due to the uncertainty in the stochastic wind environment. Another important factor 

is the lack of basic howledge of rotary-wing airfoil stall performance. Through an earlier effort, 

BuUerfield (1989) found that dynamic stall can exist on wind-turbine blades during normal operating 

conditions. There is also mounting evidence that this may contribute to higher dynamic loads. Wright and 

Butterfield (1991) and Hansen and Butterfleld (1990) showed improved correlations with measured 

dynamic loads and yaw loads when dynamic stall was introduced into their analyses. Semi-empirical 

dynamic-stall models used by the helicopter industry may be applicable to wind Wines for predicting 

these dynamic loads. However, they wilI require validation using measurements from operating wind 

turbines. Tfie dynamic-stall data collected during the Combined Experiment have provided researchers 

with an opportunity to perform this validation and adapt these models for wind turbines. Some of the 

work being conducted at the University of Utah is focused on this code development. 

This chapter gives a complete summaq of the work performed by Buttefield (1989) along with the most 

recent empirical findings. Dynamic-smll predictions are not discussed. Dynamic-stall measurements rn 

described at four blade spanwise stations of a rotating wind--turbine blade. Loads are correlated with the 

measured airloads. Fi'iy, dynamic stall is related to increased yaw moments. 

42 Data Preparation 

42.1 Test Cases 

The sign convention used for this analysis assumes that the yaw error is the compass angle of the turbine 

minus the wind direction. It is important to note that the rotor's direction of rotation has a major influence 

over dynamic-stall response caused by yawed flow. For this turbine, the rotor spins dockwise when 

viewed from a downwind location. The instrumented blade is pointing up when in the zero-degree 

azimuth position. 

Two yaw cases were chosen for analysis.' The first was a? 30 deg yaw emr and spanned 20 seconds at 

wind speeds of 13.5 mfs. The second case was at zero yaw error and spaaned 10 seconds at wind speeds 

of 15 ds. These two cases are compared in Table 4.1 and were chosen to illustrate the conditions that 

cause dynamic stall. 
Table 4.1 

30" Yaw Case Zen, Yaw Case 

VPA Ave. Wmd Speed (mi$) 

Ave. Turb. Intensity 

Shear (mk) 

Pitcb Angle (degree) 

Pitch Std Dev. (degree) 

Rotor Tq. (N-m) 

The 30 deg yaw error case is typical of conditions that normally occur for yaw-driven and free-yaw 

HAWS. Yaw-driven wind turbines typically respond to wind-dinction changes at low yaw rates 

(1-5 deg per second) to limit gyroscopic bending moments on the main&& Wind direction can change 

at much higher rates and result in yaw errors of 30-40 deg while the turbine's yaw system is catching up 

with wind direction. In &=-yaw wind turbines, tracking pmblem, slow yaw response, and dynamic yaw 

overshoot can lead to the same high yaw errors experienced by yaw-driven machines. In either case, the 

yawed flow case represents a realistic operating condition that may be typical for many other wind-Wine 

configuations. 

4.2.2 Pressurn Coefficient Normalization 

All the pressure data wen digitally sampled at a 520 Hz during testing. Dnring post-processing, data were 

filtered at 10 Hz and block-avefaged by a factar of 10 to obtain a find 10 Hz bandwidth and 52 Hz 

sample rate. These rates are different from those in Section 3.0. 'Ihese data were then sorted into bins 

using We measured AOA as the independent variable. 'Ihis ged approach was used for results 

presented in Figures 4.3 through 4.19. 

Three different memods can be used to determine the dynamic pressure needed to normahe measured 

pressure distributions. The &st two methods are described in Equations 3.4 and 3.5 of Section 3.0. 

Equation 3.4 shows the ernpitical method It &pea solely on measured quantities and therefore reflects 

all the flow asymmetries in dynamic pressure. The second me$hod uses Equation 3.5 and yields a constant 

value for a given wind speed It does not comd for wind shear, tower shadow, or yawed flow conditions, 

all of which are common for wind-turbine rotors. The third method is an analytical approach that was 

developed to correct for the effects of yawed flow by allowing the dynamic pressures to vary azimuthdlly. 

This third method and its effect on unsteady data are illustnrted in Figure 4.1. Tbis figure shows measured 

and predicted dynamic pressures during 30 deg yawed operation at the 67% span 'Ihe effects of the tower 

shadow are visible on the measured dynamic pressme (Q) awe at approximately 180 deg. Equations 4.1 

and 4.2 were used to calculate the predicted dynamic pressure cwe in Figure 4.1. The resulting values 

include the vector sum of the free-stream velocity, and the in-plane blade-velocity component, which take 

into account the effects of the advancing and retreating blade in yawed flow. A comparison of these two 

curves shows very good com1ation between the measured data and the data normalized by the third 

method This agreement provides a good validation of the measured daQ 

Figure 4.1. Dynarniepressure variations during yaw 

$o = Yaw Angle 

= Azimuth Angle 

v =windspeed 

p = Air Density 

r = Local Blade Radius 

Q = Dynamic Pressure. 

Figure 4.2 shows the lift coefficient during dynamic stall with constant (ucolfected) Q, which was 

calculated prr Equation 35, and azimuthally varying (corrected) Q, which was calculated using Equations 4.1 and 4.2. This shows that the effects of dynamic stall can vary depending on the no~zation 

used. 

figure 42. Effects of timevarying dynambpressure normalhation 

There is no clear consensus among aerodynamicists about which normalizaton should be used on rotating 

airfoils. The measured data &om Equation 3.4 might seem to provide the most realistic normalization; but 

because the measurements are made directly in front of the rotating blade, they may be subject to wakeinduced velocities that could affect the measurement accuracy. Therefore this technique is met with some 

skepticism. The corrected, azimuthally varying method is also subject to measurement errors caused by 

the calculation of yaw emr, which combiis the yaw angle measurement made on the turbine with the 

instantaneous wind direction measurement made on the Vertical Plane Array (VPA). Spatial separation 

between the mine and the VPA will introduce an uolmown amount of error. In spite of this, the 

corrected method has merit and is probably a valid technique, because it coneladed well with the measured 

data. However, the cumnt accepted practice is to use the constant-norm-tion method described by 

Equation 35. Therefore, in this report, all chordwise pressure-disttibution data are normalized by local 

dynamic pressure based on the free-stream velocity using Equation 35. In fbture analyses of these data 

it may be insightful to use the corrected normalization. 

For spanwise airloads, it is appropriate to normalize by the dynamic pressure calculated at the blade tip 

using the constant C, method 'Ibis technique provides a more representative full-blade load distribution 

of the dimensiondl pressures, because the pressures are all normalized by the same constant. Normalization 

of each spanwise location with its unique local dynamic pressure would make station-to-station 

comparisons difficult. Therefore, all spanwise pressure distributions presented in Section 4.5 are 

normalized by blade-tip dynamic pressure. 

4.3 Azimuth-Averaged Results 

Figures 4.3 through 4.6 illustrate angle of attack (AOA) and lift coefficient (CJ variations azimuthaveraged over 25 revolutions for the 30 deg yawed case at 80%, 63%, 47%, and 30% blade spans. For 

reference, the wind-tunnel static stall CUWp equal to 0.95, is provided on each me. If the airfoil did 

not stall and the lift curve remained linear even at high values of AOA, then C, would follow the cosine 

shape of the AOA carves shown in these figares. This is clearly not the case with these data The inboard 

stations reach Be highest values of Cu-I while the blade is rising (advancing), at azimuth angles of 

270 deg. Minimum values of C, occur aftex the airfoil has stalled (begun retreating), when the blade 

azimuth angle is between 0 and 90 deg on the downwind side of the blade. The large differences in lift 

that exist from the upwind side of the yawed rotor (200 to 300 deg azimuth angle) to the downwind side 

of the rotor (0 to 100 deg azimuth angle) are caused by dynamic stall on the airfoils. This is evident 

because peak C, values exceed even the higher steady-stall performance described in Chapter 3.0. These 

dymdc loads are responsible for high yaw moments and increased low-speed shaft WS) cyclic loads. 

Figure 4.3. Mmrdh-averaged AOA and lift coefficient at 80% span for 30° yaw case 

Figute 4.4. ~rnuth-everaged AOA and Slt coefficient at 63% span for 30' yaw case 

Figure 4.5. Azimuthaveraged AOA and lift coefficient at 47% span for 30' yaw case 

Figure 4.6. Azimutheveraged AOA and lift toefficient at 30% span for 30' yaw case 

The tower shadow can be seen in some of the cwes as a dip just befme an azimuth angle of 180 deg is 

reached. In zero-yaw flow this dip would be at exactly 180 &g. It is important to note that the 

magnitude of the tower-shadow effect is dwarfed by the effects of yaw. 

These figures show that peak-to-peak values of AOA range from 11 deg at the 80% span to 26 deg at the 

30% span. Because 30 &g yaw errors are not unusual? most horizontal-axis rotors experience AOA cyclic 

amplitudes of this magnitude, which are large enough to cause dynamic stall. Both fixed-pitch and pitchcontrol romrs would be affected by this. 

Figures 4.7 through 4.10 show similar plots of C, and AOA variations for the zero-yaw enor case. As 

can be seen, AOA variations are small compared to the yawed case. In this case, asymmetrical inflow is 

due only to a wind shear of 0.7 ds across the rotor and to the tower shadow. Unlike the yawed case, 

tower shadow is the major contributor to inflow disturbances. This is obvious from the rapid change in 

AOA and C, at a 180 deg azimuth angle. This disturbance is large enough to cause dynamic stall, as is 

evident from the sudden rise in C, above the static stall C,,, of 0.95. 

Figure 4.7. Azimuth-averagod AOA and lift coefficient at 80% span for no yaw case 

Figum 4.8. Azimuthgveraged AOA and lift coefficient at 63% span for no yaw case 

Figure 4.9. Azimuthsveraged AOA and lift coefficient at 47% span for no yaw case 

Figure 4.1 0. Azimuthaveraged AOA and lift coefficient at 30% span for no yaw case 

4.4 Dynamic Stall Results 

Figures 4.11 through 4.14 show azimuth-averaged CL vs. AOA curves compared to lift awes measured 

in the CSU wind-tunnel tests (Butterfield, Scott, and MusidI, 1990). The dynamic-stall behavior is evident 

in the large hysteresis loops that surround the static cwes. At the 80% span the rotating blade CL(-) 

values do not significantly exceed static values. However, at inboard stations the effect on C,,-, becomes 

more pronounced. At spanwise stations 6396,4796, and 3096, shown in Figures 4.12 through 4.14, CU,, 

values exceed static values by 32% to 110%. The labeled symbols indicate blade azimuth angle. The 

variation in lift at azimuth angles of 90 deg and 270 deg, discussed in Section 4.3, is again obvious in 

these mes. As the hyskzesis loops grow larger, the differences inaease and the resulting yaw moments 








inaease. Hansen and Butterfield (1990) showed that predicted yaw moments double when dynamic stall 

is introduced into YAWIIYN, a structural dynamics model. 

Figure 4.11. Dynamic stall at 80% span 

Figure 4.12. Dynamic stall at 63% span compared with wind-tunnel dynamic stall 

Figure 4.13. Dynamic stall at 47% span 

Figure 4.14. Dynamic stall at 30% span 

Drag is also subject to the effects of dynamic stall. Figures 4.15 through 4.18 compare dynamic stall 

measurements for the 30 deg yaw emr case with data hm the CSU wind tunnel. The 80% span is 

affected minimally, but inboard WOOS experience nearly double the drag fiom one side of the rotor to 

the other. This is illustrated for the 47% span in Figure 4.17, which shows a Cb of 0.15 at 270 deg 

azimuth angle and 0.07 at 90 deg, an increase of more than a factor of two. 

Figurs 4.15. Drag dynamic stall at 80% span 

Figure 4.16. Drag dynamic stall at 63?& span 

Figure 4.17. Drag dynamic stall at 470h span 

Figure 4.18. Drag dynamic stall at 30% span 

Pitching-moment coefficients also experience dynamic stall. Figure 4.19 shows C, hysteresis loops 

measured at the 63% blade span. These moments can have a significant effect on blade pitch angle if 

there is flexibility in the pitch system of pitch-control rotors. This can also cause increased fatigue loads 

on pitch linkages and pitch actuators. 

Figure 4.19. Pitching moment dynamic stall at 63?h span 

45 Transient Pressure Did but ions 

Figure 4.20 shows the suction-side pressure distributions of a mtating turbine blade with zero yaw taken 

at 30% span a$ a wind velocity of 14.7 mkc over a single rotational cycle. At 0 deg azimuth, the turbine 

blade is pointing up, and at 180 deg the blade is straight down behind the tower. Three-dimensional 

surface plots illustrate the chordwise pressure distribution and its variation throughout the rotation cycle. 

Chordwise pressure distributions are shown for every 0.82 deg of rotation. Tbe axis of this plot showing 

"azimuth angle" can also be converted to "timen with 0.833 second passing for each 360 deg. This figure 

shows a shift in the suction pressure maxima toward the trailing edge as blade advances through the 

rotation cycle. Robinson (1985) used flow visualization in conjunction with surface-pressure data to 

correlate dynamic-stall vortex formation with unsteady surface-pressure signatures. He observed a 

temporal shift in maximum suction pressure toward the trailing edge, which corresponded to passage of 

a dynamic-stall vortex. Current results seem consistent with this observed vortex formation and with 

convection over the suction surface of wind-turbine blades. The transient pressure signature becomes more 

obvious for a yawed HAWT, as will be demonstrated later. 

Figure 420. Chordwise surface-pressure distribution vs. azimuth angle. V = 14.7 mls, O0 yaw, 30% 

span. One cycle of data. 










Figure 4.21 shows a comparison of measured and theoretical AOAs . theoretical model includes the 

vector ation of he-strearn velocity, local in-plane velocity, the effect of the yawed turbine, and a 

simple tower-shadow model. The agreement is generally good, but there are some discrepancies. At 

180 deg, the frequency response of the transducer is not fast enough to charstcterize rapid changes in flow 

angles due to the tower shadow. At high AOA, there is a discrepancy that may be caused by induced 

velocity effects or flow irregularities that are not included in the theoretical-model curve. This comparison 

demonstrates that the measured AOA yields reasonable results and has sufficient frequency response to 

give accurate dynamic-stall characterization for yawed flow conditions. However, the flow-angle sensor 

cannot accutately respond to the impulsive nature of the tower shadow. 

Figure 421. Measured and geometric lml AOA vs. azimuth angle 

Figure 4.22 shows the theoretical AOA va&ion of a yawed downwind HAWT for a yaw angle of 30 deg 

and wind velocity of 16.7 &see. Figure 4.23 shows a bin-averaged upper-surface pressure distribution 

at 30% span for 30 &g yaw. This may be contrasted with single-revolution data shown in Figure 4.24 

for similar test conditions. In both cases, two leadingedge suction-pressure peaks were seen over the 

averaged rotational cycle. Data selected for the single-revolution show that the suction pressure peaks can 

be larger and more transient in me. Convection of a vomx structure may be inferred by the temporal 

shift in maximum suction pressure away fiom the leading edge, which is consistent with results seen for 

pitching airfoils, 

Figure 422. Theoretical geometric AOA vs. azimuth angk. m0 yaw, V = 16.7 m/s 

Figure 423. Chordwise surface-pressure distribution vs. azimuth angle. 30" yaw, V = 14 mls, 30% 

span. Birr-averaged data. 

Figure 424. Chordwise surface-pressure distribution vs. azimuth angk. 30° yaw, V = 16.7 mls, 30?h 

span. One cycle of data. 

Figure 4.25 shows the upper-surface pressure distribution for a turbine blade opetating at -33 deg yaw over 

one revolution at the 30% span The suction-pressure peaks are approximately 180 deg out of phase with 

respect to the positive yaw case. Four discrete suction-pressure peals were observed, implying formation 

of a number of vortices throughout the rotation cycle. Convection of these vortices toward the trailing 

edge may be infened at both 75 deg and 270 deg azimuth. 

Figure 4.25. Chordwise surfacepressure distribution vs. azimuth angle. -33O yaw, V = 13.3 mfs, 

30% span. One cycle of data. 

'he upper-surface pressure distribution at 63% span is shown in figure 4.26. A suction-pressure spike 

was observed at approximately 120 deg azimuth, and a smaller spike was seen at 210 deg. The suctionpressure peak persists over a larger portion of the cycle (90 deg-120 deg), and convection of this vortex 

can be inferred in the suction-pressure ridge. This convection exfends only back to 50% chord, in contrast 

to the situation at 30% span, shown in Rgure 4.25, where well-defined vortex convection is seen dl the 

way to the trailing edge. 

Figure 4.26 Chordwise surface-pressure distribution vs. azimuth angle. -33O yaw, V = 13.3 mls, 

63% span. One cycle of data. 

Figures 427 and 4.28 show bin-averaged spanwise pressure distributions taken at the 4% chord location 

for zero yaw error. All spanwise pressures were normalized with respect to the tip velocity using 

Equation 35 where R = 5.05 m. As can be seen in Figure 4.27, for the low wind velocity case of 






7.7 dsec, dace pressure remains approximately constant along the span. The dip at 180 deg is due to 

the tower shadow. A suction-pressure maximum appears at approximately 4746 span, with suction 

pressure decreasing modestly inboard and outboard This may be contrasted with the high wind velocity 

case of 18.7 mhec seen in figure 438. For this case, suction pressure demeases slightly from 30% to 

47% span, and little evidence of a tower-shadow-induced vortex can be seen. From 50% span outboard, 

however, overall suction pressure increases appreciably. h addition, a tower-shadow-induced vortex 

signature becomes better defined at 180 &g azimuth for outboard stations. 

Figure 427. Spanwise pressure distribution vs. azimuth angle. 7.7 mls wind speed, 4% chord, tip 

velocity normalization, binaveraged data. Q' 'ip"~ 

Figure 428. Spanwise pressure distribution vs. azimuth angle. 18.7 mls wind speed, 4% chord, tip 

velocity normalization, bi~veraged dab. 

Figures 4.29 and 4.30 show surface plots of bin-avewged surface-pressure distributions taken at 4% chord 

for -26.5 deg and 30 deg yaw cases. Again, P swface-pressure data were normalized with respect to the 

tip velocity. Figure 429 shows the -265 deg yaw case. In general, maximum suction pressures occurred 

as the blade reached maximum height (0 deg or 360 deg azimuth), and minimum pressures were 

encountered at 180 deg. These data exhibit some interesting characteristics. From 30% to 47% span, 

suction pressure deneased appreciably. Rom 47% to 63% span, suction pressure increased, and from 

63% to 80% span, it leveled In addition, at 30% p, thae was no evidence of a tower-induced 

vortex (which would likely occur near 180 &g azimuth), but this vortex did appear in the pressure 

signature near 63% span and further outboard. 

Figure 429. Spanwise pressurn distribution vs. azimuth angle. 18 mls wind speed, 4% chord, tip 1 

velocity norrnaliwtion, bin-averaged data, -26S0 yaw. 

Figure 430. Spanwise pressure distribution vs. azimuth angle. 14 mls wind speed, 4% chord, tip 

velocity nonnaliion, bin-averaged data, 30" yaw. 

Figure 4.30 shows a cornpafable surface plot of spanwise pressure distributions for the 30 deg yaw case. 

As expected, maximum suction pressures occurred approximately 180 deg out-of-phase with respect to 

the negative yaw case. There is no evidence of a tower-induced vortex in the presswe profile except at 

80% span. A pressure ridge does pist at approximately 200 deg azimuth along the entire span, possibly 

indicating uniform voxtex initiation. This suction-pressure peak persists over a relatively short azimuth 

range at 3046 span, but pasists longer at outboard stations. At 80% span, the suction-pressure maxima 

persist over 45 deg of the rotation cycle. 

Figure 4.31 is identical to Figure 4.30 except that single-revolution instead of bin-averaged results are 

presented. In general, the charactaistics are identical to the bin-averaged resul?s with a few exceptions. 

The overall suction pressures are higher for the single-revolution case selected. Turbulence levels, 

indicated by random pressure fluctuations, were more apparent for the single-revolution case. This effect 

was greater at outboard stations. 

Figure 4.31. Spanwise pressure distribution vs. azimuth angle. 16.7 mls wind speed, 4% chord, tip 

velocity normalization, bi~veraged data, 30" yaw. 

4.6 Conclusions 

The influences of unsteady flow were examined on the Combined Experiment wind-turbine rotor. The 

effects of yawed flow and tower shadow wexe investigated extensively. Some general conclusions are: 





Dynamic stall was found to exist on a HAWT operating at 30 deg yaw angle. 

Dynamic stall was also found for zero yaw mr when tower shadow, wind shear, or inflow turbulence 

caused large AOA excursions. 

Increased aerodynamic loads due to dynamic stall cause increased structural loading. 

Dynamic stdl forces caused by tower shadow are shorter in duration and lower in ~tude than 

those caused by yawed flow. 

Dynamic stall should be included in structural dynamic rotor models in order to acarately predict 

savice loads. The data presented in this report plus future data will provie the basic information 

needed to develop dynamic-stall models. 

Surface plots of suction-pressure distributions on a rotating airfoil demonstrated the presence of 

discrete vortex strums. 

Temporal shifts in maximum suction pressures toward the trading edge suggested the convection of 

these vortex structures, which was consistent with presmre signatures produced by vortex formafions 

on pitching airfoils. 

Future analysis is required to more accllrately predict the formation of vortex structures on rotating 

wind turbines. 'Ibis will allow for better modeling of dynamic stall, structural loading, and rotor 

performance. 
Flow-Visualization Tests 

Flow visualization was used to establish a correlation with the surface-flow pattems ad pressure data. 

Section 5.1 describes a liquid-crystal surface-shear-stress coating that was used to examine the boundarylayer characteristics. Section 5.2 describes the correlation of pressure distributions with video-taped flow 

patterns using tuftr. 'LAis technique was used to prove that separated flow conditions coexist with highnegative-pressure coefficients on the inboard sections of the rotating blade. In general the video 

correlation technique is useful for a variety of data-analysis tasks, but in this case it is used to correlate 

surface-flow patkms with surhce-pressure distributions. 

5.1 Liquid Crysbl Tests 

A diagnostic technique capable of "visualizing" the instantaneous surEdce-shear-stress pattern in dynamic 

flow fields, in a continuous and reversible manmr, would be a valuable research tool. The potential of 

liquid crystals to meet this objective has been under investigation a# Sandia National Laboratories. A 

desciption of the technique and results obtained to date, from both the laboratory and an operating 

vertical-axis wind turbine, are given in Re& (1988% b). 

A coopemthe research effort was jointly conducted by Sandia Nationdl Laboratories and the National 

Renewable Energy Laboratory to investigate the feasibility of applying the liquid-crystal technique to 

horizontal-axis wind turbines operating in field environments. 

Initial experiments were nu! under light-wind conditions while the machine was motordriven. A portion 

of the blade surface near the pressure taps at 80% span was coated with the liquid-crystal solution. Under 

these conditions, the airfoil AOA was essentially equal to the airfoil pitch setting. Both thennochromic 

(shear/tempemturedependent) and shear-sensitive-only Liquid-aystal mixhues were employed Liquidcrystal response was recorded by a boom-mounted (downwind) video camera, Test conditions and test 

results are s-zed in a 10-minute color video, obtainable upon request from the authors. 

The technical feasibility and viability of the liquid-crystal technique in WECS field environments were 

further demonstmad Results for the SEN-SS09 aidoil showed the existence of an adverse-pressuregradient-induced (contour-generated) I- separation bubble near the airfoil mid-chord at low AOAs. 

For AOAs near 10 deg, this bubble quickly moved to the doil leading edge. In both cases, transition 

to turbulence occun:ed in the shear layer above the local revme-flow region, resulting in a high-shearstress turbulent reattachment zone immediately downstream of the bubble. At low AOAs, the turbulent 

boundary layer remained attached to the airfoil fluface all the way to the trailing edge. However, at 

10 deg AOA, turbulent-boundary-layer separation occurred at the 60% to 70% chord location. Higher 

AOAs could not be tested on the wind mine but were tested in the wind tunnel at Ohio State University. 

Wind-tunnel tests demonstrated that the boundary-layer behavior was very similar to the boundary-layer 

behavior in the rotating-blade case at low AOAs. The chordwise transition locations were not measurably 

different, and the transition AOAs appeared to be the same. The laminar separation bubble occurred at 

the same chordwise location, and moved forward toward the leading edge at AOAs larger than 8 deg. 

The airfoil was dynamically pitched to induce unsteady flow to see if the resulting unsteady-pressure 

distributions would affect the boundary-layer-transition locations. The blades were tested at reduced 

frwluencies (CWr, where C is chord length, o is cyclic frequency, and V, is relative velocity) up to 0.05, 

which co~~esponds to the once-per revolution siwsoidal pitch variation that the turbine experiences during 

yawed operation). Transition locations occurred at the same chordwise locations when the unsteady AOA 
corresponded to the static AOA. Re& and Buttemeld (1989) examined this unsteady behavior in detail 

on a similar symmetrical airfoil. 

Dynamic stall is known to exist at this reduced frequency for large AOA amplitudes. The liquid crystals 

did not show a distinct color change at the turbulent separation boundary. This characteristic made it 

difficult to quantify the unsteady turbulent separation location. Therefore, the effects of dynamic stall may 

have been present but unnoticed in the liquid-crystal results. 

5.2 Correlations of Pressure Distribution with Flow Patterns 

In the past, the research commuIIity has not been able to predict dynamic loads on wind-turbine blades 

resulting from complex inflow. lhis prediction is particularly difficult during high-wind-speed operation, 

when stall occurs throughout the blade span (Viterna and Corrigan, 1981; Madsen et al., 1988). Flow 

visualization has been used to determine if stall separation is occurring expected. Correlating these 

flow-visualization data with operating data such as wind speeds and power levels has not been done 

previously because of the difEculty in establishing a link between video data and recorded engineering 

data. 

This Link has been established as part of Phase II of the Combined Experiment. Correlating the flow 

visualization with the recorded time-series data gave researchers a new tool for validating and interpreting 

unsteady-flow phenomena 

This work has been described in more detail in the paper by Scott et al. (1991). 

52.2 Flow Visualiwtion Test Sefup 

Two rotor-mounted black-and-white high-resolution video cameras spin with the hub and give stationary 

(in the rotating coordinate fkame) images of the downwind side of the blade. One camera is mounted on 

the blade, giving a low-angle (oblique) view of the entire blade, and the other is mourned at the end of 

a rigid boom extending 3 m downwind. The blade-mounted camera can be remotely zoomed, although 

it is typically set so that the low-angle view of the blade fills the bottom half of the video frame. The 

second (boom-mounted) camera can be panned to view almost any part of the blade. The video signals 

from the cameras and dl the pressure data are passed through slip rings to cables to the recorders. 

Figure 5.1 shows the turbine setup. 

Figure 5.1. Wind turbine with video cameras 

To visualize thk air flow over the blade, half-inch-long tufts of white thread (polyester #60) are attached 

to the blade. When flow is attached, the boundary layer is thin, and the tufts remain close to the blade 

and align themselves with steady streamlines. When the flow becomes separated, the tufts' movement 

becomes random as the tufts are caught up in the recirculating turbulence. This difference in behavior 

makes it easy to see the bun- between attached and separated flow on the video images. 

Additional video equipment consists of a video mixer and a Super-VHS video cassette recorder. The 

Super-VHS recorder, when used with the high-resolution cameras, gives about a 25% improvement in 

horizontal resolution of the video image over a standard VHS image. The mixer allows a combination 

of the two camera signals to be recorded on a single video tape. Because the view of the blade fkom the 

blade-mounted camera fills Iess than half the video frame, the upper half of the frame is usually filled with 

part of the view Born the boom-mounted camera. Figures in this report show only the view from the 

blade-mounted camera Figure 5.2 shows the video signal path. 

Figure 51. Video signal path 

A good video image of the tufts on the blade requires high contrast between the white tufts and the flat 

black blade. The best way to achieve this is to record aIl video data at night. During recording, the blade 

is illuminated by 12 100-W tugstenhalogen spotlights that an mounted on the boom and that rotate with 

the blade. 

The master system clock is a Datum 9100 IRIG-B Time Code Generator. The same RIG-B time code that 

is recorded on tape with the pulse code modulated (PCM) data streams is fed to a Datum Video Time 

Inserter, which overlays a block containing the time code on the video image. 

The final product of the video system is a Super-VHS tape showing views of the blade. 'This tape is then 

taken to the NREL offiw for further processing. The video system in the NREL office consists of a 

25-MHz 386-based PC with a Truevision Targa-16 video board and a BCD-KKIO VCR controller that 

controls two Panasonic editing VCRs. Software on the system allows the researchers to grab still images 

from video tape, modify them to improve visibility, overlay other graphics on top of the video image, and 

produce an animated sequence of still frames on video tape. 

52.3 Data Processing 

Video Data 

Standard NTSC video is recorded at a rate of 30 6rames per second, while the PCM data stream from the 

Wine has a sample rate of 520.8 Hz. To correlate the video data with the PCM data, a program was 

written that reads a list of frame time codes, picks the closest data record firom a disk file of PCM data, 

and writes the time code and the upper-surf' pressure record to anotha file. This file is then transferred 

to the PC for use by the pressure overlay program. 

Picking the closest record for a given time code is somewhat arbitrary, because a video frame actually 

requires 1BWr of a second to generate, a time period that spans 17 PCM data records. The procedure does 

guarantee that the data record was recorded at some point during the frame generation, and that PCM data 

records are separated by 1/3m of a second, plus or minus one PCM data-sample interval (0.33 2 

0.00192 sec). 

Video Image Processing 

When a segm& of interest has been chosen, it must be saved as a series of individual frames. First a 

channel of a proprietary frame code is added to the video tape. This frame-code information is required 

by the VCR controller. Next, a table is prepared listing frame code versus inter-range instrumentation 

group (IRIG) time code for each frame in the segment An automated he-grabbing program grabs each 

frame fiom the video tape and field copies it Because of the interlaced nature of NTSC video, adjacent 

horizontal lines are actually recorded 1160th of a second apart, a time long enough to blur a moving 

object Field copying replaces each even-numbered line with another copy of the previous &numbered 

line, reducing the vertical resolution by a factor of 2, but greatly reducing inter-field jitter. The resulting 

cleaned-up frame is then saved as an image file on the hard disk of the PC. 

The pressure overlay program loads each frame image file, finds the proper pressure record in the data 

file, draws the pressure graphs on the blade image, and stores the result as another PC image file. The 

animation program writes a specified number of fiames of each image file to video tape. For example, 

if 10 frames of each image file are written to tape, the resulting animated sequence runs at one-tenth the 

original speed, allowing rapid transitions in flow on the blade to be observed Single hmes can also be 

studied to map boundaries between attached and separated flow. 

Figure 5.3 shows an example of a pressure distribution srrperimposed on a video lime. The video be 

shows tufts attached to the rotating wind-turbine blade. Angle-of-attack probes can be seen extending 

from the leading (right-hand) edge of the blade. This display allows the researcher to correlate the 

separation boundary displayed by the tufts with pressure distributions measured by transducers mounted 

inside the blade. 

Figure 5.3. View of blade at low AOA 

A varieq of data-display programs are available on the UNIX system. 'Ihese programs give a more 

detailed view of pressure profiles, and also show wind speeds, time histories of attack angle, pitch angle, 

and other variables fkom the FCM data stream. It is very iaformative to simultaneously study both the 

video and the output of these graphics programs. The VCR is advanced a fnune at a time, and the time 

code displayed on each video we isnoted. The graphics program can then be set to display the 

corresponding PCM data record. 

The resolution of the camera and video tape is not mfEcient to resolve individual tufts near the blade tip. 

Thus, it is very difficult to map the boundary between separated and attached flow at positions farther out 

than about 65% span. Perhaps in the future, we can use the high-angle (boom-mounted) camera to map 

this transition. 

5.2.4 Pressum Distributions 

Figures 5.3, 5.4, and 5.5 show low-angle views of the blade and tufts at low, medium, aod high angles 

of attack (AOAs). Table 5.1 gives the AOA at each probe for each figure. Superimposed on each figure 

are the graphs of the four chordwise pressure distributions. The bottom edge of each figure is located at 

approximate'Iy 32% span. 

Table 5.1. Angles of Attack 

Figure 34% 51 % 67 % 86% 

In Figure 5.3, the low-attack-angle case, there is no stall, and the flow is attached everywhere. The 

outboard pressure profiles show the characteristic rounded distribution that is seen in the wind tunnel at 

low attack angles, while the inboard (30%) profile has a strong suction peak. A strong suction peak is 

characteristic of high AOA pnstall operation. 

Figure 5.4, the moderate-&tack-angle case, shows separated flow from about 40% inboard 'Ihe inboard 

profile has lost its suction peak, a characteristic of leadingedge stall, and is approaching a flat profile. 

The leadingdge suction peak has reappeared at the 47% profile. 

Figure 5.4. View of blade at medium AOA 

In the high-attack-angle case of Figure 55, the two inboard profiles are stalled, and the suction peak has 

moved out to the 67% pro6le. Flow at the inboard stations is definitely sepmted, but the pressure 

gradient is mn-zero. 

Figure 5.5. View of blade at high AOA 

The pressure measurements made on me rotating blade are significantly different hrn those made in the 

wind -el. Figure 5.6 shows a comparison of a wind-tunml test at an AOA of 26.2 deg, and a sample 

from a winbtmbine test at an AOA of 265 deg at the 30% blade span. Both upper- and lower-surface 

pressure coefficients are shown for each case. The rotating-blade tests show suction peaks with values 

up to about -3.0; the wind-tunnel data show a flat pressure distribution at the same AOA. 'Ibis flat 

distribution is typical of a fully stalled two-&:ensional airfoil. Local pressure coefficients on the rotating 

blade were measured at up to three times thme ie the wind tunnel. 

Figure 5.6. Comparison of wind-tunnel and rotating-blade pressures 

Figure 5.7 compares lift coefficients (Cd measund in the wind tunnel with data measured from the wind 

turbine. The wind filnneI shows a rapid drop in CL at an AOA of 18 deg. This drop is caused by 

leadingedge stdl (the loss of the leading* suction peak). The wind-turbine data show no drop 

because the suction peak and pressure gradients persist at high AOAs, as shown in Figure 5.6. 

Figure 53. Comparison of lift coefficients from wind tunnel and wind turbine 

Figure 5.8 shows the corresponding pnssure drag coefficient (C curves. This comparison shows that 

the wind-turbine drag is greater than that in the wind tunnel at & AOAs. If the delayed stall (high lift 

coefficients) shown in Figure 5.7 was caused by extended attached flow (less sepafation), one would 

expect lower pressure drag brn tht wind-turbine data Instead the pressure drag is higher than the windtunnel results, implying more sepGlrated flow. 'Ibis Iift results hm high negative vdlues of Cp in regions 

of separated flow. 

Figure 5.8. Comparison of pressure dag coefficients from wind tunnel and wind turbine 

Performance analyses of stall~atrolled wind turbines depend on accurate inputs of CL and CDp to predict 

the peak perfofnxmces. The differences shown in Figures 5.7 and 5.8 explain why analyses based on 

wind-tunnel data often underpredict wind-turbine performance. 

5.3 Conclusions 

A method of conelating video flow-visualization data and standard time-series pressure data recorded on 

an operating wind turbine has been shown. A key to this correlation is to use a single clock signal and 

to simdtaneousZy re& it on the video tape using a time code inserter and on the PCM &ta stream. 

Significant differences between the wind-turbine-blade field results and wind-tunnel tests have been 

observed. Beyond stall, leading suction peah persist to high angles of attack. At inboard stations on the 

blade, non-zero pressure gradients exist in regions of separated flow. These gradients do not occur in the 

wind tunnel. These results help explain why pcrforance analyses typically under predict the peak 

performance on stal1control wind turbines. 
6.0 Structural Loads 

6.1 Introduction 

Structural loads are cmud by airloads, whi@turn, are caused by inflow conditions. Understanding 

the connection between strum loads and mflow was one of the major reasons for conducting this 

experiment Various techniques for establishing the correlation between airloads and structural loads were 

investigated. 

There was a second motivation for investigating structural loads. During the test plan, design attempts 

were made to include redundant measurements or means to check primary measurements. Blade bending 

moment distribution measurements were made in order to co&m aerodynamic load distributions. They 

were also made so that blade motions or amelastic interactions could be isolated hm the natural 

aerodynamic forces caused by inflow alone. Steady load distributions are affected by both airloads and 

centrifugal forces. Dynamic loading is cyclic in nature and involves blade flapwise motions. It is more 

difficult to use dynamic blade loads to estimate unsteady aerodynamic loads. 

Section 6.2 describes a technique that uses dynamic blade loads to estimate unsteady aerodynamic loads. 

This technique was used to estimate airloads that were then compared to integrated airloads measured by 

pressure taps. Although the greatest degree of confirmation of the pressure measurements was gained 

through comparisons with wind tunnel data, this method provided another piece of conMence-building 

idormation. It was also usem to develop the technique and validate it for later use on turbines that 

cannot be instrumented with pressure taps. 

Section 6.3 describes "steady" or bin-averaged load data that can be used for comparison with steady 

prediction codes. 

Structural loads are the important end result of aerodynamic applied loads. It is important to determine 

the transfer function that relates aerodynamic loads to structd loads, so that the wind turbine designer 

can determine the frequency band of aerodynamic loads with which he should be concerned. Steady 

stmctural loads are important as well. Section 62 describes a technique adapted fiom the helicopter 

industry, which uses measwed structural loads plus an analytical dynamics model (transfer function) to 

estimate airloads acting on the rotor. 'Ihis technique was validated by comparing its predictions to the 

loads measured on the Combined Experiment turbine. A good correlation with measured airloads is 

demo- for a variety of operating conditions. 

Section 6.3 briefly describes the bin-averaged load data base presented in Appendix E. 

62 Air Load Estimating Tool (ALEST) 

Two methods can be used to determine aerodynamic loads on a rotating wind-turbine blade. The first is 

to make direct pressure measurements on the blade surface. This is a difficult process requiring costly 

pressure instrumentation. The second method uses measured flap bending moments in conjunction with 

analytical techniques to estimate airloads. Estimating airloads using flap bending moments is much 

simpler and less costly because measurements can be made with conventional strain gages and equipment. 

This section presents results of airload estimates obtained using both methods under a variety of operating 

conditions. Insights on the limitations and usefulness of the bending-moment technique are also included. 

Surface pressures provide a direct measurement of spanwise blade aerodynamic load distribution. Air-load 

distribution can dso be estimated using strain-gage bending-moment measurements (DuWaldt and Statler, 
1966). The Combined Expeximent data set provides an excellent resource for comparing airloads obtained 

using either method. Because accurate pressure measurements from a rotating blade are difficult and 

expensive to obtain, a simpler strain-gage method could be a valuable tool in better understanding 

aerodynamic behavior. Blade airload data are especially usefnl for validating wind-turbine analytical tools. 

To explore this further, we have evaluated a technique in which flapwise bending-moment data are used 

in conjunction with known blade mass and stiffness properties to estimate airloads. This techuique was 

originally developed for use on helicopter rotors as a computer code called ALEST (Air Load 

ESTimation) (Bousman, 1987). 

In ALEST, free vibration modes of the blade are used in a single-rotating-blade model to determine modal 

displacements based on measured bending-moment distributions. An empirical leasf-squares method is 

used to determine modal amplitudes. This information is used in the blade model to estimate aerodynamic 

loads. The airloads are a function of the mode shapes, modal amplitudes, mass distribution, md frequency 

information. ALEST solves the dynamic uncoupled flap equation of motion to determine resultant 

aerodynamic forces. 

The modification of ALEST for wind turbine use required adding terms to include gravity loading affected 

by precone angle and blade pitch. A set of test conditions was devised to verify modifications made to 

ALEST. First, the code was checked by cumpGning estimated airloads and bending moments with those 

obtained lkom simple beam theory using an analytical continuous beam model. Estimated results fiom 

ALEST were identical to those hm theory (Schnepp, et al., 1992). Second, the modifications to ALEST 

were checked by comparisons with the Force and Loads Analysis Program (RAP) (Wright and Thresher, 

1987). FLAP uses wind inflow characteristics and machine properties in conjunction with known blade 

aerodynamic data to calculate blade loads. Bending moments calculated by FLAP were input directly into 

ALEST. Comparisons between ALEST and FLAP showed good correlation (Sirnms et al., 1990). 

In this section, measured airloads are compared to those deterruined from ALEST using corresponding 

measured bending-moment data The second phase of the Combined Experiment provided meamred 

airload distributions from pressure data at four span locations: 3096,4746, 63%, and 80%. At each of 

these locations, approximately 28 pressure taps were disbributed around the blade chord Tap distribution 

was concentrated more toward the leading-edge suction side of the blade to better characterize the active 

peak-pressure region. Each individual pressure measurement was normalized into a pressure coefficient 

by dividing by the total pressure, P, which was obtained using: 

where p is the& density, (D is the blade mtatioa speed, r is the 

velocity. 

(6.1) 

radius, and V, is the hub-height wind 

The aerodynamic normal force coefficient, C,, is betermined by integrating the blade-normal component 

of each pressure coefficient over all pressure coefficients around each chord section. Spwise airloads, 

Fs, are then obtained at each span location using: 
where C, is the normal force coefficient, P, is the ma pressure, and c is the blade chord length 

(0.457 m, or 18 in.). Because spanwise airloads are calculated at a particular span location, they are in 

units of force per length of span. They can be multiplied by span length to determine overall blade load 

in the blade-normal direction. 

Strain-gage bridges configured to measure flapwise bending moments were located at the blade root and 

at the 2046, 40%, SO%, 7096, and 90% span locations. These were used to provide bending-moment 

inputs to ALEST. Because ALEST requires inputs expressed in terms of harmonic content, strain-gage 

data were azimuth-averaged over the number of blade cycles shown in Table 6.1 into 5 deg bins and then 

converted into an eighth-order Fourier expansion. Because the blade rotates at 1.2 Hz, this provides 

bandwidth close to 10 Hz. The original pressure data were sampled with 100 Hz bandwidth, and were 

digitally low-pass filtered to I0 Hz for these comparisons. Higher-bandwidth data could be used in 

ALEST; howevn, this would require characterizing higher frequency response with increasing numben 

of harmonic terms* 

Table 6.1. Case Study Parameter Statistics 

Parameter Low-Wind Eigh-Yaw ~igh-whd - - - - - - - - -- 

Time duration (sec) 30 20 60 

# of blade cycles 36 24 72 

Wmd speed (mk) 83 13.7 18.3 

Wmd direction (deg) 279 

Yaw angle (kg) 287 

Pitch angle (deg) 12.1 

Tbree test cases were chosen: low wind, high yaw, and high wind. Minimal yaw error existed in both 

low-wind and high-wind cases. In the high-yaw case, dynamic stall causes large cyclic aerodynamic load 

variations. In all cases, there was no significant yaw motion. ALEST was not set up to model yaw 

I motion effects. Statistics summarizing the three cases are shown in Table 6.1. 

ALEsT also rewed blade natural frequency flap mode shapes. These were obtained fkom blade mass 

and stiffness distribution characteristics and were estimated using a program called TMQ. TMQ uses the 

transmission matrix method (Murthy, 1976) to provide flap natural frequency mode shapes, slopes, 

bending moments, and shear. TIE first thne flapwise modes for the Combined Experiment blade occurred 

at approXimateIy 5,20, and 50 Hz. Only the first mode shape data were used in these comparisons. An 

alternative approach would be to conduct a modal test to directly measure blade natural frequencies and 

mode shapes. 'Ihe measured results could be input to ALEST instead of using TMQ. TWO other operational parameters, precone angle and mean pitch angle, are the final required inputs to ALEST. 

Comparisons of blade airload distributions obtained using ALEST verms direct blade-measured airloads 

were performed on data &om three test cases. Results are summarized in Figures 6.1 through 6.4. In 

Figures 6.1 through 6.3, each plot shows an airload distribution over the full (azimuth-averaged) blade 

cycle. Zero degrees corresponds to the instrumented blade pointing straight up, and 180 deg is in the 

tower shadow. Airload distributions fkom the 30%, 47%, 63%, and 80% span locations are shown in the 

four rows in each figure. Data &om each of the test cases are shown in the three columns. In each plot, 

ALEST results are shown as a dotted line, and direct pressure measurements are a solid line. 
Figure 6.1. Sensitivity to input bending moment 

Figure 62. Sensitivity to precone angle 

Figure 6.3. Sensitivity to blade mass distribution 

Airloads estimated by ALEST show good agreement with those observed in both the high-wind and highyaw cases. Mean values are close, and harmonic content is similar. ALEST works best when predicting 

outboard airloads in higher winds. In the low-wind case, however, ALEST does not work well, especially 

inboard. This is because the aerodynamic forces under these conditions are extremely small compared 

to the other gravitational, inertial, and centrifugal forces. The code can not extract small differences Born 

large load values with enough precision to provide accurate estimates for this case. 

Sensitivity studies were performed to show how variations in input parameters affect resulting airload 

estimation. Shaded regions in Figures 6.1 through 6.3 show these results. The sensitivity studies also give 

an idea as to the level of accuracy required of the measured input parameters. 

Figure 6.1 shows the sensitivity to input bending moment The upper boundary of the shaded region was 

obtained by adding a 5% full-scale bias error with a t5% of measured-value random error to the input 

data Similarly, the lower boundary was obtained by subtracting a 5% bias error, and then adding the 

oscillating 5% random mr. This was done to strain-gage data at the six spanwise locations as 

summarized in Table 6.2. 

Table 6.2. Input Bending Moment Channels 

- - - - - 

Full-Scale 

Location Measurement Range Bias Error Random Error 

(% of Span) (N-m) W-m) (W -- 

0 mot) G200 *la +5 

20 do00 2150 -5 

40 *I15 +5 

50 *I400 k70 -5 

70 k800 +5 

90 600 +15 -5 

Figure 6.2 shows the sensitivity to a =lo% variation in precone angle. The measured precone wgle is 

3.5 deg. The upper and lower limits of the shaded regions correspond to precone angles of 3.85 deg and 

3.15 deg, respectively. Figure 6.3 shows the sensitivity to blade-mass distribution. Blade mass (and 

stiffness) are input in segments as shown in Table 6.3. The blade-mass input values were varied by t10% 

to simulate random measurement uncertainty. Similar parameter variations were performed on the input 

stifbess distribution values, but results are not shown because effects were minimal. 

The sensitivity studies show that measurement error does not adversely affect results. All cases show 

similar error band width, with measurement errors having increased impact outboard. This is somewhat 

unfo- because outboard airloads are usually of greatest interest It is also unfortunate because 

outboard bending-moment measurements are most susceptible to error from low strain-gage signal level 

and high gain. This means that care must be taken to ensure accurate measurements outboard. OveraIl, 



the technique is shown to be robust in predicting airloads and tolerating typical measmement uncertainty 

levels. Furthermore, the relative effects of measurement error become less significant as the blade is 

subjected to higher loading conditions. 

Figure 6.4 shows full spanwise blade airload estimates from ALEST averaged over a complete blade cycle 

for each test case. The averaged measured pressures are also indicated with triangular symbols. This is 

probably the most useful form of re& that could be used to verify airload inputs to a blade performance 

model such as PROP. Cydic data, as shown in Rgures 6.1 through 6.3, would be useful in verifying 

airloads in analytical tools such as FLAP. 

Figure 6.4. Blade airload estimates 

Table 6.3. Blade Mass Distribution 

- -- 

]Location Mass Stiffness Random Error 

Figure 6.5 summarizes the ALEST analysis procedure, showing how flap bending moments, blade 

properties, and machine operating conditions are used to produce an airload distribution. Figure 6.6 is a 

schematic representation of the data-processing procedure. It shows an example in which the 80% span 

pressure-measurement data are used to calculate airload at one span location. Figure 6.6 also shows how 

this airload value compares to the corresponding ALEST airload distribution. It is important to note that 

results are produced in terms of rotational harmonics, which enable comparisons to be made over a full 

blade cycle. ' 

Figure 65. ALEST analysis procedure 

Figure 6.6, Comparison of estimated airloads with measured airloads 

6.3 Blade-Load Data Base 

Blade loads were measured at 10 spanwise Iocations on the Combined Experiment turbine in order to 

correlate structural-load distributions with aerodynamic-load distributions. Appendix E contains plots of 

bin-averaged blade bending moments vs. wind speed for all the spanwise locations. Rotor torque and 

power measurements are also compared with predictions from PROPPC, an aerodynamic loads and 


performance program. Angle of attack and local total pressure (dynamic pressure) data are correlated with 

wind speed. A measured generator efficiency nwe is also presented. To date, very little effort has been 

expended on making analytical comparisons, but it was felt that the existing data should be presented to 

enable other researchers to make their own comparisons. 

6.4 Conclusions 

ALEST requires measured strain-gage flap bending moments, blade properties, and machine operating 

parametem as input to estimate blade aexodynamic loads. It works we11 during higher-wind loading 

conditions, even if input parameters have high measurement uncertainty. ALEST accurately depicts both 

mean and cyclic airload content under these conditions. 

The ALEST technique becomes less reliable as airloads decrease. The point at which this occurs cannot 

easily be determined. It depends on blade configuration, aerodynamic loads relative to other loads, mode 

shape, measured bending-moment accuracy, and deflection. 

WST could be used to show the impact of aerodynamic phenomena, such as unsteady effw and 

delayed stall, because these are most likely to occur at higher wind conditions. An example is seen in the 

high-yaw case presented, where dynamic stall is occurring as seen in the measured airloads, and is 

comborated in the ALEST results. This lends support to the hypothesis that aerodynamically induced 

loads can be transmitted into a machine and adversely affect structtual response. 

The technique works well on the particular blade co~guration presented here because of the simple blade 

planform and zero twist, and because the blade does not have any adgewise or torsional mode hquencies 

near the first flap mode frequency that might introduce fdse flap bending-moment signals. Other blade 

~0nf7gurations would have to be carefulIy considered as to their feasibility for this technique. 

ALEST is probably most suited for use as a research tool to provide airload estimations for verifying 

analytical models such as FLAP or PROP. It might also be used to study effects of relative changes made 

to a blade coofiguration. 

The code is undergoing refinement by incorporating a weighting matrix technique to be used to equalize 

strain distribution and thus improve mode-shape cume fitting. This should provide better airload estimation under low-load conditions. However, it requires a more in-depth knowledge of the ALEST analysis 

technique. 
7.0 Future Work 

Blade geometry appears to affect airfoil performance. To understand the effect of blade twist and taper 

on airfoil stall performance, a tapefed and twisted blade will be developed and tested. Results will be 

compared to those kom the existing blade, which has no twist or taper. 

The flow conditions adjacent to the blade but off the surface (outer flow condition) may reveal the cause 

of airfoil paformance abmrmdlity. These flow states can be tested by observing smoke flow pattern as 

the blade is rotating through the smoke. Video cameras will be used to record these smoke patterns. Video 

image processing will be used to correlate the pattans with pressure distributions and other operating 

conditions. This information will be used to improve stalI models for wind turbines. 
8.0 Conclusions 

Never before, in wind turbine research has such a complete investigation of wind turbine aerodynamics 

been conducted, The results have led to new insights in both steady and unsteady aerodynamic behavior. 

Designers have always used wind tunnel data to describe airfoil performance on wind turbine blades and 

assumed that adjacent spanwise sections can be treated as though they were independent. The test results 

presented in this report show thai under low wind speed conditions, many of these assumptions were true. 

But under high wind speed conditions, they were not true. 

Under high wind conditions, steady and unsteady stal1 behavior of airfoils does not correlate well with 

wind tunnel data Unfortunately, these conditions occur at maximum load operating conditions and 

prevent the designer from making accnrate predictions of maximum load or performance predictions. The 

data presented in this report helps the designer how what the origins of the inauntracies an and help him 

estimate theii magnitude. This report also presents a comprehensive body of wind turbine sterodynzmic 

data that can be used for code validation. 

The data presented demonstrate that airfoils on wind turbines behave as they would in the wind tunnel for 

low to moderate AOAs. Near stall, the wind-turbine airfoils show a delay in stall due to a combination 

of suction peak persistence and high negative pressures on the suction side of the airfoil. This results in 

high normal forces and high tangent forces in the post-stall regioa Because of this modified airfoil 

behavior, wind-tunnel stall data may not be acmrate for stall-control wind-turbine design purposes. 

However, it is not clear what causes this behavior or what effect blade pldonn (twist and taper) will have 

on the airfoil performance. 

Dynamic stall was shown to exist on a HAWT operating at 30 deg yaw angle. Dynamic stall also occurs 

for low-yawerror operation when tower shadow, wind shear, or inflow turbulence cause large AOA 

excursions. These increased aerodynamic loads cause increased structural loading. It was shown that 

dynamic stall forces caused by tower shadow are shorter in duration and lower in magnitude than those 

caused by yawed flow. It is impatant to model dynamic stall for yawed rotors for this reason. The data 

presented in this report plus future data win provide the basic information needed to develop dynamic-stall 

models. 

We have shown how video flow-visu~on data can be correlated with standard time-series pressure 

data recorded on an operating wind turbine. A key to this correlation is to use a single clock signal and 

to record it on the video tape using a time-code inserter as well as recording it as another item in the PCM 

data stream. 

We have observed significant differences between the wind-turbine-blade field-test results and wind-tunnel 

tests. Beyondstall, leading suction peaks persist b high AOAs. At inboard stations on the blade, nonzero pressure gradients exist in regions of separated flow. These gradients do not occur in the wind 

tunnel. These results help explain why performance analyses typically under-predict the peak performance 

on stall-control wind turbines. 

Many of the specific results presented in this report have led researchers to a better understanding of 

airfoil performance on rotating wings as well as a realization that airfoil may be affected by overall blade 

geometry. The variation of airfoil performance with spanwise location suggests that optimum blade 

geometries developed based on wind tunnel data may not be true optimums. Different geometries may 

produce better pedorming blades, and still others may produce blades that stall in a mae progressive 

manner, reducing dynamic loading. The concept of controlling stall progression to achieve a balance 
between high performance and low dynamic loads has been suggested as a result of examining stall 

behavior measured on this test turbine. 

Dynamic stall measurements have proven that this phenomenon does occur on wind turbines opbg 

in yawed conditions, and it can significantly increase yaw loads and dynamic loads. These results have 

convinced rsearchers and designers that dynamic stall must be introduced into structural dynamics codes 

in order to improve the accuracy of the codes' fatigue life predictins. 
Bousman, W.G., 1987, Estimation of Blade Airoads from Rotor Blade Bending Moments, NASA 

Technical Memoraadum 100020, USAAVSCOM Technical Report 87-A-8. 

Butterfield, C.P., 1989, Three Dimensional Airfoil Pe~onruuzce Meawements on a Rotating Wing, 

SEW-217-3505, Golden, CO: National Renewable Energy Laboratory. 

Butterfield, C.P., D.A. Simms, W.P. Musid, and G.N. Scott, 1990, Spanwise Aerodynmnic Loads on a 

Rotating Blade, SERVTP-257-3983, presented at Windpower '90, Golden, CO: National Renewable 

Energy Laboratory. 

Butterfield, C.P., D.A. Simms, W.D. Musial, and G.N. Scott, 1992, NREL Combined Fxperhent- Phase I , 

Final Report, NRELrTP-257455, Golden, CO: National Renewable Energy Laboratory. 

ButterlieId, CP., G.N. Scott, and W.P. Musial, 1990, Comparison of Wind Tunnel Ai~oiil Perfomme 

Data with Wind Turbine Blade Da@, SERVLP-254-3799, Golden, CO: Nationdl Renewable Energy 

Laboratory. 

DuWddt, FA, and LC. Statler, 1966, Derivation of Rotor Blude Generalized Air Loadsfrom Measured 

Flapwise Bending Moment and Measured Pressure Distributions, USAAVLABS TR 66-13, 

Natxsen, A.C., and CP. Butterfield, 1990, "Yaw Loads and Motions of a Horizontal Axis Wind Mine," 

AWE Journal of Soh Energy, Vol. 112. 

Hurst, D.W., and D.T. Owen, Nacelk Ittsrall-n E'ects on Propeller Blade Su$izce Pressure 

Distriburions, 0096-736X/88/9606-0940, Southampton, England: Society of Automotive Engineers. 

Madsen, H.A., F. Rasmussen, and T.F. Pedersen, 1988, Aerodynamics of a Full-Scale HA WT Blade, 

presented at European Wind Energy Conference, Conferellce FWcedings. 

Murthy, V.R., 1976, "Dynamic C-cs of Rotor Blades," Jounral of Sod and VBrations, Vol. 49, 

NO. 4, pp. 483-500. 

Musial, W., and C.P. Butterfield, 1990, A Comparison of Two- and Three-Dimensional S809 Airjoil 

Properties for Rough and Smooth HAWT Operetion, SEW-257-3603, Golden, CO: National 

Renewable Energy Laboratory. 

Pope, A, and J.J. Harper, 1966, Low-Speed Wind Tmel Testing, Wiley & Sons, NY. 

Reda, D.C., 1988a, "Liquid Crystals for Unsteady Suface Shear Stress Visualization," ABBE-3841CP, 

Proceedings, First National Fluid Dynamics Congress, Cincinnati, OH: July 1988. 

Reda, D.C., 1988b, "Liquid Crystals for Surface Shear Stress Visualization on Wind Turbine Airfoils," 

Proceedings, Windpower '88, Honolulu, HI, Sept. 1988. 

Re&, D.C., and C.P.. Butterfield, 1989, "Viualization of Boundary Layer Transition Zone Movement on 

Oscillating Airfoils with Shear-Sensitive Liquid Crystd Coatings," Proceedings, Symposium on Flow 

ViszuzZiZatiOrt, ASME Winter AMual Meeting, San Francisco, CA, Dec. 10- 15. 
Robinson, MC., 1985, Development of Vorticity and Vollices fiom Forced Unsteady Flav Separation, 

PhD. Thesis, Boulder, CO: University of Colorado at Boulder. 

Schaepp, R.R., A.C. Hansen, and AD. Wright, 1992, A Method for Determining Aerodynamic Loads on 

a Wind Turbine from Blade Flap Bending Moments, Proceedings of the 11th ASME Wind Energy 

Symposium, Houston, TX. 

Scott, G., CP. Butterfield, D. Simms, and W. Musial, 1991, Correlation of Flow Visualization and 

Pressure Distribution Dara on a Wind Turbine Blade, 10th ASME-ETCE Conference Proceedings, 

Houston, TX. 

Simms, D.A., C.P. Buttemeld, and AD. Wright, 1990, "A Procedure for Estimating Aerodynamic Loads 

on Horizontal Axis Wind Turbines Using Measured Bending Moments," Proceedings of llre Ninth ASME 

Wind Energy Symposium, New Orleans, LA. 

Tangler, J.L., 1983, Horizontal-Axis WW System Rotor Pe@ionnance Model Compa&on-A Compendium, 

RFP-3508, NTIS. 

Vitema, LA, and R.D. Comgan, 1981, Fixed Pitch Rotor Perfonncz~cce of Lage Horizontal Axis Wind 

Turbines, DOUNASA Workshop on Large Horizontal Axis Wind Tbrbines, Cleveland, OH, July 28-30. 

Wright, A, and CP. Butterfield, 1991, The NREL Teetering Hub Rotor Code: Final Results and 

Conclus1*0m, NREIJTP-257-4517, presented at Windpower '91, Golden, CO: National Renewable Energy 

Laboratory. 

Wright, AD., and R.W. Thresher, 1987, "Accurate Rotor Loads Prediction Using the FLAP Dynamics 

Code," SEWrP-217-3315, WMpower '87 Proceedings, Alexandria, VA: American Wind Energy 

Association, 
Appendix A 

Blade Layouts 


Appendix B 

Bin-Averag ed Aerodynamic Coefficients 




























Appendix C.1 

Moderate Wlnd Speed Case 
Appendix C. I. I 

Statistics for All Channels for Tape 65, Case 2 





Appendix C. 1.2 

Pressure Distributioos fo/ 80% Span 










Appendix C. 1.3 

Pressure Distributions for 63% Span 


















Appendix C. 1.4 

Pressure Distributions for 47% Span 




























Appendix C. 1.5 

Pressure Djstributions for 30% Span 








































Appendix C2 

High Winds' Cage - 
Appendix C.2. I 

Statistics for All Channels for Tape 68, Case 1 




Appendiw C.2.2 

Pressure Distributions for 80% Span 















































Appendix C.2.3 

Pressure Distributions for 63% Span 





















Appendix C.2.4 

Pressure Distributions ffor 30% Span 


























Appendix D 

Pressure Distributions Based on Measured Wind Speed 













































Appendix E 

Bindveraged Blade Loads 

Table of Coefficients