NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Rotational Augmentation on a 

2.3 MW Rotor Blade with Thick 

Flatback Airfoil Cross-Sections 

Preprint

S. Schreck and L. Fingersh

National Renewable Energy Laboratory

K. Siegel, M. Singh, and P. Medina

Siemens Energy, Inc.

To be presented at the 51st AIAA Aerospace Sciences Meeting

Grapevine, Texas

January 7-10, 2013

Conference Paper

NREL/CP-5000-57372

January 2013
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1 

Rotational Augmentation on a 2.3-MW Rotor Blade with 

Thick Flatback Airfoil Cross Sections 

S. Schreck and L. Fingersh

NREL’s National Wind Technology Center, 15013 Denver West Parkway, Golden, CO 80401

K. Siegel, M. Singh and P. Medina

Siemens Energy, Inc., 1050 Walnut Street, Suite 303, Boulder, CO 80302

Rotational augmentation was analyzed for a 2.3-MW wind turbine that was equipped 

with thick flatback airfoils at inboard radial locations and extensively instrumented for 

acquisition of time varying surface pressures. Mean aerodynamic force and surface 

pressure data were extracted from an extensive field test database, using stringent criteria 

for wind inflow and turbine operating conditions. Analyses of these data showed 

pronounced amplification of aerodynamic forces and significant enhancements to surface 

pressures in response to rotational influences, relative to two-dimensional, stationary 

conditions. Rotational augmentation occurrence and intensity in the current effort was 

found to be consistent with that observed in previous research. Notably, elevated airfoil 

thickness and flatback design did not impede rotational augmentation.

Nomenclature

c = blade chord (m)

cp = surface pressure coefficient

Cn = normal force coefficient

Ct = tangential force coefficient

D = rotor diameter (m)

f = frequency (Hz)

Hz = Hertz

h = height above ground (m)

IEC = International Electrotechnical Commission

MW = megawatt

r = radial location (m)

R = blade tip radius (m)

α = angle of attack (deg)

I. Introduction

Horizontal axis wind turbines (HAWTs) experience large aerodynamic loads on a routine basis. Elevated 

aerodynamic loads impose high stresses on major components, and can produce torque variations that adversely 

impact power quality. These and other factors arising from large, unpredicted aerodynamic loads drive up the 

overall cost of energy. More thorough understanding of wind turbine aerodynamics will facilitate improved

accuracy in wind turbine aerodynamics models, which will enable continued reductions in cost of energy.

Rotational augmentation is incompletely characterized and understood. The widely cited experiments of 

Himmelskamp1 showed that stall delay and lift enhancement due to rotation occur on aircraft propellers. To explain 

these results, radial thinning and chordwise acceleration of the steady boundary layer caused by centrifugal forces 

and the Coriolis effect were postulated. A complementary theoretical analysis was performed by Banks and Gadd2

for steady laminar boundary layers on a rotating blade at small incidence. They concluded that rotational effects 

could stabilize the boundary layer against separation, if the linear adverse external velocity gradient is sufficiently 

small.
2 

Subsequent experimental and analytical research has been conducted in the rotorcraft field. McCroskey and 

Yaggy3 conducted a theoretical analysis for quasi-steady rotor blade flows with small crossflows. For zero or 

favorable chordwise pressure gradient, the effects of crossflow due to rotor rotation were judged beneficial, 

especially in regions of incipient separation. However, the effects of crossflow due to rotor rotation were considered 

to be of smaller magnitude than the effects of crossflows caused by rotor translation. It was also speculated that, in 

strong adverse pressure gradients, the rotationally induced crossflows played a more influential role. Further rotor 

experiments4 suggested that centrifugal forces move the fluid significantly outward in separated regions, but are 

relatively unimportant regarding aerodynamic force augmentation under these conditions.

More recent HAWT experiments employing surface pressure measurements reached differing conclusions 

regarding the impact of blade crossflows. Using test data, Madsen and Christensen5 concluded that rotational effects 

are of minor importance compared to the influences of aspect ratio and radial pressure gradient. Two other 

experiments conducted in wind tunnels arrived at different conclusions. Using two-dimensional airfoil performance 

as a baseline, Barnsley and Wellicome6 stated that the combination of rotational and three-dimensional effects 

appeared to suppress the loss of leading edge suction across the entire span, compared to two-dimensional behavior. 

Ronsten7 used nonrotating blade data as a baseline and noted that rotation generated significant differences in lift 

behavior only at the pressure measurement station farthest inboard on the blade.

Using full-scale HAWT data acquired in a wind tunnel, Schreck and Robinson8 concluded that rotational 

augmentation was not driven by Reynolds number, prominent global unsteadiness, or sweep effects. Rather, it was 

associated with distinctive chordwise and radial pressure signatures that varied in an orderly manner in response to 

changes in radial location and inflow conditions. Subsequent work9 exploited rotating blade surface pressure data to 

characterize boundary layer separation and shear layer impingement, and flow field structure evolution with 

rotational augmentation of the flow field. Closely related research10,11 correlated experimentally and 

computationally derived surface flow topologies for the rotating blade, finding excellent agreement and significantly 

generalizing comprehension of the flow field. Other work12 isolated tip speed ratio as in independent variable and 

quantified the influence of tip speed ratio on aerodynamic forces and underlying flow field structure. In addition, 

flow structure sources of unsteadiness in rotational augmented flows have been experimentally identified and

characterized for a bounded set of operating conditions.

13 Related work has quantified key relationships that link

mean structure and unsteadiness intensity in rotationally augmented flows, and quantitatively determined that the 

intensity of aerodynamic force unsteadiness can exceed that elicited by atmospheric turbulent inflows of maximal 

intensity as specified in design standards.

14 In addition, analyses of rotationally augmented flow fields using 

spectral decompositions have furnished unique information regarding shedding kinematics.15

To date, the principal structures and interactions underlying blade rotational augmentation have been 

characterized in some detail for subscale geometries, and comprehension has greatly improved. However, rotational 

augmentation has not been explored in detail for utility-scale turbines. In particular, the occurrence and prominence 

of rotational augmentation in connection with thick and flatback airfoils has not been examined. The current work 

addresses this shortfall by analyzing field experiment data acquired from the Siemens 2.3-MW turbine at the 

National Renewable Energy Laboratory’s (NREL’s) National Wind Technology Center.

II. Experimental Methods

A. Wind Inflow Measurements

Aerodynamic inflow data presented in this paper were acquired from an 80-m meteorological tower 

approximately 2.5 rotor diameters (D) upwind of the turbine at NREL’s National Wind Technology Center. This 

tower is instrumented with one sonic anemometer, four levels of cup anemometers and directional vanes, and two 

levels of temperature and barometric pressure sensors. For this project, turbine hub height instrumentation was IECClass. This included an IEC Class-1 cup at a height of 80 m and another at 78.5 m along with a vane at that level. A 

barometric pressure sensor and temperature sensor were installed immediately below that. The meteorological 

tower data acquisition system acquired data continuously at a sampling frequency of 20 Hz. To accurately 

characterize the atmospheric boundary layer, as well as the inflow across the turbine rotor, a 135-m meteorological 

tower was installed approximately two rotor diameters upwind of the turbine in the dominant wind direction. The 

135-m tower was equipped with six levels of cup anemometers and directional vanes, six levels of sonic

anemometers with three-axis accelerometers, four levels of temperature sensors, a barometric pressure sensor, and a 

precipitation sensor. All of these sensors were placed at different heights between 3 m and 134 m above ground. 

The inflow sensor array is documented in greater detail in ref. 16. 
3 

B. Blade and Probe Pressure Measurements

To obtain surface pressure data, one of the Siemens 2.3-MW turbine blades was extensively instrumented to 

acquire surface pressure measurements at nine radial locations. Each of the nine radial locations had approximately 

60 surface pressure taps that were distributed over the suction and pressure surfaces. Pressure tap placement was 

governed by anticipated pressure distributions and the need to validate both aeroelastic and computational fluid 

dynamics (CFD) models. Figure 1 shows the nine radial locations on the blade for pressure measurements. At the 

7-m and 13-m radial stations, the blade airfoil sections were approximately 60% and 40% thick, and both had

flatback configurations. 

The surface pressures at each of these stations were acquired using a combination of Scanivalve ZOC33 and 

ERAD-4000 modules, discretized at 25 Hz, and transmitted wirelessly to a remote computer for storage and postprocessing. To avoid sensor drifts due to temperature variations, the pressure modules were housed in temperature 

control units (TCU) that maintained the temperatures of the modules at 25° C. Additionally, to reduce the maximum 

rotational acceleration of the pressure sensors, all the instruments were mounted inside the 25-m blade radius and 

aligned appropriately as specified by the manufacturer.

Four five-hole probes, also shown in Figure 1, were installed to measure local inflow angles and velocities. 

These probes extended approximately 0.6 m ahead of the blade leading edge. The setup for five-hole probe data 

acquisition was essentially the same as that described for the blade surface pressure measurements. Both blade and 

probe pressure data were post-processed to correct for the effects of hydrostatic pressure variation and reference 

static pressure deviation. Surface pressure data were integrated over the blade section surface to obtain normal force 

coefficient (Cn) and tangential force coefficient (Ct) data. Blade and probe pressure instrumentation and data 

processing procedures were previously documented.

16

Because of the spatial extent of the blade, tubing lengths between the surface pressure taps and the Scanivalve 

sensors frequently were of significant length, and pressure signals underwent corresponding distortions. To 

compensate for these effects, a Wiener inverse filter based on tubing geometry, local temperature, and local pressure 

was constructed. This inverse filter was applied to measured data to compensate for the extended tubing lengths and 

to reconstruct the original signal.17

III. Results and Discussion

A. Aerodynamics Database and Filtering

The Siemens 2.3-MW turbine field experiment database contained 1987 ten-minute data sets discretized at 25 

Hz. These were acquired over a six-month period for a broad range of wind inflow and turbine operating states. To 

isolate those data for which rotational augmentation was present and that significantly impacted rotor operation, the 

following filtering procedure was used to extract data from the database.

First, all of the ten-minute data sets were segmented into one-minute data sets. One-minute analysis periods 

were short enough to avoid averaging out wind inflow conditions and turbine operating responses of interest. At the 

same time, one-minute periods were long enough to retain a degree of correlation between inflow and turbine data. 

For each of these one-minute data sets, averages were computed for several inflow and turbine operating parameters, 

and one-minute data sets were chosen for the current study according to the following filtering criteria.

To ensure that the turbine was operating at a controlled tip speed ratio, blade pitch angle was required to be less 

than 70°, wind speed required to be 4 m/s or higher, and net power generation required to be positive. In addition, 

yaw error needed to be between -10° and +10°, to approximate axisymmetric conditions in which rotational 

Figure 1. Planform view schematic of Siemens 2.3 MW pressure measurement blade.
4 

augmentation dominates blade aerodynamics. Finally, wind direction had to be between -20° and +20° of 

meteorological tower azimuth with respect to the turbine, so that winds impinging on the turbine were the same 

winds measured by the inflow instrumentation.

Application of these filtering criteria to the 19,870 one-minute data sets identified 157 one-minute data sets that 

were analyzed in the current study. That less than 1% of all one-minute data sets met the filtering criteria is 

consistent with the stringent criteria and high turbulence environment at the National Wind Technology Center.

One-minute average wind speed data were binned at 1 m/s intervals and plotted as a histogram in Figure 2. 

Figure 2 shows that average wind speeds for the 157 one-minute data sets ranged from 4 m/s to 17 m/s. Most oneminute average wind speeds were in the 4 m/s to 13 m/s range, corresponding closely to the Region 2 operating 

range for the Siemens 2.3-MW turbine. Average wind speeds in the 14 m/s to 17 m/s range, in Region 3, occurred 

only three times. Overall, the distribution of one-minute wind speeds was skewed slightly to lower wind speeds, 

with the mean across the 157 one-minute wind speeds being 7.7 m/s, and the mode of 30 realizations occurring at 6 

m/s. 

Average yaw error data for the 157 one-minute data sets were binned at 1° intervals and are plotted as a 

histogram in Figure 3. Again, approximate axisymmetric conditions were important to the current study, to provide 

conditions in which rotational augmentation dominates blade aerodynamics. Mean one-minute yaw error

realizations occupied bins from -9° to 10°. Mean one-minute yaw error across 157 data sets was 3.4°, and 67 of the 

157 data sets had average yaw errors between -5° and 5° (inclusive). However, yaw error realizations were visibly

skewed to higher yaw error, with 71 of the one-minute averages lying between 7° and 10° (inclusive). 

B. Normal and Tangential Forces

Accompanying the wind inflow data described above in the oneminute data sets were blade surface pressure data. Blade surface 

pressures were measured at each of the nine radial locations and were 

discretized at f = 25 Hz. Blade surface pressure data were integrated 

over the blade surface at each of the nine radial locations to yield time 

records of Cn and Ct. As shown in Figure 4, Cn is directed perpendicular 

to the local chord line and is positive pointing outward from the suction 

surface. Ct is directed parallel to the local chord line and is positive 

pointing aft from the trailing edge.

For the current study, Cn and Ct time records for each of the 157 

data sets were averaged over the one-minute record to obtain oneminute average data. Typical one-minute average Cn and Ct data for the 

7.0 m and 13.0 m blade radius locations are shown in Figures 5 and 6, with −Ct as the abscissa and Cn as the 

ordinate. Here, these data are plotted as blue circles, with each circle corresponding to one of the 157 one-minute 

averages. 

Figure 2. Wind speed histogram for 157 oneminute data files used in the current study.Figure 3. Yaw error histogram for 157 one-minute 

data files used in the current study.

Figure 4. Cn and Ct orientation.
5 

A two-dimensional, nonrotating reference datum was needed against which three-dimensional, rotational 

influences associated with rotational augmentation could be assessed. These two-dimensional, nonrotating data 

were produced using CFDs for airfoil envelopes corresponding to sectional cuts through the blade at the pressure tap 

rows. These data are represented by the red squares in Figures 5 and 6. 

Figure 5 (7-m blade radius) shows that force

coefficient levels measured on the rotating blade ranged 

from (–Ct, Cn) = (–0.14, –0.77) at the low end of the 

point distribution to (–Ct, Cn) = (1.21, 2.78) at the upper 

extreme of the distribution. Though the ensemble of 

points corresponds to a range of wind speeds (Figure 2) 

and yaw errors (Figure 3), it is worth noting that the 157 

points are organized in an approximately linear region. 

However, also noticeable for the data point ensemble is 

the contrast between the tighter, denser point 

distribution near the lower end and the broader, sparser 

spread near the upper end. It should be noted that 

instantaneous Cn and –Ct magnitudes (not documented 

herein) exceeded the one-minute average data plotted in

Figure 5. 

Also shown in Figure 5 (7 m blade radius) are force 

coefficients computed for two-dimensional, stationary 

conditions using a fully turbulent boundary layer. The 

airfoil section used in these computations corresponded 

to that of the blade section at the tap distribution. Force 

coefficients ranged from (–Ct, Cn) = (–0.12, –0.35) at the 

low end of the airfoil curve to (–Ct, Cn) = (0.13, 1.06) at the upper extreme. The three-dimensional, rotating blade (–

Ct, Cn) data deviated visibly from the two-dimensional, stationary airfoil (–Ct, Cn) point distribution, with the slope 

of the blade data distribution being visibly lower than that for the airfoil. Most importantly, because of rotational 

augmentation effects, maximum rotating blade Cn (Cn = 2.78) exceeded maximum stationary airfoil Cn (Cn = 1.06) 

by a factor of 2.62.

In Figure 6 (13-m blade radius), rotating blade force

coefficients ranged from (–Ct, Cn) = (–0.21, –0.78) at the 

lower part of the point distribution to (–Ct, Cn) = (0.77, 

2.17) at the upper region. It is worth noting that the 

ensemble of 157 points is approximately linear in shape, 

even though these data points represent a spectrum of 

wind speeds (Figure 2) and yaw errors (Figure 3). In the 

lower part of the point ensemble, the distribution tends to 

be more condensed, while the upper portion tends to be 

more expansive. Notably, instantaneous Cn and –Ct

magnitudes were greater than the one-minute average 

data in Figure 6. 

Figure 6 also contains Cn and –Ct predicted by CFD

for a two-dimensional, stationary airfoil with a fully 

turbulent boundary layer. The airfoil section used in 

these computations was that of the blade section at the 

13-m blade radius tap distribution. Force coefficients 

ranged from (–Ct, Cn) = (0.00, –0.59) at the curve lower 

end to (–Ct, Cn) = (0.39, 1.41) at the upper extreme. The 

two-dimensional, stationary airfoil (–Ct, Cn) data deviated

from the three-dimensional, rotating blade (–Ct, Cn) point 

distribution, with the slope of the stationary airfoil curve being slightly higher than that for the rotating blade point 

ensemble. Notably, maximum rotating blade Cn at 13-m blade radius (Cn = 2.24) exceeded maximum stationary 

airfoil Cn (Cn = 1.41) by a factor of 1.59, due to the influence of rotational augmentation effects.

Comparing Figures 5 and 6, trends in the two-dimensional, nonrotating data are apparent and consistent with 

blade geometry. At the 7-m blade radius, two-dimensional, nonrotating Cn reached a maximum of 1.06, and at the 

Figure 5. 7-m blade radius Cn vs. –Ct for twodimensional stationary airfoil CFD (red squares) 

and three-dimensional rotating blade data (blue 

circles).

Figure 6. 13 m blade radius Cn vs. –Ct for twodimensional stationary airfoil CFD (red squares) 

and three-dimensional rotating blade data (blue 

circles).
6 

13-m station, it attained a maximum of 1.41. Increasing Cn maxima at greater blade radius is consistent with 

decreasing airfoil thickness ratio, and the accompanying delay in boundary layer separation as adverse pressure 

gradients were reduced.

Most significant for blade aerodynamics are differences between the two-dimensional, nonrotating airfoil and 

the three-dimensional, rotating blade. The 7-m blade radius (Figure 5) showed a difference of 1.47 between Cn

maxima for the two-dimensional stationary airfoil and the three-dimensional rotating blade. At the 13-m radius 

(Figure 6), the difference between these two Cn maxima was 0.63 and significantly less than that inboard at the 7-m 

location. The Cn increment for three-dimensional rotating conditions relative to two-dimensional stationary 

conditions and the decrease in this increment with radial location is attributable to rotational augmentation and is 

consistent with previous measurements.

8,19

Also identified above were differences in slope between the two-dimensional, nonrotating airfoil and the threedimensional, rotating blade. Though not quantified, this difference was substantial at the 7-m blade radius (Figure 

5). At the 13-m blade radius (Figure 6), this slope difference still was apparent but was less than that for the 7-m 

station. Similar slope increments and trends with respect to blade radial location have been noted in previous 

efforts.

8,19 Here, these phenomena can be attributed to three-dimensionality mediated by potential flow interactions 

acting in the blade root vicinity. 

C. Surface Pressure Distributions

To understand the fluid dynamics underlying three-dimensional rotationally augmented blade Cn levels 

documented above, as well as the two-dimensional stationary airfoil reference datum, blade surface pressure

distributions were examined for select Cn conditions. To do so, surface pressure data for three-dimensional

rotationally augmented conditions were obtained from the same 157 one-minute Siemens 2.3-MW turbine data sets 

discussed above. Surface pressure data for the two-dimensional stationary airfoil reference cases were derived from

Siemens proprietary wind tunnel testing databases.

These surface pressure data are shown in Figures 7 through 10, where Figure 8 and Figure 10 cp distributions 

represent means computed from the one-minute data sets, and data in Figures 7 and 9 were acquired from twodimensional wind tunnel experiments. In Figures 7 through 10, to avoid compromising Siemens proprietary data, 

pressure surface data have been omitted to conceal stagnation point cp and thus eliminate any zero reference. For 

the same reason, tick marks and labels have been removed from the vertical axis, to eliminate any scaling

information. However, zero references and vertical axis scaling are consistent for Figures 7 through 10, to enable

comparisons of suction surface cp distributions. 

Figure 8 shows suction surface cp distributions for rotating conditions at the 7-m blade radius, and Figure 7 

shows the same data for two-dimensional stationary conditions as measured in a wind tunnel airfoil test. Wind 

tunnel data were not available for the airfoil section at the 7-m blade location, and instead wind tunnel data for the 

airfoil section corresponding to the 9-m radius were used. Between the 7-m and 9-m blade radii, the difference in 

airfoil thickness was approximately 5%. 

Figure 7. Suction surface cp vs. x/c for twodimensional stationary wind tunnel measurements.

Figure 8. Suction surface cp vs. x/c for threedimensional rotating blade measurements at 7-m 

blade radius.
7 

The three cp distributions in Figure 8 correspond to data points in Figure 5 for which Cn > 2.0, and were selected 

as being representative of rotationally augmented conditions. Like the Cn data in Figure 5, the cp data in Figure 8 

also are mean data computed from one-minute data sets. Using the stagnation point location (not shown herein) for 

these three blade cp distributions as a reference, the three two-dimensional airfoil cp distributions in Figure 7 were

identified as being at the same angles of attack (α). 

In Figure 7, all three cp distributions were similar and displayed attributes typical of two-dimensional airfoils at 

elevated α. Peak cp magnitudes occurred near the leading edge, at x/c = 0.033 and x/c = 0.053. Aft of these 

locations, suction decreased rapidly with chord and began to level off in the range 0.314 ≤ x/c ≤ 0.378. Downstream 

of this chord region, the surface pressure distribution was virtually flat, indicating that the boundary layer was 

separated over much of the aft portion of the airfoil suction surface.

Figure 8 shows suction surface pressure distributions for three-dimensional rotating conditions at the same 

angles of attack as the two-dimensional stationary cp distributions in Figure 7. Significant differences between 

stationary two-dimensional (Figure 7) and rotating three-dimensional (Figure 8) conditions were apparent. In Figure 

8, peak cp magnitudes occurred at x/c = 0.117, approximately 0.05c to 0.08c aft of those in Figure 7. In addition, the 

Figure 8 peak cp magnitudes exceeded those in Figure 7 by factors of 1.30 to 1.71. Finally, Figure 7 cp magnitudes 

decreased steeply over the 0.15c to 0.25c aft of the peak location, and thereafter remained level over the rear 0.7c of 

the airfoil. In contrast, the Figure 8 cp magnitudes decreased in pseudo-linear fashion from the peaks to the trailing 

edge vicinity, and level regions in the cp distribution were virtually nonexistent, implying that separation was 

negligible, having been precluded by rotational augmentation of the blade boundary layer.

Figure 10 shows suction surface cp distributions for rotating conditions at the 13-m blade radius, and Figure 9 

shows the same data for two-dimensional stationary conditions as measured in a wind tunnel airfoil test. Wind 

tunnel data were not available for the airfoil section at the 13-m blade location, and wind tunnel data for the airfoil 

section corresponding to the 14-m radius were used instead. Between the 13-m and 14-m blade radii, the difference 

in airfoil thickness was approximately 2%. 

The three Figure 10 cp distributions correspond to Figure 6 data points where Cn > 1.5 and were chosen as 

typical of rotationally augmented conditions. Like the Cn data in Figure 6, the cp data in Figure 10 also are mean 

data extracted from one-minute data sets. Using the location of the stagnation point (not shown herein) for the three 

blade cp distributions, the two-dimensional airfoil cp distribution in Figure 9 was identified as corresponding to the 

same α. 

In Figure 9, the airfoil cp distribution was consistent with those produced by two-dimensional airfoils at 

elevated α. Peak cp magnitude occurred near the leading edge, at x/c = 0.055. Downstream of this location, suction 

declined progressively with chord and began to level off at approximately 0.40c. Downstream of 0.40c, the surface 

pressure distribution was virtually flat, implying that boundary layer separation dominated the aft region of the 

airfoil suction surface.

Figure 10. Suction surface cp vs. x/c for threedimensional rotating blade measurements at 13-m 

blade radius.

Figure 9. Suction surface cp vs. x/c for twodimensional stationary wind tunnel measurements.
8 

Figure 10 shows suction surface cp distributions for three-dimensional rotating conditions at the same α as the 

stationary airfoil cp distribution in Figure 9. Here, as at the 7-m blade radius, differences existed between twodimensional stationary conditions (Figure 9) and three-dimensional rotating conditions (Figure 10). In Figure 10, 

peak cp magnitudes occurred at x/c = 0.055c and 0.082c, which were aft of the peak at 0.061c in Figure 9. Figure 10

peak cp magnitudes were 94% to 123% of that in Figure 9. Finally, Figure 9 cp magnitudes dropped off through

0.05c to 0.40c downstream of the peak location, and then remained virtually flat over the aft 0.6c. In contrast, cp

magnitudes in Figure 10 decreased in pseudo-linear fashion from the suction peaks to the neighborhood of the 

trailing edge vicinity. As at the 7-m blade radial station, flat cp distribution regions were not apparent in the Figure 

10 rotating blade cp data, indicating that separation was similarly insignificant, due to rotational augmentation of the 

blade boundary layer.

D. Rotational Augmentation Context

In the current effort, clear differences were

apparent between two-dimensional stationary airfoil 

performances and three-dimensional rotating blade 

responses at both the 7-m and 13-m blade radius 

locations. However, the most pronounced differences 

occurred at the inboard location at 7 m, with respect to: 

1) Cn increment for three-dimensional rotating 

conditions relative to two-dimensional stationary 

conditions, 2) Peak cp magnitude, 3) Peak cp chordwise 

location, and 4) cp distribution shape aft of the peak. 

This radial variation in rotational augmentation is 

consistent with radial variation in the ratio c/r, as shown 

previously by Snel et al.18

Figure 11 shows a plot of c/r for the Siemens 2.3-

MW turbine blade. Across the entire blade, c/r varied

between 0.40 at 0.17R, to 0.02 at 0.94R. The 7 m and 

13 m blade radius locations analyzed in the current 

work are identified by circles, where c/r = 0.40 and 0.24, respectively. That rotational augmentation observed at 

these two radial locations is consistent with previous research. Analyses of UAE Phase VI8 and MEXICO19 data

showed that rotational augmentation influences were prominent down to c/r = 0.17, and that rotationally 

augmentation was detectable for c/r levels as low as c/r = 0.1.

IV. Conclusions

The current work is aimed at exploiting data acquired from the Siemens 2.3-MW turbine at NREL’s National 

Wind Technology Center to improve fundamental comprehension of rotationally augmented blade aerodynamic 

response. In particular, analyses were directed at determining whether thick, flatback airfoil sections significantly 

alter rotational augmentation. The current effort supports the following conclusions regarding rotational 

augmentation on megawatt-scale turbines using thick, flatback airfoils: 

• Rotating blade aerodynamic time-averaged forces are amplified by factors as high as 2 to 3, relative to 

stationary two-dimensional conditions.

• Rotating blade time-averaged leading edge suction nearly doubles under some conditions, again relative to

stationary, two-dimensional conditions. 

• Rotating blade time-averaged pressure distributions are pseudo-linear, and pressure recovery indicates 

greatly reduced boundary layer separation, compared to stationary, two-dimensional conditions.

• Rotationally augmented blade response persisted as indicated by mean statistics, though inflows at the field 

test site are highly turbulent.

• Prevalence and intensity of rotational augmentation across the blade radius is consistent with that observed 

for blades having lower thickness ratios and conventional trailing edges, and is not impeded by elevated 

airfoil thickness or flatback design. 

The current work provides novel insights into rotational augmentation on megawatt-scale turbines using thick, 

flatback airfoils. These findings provide a solid foundation for future research in two separate but equally important 

Figure 11. (c/r) vs. (r/R) for Siemens 2.3-MW rotor 

used in current study.
9 

directions. First, the understanding and data furnished herein can be used as a basis for validating and improving 

post-stall models in aeroelastic design codes. Second, the time-average analyses used in the current work can be 

enhanced with time-varying data and analyses to characterize and better understand fatigue loading generated by 

rotationally augmented blade flows.

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Max-Planck-Institut fur Stromungsforschung Gottingen Nr. 2, 1950. 2

Banks, W., and Gadd, G., “Delaying Effect of Rotation on Laminar Separation,” AIAA J., v. 1, n. 4, Apr. 1963, pp. 

941-942. 3

McCroskey, W., and Yaggy, P., “Laminar Boundary Layers on Helicopter Rotors in Forward Flight,” AIAA J., v. 

6, n. 10, Oct. 1968, pp. 1919-1926. 4

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