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

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Wind Turbine Blade Flow Fields 

and Prospects for Active 

Aerodynamic Control 

Preprint 

S. Schreck and M. Robinson 

To be presented at the ASME 2007 Fluids Engineering Division 

Summer Meeting 

San Diego, California 

July 30–August 2, 2007 

Conference Paper 

NREL/CP-500-41606 

August 2007 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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WIND TURBINE BLADE FLOW FIELDS AND 

PROSPECTS FOR ACTIVE AERODYNAMIC CONTROL 

S. Schreck and M. Robinson 

Applied Research Division 

NREL’s National Wind Technology Center 

Golden, CO 80401 

ABSTRACT 

As wind turbines continue to grow larger, problems 

associated with adverse aerodynamic loads will grow more 

critical. Thus, the wind energy technical community has begun 

to seriously consider the potential of aerodynamic control 

methodologies for mitigating adverse aerodynamic loading. 

Spatial and temporal attributes of the structures and processes 

present in these flow fields hold important implications for 

active aerodynamic control methodologies currently being 

contemplated for wind turbine applications. The current work 

uses complementary experimental and computational 

methodologies, to isolate and characterize key attributes of 

blade flow fields associated with axisymmetric and yawed 

turbine operation. During axisymmetric operation, a highly 

three-dimensional, shear layer dominated flow field yields 

rotational augmentation of both mean and standard deviation 

levels of aerodynamic forces. Under yawed operating 

conditions, pseudo-sinusoidal inflow angle oscillations elicit 

dynamic stall, which significantly intensifies aerodynamic load 

production. Both rotationally augmented and dynamically 

stalled flows possess attributes likely to pose central challenges 

for turbine flow control. Whether active control of turbine 

aerodynamics can help alleviate adverse aerodynamic loads 

will depend on comprehension and command of the issues 

documented herein. 

NOMENCLATURE 

Cn normal force coefficient 

c chord length (m) 

cp pressure coefficient ((p-p∞)/q) 

Hz Hertz 

LFA local inflow angle (deg) 

Employees of the Midwest Research Institute under Contract No. DE-AC36-

99GO10337 with the U.S. Dept. of Energy have authored this work. The U.S. 

Government retains, and the publisher, by accepting the article for publication, 

acknowledges that the U.S. Government retains a non-exclusive, paid-up, 

irrevocable, worldwide license to publish or reproduce the published form of 

this work, or allow others to do so, for U.S. Government purposes. 

m meter 

msec millisecond 

p static pressure (Pa) 

p∞ freestream static pressure (Pa) 

q dynamic pressure (Pa) 

r radial distance from hub (m) 

R blade length (m) 

RPM revolutions/minute 

s second 

U∞ test section speed (m/s) 

Uloc local inflow velocity (m/s) 

x chord location (m) 

Ψ blade azimuth angle (deg) 

Ω blade rotation rate (rad/s) 

γ rotor yaw angle (deg) 

INTRODUCTION 

Wind turbine aerodynamics remains a challenging and 

crucial research area for wind energy. Clearly, steady-state 

aerodynamic performance is essential to turbine energy capture, 

since blade aerodynamic forces produce mechanical energy that 

is subsequently converted to electrical energy. However, more 

recent inquiry has focused on adverse time varying 

aerodynamic loads that wind turbines frequently suffer during 

routine service. These undesirable aerodynamic loads impose 

excessive stresses on turbine blades and gear boxes, and 

appreciably shorten machine service life. At present, the wind 

energy technical community is contemplating the utility of 

various aerodynamic control methodologies for mitigating 

adverse aerodynamic loading. 

Wind turbine blade aerodynamic phenomena can be 

broadly categorized according to the operating state of the 

machine, and two particular aerodynamic phenomena assume 

crucial importance. At zero and low rotor yaw angles, 

rotational augmentation determines blade aerodynamic 

response. At moderate to high yaw angles, dynamic stall 

dominates blade aerodynamics. As described herein, the spatial 

and temporal attributes of the structures and processes present 

1 
in these flow fields hold important implications for active 

aerodynamic control methodologies currently being considered 

for wind turbine applications. 

Rotational Augmentation

Augmentation of rotating blade aerodynamic properties, 

including stall delay and lift enhancement, was first observed 

for airplane propellers and qualitatively explained in terms of 

centrifugal and Coriolis accelerations.[1] Some time later, 

analytical modeling quantitatively accounted for key elements 

of the rotating blade flow field.[2]

Subsequent rotational augmentation research was carried 

out for helicopter rotors. Analytical modeling determined that 

rotationally induced cross flows played an important role in 

blade lift production.[3] Experimental research [4] suggested 

that centrifugal forces are important in the presence of flow 

separation, but of limited influence otherwise. 

Prior research concerning rotational augmentation of 

airplane propeller and helicopter rotor aerodynamics aided later 

work aimed at wind turbines. However, wind turbines extract 

energy from flowing air, while propellers and rotors inject 

energy. This key distinction pointed out the need for rotational 

augmentation research specific to wind turbines. 

Early wind turbine field testing aimed at rotational 

augmentation affirmed the importance of blade geometry with 

respect to rotational influences.[5] A wind tunnel experiment 

showed that blade geometry coupled with blade rotation 

maintained blade lift under conditions in which lift otherwise 

would significantly decline.[6] Subsequent wind tunnel 

research determined that rotational augmentation was most 

active at the inboard portion of the turbine blade.[7] 

Concurrent analytical modeling of rotational augmentation has 

furnished better comprehension of the aerodynamics underlying 

this phenomenon, and provided foundational predictive 

capabilities for design and analysis.[8-13] 

More recently, analysis of turbine aerodynamics 

measurements acquired during controlled wind tunnel 

experiments, in conjunction with validated computational 

results, have provided key physical insights regarding 

rotationally augmented flow fields.[14-17]

Dynamic Stall

Viewed simplistically as a two-dimensional process, 

dynamic stall initiates when aerodynamic surface angle of 

attack (α) dynamically exceeds the static stall α. Soon 

thereafter, unsteady boundary layer separation gives rise to a 

small but energetic dynamic stall vortex, which appears near 

the leading edge. This vortex quickly grows, convects rapidly 

downstream, and soon sheds from the lifting surface. 

During this process, the dynamic stall vortex generates a 

region of low pressure on the lifting surface, causing dramatic 

lift amplification beyond static levels, followed by abrupt deep 

stall at vortex shedding. [18,19] Surface pressure signatures 

confirm that dynamic stall occurs on horizontal axis wind 

turbine blades [20-24], and contributes significantly to wind 

turbine rotor loads and yaw dynamics. [25] 

The complexity of dynamic stall is compounded by threedimensionality, which corresponds to nonunformity of the 

vortex structure along the length of the vortex tube. Dynamic 

stall vortex three-dimensionality has been visualized for a 

rectangular wing pitching in a wind tunnel.[26] These and 

other visualizations have been corroborated with time varying 

surface pressure measurements, which also were acquired using 

rectangular wings pitching in wind tunnels.[27-31]

More recently, dynamic stall vortex three-dimensionality 

was observed clearly and analyzed in detail for wind turbine 

blades during yawed turbine operation. This was accomplished 

via analysis of time varying surface pressure data acquired from 

the NREL Unsteady Aerodynamics Experiment field test [32]

and wind tunnel experiment [33,34] 

EXPERIMENTAL PROCEDURES 

All surface pressure data used in the current work were 

acquired during wind tunnel testing of the NREL UAE 

(National Renewable Energy Laboratory Unsteady 

Aerodynamics Experiment) horizontal axis wind turbine. This 

machine was documented in detail for earlier field testing 

phases of the experiment.[35-38] Subsequently, several UAE 

configurations were tested in the NASA Ames 80 ft x 120 ft 

wind tunnel, all of which are described by Hand, et al.[39]

Wind Turbine

Data analyzed in the current work were acquired from a 

two bladed upwind rotor, 10.1 m in diameter, with zero cone 

angle. The rotor turned counterclockwise (viewed from 

upwind) at a constant 71.6 RPM, was stall regulated, and had a 

maximum rated power of 19.8 kW. A cylindrical tower 0.4 m 

in diameter supported the turbine at a hub height of 12.2 m (test 

section centerline), with 1.32 m rotor overhang. This UAE 

configuration is depicted in Figure 1. 

Figure 1. NREL Unsteady Aerodynamics Experiment wind 

turbine in NASA Ames 80 ft x 120 ft wind tunnel. 

2 
Blade Geometry

The blades used throughout the NASA Ames wind tunnel 

test were both twisted and tapered. Blade taper distribution is 

apparent in Figure 2, with chord tapering from 0.737 m at 

0.25R to 0.356 m at the tip. Blade section twist decreased from 

22.1° at 0.25R to 0.0° at the tip. Between 0.25R and the tip, 

blade cross-section was uniform, corresponding to the S809 

airfoil. The airfoil section at 0.25R was joined to the pitch 

shaft section at 0.12R using linear segments to yield an 

uninterrupted transition between these two disparate contours. 

The blade pitched about an axis located 0.30c aft of the leading 

edge, and centered between the blade upper and lower surfaces 

at that chord location. Design procedures, constraints, and 

measures of merit for this blade have been documented in 

detail.[39,40] 

Figure 2. Blade cross-section and planform, showing 

pressure tap locations. 

Pressure Instrumentation

The black blade shown in Figure 1 was equipped with 

pressure taps at the locations indicated in Figure 2 to acquire 

detailed surface pressure data. A full pressure tap distribution 

consisted of 22 taps distributed on the airfoil section as shown 

in the upper portion of Figure 2. At the full distributions, taps 

were more densely distributed near the blade leading edge to 

better resolve the steep gradients typically present there, as well 

as the small structures that initiate there during dynamic stall. 

Pressure taps were flush mounted at the blade surface, and 

had inside diameters of 0.69 mm. From the taps, stainless steel 

hypodermic tubes having inside diameters of 0.69 mm 

transmitted the surface pressures to the pressure transducers. 

Hypodermic tubing lengths were minimized to mitigate 

pressure delay and dispersion effects. Pressures were measured 

by five Pressure Systems Incorporated ESP-32 electronically 

scanned pressure transducers located inside the blade near the 

five full pressure tap distributions. Each of the transducer 

pressure inputs was scanned at 520.8 Hz. In conjunction with 

the tubing frequency response, this provided antialiased 

digitization and minimal gain variation out to 55 Hz.[35] Test 

section flow speed and air properties were measured using the 

NASA Ames 80 ft x 120 ft wind tunnel air data system 

described by Zell.[41]

Time records of cp were integrated over the sectional chord 

to obtain time records of Cn. Then, time records of cp and Cn

were processed to obtain means and standard deviations for cp

and Cn. For the zero yaw measurements, only those data 

corresponding to the upper half of the rotor disk were extracted 

and analyzed in the current study. This was done to eliminate 

tower influences from rotational flow modifications. 

Local Inflow Measurement

Inflow angles were measured near the five full pressure tap 

distributions using five-hole probes. Probes were mounted on 

cylindrical stalks at 0.34R, 0.51R, 0.67R, 0.84R, and 0.91R, 

with the probe tip 0.80c upstream of the blade leading edge. 

Probes were angled 20° downward relative to the local chord 

line, allowing measurement of local inflow angles between –

20° and 60°. Five-hole probe pressures were measured using 

the ESP-32 transducers described above, and local inflow angle 

(LFA) was derived from these measurements. LFA was 

defined as the angle between the local inflow vector (Uloc) and 

the local blade chord line, measured at the probe tip, as 

depicted in Figure 3. LFA was defined in a plane parallel to the 

local chord line and orthogonal to the blade axis. 

Figure 3. Definition of local inflow angle (LFA) and normal 

force (N). 

3 
Data Acquisition Protocol

Data acquisition procedures were designed to decouple the 

principal physical phenomena that govern blade aerodynamics, 

and thus were structured in an incremental manner. First, blade 

aerodynamics were characterized for nonrotating conditions. 

Then, data were acquired with the blades rotating at zero yaw, 

for a range of wind speeds. Finally, with the rotor still turning, 

yawed conditions were characterized for a matrix of wind 

speeds and yaw angles. 

To isolate the effects of rotational influences, a baseline 

was needed that excluded rotational effects. This was 

accomplished by halting turbine blade rotation and acquiring 

pressure data for the stationary (parked) blade. To achieve 

stationary blade conditions, the instrumented turbine blade was 

first fixed at the 12 o’clock azimuth position. Then, for U∞ = 

20 m/s, turbine blade pitch was incremented in 5° steps, from 

90° to -15° and -15° to 90°. Blade pitch was incremented in 

both negative and positive directions to disclose any hysteresis 

in blade aerodynamic force response. At each pitch angle, one 

13 second data record was acquired, and cp data from each 

record were integrated over the sectional chord to obtain time 

records of Cn. Finally, these 13 second time records of cp and 

Cn were time averaged to yield mean values for each blade 

pitch angle. Prior to acquiring data at each pitch angle, 

appropriate delays were included to allow time for pitching, 

blade flow field equilibration, and wake relaxation. 

To quantify the effects of rotational influences, data were 

collected with the rotor turning at 71.6 RPM. Turbine blade 

plane of rotation was maintained orthogonal to the test section 

centerline, yielding γ = 0°. Blade pitch angle was held constant 

at 3.0°. U∞ was varied between 5 m/s and 25 m/s, in nominal 1 

m/s increments. At each U∞, a data record of 30 seconds 

duration was acquired, corresponding to 36 blade rotations. 

Characterizations of yawed conditions also were carried 

out with the rotor turning at 71.6 RPM. Again, U∞ was varied 

between 5 m/s and 25 m/s, in increments of approximately 1 

m/s. At each U∞, turbine blade plane of rotation was yawed 

through the range 10° ≤ γ ≤ 60°, in increments of 10°. At each 

combination of U∞ and γ, a 30 second data set corresponding to 

36 blade rotations was acquired. 

The zero yaw experiments were carried out using two 

blade configurations. Initially, the five-hole probes and stalks 

were mounted on the blade, and LFA was measured 

concurrently with other blade aerodynamic properties. Later, 

the probes and stalks were removed, yielding a 'clean' blade 

with a flow field free of any disruptions due to the stalks or 

probes. Measurements then were repeated using this otherwise 

identical machine configuration and virtually identical inflow 

conditions. This approach allowed accurate measurement of 

LFA, provided a blade flow field free of intrusions, and 

allowed for assessment of flow field stability and repeatability. 

Because of the greater robustness of dynamically stalled flow 

fields, yawed rotor experiments were carried out with the fivehole probes and stalks mounted on the blade during surface 

pressure data acquisition. 

Inflow Angle and Force Nondimensionalization

Derivation of angle of attack and lift coefficient for 

rotating blades using measurements on or near the blade 

remains a challenging and essential area of inquiry [5,7,42-46]. 

However, in the current work, these dependencies were 

excluded in order to simplify physical relationships and 

concentrate analyses on the blade flow field. This was 

accomplished by analyzing measured LFA and Cn in lieu of 

derived α and Cl. 

Normalization of aerodynamic forces also presents 

challenges. In the current work, normal force was 

nondimensionalized by local dynamic pressure. Local dynamic 

pressure was computed as the difference between test section 

static pressure (p∞) and local total pressure. Local total 

pressure was sensed at each full pressure tap distribution as the 

highest pressure in the tap distribution. This methodology for 

quantifying dynamic pressure has been analyzed previously, 

and found to induce average errors in dynamic pressure of 

approximately 1.0 percent.[14] 

RESULTS AND DISCUSSION 

Results and discussion below are divided according to 

turbine operating state into two sections, axisymmetric 

operation and yawed operation. As summarized above, these 

two operating states produced flow fields dominated by 

rotational augmentation and by dynamic stall, respectively. 

Within each of these two sections, results and discussion follow 

the same progression. First, inflow relationships are described. 

Then, typical data for mean and standard deviation of normal 

force are presented. Finally, representative two-dimensional 

flow field sections are shown, followed by three-dimensional 

planform representations. 

Axisymmetric Inflow Relationships

Figure 4 shows measured local inflow angle (LFA) as a 

function of U∞ for 5 m/s ≤ U∞ ≤ 25 m/s, at three five-hole probe 

locations on the UAE blade, for axisymmetric (zero yaw) 

operation. 

Figure 4. LFA vs. U∞, for three radial locations. 

4 
At U∞ = 5 m/s, LFA began at approximately 5° for all three 

radial locations. As U∞ increased, plots corresponding to all 

three radial locations rose smoothly, but diverged with 

increasing U∞. Divergence was prompted by differing LFA 

sensitivities to U∞, which was greatest at 0.34R and declined 

progressively for radial stations farther outboard on the blade. 

LFA data acquired at 0.51R and 0.84R were consistent with 

these trends, but are not presented here for plot clarity. Data in 

Figure 4 correspond to a fixed blade pitch angle of 3°. 

These LFA-U∞ relationships are governed by a velocity 

vector triangle that describes local inflow relationships for zero 

yaw operation. In general (neglecting axial and rotational 

induction), LFA is proportional to tan-1(U∞/Ωr). Thus, 

assuming that local blade section orientation (pitch) and 

rotation rate (Ω) both remain constant, higher U∞ will prompt 

higher LFA. Clearly, this statement applies directly to constant 

speed, stall controlled turbines. However, the same remains 

applicable to variable speed, pitch controlled machines, within 

the bandwidth constraints imposed by component inertia, drive 

system authority, control parameter settings. 

Axisymmetric Mean Cn Augmentation

Figure 5 shows a plot of Cn as a function of LFA at 0.30R, 

for both the stationary (parked) and rotating blade. For the 

parked blade, U∞ = 20 m/s corresponds to Re = 0.98 x 106

, 

while U∞ = 30 m/s yields Re = 1.46 x 106

. The stationary blade 

Cn curve for each U∞ consists of two data sets, one 

corresponding to increasing LFA, and the other for decreasing 

LFA. This was done to disclose any hysteresis in the stationary 

blade response to LFA. Generally, the four curves exhibit close 

agreement, testifying to the absence of hysteresis, and showing 

an apparent maximum of Cn = 0.94 at LFA = 32.2° for the 

parked blade. 

Figure 5. Mean Cn vs. LFA at 0.30R, for parked 

(stationary) and rotating blade. 

Also plotted in Figure 5 is Cn for the rotating blade. The 

rotating blade Cn curve consists of 21 data points, 

corresponding to 21 U∞ values at 1 m/s intervals, in the range 5 

m/s ≤ U∞ ≤ 25 m/s. Between LFA = 4.7° and 32.6°, the 

rotating blade Cn curve rises in approximate linear fashion from 

0.38 to 1.62. In this range, rotating blade Cn curve slope 

closely matches that for the stationary blade linear range. 

Subsequently, the Cn curve begins to rise steeply, and shortly 

thereafter attains a maximum Cn of 2.41 at LFA = 45.0°. This 

stall Cn represents an amplification of nearly threefold relative 

to the stationary blade stall Cn. Thereafter, Cn decreases 

gradually with LFA, and finally descends to 2.23 at LFA = 

57.6°. 

For the most part, the 0.30R rotating blade Cn curve differs 

significantly from that of the stationary blade. At all values of 

LFA, rotating blade Cn significantly exceeds stationary blade 

stall Cn. Notably, both stall LFA and stall Cn for the rotating 

blade exceed those for the parked blade. On the rotating blade 

curve in Figure 5, the data points representing the 11 highest 

LFAs have been filled in. These data points correspond to 

Reynolds numbers that lie within the range demarcated by the 

stationary blade conditions (0.98 x 106

 < Re < 1.46 x 106). 

Thus, at 0.30R, it is clear that Reynolds number influences do 

not account for the pronounced Cn amplification. 

The trends shown in Figure 5 for 0.30R are typical of those 

observed at 0.47R, 0.63R, and 0.80R.[14] At all four radial 

locations the rotating blade exhibited considerable stall delay 

and stall Cn amplification, compared to the stationary blade. In 

general, at radial locations farther inboard, the conformation of 

the rotating blade Cn curve diverged more dramatically from 

the stationary blade Cn curve. Specifically, stall delay and Cn 

amplification were consistently greater for radial locations 

farther inboard on the blade. 

Axisymmetric Cn Unsteadiness Amplification

To disclose any time varying components in the UAE test 

data, standard deviations were extracted from the Cn time 

records. Typical data are presented in Figure 6, and are 

identified in the legend as “UAE Test”. 

Figure 6. Cn standard deviations measured by UAE and 

predicted by YawDyn/TurbSim at 0.30R. 

5 
It should be noted that Cn standard deviations extracted 

from UAE test data were caused solely by interactions 

originating in the blade flow field. This exclusion applies 

because of two factors. First, as stated above, data were 

analyzed in a manner that excluded tower influence and any 

associated time variations due to blade passage through it. 

Second, the NASA Ames 80 ft x 120 ft wind tunnel provided 

an inflow environment free of significant inflow turbulence or 

velocity gradients.[41]

To quantify Cn standard deviations that would occur in 

response to turbulent atmospheric inflow, UAE operation was 

simulated using YawDyn[47] with AeroDyn[48]. Turbulent 

inflow input files were generated using TurbSim[49]. 

YawDyn/TurbSim simulations were carried out for three 

turbulence intensities (5, 10, and 15 percent) and five mean 

wind speeds (U∞ = 5, 10, 15, 20, and 25 m/s). These data are 

designated in the Figure 6 legend as TI = 5%, TI = 10%, and TI 

= 15%. All data shown in Figure 6 apply to the 0.30R radial 

location. 

In Figure 6, Cn standard deviations for the UAE test data 

exhibited subdued levels in the range 5 m/s ≤ U∞ ≤ 9 m/s. 

Thereafter, Cn standard deviation rose rapidly with increasing 

U∞, and soon attained substantial magnitudes. Ultimately, Cn

standard deviation for the UAE test data reached a maximum of 

0.214 at U∞ = 20 m/s, and declined only slightly for higher 

values of U∞. It should be noted that the turbulence intensity 

range of 5 to 15 percent in Figure 6 approximates the 

turbulence intensity range of 13 to 20 percent encompassed by 

various wind turbine design standards.[50] 

UAE Cn standard deviation behaviors seen in Figure 6 for 

0.30R are representative of those at 0.47R, 0.63R, and 

0.80R.[51] At all four radial locations for the rotating blade, 

U∞ values below 9 or 10 m/s were accompanied by attenuated 

Cn standard deviation levels, which remained well below Cn

standard deviation levels predicted for 5 percent inflow 

turbulence intensity. However, modest increases in U∞ elicited 

a rapid and substantial rise in UAE Cn standard deviation, 

which peaked in the range 15 ≤ U∞ ≤ 20 m/s. These elevated 

Cn standard deviation levels exceeded those predicted for 15 

percent turbulence intensity, and did so over a broad U∞ range. 

Overall, Cn time variations due to spontaneous blade flow 

field interactions were comparable to those prompted by 

elevated turbulent inflow intensities. Adverse structural 

impacts, including fatigue life expenditure, need to be 

considered comparable, as well. 

Axisymmetric Flow Field Structure

The dramatic rise in Cn mean and standard deviation levels 

during rotational augmentation has been conclusively linked to 

abrupt transformation of the flow field from a conventional 

separating topology to one dominated by an impinging shear 

layer.[16] Figure 7 shows a two-dimensional section of the 

mean flow field on the rotating UAE blade, for U∞ = 15 m/s 

and r/R = 0.30. This two-dimensional section was extracted 

from a three-dimensional Ellipsis3D computation.[17]

Figure 7. Two-dimensional section of computed flow field, 

showing typical shear layer impingement (U∞ = 15 m/s, r/R 

= 0.30). Courtesy of N. Sørensen, Risø National 

Laboratory. 

In Figure 7, separation at the leading edge near the left 

border of the frame gives rise to a shear layer that arches over 

the blade suction surface. This shear layer impinges farther aft 

on the chord, over an extended region approximately 0.25c in 

length and centered near 0.70c. As the shear layer arches over 

the blade, it surrounds a region of intense recirculation 

reminiscent of a vortical structure. This structure produces low 

surface pressures over the extended chordwise region beneath 

it, thus accounting for augmented mean Cn levels. Recent 

research has shown that Cn standard deviation levels correlate 

with the chord length enveloped by the shear layer.[51]

The image shown in Figure 7 is a two-dimensional section 

extracted from a strongly three-dimensional flow field. To gain 

an appreciation for the complex three-dimensional character, 

Figure 8 shows computed surface limiting streamlines on the 

suction surface of the UAE blade, for U∞ = 15 m/s. These 

limiting streamlines were derived from the same threedimensional Ellipsis3D solution described in connection with 

Figure 7.[52]

Figure 8. Computed boundary layer topology on blade 

suction surface for U∞ = 15 m/s and γ = 0°. Courtesy of N. 

Sørensen, Risø National Laboratory. 

6 
In Figure 8, the computed separation line extends along the 

blade leading edge from 0.20R to 0.85R. From 0.85R, the 

separation line travels outboard and aft, demarcating a small 

triangular region containing chordwise streamlines and twodimensional flow. Impingement is prominent on the blade 

surface, signified by an aggregation of limiting streamlines that 

crosses the blade from 0.30R at midchord to 0.65R at the 

leading edge. Within the triangular region between separation 

and impingement, the streamlines proceed outboard and 

forward toward the leading edge, suggesting the presence of a 

recirculating region over this blade surface area. 

The boundary layer topology shown in Figure 8 

corresponds to near-peak augmentation of mean Cn and 

amplification of Cn standard deviation. Topology shifts 

substantially with U∞, and attached flow areas may expand 

somewhat for lower U∞. However, the topology always 

remains strongly three-dimensional with extended regions of 

spanwise flow, and thus must be considered highly complex in 

general. These and related computational results have been 

corroborated through detailed comparisons with UAE wind 

tunnel measurements.[17,52] 

Yawed Inflow Relationships

Figure 9 shows mean LFA as a function of Ψ, at four fivehole probe locations, for U∞ = 13 m/s and γ = 40°. Mean LFA 

data shown in Figure 9 were derived from the 36 cycles 

contained in the 30 second data set acquired at U∞ = 13 m/s and 

γ = 40°. At Ψ = 0.0° the instrumented blade is situated at the 

12 o’clock position, and the period for one 360° rotor 

revolution encompasses 0.838 s. 

Figure 9. LFA vs. Ψ at four radial locations, for U∞ = 13 

m/s and γ = 40°. 

Since the rotor plane is yawed with respect to U∞, the 

vectors Ωr and U∞ are no longer perpendicular. Thus, local 

inflow magnitude and LFA vary during the rotation cycle, 

depending upon whether the blade section is advancing 

upstream into the wind or retreating downstream from it. As a 

consequence, LFA no longer remains constant as the blade 

rotates in azimuth, but varies in pseudo-sinusoidal fashion, as 

shown in Figure 9. 

These pseudo-sinusoidal oscillation amplitudes are largest 

at 0.34R, with LFA varying through the range 6.6° ≤ LFA ≤

71.4°. LFA oscillation amplitudes decrease progressively at 

radial stations farther outboard on the blade. However, even at 

0.84R, LFA oscillation amplitude remains substantial, 

traversing the range 10.8° ≤ LFA ≤ 22.9°. Notably, all four 

LFA histories intersect the S809 stall LFA of 21°, which was 

determined during wind tunnel testing of a two-dimensional 

S809 airfoil. 

A disruption to the 0.34R plot in Figure 9 is apparent in the 

range 41.9° ≤ Ψ ≤ 98.4°. This was caused by the wake shed 

from the instrumentation package mounted on the rotor hub, 

which was directly upstream of the 0.34 probe for γ ≥ 40°. 

However, it should be noted that the aerodynamics of interest 

in this work occurred outside this Ψ range, and so were not 

influenced by this disruption. 

Yawed Mean Cn Augmentation

At different points in the approximate range -180° ≤ Ψ ≤ -

90°, LFA at all four radial locations dynamically exceeded the 

S809 static stall threshold (LFA = 21°), thus initiating dynamic 

stall. The flow field response is shown in Figure 10, which 

contains records of mean Cn compiled from data acquired 

during 36 consecutive blade rotation cycles. Here, mean Cn is 

plotted as a function of Ψ, at four radial locations on the UAE 

blade, for U∞ = 13 m/s and γ = 40°. Prior research has shown 

that dynamic stall similar to that in Figure 10 is common for a 

broad range of U∞, yaw angle, and radial location.[34] 

Figure 10. Mean Cn vs. Ψ at four radial locations, for U∞ = 

13 m/s and γ = 40°. 

As shown in Figure 10, Cn at 0.30R increased rapidly with 

Ψ, and quickly reached a maximum of 3.18 at Ψ = 6.3°. Cn

maxima at the remaining three locations decreased 

progressively with distance outboard. Though the 0.80R Cn

7 
maximum of 1.31 was substantially lower than those farther 

inboard, it still significantly exceeded the S809 stall Cn of 1.00 

for static, two-dimensional conditions, testifying to the 

presence and influence of dynamic stall. 

Spectral components predominant in the mean Cn records 

occurred once per revolution, which corresponded to 1.2 Hz for 

the experiments analyzed in the current work. Features 

suggesting higher frequency content, which occurred 

approximately twice per revolution can be seen in Figure 10. 

However, these features reside in the approximate range 0.0° ≤

Ψ ≤ 90°, and thus may be artifacts associated with the LFA 

disruption apparent in Figure 9, in the range 41.9° ≤ Ψ ≤ 98.4°. 

Rise times for Cn in Figure 10 ranged from 0.39 s at 0.30R to 

0.29 s at 0.80R. 

Yawed Cn Unsteadiness Amplification

Figure 11 shows a superposition of Cn records for all 36 

blade rotation cycles, at 0.30R and 0.80R, for U∞ = 13 m/s and 

γ = 40°. The mean Cn data for these two radial locations also 

are plotted, as thin white traces in the approximate center of the 

multiple overlapping black traces. 

Figure 11. Instantaneous Cn vs. Ψ for 36 blade rotation 

cycles at two radial locations, for U∞ = 13 m/s and γ = 40°. 

These plots confirm that the principal dynamic stall Cn

features, which occur once per revolution, are sufficiently 

repeatable from cycle to cycle to confer statistical significance 

on the mean data. At the same time, Figure 11 reveals the 

presence of Cn oscillations having higher frequencies of 

approximately 30 to 50 Hz. Peak-to-peak amplitudes of these 

higher frequency components correspond to approximately 50 

to 70 percent of mean levels. In Figure 11, data for 0.47R and 

0.63R have been omitted for plot clarity. However, these two 

intermediate radial locations exhibit behaviors consistent with 

those shown by the 0.30R and 0.80R data. 

Both the once per revolution and the higher frequency Cn

time variations associated with dynamic stall constitute 

significant components of the aerodynamic load spectrum. 

Amplitude of the higher frequency components varies with Ψ, 

attaining largest magnitudes in the approximate range -90° ≤ Ψ

≤ -90°, simultaneous with dynamic stall. Outside this Ψ range, 

these higher frequency Cn components are virtually absent, 

convincingly linking them to dynamic stall rather than to some 

artifact. 

Yawed Flow Field Structure

Visualization of dynamically stalled flow fields can reveal 

flow field structures responsible for the dramatic Cn

amplification. Unfortunately, dynamic stall visualization is 

extremely difficult on rotating turbine blades and has not yet 

been accomplished. However, analogous dynamically stalled 

flow fields occur on nonrotating aerodynamic surfaces 

subjected to dynamic pitching, and are readily visualized. 

Figure 12 shows smoke flow visualization of a twodimensional airfoil with c = 0.15 m, pitching from 0° to 60° at a 

constant rate of 115°/s. The airfoil pitch axis is located at 

0.25c. Air flows from left to right at 6.1 m/s. The three panels 

show instantaneous angles of attack of 20°, 25°, and 30°. The 

upper surface of the airfoil is visible in each panel as a brightly 

lit arc inclined with respect to the panel borders. 

Figure 12. Smoke flow visualization of dynamic stall on an 

airfoil pitching in a wind tunnel. 

8 
In the upper panel, the diffuse smoke filaments 

immediately above the airfoil indicate that separation has 

enveloped the entire upper surface. However, the smoke line 

emanating from the leading edge is well defined, curves back 

toward the airfoil, and impinges on the surface at 

approximately 0.20c. Subsequent panels will show this 

structure to be an energetic dynamic stall vortex. The dynamic 

stall vortex produces a surface pressure minimum of significant 

magnitude on the airfoil surface beneath the vortex. This 

surface pressure minimum is responsible for the strongly 

amplified Cn levels observed during dynamic stall. 

The middle panel shows that the dynamic stall vortex has 

grown substantially and has convected aft on the airfoil chord, 

residing approximately over midchord. The smoke line 

defining the outer perimeter of the vortex almost completely 

encircles the vortex, indicating the strongly rotational and 

energetic nature of the vortex. Consistent with vortex size, 

surface pressure minimum has grown and Cn has increased. 

In the lower panel, the vortex has grown still larger, and 

continues to exhibit a strongly rotational appearance, indicative 

of the energy contained in the vortex. However, the dynamic 

stall vortex has reached the airfoil trailing edge, and has begun 

to lift away from the airfoil surface. As the vortex sheds from 

the airfoil surface, the surface pressure minimum weakens and 

Cn undergoes a catastrophic decrease, or stall. 

Again, no flow visualization has been accomplished for the 

UAE turbine blade. However, using the UAE surface pressure 

instrumentation, pressure signatures elicited by a dynamic stall 

vortex have been identified and tracked on the UAE blade 

surface. These cp data are shown in Figure 13, for U∞ = 13 m/s 

and γ = 40°, at 0.47R. The cp distributions in Figure 13 are 

averages computed from data in 36 successive rotor cycles. 

Figure 13. cp vs. x/c at three Ψ values, for U∞ = 13 m/s, γ = 

40°, and 0.47R. 

In Figure 13, the dynamic stall vortex was first detected 

just aft of the leading edge, as indicated by a high, narrow 

surface pressure minimum, or suction peak, centered at 0.04c, 

and corresponding to Ψ = -72.8°. Then, 35 msec later when Ψ

= -57.8°, the suction peak had moved aft to 0.36c, where it had 

broadened and magnitude decreased to 2.42. Finally, after an 

additional 66 msec had passed and Ψ = -29.4°, the suction peak 

had continued aft to 0.68c, had broadened further, and the cp

magnitude was 1.42. Clearly, aftward movement of the peak 

corresponded to dynamic stall vortex convection, and peak 

broadening was associated with vortex growth. Dynamic stall 

vortex convection was rapid, traversing 0.75c in approximately 

0.1 s. 

Using the suction peak tracking methodology illustrated in 

connection with Figure 13, a detailed history was assembled at 

each full tap distribution (r/R = 0.30, 0.47, 0.63, and 0.80), 

documenting vortex chordwise position as a function of time. 

Then, for each combination of U∞ and γ, the vortex convection 

data from the four individual radial locations were interpolated 

and assembled into vortex convection topologies like those 

presented below in Figure 14. These analyses have been 

documented in detail previously by Schreck, et al. [43]. As 

shown by the topologies, vortex structure evolved rapidly and 

dramatically, undergoing complex structural deformations. 

Although complex, these deformations generally were 

systematic with respect to U∞ and γ, and are typified well by the 

cases presented in Figure 14. 

Figure 14. Dynamic stall vortex topologies for γ = 20°, 30°, 

40°, and 50° (top to bottom), all at U∞ = 13 m/s. 

Figure 14 consists of four panels. Each panel contains the 

UAE blade planform outline between 0.25 ≤ r/R ≤ 1.00, with 

the blade tip to the right and the blade leading edge near the 

bottom of the panel. Dynamic stall vortex position data are 

superimposed on each blade planform at r/R = 0.30, 0.47, 0.63, 

9 
and 0.80, consistent with the radial locations of the full tap 

distributions. At these radial locations, measured suction peak 

position data are represented by dot symbols. Symbols at 

adjacent radial locations corresponding to simultaneous times 

are connected by linear segments, depicting vortex topology 

evolution with time. Symbols not connected by linear 

segments indicate that the dynamic stall vortex was not 

observed at the adjacent radial location. Near the blade tip, the 

time interval between successive topologies is annotated as Δt. 

Time intervals vary, having been chosen to yield easiest 

viewing and clearest interpretation of the data shown in the 

figure. 

Proceeding down from the top panel in Figure 14, the four 

panels represent γ = 20°, 30°, 40°, and 50°, all for U∞ = 13 m/s. 

At all yaw angles, the dynamic stall vortex was observed at 

0.30R, reaching progressively farther outboard across the blade 

radius as γ increased. For the experimental range of U∞ and γ, 

dynamic stall vortices were not observed outboard of the 0.80R 

location. At 0.30R, 0.47R, and 0.63R, the dynamic stall vortex 

was first observed at the blade leading edge. In contrast, at 

0.80R, the dynamic stall vortex was first detected aft of the 

leading edge. 

In Figure 14, dynamic stall initiation time varied 

considerably with radial location. Accordingly, initiation 

phasing played a prominent role in shaping the dynamic stall 

vortex. For the experimental range of U∞ and γ, vortex 

initiation always was detected first at 0.30R, and subsequently 

progressed outboard. This progression produced vortices 

initially skewed with respect to the leading edge, with the 

inboard part of the vortex situated farther aft on the blade 

surface. 

After initiation, dynamic stall vortex convection was least 

dynamic at 0.30R, implying the existence of pinning influences 

at the inboard vicinity of the vortex [26-30,33,53-56]. 

Depending on γ, convection was most active at 0.47R or 0.63R. 

At γ = 20° and γ = 30°, dynamic stall vortex convection speed 

was fastest at 0.47R, where the vortex initially arrived at the 

trailing edge. At γ = 40°, convection speed was highest at 

0.63R, where the vortex first passed the trailing edge, and at γ = 

50°, convection speed was highest at 0.80R. Although not true 

for γ = 50°, convection rates at the outboard end of the vortex 

generally were lower than those observed at locations farther 

inboard, consistent with pinning interactions functioning near 

the outboard end of the vortex [26-30,33,53-56]. 

In general, disparities in convection rate between radial 

locations were large enough to drive the vortex downstream of 

the trailing edge at 0.47R or 0.63R, while the vortex remained 

over the blade surface at the inboard and outboard vortex ends, 

at 0.30R and 0.80R. 

Observations in conjunction with Figures 13 and 14 

regarding dynamic stall vortex convection kinematics are 

consistent with those noted in previous efforts employing 

rotating wind turbine blades [33,34], and pitching rectangular 

wings [26-30,53,56]. In addition, analogous structures have 

been observed over stationary lifting surfaces [57] and bluff 

bodies [58]. All such flow fields are known to be consistent 

with the vortex theorems of Helmholtz.[59]

Frequency of Occurrence

Rotational augmentation and dynamic stall, as described 

above, are not anomalous events. Rather, both take place 

routinely during wind turbine operation, and pervade the 

turbine operating envelope. Figure 14 illustrates the operating 

conditions during which these phenomena occurred for the 

Unsteady Aerodynamics Experiment turbine during the NASA 

Ames wind tunnel test. 

Figure 15. UAE operating envelope, showing typical gusts 

and direction changes. 

The square marked “Nom” (nominal) near the origin of the 

plot represents the range of yaw error and U∞ not influenced by 

rotational augmentation or dynamic stall. Rotational 

augmentation predominates for yaw error and U∞

corresponding to the rectangular region labeled “Rot Aug”, 

which lies immediately above the “Nom” square. Dynamic 

stall is dominant for conditions demarcated by the large 

rectangular region marked “Dynamic Stall” that fills the upper 

right portion of Figure 14. 

Wind gusts and direction changes encountered during 

routine operation are sufficient to drive turbine operating state 

out of the nominal region and into rotational augmentation or 

dynamic stall. These departures are shown by the two vectors 

originating on the nominal region upper right corner. One of 

these vectors points to the right and is labeled “30° Operating 

Direction Change”, while the other points upward and is 

labeled “9 m/s Operating Gust”. Established international wind 

turbine certification standards were used to establish these 

vector magnitudes for operating direction change [60] and 

normal operating gust [61]. 

It may be noted that the UAE was a stall controlled turbine 

and was intentionally operated at large yaw errors. Admittedly, 

these two characteristics differ from state-of-the-art utility class 

machines. However, wind gusts and direction changes that 

10 
outpace turbine pitch and yaw actuation rates are likely to lead 

to analogous circumstances. 

It also is important to remember that the current work 

relies on wind tunnel measurements and computational 

predictions that purposely exclude turbulent inflow. Thus, the 

current work cannot quantify the extent to which turbulent 

inflow may compound the adverse effects documented herein. 

Implications for Control

The results and discussion above show conclusively that 

rotational augmentation and dynamic stall occurring on wind 

turbine blades can be accurately characterized as follows. 

• Amplified aerodynamic loads 

• Pronounced unsteady content 

• Extensive frequency spectrum 

• Large scale vortical structures 

• Leading edge vorticity production 

• Three-dimensional flow fields 

• Pervasive across operating regime 

These attributes are likely to pose difficult challenges for 

wind turbine flow control, specifically with respect to the 

following considerations that typically impact sensors, 

actuators, and controllers. 

• Bandwidth 

• Nonlinearity 

• Sensor/actuator placement 

• Flow state identification 

• Actuator authority 

CONCLUSIONS 

Using complementary experimental and computational 

methodologies, key attributes of blade flow fields were isolated 

and characterized for axisymmetric and yawed turbine 

operation. These characteristics are likely to have strong 

implications for control of turbine blade flow fields. 

For axisymmetric operation, elevated inflow angles drive 

Cn to mean and standard deviation levels that are significantly 

augmented relative to two-dimensional non-rotating conditions. 

Both mean and standard deviation levels are substantial, and 

are caused by a highly three-dimensional, shear layer 

dominated flow field. 

Under yawed operating conditions, pseudo-sinusoidal 

inflow angle oscillations elicit dynamic stall. The dynamic stall 

flow field is strongly unsteady, driven by rapid vortex growth 

and convection. Mean Cn levels are strongly amplified 

compared to static conditions. In addition, The highly 

energetic vortices undergo complex three-dimensional 

modifications and are associated with broad frequency content. 

Rotationally augmented and dynamically stalled flows 

possess attributes likely to pose central challenges for turbine 

flow control. Specifically, stringent demands will be imposed 

on flow state identification, actuator effectiveness, controller 

nonlinearity, and system bandwidth. 

As wind turbines continue to grow larger, problems 

associated with adverse aerodynamic loads will be exacerbated. 

Whether active control of turbine aerodynamics can help solve 

this problem will depend on comprehension and command of 

the issues documented herein. 

ACKNOWLEDGMENT 

The experimental data used in this investigation were 

acquired from the National Wind Technology Center (NWTC) 

Unsteady Aerodynamics Experiment (UAE). The wind turbine 

and instrumentation were designed, fabricated, installed, and 

maintained by Mr. Jason Cotrell, Mr. Lee Jay Fingersh, and 

Mr. David Jager. The UAE was installed and tested in the 

NASA Ames 80 ft x 120 ft National Full-Scale Aerodynamics 

Complex by these NWTC personnel, with the assistance, 

advice, and encouragement of numerous NASA Ames 

engineers, scientists, and technicians. 

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12 
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Wind Turbine Blade Flow Fields and Prospects for Active 

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NREL 

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National Technical Information Service 

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14. ABSTRACT (Maximum 200 Words)

This paper describes wind turbine flow fields that can cause adverse aerodynamic loading and can impact active 

aerodynamic control methodologies currently contemplated for wind turbine applications. 

15. SUBJECT TERMS

wind turbine design; wind turbine flow fields; wind turbine aerodynamic; wind energy 

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a. REPORT 

Unclassified 

b. ABSTRACT 

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c. THIS PAGE 

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