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Rotationally Augmented Flow 

Structures and Time Varying 

Loads on Turbine Blades 

Preprint 

S.J. Schreck 

To be presented at the 45th AIAA Aerospace Sciences Meeting 

and Exhibit, Wind Energy Symposium 

Reno, Nevada 

January 8–11, 2007 

Conference Paper 

NREL/CP-500-40982 

January 2007 

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Rotationally Augmented Flow Structures and 

Time Varying Loads on Turbine Blades 

Scott J. Schreck1

NREL’s National Wind Technology Center, 1617 Cole Boulevard, Golden, CO 80401 

[Abstract] Blade rotation routinely and significantly augments aerodynamic loads during 

zero yaw operation of horizontal axis wind turbines. To better understand the flow physics 

underlying this phenomenon, time dependent blade surface pressure data were acquired 

from the NREL Unsteady Aerodynamics Experiment, a full-scale HAWT tested in the NASA 

Ames 80 ft x 120 ft wind tunnel. Records of time varying surface pressure were analyzed to 

extract mean flow field structure and normal force coefficient standard deviation during 

operating conditions that included rotational augmentation. Clear relationships were 

identified between mean flow field state and the magnitude of time variations in normal 

force coefficient. Model results showed that the level of these time variations in normal force 

coefficient were significant compared to time varying aerodynamic loads elicited by 

turbulent inflows. 

Nomenclature 

Cn normal force coefficient (N/qc) 

c chord length (m) 

cp pressure coefficient ((p-p∞)/q) 

HAWT horizontal axis wind turbine 

LFA local inflow angle (deg) 

LIMP impingement length (x/c) 

LSEP separated length (x/c) 

N Normal force or Newton 

NFAC National Full-Scale Aerodynamics Complex 

p static pressure (N/m2

) 

p∞ freestream static pressure (N/m2

) 

q dynamic pressure (N/m2

) 

r radial distance from hub (m) 

R blade length (m) 

RPM revolutions/minute 

SD standard deviation 

Uloc local inflow speed (m/s) 

U∞ test section speed (m/s) 

x chord location (m) 

α angle of attack (deg) 

θ blade pitch angle (deg) 

UAE Unsteady Aerodynamics Experiment 

 

1

 Senior Engineer II, Applied Research Division, Associate Fellow 

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. 

1
I. Introduction 

Horizontal axis wind turbines (HAWTs) routinely experience unanticipated aerodynamic loads. Elevated 

aerodynamic loads impose high stresses on blades and structural components, appreciably shortening machine 

service life. In addition, aerodynamic load fluctuations produce torque variations that adversely impact power 

quality and degrade transmissions. These and other factors arising from unpredicted and excessive aerodynamic 

loading drive up the overall cost of energy. Failure to reliably predict aerodynamic loads is due principally to a lack 

of accurate methods for modeling wind turbine blade aerodynamics. This situation, in turn, derives strongly from 

the complex nature of wind turbine blade aerodynamics and incomplete comprehension of the fluid dynamics that 

govern them. 

Flow fields elicited by HAWT blades are highly complex due to the simultaneous presence and interaction of 

three-dimensionality, unsteadiness, separation, and rotational influences. Previous field experiments have 

established that dynamic stall routinely occurs on HAWT blades, producing energetic vortex structures responsible 

for dramatic force and moment amplifications.[1-5] Recently, dynamically stalled blade flow fields have been 

investigated in detail, significantly improving comprehension of the three-dimensional vortex dynamics responsible 

for these flows.[6-8] 

Typically, dynamic stall dominates turbine blade flow fields at large to moderate yaw angles, and remains 

prominent even under low yaw conditions. However, low yaw angles appreciably attenuate dynamic stall effects, 

permitting emergence of rotational influences. At zero yaw, rotational effects comprise the principal increment to 

blade aerodynamic response. 

Currently, rotational influences remain incompletely characterized and understood. The widely cited 

experiments of Himmelskamp [9] indicate that stall delay and lift enhancement due to rotation were first noted for 

aircraft propellers. To explain these results, radial thinning and chordwise acceleration of the steady boundary layer, 

due to centrifugal forces and Coriolis effect, were postulated. A complementary theoretical analysis was performed 

by Banks and Gadd [10] 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. 

Subsequent experimental and analytical research was carried out in the rotorcraft field. McCroskey and Yaggy 

[11] carried out 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, but of smaller magnitude than crossflows due to rotor translation. It was also 

speculated that, in strong adverse pressure gradients, the rotationally induced crossflows played a more influential 

role. Further rotor experiments [12] 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, all employing surface pressure measurements, reached differing conclusions 

regarding the impact of blade crossflows. Using field test data, Madsen and Christensen [13] concluded rotational 

effects to be of minor importance compared to the influences of aspect ratio and spanwise pressure gradient. Two 

other experiments were carried out in wind tunnels and arrived at different conclusions. Using two-dimensional 

airfoil performance as a baseline, Barnsley and Wellicome [14] stated that the combination of rotational and threedimensional effects appeared to suppress the loss of leading edge suction across the entire span, compared to twodimensional behavior. Ronsten [15] 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 Robinson [16] concluded that rotational 

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

it was associated with distinctive chordwise and spanwise pressure signatures that varied in an orderly manner in 

response to changes in radial location and inflow conditions. Subsequent work [17] 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 research [18,19] correlated experimentally 

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

significantly generalizing comprehension of the flow field. Other work [20] isolated tip speed ratio as in 

independent variable, and quantified the influence of tip speed ratio on aerodynamic forces and underlying flow 

field structure. More recently, unsteadiness in rotational augmented flows has been experimentally identified and 

characterized for a bounded set of operating conditions.[21] 

Attempts have been made to formalize extant knowledge of HAWT flow field rotational influences using 

approximate and empirical models. A model formulated by Eggers and Digumarthi [22] scaled the effects 

centrifugal and Coriolis effects on a low energy, deeply stalled wake residing on the blade suction surface. Snel, et 

2
al. [23] developed a computational model that employs a quasi-two-dimensional approximation that retains the 

important effects of centrifugal and Coriolis forces. Tangler and Selig [24] applied the empirical stall delay model 

of Corrigan and Schillings [25] to wind turbine blade flows to account for rotational and inertia effects. 

To date, the principal structures and interactions underlying the mean component of blade rotational 

augmentation are approaching adequate characterization. In contrast, the physical relationships that give rise to the 

time varying component of rotational augmentation remain relatively obscure. To address this disparity, a portion of 

the blade surface pressure data acquired via the NREL UAE during the NASA Ames test have been analyzed to 

quantify the unsteady component of aerodynamic load enhancement due to rotational influences. Analysis 

methodologies have been applied to these data to better understand the time varying fluid dynamic mechanisms 

present under rotational, three-dimensional conditions. In particular, these analyses have been directed toward 

revealing relationships extant between the mean and unsteady components operating in the rotationally augmented 

blade flow field. 

II. Experimental and Modeling Procedures 

A. Experimental Measurements 

The UAE HAWT has been documented in detail for earlier field testing phases of the experiment.[26-29] 

Several UAE configurations were tested in the NFAC 80 ft x 120 ft wind tunnel, and are described by Hand, et 

al.[30] Data analyzed herein were acquired from a two bladed upwind rotor, 10.1 m in diameter, with zero cone 

angle. The rotor turned clockwise (viewed from downwind) 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, mounted in the NASA Ames 

80 ft x 120 ft wind tunnel, is shown in Figure 1. 

The black blade on the left side of the rotor in Figure 1 was equipped with pressure taps to acquire detailed 

surface pressure data. A full pressure tap distribution consisted of 22 taps distributed on the S809 airfoil section as 

shown in the upper portion of Figure 1. Pressure taps were more densely distributed near the blade leading edge to 

Figure 1. UAE wind turbine mounted in the 

NASA Ames 80 ft x 120 ft wind tunnel. Figure 2. Blade cross-section and planform, 

showing pressure tap locations. 

3
better resolve the pronounced gradients typically present there, as well as the small structures that initiate there 

during dynamic stall. The lower portion of Figure 2 shows that full pressure tap distributions were located at r/R = 

0.30, 0.47, 0.63, 0.80, and 0.95. Interspersed between these five full tap distributions were ten partial pressure tap 

distributions, each having taps only on the suction surface at 0.04c and 0.36c. 

The blades used throughout the NASA Ames wind 

tunnel test were both twisted and tapered. The blade 

taper distribution is apparent in Figure 2, with 

maximum blade chord being 0.737 m at 0.25R, and 

tapering to 0.356 m at the tip. Figure 3 documents 

blade twist, which decreases 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.[31] 

Figure 3. UAE blade twist distribution. 

Surface pressure taps were flush mounted at the blade surface, and had inside diameters of 0.69 mm. From the 

taps, stainless steel hypodermic tubes with inside diameters of 0.69 mm transmitted 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.[26] Test section flow speed and air properties were measured using the 80 ft x 

120 ft wind tunnel air data system described by Zell.[32]

Inflow angles were measured near the five full 

pressure tap distributions using five-hole probes. As 

shown in Figure 4, 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. Consistent with Figure 4, LFA is defined 

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

local blade chord line, measured at the probe tip. Also 

defined in Figure 4 is normal force (N), which is oriented 

perpendicular to the blade chord line, and from which Cn

is obtained. 

Figure 4. Definition of local inflow angle (LFA) 

and normal force (N). 

All data were collected with the turbine rotating at a constant speed of 71.6 RPM. Turbine blade plane of 

rotation was maintained orthogonal to the test section centerline, yielding a yaw angle of 0°. Blade pitch angle was 

held constant at 3.0°. Test section velocity was varied between 5 m/s and 25 m/s, in nominal increments of 1 m/s. 

At each test section velocity, a 30 second data record was acquired. 

The experiments described above were carried out twice. Initially, the five hole probes and stalks were mounted 

on the blade, and LFA was measured concurrently with other blade aerodynamic properties. Subsequently, the 

probes and stalks were removed, and measurements were repeated using an otherwise identical machine 

configuration and virtually identical inflow conditions. This approach allowed accurate measurement of LFA while 

maintaining the blade flow field free of any possible intrusions due to the stalks or probes. 

Time records of cp were integrated over the sectional chord to obtain time records of Cn. Time records of cp and 

Cn were processed to obtain means and standard deviations for cp and Cn. To separate time variations due to 

4
rotational flow physics from tower wake influences, only those data corresponding to the upper half of the rotor disk 

were extracted and analyzed in the current study. 

The work documented herein employs standard deviations to quantify surface pressure temporal fluctuations. To 

ensure that pressure measurement system frequency responses did not corrupt standard deviations, power spectral 

density calculations were carried out. Results confirmed that pressure fluctuations were restricted to frequencies 

well below previously measured pressure system cutoff frequencies.[26] Thus, pressure measurement system 

frequency responses were deemed negligible error sources. 

B. Computational Modeling 

The NASA Ames 80 ft x 120 ft wind tunnel test section provided an inflow environment free of significant 

inflow turbulence or velocity gradients.[32] To quantify the Cn standard deviations that would occur in response to 

turbulent atmospheric inflow, the NWTC YawDyn[33] and AeroDyn[34]models were employed in conjunction with 

turbulent inflow files generated by TurbSim[35]. 

All modeling results in the present work were generated using YawDyn version 12.14, with AeroDyn version 

12.50. In the YawDyn input file, machine geometry and kinematics were specified consistent with documentation in 

ref. 30 for the “S” sequence. The “S” sequence was one of several UAE test sequences carried out during testing in 

the NASA Ames 80 ft x 120 ft wind tunnel. This yielded a model representation having a rigid hub, rotating at 

constant speed, at a fixed zero yaw angle. 

In the AeroDyn input file, the blade was discretized into 21 radial elements, five of which were centered over the 

full pressure tap distribution locations at r/R = 0.30, 0.47, 0.63, 0.80, and 0.95. Aerodynamic input files were 

generated with existing stall delay modeling techniques, modifying experimental two-dimensional S809 

aerodynamics data to account for UAE blade geometric and aerodynamic three-dimensionality. Both the 

generalized dynamic wake and the Leishman-Beddoes dynamic stall model options available in AeroDyn were used 

for all modeling results described herein. 

 Aerodynamic response of the YawDyn/AeroDyn model was calibrated by comparing model mean Cn predictions 

for r/R = 0.30, 0.47, 0.63, 0.80, and 0.95, against mean Cn measurements acquired by the UAE during sequence “S” 

wind tunnel testing. Then, based on these comparisons, AeroDyn aerodynamics input files were adjusted 

accordingly. To replicate wind tunnel conditions, calibration model runs were carried out with zero vertical and 

horizontal shear specified in the model input files. To exclude tower effects to the greatest extent possible and to 

ensure consistency with processing applied to experimental data, mean Cn data were computed using only data from 

the upper half of the rotor disc. 

Following YawDyn/AeroDyn model calibration for mean aerodynamic response, the same model was used to 

predict time varying aerodynamic response. Prediction of time varying aerodynamic response was accomplished by 

providing turbulent inflow input files to the calibrated YawDyn/AeroDyn model. These turbulent inflow files were 

generated using the NWTC TurbSim model, with inputs specified as follows. 

The TurbSim grid was 13 points high by 13 points wide, corresponding to a height of 13.0 m and width of 13.0 

m. An IEC Kaimal model was chosen consistent with IEC Standard 61400-1, edition 3. Turbulence intensity was 

specified at 5.0, 10.0, and 15.0 percent, for each of five mean wind speeds (U∞ = 5, 10, 15, 20, and 25 m/s), yielding 

a total of 15 turbulent inflow input files. A power law wind profile with an exponent of zero was specified, to 

replicate the uniform mean inflow present during wind tunnel testing. To achieve stationary statistical 

characteristics for the turbulent inflow time series, an output time series of 600 seconds duration was specified. 

III. Results and Discussion 

A. Reference Angle and 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 [13,15,36-40]. However, in the current work, these dependencies 

were excluded to simplify physical relationships and focus on the blade flow field. This was done by analyzing 

local inflow angle (LFA) and normal force coefficient (Cn) instead of angle of attack and lift coefficient. 

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. 

5
B. Identification of Separation and Reattachment 

Previous works have established the connection between airfoil boundary layer separation and reattachment, and 

surface pressure mean levels.[41] A surface pressure distribution exhibiting significant chordwise pressure gradient 

(dp/dx) signals attached or reattached flow, while an attenuated pressure gradient indicates separated flow. Other 

efforts established the correlation between lifting surface boundary layer separation and reattachment, and surface 

pressure temporal fluctuations for two-dimensional [42] and three-dimensional [43,44] flows. For both types of 

flows, boundary layer separation and reattachment elicit localized surface pressure fluctuations significantly greater 

than those observed at locations occupied by attached flow. 

These generalized relationships for stationary geometries were successfully adapted to track boundary layer 

separation and shear layer impingement on the surface of the rotating UAE wind turbine blade during the NASA 

Ames wind tunnel experiment.[17,18] Later, these experimental surface characterizations were combined with 

validated computational results [19] to provide a cohesive portrayal of mean flow field behavior underlying 

rotational augmentation of aerodynamic loads on turbine blades. 

To briefly summarize these previous works, for each 

radial location, the tap where maximum standard 

deviation in surface pressure occurred was identified as a 

location of either boundary layer separation or shear layer 

impingement. Boundary layer separation was 

distinguished from shear layer impingement by 

examining the change in pressure gradient. If the 

upstream gradient was appreciable and the downstream 

one essentially zero, then the feature was identified as 

boundary layer separation. Alternatively, if the upstream 

gradient was essentially zero and the downstream 

gradient was appreciable, then the feature was identified 

as shear layer impingement. Mean flow fields for 

boundary layer separation and shear layer impingement 

are schematically depicted in Figure 5. 

In Figure 5, separation and impingement points are 

identified by the filled circular symbol on the blade upper 

(suction) surface. Chordwise distance between the 

separation point and trailing edge is defined as the 

separated length (LSEP). Similarly, the chordwise 

distance between the impingement point and leading edge 

is termed the impingement length (LIMP). 

Figure 5. Definition of separated length (LSEP) and 

impingement length (LIMP). 

C. Separation/Impingement Location and Cn Standard Deviation 

Using the criteria explained above, the mean chordwise locations of boundary layer separation or shear layer 

impingement were identified for test section speeds from 5 m/s to 25 m/s, at 0.30R, 0.47R, 0.63R, and 0.80R. Data 

for 0.95R were available, but were not analyzed because of blade tip vortex proximity and associated confounding 

effects. Results are shown in Figures 6 through 9, along with Cn standard deviation data. In each of these figures, 

the 21 values of LFA correspond to the 21 test section speeds from 5 m/s to 25 m/s. In Figures 6 through 9, 

separation is indicated by open circular symbols, and filled circular symbols denote impingement. 

1. 0.30R 

Figure 6 shows separation/impingement chordwise movement and Cn standard deviation data for r/R = 0.30. For 

LFA = 4.7°, 9.3°, and 13.9°, separation was detected at 0.56c. This is consistent with visualization results obtained 

using a two-dimensional S809 airfoil.[45] At LFA = 18.5°, separation initially was detected at the trailing edge, at 

LFA = 22.6° progressed forward to 0.80c. Shortly thereafter, separation arrived in the leading edge vicinity (0.02c) 

at LFA = 26.4° and remained there until LFA = 29.4°. 

Impingement was first apparent at LFA = 32.5° and 0.06c, as the point of maximum cp standard deviation 

retreated from 0.02c. Initial impingement movement was mildly responsive to LFA, with impingement moving aft 

to 0.10c as LFA increased to 35.2°. Further aft movement was more sensitive to LFA, with impingement advancing 

to the trailing edge as LFA increased to 42.3°. Thereafter, impingement remained at the trailing edge until LFA = 

6
48.5°. At this point, impingement began to advance forward again, halting at x/c = 0.56 and remaining there until 

data acquisition ended. 

Cn standard deviation fluctuations generally were correlated with separation and impingement movement. 

Forward advance of the separation point from the trailing edge (x/c = 1.0) to the leading edge vicinity was 

accompanied by a significant and steady increase in Cn standard deviation levels. As separation transitioned to 

impingement and moved aft from the leading edge, Cn standard deviation initially increased to a local maximum of 

0.178, and then briefly decreased to 0.122. Thereafter, Cn standard deviation again began to rise with aft movement 

of impingement. This trend continued until maximum Cn standard deviation was attained with impingement at the 

trailing edge. Finally, as impingement traveled forward toward midchord, Cn standard deviation descended from 

0.214 to 0.183. 

Figure 6. Separation/impingement chordwise 

movement (upper panel) and C

Figure 7. Separation/impingement chordwise 

movement (upper panel) and C

2. 0.47R 

Chordwise separation/impingement movement and Cn standard deviation data for r/R = 0.47 resembled those at 

0.30R, and are shown in Figure 7. For LFA = 9.1°, 12.7°, and 15.8°, separation was detected at 0.56c, again 

consistent with two-dimensional visualizations of the S809.[45] At LFA = 18.9°, separation initially appeared at the 

trailing edge, and remained there until LFA = 22.1°. Immediately thereafter, separation movement responded 

strongly to increasing LFA, reaching the leading edge vicinity at 0.005c at LFA = 27.8°. 

Impingement was first observed immediately after separation arrived at the leading edge, at LFA = 30.6°. 

Subsequent impingement movement was responsive to LFA, with impingement progressing to the trailing edge as 

LFA increased to 35.1°. Thereafter, impingement remained on the aft portion of the blade, apparently oscillating 

between 1.0c and 0.56c while LFA increased to 55.1°. 

n standard 

deviation (lower panel) at 0.30R. n standard 

deviation (lower panel) at 0.47R. 

7
Cn standard deviation fluctuations were again well correlated with separation and impingement movement. As 

the separation point moved from the trailing edge to the leading edge neighborhood, Cn standard deviation levels 

increased monotonically and substantially to 0.129. As separation transitioned to impingement and moved aft from 

the leading edge, Cn standard deviation briefly increased to 0.165, and then temporarily decreased to 0.083. 

Thereafter, Cn standard deviation increased and decreased in irregular fashion as impingement oscillated between 

0.56c and 1.00c. 

Figure 8. Separation/impingement chordwise 

movement (upper panel) and Cn standard 

deviation (lower panel) at 0.63R. 

Figure 9. Separation/impingement chordwise 

movement (upper panel) and Cn standard 

deviation (lower panel) at 0.80R. 

3. 0.63R 

Separation movement at r/R = 0.63, as shown in Figure 8, was similar to separation movement at 0.30R and 

0.47R. For LFA = 4.9°, 7.6°, and 10.0°, separation was observed at 0.56c. As before, this early midchord detection 

of separation agreed with two-dimensional visualizations of the S809.[45] At LFA = 12.7°, separation was first 

detected at the trailing edge, and then moved responsively forward with increasing LFA. At LFA = 19.9°, 

separation had reached the leading edge, and remained there until LFA = 22.0°. 

Impingement was first observed at 0.04c, at LFA = 23.7°. Subsequently, impingement movement was somewhat 

erratic and only moderately responsive to LFA. In contrast to the 0.30R and 0.47R locations, impingement did not 

reach the blade trailing edge region. Instead, impingement retreated in irregular fashion aft to 0.36c as LFA 

increased to 34.7°. Subsequently, impingement advanced steadily forward with further increases in LFA, reaching 

0.005c at LFA = 44.1°. 

At 0.63R, Cn standard deviation fluctuations again correlated well with separation and impingement movement. 

Forward travel of the separation point from the trailing edge to the leading edge occurred concurrently with a 

substantial and monotonic rise in Cn standard deviation to a maximum of 0.189. Immediately after impingement 

supplanted separation, the impingement point moved aft in erratic fashion to its 0.36c, at the same time that Cn

standard deviation varied in an irregular manner. Local maxima in aft impingement point travel at LFA = 29.6° and 

34.7° were closely correlated to local maxima in Cn standard deviation that occurred at LFA = 29.6° and 33.1°. 

8
4. 0.80R 

Figure 9 shows separation/impingement and Cn standard deviation data for r/R = 0.80. For LFA = 5.3°, 7.4°, and 

9.4°, separation was detected at 0.56c. As at the other three radial locations, this observation was consistent with 

surface flow visualizations performed on a two-dimensional S809.[45] At LFA = 11.6°, separation initially was 

observed at the trailing edge, progressed rapidly forward in response to rising LFA, and at LFA = 21.3° reached the 

leading edge. 

Impingement was first observed at 0.20c, at LFA = 22.9°. Subsequently, impingement generally moved in an aft 

direction, but did so in sporadic fashion, stabilizing temporarily at 0.10c, 0.20c, and 0.36c before ending at 0.10c at 

LFA = 38.2°. As at 0.63R, impingement moved aft no farther than 0.36c. 

Cn standard deviation levels again were linked closely to mean separation and impingement movement. At 

0.80R, separation moved steadily forward from the trailing edge to the leading edge. At the same time, Cn standard 

deviation increased substantially and uniformly from 0.015 to 0.091. Thereafter, the impingement point traveled 

generally aft with infrequent disruptions, at the same time that Cn standard deviation decreased from 0.164 to 0.116, 

also exhibiting sporadic interruptions. 

D. Cn Standard Deviation Correlation with Separation/Impingement Length 

Figures 6 through 9 imply that a consistent relationship exists between Cn standard deviation levels and mean 

separation/impingement length. To more clearly distinguish this relationship, Cn standard deviation was correlated 

with separation length in Figure 10, and with impingement length in Figure 11. As before, separation length and 

impingement length are defined as shown in Figure 5. 

In Figure 10, for a separation length of 0.0c 

(separation at trailing edge), Cn standard deviations for all 

four radial locations were grouped tightly around a value 

of approximately 0.02. As separation moved toward the 

leading edge and separation length increased, Cn standard 

deviation increased in steady, monotonic fashion for all 

four radial stations. The solid line extending diagonally 

across the lower part of the plot is an aggregate linear 

least squares fit to all plotted data points, and thus 

represents an average correlation across all four radial 

locations. Slope of this linear fit is 0.100. 

Clearly, data points corresponding to the four radial 

locations deviate from the linear fit. Irregular deviation 

from the linear fit can be partially attributed to 

comparatively coarse chordwise pressure tap resolution 

over the aft portion of the blade surface. However, 

careful examination of Figure 10 reveals that the points 

for the 0.47R and 0.63R locations assume a consistently steeper slope than those for 0.30R and 0.80R. Thus, three 

dimensional aerodynamic interactions can be identified as a source for systematic deviations from the aggregate 

linear fit. 

Figure 10. Correlation between Cn standard 

deviation and separation length (LSEP), for all 

four radial locations. 

In Figure 11, for impingement lengths of 0.0c 

(impingement at leading edge) to 1.0c (impingement at 

trailing edge), Cn standard deviation for all four radial 

locations was confined to a range of approximately 0.1 to 

0.2. As before, a linear least squares fit was applied to all 

data points in the Figure 11 plot, and is shown by the solid 

line extending across the center of the plot. The slope of 

this aggregate linear fit is 0.023, which is significantly 

shallower than the slope of 0.100 for the line fit to the 

separation length data in Figure 10. 

As in Figure 10, data points in Figure 11 deviate 

visibly from the linear fit. Sporadic deviation from the 

linear fit is partially attributable to sparse chordwise 

pressure tap spacing over the aft blade chord. However, 

strong aerodynamic three-dimensionality previously has 

Figure 11. Correlation between Cn standard 

deviation and impingement length (LIMP), for all 

four radial locations. 

9
been identified in the UAE operating range corresponding to the Figure 11 data.[19] This blade flow field change 

introduces spanwise flow components that alter impingement length, and modifies the mechanisms for time varying 

aerodynamic force generation. 

Comparing the aggregate linear least squares fit for separation (Figure 10) with that for impingement (Figure 11) 

leads to two clear observations. First, time varying aerodynamic force levels are, with only isolated exceptions, 

significantly higher for an impinging flow field than for a separated flow field. Second, the slope of the linear fit for 

separation (0.100) is four times steeper than that for impingement (0.023). This indicates that above-surface fluid 

dynamics responsible for time varying aerodynamic force production respond more sensitively to aerodynamic 

surface state during separation than during impingement. 

E. Cn Standard Deviation from Turbulent Atmospheric Inflow 

Thus far in the current work, the only Cn standard deviations considered have been those produced by 

interactions occurring spontaneously in separated and impinging blade flow fields. This exclusion was enabled by 

the NASA Ames 80 ft x 120 ft wind tunnel, which provided an inflow environment free of significant inflow 

turbulence or velocity gradients.[32] To quantify Cn standard deviations that would occur in response to turbulent 

atmospheric inflow, the UAE was modeled using the YawDyn[33] and AeroDyn[34] models, in conjunction with 

turbulent inflow files from TurbSim[35]. 

Figures 12 and 13 show Cn standard deviation data from the UAE wind tunnel test along with Cn standard 

deviation data extracted from YawDyn/TurbSim model output files. For each radial location, Cn standard deviation 

data were extracted from model output files for three turbulence intensities (5, 10, and 15 percent), for each of five 

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

Figure 12 shows Cn standard deviation test measurements and model predictions for the 0.30R radial location. 

UAE test data in this figure are the same as those in the lower panel of Figure 6, except that the test data in Figure 

12 are plotted as a function of U∞ to maintain consistency with the model data. Similar to Figure 6, Cn standard 

deviation levels for the test data exhibit subdued levels for low U∞, and increase steadily with U∞. The three 

modeled turbulence intensity levels produce three widely separated Cn standard deviation levels, all of which are 

significant in magnitude. UAE test data points representing a separated flow field reach Cn standard deviation levels 

corresponding to turbulence intensities of 5 to 10 percent. Some UAE test data points for an impinging flow field lie 

between the 10 and 15 percent turbulence intensity curves, and several more reach Cn standard deviation levels 

exceeding those elicited by turbulence intensities of 15 percent. 

Figure 12. C Figure 13. C

Figure 13 shows Cn standard deviation test measurements and model predictions for the 0.80R radial location. 

UAE test data in Figure 13 are the same as those shown in the lower panel of Figure 9, though the test data in Figure 

13 have been plotted against U∞ for consistency with model data. As in Figure 9, test data Cn standard deviation 

levels assume attenuated levels for low U∞, and increase substantially with U∞. The three modeled turbulence 

intensities of 5, 10, and 15 percent yield three clearly delineated Cn standard deviation levels. While magnitudes of 

these curves are lower than those at 0.30R, all three nonetheless remain significant in magnitude. UAE test data 

points for separated flow fields reach Cn standard deviation levels represented by turbulence intensities of 10 to 15 

n standard deviations measured by 

UAE and predicted by YawDyn/TurbSim at 

0.80R. 

n standard deviations measured by 

UAE and predicted by YawDyn/TurbSim at 

0.30R. 

10
percent. All of the UAE test data points representing impinging flow fields lie above the 15 percent turbulence 

intensity curve, with many of these points lying significantly above this curve. 

Figures 12 and 13 clearly show that blade operation in either the stalled regime in quiescent inflow, or in the 

unstalled regime in turbulent inflow, produces Cn standard deviations of comparable and substantial magnitude. The 

turbulence intensity range of 5 to 15 percent in Figures 12 and 13 approximates the turbulence intensity range of 13 

to 20 percent encompassed by various design standards.[46] Similar data were analyzed for 0.47R and 0.63R, 

revealing trends consistent with those shown by Figures 12 and 13. It is important to note that these data and 

analyses are not intended to predict Cn standard deviation levels that could be expected for a blade operating in the 

stalled regime in a turbulent inflow. 

IV. Conclusions 

Blade surface pressure data were acquired from a full-scale horizontal axis wind turbine tested under 

axisymmetric conditions in the NASA Ames 80 ft x 120 ft wind tunnel. Records of time varying surface pressure 

and normal force were analyzed to extract mean separation and impingement location and normal force coefficient 

standard deviation during operating conditions that included rotational augmentation. Complementary model 

predictions of time varying normal force coefficient standard deviation also were provided for turbulent inflow 

conditions. These data and analyses support the following conclusions. 

Normal force coefficient standard deviation varies considerably with local inflow conditions, which in turn 

determine blade flow field structure. 

Normal force coefficient standard deviation correlates well with mean flow field spatial scales at the blade 

surface, specifically mean separated length and mean impingement length. 

Normal force coefficient standard deviation is substantially higher for impinging blade flow fields than for 

separating blade flow fields. 

Normal force coefficient standard deviation responds more sensitively to blade surface mean flow field state for 

separating flow fields than for impinging flow fields. 

Normal force coefficient standard deviations observed for quiescent inflow are of the same magnitude as normal 

force coefficient standard deviations produced by routine turbulent inflows. 

This work characterizes previously undocumented physical relationships that govern aerodynamic force time 

variations that take place in connection with rotational augmentation on rotating wind turbine blades. This and other 

research will help improve the accuracy and reliability of aerodynamic predictions for wind energy machine design 

and analysis. 

Acknowledgment 

The wind turbine 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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13
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4. TITLE AND SUBTITLE

Rotationally Augmented Flow Sturctures and Time Varying Loads 

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14. ABSTRACT (Maximum 200 Words)

Blade rotation routinely and significantly augments aerodynamic loads during zero yaw operation of horizontal axis 

wind turbines. To better understand the flow physics underlying this phenomenon, time dependent blade surface 

pressure data were acquired from the NREL Unsteady Aerodynamics Experiment. 

15. SUBJECT TERMS

wind energy; wind turbines; wind turbine blades; Unsteady Aerodynamics Experiment; NASA Ames; 

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