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

Inflow Characterization and 

Aerodynamics Measurements 

on a SWT-2.3-101 Wind Turbine

Preprint

P. Medina and M. Singh 

Siemens Energy, Inc.

J. Johansen and A. Jove 

Siemens Wind Power A/S

L. Fingersh and S. Schreck 

National Renewable Energy Laboratory

To be presented at the 50th AIAA Aerospace Sciences 

Meeting/Wind Energy Symposium

Nashville, Tennessee

January 9-12, 2012

Conference Paper

NREL/CP-5000-53666

January 2012
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Inflow Characterization and Aerodynamic Measurements

on a SWT-2.3-101 Wind Turbine

Paul Medina and Manjinder Singh, Jeppe Johansen and Anna Rivera Jové

Siemens Energy Inc., Boulder, CO, USA Siemens Wind Power A/S, Taastrup, Denmark 

Lee Fingersh and Scott Schreck

National Renewable Energy Lab, Golden, CO, USA 

Post processing techniques for aerodynamic data acquired from a Siemens’ SWT-2.3­

101 turbine have been developed and applied in this paper. The turbine is installed at 

the National Wind Technology Center (NWTC) as part of Cooperative Research and Development Agreement between Siemens Wind Power and the National Renewable Energy 

Laboratory (NREL) under U.S. Department of Energy (DOE) sponsorship. The results 

indicate that the use of these corrections is essential for accurate analysis of the data. An 

example of local inflow angles, velocities, and inflow velocity over the rotor plane derived 

from measurements from a 5-hole probe is also presented. Finally, the pressure measurements are used to characterize unsteady phenomenon, namely, rotational augmentation and 

dynamic stall on an inboard station. The results show that the rotational augmentation 

can considerably increase the attached flow regime compared to the 2D Computational 

Fluid Dynamics (CFD) results. The dynamic stall event was seen to significantly delay the 

stall. Furthermore, the non-dimensionalized vortex convection derived from the dynamic 

stall event was found to agree well with results from others studies. 

I. Introduction and Background

Over the last few decades the trend of the wind industry has been for larger turbines capable of producing 

power at multi-megawatt levels using rotor diameters 100+ meters. Furthermore, increased focus on offshore 

development has led to even larger rotor diameters to reduce the total cost of energy produced. As a result, 

new rotor designs are driven to improve aerodynamic performance to maximize the energy captured while 

minimizing the loads acting on the blades. However, with larger rotor diameters, an additional design 

challenge must be addressed due to the ability of the rotor to interact with large energetic turbulent structures 

in the atmospheric boundary layer (ABL). Additionally, other features of the inflow, like high shear and yaw 

errors, have the potential to effect the performance of the rotor. Thus, for higher reliability, blade performance 

must be characterized for a wide and highly dynamic range of parameters over which the turbine can be 

anticipated to operate. 

To provide a test bed for such a characterization, Siemens Wind Power and the National Renewable 

Energy Laboratory (NREL) entered into a Cooperative Research and Development Agreement (CRADA) 

in December 2008. Under this agreement, a Siemens 2.3 MW turbine was installed on the National Wind 

Technology Center (NWTC) grounds in Golden, Colorado, to support numerous research activities that 

will result in improved understanding of wind turbine rotor-dynamics and provide high quality data that 

will lead to better and more efficient rotor designs. The site is particularly suitable for this study because 

of its extreme wind conditions, with strong shear, high turbulence intensity, severe wind ramps, and gust 

events. The test turbine erected under the CRADA agreement is a Siemens SWT-2.3-101 turbine. It is a 

pitch-regulated variable-speed turbine with a rated power of 2.3 MW and a rotor diameter of 101m. The 

tower height is 80m and the 49m long blades are designed specifically for reducing loads and increasing power 

at moderate wind speeds. The blades are made of fiberglass reinforced epoxy using a Siemens’ proprietary 

manufacturing process. The turbine is designed to run between wind speeds of 4 and 25 m/s, with nominal 

power being reached at 12 to 13 m/s. The rotor speed varies between 6 and 16 rpm. 

A wide range of research studies are being conducted under the CRADA, a good overview of which can 

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be found in reference 1. The focus of the work presented in this paper is to (1) demonstrate the development 

and implementation of data reduction algorithms for the measured pressures, (2) demonstrate an example 

for use of a five hole probe to obtain local inflow velocities and angles, and (3) demonstrate the use of data 

being collected for studying features like rotational augmentation and dynamic stall that are most likely 

to influence the blade performance on inboard sections. The overall objective of these tests is to develop 

a broad database that can be used for validation and improvement of the tools that are used in the blade 

design cycle. 

II. Data Acquisition and Analysis

A. Inflow Measurements 

Most of the aerodynamic data presented in this paper was characterized from a 80m tower approximately 

2.5 rotor diameters (D) upwind of the turbine. This tower is instrumented with one sonic anemometer, 

four levels of cup anemometers and directional vanes, two levels of temperature, and barometric pressure. 

For this project, turbine hub height instrumentation is IEC-class. This includes a class-1 cup at 80m, 

another at 78.5m along with a vane at that level, and a barometric pressure sensor and temperature sensor 

just below that. The data acquisition system runs continuously at 20 Hz. Due to the need to accurately 

characterize the ABL, as well as the inflow across the turbine rotor, a 135m meteorological tower was installed 

approximately 2 rotor diameters away from the turbine in the dominant wind direction and will be used 

for future measurements. The 135m tower consists of six levels of cup anemometers and directional vanes, 

six levels of sonics with 3-axis accelerometers, four levels of temperature, a barometric pressure sensor and 

a precipitation sensor. All of these sensors are placed at different heights between 3 and 134m from the 

ground. 

Additionally, in collaboration with University of Colorado, Boulder, a portable NRG System’s Windcube 

LIDAR has been installed on-site approximately 2.8 rotor diameters upstream of the turbine in the dominant 

wind direction to characterize the inflow. The LIDAR is capable of measuring three velocity components 

(between 0 to 60 m/s) within a 40 to 200m range, with scanning cone angle of 15◦ and 30◦. Though the 

system is capable of producing high temporal resolution data, currently it is being used to acquire averaged 

data (over 10 minutes) at 40, 50, 60, 80, 100, 120, 140, 160, 180, and 200m above the ground. 

B. Pressure Measurements 

To obtain useful surface pressure data, one of the blades is extensively instrumented to provide the pressure 

measurements at nine span-wise locations. Each of the nine span-wise locations has approximately 60 to 

64 pressure taps, distributed based on expected pressure distributions, that provide sufficient resolution to 

develop reliable models and CFD comparisons. Figure 1 depicts the span-wise locations used on the blade 

for pressure measurements and 5-hole probes. The pressure at each of these stations is acquired using a 

combination of ZOC33 and ERAD-4000 modules, from Scanivalve Corporation, at 25 Hz and is transmitted 

wirelessly to a remote computer for storage and post-processing. To avoid the sensor drifts due to variation 

in temperature, the pressure modules are housed in a temperature control unit (TCU) that maintains the 

temperature of the modules at � 25◦C. Additionally, to reduce the maximum rotational acceleration of the 

5-hole probes 

Figure 1. Schematic of the instrumented blade showing the locations of measurements stations and 5-hole 

probes. 

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pressure sensors, all the instruments are mounted inside of 25m blade radius and aligned appropriately as 

suggested by the manufacturer. Furthermore, four 5-hole probes also shown in Figure 1 are installed to 

measure local inflow angles and velocities. These probes extend approximately 0.6m ahead of the blade 

leading edge. The setup for data acquisition is essentially the same as was described for the pressure 

measurements. 

C. Post-Processing: Data Corrections 

Past studies2, 3 have shown that certain physical phenomenon manifest themselves on the actual pressure 

distribution and must be accounted for in the pressure measurements conducted on a turbine. While most 

of these corrections are well understood and have been successfully implemented in the past studies, the 

nature of the measurements being conducted here also requires an additional correction that has not been 

previously used on turbine-scale experiments. The following discussion provides a brief description of the 

corrections that are used in this study. 

1. Pressure correction for the tubing effect 

In the current setup the pressure measurements are conducted remotely. The sensors are not flush with the 

surface but are connected to the surface ports through long pressure tubing of varying geometry. In such 

cases, the tubing affects the pressure measurements in a manner similar to a low pass filter by attenuating 

the amplitude and introducing a phase shift as compared to the original signal.4 The tubing correction is 

used to determine a transfer function that removes this attenuation and phase shift. The mathematical 

development of this correction is quite intensive and can be found in the work by Strike et al.4

Depending on the measurement station, the tubing configuration changes (see Figure 1). The worst case 

scenario (that produces the most amplitude attenuation and phase shift between measured and actual signal) 

is expected at station 9, which uses the longest tubes, in three different diameters. Thus, wind tunnel tests 

for validating the tubing effect correction were conducted by exactly replicating the tubing layout used for 

station 9. The tubing system was installed on a pitching airfoil, which also had a surface mounted transducer 

for validating the reconstructed signal. Figure 2 (a) shows results from one such test in which the airfoil was 

2 2.1 2.2 2.3 2.4 2.5

-1200

-1000

-800

-600

-400

-200

0

Time (secs)

Pressure (Pa)

corrected

reference

sensed

100102

10-4

10-3

10-2

10-1

100

101

102

Frequency (Hz)

Transfer Function (amplitude)

overall

T1

T2

T3

(a) (b) 

Figure 2. (a) Validation measurements for the tubing corrections using a pitch oscillating airfoil at 8 Hz.(b) 

Overall transfer function and its decomposition to show the individual contribution of the tubing used in 

cascaded connection. 

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0 50 100 150 200

−260

−250

−240

−230

−220

−210

−200

−190

−180

−170

data index

pressure (Pa)

before

after

Figure 3. Application of the tubing correction on an arbitrarily selected time series from station 2. 

oscillating at 8 Hz. As can be observed, the reconstructed signal almost exactly follows the reference signal 

(acquired by the surface mounted transducer). For the tubing correction, the transfer function calculation 

is based not only on the geometrical features of the tube, but also on the atmospheric pressure and ambient 

temperature at the time of measurements. The transfer function is then applied to the measured signal. 

The transfer function for station 9 used in this study is shown in Figure 2(b). Additionally, the individual 

contribution of each of the tubing (T1 - 1mm diameter 0.1m long, T2 - 1.6 mm diameter 0.2m long, and T3 

- 3mm diameter 25.8m long) is also shown in this figure. As expected, most of the attenuation is due to the 

longest tubing in this system. To demonstrate that the tubing effect can be significant for smaller lengths of 

tubing also, Figure 3 shows a signal at arbitrary chord wise location on station 2, which uses 1.6mm diameter 

tubing 4.25m long and a 1mm diameter tubing 0.1m long. As the Figure depicts, even though the amplitude 

is not affected much by the smaller tubing, a significant phase shift still exists in the signal. 

2. Hydrostatic correction 

Consider Figure 4 that depicts the installation of differential transducers used for measurements. As the 

blade rotates, the transducer undergoes vertical translation. As a result, the transducer sees a hydrostatic 

pressure variation of approximately 10 Pa/m at the site. Since the transducer measures differential pressure 

differences, one would expect that this hydrostatic variation will not influence the transducer measurements. 

However, the presence of tubing on either side of the transducer introduces phase and amplitude shifts 

in hydrostatic pressure sensed by the transducer, due to same reasons that were discussed for the tubing 

correction. Thus, the sensed signal must be corrected to remove this effect. If, ψ is the azimuthal position 

of the rotor, r is the radial position of the transducer, w.r.t to hub center, and ρ the air density, then the 

hydrostatic correction, ∆PHS , is given by, 

∆PHS = −Aprρgr cos(ψ − φpr) − (−Aref ρgr cos(ψ − φref )). (1) 

Where, φpr and φref represent the phase lag introduced by the tubing, and Apr and Aref represent the 

amplitude changes on the pressure and reference side of the transducer respectively. As expected, in the 

absence of tubing, Apr = Aref and φpr = φref leading to ∆PHS = 0. However, the current measurement 

system uses a static basket with a slow leak. Due to this, for the current analysis, the latter term used to 

model the reference side of transducer is neglected. Apr and Aref were determined using the same theory 

as that used for the tubing correction. Figure 5 shows the effect of the hydrostatic correction on measured 

signal on station 2 (after correcting for tubing effect). The low frequency oscillations seen in the "before" 

signal corresponds to 1-p frequency. As the figure shows, the 1-p frequency content is largely removed by 

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taking into account the hydrostatic variation. The remaining fluctuation is attributed to variations in inflow 

conditions. 

3. Reference pressure correction 

As was mentioned previously, the reference side of the transducers is connected to a static basket located in 

the hub with a small leak. Additionally, the axial pressure variation based on the actuator disc model (see 

Figure 6) also predicts that the basket would not sense the freestream static pressure irrespective of the size 

Pr 

Ref 

r 

θ

r 

Transducer 

Tubing 

Schematic of the transducer used for development of the hydrostatic correction. 

0 50 100 150 200

−260

−250

−240

−230

−220

−210

−200

−190

−180

−170

data index

pressure (Pa)

before

after

Figure 5. Application of hydrostatic correction on an 

arbitrarily selected time series on station 2. The "before" signal is corrected for the tubing effect. 

P∞ P∞

P

+

P

-

Rotor 

plane 

variation from actuator disc 

0 50 100 150 200

−240

−230

−220

−210

−200

−190

−180

−170

−160

−150

data index

pressure (Pa)

before

after

Figure 4. 

Figure 7. Application of reference pressure correction 

to an arbitrarily selected times series on station 2. The 

Figure 6. Axial pressure "before" signal is already corrected for tubing and hytheory. drostatic effects. 

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of the leakage. Thus, the measured values are not actually referenced to the freestream static pressure, P∞, 

but the pressure in static basket, Psb. The measured signal must, therefore, be corrected to account for the 

error, Pref P∞ − Psb. From Bernoulli’s theorem, the freestream total pressure can be written as 

Patm Qmet + P∞ (2) 

Where, Qmet is the dynamic pressure based on freestream velocity V∞ measured at hub height. Furthermore, Bernoulli’s theorem also allows to state that the absolute stagnation pressure (Pstag) as, 

Pstag Patm Qmet + P∞. (3) 

Also, since the measured pressure is w.r.t to the static basket, we have 

Patm Pstag,measured + Psb. (4) 

Where Pstag,measured is the measured stagnation w.r.t. Psb (recall that Pstag is an absolute quantity.) The 

correction can then be determined as, 

∆Pref P∞ − Psb Pstag,measured − Qmet (5) 

The corrected pressure is then computed as Pcorrected Pmeasured − Pref . Experience has shown that 

a minimum of 2 minute data (25 sample/sec*120 sec 3000 samples) is required to obtain a converged 

correction value. As an example Figure 7 shows an arbitrarily corrected sample using the above formulation. 

As the figure shows, the correction acts by offsetting the measured pressure. 

4. Centrifugal Correction 

Much like the hydrostatic correction, another phenomenon affects the pressure measurements as the blade 

rotates. This is due to the centrifugal force that the air trapped in the tubing exerts on the pressure and the 

reference side of the transducer. However, unlike the hydrostatic pressure, the centrifugal pressure acting 

on the transducer diaphragm depends on the speed of the rotor and not its azimuthal position. Figure 8 

depicts the layout that will prove useful in developing the model for the centrifugal correction. In the figure, 

the pressure due to the centrifugal forces is shown at each tubing end. The unmarked side of the reference 

tubing terminates in the hub and does not experience any effect due to centrifugal pressure. These equations 

Ref 

side

Pr 

side

2

2 2 1

2

cent tip

r P V

R

ρ

  =    

Pr 

Ref 

r1 

rsec 

2

2 2 1

2

cent tip

r P V

R

ρ

  =    

r2 

2 1 2 sec

2

cent tip

r P V

R

ρ

  =    

showing centrifugal pressure on 

pressure and reference side of the transducer. 

0 50 100 150 200

−200

−150

−100

−50

0

50

data index

pressure (Pa)

before

after

=

=

= =

=

= =

=

= 

Figure 9. Application of the centrifugal correction to 

an arbitrarily selected signal on station 2. The "before" 

Figure 8. Schematic signal is already corrected for tubing, hydrostatic, and 

reference effects. 

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assume that the air density is not changing with height within the rotor diameter. These pressures can be 

combined to show that the centrifugal pressure correction is only a function of blade rotation speed and the 

location of the station at which the pressure is being measured and is given by, 

∆Pcent

1

2

ρV 2

tip (rsec

R

)2

(6) 

Where, Vtip is the tip speed, R is the rotor radius, and rsec is the radius of the measurement station for 

which the correction is being calculated. To demonstrate the effect of this correction, Figure 9 shows an 

arbitrarily selected signal at station 2 before and after applying the centrifugal correction. As the figure 

shows, centrifugal force is responsible for causing the most offset in the measured signal and, thus, is a 

critical quantity to account for while analyzing the results. 

The pressure measurements from the surface ports are post-processed to remove these effects. However, 

for the pressure measurements from the 5-hole probes, in addition to the above mentioned corrections, a 

data reduction method technique is used to obtain the instantaneous inflow velocity, local inflow angle, and 

the local slip angle. 

D. Inflow angle and velocity: 5-hole probes 

To correctly identify the comparison points for the experimental and computational data, it is important to 

determine the correct inflow angle and velocity at which the data is acquired. The 5-hole probes that are 

mounted on the leading edge of the blade are one of the most direct methods to instantaneously determine 

the inflow angles and velocities the turbine blades are subjected to as they undergo rotation. In principle, 

such direct measurements eliminate the need to account (in post-processing) for turbulence, yaw, and shear 

in the flow. Figure 10 provides an example of the type of data before and after post-processing. The 

acquired pressures P1-6 are shown in the two figures in the first column and the variables obtained after 

post-processing (local inflow and slip angles and local velocity at the probe tip) are shown in the figures in 

the second column. The steps required to obtain the local inflow angles and velocities are standard and well 

understood. However, the measurements from a 5-hole probe on an blade are also susceptible to induced 

0 5000 10000 15000

−0.15

−0.1

−0.05

0

0.05

frames

Pressure [PSI]

Pitot tube @ R =31.5 m Wsp =7.4 m/s

P1

P2

P3

0 5000 10000 15000

−10

0

10

20

30

frames

Angles [deg.]

Local Inflow Angle

Local Slip Angle

0 5000 10000 15000

−0.2

−0.15

−0.1

−0.05

0

0.05

frames

Pressure [PSI]

P4

P5

P6

0

10

20

30

40

50

60

frames

Velocity [m/s]

0 5000 10000 15000

8

9

10

11

12

13

14

Wsp80m

Turbine Speed [rpm]

Vmag

RPM

=

(raw) (processed) 

Figure 10. An example to demonstrate the data acquired and analyzed from 5-hole probes. 

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velocity due to bound circulation. The induced velocity leads to higher measured angles of attack at the 

probe tip and must be accounted for. This so called upwash correction requires extensive use of simulations 

and thus is very dependent on local blade profiles. The upwash correction, though has been implemented 

and tested, is beyond the scope of work presented here and is, thus, not discussed further. 

III. Results and Discussion

The first step after collecting the measurements is to apply the corrections that were discussed in section 

IIC. These corrections remove the "artifact" effects caused by acquiring the data on a rotor in field. Figure 

11 demonstrates an example of the Cp distribution resulting from acquisition, after corrections and also 

compares the results to the computed Cp from CFD. The gaps in the measured data are due to a filtering of 

channels showing bad data resulting from freeze, and plug. These frozen/plugged channels measure pressures 

very close to zero and are easily identified by observing the pressure distribution. In general, the experimental 

and CFD data are in better agreement at the outboard stations than at the inboard stations. This may 

be attributed to the inability of CFD tools to fully capture the complex flow physics associated with 3-D 

separated flows occurring near the root and behind flat back airfoils. It must be noted here that the pressure 

distributions on the inboard sections are more sensitive to the wind speed, and the 5% difference in the 

measured (21 m/s) and simulated wind speed (20 m/s) may have added to the noted disagreement on the 

inboard sections. Nonetheless, this is a perfect example that shows an application that can benefit from the 

data being collected in this campaign. On the outboard stations, CFD does a very good job of predicting the 

pressure distribution. Although not shown here, additional comparisons at other wind velocities also showed 

a similar agreement between post-processed pressure distributions and the predicted pressure distribution 

using CFD. 

0 0.5 1

r = 13.0 m

pressure

0 0.5 1

r = 25.2 m

0 0.5 1

r = 37.5 m

Measured

Corrected

CFD

x/c

Figure 11. Comparison of a sample measurement that has been corrected with the CFD results at Vo = 20m/s 

at three stations. Scale on Y-axis has been removed due to confidentiality. 

The corrected data shown in Figure 11 represent a relatively low angle of attack, steady blade operating state, in which separation and unsteadiness exercised negligible effects. However, turbine testing also 

captured periods wherein separation and dynamic effects were prominent, as in rotational or dynamic stall. 

Occurrences typifying these two phenomena are documented below. 

A. Inflow profile from 5-hole probes 

The derivation of local inflow angles and speed from 5-hole probes was discussed earlier. In addition to these 

parameters, the data acquired from a 5-hole probes can also be used to derive inflow profiles. As the blade 

undergoes a full rotation, the most outboard probe (at r = 43m) provides an envelope that covers most of 

the area swept by the rotor. Thus, the useability of the inflow velocity at the probe tip can be extended by 

creating a inflow profile. For example, Figure 12 shows a inflow profile (averaged over 10-minutes) obtained 

from the 5-hole probe measurements that have been binned based on the blade azimuthal position during 

the downstroke (U1) and upstroke (U2). The figure also shows the mean profile (Umean) from measurements 

and those from a 135m met tower and a Lidar. The calculation of incoming velocity profile is essentially 

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4 6 8 10 12

0

20

40

60

80

100

120

140

Wind speed (m/s)

Height (m)

U1

U2

Umean

Lidar

80-m met mast

135-m Met mast

Figure 12. Estimation of inflow profile using 5-hole probe located at 43m radius. U1 represents the measurement 

during downstroke and U2 represents the measurements during the upstroke. Umean is the mean velocity from 

derived by interpolating the correlation between the wind speed at the probe tip and the freestream velocity. 

was obtained using a Siemens in-house aeroelastic design code. As the figure shows, the 

measurements obtained from the probe provide a very good estimate of shear in the incoming flow. The 

difference in the profiles during the upstroke and downstroke can be attributed to the yaw error. Nonetheless, 

the mean profile shows a good match to the meteorological tower and Lidar data with acceptable accuracy. 

Rotational Augmentation 

To isolate and analyze rotational augmentation, inflow conditions were identified during which wind speed 

and direction varied to a minimal degree. This results in low yaw error turbine operation approximating an 

axisymmetric flow state. Under these conditions, surface pressure distributions consistent with a rotationally 

augmented flow field were observed. Representative suction surface Cp distributions (obtained by normalizing the pressure distribution with maximum pressure assumed to be the stagnation pressure) acquired on 

November 23, 2011, during 2040 2050 hrs (MST), at the 11.3 m radius location are shown in Figure 13, 

and correspond to cycle 50 of the 10 minute data set. 

Cp distributions are shown for rotor azimuths of 0◦, 90◦, 180◦, 270◦, and 360◦. Analysis 

showed that the angle of attack during this time was approximately 24◦. Minimum Cp values were recorded at 

U1 and U2. 

This correlation 

B. 

-

In Figure 13, 

Figure 13. Typical rotational augmentation suction surface Cp histories, at the 11.3m radial station. 

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x/c = 0.094, and ranged from -1.9 to -2.6 for the five rotor azimuths. A predominantly linear Cp distribution 

having a moderate adverse gradient was present over 0.094 � x/c � 0.344, and over 0.344 � x/c � 1.00 was 

supplanted by a similarly pseudo-linear Cp distribution, though of decreased gradient. The strongly linear 

character is reminiscent of the Cp distributions observed by Ronsten,5 but differs from the Cp distributions 

documented for the UAE Phase VI and MEXICO experiments.6

To establish a baseline for the current measurements, which included three-dimensionality and rotation, 

computations were carried out for the r = 11.3m blade section under two-dimensional, non-rotating conditions. The computational results were obtained from steady RANS (Reynolds Averaged Navier-Stokes 

equations) using the SST (Shear Stress Transport) turbulence model. The results are shown in Figure 13, 

for laminar and fully turbulent boundary layers. 

The laminar boundary layer computations deviate substantially from the measured data and apparently 

offer little insight into the rotational augmentation flow physics. However, for most rotor azimuths, the 

turbulent boundary layer predictions generally agree with the measurements over the forward part of the 

chord (x/c < 0.175). In contrast, aft of x/c = 0.175, the computational predictions and the measured data all 

deviate widely. The most notable difference between the two is the absence of pressure recovery aft of x/c = 

0.25 for the computational surface pressures, and the persistence of an appreciable surface pressure gradient 

over the same chord region in the measured data. This distinction credibly confirms the augmenting influence 

of rotational effects on the boundary layer and the resulting effect of delaying or precluding separation. 

C. Dynamic Stall 

Blade suction surface Cp distributions were examined to clarify the physics of the vortex dominated flow 

field via detection and tracking of the dynamic stall vortex. Blade Cp histories are shown in Figure 14, which 

were acquired, on May 5, 2011 during 2030 - 2040 hrs (MST), at the 11.3m rotor radial station. It must 

be noted here that this event was captured while the turbine operated in off-design conditions/parameters 

that were selected specifically to enable capturing of such data. The data shown in Figure 14 correspond 

to cycle 100 of the 10 minute data set, and were typical of dynamic stall episodes observed in the database. 

During cycle 100, average local inflow speed (Uloc) at the 11.3m radius location was 19.0 m/s. Using this 

inflow speed along with local blade chord (c) and rotor rotation rate (Ω) yielded a reduced frequency K = 

0.095 (K = cΩ/2Uloc). 

Figure 14 contains 13 suction surface Cp histories plotted versus rotor azimuth angle (ψ). ψ = 240◦

corresponds to the 12 o’clock position for the pressure tapped blade, and the blade rotates clockwise as 

viewed from upwind. Trace 1 at the bottom of the panel corresponds to the leading edge pressure tap, 

with successively higher traces corresponding to tap locations progressively farther aft on the suction surface 

blade chord. Traces are tagged with blade chord location (x/c) near the right border of the plot. Surface 

pressure minima corresponding to dynamic stall vortex presence7, 8 have been highlighted with a filled circle 

symbol. 

In Figure 14 soon after Ψ = 0◦ , Cp began to decrease at all 13 taps, first at tap locations near the 

leading edge and shortly thereafter at tap locations farther aft. Decreasing surface pressures culminated 

in well defined Cp minima corresponding to dynamic stall vortex presence and marked by filled circles. 

After Cp minima were attained, surface pressures again increased, signaling departure of the dynamic stall 

vortex. These Cp minima occurred at later times for taps farther aft on the blade chord, consistent with a 

dynamic stall vortex that initiated near the leading edge, and then convected aft toward the trailing edge. At 

x/c = 0.34, Cp time variation yielded troughs that were deep and narrow, indicating passage of a condensed 

vortex structure. By the time the dynamic stall vortex reached the trailing edge, deep, narrow Cp troughs 

had been supplanted by broader, shallower Cp depressions, indicating alteration to the vortex structure, and 

passage of a more expansive vortex structure. 

When present, the dynamic stall vortex dominated the flow field over the blade. To better comprehend the 

physics of the vortex and the influences it exercised on the blade aerodynamics, the kinematics of the dynamic 

stall vortex were documented in further detail. Dynamic stall vortex convection history was extracted from 

Figure 14. This showed that the dynamic stall vortex convected from the leading edge vicinity toward the 

trailing edge with only minor deviations from constant speed. A linear least squares fit was applied to the 

vortex convection history to obtain the average chordwise convection speed. Nondimensionalizing average 

vortex convection speed (Vdsv) with respect to the local inflow speed (Uloc) showed that Vdsv was 22 percent 

of Uloc. This value of Vdsv/Uloc was consistent with observations reported for the UAE Phase IV field test9

and for oscillating airfoil experiments in the Ohio State University wind tunnel.10

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Figure 14. Typical dynamic stall suction surface Cp histories, at the 11.3 m radial station. The traces have 

been offset vertically to facilitate viewing and zero references have been omitted. 

IV. Conclusions

Aerodynamic data acquired on a Siemens’ 2.3-101 turbine were analyzed to (1) develop and demonstrate 

post processing method, (2) demonstrate the use of 5-hole probe data for obtaining local inflow velocity, 

angles, and (3) confirm the detection of dynamic stall and rotational augmentation events. The acquired data 

are corrected to remove effects of four phenomena, namely, the filtering effect of the tubing, the hydrostatic 

pressure variation, the reference pressure, and the centrifugal effects. While the first two corrections provide 

the correct amplitude and phase, the latter corrections help by removing the temporal offsets from the signal 

that result from the rotation of the blades. The results indicate that all of these corrections are essential for 

improving the accuracy and quality of data analysis. The 5-hole probes provide instantaneous local inflow 

velocities and angles, which are useful for comparing the actual forces from the blade cross-sections to those 

obtained from CFD. Use of 5-hole probe data for deriving the inflow to rotor has also been explored. 

Rotational augmentation and dynamic stall that result from wind unsteadiness and high angles of attack 

have also been studied. Compared to the 2D CFD results at r = 11.3m, which showed that almost 75% of 

the airfoil stalled at 24◦, the measured pressure did not show any separation. These results indicate that on 

the inboard station covered in this work, separation, and thus, the stall angle is delayed significantly due to 

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rotational augmentation. The vortex convection rate calculated from a dynamic stall event captured during 

high winds was found to agree well with other studies. 

Data and analysis in the current work show that the Siemens SWT-2.3-101 turbine sited at the NWTC 

is capable of furnishing research grade rotor aerodynamics measurements. In the recently initiated phase of 

the project, blade aerodynamic measurements will be complemented with high quality inflow data from a 

135m meteorological tower. In subsequent work, blade aerodynamics and meteorological tower inflow data 

will be correlated to further clarify rotor and inflow fluid dynamic phenomena responsible for the spectrum 

of aerodynamic loads produced by multi-megawatt turbines. Clear understanding and accurate prediction 

of these loads will enable turbines that are more aerodynamically efficient and structurally reliable, which 

will facilitate continued reductions in the cost of wind energy. 

Acknowledgements

The authors would like to acknowledge the contributions of Ewan Machefaux, (graduate student from 

Technical University of Denmark, past intern at Siemens Wind Power) for implementing the programs used 

for post-processing of the data presented in this paper. The authors also gratefully acknowledge the support 

of the Department of Energy Wind and Water Program Office under the contract CRD-08-303. 

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