IEA Wind Annex XX

Final Report

December 2008

HAWT Aerodynamics and Models 

from Wind Tunnel Measurements
Final Report 

IEA Wind Annex XX: 

HAWT Aerodynamics and Models from

Wind Tunnel Measurements 

NREL/TP-500-43508 

December 2008 

Operating Agent Representative: 

S. Schreck 

National Renewable Energy Laboratory 

National Wind Technology Center 

Golden, Colorado 

United States 

Report Contributors: 

C. Masson, École de Technologie Supérieure (ETS), Canada 

J. Johansen, N. N. Sorensen, F. Zahle, C. Bak, and H.A. Madsen, Risoe DTU, Denmark 

E. Politis, Centre for Renewable Energy Sources, Greece 

G. Schepers, K. Lindenburg, H. Snel, Energy Research Centre, The Netherlands 

R.P.J.O.M. van Rooij, E.A. Arens, G.J.W. van Bussel, G.A.M. van Kuik, F. Meng, T. Sant 

Delft University of Technology, The Netherlands 

A. Knauer, G. Moe, Institute for Energy Technology, NTNU, Norway 

X. Munduate, A. González, E. Ferrer, S. Gomez, G. Barakos, National Renewable Energy 

Centre, Spain 

S. Ivanell, Gotland University and the Royal Institute of Technology, Sweden 

S. Schreck, National Renewable Energy Laboratory, U.S.A. 
The IEA Wind agreement, also known as the Implementing Agreement for Co-operation in 

the Research, Development, and Deployment of Wind Energy Systems, functions within a 

framework created by the International Energy Agency (IEA). Views, findings, and 

publications of IEA Wind do not necessarily represent the views or policies of the IEA 

Secretariat or of all its individual member countries.
iii

Table of Contents

Table of Contents...................................................................................................................iii

1.0 Report Introduction.......................................................................................................... 1

1.2 Advantages of HAWT Wind Tunnel Testing ................................................................. 1

1.3 Early UAE Database Findings Leading to Annex XX ................................................... 2

1.4 Research Thrust Areas.................................................................................................... 3

1.4.1 Data Acquisition, Processing, and Quality Assurance .......................................... 3

1.4.2 Data Analysis and Exploitation ............................................................................. 3

1.4.3 Model Subcomponent Construction or Modification............................................ 4

1.4.4 Subcomponent Integration and Model Validation................................................. 4

1.5 Unsteady Aerodynamics Experiment Wind Tunnel Database........................................ 4

1.5.1 Sequences 8 and 9: Downwind Sonics and Downwind Sonics Parked................. 4

1.5.2 Sequences B and D: Downwind Baseline and Downwind High Pitch.................. 5

1.5.3 Sequence F: Downwind High Cone ...................................................................... 5

1.5.4 Sequence G: Upwind Teetered .............................................................................. 5

1.5.5 Sequences H, I, and J: Upwind Baseline, Low Pitch, and High Pitch .................. 5

1.5.6 Sequence M: Transition Fixed............................................................................... 6

1.5.7 Sequences S and U: Upwind, No Probes; Upwind 4° Pitch.................................. 6

1.5.8 Sequence V: Tip Plate ........................................................................................... 6

1.5.9 Sequence W: Extended Blade................................................................................ 7

1.5.10 Sequence X: Elevated RPM ................................................................................ 7

1.5.11 Example of UAE Wind Tunnel Database Website User Session........................ 7

1.6 Administration and Management.................................................................................. 12

1.6.1 Operating Agent Duties....................................................................................... 12

1.6.2 Participating Countries........................................................................................ 12

1.6.3 Funds Received and Expended............................................................................ 12

1.6.4 Intellectual Property ............................................................................................ 13

1.6.5 Research Documentation..................................................................................... 13

1.7 Reference....................................................................................................................... 13

2.0 Research Summary—Canada........................................................................................ 14

2.1 Background and Motivation.......................................................................................... 14

2.2 Data Used and Methods Employed............................................................................... 14

2.3 Research Results........................................................................................................... 15

2.4 Conclusions and Future Work....................................................................................... 15

2.5 Bibliography.................................................................................................................. 15

3.0 Research Summary—Denmark..................................................................................... 16

3.1 Background and Motivation.......................................................................................... 16

3.1.1 CFD Background and Objectives........................................................................ 16

3.1.2 Engineering Models Background and Objectives............................................... 16

3.2 Data Used and Methods Employed............................................................................... 17

3.3 Applied Models and Software....................................................................................... 19

3.4 Research Results........................................................................................................... 20

3.5 Conclusions and Future Work....................................................................................... 20

3.6 Bibliography.................................................................................................................. 20

4.0 Research Summary—Greece ......................................................................................... 24
iv

5.0 Research Summary—Netherlands (ECN).................................................................... 25

5.1 Background and Motivation.......................................................................................... 25

5.2 Data Used...................................................................................................................... 26

5.2.1 Selected Data at Nonyawed Flow........................................................................ 26

5.2.2 Selected Data at Yawed Flow.............................................................................. 28

5.2.3 Selected Dynamic Inflow Measurements............................................................ 29

5.3 Methods Employed ....................................................................................................... 30

5.3.1 PHATAS.............................................................................................................. 30

5.3.2 AWSM................................................................................................................. 32

5.3.3 Comparison between Calculated and Measured Results..................................... 33

5.4 Research Results........................................................................................................... 33

5.4.1 Nonyawed Conditions ......................................................................................... 33

5.4.2 Yawed Conditions ............................................................................................... 35

5.5 Conclusions and Future Work....................................................................................... 40

5.6 Bibliography.................................................................................................................. 42

6.0 Research Summary—The Netherlands, Delft University of Technology .................. 44

6.1 Background and Motivation.......................................................................................... 44

6.2 The UAE Measurements............................................................................................... 45

6.2.1 UAE Research Results......................................................................................... 45

6.2.2 UAE Conclusions and Future Work.................................................................... 47

6.3 The Free-Wake Vortex Model ...................................................................................... 48

6.3.1 Estimating the Angle of Attack from Blade Pressure Measurements on the 

NREL Phase VI Rotor using a Free-Wake Vortex Model ........................................... 48

6.3.2 Free-Wake Vortex Model Research Results ....................................................... 49

6.3.3 Free-Wake Vortex Model Conclusions and Future Work................................... 52

6.4 Engineering versus CFD ............................................................................................... 54

6.4.1 Engineering Models versus CFD Research Results............................................ 55

6.4.2 Engineering versus CFD Conclusions and Future Work..................................... 59

6.5 Conclusions and Future Work in All Three Areas........................................................ 59

6.6 Bibliography.................................................................................................................. 60

7.0 Research Summary—Norway ....................................................................................... 62

7.1 Background and Motivation.......................................................................................... 62

7.2 Data Used and Methods Employed............................................................................... 62

7.3 Research Results........................................................................................................... 63

7.4 Conclusions and Future Work....................................................................................... 63

7.5 Bibliography.................................................................................................................. 63

8.0 Research Summary—Spain ........................................................................................... 65

8.1 Background and Motivation.......................................................................................... 65

8.2 Data Used and Methods Employed............................................................................... 65

8.3 Experimental Analysis.................................................................................................. 66

8.4 CFD Computations ....................................................................................................... 66

8.5 Research Results........................................................................................................... 67

8.6 Conclusions and Future Work....................................................................................... 70

8.7 Bibliography.................................................................................................................. 70

9.0 Research Summary—Sweden........................................................................................ 72

9.1 Background and Motivation.......................................................................................... 72
v

9.2 Data Used and Methods Employed............................................................................... 73

9.3 Research Results........................................................................................................... 73

9.4 Conclusions and Future Work....................................................................................... 74

9.5 Bibliography.................................................................................................................. 74

10.0 Research Summary—United States............................................................................ 75

10.1 Background and Motivation........................................................................................ 75

10.2 Data Used and Methods Employed............................................................................. 76

10.2.1 Rotating Blade Data ......................................................................................... 76

10.2.2 Parked Blade Data............................................................................................ 76

10.3 Research Results......................................................................................................... 77

10.3.1 Flow Field Topology........................................................................................ 77

10.3.2 Unsteady Load Magnitudes.............................................................................. 78

10.3.3 Unsteady Load Severity ................................................................................... 79

10.4 Conclusions and Future Work..................................................................................... 81

10.5 Bibliography................................................................................................................ 82

11.0 Report Conclusion......................................................................................................... 83
1

1.0 Report Introduction

1.1 Uncertainty Limits in HAWT Field-Testing

Experimental uncertainties constrain our ability to understand horizontal axis wind turbine 

(HAWT) aerodynamics, or to rigorously formalize understanding in accurate, reliable 

predictive models. Prior to undertaking Annex XX, turbine aerodynamics measurement 

uncertainties had been driven down substantially over the previous decade. Concurrent 

efforts at Netherlands Energy Research Foundation, Delft University of Technology, National 

Renewable Energy Laboratory, Risoe National Laboratory, and Imperial College significantly 

advanced the state of the art for performing research-grade measurements in the field 

environment. These successes in acquiring highly resolved, quantitative aerodynamic and 

structural measurements were fostered and documented under the auspices of IEA Wind 

Annex XIV and Annex XVIII.

However, improved field measurement systems and enhanced data quality further accentuated

a harsh dilemma that had long confronted turbine aerodynamicists. Full-scale geometries 

could be densely instrumented and subsequently tested, but only in the uncontrollable and 

sparsely characterized inflows of atmospheric winds. Here, overriding uncertainties were 

introduced into the aerodynamics data by inflow fluctuations and anomalies. Alternatively, 

the wind tunnel environment offered controlled and uniform inflows, but at the expense of 

severe dynamic similarity parameter mismatches. In the wind tunnel, aggregate data 

uncertainties were acceptably low, but differences between experiment and field similarity 

parameter levels led to uncertainties regarding the validity of extrapolating otherwise high 

quality data to potentially dissimilar physical regimes. Thus, in years leading up to Annex 

XX, uncontrolled and nonuniform inflows, as well as pronounced similarity parameter level 

disparities had thus been deemed significant impediments to deeper comprehension of turbine 

aerodynamics and to further advances in turbine aerodynamics modeling capability.

1.2 Advantages of HAWT Wind Tunnel Testing

In the United States, this quandary led to plans for testing the NREL Unsteady Aerodynamics 

Experiment (UAE) HAWT in the NASA Ames 80 by 120 ft (24.4 m × 36.6 m) wind tunnel.

This test was designed to produce accurate and reliable experimental measurements with high 

spatial and temporal resolution, for a realistic rotating blade geometry, under closely matched 

conditions of dynamic similarity, and in the presence of strictly controlled inflow conditions. 

To maximize test benefits, NREL convened an international scientific advisory panel 

consisting of wind turbine aerodynamicists and modelers from around the world. This panel 

provided guidance for developing a prioritized test plan to ensure that the resulting body of 

data would be of high quality and pertinent for attacking central issues regarding wind turbine 

aerodynamics and modeling. Because of concerted planning and resolute execution, this wind 

tunnel test was judged a decisive success at its May 2000 completion.

During this same time period, planning was initiated in Europe for a complementary wind 

tunnel wind turbine test designated MEXICO (Model Rotor Experiments In Controlled 

Conditions). The MEXICO project would acquire high quality experimental data by testing a 

well-instrumented model rotor of appropriate dimensions in a high-quality large wind tunnel.
2

MEXICO culminated in December 2006 with testing in the DNW 9.5 m × 9.5 m wind tunnel, 

which produced 100 GB of data high-quality turbine aerodynamics data. These data will be 

used to validate or adapt the aerodynamic portions of design codes based on blade element 

momentum theory. These data also will be used as reference data to validate next generation 

turbine aerodynamic models, including free vortex wake calculations and Navier-Stokes 

models. The NASA Ames experiment and the MEXICO project shared several key high-level 

goals and approaches, yet subtle distinctions enabled each experiment to compensate for 

limitations in the other.

Figure 1-1. NREL UAE in NASA Ames 80 ft × 120 ft wind tunnel

1.3 Early UAE Database Findings Leading to Annex XX

Early analyses of the data acquired during the NREL UAE wind tunnel experiments in the 

NASA Ames 80 ft × 120 ft wind tunnel led to some findings regarding HAWT aerodynamics 

that previously were incompletely characterized or had remained undiscovered. Initial 

analyses of zero yaw aerodynamics data provided unique portrayals of rotational stall delay.
3

At inboard span stations, blade rotation amplified maximum normal forces to nearly three 

times the levels observed under nonrotating conditions. These amplified normal forces were

associated with unconventional blade surface pressure distributions that have been observed 

infrequently in the past. In addition, detailed analyses of aerodynamics data acquired under 

yawed operating conditions furnished some surprising conclusions regarding the structure of 

the vortex generated during blade dynamic stall and responsible for force and moment 

augmentations. Using reconstructions of surface pressure topologies, dynamic stall vortex 

structure and dynamics thus inferred varied significantly along the blade span, being highly 

three-dimensional.

In addition to flow physics characterizations, select data were employed as a reference 

standard in a blind comparison exercise designed to evaluate wind turbine aerodynamics code 

fidelity and robustness. In this exercise, participants were given the UAE geometry and 

structural properties, and then attempted to predict aerodynamic response for a modest 

number of test cases representing diverse aerodynamic regimes. Code comparison 

participants did not have access to the experimental aerodynamics data until well after their 

model predictions were completed and submitted to NREL. Represented in the field of 

models were blade element momentum (BEM) models, prescribed wake models, free wake 

models, and Navier-Stokes codes. Model predictions were requested for axisymmetric 

operation, in which rotational stall delay is strongly manifest, and for yawed operation, 

wherein dynamic stall predominates. In general, results showed unexpectedly large margins of 

disagreement between the predicted and measured data. In addition, no consistent trends were 

apparent regarding the magnitudes or the directions of these deviations. Altogether, these 

early findings resulted in the Annex XX organizational structure documented here.

1.4 Research Thrust Areas

Annex XX encompassed the four research thrust areas summarized in the subsections that 

follow. Participants did not address all four areas nor did they proceed in a strictly sequential 

manner. Rather, because of the magnitude and complexity of the research tasks, participants 

undertook various subsets of the four areas and carried them out in ways best suited to 

individual research objectives.

1.4.1 Data Acquisition, Processing, and Quality Assurance

The raw data acquired during the UAE test in the NASA Ames 80 ft × 120 ft were reduced to 

engineering units data and were readily accessible in that form. In response to studies in 

conjunction with Annex XX, however, reprocessing to alternate formats occasionally was 

deemed advantageous or necessary. In addition, Annex XX research occasionally identified

areas of HAWT aerodynamics that could benefit from supplementary experiments to 

complement the existing NREL UAE wind tunnel database. Regardless of the source of the 

data used in Annex XX, participants carried out uncertainty analyses of the data to establish 

error bounds before proceeding with their actual research.

1.4.2 Data Analysis and Exploitation

Once participants obtained the desired data, converted them to the desired format, and 

established uncertainties, data analysis and exploitation began. To facilitate flow field 
4

comprehension, data were analyzed in a physically pertinent or rationally motivated manner 

that disclosed key flow physics. This analysis task posed a nontrivial challenge because of the 

inherent complexity of HAWT aerodynamics as well as the sheer volume of data in the UAE 

wind tunnel database. Because of these complexities, participants found it beneficial to 

construct software specifically for exploring, displaying, and analyzing database files. This

enabled compiling quantitative and qualitative knowledge concerning turbine flow physics.

1.4.3 Model Subcomponent Construction or Modification

Having knowledge of the HAWT flow field gained through data analysis and exploitation, a 

central thrust of Annex XX was the formalization this knowledge in physics physics-based 

computational model subcomponents. Some participants constructed novel subcomponent 

models based on data analysis and exploitation, while others modified existing subcomponent

models. Flow physics knowledge gained through data analysis and exploitation not only 

enabled model subcomponent construction, but also conferred enhanced robustness on these 

subcomponents. Finally, participants used UAE wind tunnel data, or data derived via analysis 

and exploitation in conjunction with this Annex, to validate new or modified subcomponent 

accuracy and reliability. 

1.4.4 Subcomponent Integration and Model Validation

Once constructed and validated in isolation from other model subcomponents and code 

segments, the new or modified subcomponents were integrated into models. Then, the 

comprehensive software packages were validated, again using the UAE wind tunnel database.

Global validation studies sought to establish bounds on accuracy or reliability or to quantify 

pertinent sensitivities to input quantities or model parameters. Validated codes were used to 

study turbine flow physics or for design and analysis.

1.5 Unsteady Aerodynamics Experiment Wind Tunnel Database

Below is a very abbreviated description of the UAE wind tunnel database. This brief 

description contains short summaries of the data sequences available in the database and an 

illustrated example of website appearance and operation. For complete information, please see

Hand et al. (2001).

1.5.1 Sequences 8 and 9: Downwind Sonics and Downwind Sonics Parked

This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged 

from 5 to 25 m/s. Yaw angles of 0° to 60° were achieved, and the blade tip pitch was 3°. The 

rotor rotated at 72 RPM during Sequence 8, but was parked during Sequence 9. Blade 

pressure measurements were collected. The five-hole probes were removed and the plugs 

were installed. Plastic tape 0.03 mm thick was used to smooth the interface between the plugs 

and the blade. Teeter dampers were replaced with rigid links, and these two channels were 

flagged as not applicable by setting the measured values in the data file to –99999.99 Nm. 

The teeter link load cell was pretensioned to 40,000 N. During postprocessing, the probe 

channels were set to read –99999.99.

Sonic anemometers were mounted on a strut downwind of the turbine. The strut was mounted 

to the T-frame, which was rotated to align the anemometers aft of the 9% and 49% radius 

locations at hub height. Because of this configuration, the tunnel balance data are considered 
5

invalid. Sequence 9 was designed to compare the downwind sonic anemometer readings with 

the upwind sonic anemometers without interference from the turbine. The rotor was parked 

with the instrumented blade at 0° azimuth. All pressure measurements obtained in Sequence 9 

are invalid because sufficient time for temperature stabilization did not occur; thus all 

associated data values were flagged as not applicable by setting the measured values in the 

data file to 0.0000 Pa.

1.5.2 Sequences B and D: Downwind Baseline and Downwind High Pitch

This test sequence used a downwind, teetered turbine with a 3.4°cone angle. The wind speed 

ranged from 5 to 25 m/s. Yaw angles of ±180° were achieved at low wind speeds, and yaw 

angles of –20° to 10° were achieved for high wind speeds. The blade tip pitch was 3°for 

sequence B and 6°for sequence D. These two sequences were interleaved during testing 

because the turbine operator could easily change the pitch angle change. The rotor rotated at 

72 RPM. Blade and probe pressure measurements were collected.

1.5.3 Sequence F: Downwind High Cone

This test sequence used a downwind, rigid turbine with an 18° cone angle. The wind speed 

ranged from 10 m/s to 20 m/s. Excessive inertial loading caused by the high cone angle 

prevented operation at lower wind speeds. Yaw angles of ±20° were achieved, and blade tip 

pitch was 3°. The rotor rotated at 72 RPM. Blade and probe pressure measurements were 

collected. The teeter dampers were replaced with rigid links, and these two channels were 

flagged as not applicable by setting measured values in the data file to –99999.99 Nm. The

teeter link was replaced with a shorter bar so the load cell was not installed during this test. 

The teeter link load cell channel was flagged as not applicable by setting the measured values 

in the data file to –99999.99 N.

1.5.4 Sequence G: Upwind Teetered

Test sequence G used an upwind, teetered turbine with a 0° cone angle. The wind speeds 

ranged from 5 to 25 m/s. Yaw angles of ±180° were achieved at low wind speeds and angles 

of ±10° were achieved at the high wind speeds. The blade tip pitch was 3°. The rotor rotated 

at 72 RPM. Blade and probe pressure measurements were collected.

1.5.5 Sequences H, I, and J: Upwind Baseline, Low Pitch, and High Pitch

This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged 

from 5 to 25 m/s. Yaw angles of –30° to 180° were achieved at low wind speeds, and angles 

of ±10° were achieved for high wind speeds. The blade tip pitch was 3° for sequence H, 0° for 

sequence I, and 6° for sequence J. These three sequences were interleaved during testing 

because the turbine operator could easily change the pitch angle. The rotor rotated at 72 RPM.

Blade and probe pressure measurements were collected. The teeter dampers were replaced 

with rigid links, and these two channels were flagged as not applicable by setting the 

measured values in the data file to –99999.99 Nm. The teeter link load cell was pretensioned 

to 40,000 N. In addition to the standard 30-second campaigns, yaw sweeps were done at 7 and 

10 m/s. These 6-minuteute campaigns were collected while the yaw drive rotated the turbine 

360° at a rate of 1°/s. The file names for these campaigns use the letter designation, followed 

by two digits for wind speed, followed by YS, followed by 000.
6

1.5.6 Sequence M: Transition Fixed

Test sequence M used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged 

from 5 to 15 m/s. Yaw angles ranged from 0° to 90°, and the blade tip pitch was 3°. The rotor 

rotated at 72 RPM. Blade pressure measurements were collected. The five-hole probes were 

removed and the plugs were installed. Plastic tape 0.03 mm thick was used to smooth the 

interface between the plugs and the blade. The teeter dampers were replaced with rigid links, 

and these two channels were flagged as not applicable by setting the measured values in the 

data file to –99999.99 Nm. The teeter link load cell was pretensioned to 40,000 N. During 

post-processing, the probe channels were set to read –99999.99. Zigzag tape was installed 

near the leading edge of the instrumented blade on both the upper and lower surfaces as 

described in Appendix K of Hand, et al. (2001). In addition to the standard 30-second

campaigns, yaw sweeps were done at 7 and 10 m/s. These 90-second campaigns were 

collected while the yaw drive rotated the turbine 90° at a rate of 1°/s. The file names for these 

campaigns use the letter designation, followed by two digits for wind speed, followed by 

YSU, followed by 00.

1.5.7 Sequences S and U: Upwind, No Probes; Upwind 4°Pitch

This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged 

from 5 to 25 m/s. Yaw angles of 0° to 180° were achieved for Sequence S, but the yaw angle 

remained at 0° for Sequence U. The blade tip pitch was 3° for Sequence S and 4° for 

Sequence U. These three sequences were interleaved during testing because the pitch angle 

change was easily made by the turbine operator. The rotor rotated at 72 RPM. Blade pressure 

measurements were collected. The five-hole probes were removed and the plugs were 

installed. Plastic tape 0.03 mm thick was used to smooth the interface between the plugs and 

the blade. The teeter dampers were replaced with rigid links, and these two channels were 

flagged as not applicable by setting the measured values in the data file to –99999.99 Nm.

The teeter link load cell was pretensioned to 40,000 N. During postprocessing, the probe 

channels were set to read –99999.99. In addition to the standard 30-second campaigns, yaw 

sweeps were done at 7 and 10 m/s for the Sequence S configuration. These 6-minute 

campaigns were collected while the yaw drive rotated the turbine 360° at a rate of 1°/s. The 

file names for these campaigns use the letter designation, followed by two digits for wind 

speed, followed by YSU, followed by 00.

1.5.8 Sequence V: Tip Plate

This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged 

from 5 to 25 m/s, and the yaw angle was held at 0°. The blade pitch angle was 3°. The rotor 

rotated at 72 RPM. Blade pressure measurements were collected. The five-hole probes were 

removed and the plugs were installed. Plastic tape 0.03 mm thick was used to smooth the 

interface between the plugs and the blade. The teeter dampers were replaced with rigid links, 

and these two channels were flagged as not applicable by setting the measured values in the 

data file to –99999.99 Nm. The teeter link load cell was pretensioned to 40,000 N. During 

postprocessing, the probe channels were set to read –99999.99. The standard tip blocks were 

replaced with a tip plate to simulate the effect of an undeployed tip-mounted aerodynamic 

brake as shown in Appendix A. of Hand, et al. (2001). Note that the blade radius was not 

changed during postprocessing, so the pressure tap locations are at the same radial location, 
7

but the reference to 30% represents 30% of 5.029 m not 4.943 m. Throughout this report, 

references to the blade span are made for the 5.029-m radius, not the 4.943-m radius.

1.5.9 Sequence W: Extended Blade

This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged 

from 5 to 21 m/s, and the yaw angle was held at 0°. The blade pitch angle was 3°. The rotor 

rotated at 72 RPM. Blade pressure measurements were collected. The five-hole probes were 

removed and the plugs were installed. Plastic tape 0.03 mm thick was used to smooth the 

interface between the plugs and the blade. Rigid links replaced The teeter dampers were 

replaced with rigid links, and these two channels were flagged as not applicable by setting the 

measured values in the data file to –99999.99 Nm. The teeter link load cell was pretensioned 

to 40,000 N. During postprocessing, the probe channels were set to read –99999.99. The 

standard tip blocks were replaced with blade extensions that created a 5.532-m radius rotor as 

shown in Appendix A of Hand, et al. (2001). The extension used the S809 airfoil throughout, 

and the linear taper of the blade continued along the extension. Note that the blade radius was 

not changed during postprocessing, so the pressure tap locations are at the same radial 

location, but the reference to 30% represents 30% of 5.029 m not 5.532 m. Throughout this 

report, references to the blade span are made for the 5.029-m radius, not the 5.532-m radius.

1.5.10 Sequence X: Elevated RPM

This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speed ranged 

from 5 to 12 m/s, and yaw angles of ±30° were obtained. The blade pitch angle was 3°. The 

rotor rotated at 90 RPM. Blade pressure measurements were collected. The five-hole probes 

were removed and the plugs were installed. Plastic tape 0.03 mm thick was used to smooth 

the interface between the plugs and the blade. The teeter dampers were replaced with rigid 

links, and these two channels were flagged as not applicable by setting the measured values in 

the data file to –99999.99 Nm. The teeter link load cell was pretensioned to 40,000 N. During 

postprocessing, the probe channels were set to read –99999.99. The hydrostatic correction 

derived for operation at 72 RPM as described in Appendix F was applied to these data.

1.5.11 Example of UAE Wind Tunnel Database Website User Session

Figure 1-2 shows the web page first seen by a user when entering the NREL Unsteady 

Aerodynamics Experiment database web site. Each of the 15 links shown on this page 

corresponds to an entire data sequence, each of which includes several individual data files.

Clicking on any of the 15 links takes the user directly to the web page for that data sequence.
8

Figure 1-2. Web page first seen when entering the NREL Unsteady Aerodynamics Experiment 

database web site.
9

Figure 1-3 depicts the page that a user would access by clicking on the “(H) Upwind 

Baseline” link on the page in Figure 1-2. This data sequence contains more data files than any 

other sequence, and so presents a good example of the manner and effectiveness of the 

database organization. It is easy for the user to find the conditions they want and to click on 

the corresponding link to access the desired file.

Figure 1-3. Web page accessed by clicking on the “(H) Upwind Baseline” link on the page in 

Figure 1-2
10

Clicking the link corresponding to 10 m/s and 60° yaw would lead to the web page shown in 

Figure 1-4. On this web page, the user can select the channels that they want to download and 

designate these using the check boxes.

Figure 1-4(a). Web page accessed by clicking the link corresponding to 10 m/s and 60° yaw on 

the web page shown in Figure 1-3.
11

After selecting the desired channels using the check boxes, the user then has the option to 

download 1) entire raw channel selection, 2) channel average, 3) cycle average, or 4) azimuth 

average. In addition, the user may choose whether they want the file delivered to them as 

ASCII text or as a ZIP compressed file.

Figure 1-4(b). Continuation of web page begun in Figure 1-4(a).
12

1.6 Administration and Management

The following administrative and managerial provisions were established at annex inception,

and were observed throughout the life of the annex. All provisions were consistent with IEA 

policies and regulations, and were responsive to IEA Wind Executive Committee oversight 

and direction.

1.6.1 Operating Agent Duties

The National Wind Technology Center (NWTC) of NREL was not the Contracting Party, but 

accepted the rights and powers and carried out the obligations and functions of the Operating 

Agent as provided in the IEA Wind Implementing Agreement. Principal Operating Agent 

duties consisted of the following:

• Establishing and managing the NREL UAE wind tunnel experiment database.

• Organizing annual technical meetings in cooperation with the host country 

representative, which were attended by participating researchers.

• Preparing semiannual reports regarding Annex XX scientific progress and 

administrative status, which were submitted to the IEA Wind Executive Committee.

1.6.2 Participating Countries

To be eligible to participate in Annex XX, countries were required to be members of the IEA 

Wind Implementing Agreement. Of those who were members of the agreement and thus 

eligible, the following eight countries elected to participate in Annex XX: Canada, Denmark, 

Greece, the Netherlands, Norway, Spain, Sweden, and the United States.

1.6.3 Funds Received and Expended

Principal costs associated with Annex XX were those for Operating Agent salary, travel, and 

operating expenses. The three year aggregate Operating Agent budget that was approved for 

Annex XX is as follows:

Salaries 3.0 person-months $ 64,500

Travel 3 meetings travel $ 9,000

Expenses publishing, mailing $ 1,500

TOTAL $ 75,000

These Operating Agent costs were paid jointly and in equal shares by the eight countries

participating in Annex XX. During 2004, 2005, and 2006, participating countries paid

$57,543. Combined with the three year NREL Operating Agent cost share of $9,375, this 

brings total receipts for the life of Annex XX to $66,918. Total receipts fell short of $75,000 

because program priorities and funding commitments for some participating countries 

precluded them from contributing as originally projected. By the end of December 2007, the 

Operating Agent had expended the entire amount of $66,918 to defray the costs of annex 

administration.
13

In addition to costs for Operating Agent administration and management, each of the eight 

participating countries paid their own costs for carrying out research under Annex XX, 

including reporting and travel expenses. Each year, the country hosting the annual technical 

progress meeting paid the costs associated with holding the meeting.

1.6.4 Intellectual Property

For purposes of Annex XX, proprietary information consisted of information of a confidential 

nature, such as trade secrets and know-how appropriately identified as such. During the 

course of Annex XX, no proprietary information was produced or maintained by the 

Operating Agent or any of the participants. Similarly, no patentable inventions were made or 

conceived in the course of or under Annex XX.

The NWTC as part of NREL owns and will continue to maintain the UAE wind tunnel 

database, and will continue to distribute data from it to former Annex XX participants as well 

as other researchers who need the data. Data will continue to be available from the UAE wind 

tunnel database website and directly from the Operating Agent Representative, Scott Schreck 

(scott_schreck@nrel.gov). Those who wish to request data should first consult Hand et al. 

(2001), which gives an excellent description of the test and the resulting data.

1.6.5 Research Documentation

Participants constructed or modified model subcomponents and integrated codes during

Annex XX. Although they documented methodologies and results in detail, generally they did 

not exchange actual software.

All studies, assessments, analyses, and evaluations carried out in conjunction with Annex XX 

were formally documented. Each participant prepared at least one report a year during the 

Annex, and these reports were made available to all participants.

Most importantly, annex research was thoroughly and credibly documented in numerous 

journal articles, conference papers, laboratory reports and dissertations, which are listed in 

this report in the summaries provided by each of the participating countries.

In addition to these individual reports, semiannual summaries of Annex activities were

submitted to the IEA Wind Executive Committee by the Operating Agent Representative.

1.7 Reference

Hand, M.; Simms, D.; Fingersh, L.; Jager, D.; Cotrell, J.; Schreck, S.; Larwood, S. (December 

2001). “Unsteady Aerodynamics Experiment Phase VI: Wind Tunnel Test Configurations and 

Available Data Campaigns,” NREL/TP-500-29955. Golden, CO: NREL. Available at 

http://www.nrel.gov/docs/fy02osti/29955.pdf.
14

2.0 Research Summary—Canada

Organization: École de Technologie Supérieure (ETS), Mechanical 

Engineering Department

Researcher: Christian Masson, christian.masson@etsmtl.ca

2.1 Background and Motivation

One weakness of numerical models for analyzing wind turbine aerodynamics lies in their 

limited capacity to take account adequately of the induction of the wind turbine wake on the 

blade aerodynamics. The main objective is to improve the representation of the blade 

aerodynamics in Navier-Stokes/actuator disk approach by extending it to an actuator-surface 

model in which wake induction is naturally taken into account instead of using tip loss factor 

models. In the actuator-surface model, singular surfaces represent the action on the flow of 

the rotor blades. These surfaces are porous, have zero thickness, and carry velocity and 

pressure jumps. The shape and movement of these surfaces depend in fact on the object of 

analysis: a fixed conical disk is used for wind turbine rotor analysis in the framework of 

actuator disk-type analysis, and planar surfaces in rotation are used in the framework of 

actuator-surface analysis. Velocity jumps are prescribed so that circulation of velocity 

respects important physical relations linked to lift force and vorticity conservation. In the 

Navier-Stokes equations, this will be guaranteed by including additional surface and volume 

forces. These features of actuator surfaces will allow accounting for the effects of tip vortices 

while respecting the local physics of the flow. Using a tip loss factor in classical blade 

element momentum (BEM) integral methods has a similar effect. However, it is derived from 

a unique configuration of a turbine (zero yaw, ideal wake) that may not correspond to any 

configuration and loading of a rotor.

2.2 Data Used and Methods Employed

Detailed experimental data gathered when testing the National Renewable Energy Laboratory 

(NREL) Unsteady Aerodynamics Experiment (UAE) in the NASA Ames 80 ×120 ft wind 

tunnel supported developing proposed mathematical models and numerical methods and their 

validations.

The method developed involves a spatial resolution capable of efficiently studying the range 

of scales from the turbine wakes to the atmospheric boundary layer. This approach 

encompasses spatial resolutions between very high resolution computational fluid dynamics 

(CFD) modeling suitable for three-dimensional separated flow in the vicinity of the blade, and 

large-scale modeling typically used to model the atmospheric boundary layer over 

topography. The method combines the Navier-Stokes solver with the actuator-disk approach. 

It has been used in various wind turbine configurations, including light and strong yaw angles, 

dynamic inflow, rapid blade pitch changes, and rotor/tower interactions. In addition, this 

method has been extended to model the turbulent flow field around the turbine using a twoequation closure model to study wind turbine interactions within a wind park in stable, 

neutral, or unstable atmospheric conditions.
15

Adapting the method to modeling actuator surfaces requires prescribing the velocity jumps 

along one wing or blade using the spanwise lift distribution, which is calculated using blade 

element theory: for a blade section, simple analysis of local flow components raise values of 

local wind speed and angle of attack which are then used, based on experimental results—

two-dimensional characteristics of the same airfoil for the simpler estimate—to derive lift 

values. Numerical methods based on control-volume approaches have been developed to 

study 2D or 3D problems. Specific contributions to the numerical methods are specialized 

treatments for the velocities and pressure discontinuities as well as second-order models for 

computing convection fluxes.

2.3 Research Results

The first results obtained have demonstrated and validated satisfactorily the new model 

against elementary problems for which an analytical solution is available: continuous loading 

of vorticity on a segment or on an actuator disk (2D) and finite wings with different shapes 

immersed in uniform inflow. The recent application of the model to a wind turbine using 

rotating surfaces has shown that the new model increases accuracy in calculating the wake 

induction on the blade aerodynamics compared to models based on the actuator disk 

representation.

2.4 Conclusions and Future Work

Future results will be produced for other case studies of wind turbines and comparisons with 

other models of wind turbine aerodynamics will hopefully raise interesting conclusions about 

the new model proposed. This model is expected to be used to study the wind turbine wake 

with more attention, especially regarding the far wake development (one rotor diameter 

downstream and more).

2.5 Bibliography

Leclerc, C.; Masson, C. (2004). “Wind Turbine Performance Predictions using a Differential Actuator 

Disk Modeling.” Prepared for the Special Topic Conference of EWEA: The Science of Making 

Torque from Wind, Delft, Netherlands, April 2004.

Leclerc, C.; Masson, C. (2005). “Wind Turbine Performance Predictions using a Differential Actuator 

Disk Modeling,” Journal of Solar Energy Engineering (127:2); 200–208.

Sibuet-Watters, C.; Masson, C. (2007). “Recent Advances in Modeling of Wind Turbine Wake 

Vortical Structure Using a Differential Actuator Disk Theory.” Prepared for the Science of Making 

Torque from Wind Conference. Journal of Physics: Conference Series 75, doi:10.1088/1742-

6596/75/1/012037.
16

3.0 Research Summary—Denmark

Organization: Risoe DTU Department of Wind Energy

Researchers: Jeppe Johansen, jeppe.johansen@risoe.dk

Niels N. Sørensen, niels.soerensen@risoe.dk

Frederik Zahle, frederik.zahle@risoe.dk

Christian Bak, christian.bak@risoe.dk

Helge Aa. Madsen, helge.aagaard.madsen@risoe.dk

3.1 Background and Motivation

The research has been within two main areas: one on computational fluid dynamics (CFD)

modeling and one on developing and validating the aerodynamic engineering models in the 

aeroelastic codes HAWC and HAWC2 and correcting the airfoil data to be used in these 

codes.

3.1.1 CFD Background and Objectives

Risoe has for the last 15 or more years been developing CFD methods for wind turbine rotor 

aerodynamics, and a critical bottleneck has been the lack of detailed high quality experimental 

data for verification. Thanks to the National Renewable Energy Laboratory/National 

Aeronautics and Space Administration (NREL/NASA) Ames wind tunnel experiment, this 

picture has now changed. Risø participated in the NREL Blind Code Comparison, and the 

CFD method showed some very promising results for the upwind zero yaw computations. 

Since then, a large effort has been dedicated to comparisons to the NREL/NASA Ames data 

released after the blind comparison.

The objectives of participating in IEA Wind Annex XX were twofold: First, we wanted to

improve and validate our Navier-Stokes CFD solver, both the standard Reynolds Averaged 

Navier-Stokes (RANS) approach and the Detached Eddy Simulation (DES) methods. Second, 

the details of the CFD simulations are complementary to the wind tunnel data and would 

provide additional insight that would help interpret the available measurements. In this respect 

the CFD method is one of the only methods used with wind energy that directly produces

pressure data.

3.1.2 Engineering Models Background and Objectives

Aerodynamics and aeroelasticity of horizontal axis wind turbines (HAWT) is a key research 

area in the Department of Wind Energy at Risoe DTU. A combination of theoretical and 

experimental research activities is considered vital to improving the modeling tools. Risoe 

DTU thus conducted a comprehensive field rotor experiment from 1987 to 1993 and sent the 

data to the database established within Annex XIV and Annex XVIII, in which Risoe DTU 

participated. With the field rotor data it was possible to qualify and to some extent quantify 

the importance of three dimensional and unsteady effects on a HAWT rotor in stall. The 

turbulent inflow inherent in these experiments, however, complicated interpreting the data. 

Therefore the new NREL data set acquired in a wind tunnel constitutes an excellent 

completion of the field rotor data for further development and validation of engineering 

aerodynamic submodels.
17

The main objective was to further develop and validate the engineering aerodynamic models 

in the aeroelastic codes HAWC and HAWC2. The following models were considered:

• Model for delayed stall on the inner part of the blade

• Model for unsteady blade section aerodynamics – dynamic stall model

• Tip correction model 

• Model for correction of induction in yawed flow

• Model for tower influence upstream as well as downstream.

3.2 Data Used and Methods Employed

Standstill. The UAE data were used to determine aerodynamics of a blade during standstill in 

comparison with computed results using the EllipSys3D code. Both steady blades (fixed 

angles of attack) and pitching blades (dynamic stall) at low and high angles of attack have 

been analyzed. Turbulence was modeled using both k-ω Shear-Stress Transport (SST) and 

DES models.

Uniform inflow conditions. The UAE data in the uniform upwind configuration have been 

used for validating the EllipSys3D code based on comparisons with surface pressure 

measurements and low-speed-shaft torque. Different turbulence models have been applied,

including and Wilcox’s k-ω model, Menter’s k-ω SST model, and the DES turbulence model. 

3D corrected airfoil data. Fully converged CFD solutions of the flow around the NREL 

Phase VI rotor using EllipSys3D were used to develop a new method for determining axial 

and tangential induction factors. Together with the sectional normal and tangential forces and 

local twist, airfoil characteristics Cl and Cd have been determined and applied in a standard 

blade element momentum method. Excellent agreement was obtained.

Investigators analyzed the pressure distributions from the measurements and compared them 

to measurements from 2D wind tunnel tests. An analytical model describing the difference 

between the pressure distributions from the two kinds of measurements made 3D corrections 

on 2D airfoil data. The model was validated against power and thrust for the NREL Phase VI 

turbine, the Tellus wind turbine at Risø, and an active stall-regulated megawatt wind turbine. 

The model showed good agreement with measurements and good correspondence between 

power and thrust.

Yawed flow conditions. With the increase in computational power, it is today both possible 

and affordable to do yaw computations using CFD. In contrast to the axial flow cases, the 

total rotor must be modeled in yaw simulations, thereby increasing the number of mesh points 

typically by a factor of three. Additionally, the azimuth variation inherent in yaw simulations 

dictates a time accurate simulation, because no steady-state solution can exist, thereby greatly 

increasing the computing time. The yaw data for 10°, 30°, and 60° yaw error at a wind speed 

of 15 m/s were used for CFD validation, with good qualitative agreement; see Figure 3-1.
18

Figure 3-1. Azimuth variation of normal and tangential force coefficient for the NREL Phase VI 

turbine at the r/R=0.4 section during a 60° yaw error at 15 m/s wind speed

Operation with step pitch. In the design of wind turbines and their control algorithms, the 

transient loads, especially those generated by time-varying blade loads, are very important. In 

the blade element momentum method, the most widespread tool in the wind turbine industry, 

the time constants necessary to describe these problems are not an inherent part of the model. 

The present study used two different approaches to determine these time constants, the 

EllipSys3D Navier-Stokes solver and an actuator-disk method. The time constants estimated 

by the actuator disc method is afterward used in a standard BEM method and a BEM method 

coupled with a near wake method. The resulting transients were compared to measured values

for a step pitch change taken from the unique data set of the NREL/NASA Ames experiment, 

with good agreement for the case with low loading. 

Downwind operation. The EllipSys3D code was recently adapted for computation of rotortower interaction on wind turbines. As part of the validation of the code, the downwind data 

from the Phase VI experiment have been used extensively. The numerical results were in very 

good agreement with the experiment, capturing the interaction between the rotor blades and 

the unsteady wake from the tower. 

Figure 3-2 shows the flow development downstream of the turbine and a comparison between 

computations and experimental data of the normal force coefficient at 80% span.
19

 

Figure 3-2. (a) Vorticity magnitude isosurface plot showing rotor tip and root vortices and 

tower wake, (b) normal force coefficient at 80% span showing the effect of the tower wake on 

the rotor load

3.3 Applied Models and Software

CFD. The in-house CFD solver EllipSys3D was used in the Annex XX work. The code was

developed in cooperation with the Department of Mechanical Engineering at DTU and the 

Department of Wind Energy at Risø National Laboratory; see Michelsen (1992, 1994) and 

Sorensen (1995). The EllipSys3D code is a multiblock finite volume discretization of the 

incompressible RANS equations in general curvilinear coordinates, and is second order 

accurate in both time and space. The code is parallelized with MPI for executions on 

distributed memory machines using a nonoverlapping domain decomposition technique. This 

work modeled the turbulence in the boundary layer with the k-ω SST eddy viscosity model

(Menter 1993). A moving mesh formulation in a fixed frame of reference modeled the 

rotation and pitch of the rotor. The moving mesh option has been implemented in the 

EllipSys3D solver in a generalized way, allowing arbitrary deformation of the computational 

mesh, following Demirdzic and Peric (1988). It has been used for doing unsteady simulations 

for several years for both stiff rotors in yaw and fully coupled aeroelastic computations

(Bertagnolio et al. 2002, 2003; Sørensen, Michelsen, and Schreck 2002; Madsen, Sørensen, 

and Schreck 2003).

For the downwind configuration, the EllipSys3D code was altered to take into account the 

relative movement between the rotor and the tower. Using the overset grid method (also 

known as overlapping or chimera grid method) makes it possible to simulate the entire 

turbine, including rotor, tower, nacelle, and ground boundary.

Engineering models. The two aeroelastic codes HAWC and HAWC2 were used and some of 

the engineering submodels within these codes have been validated. In particular, this work 

considered the dynamic induction modeling in HAWC 2 as well as the yaw modeling. 

A near wake model for dynamic induction modeling was also applied on the cases of pitch 

step measurements (Madsen and Rasmussen 2004).
20

3.4 Research Results

Since the release of the Annex XX database, much of the validation work at Risoe-DTU in 

connection with CFD focused on these data, using them to validate the new overset version of 

the EllipSys3D solver. In addition, the NREL data have demonstrated how CFD computations 

can produce results for rotor aerodynamics, for simple axial flow, yawed cases, and for rotors 

performing step pitch motions. A second issue highlighted by studying the NREL data is that 

CFD is a valuable tool to obtain qualitative knowledge about flow physics to interpret 

experimental data.

For engineering submodels, researchers verified the time constants in the dynamic induction 

modeling as well as the correction terms in the modeling of yawed flow.

The aerodynamic characteristics for the airfoil sections used in aeroelastic calculations are 

important to predict correct loads. 3D effects such centrifugal and Coriolis forces, however,

change the characteristics compared to 2D wind tunnel tests. Pressure distributions measured 

in the NREL/NASA Ames test allowed researchers to formulate a new model to correct for 

these 3D effects. This model can be used to correct 2D wind tunnel data before carrying out 

aeroelastic calculations.

3.5 Conclusions and Future Work

The Annex XX database has been valuable for developing and validating several different 

models within CFD and engineering modeling. It is expected that the database will be used in 

for similar purposes in the future.

3.6 Bibliography

Bak, C.; Johansen, J. (2005). “3D Correction of Airfoil Data Based on Pressure.” Prepared 

for the IEA Joint Action Committee on Aerodynamics, Annex XI and XX Aero Experts 

Meeting, Pamplona, Spain, May 25–26.

Bak, C.. (2005). “Metode til 3D korrektion af profildata.” Forskning i Aeroelasticitet – EFP2004, Risø-R-1509(DA), May 2005, pp. 11–25.

Bak, C.; Johansen J.; Andersen, P.B. (2006). “Three-Dimensional Corrections of Airfoil 

Characteristics Based on Pressure Distributions.” Presented at EWEC-2006, Athens, Greece, 

February 27–March 2.

Bertagnolio, F.; Gaunaa, M.; Hansen, M.; Sørensen, N.N.; Rasmussen, F. (2002).

“Computation of Aerodynamic Damping for Wind Turbine Applications.” Prepared for the 

4th GRACM Congress on Computational Mechanics, Patras, Greece, June 27–29.

Bertagnolio, F.; Gaunaa, M.; Sørensen, N.N.; Hansen, M.; Rasmussen, F. (2003).

“Computation of Modal Aerodynamic Damping Using CFD.” Prepared for the 22nd ASME 

Wind Energy Symposium, Reno, Nevada, January 6–9.
21

Demirdzic, I.; Peric, M. (1988). “Space Conservation Law in Finite Volume Calculations of 

Fluid Flow.” Int. J. Numer. Methods Fluids (8); pp. 1037–1050.

Hansen, M.O.L.; Johansen, J. (October/December 2004). ”Tip Studies Using CFD and 

Comparison with Tip Loss Models.” Wind Energy (7:4); pp. 343–356.

Hansen M.O.L. and Johansen J., (2004). “Tip Studies Using CFD and Comparison with Tip 

Loss Models.” Prepared for the Special Topic Conference, The Science of Making Torque 

from Wind, Delft, Netherlands, April 19–21.

Johansen J.; Sørensen, N.N. (2003a). “Method for Extracting Airfoil Data using 3D CFD 

Computations.” Prepared for the IEA Joint Action Committee on Aerodynamics Annex XI 

Aero Experts Meeting, Boulder, Colorado, May 5–6.

Johansen J. and Sørensen N.N. (2003b). “Risø’s CFD contribution to Annex XX.” Prepared 

for the IEA Annex XX, Aerodynamics and Models from Wind Tunnel Measurements 

Meeting, Boulder, Colorado, May 7–8.

Johansen J., Sørensen N.N. (October/December 2004). “Aerofoil Characteristics from 3D 

CFD Rotor Computations.” Wind Energy (7:4); pp. 283–294.

Johansen, J.; Sørensen, N.N. (2004). “Airfoil Characteristics from 3-D CFD Rotor 

Computations.” Pages 9–15 in Proceedings, Special Topic Conference: The Science of 

Making Torque from Wind, Delft (NL), 19-21 April 2004. Delft, Netherlands: Delft University 

of Technology.

J. Johansen and N.N. Sørensen. (2006). “Aerodynamic Investigation of Winglets on Wind 

Turbine Blades Using CFD.” Prepared for the IEA Annex XI/XX Joint meeting, Kiel, 

Germany, April 25–26.

Johansen, J.; Sørensen, N.N.; Michelsen, J.A. (2002). “Navier-Stokes Simulation of Three Tip 

Shapes for Wind Turbine Blades.” Prepared for the 4th GRACM Congress on Computational 

Mechanics, Patras, Greece, June 27–29.

Johansen, J., Sørensen, N.N., Michelsen, J.A., Schreck, S. (2001). “Detached-Eddy 

Simulation of Flow around the S809 Airfoil.” Pages 414–417 in Wind Energy for the New 

Millennium. Proceedings, 2001 European Wind Energy Conference and Exhibition (EWEC 

‘01), Copenhagen (DK), 2–6 July 2001. Helm, P.; Zervos, A., eds. Munich: WIP Renewable 

Energies. 

Johansen, J., Sørensen N.N., Michelsen, J.A. and Schreck, S. (2002). “Detached-Eddy 

Simulation of Flow around the NREL Phase-VI Blade.” Wind Energy (5); 185–197.

Johansen, J.; Sørensen, N.N.; Michelsen, J.A.; Schreck, S. (2003). “Detached-Eddy 

Simulation of Flow around the NREL Phase-VI Rotor.” In Proceedings, European Wind 

Energy Conference and Exhibition 2003 (EWEC 2003), Madrid, June 16–19. Brussels: 

European Wind Energy Association.
22

Madsen, H.A. (2003). “Risoe Activities in IEA Annex XX.” Prepared for the IEA Annex XX, 

Aerodynamics and Models from Wind Tunnel Measurements Meeting, Boulder, Colorado, 

May 7–8.

Madsen, H.A.; Rasmussen, F. (2004). ”A Near Wake Model for Trailing Vorticity Compared 

with the Blade Element Momentum Theory.” Wind Energy (7); pp. 325–341.

Madsen, H.A.; Sørensen, N.N.; Schreck, S. (2003). Yaw Aerodynamics Analyzed with Three 

Codes in Comparison with Experiment. In: AIAA Paper 2003-519. 41. Aerospace sciences 

meeting and exhibit, Reno (US), 6–9 Jan 2003. (American Institute of Aeronautics and 

Astronautics, Inc., Reston, VA, 2003)

Menter, F.R. (1993). “Zonal Two Equation k-ω Turbulence Models for Aerodynamic Flows.” 

AIAA paper 93-2906.

Michelsen, J.A. (1992). “Basis3D: A Platform for Development of Multiblock PDE Solvers.” 

Technical Report AFM 92-05. Lyngby: Technical University of Denmark.

Michelsen J.A. (1994). “Block Structured Multigrid Solution of 2D and 3D Elliptic PDE’s.” 

Technical Report AFM 94-06. Lyngby: Technical University of Denmark.

Schreck, S.J.; Sørensen, N.N.; Robinson, M.C. (2006). “Aerodynamic Structures and 

Processes in Rotationally Augmented Flow Fields.” Wind Energy (10); pp. 159–178.

Schreck S.J.; Sørensen, N.N.; Robinson M.C. (2003). “Aerodynamic Structures and Processes 

in Rotationally Augmented Flow Fields.” In Proceedings, European Wind Energy Conference 

and Exhibition 2003 (EWEC 2003), Madrid, June 16–19. Brussels: European Wind Energy 

Association.

Sørensen, N.N. (1995). ”General Purpose Flow Solver Applied to Flow over Hills.” Risø-R827-(EN). Roskilde, Denmark: Risø National Laboratory.

Sørensen, N.N.; Madsen, H.A. (2006). “Modelling of Transient Wind Turbine Loads during 

Pitch Motion (paper and poster). In Proceedings, 2006 European Wind Energy Conference 

and Exhibition, Athens, Greece, February 27–March 2. Brussels: European Wind Energy 

Association.

Sørensen, N.N.; Michelsen, J.A. (November 2004). “Drag Prediction for Blades at High 

Angle of Attack Using CFD.” Journal of Solar Energy Engineering, v. 126, n. 4, pp. 1011-

1016.

Sørensen, N.N.; Michelsen, J.A.. (2004). “Drag Prediction for Blades at High Angle of Attack 

Using CFD.” 2004 ASME Wind Energy Symposium Technical Papers Presented at the 42nd 

AIAA Aerospace Sciences Meeting and Exhibit, pp. 386–393

Sørensen, N.N.; Johansen, J.; Conway, S. (2004). “CFD Computations of Wind Turbine 

Blade Loads during Standstill Operation KNOW-BLADE.” Task 3.1 report. Risø-R1465(EN). Roskilde, Denmark: Risø National Laboratory.
23

Sørensen, N.N.; Michelsen, J.A.; Schreck, S. (2001). “Detailed Aerodynamic Prediction of 

the NREL/NASA Ames Wind Tunnel Tests using CFD.” In: Wind energy for the new 

millennium. Proceedings. 2001 European wind energy conference and exhibition (EWEC 

‘01), Copenhagen (DK), 2–6 Jul 2001. Helm, P.; Zervos, A. (eds.), (WIP Renewable 

Energies, München, 2001) p. 48–53. 

Sørensen, N.N.; Michelsen, J.A.; Schreck, S.. (2002a). “Navier-Stokes Predictions of the 

NREL Phase VI Rotor in the NASA Ames 80 ft × 120 ft Wind Tunnel.” Wind Energy (5); pp. 

151–169.

Sørensen, N.N.; Michelsen, J.A.; Schreck, S.. (2002b). “Application of CFD to Wind Turbine 

Aerodynamics.” Presented at the 4th GRACM Congress on Computational Mechanics, 

GRACM 2002, Patras, Greece, June 27–29.

Sørensen, N.N.; Johansen, J.; Conway, S.; Voutsinas, S.; Hansen, M.O.L.; Stuermer, A. 

(2005). “KNOW-BLADE Task-3.2 report; Tip Shape Study.” Risø-R-1495(EN). Roskilde, 

Denmark: Risø National Laboratory.

Zahle, F. (2006). Wind Turbine Aerodynamics Using an Incompressible Overset Grid Method.

Ph.D. Thesis. London: Imperial College.

Zahle, F.; Sørensen, N.N. (2007a). “Investigation of the Influence of Wake Resolution on 

Wind Turbine Rotor Flow Simulations.” Presented at the IEA Annex XI/XX Joint Meeting, 

Risø National Laboratory, Roskilde, Denmark, June 14–15.

Zahle, F.; Sørensen, N.N. (2007b). “On the Influence of Far-Wake Resolution on Wind 

Turbine Flow Simulations. International conference: The science of making torque from 

wind, Lyngby (DK), 28-31 Aug 2007. J. Phys.: Conf. Ser. (2007) 75, 9 p.

Zahle, F.; Johansen, J.; Sørensen, N.N. (2007). “Wind Turbine Aerodynamics Using an 

Incompressible Overset Grid Method.” Pages 9–19 in Scientific Proceedings, 2007 European 

Wind Energy Conference and Exhibition, Milan (IT), May 7–10. Brussels: European Wind 

Energy Association.

Zahle, F.; Sørensen, N.N.; Johansen, J.; Graham, J.M.R. (2006). “Rotor Tower Interaction 

Using an Incompressible Overset Grid Method. Presented at the IEA Annex XI/XX Joint 

Meeting, Kiel, Germany, April 25–26.
24

4.0 Research Summary—Greece

Organization: Centre for Renewable Energy Sources (CRES)

Researcher: Evangelos Politis, vpolitis@cres.gr

Budgetary constraints restricted the participation of the Centre for Renewable Energy Sources 

(CRES) in Annex XX to less than originally planned. Nevertheless, CRES co-financed the 

activities of Annex XX and benefited from the knowledge gained through the results 

produced by the other participants. Because the results of the work of the other participants 

were disseminated to CRES, the Centre was able to access the state-of-the-art developments 

in the aerodynamics of wind turbine research for potential use in the years to come. 
25

5.0 Research Summary—Netherlands (ECN)

Organization: Energy Research Centre of the Netherlands (ECN)

Researchers: *

Gerard Schepers, schepers@ecn.nl

Koert Lindenburg, lindenburgl@ecn.nl

Herman Snel, snel@ecn.nl

5.1 Background and Motivation

This report summarizes the most important results of ECN’s contribution to IEA Wind Annex 

XX. Annex XX is an international cooperation between several parties from eight countries, 

coordinated by the U.S. National Renewable Energy Laboratory (NREL). The Dutch 

contribution to Annex XX was sponsored by SenterNOVEM.

The work of this project extended from 2003 to 2007.

The main aim of Annex XX was to analyze the measurements that were taken by NREL on a 

wind turbine placed in the large (24.4 m × 36.6 m) National Aeronautics and Space 

Administration (NASA)-Ames wind tunnel. This wind turbine was two-bladed with a rotor 

diameter of 10 m. One blade was instrumented with pressure taps at five radial positions to 

measure local pressure distribution and resulting sectional aerodynamic loads. Many

measurements at very different conditions were stored in a database, which was made 

accessible to the Annex XX participants. 

The present set of measurements offers a unique base for investigating aerodynamic effects 

and for validating and improving wind turbine design codes. An important advantage is that 

aerodynamic forces are measured at different radial positions, by which local aerodynamic 

effects can be assessed. Usually wind turbine measurements give only integrated blade and 

rotor loads, which hide the details on sectional level. 

Local aerodynamic loads were also measured in Annex XVIII (Schepers et al. 2002), but they

were taken on turbines in the free atmosphere, where the uncertainty resulting from the 

nonstationary, inhomogeneous, and uncontrolled wind conditions formed an important 

problem (as in all field measurements). Hence a very important additional advantage of the 

present measurements lies in the wind tunnel environment, which yields (almost) stationary 

and homogeneous conditions. The huge size of the wind tunnel allows a rotor diameter of 10 

m, with very few blockage effects. Obviously this rotor diameter is still (much) smaller than 

the diameter of today’s commercial wind turbines; nevertheless the blade Reynolds number 

(approximately 1 million) is sufficiently high to make the aerodynamic phenomena at least to 

some extent representative for modern wind turbines.

In the ECN contribution to Annex XX, particular emphasis has been put on the validation of 

an aero-elastic code PHATAS (Lindenburg, 2005) and a free wake lifting line model AWSM

 *

Financial support for this research was given in part by Senter-Novem, Project: IEA Annex XX: Analysis of 

NASA-Ames Wind Tunnel Measurements. Project Reference: 2020-02-11-10-005. ECN project number: 74189. 

The present project could not be performed without the data supplied by NREL, where in particular support from 

Scott Schreck is highly appreciated.
26

(van Garrel, 2003). Special attention was paid to 1) non-yawed conditions, 2) yawed 

conditions and 3) dynamic inflow at fast pitching steps. The background/motivation to select 

data at these particular conditions, can be described as follows.

The comparison at nonyawed flow and at relatively low tunnel speeds offers insight into the 

basic accuracy of design codes at normal conditions. The results at high tunnel speeds offer

useful insights on three-dimensional stall effects. 

The ECN study on yawed conditions offered insight into the load variations, which result 

from two effects:

• The variation in induced velocity caused by the skewed (asymmetric) wake geometry 

• The advancing and retreating blade effect in combination with dynamic stall effects.

Both effects determine the cyclic load fluctuation (which is relevant for fatigue load 

calculations). The first effect also contributes to the yawing stability. The variation of the 

induced velocity obviously mainly plays a role at high axial induction factors (i.e., relatively 

low wind speeds) where the advancing and retreating blade effect is important at high tunnel 

speeds because the high lateral component of the skewed wind speed, which, in combination 

with the high angle of attack, leads to strong dynamic stall effects. 

The results at fast pitching transients offer insight into the dynamic inflow phenomenon. 

Dynamic inflow is characterized by a load overshoot followed by a gradual approach to the 

new equilibrium value. These effects have a large practical importance, not only in view of 

the higher dynamic loads on a turbine but also because of its impact on the aerodynamic 

damping characteristics and in particular in the design of pitch control algorithms; see van 

Engelen and van ‘t Hooft (2004).

Furthermore a model has been developed that covers instationary effects at attached flow. 

These effects are important for the correct prediction of the stability of edgewise vibrations at 

pitching actions. This report gives a summary of only the most important results obtained by 

ECN. More detailed information can be found in ECN task reports; see e.g., Schepers (2007a,

2007b, 2007c).

5.2 Data Used

NREL performed measurements for a wide variety of conditions (tunnel speeds, pitch angles,

and yaw angles) and different configurations (upwind, downwind, teetered, and nonteetered). 

The data are stored in a (password protected) database. As mentioned in Section 5.1, ECN’s 

investigations focused on the aerodynamic effects at nonyawed flow, yawed flow, and fast 

pitching steps.

5.2.1 Selected Data at Nonyawed Flow

As a first step in the project, the measurements at nonyawed conditions from the NREL UAE 

Phase VI H, I, and J sequences. In these sequences the rotor is rigid (i.e., nonteetered) and 

located upwind from the tower. The difference between the sequences lies in the pitch angle: 
27

The pitch angle for the H sequence is nominal, i.e., 3°. The pitch angle for the I sequence is 0°

and the pitch angle for the J sequence is 6°. 

For every sequence, time series have been selected at tunnel speeds that vary between the 

lowest possible speed of 5 m/s and the highest possible speed of 25 m/s, with an interval of 1 

m/s. In all sequences the rotor speed is 72 rpm, which in combination with these tunnel speeds 

leads to a relatively low tip speed ratio. 

In the analysis of the results, it is important to have a global idea on the local aerodynamic 

conditions at the different campaigns. The PHATAS calculation code was thus used to 

calculate the axial induction factor and the angle of attack at three radial positions (30% or 

47% span, 63% span, and 80% span). The results are shown graphically as function of tunnel 

speed and the three pitch angles in Figures 5-1 and 5-2. As expected, the angle of attack 

increases with tunnel speed and decreases with pitch angle and radial position. Because of the 

low tip speed ratio, most angles of attack have already exceeded the two-dimensional (2D)

stalling angle of attack of ~ 10° at a tunnel speed of 10 m/s. Furthermore, substantial 

induction factors are only found at a tunnel speed of 5 m/s and a pitch angle of 0°. At a tunnel 

speed of 10 m/s, the axial induction factors are between 0.1 and 0.15, and at 15 m/s, the 

induction factors are less than 0.1.

Figure 5-1. Angle of attack calculated by PHATAS for different pitch angles, radial positions,

and wind speeds
28

Figure 5-2. Axial induction factors calculated by PHATAS for different pitch angles, radial 

positions, and wind speeds

5.2.2 Selected Data at Yawed Flow

The study on yawed flow took measurements from the H, I, and J sequences; see Section 

5.2.1. Two yaw angles were considered: 10° and 30°. The main emphasis here is on the 

results at 30°. Qualitatively speaking, the results for both yaw angles turned out to be very 

similar (the only difference appears to be in the magnitude of the load variations, which is 

obviously smaller for a yaw angle of 10°). Measurements at very large yaw angles (greater 

than 30°) were taken, but these are not investigated here.

As mentioned in Section 5.1, the aim of ECN’s study on yawed flow was to investigate the 

load variations that result from two effects: the variation in induced velocity caused by the 

skewed (asymmetric) wake geometry, and the advancing and retreating blade effect in 

combination with dynamic stall effects. The first effect plays a role mainly at low tunnel 

speeds (i.e., high axial induction factors), and the second effect is particularly important at 

high tunnel speeds.

The minimum tunnel speed for the yawed campaigns was similar to the one for the nonyawed 

cases (5 m/s), where the maximum tunnel speed was slightly lower (16 or 17 m/s). As 

explained in Section 5.2.1, the low rotational speed of the present configuration makes the tip 

speed ratio relatively low, which in turn leads to a low induction factor and a high angle of 

attack.

With these considerations in mind, measurements have been selected at Vtun = 5, 10, and 15 

m/s for all sequences. The measurements at Vtun = 5 m/s are expected to be useful for the 

investigation of the skewed wake effect on the inflow because the averaged angle of attack is 

below 5° and the axial induction factor is approximately 0.2 for Sequence H and 0.3 for 

Sequence I (see Section 5.2.1). These axial induction factors are sufficiently high to expect at 

least some effect from the variation in induced velocity at yawed conditions, where the low 

angle of attack limits the “disturbance” from dynamic stall effects.

The measurements at higher tunnel speeds are expected to provide information on the 

advancing and retreating blade effect in combination with dynamic stall effects because of the 

high angles of attack and the low induction.
29

The definitions of yaw angle and azimuth angle used in this study differ from the NREL 

definitions. The actual rotor placed in the NASA-Ames tunnel rotated counterclockwise and 

applied the 0 azimuth angle at the 12 o’clock position. The measurement data have been 

transformed to a clockwise rotating turbine with 0° azimuth at the 6 o’clock position and a 

yaw angle with sign opposite to the NREL yaw angle.

5.2.3 Selected Dynamic Inflow Measurements

For dynamic inflow analyses, NREL measured time series at fast stepwise pitch changes, 

similar to the ones measured by the Technical University of Denmark in the European 

Dynamic Inflow projects; see Snel and Schepers (2004).

NREL performed the pitching steps at different tunnel speeds, but dynamic inflow effects 

become more prominent at high induction factors. As explained earlier, a high induction 

factor is associated with a low tunnel speed, and for this reason, the time series at the lowest 

tunnel speed of 5 m/s (with NREL identification Q0500000) was obtained from NREL.

Figure 5-3 shows the measured time series of the pitch angle at his tunnel speed. The 

measurement period is 600 s, and within this period 20 pitch angle steps are performed. The 

pitch angle was repeatedly increased with a fast pitching speed from a pitching angle of 

approximately -5.9° to 10.02°, after which it remains constant for some 15 s. Thereafter it 

decreases again to a pitch angle of -5.9°.

Figure 5-3. Pitch angle variation during dynamic inflow transient 

The axial induction factors (at the instrumented section 30% to 95% span) have been 

calculated with ECN’s aero elastic code PHATAS. The results are shown in Table 5-1 for the 

five instrumented sections.

Table 5-1. Axial induction factor at fast pitching step

Pitch angle(°) a30 a47 a63 a80 a95

-5.9 0.32 0.42 0.58 0.68 1.06

10.02 0.039 0.005 0.008 -0.017 -0.03
30

The rotor is heavily loaded at a pitch angle of –5.9°, but very lightly loaded at a pitch angle of 

10.02°. As such, the pitch angle steps should be considered artificial, but they are suitable for 

validation.

To smooth the load signals, ECN applied an averaging procedure, similar to the one used by 

the Technical University of Denmark in the Dynamic Inflow project (see Snel and Schepers, 

1994). The different pitching steps are synchronized by initializing them to the start of the 

pitching step transient. The synchronized time series are then averaged to filter out as many of 

the fluctuations as possible. Figure 5-4 shows the resulting time series of the pitch angles of 

both blades for the upward and downward step.

Figure 5-4. Pitch angles of both blades, averaged over all transients, for upward and downward 

pitching step

5.3 Methods Employed

ECN’s main activities consisted comparing the NASA-Ames measurements with calculated

results from two of ECN’s codes:

• The aeroelastic wind turbine design code PHATAS. This code has been used to 

calculate all the measurement cases.

• The free wake lifting line code AWSM. This code has been used to calculate the cases 

only at low tunnel speeds because for high tunnel speeds (say tunnel speeds > 10 m/s) 

the angle of attack exceeds the stall angle of attack, where, in principle, AWSM does 

not account for stall effects. Furthermore, the potential advantage of AWSM lies in its 

calculation of induction. As such, comparing AWSM and the measurements makes 

most sense for conditions where the induction is relatively large, i.e., for low tunnel 

speeds; see Figure 5-2.

5.3.1 PHATAS

The calculations described here were performed with PHATAS release "Apr-2005". 

Lindenburg (2005) described this version of the code. The following remarks can be made 

about the PHATAS simulations:

• The input on the NREL wind turbine is based on the description given in Lindenburg 

(2003). Several degrees of freedom are taken into account, e.g., blade flexibility in the 
31

edgewise and flatwise direction, blade torsion, shaft torsion, and dynamics of the 

tower. Furthermore, the asynchronous generator has been modeled.

• The tunnel speed is assumed to be homogeneous and constant in time. Tower shadow 

has been included.

• The pitch angle is assumed to be constant (except for the dynamic inflow transients, 

where the measured time series of the pitch angle is prescribed).

• The calculations were performed over a period of 20 s (except for the dynamic inflow 

transient where the measured time series is simulated).

• The aerodynamic modeling of PHATAS is based on the blade element momentum 

theory. This requires tables of aerodynamic characteristics [i.e., cl(α), cd(α), and 

cm(α)]. The airfoil section along the blade is the NREL S809 profile.

• The 2D, stationary (nonrotating) aerodynamic characteristics of this airfoil were

constructed by combining wind tunnel measurements from the University of Delft 

with wind tunnel data from Ohio State University. The data were extended to deep 

stall according to the method described in chapter 2 of Lindenburg (2003). Figures 5-5 

and 5-6 present the resulting 2D aerodynamic characteristics.

• In subsequent work, much attention will be paid to the modeling of three-dimensional

effects in stall. These effects are accounted for by the model from Snel et al. (1993), 

where a factor fcl is used to correct the available 2D lift coefficients cl,2D. The factor fcl

is the ratio of the actual increase in cl (i.e., cl,3D – cl,2D) and the difference dc_l between 

the nonviscous lift coefficient cl,potential (with “potential” slope dcl/dα =2π α) and the 

2D value of cl,2D. Hence:

cl,3D = cl,2D + fcl (cl,potential – cl,2D); the factor fcl is given by fcl = 3 (c/r)2

.

• Note that the method corrects only the lift coefficients and not the drag and the 

moment coefficients.

• Dynamic stall is modeled with the first order dynamic stall model from Snel (1997). 

Again only the lift is corrected.
32

Figure 5-5. S809 airfoil: Basic 2D aerodynamic coefficients for –180 < α < 180

Figure 5-6. S809 airfoil: Basic 2D aerodynamic coefficients for 0 < α < 45

5.3.2 AWSM

The AWSM code (van Garrel 2003) is based on a nonlinear lifting line vortex wake theory. 

The code is of a free wake character, but for distances greater than four rotor diameters 

downstream of the rotor, the wake is assumed to be “frozen.”

The blades are modeled as lifting lines. This implies that, similar to the PHATAS code, 

aerodynamic profile data should be prescribed as a function of the angle of attack. The same 

airfoil data as used in PHATAS have thus been applied. 3D and dynamic stall corrections are 

not taken into account (although instationary effects that result from the shed vorticity are 

modeled).

In principle, a 3D stall correction could be added to the airfoil characteristics, but AWSM has 

been used to simulate only low tunnel speed cases, for which stall corrections are less 

relevant. 
33

The AWSM calculations were made under the following assumptions:

• The construction is assumed to be rigid and mass-induced loads are neglected.

• The wind speed is constant in time and homogenous; the aerodynamic tower shadow 

effect has been neglected. 

• The rotor speed and pitch angle are constant (except for the dynamic inflow transient 

where the measured pitch angle is prescribed).

• The calculations have been performed for a period of 11 s (except for the dynamic 

inflow transient where the measured time series is simulated).

5.3.3 Comparison between Calculated and Measured Results

The comparisons between calculations and measurement at nonyawed conditions are mostly 

made in terms of mean blade segment loads (normal and tangential forces), flatwise moments 

at the blade root, and the rotorshaft torque; i.e., the values are averaged over the calculation or 

measurement time. Such comparison is based on dimensional quantities that are calculated at 

given external conditions [although the comparison on normal and tangential forces is of cn

(ct) 0.5ρV2

, which differ from the actual forces through the (constant) value of the chord].

Such comparison avoids the uncertainty in the measured angle of attack and dynamic pressure 

experienced when the comparison is of dimensionless aerodynamic characteristics such as 

function of the angle of attack; see, e.g., Schepers et al. (2002).

The comparison at yawed conditions is mostly made in terms of azimuthally binned averaged 

values. The comparison of the dynamic inflow cases is made on basis of time series, where

the averaged measured pitch angle step has been prescribed to PHATAS and or AWSM.

5.4 Research Results

5.4.1 Nonyawed Conditions

The results at nonyawed (stationary) conditions led to the conclusions described in this 

section. Most investigated loads are predicted well with ECN’s aeroelastic code PHATAS as 

long as the tunnel speed is relatively low (e.g., less than 10–12 m/s, which corresponds to an 

angle of attack below the stall angle of attack). The most pronounced exception is the 

overprediction of the normal force at 95% span. Figures 5-7 and 5-8 show the ratio between 

calculated and measured normal forces at 30% and 95% span as a function of tunnel speed for 

the three different pitch angles (a ratio of 1 corresponds to an optimal agreement).
34

Figure 5-7. Nonyawed conditions: Ratio between calculated and measured normal force at 30% 

span for three different pitch angles

Figure 5-8. Nonyawed conditions: Ratio between calculated and measured normal force at 95% 

span for three different pitch angles

At higher tunnel speeds, the PHATAS code underpredicts the normal forces and overpredicts

the tangential forces. The latter observation implies the prediction of too strong a force toward 

the leading edge. These deficiencies are most likely caused by 3D effects in stall. It implies 

that the 3D correction from Snel et al. (1993), as implemented in PHATAS, yields a too low 

increase in lift coefficient, where at the same time the drag should be increased. The 

Annexlyse project (Schepers 2004), which analyzed field measurements on different turbines,

made similar observations. The empirical corrections derived from those measurements, 

however, lead for the present configuration to an “overcorrected” lift increase and drag 

increase.

The overpredicted tangential force is reflected by an overprediction of the rotor shaft torque at 

the high tunnel speeds. The underpredicted normal force is reflected by an underpredicted

flatwise moment;
35

The calculations from the free wake lifting line code AWSM (which are performed only for 

low tunnel speeds) show good agreement with the measured loads. Even at the 95% span the 

agreement is reasonable and better than the agreement from the corresponding PHATAS 

calculation. Figure 5-9 shows the measured normal force for a tunnel speed of 5 m/s (θ = 3°) 

as function of radial position compared to the normal forces predicted by PHATAS and 

AWSM.

Figure 5-9. Comparison of measured normal force and normal forces calculated by AWSM and 

PHATAS

5.4.2 Yawed Conditions

At yawed flow, the investigation put particular emphasis on the azimuthal variation of the 

normal forces by (1) the advancing and retreating blade effect and (2) the variation in induced 

velocity from the skewed wake geometry, where the second effect is expected to be dominant 

at a low tunnel speed of 5 m/s and the first effect is expected to be dominant at higher tunnel 

speeds.

Figure 5-10 shows the measured normal force variation at the five instrumented sections, for 

the case with the highest induction (θ = 0°) at a tunnel speed of 5 m/s. At this wind speed, the 

variation of the normal forces is clearly influenced by the skewed wake effect, which leads to 

a strong radial dependency on the azimuthal variation of the normal forces. At the outboard 

part of the blade, the normal force varies more or less sinusoidally such that the maximum 

force occurs at the upwind part of the rotor plane (which is between an azimuth angle of 0°

and 180°). Such load variation leads to a stabilizing yawing moment. The variation is in line 

with the outcome of a conventional wake model for calculating induced velocities, which 

assumes that these velocities are induced by tip vortices only; see Schepers and Snel (1995).
36

Figure 5-10. Azimuthally binned averaged normal force at Vtun = 5 m/s (θ = 0°) and a yaw angle = 

30°: Measured data at five radial positions

At the inboard sections of the blade, however, the maximum value of the normal force shifts

toward the downwind side of the rotor plane. This is consistent with previous wind tunnel 

measurements of the inflow velocity, which showed the inflow velocity at the inboard station 

to reach a maximum at the downwind side of the rotor plane; see Schepers (1999). The shift 

can be explained by the velocities induced by the root vortex, where conventional yaw models 

consider only the tip vorticity. The resulting load variation leads to a destabilizing yawing 

moment.

The AWSM code predicts the above-mentioned radial dependency in azimuthal variation of 

the normal force very well. Figures 5-11 and 5-12 illustrate this, showing the normal forces at 

47% and 95% span. The agreement between the AWSM results and the measurements is 

excellent in the shape of the normal force distribution (the shape determines the yawing 

moment and the offset in level at 95% span is consistent with the overpredicted normal force 

as discussed in Section 5.4.1). The agreement from the PHATAS code is slightly poorer but 

opposite to common (engineering) yaw models; it predicts a destabilizing yawing moment 

near the inboard part of the blade in agreement with the measured results.
37

Figure 5-11. Azimuthally binned averaged normal force at 47% span at Vtun = 5 m/s (θ = 0°) and 

yaw angle = 30°: Measured result compared with calculated results

Figure 5-12. Azimuthally binned averaged normal force at 95% span at Vtun = 5 m/s (θ = 0°) and 

yaw angle = 30°: Measured result compared with calculated results

To quantitatively assess the prediction of the skewed wake effect, researchers compared the 

calculated and measured “sectional yawing moments” were compared. The n(φr) curves are 

thus transformed into their contribution to the yawing moment: Myaw = –n1(φr,1)sin(φr,1) –

n3(φr,3)sin(φr,3). In this equation, index 1 denotes blade 1 and index 3 denotes blade 3. The 

blade numbering here is kept similar to the NREL blade numbering, in which blade 3 is the 

instrumented blade. Hence φr,1 = φr,3 + 180. Obviously the n1(φr,1) curve has not been 

measured, but it is assumed to be similar to the measured n3(φr,3). The equation indicates 

whether or not the normal force distribution yields a stabilizing yawing moment contribution:

a negative value indicates a stabilizing yawing moment and a positive value indicates a 

destabilizing yawing moment. Table 5-2 lists the (rotor averaged) yawing moment 

contributions (for the scenario with θ = 0°). The results are consistent with the qualitative 

observations:
38

• The measured yawing moment contribution stabilizes at the outboard sections (63% to 

95% span), and is destabilizing at the inboard sections. PHATAS and AWSM also 

predict this.

• The AWSM calculated yawing moment and the measured values have good 

agreement. The AWSM calculated yawing moment is generally closer to the measured 

values than the PHATAS calculated yawing moment.

In addition, the induced velocities—as calculated by the PHATAS and AWSM codes—

generally show a good mutual agreement, even though the underlying models have very 

different bases.

The calculated results at yawed flow and higher tunnel speeds show much poorer agreement 

with the measurements (Figure 5-13). Differences occur in amplitude and phase of the normal 

force variation. The deviations seem to increase with angle of attack. The most likely cause 

for these differences is dynamic stall effects, which are known to be a source for deviations 

between calculations and measurements. Using a dynamic stall model leads to only a minor 

improvement in results. As such, a more detailed analysis of dynamic stall effects should be 

done, using measurements recently taken in the MEXICO project. These measurements were 

taken on a model rotor with a diameter of 4.5 m in the German Dutch Wind Tunnel (DNW). 

Among others, pressure measurements at a very high sampling rate (5 kHz effectively) have 

been taken together with measurements of the flow field around the blade.

Table 5-2. Contribution to sectional yawing moment, measured and calculated by AWSM and PHATAS 

at Vtun= 5 m/s, yaw angle = 30°, and pitch angle = 0°

dMyaw,30 dMyaw,47 dMyaw,63 dMyaw,80 dMyaw,95

Measurements 6.9 6.3 -6.6 -24.0 -39.8

AWSM 3.9 4.7 -4.9 -27.9 -41.0

PHATAS 8.0 15.0 5.7 -34.1 -102.0
39

Figure 5-13. Vtun = 15 m/s, θ = 0°, and yaw angle = 30°: Normal force at 0.30R as function of

azimuth angle: Calculated by PHATAS with and without dynamic stall

The study on dynamic inflow considered fast pitching steps from –5.9° to 10° (i.e., an upward 

pitching step) and vice versa. At a tunnel speed of 5 m/s, strong dynamic inflow effects 

appear in the normal forces at all radial positions. Figure 5-14 shows a clear overshoot 

followed by a gradual approach of the new equilibrium values. As explained in Snel and 

Schepers (1994), such behavior is typical for dynamic inflow. 

Figure 5-14. Measured normal force at five radial positions for the upward pitching step

The upward pitching step is much more suitable for studying dynamic inflow effects than the 

downward pitching step. For the downward pitching step, the angle of attack exceeds the stall 

angle of attack. The analysis of dynamic inflow effects is then complicated by the 

uncertainties that result from the stall effects.

The agreement between the measured results and the calculated results from the newly 

developed free wake lifting line model AWSM (for the upward pitching step) was excellent in 

terms of equilibrium values, overshoots, and time constant. Figure 5-15 compares measured 

normal force during upward pitching step and normal force calculated by AWSM for the 47% 

and the 80% span.
40

Figure 5-15. Comparison between AWSM calculated and measured dynamic inflow transient

Because dynamic inflow effects were measured at different radial positions, the present 

measurements allowed, for the first time ever, an assessment of the radial dependency on the 

dynamic inflow effect. Table 5-3 shows the time constants as derived from the measured 

transients in normal force, the AWSM calculated transients, and the engineering model 

implemented in PHATAS. The time constant in the measured and AWSM results hardly 

reduces toward the tip. This is opposite from the expectations from previous projects on 

dynamic inflow (Snel and Schepers, 2004) and from the results from the engineering model. 

A similar study on the recent MEXICO measurements should investigate the radial 

dependency of the time constant for that configuration. Thereafter, if necessary, the time 

constants in the engineering models on dynamic inflow need to be adjusted.

Table 5-3. Time constant for different radial positions at dynamic inflow transient

Radial Position fmeas [s] fAWSM [s] fengineering model [s]

30% 0.94 1.12 0.93

47% 0.83 1.04 0.83

63% 0.76 1.04 0.68

80% 0.73 1.00 0.44

95% 0.77 1.03 0.14

5.5 Conclusions and Future Work

NREL’s measurements in the NASA-Ames tunnel presented a unique opportunity to 

investigate different aerodynamic aspects and to validate wind turbine design codes. Local 

aerodynamic quantities (pressure distributions, normal forces, and tangential forces) were

obtained at different radial positions under controlled and stationary conditions. The present 

analysis examined nonyawed conditions, yawed conditions, and dynamic inflow effects at fast 
41

pitching steps. The measurements at these conditions were compared with the wind turbine 

design code PHATAS and the free wake lifting line code ASWM. To recap the results,

• At nonyawed conditions, the agreement between PHATAS calculated results and 

measurements is generally good, as long as the tunnel speed is low (< 10 m/s),

although some differences appeared at the outer part of the blade (95% span). 

Furthermore, differences appeared at high angles of attack (high tunnel speeds). 

Although the 3D model on the lift coefficient, as implemented in PHATAS, does 

improve the agreement with the measurements, this correction needs to be 

“amplified.” Furthermore, a correction on the drag is needed.

• At yawed conditions (and low tunnel speeds), a clear influence from the skewed wake 

on the induced velocity appears, which in turn affects the normal forces variation with 

azimuth angle.

• At yawed conditions and low tunnel speeds (i.e., relatively high induction factors),

some discrepancies appear between the PHATAS results and the measurements, but 

opposite to common engineering models, PHATAS does predict a destabilizing 

yawing moment contribution at the inner part of the blade in agreement with the 

measured results. 

• At yawed conditions and high tunnel speeds, the agreement between the PHATAS 

results and the measurements is poor. Using the dynamic stall model as implemented 

in PHATAS does not improve the results. 

• At fast pitching steps, clear dynamic inflow effects were observed as an overshoot in 

the measured normal force, followed by a gradual approach of the new equilibrium 

value.

• The results from the AWSM code are very similar to the results of the PHATAS code,

and therefore the agreement between AWSM results and the measurements can 

generally be considered good as well. Where the PHATAS code performs more poorly 

and where the induction is significant (e.g., at the outer part of the blade, at yawed 

conditions or at dynamic inflow), however, the agreement from the AWSM code is 

much better and sometimes even excellent. 

The present study considered data only at the default configuration with a rotor radius of 5 m 

and a rotational speed of 72 rpm. Some measurements, however, were also taken on an 

extended blade (radius 5.5 m) and at a higher rotational speed (90 rpm). These measurements 

are still unexplored. The measurements at an extended blade can help derive better tip 

correction models, and the measurements at a higher rotational speed may provide additional 

information on phenomena that are related to high induction factors. Related work should also 

use the recently available MEXICO measurements. These measurements are taken on a 4.5-m 

diameter rotor in the DNW wind tunnel (Snel et al. 2007). Absolute pressure measurements 

around the blade at 5 kHz were taken along with PIV (particle image velocimetry)

measurements on the flow field. These measurements will provide additional insights on, e.g.,

dynamic inflow, dynamic stall, yawed conditions, and tip effects. 
42

5.6 Bibliography

Lindenburg, C. (2003). Investigation into Rotor Blade Aerodynamics: Analysis of the 

Stationary Measurements of the UEA Phase IV Rotor in the NASA-Ames Wind Tunnel. ECNC-03-025. Petten: ECN.

Schepers, J.G. (1999). “An Engineering Model for Yawed Conditions Developed on the Basis 

of Wind Tunnel Measurements.” Presented at the ASME Wind Energy Symposium, Reno, 

Nevada, American Institute for Aeronautics and Astronautics, January.

Schepers, J.G. (2007a). IEA Annex XX: Comparison between Calculations and Measurements 

on a Wind Turbine in the NASA-Ames Wind Tunnel. ECN-E-07-066. Petten: ECN.

Schepers, J.G. (2007b). IEA Annex XX: Comparison between Calculations and Measurement 

on a Wind Turbine in Yaw in the NASA-Ames Wind Tunnel. ECN-E-07-072. Petten: ECN.

Schepers, J.G. (2007c). IEA Annex XX: Dynamic Inflow Effects at Fast Pitching Steps on a 

Wind Turbine Placed in the NASA-Ames Wind Tunnel. ECN-E-07-085. Petten: ECN.

Schepers, J.G.; Snel, H. (1995). JOULE2: Dynamic Inflow: Yawed Conditions and Partial 

Span Pitch. ECN-C-95-056. Petten: ECN.

Schepers, J.G. et al. (1997). Final Report of IEA Annex XIV Field Rotor Aerodynamics. ECNC-97-027. Petten: ECN. Available at http://www.ecn.nl/wind/other/IEA/index.en.html.

Schepers, J.G. et al. (2002). Final Report of IEA Annex XVIII Enhanced Field Rotor 

Aerodynamics Database. ECN-C-02-016. Petten: ECN. Available at 

http://www.ecn.nl/wind/other/IEA/index.en.html.

Schepers, J.G.; Snel H.; van Garrel A. (2005). “Dynamic Inflow Effects in NASA-Ames 

Measurements; Radial Dependency and Comparison with Free Wake Lifting Line Model 

AWSM.” Presented at IEA Annex 11/20 meeting, Pamplona, Spain, May.

Schepers, J.G.; Feigl, L.; Van Rooij, R.; Bruining A. (November 2004). “Analysis of Detailed 

Aerodynamic Field Measurements using Results from an Aeroelastic Code.” Journal of Wind 

Energy (7:4); pp. 357–371.

Snel, H. (1997). “Heuristic Modelling of Dynamic Stall Characteristics.” Pages 429–433 in 

Proceedings of the EWEC 1997 Conference, Dublin, October.

Snel, H.; Schepers, J.G. (December 1994). JOULE1: Joint Investigation of Dynamic Inflow 

Effects and Implementation of an Engineering Method. ECN-C-94-107. Petten: ECN.

Snel, H.; Schepers, J.G.; Montgomerie, B. (2007). “The MEXICO Project (Model 

Experiments in Controlled Conditions): The Database and First Results of Data Processing 

and Interpretation.” In Proceedings of EAWE conference on The Science of Making Torque 

from the Wind. Available at http://www.iop.org/EJ/toc/1742-6596/75/1.
43

Snel, H.; Houwink, R.; Bosschers, J. (May 1993). Sectional Prediction of Lift Coefficients on 

Rotating Wind Turbine Blades in Stall. ECN-C-93-052. Petten: ECN.

van Engelen, T.G.; van der Hooft, E.L. (November 2004). “Dynamic Inflow Compensation 

for Pitch Controlled Wind Turbines.” In Proceedings EWEC conference, London, 22–25 

November 2004 (on CD-ROM).
44

6.0 Research Summary—The Netherlands, Delft University of 

Technology

Organization: Delft University of Technology

Researchers: R.P.J.O.M. van Rooij, R.P.J.O.M.vanRooij@tudelft.nl

E.A. Arens

G.J.W. van Bussel, G.J.W.vanBussel@tudelft.nl

G.A.M. van Kuik, G.A.M.vanKuik@tudelft.nl

F. Meng, F.Meng@tudelft.nl

T. Sant, tonio.sant@um.edu.mt

6.1 Background and Motivation

Motivation concerning the National Aeronautics and Space Administration (NASA) Ames

database and this International Energy Agency (IEA)Annex XX is twofold: first, to research 

the flow around the blade and its corresponding loads and second, to validate numerical 

approximations of blade and rotor loads. 

The first approach starts with carefully unraveling the characteristics to understand the flow 

behavior. Then determining a proper inflow angle plays a key role. A simple adjustment of 

the measured flow probe angle is proposed, but also a more comprehensive method was

derived using a free vortex wake model. The method could be of great value in particular for

stalled and yawed conditions. The newly obtained blade characteristics are compared with 

other model measurements like field experiments (part of IEA Wind Annexes XIV and 

XVIII) but also Sweden’s FOI wind tunnel experiments (in the CARDC (China

Aerodynamics Research and Development Center)) and the recent measurements from the 

MEXICO (Model Rotor Experiments In Controlled Conditions)partners (EU project) in the 

DNW [Au: please define], Netherlands. Such gives insight in aeroelastic behavior and shows 

dependencies in the rotor configuration (rpm and pitch angle), blade geometry (chord and 

twist distribution), and airfoil characteristics. Delft University of Technology investigated

rotating and nonrotating configurations.

Most studies, however, focus on validating and developing numerical tools. The blade

element momentum (BEM) methods are still the most common methods used to predict the 

aerodynamics loads of wind turbine blades. These codes rely on proper input characteristics. 

The database helps to understand and approximate rotational augmentation and in verifying

the constants in “classical” stall models. Delft University also investigated validating the 

results from more sophisticated aerodynamic methods such as full Navier-Stokes codes with 

proper physical description .

The contributions of Delft University of Technology are described briefly and three areas are

highlighted:

1. Investigating the Unsteady Aerodynamics Experiment (UAE) measurements

(abbreviated hereafter as “UAE”)
45

2. Estimating Angle of Attack from Blade Pressure Measurements on the NREL Phase 

VI Rotor using a Free-Wake Vortex Model (abbreviated as “Free-Wake Vortex 

Model”)

3. Engineering Models versus CFD Methods with respect to augmented lift caused by 

blade rotation (abbreviated as “Engineering versus CFD”)

All research under the Annex XX has been published in conference proceedings, expert 

meeting, and journals, and listed in the bibliography of this section.

6.2 The UAE Measurements

This study investigated the rotating experimental data from the UAE experiment designated 

as B, H, and S series. The analyses examined the steady-state conditions and a selection was 

made from these data containing for Yaw = 00 and U = all wind speeds with special emphasis 

on 5, 6, 8, 10, 12, 13, 14, 15, and 20 m/s.

The study of the parked conditions (not described here) was based on the nonrotating data. 

These campaign data were more detailed than those available on the website and sent by the 

Annex XX Operating Agent Representative S. Schreck. The study was extended with the 

nonrotating configuration of the FOI measurements carried out on a STORK WP5 blade in 

the LT1 low speed wind tunnel (in Stockholm, Sweden).

6.2.1 UAE Research Results

The research on the rotating measurements focused on the variation in flow behavior and 

quality of the dataset. This was combined with finding the most representative average 

steady-state data. The effects of the test set-up were one of the first issues to be investigated. 

To quantify these effects, the rigid blade conditions (H sequence) at a relatively low wind 

tunnel speed were chosen because the corresponding attached flows respond promptly to 

small variations in tunnel speed, revolution speed, and pitch changes. This procedure,

however, does not give any information on the accuracy of the measurement system itself. 

Researchers investigated all segment sections, and the 30% and 80% span locations represent 

the overall findings quite well. Within one measurement campaign the rotor speed was 

(almost) constant at 72 rpm and the effect of tunnel speed variations were relatively small. 

The fluctuations in pitch angle were limited to approximately ±0.4°, representing a variation 

of about ±0.04 in Cn in an attached flow.

With the installed pressure system, researchers collected information about the local flow 

angles and pressure distributions. From this the stagnation pressure, normal and tangential 

force coefficients could be derived, giving information on variation in flow behavior. 

Figure 6-1 shows the variation in Cn with tunnel speed, and the error bars represent the 

minimum and maximum values of the binned averages. Variation in Cn increases with tunnel 

speed, and beyond a velocity of 12 m/s the error bars become quite considerable. This is in 

particular true at the inboard segment where the flow is dominated by large turbulent 

separated areas. This is consistent with the pressure distributions, which indicate leading edge 

separation at the inboard segment starting at 12 m/s tunnel speed. Also the accompanying 

large LFA (local inflow angle) values indicate that large inflow angles can be expected.
46 

Figure 6-1. Variation of Cn and local flow angle (LFA) 

The variation in normal force coefficient can largely be attributed to the set-up, and Figure 6-

2 shows a representation along the azimuth angle. An inquiry with an FFT procedure show 

the dominant 1P (1.2 Hz) in the power spectra and corresponds with the blade passing 

frequency of the instrumented blade. 

Figure 6-2. Behavior of local flow angle and Cn with azimuth angle (a period) and as power 

spectra for inboard span position 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

5 10 15 20

Average tunnel speed (m/s)

Cn

30% section

80% section

0

10

20

30

40

50

5 10 15 20

Average tunnel speed (m/s)

LFA (deg.)

30% section

80% section
47 

A 2P peak is also very clear at the inboard segment, and this represents the blade passing 

frequency of the rotor, which has two blades. This suggests that the instrumented blade is 

affected by the noninstrumented blade passing the tower. Structural quantities of the rotor or 

the control system, which could not fully switched off, could have provoked this. The 

influences of the tower are smaller, less pronounced, and toward the outboard span locations. 

Higher local velocities and a smaller shadow area probably cause this behavior. At tunnel 

speeds beyond 12 m/s, the variations in Cn and LFA are more random and no clear resonance 

frequency in the power spectra could be observed. This indicates that flow disturbances 

caused by flow separation are dominating the flow. 

The preceding analysis clearly shows that the set-up affects the acquired data and that 

representative characteristics for the steady-state nonyawed conditions are not easy to 

distinguish. Arranging the measurements along the azimuth angle show some alarming 

behavior, in particular for LFA between 270° and 360° azimuth angle at the inboard segment 

(Figure 6-3). This is surprising because only the area coinciding with the tower was expected 

to be disturbed, which is more or less true at the outboard span position. Comparable behavior 

was detected for the other segments as well and was consistent for all tunnel speeds. It seems 

that steady behavior of the local flow angle and normal force coefficient, the most important 

parameters of the characteristics, is achieved in the first quadrant of the rotor plane only. 

Figure 6-3. The behavior of the local flow angle and normal force coefficient averaged per 

azimuth angle for the inboard and outboard position 

6.2.2 UAE Conclusions and Future Work 

Analysis of the UAE data series in the configuration with rigid blade, no cone angle, no yaw, 

and upwind rotor on the attached blade flow revealed the following: 
48

• The 1P and 2P blade passing frequencies can be observed in almost all aerodynamic 

parameters for the four investigated span locations and is caused by the downwind 

tower. The variations are most pronounced at the inboard segment.

• The first quadrant of the rotor plane showed the most steady average aerodynamic 

behavior and should be used by preference to distinguish the characteristics at the span 

locations along the blade.

Applying the recommendations still left some doubts concerning the inflow angle at the 47% 

and 63% span locations. Further investigations of the normal force coefficient in the 

nonrotating, parked configuration showed similar deviations at those locations, and so an 

additional correction for LFA at these two segments is proposed: 3° at the 47% span segment 

–and 1°at the 63% span segment.

6.3 The Free-Wake Vortex Model

6.3.1 Estimating the Angle of Attack from Blade Pressure Measurements on the 

NREL Phase VI Rotor using a Free-Wake Vortex Model

This study used the experimental data collected from the NREL Phase VI rotor when 

operating in the S configuration in the NASA Ames wind tunnel. The analysis was performed 

at various wind speeds, yielding attached and stalled flow conditions over the blades. Both 

axial and yawed rotor conditions were studied, as follows: 

• Yaw = 0° and U = 5, 7, 10, 13, 15, 20, and 25 m/s

• Yaw = 30° and U = 5, 7, 10, 13, and 15m/s

A novel and comprehensive method involved using the blade pressure measurements with a 

free wake vortex model to estimate the angle of attack (definition given in Figure 6-4) at the 

blades more accurately, together with the induced velocity distributions at the rotor plane and 

the wake geometry. 

Figure 6-4. Blade section aerodynamic load coefficients
49 

This method consisted of the following sequence of steps: Initially, a spanwise distribution for 

the angle of attack was assumed as well as Cn and Ct, which were used to determine the lift 

coefficients at the blades. Using the Kutta-Joukowski law, the bound circulation distribution 

at the blades was determined and prescribed to the free wake vortex model to generate the 

free vortical wake. The induced velocity at the blades was estimated and used to calculate a 

new angle of attack distribution. The process was repeated until convergence in the angle of 

attack was achieved. One advantage of this method is that the solution is itself unsteady and 

could be readily applied to yawed conditions, under which complex aerodynamic phenomena 

are known to occur (e.g., dynamic stall and unsteady induction). A second advantage is that 

because the wake geometry is inherently part of the solution, it is possible to derive the pitch 

and expansion of the helical wake from the measured Cn and Ct. 

Delft University of Technology created the free wake code. The code models rotor wakes in 

both axial and yawed conditions from knowledge of the aerodynamic loads on the blades. The 

input to the code is a prescribed spanwise distribution of bound circulation instead of the 

usual airfoil characteristics. From this prescription, the code generates the lifting line values 

and the vortex wake sheets and calculates the 3D induced velocities at the rotor plane. The 

near wake consists of vortex sheets, one per blade, modeled as a mesh of straight line vortex 

filaments to account for trailing and shed vorticity. The far wake model consists of a helical 

tip vortex at each blade, representing the fully rolled up tip vortices. Each helical tip vortex is 

attached to the outboard end of the corresponding near wake. It is assumed that, at the transfer 

of the near to the far wake model, each near wake vortex sheet rolls up immediately into a 

concentrated tip vortex. This assumption is not physical but yielded accurate predictions for 

the inflow at the rotor plane and near wake geometry in a validation study on the subject free 

wake vortex model. 

6.3.2 Free-Wake Vortex Model Research Results 

Figure 6-5 shows the converged angle of attack distributions for axial conditions at yaw 0°

. 

The figure also shows experimental values for the local inflow angles at the probe tips at each 

wind speed, as well as the large differences between the computed angle of attack and the 

measured LFA results. 

Figure 6-5. Converged angle of attack distributions at different wind speeds for Yaw 0°

. The 

LFA values measured with the probes are also shown. 

0

10

20

30

40

50

60

0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

r/R

α or LFA(deg)

U = 5 m/s

U = 7 m/s

U = 10 m/s

U = 13 m/s

U = 15 m/s

U = 20 m/s

U = 25 m/s

LFA, U=5m/s

LFA, U=7m/s

LFA, U=10m/s

LFA, U=13m/s

LFA, U=15m/s

LFA, U=20m/s

LFA, U=25m/s
50

Figure 6-6 summarizes the aerofoil data derived from the measured values of Cn and Ct and 

the angle of attack distributions of Figure 6-5. Both the lift and drag coefficients depend on 

the radial location. The 2D Delft wind tunnel data for the S809 aerofoil are plotted for a 

Reynolds number of 1 million. 

Figure 6-6. Derived lift and drag coefficients for different radial positions for Yaw 0°

The derived lift and drag coefficients were used together with the calculated induced 

velocities at the blades to calculate the low-speed shaft torque (LSSTQ) and the blade rootedge moments (RFM and REM). These were compared with those measured experimentally 

using strain gauges (Figure 6-7a–c). The latter are plotted together with the corresponding ±1 

standard deviations. The cyclic gravitational loads caused the large standard deviations in the 

REM. The aerodynamic loads were also derived directly from the pressure measurements by 

calculating the torque and thrust coefficients at each blade element and integrating across the 

whole blade. These compared very well with those calculated by the free wake code but not 

so well with those measured with strain gauges. Most probably this is mainly the result of

errors in the measured values of Ct. 

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30 35 40 45

α (deg)

Cl

r/R = 0.3 r/R = 0.47 r/R = 0.63 r/R = 0.80 r/R = 0.95 2D Delft

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 5 10 15 20 25 30 35 40 45

α (deg)

Cdp

r/R = 0.3 r/R = 0.47 r/R = 0.63 r/R = 0.80 r/R = 0.95 2D Delft

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25 30 35 40 45

α (deg)

Cl

r/R = 0.3 r/R = 0.47 r/R = 0.63 r/R = 0.80 r/R = 0.95 2D Delft

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 5 10 15 20 25 30 35 40 45

α (deg)

Cdp

r/R = 0.3 r/R = 0.47 r/R = 0.63 r/R = 0.80 r/R = 0.95 2D Delft
51

Fig. 4(a): Variation low speed shaft torque with wind speed

Torque

0

200

400

600

800

1000

1200

1400

1600

4 6 8 10 12 14 16 18 20 22 24 26

U (m/s)

LSSTQ (Nm)

Free-Wake

Press Meas

Strain Gauges

BEM with Prandtl correction

BEM without Prandtl correction

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

4 6 8 10 12 14 16 18 20 22 24 26

U (m/s)

RFM (nm)

Free-Wake

Press Meas

Strain Gauges

BEM with Prandtl correction

BEM without Prandtl correction

-1200

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

1200

1400

4 6 8 10 12 14 16 18 20 22 24 26

U (m/s)

REM (Nm)

Free-Wake

Press Meas

Strain Gauges

BEM with Prandtl correction

BEM without Prandtl correction

Fig. 4(b): Variation of blade root flap moment with wind spee

 

Flap Moment

Edge

Moment

Fig. 4(a): Variation low speed shaft torque with wind speed

Torque

0

200

400

600

800

1000

1200

1400

1600

4 6 8 10 12 14 16 18 20 22 24 26

U (m/s)

LSSTQ (Nm)

Free-Wake

Press Meas

Strain Gauges

BEM with Prandtl correction

BEM without Prandtl correction

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

4 6 8 10 12 14 16 18 20 22 24 26

U (m/s)

RFM (nm)

Free-Wake

Press Meas

Strain Gauges

BEM with Prandtl correction

BEM without Prandtl correction

-1200

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

1200

1400

4 6 8 10 12 14 16 18 20 22 24 26

U (m/s)

REM (Nm)

Free-Wake

Press Meas

Strain Gauges

BEM with Prandtl correction

BEM without Prandtl correction

Fig. 4(b): Variation of blade root flap moment with wind spee

 

Flap Moment

Edge

Moment

Figure 6-7. Torque, flapping moment, and edge moment in relation to tunnel speed

Finally, the derived Cl-α and Cd-α data were used in a BEM model to compute the same 

global loads. Whether to include the Prandtl tip/root loss factor or not did not have a 

significant difference on the global load predictions.
52

For yawed conditions at 30°, Figure 6-8(a,b) illustrates the converged angle of attack 

variations with the blade azimuth angle at U = 10 m/s and the measured LFA values. Large 

differences exist between the angle of attack and the corresponding LFA values at all azimuth 

positions of the blades, similar to what was noted in axial conditions. Yet in yawed 

conditions, the probe was subjected to highly unsteady flow situations. At U = 10 m/s, the 

Figure 6-8. Derived angle of attack variations derived for U = 10m/s, yaw 30°. The LFA values 

measured using the probes are shown.

free wake model estimated that the time rate of change of angle of attack reaches a peak of 

about ±78°/s at the inboard sections. Further work is required to establish the accuracy with 

which five-hole pressure probes manage to measure the LFA at such high levels of 

unsteadiness.

From the variations of Cn and Ct obtained from the blade pressure measurements and the 

results for the angle of attack presented in Figure 6-8, researchers could derive the unsteady 

aerofoil hysteresis loops for Cl-α and Cdp-α. Figure 6-9(a–d) illustrates the hysteresis loops for 

different radial locations at U = 10 m/s, yaw 30°. The figure also shows 2D steady aerofoil 

data obtained from the Delft wind tunnel experiments and the 3D steady aerofoil data were 

derived from the blade pressure measurements for nonyawed conditions together with the free 

wake code. In a yawed rotor, these loops are not only a result of the cyclic angle of attack 

variations but also caused by a cyclic variation of the flow velocity relative to the blades. 

Each hysteresis plot includes the reduced frequency k - ( ( ) Vr k = Ωc 2 . The hysteresis loops 

often change direction from counterclockwise to clockwise when moving outboard from r/R =

0.3 to 0.95 (see Figure 6-9).

6.3.3 Free-Wake Vortex Model Conclusions and Future Work

A free-wake vortex model can be used to derive the angle of attack at the blades from blade 

pressure measurements in both axial and yawed conditions. Using the derived angle of attack 

and the measured Cn and Ct values can help generate complete three-dimensional (3D),

steady and unsteady aerofoil data.

0

5

10

15

20

25

30

35

40

45

0 60 120 180 240 300 360

φ (deg)

α or LFA (deg)

Angle of attack, Free-wake

LFA, Experiment probes

0

2

4

6

8

10

12

14

16

18

0 60 120 180 240 300 360

φ (deg)

α or LFA (deg)

Angle of attack, Free-wake

LFA, Experiment probes

Fig. (a): r/R= 0.34 Fig. (b): r/R= 0.84

0

5

10

15

20

25

30

35

40

45

0 60 120 180 240 300 360

φ (deg)

α or LFA (deg)

Angle of attack, Free-wake

LFA, Experiment probes

0

2

4

6

8

10

12

14

16

18

0 60 120 180 240 300 360

φ (deg)

α or LFA (deg)

Angle of attack, Free-wake

LFA, Experiment probes

Fig. (a): r/R= 0.34 Fig. (b): r/R= 0.84
53

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2 7 12 17 22

α (deg)

Cl

3D Unsteady

2D Steady Delft

3D Steady

kmax = 0.27

kmin = 0.15

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

5 10 15 20 25

α (deg)

Cdp

3D Unsteady

2D Steady Delft

3D Steady

kmax = 0.27

kmin = 0.15

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

2 4 6 8 10 12 14 16

α (deg)

Cl

3D Unsteady

2D Steady Delft

3D Steady

kmax = 0.06

kmin = 0.05

0

0.02

0.04

0.06

0.08

0.1

0.12

5 7 9 11 13 15

α (deg)

Cdp

3D Unsteady

2D Steady Delft

3D Steady

kmax = 0.06

kmin = 0.05

Fig. (a): r/R= 0.3 Fig. (b): r/R= 0.3

Fig. (c): r/R= 0.8 Fig. (d): r/R= 0.8

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2 7 12 17 22

α (deg)

Cl

3D Unsteady

2D Steady Delft

3D Steady

kmax = 0.27

kmin = 0.15

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

5 10 15 20 25

α (deg)

Cdp

3D Unsteady

2D Steady Delft

3D Steady

kmax = 0.27

kmin = 0.15

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

2 4 6 8 10 12 14 16

α (deg)

Cl

3D Unsteady

2D Steady Delft

3D Steady

kmax = 0.06

kmin = 0.05

0

0.02

0.04

0.06

0.08

0.1

0.12

5 7 9 11 13 15

α (deg)

Cdp

3D Unsteady

2D Steady Delft

3D Steady

kmax = 0.06

kmin = 0.05

Fig. (a): r/R= 0.3 Fig. (b): r/R= 0.3

Fig. (c): r/R= 0.8 Fig. (d): r/R= 0.8

Figure 6-9. – Cl-α and Cd-α hysteresis loops for U = 10 m/s, yaw = 30°

For axial conditions in which the angle of attack is steady, the following conclusions could be 

made: 

• For small angles of attack (less than the 2D static stalling angle), the new 3D aerofoil 

data are very close to the 2D aerofoil data except at the tip and root region. In the latter 

regions, the new 3D lift coefficient was lower than the 2D aerofoil data. 

• For larger angles of attack, the new 3D aerofoil data were considerably different than 

the 2D data and the presence of stall-delay was evident, especially at the inboard 

sections of the blades. The increase in lift coefficient caused by stall delay was

accompanied by an increase in drag coefficient.

For yawed conditions in which the local angle of attack is unsteady and a function of the 

blade azimuth angle, the following conclusions could be drawn:

• The 3D unsteady coefficients could well exceed the corresponding steady values 

derived for axial conditions. 
54

• The unsteady Cl-α and Cdp-α hysteresis loops were in general closer to the 3D steady 

values than to the corresponding 2D steady data. This was observed at both attached 

and stall conditions, and it implies that 3D effects (in particular stall-delay) have a 

significant role in the unsteady flow behavior of wind turbine blades. 

A free-wake vortex model can also derive the complex 3D induction at the rotor plane 

together with the wake geometry and circulation distribution from the blade pressure 

measurements. These are vital for a better understanding of a wind turbine’s aerodynamic 

behavior in both steady and unsteady environments.

With reliable 3D aerofoil data, BEM predictions for wind turbines improve significantly, even 

for conditions of deep stall and rotor yaw. In many situations, BEM results for the blade 

aerodynamic loading distributions are unreliable, not necessarily because the BEM theory is 

deficient but because incorrect aerofoil data are used.

When using 3D lift and drag aerofoil data derived directly from the blade pressure 

measurements, the BEM calculations no longer require the tip/root loss correction model. 

This is because the tip/root loss effects are already accounted for in the 3D aerofoil data.

This study clearly demonstrated the considerable potential to improve the accuracy of BEMbased aerodynamic models. Wind turbine designers urgently need more reliable engineering 

models that predict 3D aerofoil data. Research efforts should focus on a better understanding 

of the flow physics locally around the rotating blades. More extensive blade pressure 

measurements would be indispensable to reaching this aim and further validating CFD 

models. The limited availability of accurate 3D aerofoil data for wind turbine applications 

restricts not only BEM-based models from being more accurate but also the more 

comprehensive models such as lifting-line free-wake vortex models and lifting-line/actuatordisk CFD models.

6.4 Engineering versus CFD 

This work applied the rotating UAE data designated as H and S series, in particular, the first 

quadrant of the rotor plane for yaw = 0° and U = all wind speeds with special emphasis on 5, 

8, 10, 12, 13, 14, 15, and 20 m/s.

Blade loads and rotor performances of wind turbines in the rotating case can be approximated 

with engineering methods and sophisticated aerodynamic codes. The engineering methods are 

based on 2D aerodynamic performances and result in a spanwise related adjustment. The 

newly generated segment performances can than be used to calculate blade loads and 

performances via BEM theory or vortex lattice-like techniques. The aerodynamic calculations 

include at least local blade shape and (local) flow physics. A well-known program, the panel 

code RFOIL, has an integral boundary layer description and includes a cross-flow model. It 

can be regarded as a quasi-3D code and the calculated segment characteristics are still 

independent of neighboring span sections. Full Navier-Stokes codes (or full CFD like 

FLUENT), however, include a full description of the blade geometry and the entire flow field 

and simulate the rotor environment properly. Aerodynamics influences in spanwise direction 

are considered and nacelle layout and tower presence can be modeled.
55

This comparison looked at the experimental data of the steady-state NREL Phase VI blade 

configuration versus some engineering models and CFD methods; several articles in the 

bibliography of this section describe results of other blade configurations.

6.4.1 Engineering Models versus CFD Research Results

Most engineering stall models adjust the 2D airfoil characteristics for rotation and are based 

on the idea of Snel et al., in which the (c/r) dependency plays a dominating role. They express 

the 3D correction of, for example, the lift coefficient as a fraction of the difference in Cl

between the inviscid or extended linear 2D lift. Chaviaropoulos and Hansen included twist 

and pitch in their base equation for the rotational characteristics, which reads as:

Cx,3D = Cx,2D + A (c/r)B * cosn(twist+pitch) * Cx

where x is the lift (or normal force coefficient):

Cx= Cl,INV – Cl,2D

or 

Cd = Cd,2D – Cd,2D-min

Cm = Cm,2D – Cm,INV

The most important approaches are given by:

• Snel: A = 3, B = 2, n = 0

• Chaviaropoulos and Hansen: A = 2.2, B = 1, n = 4

• Schepers and van Rooij: A=2.93, B=1.18, n=6

These constants were calculated from a limited amount of measurements, and therefore the 

applied experiments could play a rather dominant role in the value of these parameters. Figure 

6-10 shows the results for the three engineering models compared with the UAE 

measurements for two span locations. 

Figure 6-10. The NREL wind tunnel results for tapered and twisted blade compared with the 

stall model predictions for two sections (circles are UAE measurements)

NREL Phase VI: 63% span

-0.5

0.0

0.5

1.0

1.5

2.0

-10.0 0.0 10.0 20.0 30.0 40.0

Alfa (°)

Cn

Snel

Chaviaropoulos

2D Meas.

Schepers

NREL Phase VI: 30% span 

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-10.0 0.0 10.0 20.0 30.0 40.0

Alfa (°)

Cn

Snel

Chaviaropoulos

2D Meas.

Schepers
56

At the inboard segment the approximated normal force coefficient extends (by definition) 

from the 2D measurements, and the measurements strongly deviate. This could be caused by 

the influence of the transition piece toward the hub, which is very close to the measured 

segment. For the midspan position, two engineering methods show a huge increase in Cn

compared with the measurements, but the Snel option does not show such behavior. In 

general, the prediction of the maximum normal force coefficient is exaggerated, and the 

methods fell short, leading to an improper prediction of the actual loads and accompanying 

power performances. These observations are similar for the other blade sections. 

The CFD code RFOIL is a modification of panel code XFOIL extended for radial flow based 

on the Snel-Houwink model for blade rotation, and therefore includes the 3D velocity profile 

of Johnson. This velocity model, however, cannot handle negative velocities and separation 

cannot be modeled adequately. To activate “rotation,” the local solidity parameter c/r serves 

as input and two thirds of the geometric value gives the best approximations. Figure 6-11

gives the results compared with the wind tunnel measurements of phase VI. The normal force 

predictions are fairly good except for the r/R=0.30 segment, and local flow influences from 

the transition piece could obscure the measurements. The sharp nose in combination with the 

integral boundary layer solution leads to convergence problems near the nose, and this 

hampers calculation up to large angles of attack. Thicker airfoils, which are common at 

inboard segments, improve convergence, and characteristics up to 35° are in general possible.

Figure 6-11. The RFOIL predictions compared with the wind tunnel results at four span 

sections 

Investigators used full Navier-Stokes codes to model the rotating blade without nacelle and 

tower. Risoe and the Delft University of Technology performed the calculations with,

respectively, EllipSys3D and the commercial code FLUENT. The conditions were as follows:

• EllipSys3D:

o Grid: 3.1 × 106 cells, domain is half sphere with radius of 6R

o Turbulence model: fully turbulent and unsteady k-ω SST model, with maximum y+

= 2

-0.5

0.0

0.5

1.0

1.5

2.0

-10.0 0.0 10.0 20.0 30.0

Angle (°)

Cn

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

-10.0 0.0 10.0 20.0 30.0

Angle (°)

Cn

RFOIL and Phase VI planform 

c/R= 0.30

c/R= 0.63

c/R= 0.47

c/R= 0.80
57

• FLUENT:

o Grid: 3.6 × 106 cells, domain is half sphere with radius of 6R

o Turbulence model: fully turbulent and k-ω SST model, with maximum y+ = 2

Risø always applied the unsteady k-ω SST model, and Delft used the steady description for 

wind speeds less than 12 m/s and the unsteady k-ω SST model up to 15 m/s. The differences 

between the steady and unsteady result turned out to be small for the range of wind speeds 

considered. The huge difference in limited streamlines at a tunnel speed of 10 m/s (Figure 6-

12) is thus remarkable. Most interesting is the curved vortex shape near the 47% segment. 

Both front parts of the blade indicate an attached flow, and turbulent separation is about 50%–

55% with EllipSys3D and slightly more in front with FLUENT. 

Figure 6-12. Limited streamlines on the suction side of the Phase VI blade. Vertical lines 

indicate the measured sections.

This explains why the differences in normal force coefficient, derived from the pressure 

distributions, do not deviate much between the two codes. Figure 6-13 shows a comparison of 

the normal force coefficients for several wind speeds (10, 13, and 15 m/s) along the blade 

span. Behavior and deviations in values and trends are very similar. The calculated and 

measured pressure distributions (not shown here) show that integral information such as Cn

can be misleading because variation and deviations in pressure distributions near the nose can 

be cancelled out by the differences at the tail. Most of the calculated pressure distributions are 

quite comparable, however, and comparing the normal force coefficient with the 

measurements gives a fair indication of the quality of nowadays CFD codes.

To get around the uncertainty of the inflow angle, the tunnel speed is chosen as reference; 

Figure 6-14 depicts the development of the normal force coefficient for the five span 

segments. The prediction of Cn at segment r/R=0.30 is remarkably good, but the segments at 

midspan (r/R=0.47 and r/R=0.63) are quite surprising. The dip in the FLUENT results at 13

m/s at r/R=0.80 cannot be observed in the experiment, and it is not clear why. The calculated 

outboard performance at r/R=0.95 was mostly larger than the measured performance. An 

explanation could be the shaping of the tip, which is slightly approximated in the CFD 

modeling.

Comparison with other methods requires inflow angle and lift values. They are related, and 

uncertainties in the measured inflow angle must be accepted. The same inflow angles are also 
58

used to calculate the CFD lift coefficients. Figure 6-15 compares the CFD results, RFOIL and 

FLUENT, with the experimental data in.

Figure 6-13. Spanwise distribution of Cn for three tunnel speeds for EllipSys3D (left) and 

FLUENT. Circles are measurements and the solid line represents CFD results.

Figure 6-14. The normal force coefficients from the experiment and the full CFD results (full 

curves are k-ω SST model)

0.0

0.5

1.0

1.5

2.0

2.5

5 10 15 20

wind speed m/s

Cn

0.0

0.5

1.0

1.5

2.0

2.5

5 10 15 20

wind speed m/s

Cn

r/R= 0.80

r/R= 0.30

r/R= 0.63

r/R= 0.95

r/R= 0.47
59

Figure 6-15. Comparison of two CFD results with the measurements

As already demonstrated in Figure 6-14, the approximations of the lift with FLUENT at the 

r/R=0.30 segment are very good and much better than with RFOIL, and FLUENT modeling 

of the inboard transition piece seems to be very important. Still, the maximum lift prediction 

is some 20% off. At the 63% span location, the differences are small and both CFD results 

seem to be quite good. The predictions of both codes at the segments r/R= 0.47 and r/R= 0.80 

are comparable with UAE measurements.

6.4.2 Engineering versus CFD Conclusions and Future Work

The comparisons of the rotational flow characteristics for the NREL Phase VI rotor blade 

having the S809 airfoil show the following: 

• Approximations of lift or normal force coefficient with engineering methods are far 

from satisfactory. Tuning the function constants could improve the result, but then the 

result would depend on the applied case.

• The results of the CFD methods RFOIL and FLUENT (or EllipSys3D) are in general 

quite good and have to be preferred. The panel code RFOIL is fast, but at the inboard 

and tip sections the influence of the transition piece or tip shape could cause large 

deviations. Full CFD codes do not have this drawback.

6.5 Conclusions and Future Work in All Three Areas

Taking rotor measurements in a wind tunnel reduces uncertainties at the rotor plane to almost 

negligible values and is a huge improvement over field measurements. This UAE produced a 

large, good-quality database. Like other experiments, however, some uncertainties still exist

and measurement devices are often the cause. This cannot be neglected, and investigations 

showed that these influences are quite small and the given suggestions secure reliable segment 

characteristics. Most fluctuations in the flow around the blade are caused by the inevitable 

-0.5

0.0

0.5

1.0

1.5

2.0

0 10 20 30

Angle (deg.)

Cl

RFOIL 

FLUENT

UAE experiment

3D

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

0 10 20 30

Angle (deg.)

Cl

RFOIL 

FLUENT

UAE experiment, 3D

r/R= 0.30 r/R= 0.63
60

dynamic of the experiment, and defining stationary flow is then by definition impossible. The 

smallest variation in the segment characteristics was in the first quarter of the rotor plane, and 

this part should be used as reference for stationary flow. This counts only for the nonyawed 

flow.

Most of the University of Delft investigations focused on determining proper rotational 

segment characteristics, in particular for the stationary flow, and determining the correct 

inflow angle is of vital importance. A new method derived the inflow angle and it is a unique 

option in the yawed flow. Local characteristics can be predicted with engineering methods or 

more advanced aerodynamic codes. Including other rotor geometries such as the NREL field 

experiments resolved that classical engineering methods are not well suited to predict the 

segment performances. The best (and easiest) alternative seems to be the RFOIL code. 

Additional validation is needed, especially for other airfoil profiles and wind tunnel 

experiments.

Future research must include other wind tunnel experiments like the one carried out by FOI 

(CARDC tunnel, China) and the MEXICO partners (DNW tunnel, Netherlands). The focus on 

stationary segment characteristics is needed as a base, but the unsteady characteristics will 

become more important. A more elaborate study of the many yawed cases in the UAE serves 

perfectly for these investigations. The huge UAE database contains more interesting 

measurements with respect to unsteady flow like several fast pitching campaigns. Only a 

small part is studied so far, and this is true for all partners involved in the Annex XX. Many 

opportunities to research rotor flow properties from the database exist, and we have a long 

way to go before we can properly quantify the loads on the wind turbine rotor.

6.6 Bibliography

Meng, F.; van Rooij, R.P.J.O.M. (2007). “CFD Investigations with Respect to Model 

Sensitivity for the Non-rotating Flow around the NREL Phase VI Blade.” Proceedings of the 

2007 European Wind Energy Conference and Exhibition, 2007 European Wind Energy 

Conference and Exhibition in Milan, May 7–10, 2007. Brussels: EWEA.

Sant T. (January 2007). “Improving BEM-based Aerodynamic Models in Wind Turbine 

Design Codes.” ISBN: 978-99932-0-483-1. Ph.D. Thesis, Delft University of Technology,

451 pp.

Sant, T.; van Kuik, G.A.M.; van Bussel, G.J.W. (2006). “Estimating the Angle of Attack from 

Blade Pressure Measurements on the NREL Phase VI Rotor using a Free-Wake Vortex 

Model: Axial Conditions.” Wind Energy (9:6); pp. 549–577.

van Kuik, G.A.M.; van Rooij, R.P.J.O.M.; Imamura, H. (2004). “Analysis of the UAE Phase 

VI Wind Tunnel Results in the Non-yawed Flow.” Proceedings of the 2004 European Wind 

Energy Conference and Exhibition, 2004 European Wind Energy Conference and Exhibition 

in London, November 22–25, 2004. Brussels: EWEA.

van Rooij, R.P.J.O.M. (2004). “The Effect of the Test Set-up on the Segment Performance.” 

Pages 125–139 in Proceedings of the 2004 Joint Meeting of IEA R&D Wind Annex XI: Joint 
61

Action on the Aerodynamics of Wind Turbines and IEA R&D Wind Annex XX: HAWT 

Aerodynamics and Models from Wind Tunnel Measurements, June 17, 2004.

Rooij, R.P.J.O.M. van. (2005). “The effect of the test set-up on the steady state data.” 

Proceedings of the 2005 Joint Meeting of IEA R&D Wind Annex XI: Joint Action on the 

Aerodynamics of Wind Turbines and IEA R&D Wind Annex XX: HAWT Aerodynamics and 

Models from Wind Tunnel Measurements, Pamplona, Spain, May 25–26, 2005. Golden, CO: 

NREL.

Rooij, R.P.J.O.M. van. (2007). “Engineering Models vs. CFD Methods with Respect to 

Augmented Lift Caused by Blade Rotation.” Proceedings of the 2007 Joint Meeting of IEA 

R&D Wind Annex XI: Joint Action on the Aerodynamics of Wind Turbines and IEA R&D 

Wind Annex XX: HAWT Aerodynamics and Models from Wind Tunnel Measurements,

Roskilde, June 13–15, 2007. Golden, CO: NREL.

Rooij R.P.J.O.M. van, Meng F.. (2008). “Analysis of the Flow Characteristics of Two NonRotating Rotor Blades.” Journal of Solar Energy Engineering, In Press.

van Rooij, R.P.J.O.M.; Arens, E.A. (2007). “Analysis of the Experimental and Computational 

Flow Characteristics with Respect to the Augmented Lift Phenomenon Caused by Blade 

Rotation.” Journal of Physics, Conference Series, The Science of Making Torque from Wind, 

Lyngby, Denmark, August 28–31.

van Rooij, R.P.J.O.M.; Meng, F.Z. (2006). “The steady state parked configuration.” 

Proceedings of the 2006 Joint Meeting of IEA R&D Wind Annex XI: Joint Action on the 

Aerodynamics of Wind Turbines and IEA R&D Wind Annex XX: HAWT Aerodynamics and 

Models from Wind Tunnel Measurements. Golden, CO: NREL.

Rooij R.P.J.O.M. van, Meng F.. (2007). “Analysis of the Flow Characteristics of Several 

Non-Rotating Rotor Blades.” AIAA-2007-625, Proceedings from the 45th AIAA Aerospace 

Sciences Meeting and Exhibit, Reno, Nevada, January 8–11.

van Rooij, R.P.J.O.M.; Schepers, J.G. (2005). “The Effect of Blade Geometry on Blade Stall 

Characteristics.” Journal of Solar Energy Engineering (127); pp. 496–502. 

Rooij, RPJOM van, J.G. Schepers. (2005). “The Effect of Blade Geometry on the Normal 

Force Distribution of a Rotating Blade.” Pages 289–299 in A Collection of the 2005 ASME 

Wind Energy Symposium. 2005 ASME Wind Energy Symposium at the 43rd AIAA Aerospace 

Sciences Meeting and Exhibit, January 10–13. New York: ASME.
62 

7.0 Research Summary—Norway 

Organizations: Institute for Energy Technology (IFE) 

Norwegian University of Science and Technology 

(NTNU) 

Researchers: Andreas Knauer, andreask@ife.no 

Geir Moe, geir.moe@ntnu.no 

7.1 Background and Motivation 

Norway, with its moderate level of wind energy development, has an official target of 

increasing annual wind energy production to 3 TWh by 2010. Furthermore, Scanwind AS and 

StatoilHydro have industrial initiatives. The strategic Norwegian wind energy program 

(2003–2007) was launched to support development through strengthening the nation’s wind 

energy research competence. Rotor aerodynamics and aeroelastics are a vital part of the 

research, and Annex XX activities are the focus of international cooperation. 

ENOVA SF financed the Norwegian participation in Annex XX. The Annex XX activities 

were initiated at the Institute for Energy Technology (IFE) (A. Knauer), and national research 

activities were conducted in cooperation with the Norwegian University of Science and 

Technology (NTNU) (G. Moe). 

7.2 Data Used and Methods Employed 

The research activities consisted of the UAE database exploitation and analysis (Annex XX 

focus area 2), model subcomponent construction/modification (focus area 3), and 

subcomponent integration and model validation (focus area 4). The main focus was 

investigating 3D-behavior of the flow and analyzing steady/unsteady behavior of flow 

patterns at different rotor sections. 

IFE performed the basic research activities, focusing on 3D effects and stall phenomena on 

the rotor blades. To estimate stall onset and 3D effects, IFE developed a method using parts of 

the pressure distribution at the rotor blade (trailing edge pressure) combined with a Fourier 

transformation. The Unsteady Aerodynamics Experiment (UAE) experimental data were 

sampled with high frequency; this allowed using FFT on the results and transforming 

measured time series into the frequency plane. Spanwise stall onset was estimated for the 

turbine working in upwind conditions. The behavior of stall onset for yawed flow conditions 

was analyzed further. The FFT-based method allows the stall onset identification and gives a 

limited frequency spectrum induced by turbulent flow structures in different operational 

phases with distinctive maxima. 

IFE also used the UAE test results to modify aeroelastic codes. A tower wake model was 

developed for the FLEX5 code. Using tower wake measures, IFE estimated tower wake 

shapes. In the ongoing activity, potential theory is used to model suitable wake shapes. The 

modified aeroelastic model is used to investigate floating offshore wind turbine concepts. 
63

7.3 Research Results

At NTNU, Moe and two Ph.D. candidates analyzed data from the Ames wind tunnel tests for 

consistency. The work appears in EWEA 2007 conference papers and in a paper to appear in 

the journal Wind Energy (see Bibliography). The group is still working with the NREL UAE 

Phase VI database and writing two conference papers.

Further activities at NTNU investigated different stall delay models. The stall models were 

incorporated in the prescribed wake model HAWTDAWG, which comprises a more detailed 

rotor wake modeling than conventional Blade Element Momentum (BEM) methods. 

Comparing simulation results with UAE test data, the researchers identified variations in the 

prediction of power and thrust of the turbine for the stalled operation of the rotor at higher 

wind speeds. They concluded that, e.g., a combination of different stall delay models for 

distinct ranges of the angle of attack may give better results.

7.4 Conclusions and Future Work

Annex XX participation effectively supported Norwegian research efforts in rotor 

aerodynamics and aeroelastics . The UAE test database contains valuable information for 

testing, validating, and modifying various aerodynamic and aeroelastic models and will be 

used further. 

The well-organized Annex meetings contributed further to effective scientific information 

exchange and good discussions in rotor aerodynamics and aeroelastic wind turbine modeling.

The main result of the Norwegian participation in Annex XX was the strengthened

competence in rotor aerodynamics and aeroelastic modeling. These scientific areas have an 

important role in the ongoing national development of new offshore wind turbine concepts. 

Such concepts may consist of different design solutions where especially aeroelastic modeling 

is a key issue, and Annex XX research results and the knowledge gained are of great value.

7.5 Bibliography

Breton, S. P.; Coton, F.N.; Moe, G. (2007). “A Study on Different Stall Delay Models Using 

a Prescribed Wake Vortex Scheme and NREL Phase VI Experiment.” Proceedings of EWEC 

2007, Milan, Italy.

Breton, S. P., Coton, F.N, Moe, G.. “A Study on Rotational Effects and Different Stall Delay 

Models Using a Prescribed Wake Vortex Scheme and NREL Phase VI Experiment.” Wind 

Energy. In press.

Knauer, A. (2003). “Wind Energy in Norway.” Proceedings of IEA Annex XX Meeting 2003, 

Boulder, Colorado.

Knauer, A. (2004a). “Aeroelastiske simuleringer/Aero-Elastic Simulations.” Presented at the 

Norwegian Wind Seminar, Trondheim, Norway.
64

Knauer, A. (2004b). “Rotorblade Stall Investigations.” Proceedings of IEA Annex XX Meeting 

2004, Montreal, Canada.

Knauer, A. (2005). “Tip Design Issues.” Proceedings of IEA Annex XX Meeting 2005, 

Pamplona, Spain.

Knauer, A. (2007). “Initial Investigations of Offshore Wind Turbine Concept.” Proceedings 

of IEA Annex XX Meeting 2007, Risø, Denmark.

Knauer, A., Hanson, T.D. (2007a). “Undersøkelse av en ny drifttsstrategi for vindturbiner / 

Analysis of a New Operation Strategy for Wind Turbines.” Presented at the Norwegian Wind 

Seminar, Trondheim, Norway.

Knauer, A.; Hanson, T.D.. (2007b). “High Speed Operation of Wind Turbines.” Proceedings 

of EWEC 2007, Milan, Italy.

Knauer, A.; Hanson, T.D.; Skaare, B. (2006). “Offshore Wind Turbine Loads in Deep Water 

Environment.” Proceedings of EWEC 2006, Athens, Greece.

Moe, G.; Breton, S.-B.; Long, H.; Schreck, S. (2007). “Assessment of Motions and Forces in 

the NREL NASA Ames Wind Tunnel Tests.” Proceedings of EWEC 2007, Milan, Italy.
65

8.0 Research Summary—Spain

Organization: National Renewable Energy Centre (CENER )

Researchers: Xabier Munduate, xmunduate@cener.com

Alvaro González, agonzalez@cener.com

Esteban Ferrer, eferrer@cener.com

Sugoi Gomez, Sugoi.Gomez-Iradi@liverpool.ac.uk

George Barakos, G.Barakos@liverpool.ac.uk

8.1 Background and Motivation

CENER, the National Renewable Energy Centre of Spain, started its wind energy activities in 

2002, at the same time as the onset of the International Energy Agency (IEA) Wind Annex 

XX. This project represented a unique opportunity for developing and improving knowledge 

on wind turbine aerodynamics. In the ensuing years, researchers have overcome limitations on 

engineering modeling and acquired physical understanding of complex flow phenomena. 

At CENER, this work focused on acquiring reliable knowledge about aerodynamic effects in 

horizontal axis wind turbines (HAWTs) and understanding the physical phenomena involved. 

In addition, in 2005, computational fluid dynamics activities began at CENER, focusing on 

wind turbine airfoil analysis and on 3D blade simulations using full Navier-Stokes solvers 

(Fluent and recently CENER’s in-house CFD code). The National Renewable Energy 

Laboratory (NREL) Phase VI data have been valuable for validating CFD codes and 

developing methods. 

CENER’s work related to Annex XX has focused on the following issues:

• Analyzing 3D stall delay for rotating blades: To obtain a better physical 

understanding.

• Predicting dynamic stall on parked blades: To gain a better physical understanding of 

the onset and three-dimensionality of the process and to asses Beddoes-Leishman 

model for the prediction of wind turbine dynamic stall.

• Validating the Navier-Stokes tools for rotating blades: The NREL phase VI data set 

has been used to validate the commercial and the in-house Navier-Stokes solvers and 

to create new methods for using CFD techniques at CENER.

8.2 Data Used and Methods Employed

The experimental analysis of data used sequences H (yaw = 0 all velocities), S (yaw = 0 all 

velocities), L (v = 20 m/s, pitch step 5°), and O of the NREL Unsteady Aerodynamics 

Experiment (UAE) Phase VI database, in addition to the steady data for the S809 airfoil from

the Ohio State University, Colorado State University, and Delft Uuniversity of Technology

wind tunnels. The dynamic stall investigation used unsteady oscillating tests of the S809 

airfoil on the OSU wind tunnel.

CENER researchers compared CFD simulations of parked and rotating rotors to the NREL 

Phase VI wind turbine data. The parked cases used sequence L at a wind speed of 20 m/s and 

at various pitch angles. The rotating cases used sequences S and H in a nonyawed 

configuration and were compared for various wind speeds: 5, 7, 10, 13, 15, 20 and 25 m/s.
66

8.3 Experimental Analysis

To investigate the rotational effects, researchers analyzed the differences on the aerodynamic 

response between the S809 airfoil and the parked blade, and later performed the same 

comparison between the parked blade and the rotating blade. This method was employed to 

understand and separate the effects related to the 3D geometry of those related to the rotation. 

The NREL data are extensive, and Matlab was used to extract the data to useful figures or 

graphics suitable for the study. The cp and pressure data were carefully analyzed, and a PSD 

frequency study analyzed the parked and rotating blade behavior. Force and moment 

coefficients in blade sections were also revised. The cp data were used to characterize flow 

separation and reattachment for the rotating and parked blade, and researchers compared the 

flow topology.

For the dynamic stall study, the researchers used a similar procedure. Figures and graphics 

were extracted using data files containing information of unsteady oscillation cases of the 

S809 airfoil and the NREL parked blade sections. The force and moment coefficients for the 

unsteady cases were compared between the airfoil and the parked blade sections to understand 

the difference of the dynamic stall behavior related to the 3D geometry. In addition, the 

unsteady cases were compared with the steady data to establish the relation between them. 

But supporting the conclusions of the previous analysis required implementing the BeddoesLeishman model and carrying out dynamic stall computations of the airfoil and 3D parked 

blade sections.

8.4 CFD Computations

Finally, for the CFD work, Fluent 6.2 was used to compute blade loads and cp distributions on 

the NREL phase VI blade. To create the grids, Gambit (Fluent’s mesher) was used. 

Nonstructured grids were created using tetrahedral and prismatic elements containing 

approximately 3 million elements. Simulations used the Moving Reference Frame (MRF) 

capability of Fluent. Modeling flow periodicity required modeling only one blade and using 

periodic boundary conditions. The NREL phase VI validation case (two-bladed rotor) used a

180° sector. Second-order discretization schemes were used for all variables and the SIMPLE 

algorithm selected to solve the pressure-velocity coupling. Even though the resolution of the 

boundary layer (and viscous sublayer) is always recommended, reasonable results have been 

obtained when using wall functions in conjunction with the k-ω Shear-Stress Transport (SST) 

model for attached flow conditions. The results are fully turbulent converged steady-state runs 

using the k-ω SST model. All simulations were run at CENER’s cluster (36 computers 

exclusively for CFD use) using Fluent parallel capability in not fewer than four machines.

Simulations with CENER’s in-house CFD solver together with grids were generated using 

ICEM-CFD Hexa with genuinely multiblock grids. The number of available processors for 

parallel execution mainly dictates the number of blocks. A typical calculation involves about 

250 blocks per blade computed on 16–128 processors according to the employed grid density. 

The current topologies combine an H-type structure away from the blades with a C-type 

structure attached to them. The compressible unsteady Navier-Stokes equations are 

discretized on a curvilinear multiblock body conforming mesh using a cell-centered finite 
67

volume method. The convective terms are discretized using Osher’s upwind scheme. MUSCL 

variable extrapolation confers second-order accuracy with the Van Albada limiter. A central 

discretization method is used for the viscous terms. The solver includes a variety of linear and 

nonlinear eddy-viscosity models, though for this work the standard k- ω model of Wilcox has 

been used. Menter’s SST version of the k- ω model [3] was also employed for some of the 

cases. A dual-time stepping method is employed for time-accurate simulations, where a 

second-order backward difference approximated the time derivative. Integrating in pseudotime using a first-order backward difference solves the resulting nonlinear system of 

equations .

8.5 Research Results

First, the work on rotational effects generated many figures showing mainly cp and Cn related 

to the aerodynamic behavior of the S809 airfoil and the NREL parked and rotating blade. 

Analyzing and comparing the figures extracted some results about the 3D geometry and 

rotational effects on the aerodynamic behavior. The importance of rotation and 3D geometry 

on the aerodynamic behavior of the NREL blade has been clearly revealed, changing the flow 

topology over the blade and consequently changing the aerodynamic loads. For the parked 

blade, separation in one blade location may affect the flow details in the nearest locations,

such as the formation of a leading edge separation at sections close to the blade root. This 

leading edge separation might be related to the trailing edge separation delay process and 

responsible of a smoothed and delayed stall. Cn values are not amplified as a result of the 

leading edge separation. But for the rotating blade, researchers observed leading edge 

separation at inner span stations with total suppression of trailing edge separation. Within this 

separated region, a strong radial flow forced by rotation is generated, connected with the extra 

lift observed for the rotating blade. The new flow topology for inboard stations is reminiscent 

of a leading edge vortex-like structure that does not convect into the free stream remaining 

attached to the surface. Figure 8-1 shows, for the parked and rotating blade, an example of the 

extracted separation and impingement points of the flow for the 30% blade span station when 

incidence is increasing. The figure shows the change in flow topology.

The work on the dynamic stall phenomenon analyzed the aerodynamic behavior of the S809 

airfoil and different parked blade sections undergoing the same unsteady oscillating tests. The 

comparison between the unsteady airfoil behavior and the steady data showed the existence of 

a relationship between the dynamic stall behavior and the steady data for a 2D airfoil. The 

same comparison for the parked blade, using the unsteady and steady data of the blade span 

stations, showed that the dynamic stall behavior of a 3D parked blade seems to be managed 

by the steady behavior of the different sections just as the steady behavior of the profile 

manages the dynamic stall behavior of a 2D aerofoil.. After the experimental analysis, 

researchers implemented a Beddoes-Leishman model and the computations confirmed the 

previous conclusions. Figure 8-2 shows an example of computation with the BeddoesLeishman model compared with the experimental data. The case selected is a sinusoidal 

oscillation of the angle of attack for the 30% blade span station, with reduced frequency 

0.075, mean angle 20o, and amplitude of oscillation 10o. The figure shows the Cl response 

versus the angle of attack for the experimental data and for two computations of the BeddoesLeishman model with different input data. One computation is performed with the 
68

Figure 8-1. Trailing edge separation and leading edge reattachment points on the parked (a) 

and rotating (b) blade 30% span station

Figure 8-2. Unsteady oscillation for the 30% blade section, comparison between experimental 

data (red line), Beddoes-Leishman computation with the 2D steady data (green line), and 

computation with the 3D blade section steady data (blue line)
69 

2D airfoil steady data, and the agreement with the experimental data is not very good. The 

other computation used the steady data of the 30% span station, and agreement with the 

experimental data is quite good. 

Finally, the CFD results with Fluent (Figure 8-3a, top) and CENER’s in-house solver (Figure 

8-3b) showed that for low wind speeds (7 m/s) corresponding to moderated local flow angles, 

the flow can be well predicted. The depicted results correspond to the S sequence with a free 

stream velocity of 7 m/s and a rotational speed of 71.9 rpm. These figures show the cp

comparison between CFD and experimental data for the 47% radial station. The compared cp

values were calculated using the sectional stagnation pressure. The computations predict quite 

accurately the experimental cp values at these locations. 

Figure 8-3 top and bottom shows the comparison of the normal force coefficient along the 

blade. Discrepancies in the 30% and 95% station are thought to be related to the modeled 

geometry (e.g., tip not properly modeled, root-blade transition). Reasonable agreement is 

found for the intermediate stations. The results Figure 2 top show that the k-ω SST model, 

combined with wall functions and Fluent’s MRF capability, gives reasonable results for 

rotating blades as long as the flow remains attached. For higher wind speeds, when separated 

flow arises on the suction side of the blade, simulations are not as accurate when predicting 

the pressure distributions. These inaccuracies seem to be related to the mesh density, the 

turbulence modeling, and the transient nature of the flow. 

Figure 1 - Pressure distribution for 47% 

radial location for the NREL phase VI blade 

Figure 2 - Normal force coefficient vs r/R for 

the NREL phase VI blade 
70

Figure 8-3. Surface pressure and normal force coefficient distributions, comparing data from 

experiment with data from CFD models.

8.6 Conclusions and Future Work

In general, the coarse spanwise instrumented locations measured over the blade experiment is

not densely arrayed enough for the 3D analysis, mainly in the radial direction. In addition, 

data are acquired only at the blade surface, and therefore it has not been possible to analyze 

the flow behavior above the blade surface. Future flow visualizations might help researchers

understand the complete flow topology.

The rotation study analyzed cp and Cn using mainly mean values. The flow behavior for 

rotating blades, however, was extremely unsteady and complex. Therefore, further 

examination of the instantaneous measurements might offer more insight into the phenomena. 

The study of the dynamic stall on the NREL 3D parked blade sections reached one of the 

most important practical conclusions, related to the possibility of modeling the dynamic stall 

behavior of a 3D parked blade through a 2D modeling, and considering appropriate inputs 

related with the steady data of the blade sections. Analyzing dynamic stall for a 3D parked 

blade and the knowledge acquired could improve dynamic stall modeling on real 3D parked 

blade applications. Future work should investigate 3D rotating blade dynamic stall and 

develop improved modeling tools.

Finally, the Annex XX allowed CENER to produce the necessary methods and capabilities to 

compute rotor simulations using CFD. Analyzing CFD results and comparing them to 

experimental data helped CENER engineers understand complex flow phenomena on a 

rotating blade. CENER’s in-house CFD code is currently being validated and compared to 

experimental data and Fluent results. Future work will investigate 3D rotational effect and 

spanwise flow components.

8.7 Bibliography

Cabezón, D.; Ferrer, E. (2005). “Modelling Wake Effects Using Two CFD Techniques.”

EUROMECH Colloquium 464b: Wind Energy, October 04-07, Oldenburg, Germany.
71

Ferrer, E.; Munduate, X. (2005). “CFD Activities at CENER.” IEA Annex XX Symposium, 

Pamplona, Spain, May 2005.

Ferrer, E.; Munduate, X. (2006). “Preliminary CFD Simulations of the NREL Phase VI 

Rotating Blade.” EWEC 2006.

Ferrer, E.; Munduate, X. (2007). “Wind Turbine Blade Tip Comparison Using CFD.” J. 

Phys.: Conf. Ser. 75, 012005 - 10 pp.

Gomez-Iradi, S.; Barakos, G.N.; Munduate, X. (2007). “Development of a CFD Method for 

Aerodynamic Analysis of Large Diameter Horizontal Axis Wind Turbines.” IEA Annex XX, 

June 2007, Risoe, Denmark.

Gonzalez, A.; Munduate, X. (2006). “Flow Development on the NREL UAE Phase VI 

Rotating Blade.” IEA Annex XX, Kiel, Germany.

Gonzalez, A.; Munduate, X. (2007). “Three-Dimensional and Rotational Aerodynamics on 

the NREL Phase VI Wind Turbine Blade.” 45th AIAA Aerospace Sciences Meeting and 

Exhibit, Reno, NV, EEUU.

Gonzalez, A.; Munduate, X. (2007). “Experimental Analysis of Dynamic Stall on the NREL 

Phase VI Parked Blade.” IEA Annex XX, Risoe, Roskilde, Denmark.

Munduate, X. (2003). “Research Summary IEA Annex XX HAWT Aerodynamics and 

Models from Wind Tunnel Measurements.” IEA Annex XX. Golden, CO: EEUU.

Munduate, X.; Gonzalez, A. (2004). “Aerodynamic Analysis from Three Dimensional Wind 

Turbine Blade Measurements at Zero Yaw Angle.” IEA Annex XX, Montreal, Quebec, 

Canada.

Munduate, X.; Gonzalez, A. (2005). “3D Blade Geometry Stall at Parked and Rotating 

Conditions on the NREL Phase VI at Zero Yaw Angle.” IEA Annex XX, Pamplona, Navarra, 

Spain.
72

9.0 Research Summary—Sweden

Organizations:Gotland University and the Royal Institute of Technology

Researcher: Stefan Ivanell,stefan.ivanell@hgo.se

9.1 Background and Motivation

Knowledge about wind power technology has increased over the years. Lanchester and Betz 

were the first to predict the maximum power output of an ideal wind turbine. The major 

breakthrough was achieved by Glauert, who formulated the Blade Element Momentum

(BEM) method in 1935.

The BEM method still underlies today’s design codes. It has, however, been extended to 

allow for dynamic events with patchwork and ad hoc engineering methods, sometimes of 

doubtful quality. 

Therefore, aerodynamic research today is shifting toward a more fundamental approach 

because the basic aerodynamic mechanisms are not fully understood and the importance of 

accurate design models increases when turbines are larger. Recently completed Navier-Stokes 

calculations and supercomputers open new possibilities. 

Annex XX has focused on evaluating existing aerodynamic simulation methods to allow

simulations that give results that would make it possible to evaluate the flow field behind the 

turbine, i.e., the wake. These simulations support the study of the physical behavior of the 

wake. The work can be divided into three groups:

1. Simulation Method. The first aim was to find a suitable method to simulate the flow 

field around the turbine and also resolve the wake so that it would be possible to 

evaluate the flow field behind the turbine. Particular attention was given to the 

circulation directly behind the blades to give a better understanding of the physics 

close there.

2. Stability Analysis. The second aim was to understand the basic mechanisms 

underlying the breakdown of the flow structure in the wake. This becomes especially 

important when looking at interactions between two or more wakes. Optimizing the 

number of turbines and their positions in parks requires knowledge about the length of 

the wake behind the turbines. Therefore knowledge about the basic mechanisms 

underlying the breakdown of the distinct tip vortex is important.

3. Park Simulation. The overall aim is to be able to simulate an entire park. A suitable 

method, limitations in that method, basic mechanisms behind breakdown of the flow 

structure, and so on is, however, necessary knowledge before setting up an advanced 

simulation model for a park. When that is achieved and a simulation model for an 

entire park exists, researchers can study how to optimize not only one or two turbines 

but also clusters of many turbines. This opens possibilities to studying how changing 

the local energy extraction, turbine spacing, yaw angle, and park design affect total 

park efficiency. The results of interaction between the wakes will also be important 

from a fatigue load point of view. 
73

9.2 Data Used and Methods Employed

All Actuator Line Method (ACL) simulations used data from the Tjaereborg turbine. The 

Tjaereborg turbine was operational between 1988 and 1998, when researchers made extensive 

measurements and tests on the turbine. The turbine was 9 km southeast of the city of Esbjerg 

in western Denmark. Tjaereborg was a three-blade, upwind horizontal axis turbine. The blade 

profiles were NACA 4412-43 with a blade length of 29 m giving a rotor diameter of 61 m. 

The chord length was 0.9 m at the tip, increasing linearly to 3.3 m at hub radius 6 m. The 

blades are twisted 1± per 3 m. The tip speed was 70.7 m/s and the rotor solidity was 5.9%. 

The rated power was 2 MW, and a continuously variable pitch operating between 0° and 35°

degree in production mode controlled the output. The hub height is 60 m.

Researchers used the EllipSys3D code for all simulations. EllipSys3D is a general purpose,

three-dimensional (3D) solver developed by N.N. Sørensen and J. Michelsen. The blades are 

represented by volume forces. The ACL method, introduced by Sørensen and Shen was used. 

The limitation in the actuator-disk method, where the influence of the blades is taken as an 

integrated quantity in the azimuthal direction, has now been overcome using techniques in 

which volume forces are distributed along lines representing each blade. A full CFD 

simulation would require a great number of nodes at the blades to resolve the boundary layer. 

With this method, node points could be saved at the blades. This method therefore opens new 

possibilities for turbine simulations with a well-resolved wake. But the drawback is that the 

method still is based on tabulated data from which CL and CD are functions of alpha; 

therefore, they depend on the quality of these experimental data. R. Mikkelsen implemented 

this method with the EllipSys3D code.

9.3 Research Results

The simulations resulted in flow fields corresponding to expected flow according to 

experiments and experience. Verifications of the results by comparison with measurements 

data have, however, not been performed in this project. Recent data are, however, available 

from wake measurements from the MEXICO (Model Rotor Experiments In Controlled 

Conditions)project. When the work was performed, no model scale wake data were available 

to verify the simulated results against.. The result, however, was as expected, and Helmholtz’

law is valid in the wake flow (Ivanell et al. 2005).

The conclusion can therefore be made that CFD simulations using volume force approaches 

can simulate complicated wake flow. The importance of the possibility of performing

relatively fast studies will increase with the challenge of accurate calculations of the 

performance and the fatigue load of tomorrow’s large-scale wind turbine parks. The work can

also be used for further studies of basic flow features in the wake. The simulated flow fields 

could also be used as initial conditions for stability studies or as inlet in other studies of

features such as fatigue loads. Also, Ivanell and others (2006) carried out an initial numerical 

stability study of the tip vortex.
74

9.4 Conclusions and Future Work

This study examined detailed wake flow using the ACL technique, but less time-consuming 

methods such as ACD (actuator disk) methods could also be used to simulate larger turbine 

parks because the ACD method is based on the same idea of representing the blades by 

volume forces that correspond to the energy extraction.

The initial stability study summarized herein gives background knowledge to the full 

numerical stability study of the tip vortex.

After a stability study, full park simulations could be performed using the actuator-disk

methods. Future work will therefore concentrate on further stability investigations and later on 

full park simulations.

9.5 Bibliography

Ivanell, S. (2005). Numerical Computations of Wind Turbine Wakes. Licentiate thesis, ISSN 

0348-467X, KTH.

Ivanell, S.; Sørensen, J.N.; Mikkelsen, R.; Henningson, D. (2006). “Numerical Analysis of the 

Tip and Root Vortex Position in the Wake of a Wind Turbine.” Journal of Physics, 

Conference Series Volume 75, Springer.
75

10.0 Research Summary—United States

Organizations: NREL’s National Wind Technology Center (NWTC)

Researcher: Scott Schreck, scott_schreck@nrel.gov

10.1 Background and Motivation

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 derives 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

because of the simultaneous presence and interaction of three-dimensionality, unsteadiness, 

separation, and rotational influences. At zero yaw, rotational augmentation is the main 

increment to blade aerodynamics.

Using Unsteady Aerodynamics Experiment (UAE) data acquired in during the NASA Ames 

wind tunnel test, early analyses concluded that rotational augmentation 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 (Schreck and Robinson 2002).

Subsequent work 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 (Schreck and Robinson 2003a).

Closely related research correlated experimentally and computationally derived surface flow 

topologies for the rotating blade, finding excellent agreement and significantly generalizing 

comprehension of the flow field (Schreck and Robinson 2003b; Schreck, Sørensen, and 

Robinson 2007). Other work isolated tip speed ratio as an independent variable, and 

quantified the influence of tip speed ratio on aerodynamic forces and underlying flow field 

structure (Schreck and Robinson 2004).

Prior to the inception of Annex XX, analyses of UAE data had isolated several of the main 

flow field structures and processes principally responsible for the most prominent steady state 

aspects of rotational augmentation. The U.S. effort in connection with Annex XX thus 

concentrated on the time-varying components of rotational augmentation, which ultimately 

were found to be significant in magnitude and thus important to the field of wind turbine 

aerodynamics. In particular, these analyses were directed toward revealing relationships 

between the mean and unsteady components operating in the rotationally augmented blade 

flow field.
76

10.2 Data Used and Methods Employed

10.2.1 Rotating Blade Data

U.S. research in support of Annex XX used zero yaw data extracted from the H and S 

sequences. In these sequences, all data were collected with the turbine rotating at a constant 

speed of 71.6 RPM. For the zero yaw data, the 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 and 25 m/s, in nominal 

increments of 1 m/s. At each test section velocity, a 30 second data record was acquired.

For sequence H, the five hole probes and stalks were mounted on the blade, and LFA was 

measured concurrently with other blade aerodynamic properties. For sequence S, 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 caused by the stalks or probes.

10.2.2 Parked Blade Data

To isolate the effects of rotational influences, a baseline was established wherein these effects 

were excluded. This was accomplished by halting turbine blade rotation and acquiring 

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

conditions, the instrumented turbine blade was first fixed at the 12 o’clock azimuth position.

Then, 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. This process was repeated twice, once at U∞

= 20 m/s and once at U∞ = 30 m/s.

Blade pitch angle was defined as the angle between the blade tip chord line and the blade 

rotation plane, and pitching that moved the leading edge upwind was defined as positive. One 

13 second data record was acquired at each pitch angle. At each pitch angle, appropriate 

delays were included to allow time for pitching, blade flow field equilibration, and wake 

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

of Cn. Finally, time records of cp and Cn were time averaged to yield mean values for these 

quantities.

The 30 second time records of cp from the H and S sequences were integrated over the 

sectional chord to obtain time records of Cn at r/R = 0.30, 0.47, 0.63, and 0.80. Data were 

available at 0.95R, but were not analyzed because of the potentially confounding influence of 

the blade tip vortex. At these radial locations, time records of cp and Cn were processed to 

obtain means and standard deviations for cp and Cn. To separate time variations caused by 

rotational flow physics from tower wake influences, only those data corresponding to the 

upper half of the rotor disk were extracted and analyzed.

Surface pressure coefficient (cp) mean and standard deviation data were exploited to infer the 

blade flow field topology. Based on a large body of prior research, a surface pressure 

distribution exhibiting significant chordwise pressure gradient (dp/dx) was deemed to indicate 

attached flow and an attenuated pressure gradient was considered indicative of separated flow.
77

In addition, boundary layer separation and shear layer impingement elicit localized surface 

pressure fluctuations significantly greater than those observed at locations occupied by 

attached flow (Schreck and Robinson 2003a). To increase confidence in inferences of blade 

surface flow topology, and to generalize analyses above the blade surface, computations were 

used following validations involving UAE H and S sequence zero yaw data (Schreck, 

Sørensen, and Robinson 2007). These relationships were used to identify the locations and 

movements of boundary layer separation and shear layer impingement on the turbine blade 

suction surface. 

10.3 Research Results

10.3.1 Flow Field Topology

Figure 10-1 shows standard deviation in 

Cn versus local inflow angle (LFA) for r/R 

= 0.30, 0.47, 0.63, and 0.80. At all four 

radial locations, Cn standard deviation 

exhibited values between 0.01 and 0.03 at 

low LFA. In the range 17.6° ≤ LFA ≤ 

26.4°, however, Cn standard deviation 

increased rapidly with LFA and reached 

maxima ranging from 0.16 to 0.21. In 

Figure 10-1, Cn standard deviation 

increased abruptly from low values 

characteristic of nonrotating blade levels 

to elevated values that substantially 

exceeded those observed on the 

nonrotating blade. Further, although the four 

radial locations were separated by significant 

distances, the abrupt Cn standard deviation 

increase occurred over a relatively constrained 

LFA range. This sudden, substantial shift in 

aerodynamic loading, confined to a narrow LFA 

range but enveloping most of the blade radius, 

implies a radical shift in the rotating blade flow 

field structure.

Figure 10-2 shows schematics of separating and 

impinging blade flow field topologies, determined 

in previous work (Schreck and Robinson 2003a; 

2003b; Schreck, Sørensen, and Robinson 2007).

The upper portion of Figure 10-2 depicts 

boundary layer separation, with the separation site 

represented by the filled circular symbol. Flow 

is attached upstream of the separation, and a 

streamline emanates from the separation site.

The notation LSEP shows the length of the 

Figure 10-1. Cn standard deviation vs. LFA 

for rotating blade at r/R = 0.30, 0.47, 0.63, and 

0.80

Figure 10-2. Mean flow field topologies for 

boundary layer separation (upper) and 

shear layer impingement (lower)
78

separated region. The boundary layer separation flow state occurs at moderate LFA levels, 

which are below or near LFA corresponding to two-dimensional stall.

The lower part of Figure 10-2 shows shear layer impingement, in which the boundary layer 

initially separates from the surface near the leading edge. Thereafter, the shear layer arches 

over the blade and impinges on the surface aft of the leading edge, indicated by the filled 

circular symbol. Presumably, appreciable concentrations of vorticity reside within the region 

enclosed by the shear layer, producing a circulating flow structure. The notation LIMP shows 

the length of the region encompassed by impingement. The shear layer impingement flow 

state generally occurs at elevated LFA, at LFA levels that substantially exceed LFA 

corresponding to two-dimensional stall.

In Figure 10-1, the LFA at which the separation point reached the blade leading edge is 

indicated by the single filled symbol (circled for clarity) on each of the four plots. Notably, 

these four solid symbols are confined to a narrow LFA range of 19.9o ≤ LFA ≤ 26.4o. Further, 

these data points are centered in the steeply rising portion of the Cn standard deviation curve.

This implies that forward separation point movement was responsible for increasing Cn

standard deviation. This also implies that the highest Cn standard deviation levels were 

produced by an impinging flow field structure.

10.3.2 Unsteady Load Magnitudes

The linkage between Figures 10-1 and 10-2 implies that a consistent relationship exists 

between Cn standard deviation levels and mean separation/impingement length. Cn standard 

deviation was thus correlated with separation length in Figure 10-3 and with impingement 

length in Figure 10-4. Figure 10-2 defines separation length (LSEP) and impingement length 

(LIMP).

In Figure 10-3, for LSEP = 0.0 c (separation at trailing edge), Cn standard deviations for all 

four radial locations are 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.

Careful examination of Figure 10-3, 

however, 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 

Figure 10-3. Correlation between Cn standard 

deviation and separation length (LSEP)
79

aerodynamic interactions also are a likely source for systematic deviations from the aggregate 

linear fit.

For separation lengths of 1.0, corresponding to the arrival of separation near the leading edge, 

Cn standard deviation levels averaged approximately 0.1. Previous buffet research using 2D

airfoils reported similar maximum Cn standard deviation levels (Coe and Mellenthin 1954; 

Polentz, Page, and Levy 1955). These efforts did not measure separation location or extent.

Stabilization of Cn standard deviation at elevated loading conditions, however, presumably 

indicated separation arrival in the leading edge vicinity.

In Figure 10-4, for LIMP = 0.0 (impingement at leading edge) to 1.0 c (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 10-4 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 10-3.

As in Figure 10-3, data points in Figure 

10-4 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. Strong aerodynamic threedimensionality has, however, previously 

been identified in the UAE operating 

range corresponding to the Figure 10-4 

data (Schreck, Sørensen, and Robinson

2007). This flow field change causes spanwise flow components that alter impingement 

length and modify the physics of unsteady aerodynamic force generation.

Comparing the aggregate linear least squares fit for separation (Figure 10-3) with that for 

impingement (Figure 10-4) 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 actively 

to surface flow state during separation than during impingement.

10.3.3 Unsteady Load Severity

Thus far, 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 ×120 ft wind tunnel, which provided an inflow environment 

free of significant inflow turbulence or velocity gradients (Zell 1993). To quantify Cn standard 

deviations that would occur in response to turbulent atmospheric inflow, the UAE was 

Figure 10-4. Correlation between Cn

standard deviation and impingement length 

(LIMP).
80

modeled using the YawDyn (Laino and Hansen 2003) and AeroDyn (Laino and Hansen 2002)

models in conjunction with turbulent inflow files from TurbSim (Jonkman and Buhl, 2006).

Figures 10-5 and 10-6 show Cn standard deviation data from the UAE wind tunnel test along 

with Cn standard deviation data extracted from YawDyn/TurbSim model output files. “UAE 

Test Data” in these figures are the same as those in Figure 10-1, except that the test data in 

Figures 10-5 and 10-6 are plotted as a function of U∞ to maintain consistency with the model 

data. For each radial location, Cn standard deviation data were extracted from model output 

files for three turbulence intensities (5%, 10%, and 15%) for each of five mean wind speeds 

(U∞ = 5, 10, 15, 20, and 25 m/s).

Figure 10-5 shows Cn standard deviation test measurements and model predictions for the 

0.30R radial location. The 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 10-6 shows Cn standard deviation test measurements and model predictions for the 

0.80R radial location. As in Figure 10-5 for 0.30R, 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. Although the 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 

percent. All 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.

Figure 10-6. Cn standard deviations 

measured by UAE and predicted by 

YawDyn/TurbSim at 0.80R.

Figure 10-5. Cn standard deviations 

measured by UAE and predicted by 

YawDyn/TurbSim at 0.30R.
81

Figures 10-5 and 10-6 clearly show that blade operation in either the stalled regime in stable, 

uniform 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 10-5 and 10-6 approximates the turbulence intensity range of 13 to 20 percent 

encompassed by various design standards (Burton et al. 2001). Similar data were analyzed for 

0.47R and 0.63R, revealing trends consistent with those shown by Figures 10-5 and 10-6. 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.

10.4 Conclusions and Future Work

Blade surface pressure data were acquired from the NREL Unsteady Aerodynamics 

Experiment horizontal axis wind turbine tested under axisymmetric conditions in the NASA 

Ames 80 ft ×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 used for turbulent inflow conditions. These data and analyses 

support the following conclusions regarding unsteadiness associated with rotational 

augmentation.

Before the onset of rotational augmentation, boundary layer separation advances forward over 

the blade in response to increasing local flow angle. After separation arrival at the blade 

leading edge, shear layer impingement commences in the same vicinity. Subsequently, the 

impingement site advances aft on the blade chord in response to increasing local flow angle.

Forward movement of the separation point from the leading edge amplifies aerodynamic force 

standard deviations. Aerodynamic force standard deviations are most significantly amplified, 

however, as the separation point reaches the leading edge and the flow field transitions to an 

impinging flow field mediated by shear layer impingement.

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 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 stable, uniform inflows 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. These analyses have concentrated on the principal structures 

underlying rotational augmentation and examined the amplitudes of aerodynamic force 

unsteadiness. To further improve this understanding, future work will need to dissect these 

structures in more detail using combined experimental and computational approaches. To gain 
82

better resolution of physical processes through measured data, it is likely that spectral and 

statistical techniques could be productively applied.

10.5 Bibliography

Burton, T.; Sharpe, D.; Jenkins, N.; Bossanyi, E. (2001). Wind Energy Handbook. Chichester, 

UK: John Wiley and Sons, p. 22.

Coe, C.; Mellenthin, J. (1954). “Buffeting Forces on Two-Dimensional Airfoils as Affected 

by Thickness and Thickness Distribution.” NACA RM A53K24.

Jonkman, B.; Buhl, M. (September 2006). TurbSim User’s Guide. NREL/TP-500-39797.

Golden, CO: NREL.

Laino, D.; Hansen, A.C. (December 2002). AeroDyn User’s Guide. Available at

http://wind/designcodes/simulators/aerodyn/AeroDyn.pdf.

Laino, D.; Hansen, A.C. (April 2003). YawDyn User’s Guide. Available at

http://wind/designcodes/simulators/yawdyn/YawDyn.pdf.

Polentz, P.; Page, W.; Levy, L. (May 1955). “The Unsteady Normal-Force Characteristics of 

Selected NACA Profiles at High Subsonic Mach Numbers.” NACA RM A55C02.

Schreck, S.; Robinson, M. (2002). “Rotational Augmentation of Horizontal Axis Wind 

Turbine Blade Aerodynamic Response.” Wind Energy (5:2/3); pp. 133–150.

Schreck, S.; Robinson, M. (2003a). “Boundary Layer State and Flow Field Structure 

Underlying Rotational Augmentation of Blade Aerodynamic Response,” J. of Solar Energy 

Eng. (125); pp. 448–456.

Schreck, S.; Robinson, M. (2003b). “Structures and Interactions Underlying Rotational 

Augmentation of Blade Aerodynamic Response.” AIAA-2003-0520.

Schreck, S.; Robinson, M. (January 2004). “Tip Speed Ratio Influences on Rotationally 

Augmented Boundary Layer Topology and Aerodynamic Force Generation.” AIAA-2004-

0663.

Schreck, S.; Robinson, M. (January 2005). “Unsteadiness in HAWT Blade Aerodynamic 

Forces and Flow Field Structures During Rotational Augmentation.” AIAA-2005-0776.

Schreck, S.; Sørensen, N.; Robinson, M. (2007). “Aerodynamic Structures and Processes in 

Rotationally Augmented Flow Fields.” Wind Energy (10:2); pp. 159–178.

Zell, P. (1993). “Performance and Test Section Flow Characteristics of the National FullScale Aerodynamics Complex 80- by 120-Foot Wind Tunnel.” NASA TM 103920.
83 

11.0 Report Conclusion

Annex XX was established in 2003 to exploit the data acquired during testing of the National 

Renewable Energy Laboratory (NREL) Unsteady Aerodynamics Experiment (UAE) 

wind turbine in the National Aeronautics and Space Administration (NASA)Ames wind 

tunnel. Completion of this test constituted a fundamental advance because it furnished 

accurate and reliable experimental measurements having high spatial and temporal resolution, 

for a realistic rotating blade geometry, under closely matched conditions of dynamic 

similarity, and in the presence of strictly controlled inflow conditions. 

Over the four four year life of Annex XX, these data were used by dozens of researchers 

representing the eight participating countries of Canada, Denmark, Greece, The Netherlands, 

Norway, Spain, Sweden, and the United States. By the time Annex XX concluded in 2007, a 

broad variety of high quality documentation dealing with diverse aspects of wind turbine 

aerodynamics had been produced. The breadth of research would be difficult to capture in a 

reasonably brief summary. Nonetheless, some brief observations are noted here. 

Canadian researchers at ETS combined a Navier-Stokes solver with the actuator disk 

approach. In the resulting actuator-surface model, singular surfaces are used to represent the 

action on the flow of the rotor blades. The recent application of the model to the case of a 

wind turbine using rotating surfaces demonstrated that the new model increases accuracy in 

the determination of the wake induction on the blade aerodynamics when compared with 

models based on the actuator disk representation. 

In Denmark, work at Risoe National Laboratory and DTU was balanced between engineering 

models and CFD methods. Further refinement of the aeroelastic codes HAWC and HAWC2 

considered modeling aspects including inboard stall delay, dynamic stall, yawed flow 

induction, and tip correction. CFD work centered around the Navier-Stokes model Ellipsys3D, and included both Reynolds Averaged Navier-Stokes and Detached Eddy Simulation 

(DES) methods. CFD was applied to parked blade conditions, axisymmetric operation, 

correction of 2D airfoil loads to 3D, yawed flow involving dynamic stall, operation with step 

pitch, and downwind operation. 

In The Netherlands, ECN compared UAE measurements with predictions from two codes, the 

aero-elastic code PHATAS and the free wake lifting line code AWSM. UAE measurements 

used corresponded to non-yawed flow, yawed flow and fast pitching actions. Under 

axisymmetric conditions for lower wind speeds, PHATAS agreed well with the measured 

data, as did AWSM. At higher wind speeds, PHATAS displayed some discrepancies that 

were attributed to three-dimensionality in the flow field, while AWSM was not used at these 

higher wind speeds. The investigation at yawed flow emphasized azimuthal variation of 

normal force due to advancing/retreating blade effect and variation in induced velocity from 

the skewed wake. The AWSM code does predict the above mentioned radial dependence in 

the azimuthal variation of the induced velocity. The resulting azimuthal variation in normal 

force and the consequent yawing moments agree very well with the measurements at all radial 

positions. The PHATAS code is slightly less accurate, but comparison of the induced 
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velocities, as calculated by the PHATAS and AWSM codes generally shows good agreement, 

even though the underlying models use different methodologies. 

Also in The Netherlands, the Technical University of Delft undertook work in three related 

areas: 1) Analysis of the UAE measurements, 2) Angle of attack estimation from blade 

pressure measurements using a free-wake vortex model, and 3) Evaluation of engineering 

models versus CFD methods for blade rotational augmentation. In the first area, subtle but 

inevitable errors in UAE measurements were detected and methods were formulated for 

minimizing adverse impacts when analyzing the data. In the second set of studies, it was 

found that by using a free-wake vortex model it is possible to more accurately derive the 

angle of attack at the blades from blade pressure measurements in both axial and yawed 

conditions. In the third area, it was concluded that CFD methods, like RFOIL and FLUENT 

generally are reliable if carefully used and are to be preferred to conventional engineering 

methods. 

In Norway, work focused on the investigation of 3-D behavior of the flow and analysis of 

steady/unsteady behavior of flow patterns at different rotor sections. To estimate stall onset 

and 3-D effects, a method using parts of the pressure distribution at the rotor blade (trailing 

edge pressure) combined with a Fourier transformation was developed. The UAE 

experimental data were sampled with high frequency; this allowed the use of FFT on the 

results and transformation of measured time series into the frequency plane. In addition, the 

UAE test results were further used in the modification of aero-elastic codes, which were used 

in the investigation of floating offshore wind turbine concepts. 

At CENER in Spain, Annex XX research focused on three issues. The first was achieving an 

improved physical understanding of rotational augmentation for rotating blades. The second 

was predicting dynamic stall on parked blades, including the onset and three-dimensionality 

of the process, and assessment of the Beddoes-Leishman model. Finally, validation of 

Navier-Stokes computational tools for rotating blades was pursued, to validate commercial 

and in-house CFD solvers, and to create new methodologies for the use of CFD techniques at 

CENER. 

Research in Sweden involved evaluation of existing aerodynamic simulation methods to 

evaluate wake flows generated by operating turbines, especially the circulation immediately 

downstream of the blades. A supporting objective was improved comprehension of the basic 

mechanisms responsible for wake breakdown, which becomes especially important when 

considering interactions between two or more wakes. Ultimately, the overall aim is to 

simulate an entire wind park. However, knowledge about suitable method, limitations in that 

method, basic mechanisms behind breakdown of the flow structure are necessary before 

setting up an advanced simulation model for a park. 

Finally, in the United States, analyses of time varying surface pressure and normal force were 

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

standard deviation during axisymmetric conditions that included rotational augmentation. 

Complementary model predictions of time varying normal force coefficient standard 

deviation also were provided for turbulent inflow conditions. These results confirm that, prior 
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to the onset of rotational augmentation, boundary layer separation advances forward over the 

blade, and then impingement advances aft on the blade chord, both in response to increasing 

local flow angle. Forward movement of the separation point from the leading edge amplifies 

aerodynamic force standard deviations, but aerodynamic force standard deviations are most 

significantly amplified as the separation point reaches the leading edge and the flow field 

transitions to an impinging flow field mediated by shear layer impingement. Finally, normal 

force coefficient standard deviations observed for stable, uniform inflows are of the same 

magnitude as normal force coefficient standard deviations produced by routine turbulent 

inflows. 

During the four year lifetime of Annex XX, participants productively used the UAE wind 

tunnel database in support of research that significantly improved physical comprehension of 

turbine flow fields. The same data were effectively exploited to enhance the accuracy and 

reliability of a broad range of turbine aerodynamics models. However, given the extremely 

complex nature of wind turbine flow fields, important opportunities remain for better 

understanding wind turbine fluid mechanics and for achieving more accurate and reliable 

modelling capabilities. Further research is needed to address these challenges, and to support 

the continued growth of wind energy worldwide.