QBladeAE Thesis

Technische Universität Berlin

Fakultät V

Institut für Strömungsmechanik und Technische Akustik

Fachgebiet für Experimentelle Strömungsmechanik

Diploma Thesis

A BEM Based Simulation-Tool for Wind

Turbine Blades with Active Flow Control

Elements

from : Guido Weinzierl

Date : 19.04.2011

Course : Strömungslehre II

1st Supervisor : Prof. Dr.-Ing. C. O. Paschereit

2nd Supervisor : Dipl.-Ing. G. Pechlivanoglou

Preface

This diploma thesis was written in order to fulll the requirements of obtaining the

degree Dipl.-Ing. at Berlin University of Technology. The work was carried out in

collaboration with Smart Blade GmbH and the Wind Energy Group at the Institute of

Fluid Dynamics and Technical Acoustics (ISTA) at the Berlin University of Technology.

I especially want to thank Georgios Pechlivanoglou for his great support and supervi-

sion of the project. Many thanks as well to Oliver Eisele from Smart Blade GmbH, for

the numerous brainstorming sessions and Smart Blade GmbH in general, for generously

funding this thesis.

Abstract

This thesis describes the development of a software tool which provides a method to

investigate the use of dierent active ow control (AFC) concepts for load reduction

and power regulation of wind turbines. The software features an aeroelastic model

to calculate the dynamic response of the wind turbine structure. The program is an

extension of QBlade, an open-source GUI application for wind turbine calculations.

The user can easily dene a wind turbine blade on which various active elements

can be positioned. The dierent aerodynamic characteristics of the AFC-elements

are considered by their individual lift and drag polars. These polars can either be

calculated using an implemented two-dimensional panel method code (XFoil) or im-

ported to provide an interface to wind tunnel measurement data or CFD calculations.

To model the aerodynamic and structural behavior of the turbine, a binding to the

aerodynamic analysis routines AeroDyn and the structural analysis code YawDyn is

implemented. These simulation codes are provided by the National Wind Technology

Center (NWTC) of the National Renewable Energy Laboratory (NREL).

In order to control the active elements on the blade, two control approaches are

provided: a simple optimization loop, to nd an optimal actuator position for each time

step and a PID controller. Both approaches can be used, for example, to minimize the

root bending moment of the wind turbine blades, either by keeping local blade element

forces constant or by minimizing blade deections or blade deection rates.

Rajabi

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Rajabi

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Zusammenfassung

Die vorliegende Diplomarbeit beschreibt die Entwicklung einer Software, die es er-

möglicht den Einsatz von Elementen zur aktiven Strömungskontrolle (AFC) zur Las-

treduktion und Leistungsregelung an Windkraftanlagen zu untersuchen. Die Software

beinhaltet ein aeroelastisches Simulationsmodul, um den dynamischen Einuss der

AFC-Elemente auf die Struktur der Windturbine zu berechnen. Das Programm ist

eine Erweiterung von QBlade, einer open-source Anwendung zur Rotorblattentwick-

lung und -berechnung nach der Blatt-Element-Methode.

Der Benutzer kann auf einfache Art und Weise ein Rotorblatt entwerfen, auf dem

mehrere aktive Elemente platziert werden können. Der unterschiedliche aerodynamis-

che Einuss der verschiedenen Elemente, ist durch ihre individuellen Auftriebs- und

Widerstandspolare gekennzeichnet. Die Polaren können dabei entweder direkt über

eine eingebaute zweidimensionale Panelmethode (Xfoil) berechnet werden, oder sie

werden importiert, um die Verbindung zu Windkanalversuchen oder CFD Simula-

tionen herzustellen. Um das aerodynamische und strukturelle Veralten der Anlage

zu modellieren, werden die aerodynamischen Berechnungsroutinen AeroDyn und der

strukturdynamische Berechnungscode YawDyn benutzt. Die Programme werden vom

National Wind Technology Center (NWTC) des National Renewable Energy Labora-

tory (NREL) bereitgestellt.

Um die aktiven Elemente auf dem Rotorblatt zu regeln, werden zwei Herange-

hensweisen verfolgt: Zum einen eine einfache Optimierungsschleife, um für jeden

Berechnungszeitschritt die optimale Aktuatorposition zu nden, zum anderen ein PID

Regler. Beide Regelstrategien können beispielsweise dazu genutzt werden, das Blat-

twurzelbiegemoment zu reduzieren. Dazu werden entweder die lokalen Kräfte am Blat-

telement konstant gehalten, oder die Rotorblattbiegung oder deren Änderungsrate

minimiert.

Contents

1 Introduction 1

I Model Theory 4

2 Aeroelastic model 5

3 Aerodynamics 8

3.1 Wake modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.1.1 Blade element momentum theory (BEM) . . . . . . . . . . . . . 10

3.1.2 Generalized dynamic wake model (GDW) . . . . . . . . . . . . 14

3.2 Airfoil aerodynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2.1 2D static airfoil characteristics . . . . . . . . . . . . . . . . . . . 15

3.2.2 Polar extrapolation . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2.3 Unsteady blade element aerodynamics . . . . . . . . . . . . . . 22

3.2.4 Stall delay and 3D eects . . . . . . . . . . . . . . . . . . . . . 26

3.3 Tower shadow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4 Structural dynamics 28

4.1 YawDyn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5 Turbulent wind simulation 34

6 Active Flow Control 36

Contents VI

II Software 39

7 QBladeAE 40

7.1 Active Flow Control simulation . . . . . . . . . . . . . . . . . . . . . . 41

7.1.1 Optimization loop . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7.1.2 PID controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

7.2 Blade related simulation parameters . . . . . . . . . . . . . . . . . . . . 49

7.2.1 QBlade and NREL blade format . . . . . . . . . . . . . . . . . . 49

7.2.2 Blade mass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.2.3 Blade center of gravity . . . . . . . . . . . . . . . . . . . . . . . 50

7.2.4 Blade mass moment of inertia . . . . . . . . . . . . . . . . . . . 52

7.2.5 Torsional root spring constant . . . . . . . . . . . . . . . . . . . 52

7.2.6 Dynamic stall parameters . . . . . . . . . . . . . . . . . . . . . 52

7.3 Program modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

7.3.1 Blade design with active elements . . . . . . . . . . . . . . . . . 54

7.3.2 Aerodynamic representation of active elements . . . . . . . . . . 56

7.3.3 Wind eld simulation . . . . . . . . . . . . . . . . . . . . . . . . 57

7.3.4 Aeroelastic simulation . . . . . . . . . . . . . . . . . . . . . . . 57

III Simulation 60

8 Standard simulation 61

8.1 Turbine and blade model . . . . . . . . . . . . . . . . . . . . . . . . . . 61

8.2 Blade validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

8.3 Dynamic stall eects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

8.4 Yawed turbine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

8.5 Wind eld . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

8.6 Baseline simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

9 AFC simulation 71

9.1 Flap parameter study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

9.1.1 Flap positions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

9.1.2 Flap size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

9.1.3 Actuator speed and range . . . . . . . . . . . . . . . . . . . . . 79

Contents VII

9.1.4 Sensor delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

9.1.5 Multiple aps . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

9.2 Optimization loop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

10 Suggestions for future research 85

11 Conclusion 87

Bibliography 88

A Appendix 94

A.1 QBladeAE input les for YawDynAE . . . . . . . . . . . . . . . . . . . 94

A.2 Geometric blade design . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

List of Figures

1.1 Concept of a segmented wind turbine rotor blade with active elements

in form of trailing edge aps [52]. . . . . . . . . . . . . . . . . . . . . . 3

2.1 Local blade element velocities and inow angles. . . . . . . . . . . . . . 6

2.2 Local blade element forces. . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1 Summary of the various aerodynamic sources that contribute to the

airloads on a wind turbine [32]. . . . . . . . . . . . . . . . . . . . . . . 8

3.2 Rotor of a three-bladed wind turbine with the rotor radius R [18]. . . . 11

3.3 2D arfoil characteristics of a blade element. . . . . . . . . . . . . . . . . 16

3.4 Lift coecient cl(α) at dierent Reynolds numbers and xed/free tran-

sition for the DU 91-W2-250 airfoil. . . . . . . . . . . . . . . . . . . . . 17

3.5 Drag coecient cd(α) at dierent Reynolds numbers and xed/free tran-

sition for the DU 91-W2-250 airfoil. . . . . . . . . . . . . . . . . . . . . 18

3.6 Moment coecient cm(α) at dierent Reynolds numbers and xed/free

transition for the DU 91-W2-250 airfoil. . . . . . . . . . . . . . . . . . 18

3.7 Wind triangular for dierent radial positions [16]. . . . . . . . . . . . . 20

3.8 Exemplary time series of α for two radial positions at rin = 6.8m and

rout = 36.8m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.9 Extrapolated cl for the DU 91-W2-250 airfoil and cl according to the

at plate theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.10 Extrapolated cd for the DU 91-W2-250 airfoil and cd according to the

at plate theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.11 Dynamic stall events on a NACA 0012 airfoil (reprinted from [9] and [32]). 24

4.1 Components of a HWAT structural model . . . . . . . . . . . . . . . . 29

4.2 The equivalent hinge-spring model for the blade ap degree of freedom

[28]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

List of Figures IX

4.3 View of the HAWT dening selected terms and coordinate systems. All

angles are shown in their positive sense. The bold X,Y,Z axes are xed in

space and are the coordinates in which the wind components are dened

(VX, VY, VZ). Note that blade azimuth is zero when the blade is at the

6 o'clock position [28]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.1 Wind speed at hub height and inow velocity at blade tip including

rotation, wind shear and tower eect. . . . . . . . . . . . . . . . . . . . 35

5.2 3D wind eld from QBladeAE with 20x20 points. . . . . . . . . . . . . 35

6.1 Feedback ow control triad (after [25]). . . . . . . . . . . . . . . . . . . 37

7.1 QBladeAE embedded in QBlade and XFLR5. . . . . . . . . . . . . . . 41

7.2 Working principle of QBladeAE with input and output control to the

modied NREL codes. . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

7.3 Blade with two active elements, which are represented by using several

airfoil polars. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7.4 Schematic control circuit and control terminology. . . . . . . . . . . . . 44

7.5 Implementation of the optimization loop for nding the optimal polar

for each active section (element dependent). . . . . . . . . . . . . . . . 45

7.6 Implementation of the PID controller: one for each active element (blade

dependent). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

7.7 Exemplary control circuit for the PID controller using a trailing edge

ap as actuator and the blade ap rate as control variable. . . . . . . . 47

7.8 Dierent blade denition in QBlade and NREL format. . . . . . . . . . 49

7.9 Dierent blade masses over blade length and the used exponential ap-

proximation function [51]. . . . . . . . . . . . . . . . . . . . . . . . . . 50

7.10 Simplied geometric representation (rectangular cone) of a homogeneous

blade section for the calculation of the blade center of gravity. . . . . . 51

7.11 Dynamic stall related parameter using clcd-curve for automatically detect-

ing the critical static stall angle αstall. . . . . . . . . . . . . . . . . . . . 53

7.12 QBladeAE active blade design module. . . . . . . . . . . . . . . . . . . 54

7.13 QBladeAE multiple aerodynamic polar module. . . . . . . . . . . . . . 57

7.14 QBladeAE wind eld generator module (beta). . . . . . . . . . . . . . . 58

7.15 QBladeAE wind eld generator module (beta). . . . . . . . . . . . . . . 58

List of Figures X

8.1 3D view of blade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

8.2 Blade tip deection with three dierent root spring stinesses. The wind

inow is steady and constant over the whole rotor disk. . . . . . . . . . 63

8.3 Rotor power over wind speed calculated with QBlade and QBladeAE. . 64

8.4 Blade pitch over wind speed for power regulation. . . . . . . . . . . . . 65

8.5 Inuence of the dynamic stall model on the blade tip deection over

time and the angle of attack over radial position for the blade with a

pitch angle of θb = 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

8.6 Inuence of the dynamic stall model on the blade tip deection over

time and the angle of attack over radial position for the blade with a

pitch angle of θb = 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

8.7 Blade tip deection for dierent yaw angles. . . . . . . . . . . . . . . . 68

8.8 Turbulent wind speed time series in x-direction at a hub height of 89m

and a mean wind speed of 13ms. . . . . . . . . . . . . . . . . . . . . . 69

8.9 Results of baseline simulation. . . . . . . . . . . . . . . . . . . . . . . . 70

9.1 Overlapping airfoil contours for positive deection. Red: The original

DU-96-W-180 airfoil; Green: slightly deected exible ap; Red: fully

deected exible ap [41]. . . . . . . . . . . . . . . . . . . . . . . . . . 71

9.2 Overlapping airfoil contours for negative deection. Red: The original

DU-96-W-180 airfoil; Green: slightly deected exible ap; Red: fully

deected exible ap [41]. . . . . . . . . . . . . . . . . . . . . . . . . . 72

9.3 Lift and drag coecient over angle of attack cl(α) for exible ap at

four ap angles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

9.4 Extrapolated 360 cl-polar. . . . . . . . . . . . . . . . . . . . . . . . . . 73

9.5 Blade with 9 equidistant outer sections. . . . . . . . . . . . . . . . . . . 74

9.6 Load reduction of a single ap with a length of 1.5m at dierent radial

positions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

9.7 Load reduction of single aps with dierent lengths and dierent radial

positions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

9.8 Out-of-plane bending moment for baseline simulation and the single

13.5m aps with maximum load reduction. . . . . . . . . . . . . . . . . 78

9.9 Inuence of dierent actuator speeds on the load reduction for two ap

congurations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

List of Figures XI

9.10 Inuence of dierent actuator ranges and speeds on the load reduction

for a 9m ap conguration. . . . . . . . . . . . . . . . . . . . . . . . . 80

9.11 Flap angle for 9m ap over simulation time. . . . . . . . . . . . . . . . 81

9.12 Load reduction over controller time delay. . . . . . . . . . . . . . . . . 82

9.13 Local blade element force DFN at AE# 3 for the baseline, the single

PID controlled 13.5m ap and the multiple individually optimization

loop controlled aps. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

List of Tables

8.1 Turbine parameters used for the simulation. . . . . . . . . . . . . . . . 62

8.2 Blade structural parameters used for the simulation. . . . . . . . . . . . 63

9.1 Radial position of the 9 possible active elements. . . . . . . . . . . . . . 75

9.2 Load reduction for 1.5m ap at dierent radial positions. . . . . . . . . 75

9.3 Load reduction for dierent ap lengths and dierent radial positions. . 77

9.4 Load reduction for individual sections using the optimization loop. . . . 83

A.1 Blade geometric parameters in NREL format used for the simulation. . 98

Nomenclature

Greek symbols

α . . . . . . . . . . . . . . . angle of attack

β . . . . . . . . . . . . . . . local blade twist

χ . . . . . . . . . . . . . . . wake skew angle

γ . . . . . . . . . . . . . . . yaw angle

Ω . . . . . . . . . . . . . . blade angular velocity

φ . . . . . . . . . . . . . . . inow angle

ψ . . . . . . . . . . . . . . . blade azimuthal angle

ρ . . . . . . . . . . . . . . . air density

θ . . . . . . . . . . . . . . . local pitch angle

θp . . . . . . . . . . . . . . blade pitch angle

Roman symbols

a . . . . . . . . . . . . . . . axial induction factor

a′ . . . . . . . . . . . . . . tangential induction factor

B . . . . . . . . . . . . . . number of blades

c . . . . . . . . . . . . . . . chord length

cd . . . . . . . . . . . . . . drag coecient

cl . . . . . . . . . . . . . . lift coecient

cm . . . . . . . . . . . . . . moment coecient

Ct . . . . . . . . . . . . . . rotor thrust coecient

D . . . . . . . . . . . . . . blade element drag force

DFN . . . . . . . . . . blade element normal force

DFT . . . . . . . . . . . blade element tangential force

F . . . . . . . . . . . . . . force, Prandtl's tip-loss factor

L . . . . . . . . . . . . . . . blade element lift force

pn . . . . . . . . . . . . . . load normal to rotor plane

pt . . . . . . . . . . . . . . load tangential to rotor plane

Nomenclature XIV

PMA . . . . . . . . . . blade element pitching moment

Q . . . . . . . . . . . . . . rotor torque

r . . . . . . . . . . . . . . . local blade element radius

T . . . . . . . . . . . . . . rotor thrust

U∞ . . . . . . . . . . . . . inow velocity

ve,ip . . . . . . . . . . . . out-of-plane velocity due to structural deection

ve,op . . . . . . . . . . . . in-plane velocity due to structural deection

W . . . . . . . . . . . . . . incident velocity

AE . . . . . . . . . . . . . Active Element

AFC . . . . . . . . . . . Active Flow Control

AFMB . . . . . . . . . axial ap bending moment (out-of-plane)

BEM . . . . . . . . . . . blade element momentum theory

CFD . . . . . . . . . . . Computational Fluid Dynamics

GDW . . . . . . . . . . generalized dynamic wake model

GUI . . . . . . . . . . . . Graphical User Interface

HAWT . . . . . . . . . Horizontal Axis Wind Turbine

ISTA . . . . . . . . . . . Institute of Fluid Dynamics and Technical Acoustics

NREL . . . . . . . . . . National Renewable Energy Laboratory

NWTC . . . . . . . . . National Wind Technology Center

1 Introduction

The use of wind energy is continuously growing and in order to increase it's market

competitiveness, wind turbines must become even more cost eective. The key param-

eter therefore is e/kWh as in any other energy system. The cost of electricity generated

by modern wind turbines ranges from approximately 0.05 - 0.07 e/kWh at sites with very

good wind speeds to 0.09 - 0.11 e/kWh at sites with low wind speeds [14]. To cut costs,

the expenses for production, operation and maintenance have to be reduced while at

the same time the performance of wind turbines has to increase. One possibility to

achieve this is to build larger wind turbines which can extract more wind energy from

a bigger swept area. However, the continuous increase of the rotor diameter leads to

structural problems due to the enormous size of the wind turbine blades. As the wind

uctuations over the swept area get higher, the loads on the blades due to wind shear

layer, tower shadow, yaw misalignment and atmospheric turbulence increase. In ad-

dition to that, the higher blade mass introduces higher cyclic loads, which also have

negative inuence on the lifetime of the blades.

One attempt to overcome the inherent limitations of upscaling and to reduce the

structural load on the wind turbine, is the introduction of an individual pitch control.

Such a system has two major drawbacks. Firstly, the pitch actuator is relatively slow

and cannot cope with high-frequency uctuations; secondly the actuation takes place

at the innermost part of the blade, whereas the highest load contribution comes from

the outermost part. Consequently, the elasticity of the blade waters down the control

circuit.

Another approach is the use of active ow control devices (AFC) on wind turbine

blades. Actuators for active ow control can be integrated directly in the blade, with

sensors, not only behind the rotor as current anemometers and ow vanes but lo-

cally near the actuator. Measuring and controlling the loads directly where they occur,

1 Introduction 2

plus having multiple, smaller and faster actuators makes it possible to further reduce

load uctuations.

Recently a signicant amount of research has been carried out on this topic. Al-

though the main focus seems to lie on the investigation of trailing edge devices, a wide

range of other possibilities might be of interest too, such as:

• Active mini-aps

• Flexible leading-edge aps

• Inatable stall ribs

• Inclined and vertical spoilers

In order to investigate the behavior of dierent AFC-solutions a software is developed

to determine the potential of these concepts. The software allows easy denition of

a wind turbine rotor blade with several so-called active elements (AE). Figure 1.1

shows an example of how such a rotor blade, equipped with several active elements,

may look like. On the outer region, there are four trailing edge ap devices integrated.

The active elements are characterized by a variable aerodynamic performance which

is expressed by multiple lift, drag and moment polars. The response of the blade and

the wind turbine can be investigated using an unsteady aeroelastic simulation. A bind-

ing to the aerodynamic analysis routine AeroDyn [38] and the structural dynamic code

YawDyn [28] is implemented. These simulation codes are provided by the National

Wind Technology Center (NWTC). The aerodynamic package features unsteady simu-

lation with dynamic stall eect modeling as well as and full turbulent wind eld input.

The structural dynamic model is simple, yet useful for analyzing preliminary designs

and assessing aerodynamic responses, as the simulation time is very low and a lot of

parameter investigations can be carried out.

Next to the core routines, a major driving design parameter of the software is user

friendliness. The user is able to perform simulations without manual script handling or

code related operations. To achieve an easy-to-use interface, a GUI was implemented

which automatically handles all the communication between the modules and provides

dynamic data visualization. The software itself is based on the open-source application

1 Introduction 3

Figure 1.1: Concept of a segmented wind turbine rotor blade with active elementsin form of trailing edge aps [52].

QBlade [35] and as it deals with the investigation of the aforementioned user-dened

active elements, the name speaks for itself: QBladeAE.

After describing the theory of the models used by the software in Part I, the second

Part II of this work presents the working principle of the software. In Part III a param-

eter study is performed on a normal blade conguration, to investigate the behavior

of the physical models. Finally, QBladeAE is being used to simulate an exemplary

blade with exible aps in dierent congurations. The results are compared with

the baseline conguration to point out the advantages of the AFC-solution for load

reduction on wind turbines.

The focus of this project lies on the development of the software and not on the

investigation and comparison of dierent AFC-solutions.

Part I

Model Theory

2 Aeroelastic model

As mentioned in the introduction QBladeAE works in conjunction with AeroDyn. This

set of FORTRAN subroutines contains a pure aerodynamic wind turbine model de-

scription and is provided by NREL. AeroDyn is no stand alone application and needs

to be coupled with a structural program, which provides information about the dy-

namic structural deections of the wind turbine and the elastic blades, as well as the

operating conditions (e.g. rotational speed and blade pitch angles). As the structural

deections induce changes in the aerodynamic forces, the computation gets fully aeroe-

lastic. Currently AeroDyn works together with three structural programs, which dier

in their level of complexity. These are YawDyn (which is used by QBladeAE), FAST

and ADAMS (4).

The structural program controls the whole turbine simulation and calls the AeroDyn

subroutines during runtime (once for each time step, blade and blade element), in

order to obtain the aerodynamic forces on the blades. The blade is split into several

blade elements and for each element the lift and drag forces as well as the pitching

moment is determined. The computation is broken down into a two-dimensional local

blade element formulation. All element velocities are accumulated and expressed in the

local blade element coordinate system. Finally, a resulting incident velocity W with

a resulting inow angle of attack α is determined. Using airfoil polar tables for the

lift coecient cl(α), drag coecient cd(α) and moment coecient cm(α), the resulting

element forces can be calculated. After integrating the forces over each blade, the

structural model updates the dynamic deections of the blade and the wind turbine

structure in the next time step. Consequently, the local element velocities change,

which in turn aects the blade element aerodynamics again. Figure 2.1 shows the

dierent portions of the velocities and inow angles seen by a single blade element.


rotation plane

chord line

Ωr(1+a')Ve,ip

Ve,op

U∞(1-a)

ϕ

α

θ = θp + β

W

Figure 2.1: Local blade element velocities and inow angles.

The incident velocity W is given as

W = U∞(1− a) + Ωr(1 + a′) + ve,op + ve,ip (2.1)

where U∞ is the (unsteady) inow velocity, Ωr the local element circumferential speed,

a and a′ the axial and tangential induction factors and ve,op and ve,ip the in-plane and

out-of-plane velocities due to the structural deection. The total inow angle φ is a

combination of the angle of attack α and the local element pitch angle θ, which is again

consists of the blade pitch angle θp and the local blade twist β.

As described in 3.2, the ow around the airfoil produces the aerodynamic lift force

L perpendicular to the incident velocity W and the aerodynamic drag force D in ow

direction (Figure 2.2). The resulting force can then be split into a portion perpendicular

to the rotor plane, the normal load pn = L cos(φ) +D sin(φ) and a smaller tangential

portion pt = L sin(φ) +D cos(φ) which contributes to the rotation of the rotor.


rotation plane

chord line

W

D

Lϕ

pn

pt

Figure 2.2: Local blade element forces.

To calculate the aerodynamic and structural forces on the wind turbine, is the task

of the aeroelastic model of the software. The methods, working principles and assump-

tions made within the model are discussed in the following chapters. The aeroelastic

problem is split into the aerodynamic model and the structural dynamics model.

3 Aerodynamics

The aerodynamic model used by QBladeAE contains representations of several dierent

aerodynamic eects on a wind turbine (HAWT) which will be described below. This

includes wake modeling, airfoil aerodynamics (static and dynamic), yawed inow, tower

shadow, shear layer and atmospheric turbulence eects. Figure 3.1 gives a general

overview of the periodic and aperiodic aerodynamic sources on a wind turbine.

Flowfield Structure

Mostly periodic Mostly Aperiodic

Wind

Speed

Inflow Yaw Tower

Shadow

Wind

turbulence

Wake

dynamics

Blade/

wake

interactions

Figure 3.1: Summary of the various aerodynamic sources that contribute to theairloads on a wind turbine [32].

As can be seen, wind turbines operate under extreme unsteady aerodynamic condi-

tions, which are hard to dene, to measure and to predict with mathematical models

[32]. The approaches to describe these eects with the models of AeroDyn are described

below.

3.1 Wake modeling

Wind turbines extract kinetic energy from the wind. The air approaches the turbine

and is slowed down. As it passes the rotor, there is a step drop in pressure and the

velocity further decreases, as the pressure has to reach the atmospheric level again.

The dierence in the air ow velocity before and after the turbine accounts for the

extracted energy. To calculate the aerodynamic ow across the rotor, dierent models

3 Aerodynamics 9

are available. They reach from simple blade element/momentum theory over more

advanced engineering models to complicated full scale CFD simulations. The former is

the oldest and most common approach to model the aerodynamics of wind turbines and

to calculate the velocity decit in the rotor plane. The latter have high computational

costs, but provide a more realistic physical model, as they solve the Navier-Stokes

equations. In between these two ends, there are various other models which are mostly

adapted from the helicopter industry. These engineering models usually combine the

blade element theory with either a dynamic inow or a vortex wake model [32]. A major

dierence between the engineering models and the BEM theory is the the modeling of

unsteady wake eects. The time dependent changes in the inow and the blade loading

can be treated in dierent ways, by making one of the following assumptions:

Frozen wake One assumption could be that small changes in the inow have no inu-

ence on the induced velocities. The wake is only dependent on the average wind

speed over a (short) period of time. This means the unsteady wind component

passes the rotor unattenuated [8].

Equilibrium wake On the other side it could be assumed, that the wake instanta-

neously reacts on changes in the aerodynamic loading. The induced velocities

change as the inow changes and therefore the wake is always in equilibrium. For

the simulations, this means that the induction factors have to be re-calculated

for every blade element and time step. Most blade element momentum theories

make use of this assumption.

Dynamic wake In reality neither of these assumptions is correct and the truth lies

somewhere in between. Changes in the inow change the vorticity that is trailed

into the rotor wake and the full eect of these changes takes a nite time to

change the induced ow eld [5]. As indicated above, the most common method

to model dynamic inow eects is a combination of the blade element theory and

a dynamic inow model.

How important the consideration of dynamic inow eects is especially for fast

pitching transients and yawed conditions was investigated within the the European

Union JOULE 1 and JOULE 2 programs [46], where dierent models were compared

to each other, with the model of Pitt and Peters [44] probably being the most common

one. For the sake of completeness it shall be mentioned, that there are as well models,

3 Aerodynamics 10

which implement a dynamic wake formulation in the blade element momentum theory,

like outlined in [18].

AeroDyn provides two ways of calculating the induced velocities in the rotor plane:

the classic blade element momentum (BEM) theory with the equilibrium wake as-

sumption and the general dynamic wake model (GDW). Both are be described in the

following paragraphs.

3.1.1 Blade element momentum theory (BEM)

As mentioned above, the blade element momentum theory is one of the oldest methods

for wind turbine wake modeling. It is a combination of the blade element theory, in

which a blade is split in several independent sections, and a momentum theory, which

attributes the loss of momentum in the rotor plane to the aerodynamic eects of the

ow passing the blades.

Blade element theory

In the blade element theory, a blade is regarded as a number of independent blade

sections or elements. According to the airfoil theory (3.2), the aerodynamic forces

which act on the blade element can be calculated by means of two-dimensional airfoil

characteristics. With the information of the absolute value and the direction of the

incident velocityW at the blade element, the lift and drag forces as well as the pitching

moment can be determined by using airfoil tables. It is assumed that each element

cuts out an annular ring section of the rotor disc (Figure 3.2), and that the overall

rotor performance is the integration over the single annular rotor sections.

The lift and drag forces on the blade element with the chord length c (Figure 2.2)

are given as

dL =ρ

2clcdrW

2 (3.1)

dD =ρ

2cdcdrW

2 (3.2)

3 Aerodynamics 11

dr

R

r

Figure 3.2: Rotor of a three-bladed wind turbine with the rotor radius R [18].

with the trigonometric relations (Figure 2.1) for the inow angle φ = θ + α

sin(φ) =U∞(1− a)

W(3.3)

cos(φ) =Ωr(1 + a′)

W(3.4)

Blade element and momentum theory (BEM)

The combination of the blade element theory with a momentum theory allows the cal-

culation of the induction factors a and a′, which are necessary to determine the incident

velocity W in the rotor plane. It is assumed that each blade element is responsible for

the change of momentum of the air, which passes through the annulus swept by the

element [8]. A detailed derivation can be found in many textbooks, such as [18] and

only a short introduction is given here.

To determine the induction factors, the aerodynamic forces which contribute to the

thrust T and the torque Q are set equal with the change of momentum in the annulus

3 Aerodynamics 12

section. The aerodynamic forces of B blades in axial and tangential direction result in

dT = Bρ

2W 2cdr(cl cosφ+ cd sinφ) (3.5)

dQ = Bρ

2W 2cdr(cl sinφ+ cd cosφ)r (3.6)

The momentum theory gives

dT = 4πρU2∞a(1− a)rdr (3.7)

dQ = 4πρU∞(Ωr)a′(1− a)r2dr (3.8)

These equations can now be solved iteratively using two-dimensional airfoil data. Note

that AeroDyn takes the additional velocities ve,op and ve,ip into account for determining

the absolute value and inow angle φ of the incident velocity W but does not consider

them in the momentum theory, which might not be the appropriate physical model for

the element-wake coupling [38].

Despite it's simplicity, the BEM theory provides relatively accurate results. There

are other aerodynamic eects on a real turbine, which can not be modeled with the

BEM method directly, because of the assumptions made in the theory. These are eects

due to heavy loaded rotors with high induction factors, blade tip and hub losses due to

a limited number of blades and skewed inow which is not perpendicular to the rotor

plane. AeroDyn includes several corrections to account for these eects:

Tip loss model The fact that vortices are being shed from the blade tip causes high

axial induction factors, which leads to lower inow velocities at the rotor. This

causes to smaller inow angles φ and most of the aerodynamic lift contributes to

the thrust. Less torque means less power and therefore the losses near the blade

tips are higher.

AeroDyn features two models to calculate the blade tip losses. First of all, the

classic model developed by Prandtl. An additional correction term F is added to

the momentum Equations 3.7 and 3.8

F =2

πcos−1 e−f (3.9)

(3.10)

3 Aerodynamics 13

where

f =B

2

(R− r

r sinφ

)(3.11)

The other model used in AeroDyn slightly modies the Prandtl correction factor

F , using an empirical relationship for the tip losses on base of the Navier-Stokes

solutions [38]:

Fnew =F 0.85Prantl + 0.5

2, for 0.7 ≤ r/R ≤ 1 (3.12)

Fnew = 1−( rR

) 1− FPrantl(r/R=0.7)

0.7, for 0.7 ≤ r/R ≤ 1 (3.13)

Hub loss model The eect of a vortex in the hub region is described using a nearly

identical implementation of the tip-loss model. Equation 3.11 is replaced with

f =B

2

(r −Rhub

Rhub sinφ

)(3.14)

Turbulent wake state The standard momentum equation used in the BEM theory

gives negative thrust values for induction factors greater than 0.5. However,

this is not what happens in reality. As the wake becomes turbulent for heavily

loaded rotors, air (thus momentum) is transported from the outer ow region

into the wake. To account for this eect, the empirical correction of Glauert is

implemented in AeroDyn and is slightly modied, to avoid numerical instability:

CT =8

9+

(4F − 40

9

)a+

(50

9− 4F

)a2 (3.15)

Skewed wake To be able to describe the eects of yaw misalignment, AeroDyn pro-

vides a skewed wake correction. The model is based on the work of Glauert

(1926) and was extend by Pitt and Peters (1981). For steady inow conditions,

the local element induction factor askew is given with

askew = a

[1 +

15π

32

r

Rtan

χ

2cosψ

](3.16)

where ψ is the azimuthal angle that is zero at the most downwind position of the

3 Aerodynamics 14

rotor and χ being the wake skew angle, which can be approximated using the

yaw angle γ as follows [38]:

χ ≈ (0.6a+ 1)γ (3.17)

Despite the original assumption made by Glauert, AeroDyn applies the induction

factor askew to all local elements [38].

3.1.2 Generalized dynamic wake model (GDW)

The generalized dynamic wake model of AeroDyn is also known as acceleration potential

method and is based on the work of Peters and He (1989), which again is based on

the aforementioned model of Pit and Peters [44]. The main advantage over the BEM

method is the inherent inclusion of dynamic wake eects, tip losses and skewed wake

aerodynamics [38]. The equations describe the distribution of inow and are written

in the form of dierential equations, which can be solved non-iteratively. The GDW

model has several drawbacks as well. Theses are:

• Instabilities at low wind speeds when the turbulent wake state is approached.

AeroDyn uses the BEM method for wind speeds below 8m/s.

• No accounting for wake rotation. AeroDyn uses the BEM method as well to

calculate the tangential induction factor.

• Flat disk assumption makes the eect of large aeroelastic deections inaccurate.

The method itself is based on the unsteady and inviscid Euler equations. Assum-

ing the induced velocities are small against the wind velocity U∞ the conservation of

momentum can be written as

∂u

∂t+ U∞

∂u

∂x= −1

ρ

∂p

∂x(3.18)

∂v

∂t+ U∞

∂v

∂x= −1

ρ

∂p

∂y(3.19)

∂w

∂t+ U∞

∂w

∂x= −1

ρ

∂p

∂z(3.20)

3 Aerodynamics 15

and for continuity of the ow

∂u

∂x+∂v

∂y+∂z

∂w= 0 (3.21)

and the Laplace equation for the pressure distribution

∇2p = 0 (3.22)

The boundary conditions are the aerodynamic forces on the loaded blade, the pres-

sure returns to ambient pressure far behind the rotor and the equality of discontinuous

pressure and rotor thrust. The pressure eld is then split into a term for the spatial

variation and a term for the unsteadiness to split the unsteady Euler equations accord-

ingly.

A pressure distribution, which gives a discontinuous pressure drop across the rotor

and satises the Laplace equation was developed by Kinner (1937). A more detailed

description of the method is found in [38] and [8].

According to [10] the GDW method for calculating yawed and dynamic inow is

surprisingly good for its computational simplicity. However, it contains many simpli-

fying assumptions and it is proposed to implement a free vortex wake method for more

accurate results instead. On the other hand, the disadvantage of a free vortex model

is the long computation time. As noted in [47], a 10 minute time simulation with the

advanced Alcyone free wake model lasted 5 days.

3.2 Airfoil aerodynamics

3.2.1 2D static airfoil characteristics

Most wind turbine models including the ones described above make use of two-

dimensional static airfoil tables. The assumption that the ow around the blade at a

given radial position is two-dimensional, as indicated in Figure 3.3, is not always valid

especially in the blade root and tip region. On the other hand, The advantage of

having static airfoil look-up tables for the aerodynamic forces as a function of the angle

of attack α is very useful for the aerodynamic simulation. This approach in contrary

to an on-the-y calculation allows to import airfoil characteristics from wind tunnel

3 Aerodynamics 16

measurements or complex numerical computations. It is obvious that the key to an

accurate simulation lies in the careful provision of valid airfoil properties. Unfortunately

this is a hard task, as wind tunnel measurements for very high Reynolds numbers

are very costly and valid CFD simulations very time consuming and computationally

intensive.

Lift

U∞

Figure 3.3: 2D arfoil characteristics of a blade element.

Once the aerodynamic lift- drag and moment coecients cl, cd and cm are known,

the resulting forces for lift L, drag D and pitching moment M can then be calculated

using the denition:

cl(α) =L

ρ2U2∞c

(3.23)

cd(α) =D

ρ2U2∞c

(3.24)

cm(α) =M

ρ2U2∞c

2(3.25)

As stated in [38] and [49] the largest source of error in load and performance simu-

lations are errors in the airfoil data tables. Figure 3.4 - 3.6 show exemplary how the

characteristics for the same airfoil can varies under dierent conditions. The results are

computed with XFOIL, a 2D panel method which includes an estimation for viscous

ow [12]. The graphs show a calculation for two Reynolds numbers and an additional

calculation for xed transition near the leading edge.

3 Aerodynamics 17

-1

-0.5

0

0.5

1

1.5

2

-10 -5 0 5 10 15 20

c l

α

Re = 5e6, Ma = 0.1, xtrf = 1.0Re = 2e6, Ma = 0.1, xtrf = 1.0Re = 2e6, Ma = 0.1, xtrf = 0.1

Figure 3.4: Lift coecient cl(α) at dierent Reynolds numbers and xed/free tran-sition for the DU 91-W2-250 airfoil.

As the operational conditions of the wind turbine are changing during the simula-

tion, the airfoil performance changes as well. With increasing wind speed or changing

rotational speed (in spanwise direction), the Reynolds number varies. That makes it

hard to cover the whole range of operation in a simulation with only one set of polars.

AeroDyn provides the possibility to dene multiple tables for one airfoil. The user can

specify dierent tables for dierent Reynolds numbers. These tables are dynamically

accessible during simulation. As will be seen later, this functionality can be used to

dene the dierent aerodynamic characteristics of the aforementioned active elements

too.

3 Aerodynamics 18

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

-10 -5 0 5 10 15 20

c d

α


Figure 3.5: Drag coecient cd(α) at dierent Reynolds numbers and xed/free tran-sition for the DU 91-W2-250 airfoil.

-0.15

-0.14

-0.13

-0.12

-0.11

-0.1

-0.09

-0.08

-0.07

-0.06

-0.05

-10 -5 0 5 10 15 20

c m

α


Figure 3.6: Moment coecient cm(α) at dierent Reynolds numbers and xed/freetransition for the DU 91-W2-250 airfoil.

3 Aerodynamics 19

3.2.2 Polar extrapolation

Unlike airplane wings, wind turbine blades experience stalled operation. The rotational

speed of the blade gets higher towards the blade tip, but the average wind inow

velocity u∞,mean remains constant. This results in higher ow angles φ in the root

region, as can be seen in Figure 3.7. To compensate for this and to keep the angle of

attack α constant over the span width, the blades are structurally twisted inwards

more than outwards. As mentioned above the total inow angle φ is given as (assuming

a blade pitch angle of θp = 0):

φ = θp + β + α = β + α (3.26)

To keep the angle of attack α constant, the twist β must increase when the total inow

angle φ increases.

The conventional manufacturing process however, allows only a limited blade twist.

The root region is likely to operate under stalled conditions. Figure 3.8 shows the

angle of attack α for an inner and an outer section of a 40m blade with limited twist

of βroot = 13 in the root region. The turbine is operating at a rotational speed of

n = 16rpm at u∞,mean = 13m/s. The average value of the angle of attack is αmean = 20.

It's obvious that the airfoil tables need to be extended to a wider range of angles of

attack. But stall phenomena are viscous eects and it is anything but trivial to nd

valid numerical or experimental ways to determine the behavior of airfoil characteristics

beyond stall. Methods based on the potential ow theory (like used by Xfoil) are only

able to include viscous eects by semi-empirical models. Wind tunnels measurements

are also complicated, due to the high blockage in the measurement section for high

angles of attacks.

One way to overcome this problem is to use airfoil characteristics for normal operation

and extrapolate them by using the at plate theory. This approach points out the

similarity between a at plate and an airfoil at high angles of attack. This method is

refereed to as the Viterna method [55]. As wind turbine airfoils used for the root region

are relatively thick, the model can be further adapted. For the 360-extrapolation in

QBladeAE, the empirical method described in [37] is used. A similar approach is

described in [50]. Figure 3.9 and 3.10 show the extrapolation of the initial values for cl

3 Aerodynamics 20

Ωrout

Ωrmid

Ωrin

vtot

Ωrout

Ωrmid

Ωrin

u

u

u

ϕ

Figure 3.7: Wind triangular for dierent radial positions [16].

5

10

15

20

25

30

35

0 10 20 30 40 50 60

α [d

eg]

time [s]

ri = 6.8mro = 36.8m

Figure 3.8: Exemplary time series of α for two radial positions at rin = 6.8m androut = 36.8m.

3 Aerodynamics 21

-1.5

-1

-0.5

0

0.5

1

1.5

2

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180

c l

α

2*sin(x)*cos(x)Re = 5e6

Figure 3.9: Extrapolated cl for the DU 91-W2-250 airfoil and cl according to theat plate theory.

and cd seen in 3.4 and 3.5. In addition to that, the lift and drag coecients according

to the at plate theory are shown as well:

cl,fp(α) = cd,90 sinα cosα (3.27)

cd,fp(α) = cd,90 sin2 α (3.28)

As described in [35] the user can inuence the extrapolation via several control vari-

ables, to adapt the method to dierent airfoil characteristics. It shall be noted, that

the 360-extrapolation has no eect on the original two-dimensional airfoil data and

the original polar needs to cover an angles of attack range right up to the stall point.

Errors made in the extrapolation process mainly eect the root region of the blade,

whose contribution to the energy yield and the structural load is naturally smaller.

Nevertheless, the accuracy of valid airfoil data has to be pointed out again.

Next to the physical operation under high angles of attacks, the 360-extrapolation

of airfoil data is as well needed for the classical BEM-method (3.1.1). This has numer-

ical reasons and is necessary for a successful convergence of the iterations in the BEM

method.

3 Aerodynamics 22

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180

c d

α

1.98*sin(x)*sin(x)Re = 5e6

Figure 3.10: Extrapolated cd for the DU 91-W2-250 airfoil and cd according to theat plate theory.

3.2.3 Unsteady blade element aerodynamics

Until now, only the steady nature of the airfoil performance was described. The airfoil

polars are so far not more than simple look up tables. In reality, the blades operate

under highly unsteady conditions. Firstly, there is an angle of attack change α over

time, due to blade pitching or yaw misalignment. Secondly, there are in-plan inow

velocity changes U∞(t) from wind gusts as well as out-of-plane velocity changes due to

wake interactions. According to [32] it is important to distinguish between these two

inuences on the airloads and to treat them separately. However, AeroDyn does treat

every change in angle of attack equally, whether they arise from blade pitch motions,

changes in the relative wind velocity or blade ap or lag motions.

Attached ow conditions

The unsteadiness at blade element level is often put on a level with dynamic stall. It

has to be noted, that there are as well unsteady eects in the attached ow region with

low angles of attack. These eects become noticeable in moderate amplitude and phase

3 Aerodynamics 23

variations compared to the steady eects [32] provided that the reduced frequencies

are small. One way to describe the unsteady eects in the linear lift region was given

by Theodorsen and is known as the Theodorsen's theory [53]. The theory is based

on the incompressible and inviscid ow theory for thin airfoils. There are extensions

which have been developed, including compressible ow and wing rotation eects. As

AeroDyn does not include the computation of unsteady airfoil aerodynamics under

attached ow conditions, only a reference is made to [32], where a general insight in

the unsteady aerodynamics of HAWTs is given.

Dynamic stall

Leaving the linear lift region of an airfoil, stall eects occur. Dynamic stall is describing

the phenomenon of delayed stall occurrence on airfoils under unsteady conditions

either a time varying in the inow or the angle of attack. An airfoil which oscillates or

pitches through the static stall region, experiences delayed stall onset, but considerably

stronger and longer-lasting stall eects compared to the static stall development. A

cycle of a pitching airfoil, which experiences a dynamic stall hysterisis is shown in

Figure 3.11 and can be described as followed: If the static stall angle is exceeded, there

is no immediate change in the viscous or inviscid ow around the airfoil, due to a time

lag until stall takes place. After the rst appearance of ow reversal in the boundary

layer near the trailing edge, the reversal ow moves further upwards to the leading

edge. This is when rst large eddies appear and a vortex is formed at the leading edge.

This vortex rolls up, gains in strength and is shed downstream, producing an increasing

lift slope. At the same time the center of pressure moves along with the vortex, causing

a large nose down pitch moment. After the vortex has passed the trailing edge, the lift

drops rapidly and full stall takes place. When the angle of attack decreases and falls

below the static stall value again, the ow re-attaches from the leading to the trailing

edge again [43].

It is obvious that the unsteady stall eects on airfoils cannot generally be included

in two-dimensional static airfoil tables. To cover theses eects, an dynamic stall model

is implemented in AeroDyn which shall be briey described according to [43]. The

model is based on the work of Beddoes and Leishman [31]. It is a semi-empirical model

which adapts the attached ow indicial1response of an airfoil to the position where ow

1By denition, an indicial function is the response to a disturbance that is applied instantaneously

3 Aerodynamics 24

(a) STATIC STALL ANGLE EXCEEDED

(B) FIRST APPEARANCE OF FLOW

REVERSAL ON SURFACE

(c) LARGE EDDIES APPEAR IN

BOUNDARY LAYER

(d) FLOW REVERSAL SPREADS OVER

MUCH OF AIRFOIL CHORD

(e) VORTEX FORMS NEAR

LEADING EDGE

(f) LFIT SLOPE INCREASES

(g) MOMENT STALL OCCURS

(h) LIFT STALL BEGINS

(i) MAXIMUM NEGATIVE MOMENT

(j) FULL STALL

(k) BOUNDRAY LAYER REATTACHES

FRONT TO REAR

(l) RETURN TO UNSTALLED VAUES

Figure 3.11: Dynamic stall events on a NACA 0012 airfoil (reprinted from [9] and[32]).

3 Aerodynamics 25

separation actually takes place. With an indicial response function φα, the change in

the normal lift coecient ∆cn for a angle of attack change ∆α for the attached ow

region is given as

∆cCn = cnαφCα∆α (3.29)

∆cIn =4

MaφIα∆α (3.30)

with cnα being the slope of the normal coecient in the linear region, Ma the Mach

number. The lift coecient is additionally separated in one component for the circu-

latory part cCn and one for the non-circulatory part cIn. The response of the tangential

force coecient cc in chord wise direction is derived from the circulatory part of cn. The

attached ow indicial response is then adapted to the separation point f of the suction

side of the airfoil. With the static airfoil data, the separation point f is determined by

the relation

cn = cnα(α− α0)(1 +

√f

2)2 (3.31)

cc = cnα(α− α0) tan(α)√f (3.32)

where α0 denotes the angle of attack for zero lift. As these equations are derived from

an inviscid formulation, f is referred to as the static eective separation point, and

might not be the exact point of reversal ow appearance. A empirical time lag is fur-

ther applied to the movement of the eective separation point to account for the time

lag of the real separation point under unsteady conditions. In a last step, the vortex

shedding across the upper surface of the airfoil is modeled, as soon as a critical leading

edge pressure parameter indicates leading edge separation. This results in the typical

lift increase until the airloads return to their static values. The relation between cn

and cl can be found in Figure 2.2.

The dynamic stall model described above, is modied slightly in AeroDyn. The main

dierences are:

• Extension to very high/low angles of attack

• The eective separation point f(α) is not curve tted by an exponential function

at time zero and held constant thereafter; that is a disturbance given by a step function [32].

3 Aerodynamics 26

but treated in a look up table with linear interpolation in between

• Two separate point tables are used, one for cn and cc

The advantage of the model is, that it uses very few empirical coecients, mostly

derived from the static airfoil tables. The airfoil input les for AeroDyn contain the

necessary values for the dynamic stall models. QBladeAE automatically calculates and

exports the values, (7.2.6) which are:

• Angle of attack for zero lift α0

• cn slope for zero lift

• cn at stall value for positive angle of attack

• cn at stall value for negative angle of attack

• Minimum cd value

• Angle of attack for minimum cd value αcd,min

The model provides a fairly accurate way to predict the unsteady eects of dynamic

stall but it has to be noted, that none of the available models are developed to full

extend and future investigations have to be made on this topic [32].

Furthermore the dynamic model used for simulating active elements in QBladeAE is

not changed according to the specic active elements used and has to be applied with

caution. Unfortunately it is not possible to derive a generally adapted model for each

dierent kind of active ow control actuator for example leading edge or trailing edge

ap. A modied dynamic stall model for trailing edge aps can be found in [1].

3.2.4 Stall delay and 3D eects

Measurements show, that conventional HAWT simulations can under-predict the power

output compared to measured data. Beside the under estimation of delayed stall due

to dynamic stall eects, another reason for the under-prediction are three-dimensional

ow eects which are caused by centrifugal and Coriolis forces. These forces can have

a positive eect on the pressure gradient on the suction side, so that stall is delayed

[8]. Numerical investigations support these results [3] and point out their importance.

3 Aerodynamics 27

Another eect responsible for stall delay are the incident ow velocities which result

in a realtive wing sweep [32].

The eects mentioned above, are still undergoing research and not form of three-

dimensional airfoil correction is included in AeroDyn. It has to be noted as well, that

the BEM theory (3.1.1) can not handle three-dimensional eects, as the blade sections

are considered to be independent of each other by denition. If there is any desire

for implementation of three-dimensional eects, the static airfoil tables have to be

corrected and modied manually.

3.3 Tower shadow

The inuence of the tower shadow can be modeled as a velocity change seen by the

blade. The inuence is manifested in a velocity decit normal to the rotor plane.

AeroDyn uses two models to simulate the tower eects. They both use a potential ow

around a cylinder as basis and superimpose either a dam model for upwind turbines

and an additional wake model for downwind turbines. Based on the tower diameter a

drag value cd,tower is used to calculate the dimensionless velocity eld according to

u = 1− (x+ 0.1)2 − y2

((x+ 0.1)2 + y2)2+cd,tower

2π

x+ 0.1

(x+ 0.1)2 + y2(3.33)

v = 2(x+ 0.1)y

((x+ 0.1)2 + y2)2+cd,tower

2π

y

(x+ 0.1)2 + y2(3.34)

(3.35)

where u and v are the components of the horizontal wind in the x and y direction. The

parameters x and y are the upwind and crosswind distances normalized by the tower

radius [38].

For the tower wake model of downwind turbines, which are of no interest within this

report, reference is made to [38] as well.

4 Structural dynamics

Especially the exible blades and the tower make a wind turbine is a highly dynamic

system. To model the behavior of the turbine, a dynamic structural model is necessary

for several reasons: Firstly to determine extreme loads for the certication process,

secondly for the time dependent load variations on the components for fatigue load

calculation, in the third place to calculate deections which inuence the aerodynamic

model and nally to analyze the stability of the design. Dierent kind of loads are

acting on the structure [42]:

Aerodynamic loads The aerodynamic loads are listed in Figure 3.1.

Gravitational loads The weight of the blades and the nacelle resulting in a force

pointing downwards. Blade mass imbalances cause additional periodic forces.

Inertial loads They include centrifugal and gyroscopic forces as well as acceleration

forces.

Operational loads Transient turbine operation loads, initialized by the control sys-

tem, such as starting up, pitching, breaking or yawing.

Currently lots of wind turbine analysis codes available. They all include an aerody-

namic model which is either a BEM method or other engineering models (3.1) and

a structural model, which describes the motions and deformations of the wind turbine.

Furthermore the simulation models include a representation of the exible drive train,

an electric model for the generator and interfaces for implementing control strategies

for the wind turbine operations. The latter are necessary for modeling the high dy-

namic stresses a wind turbine is suering from, during maneuvers like emergency stops

or power regulation. The main components of a state-of-the-art aeroelastic simulation

code for wind turbines is shown in Figure 4.1.

Rajabi

Underline


Blades

Tower

Foundation

Generator

Break

Drive Train

Control

Nacelle

Figure 4.1: Components of a HWAT structural model


The structural model of the turbine itself, can be generally described by Newton's

second law

Mx+ Cx+Kx = F (4.1)

with M being the mass matrix, C the damping matrix and K the stiness matrix. To

solve this set of equations there are dierent approaches used in wind turbine simulation

codes. There are mainly three types of models, which vary in their level of complexity:

Assumed mode shapes An (assumed) modal representation is used for the dynamic

modeling. The modal properties of the rotating blades and the non-rotating

tower are computed independently by using information like mass and stiness

distribution.

Multi Body System A multi body system describes the dynamic structure with only

a few rigid and eventually exible elements, which are coupled with joints. The

advantage of a MBS system is, that large displacements can be modeled.

Finite Element System The nite element method is used to approximately nd the

solutions to the partial dierential equations of the mechanical system. A large

but nite number of elements are used to mesh the structure, resulting in high

computational cost. The code is usually only used for layout design and stress

calculations but not for dynamic wind turbine models.

As mentioned above, there are several design codes available. Almost every major

research center has developed their own code. There are commercial products, like

GH Bladed with license costs of ¿30.000 [15] and free open-source codes like NREL

provides them. An overview of existing codes can be found in [36] and [42]. Some of

the most common design codes are:

GH BLADED from GL Garrad Hassan,

FLEX5 from the Technical University of Denmark,

HAWC2 from the Riso National Laboratory for Sustainable Energy DTU,

DUWECS from the Delft University of Technology,

ADAMS/WT from MSC Software in collaboration with the National Renewable Re-

search Laboratories


FAST-AD from the National Renewable Energy Laboratories

YawDyn from the National Renewable Energy Laboratories

Some of these codes were compared to each other in [39] and validated against mea-

surements in [47] and [48], showing sometimes big discrepancies between each other

and between simulation and wind tunnel experimental data.

As with the aerodynamic model, it is not intended to develop a new design code

within this project. Regarding the time constraint, a comparable level of complexity

could not have been reached. As mentioned above, the AeroDyn simulation routines

from NREL are used for the aerodynamic wind turbine calculation and for simplicity

the choice for the structural model to work with AeroDyn came down to YawDyn1.

Although it is a very simple model, it provides rst insight in the aeroelastic behavior of

wind turbines. As the focus of the project lies mainly in the preliminary comparison of

dierent AFC solutions rather than on the detailed investigation of a single approach

the provided model of YawDyn seems to be sucient, although it was mainly developed

for the investigation of yaw motions. In the following, the model used in YawDyn is

described.

4.1 YawDyn

YawDyn was developed in 1992 by the National Wind Technology Center (NWTC) at

NREL. It was preliminarily used to investigate the yaw dynamics of HAWTs. Next to

the aerodynamic models used in AeroDyn, it provides the structural response of the

wind turbine at xed rotational speed in a fully turbulent wind eld.

The following assumptions are made in the structural model of YawDyn:

• Only the yaw motion γ and the blade apping motion β are used in the devel-

opment of the equations of motions.

• The rotor can be either modeled as apping rotor with two or three blades, a

teetering rotor for two blades or completely rigid.

1Recently NREL stopped the support for YawDyn and does not recommend it any more. The useof FAST, which is certied from Germanischer Lloyd WindEnergie [7], is proposed instead.


Figure 4.2: The equivalent hinge-spring model for the blade ap degree of freedom[28].

• During simulation the system can operate at either a xed yaw angle, at a xed

yaw rate or with free yaw motion using parameters for yaw spring stiness, yaw

damper coecient and constant yaw friction moment.

• Upwind/Downwind rotor simulation with tilted rotor (τ) and precone blade angle.

• Blade pitch and lag motions are not considered, as they are not important to the

yaw response.

• The tower, the rotor shaft, the nacelle and the blades themselves are treated as

rigid bodies.

• The turbine can only be modeled at xed blade pitch and at a xed rotation rate

Ω. No controller input is implemented.

• The blades are described by a uniform mass distribution, their distance from the

hinge to the blade center of gravity, their mass moment of inertia about the hinge

axis and their torsional stiness of the blade root spring.

The hinge-spring model for the blade ap degree of freedom is shown in 4.2. The model

of the wind turbine is shown in Figure 4.3. For more information it is refered to [28]

and [17].


Figure 4.3: View of the HAWT dening selected terms and coordinate systems. Allangles are shown in their positive sense. The bold X,Y,Z axes are xedin space and are the coordinates in which the wind components aredened (VX, VY, VZ). Note that blade azimuth is zero when the bladeis at the 6 o'clock position [28].

5 Turbulent wind simulation

The wind inow seen by the wind turbine is everything else but uniform. The tur-

bulent atmospheric boundary layer leads to a vertical wind prole and high turbulent

structures in the ow eld. These unsteady turbulences seen by the wind turbine are

stochastic, but not completely random (e.g. white noise). There is a spatial and fre-

quency dependent correlation of the turbulence. Veers developed a model for turbulent

wind eld computation, which can be used as input for the aforementioned simulation

codes [54]. A disadvantage of this model is, that the time histories of the wind eld for

the three velocity components u, v and w are computed independently. In other words,

there is no correlation between them. The Mann model, which is based on lineralized

Navier-Stokes equations, takes this additional correlation into account [33].

QBladeAE provides two methods to generate turbulent wind eld input les for

the simulation. An internal windeld generator and a GUI for generating input les

for NREL's TurbSim [6], which works seamlessly together with the two other NREL

modules 1. Both modules are based on Veers' model. Figure 5.1 shows the hub height

wind speed and the inow velocity seen from the rotating blade at the blade tip position,

computed with TurbSim. The mean wind speed is 13m/s. Figure 5.2 shows the x-

component of a wind eld generated in QBladeAE. The eld has 20x20 points.

1Note, that the additional coherent structures in TurbSim are only compatible to AeroDyn version13, which is incompatible with YawDyn.

5 Turbulent wind simulation 35

9

10

11

12

13

14

15

16

17

0 10 20 30 40 50 60

v x [m

/s]

Time [s]

vhubvtip

Figure 5.1: Wind speed at hub height and inow velocity at blade tip includingrotation, wind shear and tower eect.

Figure 5.2: 3D wind eld from QBladeAE with 20x20 points.

6 Active Flow Control

The preliminary task of QBladeAE is to provide a possibility to investigate elements on

a wind turbine blade, which can actively inuence and control the ow eld: so called

active elements. Flow control provides a possibility to meet the growing problems on

large scale wind turbines due to their high blade mass. The blade mass increases with

the length of the blade by the power of three: mblade ∝ R3blade, whereat the power out-

put only increases with the power of two: P ∝ R2blade. To reduce the arising uctuating

loads on the blade and to develop mass optimized blades active (and passive) load

control becomes more and more interesting.

An obvious solution to meet the challenges of load control on wind turbines is to use

already existing systems. The pitch system on modern wind turbines was introduced

for power regulation, but it can used as well to alleviate the load uctuations either

in a cyclic or individual pitch motion. As can be seen in [26] or [24] these systems

have high potential, especially for the cyclic load compensation of the 1p frequency

and multiples of it. However, the pitch system has some inherent problems. Firstly

it is too slow to react on higher turbulent load uctuations. In addition to that, the

pitch acts always on the whole blade and can not cope for local disturbances on the

blade, like local wind gusts. Thirdly the actuator is located at the blade root, but the

highest potential for load reduction lies in the outer regions of the blade.

To meet these problems, other ways of active ow control (AFC) for load reduction

can be introduced. There is a variety of dierent solutions available. These include

• Trailing edge devices (Rigid Flaps, Split Flaps, Flexible Flaps)

• Leading edge devices (Slats, Flexible Leading Edge)

• Multi-Element devices


• Gurney Flaps / Micro Tabs

• Spoiler

• Boundary layer suction/blowing devices

A more detailed is found in [40] and [19]. AFC solutions inuence the ow eld around

the airfoil section of the blade and intent to either delay transition, decrease turbulence

or avoid ow separation. This usually entails drag reduction, lift enhancement, mixing

augmentation, heat transfer enhancement, and ow-induced noise reduction [19]. Un-

fortunately the benets of one eect usually include adverse eect on others. To nd

an optimized system, which might consist of several AFC elements, is the nal goal for

active ow control on wind turbines.

Active

Flow

Control

Triad

Devices & Actuators Controls & Sensors

Flow Phenomenon

LE / TE Flaps

MicroTaps

Vortex Generators

Synthetic Jets

Active Flexible Wall

Motor

Piezoelectric

MEMs

Fluidic

Conventional

Optical

MEMS

Neural Networks

Asaptive

Physical Model-Based

Dynamical Systems Based

Optimal Control Theory

Seperation Control

Adjust Sectional Lift

Drag Reduction

Noise Suppression

Figure 6.1: Feedback ow control triad (after [25]).

The main advantage over an intelligent blade pitch control is, that several AFC ele-

ments can be located on a blade independent from each other. This means, the ability

of individual AFC elements to mitigate fatigue loads or to reduce extreme loads is more

dierentiated. On the other hand, the control strategies get more complex. The use

of simple heuristic PID controlers might not be appropriate and more sophisticated

methods like neuro-fuzzy control approaches are necessary, in order to deal with the

non-linear aeroelastic wind turbine system. In addition to that, new sensors like strain


gauges or angle of attack senors to measure the control variables are necessary.

The use of AFC solutions on wind turbines is subject to current research. The focus

lies especially on trailing edge devices, either in form of active Flaps [4] [1] or in form

of Micro Tabs and active Gurney Flaps [57] [13].

In order to get further insight in the benets of active ow control concepts, it is

the overall goal of QBladeAE, to provide a simple method and a rst approximation

to investigate the inuences of dierent AFC solutions on wind turbines. The software

itself is presented in the following Part II.

Part II

Software

7 QBladeAE

QBladeAE is used to investigate the behavior of wind turbine blades, which are

equipped with active ow control elements using an aeroelastic simulation. It is em-

bedded in the open-source software QBlade [34], which again is an extension of XFLR5

[11] (Figure 7.1). It is developed with the cross-platform C++ framework Qt, which

allows easy programming of applications with a graphical user interface. The features

of the program suite are:

XFLR5 Foil Design and XFoil analysis

• Direct geometric foil design

• XFoil direct analysis

• XFoil full and mixed inverse foil design

• (not included: wing and plane design)

QBlade Rotor and Turbine design:

• Blade design and optimization

• 360 polar extrapolation

• Turbine denition and simulation

• Rotor simulation

QBladeAE Active Flow Control Simulation

• Blade design with active elements

• Aerodynamic description of active elements

• Wind eld generator (beta)

• Aeroelastic simulation

Rajabi

Highlight

7 QBladeAE 41

XFLR5Direct Foil Desing

XFoil Direct/Inverse Design

Figure 7.1: QBladeAE embedded in QBlade and XFLR5.

As mentioned above, QBladeAE works together with the aerodynamic routines Aero-

Dyn and the structural routines YawDyn. The original FORTRAN source code of

YawDyn was extended by an input/output handling and a control structure, in order

to simulate active elements. Therefore the name YawDynAE is chosen for the modied

version. QBladeAE allows the user to dene a blade structure, to handle aerodynamic

properties (with inherent XFoil calculations and 360-extrapolation) and to create all

necessary inputs for the NREL codes via a graphical user interface. It then automati-

cally calls the aeroelastic code externally and reads in the results when the simulation

is nished. All the results are visualized within QBladeAE and a binding to gnuplot

[56] allows the export of all graphs (currently beta status). Furthermore, QBladeAE

provides as well a GUI for creating TurbSim wind eld les, which works after the

same principle as described above. The workow of QBladeAE can be seen in Figure

7.2. After dening all necessary information, the output les for YawDynAE (yaw-

dyn.ipt),for AeroDyn (aerodyn.ipt), for all the used airfoils (airfoils.dat) and for the

active elements (active.ipt) are automatically generated. TurbSim les can be gener-

ated independently. Exemplary input les can be found in A.1.

7.1 Active Flow Control simulation

Two steps are necessary to simulate a blade which contains active ow control de-

vices. At rst, a standard blade has to be designed using the blade design module

7 QBladeAE 42

AeroDynYawDynAE

QBladeAEyawdyn.ipt

aerodyn.ipt

active.ipt

airfoils.dat

wind.wnd/.hh

yawdyn.plt

yawdyn.opt

element.plt

active.plt

TurbSim

wind.iptwind.wnd

wind.sum

Figure 7.2: Working principle of QBladeAE with input and output control to themodied NREL codes.

of QBlade. In a second step, user specied active elements can be added to the

blade. Dedicated blade sections can be declared active, which means these sections

have a variable aerodynamic representation during the runtime of the simulation. The

changing aerodynamic representation is realized by multiple airfoil polars for the active

elements. The multiple polars are used to describe the dierent operation points of

an active element. When using a trailing edge ap for example, the dierent polars

represent dierent ap angles or when using a boundary layer suction device, the polars

represent dierent suction rates. During the aeroelastic simulation, a controller is used

to determine the optimal actuator operation point and therewith the optimal polar.

Figure 7.3 shows a blade with several sections, where two of them are active.

By using the AeroDyn subroutines, this approach can be realized easily. AeroDyn

can already handle multiple airfoil polars. Originally this functionality is used for

Reynolds number dependent simulations, where the dierent polars represent the air-

foil characteristic under dierent ow conditions, but can as well be used for other

runtime variable airfoil characteristics like the eect of ailerons for example. Each

airfoil polar table has an ID, which is referred to as MulTabLocvariable (Multiple

7 QBladeAE 43

cl

Active Elements

α

cl

α

Figure 7.3: Blade with two active elements, which are represented by using severalairfoil polars.

Table Location) within AeroDyn. The multiple airfoil tables are stored in the airfoil

input les for AeroDyn. For each angle of attack there is more than one lift, drag and

eventually moment coecient. Each of the dierent polar sets represent a dierent

airfoil characteristic, dened by the MulTabLoc-value. This table ID is a numerical

value and by changing it, dierent airfoil tables can dynamically be selected within the

simulation. The table ID can be used to represent any variable airfoil characteristic,

from Reynolds numbers to aileron ap angles. If the desired MulTabLoc-variable is

not directly represented in the airfoil table, linear interpolation between the adjacent

tables is performed. If a desired MulTabLoc-value exceeds the range specied in the

airfoil table, there is no extrapolation but the most outer table is taken.

In order to determine the optimal actuator position during a simulation two control

approaches are implemented: a simple optimization loop and a PID controller. Figure

7.4 illustrates the used control circuit terminology. The aforementioned MulTabLoc is

synonymous with the actuator variable y(t) which is determined by the controller.

7.1.1 Optimization loop

In order to understand the working principle of the optimization loop, the communi-

cation between YawDyn and AeroDyn needs to be described rst. Figure 7.5 shows

the three main program loops of YawDyn: the time loop, the blade loop and the blade

element loop. The structural program YawDyn calls the aerodynamic program Aero-

7 QBladeAE 44

w(t) e(t) y(t)

z(t)

x(t)

Controler System

set point error

variableactuator

variable

disturbance

variable

control

variable

Figure 7.4: Schematic control circuit and control terminology.

Dyn once for each time step, blade and blade element. The AeroDyn routines return

the aerodynamic forces on a single element, which are the normal force on the element

(DFN), the tangential force (DFT) and the pitching moment (PMA). In order to deter-

mine the incident element velocity and the element forces, AeroDyn needs information

about the current state of the element. Within each call of AeroDyn, it calls four

routines in YawDyn again, which are GetVNVT (wind and blade element velocities),

GetRotorParams (rotor parameters like rotor speed, yaw angle, etc.), GetBladeParams

(blade parameters like azimuth angle) and GetElemParams (element parameters like

element pitch angle, radius, location and MulTabLoc). If the new parameters for all

the elements of all the blade are determined, the blade and rotor related parameters

are updated again.

Figure 7.5 shows as well the implementation of the optimization control loop. It is

positioned between the blade and the blade element loop. The loop's task is to nd the

optimal active element operation point, which is expressed by the MulTabLoc-variable.

The optimization loop runs the element loop several times, but each time with a dier-

ent MulTabLoc-value. If using a trailing edge ap for example, this is synonymous with

dierent ap angles. After the loop is nished, the best MulTabLoc-value (e.g. ap

angle) is determined, according to the control variable and the desired set point. These

parameters are given by the user, who has the possibility to choose the local blade ele-

ment forces DFN, DFT and PMA as control variables and to specify a desired set point

for this parameters (the set point needs to be determined in a previous simulation). It

is important to note, that one active ow control device (= active element) can spread

over several blade elements. Logically, there is only one MulTabLoc-variable for all the

7 QBladeAE 45

BLADE Loop

ELEMT Loop

TIME Loop

YawDynAE

MulTabLocCTRL Loop

Element dependant

control variables:

- DFN

- DFT

Figure 7.5: Implementation of the optimization loop for nding the optimal polarfor each active section (element dependent).

blade elements of one active element, like the trailing edge ap can only have one ap

angle for all the covered blade elements. It is important which blade element shall be

used to compare the control variable with the set point and only this blade element

will perform optimally. In reality this represents the sensor position.

The use of the control loop has disadvantages. First of all the computational eort

can be very high, especially when the step size of the loop is very small. Secondly only

element related parameters can be used as control variables (DFN and DFT). This

means blade or rotor dependent parameters, like blade ap deections or root bending

moments can not be controlled directly. Nevertheless one can argue, that the uctu-

ations of the element forces are sources of the load uctuations of the whole blade.

In addition to that, the use of an angle of attack sensor for example would only give

local element information as well. As the local element incident inow angle is directly

coupled to the aerodynamic performance of the element, this approach still has it's

7 QBladeAE 46

right to exist.

A major advantage of the control loop is that it does not require any controller

dependent information, as it always nds the optimal actuator operation point. This

is not realistic and the results of the optimization loop have to be seen as the optimal

potential of the active ow control device.

7.1.2 PID controller

Next to the optimization control loop, a simple PID controller is implemented as well.

Each active element on the blade has a PID controller with individual characteristic

parameters. The implementation is shown in Figure 7.6. Other than the control

loop, the PID controller is positioned in the time loop of YawDyn because the control

variables for the PID controller are no longer element dependent parameters, but blade

dependent. The user can choose the control variable to be either the blade ap rate, the

blade ap angle and the out-of-plane root bending moment. Unlike the optimization

loop, the computational eort using the PID controller is much smaller, as the desired

actuator variable (MulTabLoc-value) is directly determined by the PID controller only

once per time step.

The control circuit is shown in Figure 7.7. Next to the controller itself, there is

a rate limiter and a range limiter so that dierent actuator congurations can be

investigated (small actuator with big range or large actuator with small range). The

range limiter denes the minimal and maximal actuator operation point. Note that

this parameter depends as well on the multiple airfoil tables, specied in the airfoil

input le for AeroDyn. The actuator range should not exceed the MulTabLoc-values

in the airfoil les. The rate limiter controls the maximum speed of the actuator. In

order to simulate the time lag between sensor data acquisition and actuator control,

a time delay is included. For an easy implementation in the FORTRAN routines of

YawDyn, a simple array stores and keeps the desired actuator variables for a certain

time, before using them. Note that the controller delay can not be smaller than the

simulation time step and can only be expressed by integer multiples of the time step.

7 QBladeAE 47

BLADE Loop

ELEMT Loop

TIME Loop

YawDynAE

MulTabLoc

PID

Blade dependant

control variables:

- FlapRate

- FlapAngle

- AFMB

Figure 7.6: Implementation of the PID controller: one for each active element (bladedependent).

2

0

Blade Flap Rate β

1PID

Saturation

Rate limiter Delay

Actuator Angle

Set point

Figure 7.7: Exemplary control circuit for the PID controller using a trailing edgeap as actuator and the blade ap rate as control variable.

7 QBladeAE 48

Controller tuning

The PID uses three gain variables to determine the actuator variable, according to an

error between control variable and set point. The proportional gain Kp (more error →more reaction), the integral gain Ki (more error → faster reaction) and the dierential

gain Kd (faster error → more reaction):

yp(t) = Kp · e(t) (7.1)

yi(t) = Ki ·∫e(t)dt (7.2)

yd(t) = Kd ·de(t)

dt(7.3)

y = yp + yi + yd (7.4)

In case of only one active element per blade, the optimal gains can be found by applying

a tuning method like the Ziegler-Nichols tuning rule. The system reaction on a step

function is monitored while increasing the proportional gain Kp from zero (Ki = Kd =

0) until system gets instable. After the ultimate gain Ku and the oscillation period Tuare found, the gains of the PID controller gains are determined as follows:

Kp = 0.6Ku (7.5)

Ki = 2Ku

Tu(7.6)

Kd =KpTu8

(7.7)

A step system reaction can be simulated by using a inow wind speed drop dened in

a hub-height wind le for AeroDyn (see [27]).

A problem arises when using more than one active element per blade. As the control

variables are blade dependent, a change from a rst actuator, has an inuence on the

performance of a second. The heuristic tuning rule can not be applied anymore, as a

new set of gains for one active element would change the optimal gains for another.

To nd a global optimum for all the gains of all active elements, a simple sweep loop

could be applied, similar to the one described above. All the possible gain variations

of the controllers could be tested and compared with each other. This would yield in

an enormous computational eort. To reduce computational time, a more intelligent

7 QBladeAE 49

way, like an optimization strategy as described in [45]. In QBladeAE, there is no tuning

method built in and the user has to specify all controller gains manually.

7.2 Blade related simulation parameters

The geometrical blade representation in AeroDyn and the structural blade related

parameters for YawDyn need to be specied for a simulation. As described in 4.1 a

blade has one ap degree of freedom and is represented by a sti beam and a hinge-

spring model. QBladeAE automatically determines default values for the blade related

parameters, which are described in the following.

7.2.1 QBlade and NREL blade format

The blade representation in QBlade and in AeroDyn diers, as can be seen in Figure

7.8. In QBlade a blade is dened by sections, whose radius is measured from the

center of rotation. Additionally each section has a value for the chord length and

the twist angle. In AeroDyn the blade is represented by elements. The radius of the

element (RELM) is the distance between the blade-hub connection and the center of

the element. Each element then has a length (DR), a chord length and a twist angle.

DR5

RELM5

RH

ELM#5

x

y

x

yRleft,5

Rright,5

QBlade

AeroDyn

Figure 7.8: Dierent blade denition in QBlade and NREL format.

The single elements are two-dimensional extrusions and have constant properties

over their length. The values for the chord and the twist are interpolated between two

sections. The blade geometric conversion from QBlade in AeroDyn implicates that the

last section dened in QBlade can not be represented in AeroDyn.

7 QBladeAE 50

7.2.2 Blade mass

As mentioned above, the blade is assumed to have a uniform mass distribution. In order

to approximate the blade mass mblade as a function of the blade radius, the following

relation is used:

mblade(R) = 1.7 ∗R2.3 (7.8)

This empirical approximation is derived from real blade parameters shown in Figure

7.9.

0

5000

10000

15000

20000

25000

30000

20 30 40 50 60

blad

e m

ass

[kg]

blade length [m]

f(x) = 1.7*x2.3

real blade masses

Figure 7.9: Dierent blade masses over blade length and the used exponential ap-proximation function [51].

In YawDyn the blade mass is referred to as BM .

7.2.3 Blade center of gravity

The blade center of gravity is calculated by approximating the blade elements (QBlade

format) to be truncated cones, as illustrated in Figure 7.10. The two rectangles of the

cone have the same surface area An and An+1 as the two airfoils shapes and the same

chord lengths cn and cn+1. Hence the heights of the rectangles hn and hn+1 can be

7 QBladeAE 51

cn

cn+1

An

An+1

An

An+1

hn

cn+1

ELM#i COGi,real COGxi,approx

cn

hn+1

DRRnRnx

yz

Figure 7.10: Simplied geometric representation (rectangular cone) of a homoge-neous blade section for the calculation of the blade center of gravity.

determined. The element center of gravity cogx,i is referred to as RB within YawDyn

and can be calculated with

cogx,i = Rn +DR

2· cnhn + cnhn+1 + cn+1hn + 3cn+1hn+1

2cnhn + cnhn+1 + cn+1hn + 2cn+1hn+1

(7.9)

and the element volume is given as

Vi = DR · An + An+1

2(7.10)

By weighting the element center of gravity cogx,i with the element volume Vi, the total

blade center of gravity cogx,blade can be calculated using

cogx,blade =

∑NELMi=1 cogx,i · Vi∑NELM

i=1 Vi(7.11)

The uniform density of the blade can determined with

ρblade =mblade

Vblade(7.12)

Only the center of gravity in x-direction (spanwise direction) is computed. The dis-

tance between the center of gravity and the z-axis is neglected. Furthermore the COG

distance in y-direction is not needed, as YawDyn only reads the mass moment of inertia

for the rotation about the ap axis (y-axis).

7 QBladeAE 52

7.2.4 Blade mass moment of inertia

Similar to the assumptions made above, the mass moment of inertia Jflap about the

ap axis can be calculated using

Jflap =NELM∑i=1

mi · (cog2x,i + cog2z,i) =NELM∑i=1

Vi · ρblade · cog2x,i (7.13)

neglecting the distance of cogz,i. In YawDyn the mass moment of inertia is named

BLINER.

7.2.5 Torsional root spring constant

Another value needed by the structural model, is the torsional spring constant of the

equivalent apping hinge spring at the blade root. This value is named kβ in Figure

4.2 [28]. The parameter denes how sti or soft a blade is modeled. It is not possible

to give a general rule of thumb to determine this value and QBladeAE does not auto-

matically give a default value. In order to get a reasonable stiness value, the blade

deection under steady wind conditions can be taken as an indicator. According to

[29] a number of modern wind turbines show tip deections of about 8% of their blade

radius at wind speeds of 15m/s [2].

As YawDyn does not automatically generate an output for the tip deection, an

additional function is implemented in QBladeAE. The tip deection TDblade is derived

from the ap angle β of the blade and given as

TDblade = Rblade · sin β (7.14)

7.2.6 Dynamic stall parameters

Next to the structural blade related parameters, AeroDyn needs additional aerody-

namic information for the semi-empirical dynamic stall model (if enabled). QBladeAE

automatically calculates the necessary inputs, which are derived from the airfoil polars

and stored in the airfoil les for AeroDyn. These are

• Zero lift angle of attack: α0cl

• cn slope for zero lift: cn(α0cl)

Rajabi

Underline

7 QBladeAE 53

• cn at stall value for positive angle of attack: cn(α+stall)

• cn at stall value for negative angle of attack: cn(α−stall)

• Angle of attack for minimum cd: αcd,min

• Minimum cd value: cd,min

Most of the parameters can be directly derived from the polar tables. Only the

detection of the stall angles α+stall and α−stall needs further investigation. In order to

have a reliable stall indicator, the cl/cd over α is used. As can be seen in Figure 7.11

the rst stall eects occurring on the airfoil are visible as two peaks. This is when the

drag rapidly increases due to ow separation. This clear characteristic can be used

in an automated numerical algorithm to detect the positive and negative stall angles

reliably.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180-100

-50

0

50

100

150

200

c n c l/c

d

α

cncl/cd

Figure 7.11: Dynamic stall related parameter using clcd-curve for automatically de-

tecting the critical static stall angle αstall.

To approximate the cn slope for zero lift cn(α0cl) a least square method is used, as

proposed in [27].

7 QBladeAE 54

Figure 7.12: QBladeAE active blade design module.

It shall be noted, that the active elements have no eect on the dynamic stall model.

As there is no proper aerodynamic formulation, it might be necessary to switch of the

dynamic stall model, when simulating a blade with active elements. If the actuators

are only positioned in the outer region of the blade, a negative blade pitch angle might

ensure, that there are no stall eects in the blade tip region.

7.3 Program modules

The following section gives a short overview of the four program modules used in

QBladeAE.

7.3.1 Blade design with active elements

In the active blade design menu (Figure 7.12), a new blade equipped with active el-

ements can be created. Therefore, a blade design from QBlade must be available.

Next to the 3D-visualization of the blade the geometric parameters are displayed in a

spreadsheet (QBlade format).

The Active Element group allows the denition of new active ow control devices.

The actuator parameters are specied in a separate dialog. These are

7 QBladeAE 55

Active Element Type The type of the active element species it's name and the type

of unit used for the representation of the MulTabLoc. For example a ap device

has the table ID unit [deg] for the ap angle, a boundary layer suction device has

the unit [m3/s] for the volumetric ow rate. Despite that, the correct setting of

the type and unit is not obligatory and does not change the calculation results.

Dimension The starting and end position of the active element expressed in the

QBlade blade format. Only dedicated sections can be used for the start and

end point of an active element.

Control Type The setting denes the control type of the active element. It can either

be LOOP for the optimization loop or PID for the PID controller. If there are

more than one active element, this setting is the same for all. The control types

can not be mixed.

Control Variable The control variable which can be selected depend on the controller

type. For the optimization loop the blade element dependent parameters DFN

and DFT can be selected. This is the element normal force and the element tan-

gential force. For the PID controller, the blade dependent parameters FlapRate,

FlapAngle and AFMB can be selected. This is the blade ap rate, the blade

deection angle β and the out-of-plane root bending moment (axial ap bending

moment). As can be seen in Equation 7.14 the blade deection angle and the

blade tip deection are synonymous.

Set point Set point for the controller. Depending on the selected control variable, the

units are either [kN ], [kNm], [deg] or [−].

Actuator speed This is the ± maximum actuator speed. The unit depends on the

used active element type. For a ap device the unit would be deg/s.

Min Max Range The operational range of the actuator can be limited. The range

specied here can not be bigger than the limits dened in the specic multiple

airfoil tables.

Kp, Ki, Kd The proportional, integral and derivative gain for the PID controller (only

PID).

7 QBladeAE 56

Delay The time delay in [ms] between sensor data acquisition and actuator response.

The values must be bigger than the simulation time step and is rounded to integer

multiples of the simulation time step (only PID).

Step size This is the step size for the optimization control loop expressed in active

element type dependent units. The smaller the step size the higher the controller

accuracy but the longer the simulation time (only LOOP).

Sensor position The sensor position denes the blade element from which the opti-

mization loop takes the control variable. If for example an active element covers

the blade element 10, 11 and 12 and the sensor position is set to element num-

ber 11, only this element will perform optimally. The control loop determines

the optimal actuator variable by comparing the control variable of this element

with the set point. The adjacent elements only follow the sensor element (only

LOOP).

7.3.2 Aerodynamic representation of active elements

In this module the variable aerodynamic characteristics of an active element are dened.

The inputs specied here are the basis for the AeroDyn airfoil les with multiple polar

tables. For each airfoil which is covered by an active element, a set of polars can be

added. Next to the multiple polars, the element needs a so-called mother-polar. This

is a 360-polar which was generated within QBlade. In addition to the mother-polar,

several child-polars can be added. These child polars can be either generated within

the XFLR5 module, or be imported. For the angle of attack range which is not specied

in the child-polar the coecients from it's 360-mother-polar are automatically taken

over. This assumption can be be made, as the airfoil characteristics are similar for very

high and very low angles of attack but the user can change all the polars manually as

well. Note that the polars can have dierent step sizes in the angle of attack range. By

adding child-polars to a mother-polar, the step size is automatically adapted, using a

liner interpolation. Once the child-polars are generated, the single values of the polar

can be edited manually, but single points shall not be deleted.

For each polar, which is added to the airfoil of the active element, a MulTabLoc-

values (table ID for the multiple airfoil tables) has to be specied.

7 QBladeAE 57

Figure 7.13: QBladeAE multiple aerodynamic polar module.

7.3.3 Wind eld simulation

The wind eld model of QBladeAE (Figure 7.14) is based on the Veers model [54] and

calculates correlated wind eld les for the x-direction. It includes an atmospheric

shear layer representation via a surface roughness length z0. For 3D-visualization the

the freely available Qt/OpenGl based C/C++ programming library QwtPlot3D is used.

As a turbulent inow eld can easily be generated with the more sophisticated Turb-

Sim, the wind eld module of QBladeAE is only implemented in a beta version and

will not be discussed any further.

7.3.4 Aeroelastic simulation

In the aeroelastic module of QBladeAE (Figure 7.15) all the necessary input for the

NREL routines AeroDyn ans YawDyn are provided. The user can select the available

(active) wings and dene the parameters for the aerodynamic and the structural model

in separate dialogs. The blade dependent parameters described above are automatically

updated once a new blade is selected.

After the inputs are dened, QBladeAE automatically starts YawDynAE as an ex-

ternal process and reads in the generated outputs, once the simulation is nished

7 QBladeAE 58

Figure 7.14: QBladeAE wind eld generator module (beta).

Figure 7.15: QBladeAE aeroelastic simulation module.

7 QBladeAE 59

successfully. QBladeAE automatically creates and organizes a folder structure on the

local hard drive.

For the visualization of the results, four dynamic 2D-graphs are used. They can be

used to plot the rotor parameters generated from YawDyn (yawdyn.plt) and the results

are stored in the output le from AeroDyn (element.plt). The blade element dependent

results can be either plotted over time (for a xed element) or over the radial blade

position (for a xed time step). In addition to that, the graphs can show the active

element dependent parameters as well. This is the actuator operation point and the

actuator speed over time.

A TurbSim dialog helps to dene TurbSim input les for the generation of turbulent

wind eld les.

Part III

Simulation

8 Standard simulation

This part gives the results of an exemplary simulation of a wind turbine blade, which is

equipped with active elements. It is more meant to be a description of how to approach

the set up and use of a simulation with QBladeAE, rather than a complete scientic

investigation.

Before simulating a blade with active elements, a standard wind turbine simulation

is performed. The behavior of the aeroelastic model and the sensibility to changes of

specic key input parameters is shown. A nal test case is derived which will then be

used for a simulation using one and more than one active ow control elements.

8.1 Turbine and blade model

A ctive turbine of the 2.5MW class will be used for the simulation. For simplica-

tion the turbine operates at a xed rotational speed of 15rpm, as YawDyn can not be

used for a variable speed turbine simulation. The turbine is a conventional 3-bladed

upwind turbine and has a rotor diameter of 89m. The other parameters used for the

simulation are shown in Table 8.1.


Table 8.1: Turbine parameters used for the simulation.

Parameter Symbol Value

Nominal power PN 2500kWRotor diameter D 89mBlade length lblade 43.3mHub radius Rhub 1.2mRotor tilt angle τ 4

Blade precone angle PC 2

Power regulation - pitchRotational speed (xed) n 15rpmTip speed ΩR 70m/sNumber of blades B 3Nominal wind speed vN 13m/sCut-in wind speed vin 3.5m/sCut-out wind speed vout 25m/sHub height HH 89m

The blades have a length lblade of 43.3m. The geometric shape of the blade created

with QBlade is shown in Figure 8.1. It has a limited twist of 12 in the root region

and is linearly tapered. The blade design is shown in Table A.2.

Figure 8.1: 3D view of blade

According to the assumptions described in 7.2, the structural blade parameters are

computed automatically by QBladeAE. The necessary blade parameters are the blade

mass, the distance of the center of gravity to the blade hinge axis and the mass moment

of inertia for the rotation about the ap axis. The values for the simulation are shown

in Table 8.2.


Table 8.2: Blade structural parameters used for the simulation.

Parameter Symbol Value

Blade mass mblade 9840kgDistance to center of gravity cogx,blade 10.6mFlap mass moment of inertia Jflap 1.89 · 106 kgm2

Root spring stiness (no default value) FS 2.2 · 107 Nm/rad

As mentioned in 7.2.5, another blade structural input parameter is the torsional

spring constant of the equivalent apping hinge spring at the blade root. To determine

this value, a rule of thumb is used: as stated in [2], a usual blade deection at 15m/s

steady inow is about 8% of the blade length. As the turbine already pitches at this

wind speed, a parameter variation of the spring constant is performed at 13m/s. Figure

8.2 shows the blade tip deection for three dierent stinesses. The resulting tip

deection of about 3m (6.5% of blade length) seems reasonable and therefore a spring

constant of 2.2 · 107 Nm/rad is used.

0

1

2

3

4

5

6

7

8

9

0 10 20 30 40 50 60 70 80 90

Bla

de ti

p de

flect

ion

[m]

Time [s]

FS = 1.0 106 Nm/radFS = 2.2 106 Nm/radFS = 4.0 106 Nm/rad

Figure 8.2: Blade tip deection with three dierent root spring stinesses. Thewind inow is steady and constant over the whole rotor disk.


8.2 Blade validation

The geometric blade denition in QBlade and QBladeAE diers, as described in 7.2.1.

To estimate the dierence between the two blade descriptions, a steady state rotor

performance simulation using QBlade is compared to the power output obtained with

several YawDynAE simulations. The comparison shows the inuence of the blade

translation from QBlade- to NREL-format. For the comparison, the turbine model in

YawDynAE has to be simplied: the tower and the blades are considered as rigid, so

no structural deections occur. The rotor tilt and pre-cone angle is set to zero. The

inow model in AeroDyn is set to the EQUIL-option (BEM), with a Prantl tip- and

hub-loss model. For the dierent wind speeds a totally uniform and constant inow

(no shear layer) is assumed. As can be seen in Figure 8.3 the results match up very

well.

0

500

1000

1500

2000

2500

3000

0 5 10 15 20 25

P [k

W]

v [m/s]

QBladeYawDynAE

Figure 8.3: Rotor power over wind speed calculated with QBlade and QBladeAE.

Note that modeled turbine is pitch regulated. QBlade automatically calculates the

necessary blade pitch angles θb to keep the power output at the rated nominal power

level of 2500kW if the wind speed exceeds the nominal wind speed of 13m/s. This pitch

angle is then used as well in YawDynAE. Figure 8.4 shows the used pitch angle over

the wind speed.


0

5

10

15

20

25

30

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

θ b [d

eg]

v [m/s]

Blade pitch

Figure 8.4: Blade pitch over wind speed for power regulation.

8.3 Dynamic stall eects

As mentioned in 3.2.3 AeroDyn features a dynamic stall model. The use of active

elements change the aerodynamic behavior of the airfoil and thus the semi-empirical

derived dynamic stall model might not be valid any more. To avoid any stall related

phenomena for the active simulation, the blade is pitched towards lower angles of

attack. This ensures that the outer region of the blade (where the active elements will

be positioned) operate in the attached-ow region during the whole simulation. Figure

8.5 shows the inuence of the dynamic stall model with a pitch angle of θb = 0. The

bottom graph shows the angle of attack distribution over the blade at a specic instant

of time. It indicates that also the blade outer regions operate under stalled conditions,

as the angle of attack already exceeds the value for the static stall angle of attack.

Logically, the eect of the dynamic stall phenomenon can be seen in the top graph,

where the blade deection over the simulation time is shown. The eect of dynamic

stall leads to higher blade deections, as the maximum aerodynamic forces which occur

are higher. The slopes of the blade deection curve are also higher due to the rapid

lift break-in after maximum lift.


0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

0 5 10 15 20 25 30 35 40 45

/S

ymbo

l a

[deg

]

radial position [m]

STEADYBEDDOES

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 10 20 30 40 50 60 70 80 90

blad

e tip

def

lect

ion

[m]

Time [s]

STEADYBEDDOES

Figure 8.5: Inuence of the dynamic stall model on the blade tip deection overtime and the angle of attack over radial position for the blade with apitch angle of θb = 0.

On the contrary, a simulation with a blade pitch angle of θb = 5 almost eliminates

the eects of dynamic stall. As can be seen in Figure 8.6, the angles of attack along

the blade are reduced and the outer blade region now operates in the attached ow

region. The power output for the pitched blades is logically reduced.

The eect of a the apping exible ap is currently only modeled by jumping from

one static polar table to the other. As can not be foreseen which inuence the apping

has on the dynamic stall model, it is switched o for all further simulations. Although

the pitched blade ensures a exible ap operation in the attached ow region for most

of the time, it has to be noted, that the apping itself generates unsteady eects, just

like blade pitching in the attached ow region (3.2.3). These dynamic eects are not

modeled by the software.


0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

0 5 10 15 20 25 30 35 40 45

/S

ymbo

l a

[deg

]

radial position [m]

STEADYBEDDOES

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 10 20 30 40 50 60 70 80 90

blad

e tip

def

lect

ion

[m]

time [s]

STEADYBEDDOES

Figure 8.6: Inuence of the dynamic stall model on the blade tip deection overtime and the angle of attack over radial position for the blade with apitch angle of θb = 5.

8.4 Yawed turbine

One of the key features of YawDyn is to model a yawed turbine and the opportunity

shall not be missed to show the inuence of dierent yaw conditions. YawDyn provides

dierent models, which include free yaw, xed yaw or yawing with a constant rate.

As the necessary parameters like yaw-stiness, -damping and -friction are dicult to

evaluate, the turbine is only modeled with a xed yaw model. Only dierent yaw angles

are regarded. Figure 8.7 shows the eect of three dierent yaw angles on the blade tip

deection. However, for the further simulations a yaw angle of 0 is used.


1

1.5

2

2.5

3

3.5

4

0 10 20 30 40 50 60 70 80 90

blad

e tip

def

lect

ion

[m]

time [s]

0° yaw angle2° yaw angle

10° yaw angle

Figure 8.7: Blade tip deection for dierent yaw angles.

8.5 Wind eld

The GUI interface for TurbSim is used to create a turbulent inow eld for the further

simulations. An IEC Kaimal -spectrum is used, with a turbulence intensity of 15%. The

mean wind speed is set to 13m/s at a hub height of 89m. The shear layer is simulated

with a power law exponent PLExp of 0.2 and a surface roughness length of z0 = 0.03.

This wind le will be used for all the further simulations. The wind speed time series

at the hub height is shown in Figure 8.8.

Rajabi

Pencil


7

8

9

10

11

12

13

14

15

0 10 20 30 40 50 60 70 80 90

win

d sp

eed

[m/s

]

time [s]

Figure 8.8: Turbulent wind speed time series in x-direction at a hub height of 89mand a mean wind speed of 13ms.

8.6 Baseline simulation

After combing all the aforementioned model inputs and assumptions, a baseline test

case is derived. All the simulations will be performed using these default values, unless

stated otherwise. The basic results of the simulation can be seen in Figure 8.9. The

simulation at a constant rotational speed includes atmospheric turbulence and shear

layer, blade-tower interaction, skewed inow with an unsteady inow model (GDW),

tilted hub with pre-coned and pitched blades. Note that the out-of plane bending

moment has the same curve progression as the blade tip deection. This is due to the

simple structural blade representation in YawDyn (4.1).


1

2

3

4

0 10 20 30 40 50 60 70 80 90

blad

e de

flect

ion

[m]

time [s]

1000

2000

3000

o-p

bend

ing

mom

ent [

kNm

]

1000

2000

3000

pow

er [k

W]

200

250

300

350

thru

st [k

N]

6

8

10

12

14

16

win

d sp

eed

[m/s

]

Figure 8.9: Results of baseline simulation.

9 AFC simulation

This chapter describes a simulation using a blade, which is equipped with active ele-

ments. As an example, a form-exible trailing edge device (exible ap) is used. As

mentioned above, this work does not claim to be a complete scientic investigation.

The following investigation shall rather demonstrate the performance of QBladeAE.

The exible airfoil structure was intensively investigated by Smart Blade GmbH in

the past and experimental wind tunnel data are available [41]. The aps are actuated

with pneumatic "muscles", which contract by applying air pressure. The aps can

be deected in both, upwards direction (negative deection) and downwards direction

(positive deection). The baseline airfoil is a DU-96-W-180 and the shapes for for

dierent ap deections are shown in Figure 9.1 and 9.2. The test wing in the wind

tunnel experiment had a chord of 0.6m with both the rigid and exible trailing edge

segments thus achieving a similar Re of 1, 300, 000.

Figure 9.1: Overlapping airfoil contours for positive deection. Red: The originalDU-96-W-180 airfoil; Green: slightly deected exible ap; Red: fullydeected exible ap [41].

The apping part has a chord length of 25% and the maximum apping range is

±20. The maximum actuator speed is 20/s.

9 AFC simulation 72

Figure 9.2: Overlapping airfoil contours for negative deection. Red: The originalDU-96-W-180 airfoil; Green: slightly deected exible ap; Red: fullydeected exible ap [41].

A ap deection induces changes in the circulation, and thus the aerodynamic charac-

teristics of the airfoil areb changed. In the wind tunnel measurements at the GroWiKa

of TU Berlin the performance of the exible-form airfoil was determined. The eect

on the lift and drag coecients can be seen in Figure 9.3. The overall change in the

lift coecient is about ∆cl = 2. On the other side, any ap deection increases the

drag value.

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-15 -10 -5 0 5 10 15 20

c l

α

0.0

0.1

0.1

0.2

0.2

0.2

-15 -10 -5 0 5 10 15 20

c d

α

neutral positionslight neg. deflection

full neg. deflectionslight pos. deflection

full pos. deflection

Figure 9.3: Lift and drag coecient over angle of attack cl(α) for exible ap atfour ap angles.

Using the airfoil extrapolation method of QBlade (3.2.2), 360-polars were derived,

which can be seen in Figure 9.4. The lift and drag values for a dierent ap deections

9 AFC simulation 73

are assumed to be the same for angles of attack higher than 28 and lower than −11.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-150 -100 -50 0 50 100 150

c l

α

neutral positionslight neg. deflection

full neg. deflectionslight pos. deflection

full pos. deflection

Figure 9.4: Extrapolated 360 cl-polar.

9.1 Flap parameter study

In this section a blade with dierent ap congurations is investigated. If not explicitly

mentioned, the PID controller is used for all simulations, with the ap rate as control

variable and a set point of zero. The default performance parameters of exible ap

are a range of ±20 and a maximum actuator speed of 20/s. A small parameter inves-

tigation shows the inuence of dierent ap positions, ap sizes and number of aps

as well as an investigation of the operational range, the maximum speed and the delay

of the actuator. All the controller gains are found manually for each conguration and

therefore an optimal controller tuning can not be ensured. However, the actuator rate

and range limit provides a robust controller tuning in most cases.

For the comparison of the dierent potentials for load reduction, the standard devi-

9 AFC simulation 74

ation σ is taken as indicator. It is dened as:

σ =

√√√√ 1

n− 1

n∑i=1

(xi − x)2 (9.1)

The load reduction is expressed as percentage of the standard deviation σ0 of the base-

line simulation:

χ = 1− σiσ0

(9.2)

For the parametric investigation, the outer part of the blade is divided in nine equidis-

tant parts of 1.5m, as can be seen in Figure 9.5. These sections, and combinations of

each other, will be modeled as active in the following simulations.

123456789

DU-96-W-180

43.25m1.5m

Figure 9.5: Blade with 9 equidistant outer sections.

According to the NREL blade format, the radial position of the sections is shown in

Table 9.1. According to the convention, r is the distance between blade-hub connection

and the center of the element and DR is the element length.

Some of the following simulations are as well performed to compare them with the

results obtained in [2], in which the performance of trailing edge aps was investigated.

The comparisson is used as well as a possibility to validate the obtained results.

9.1.1 Flap positions

Several simulations are performed using a single 1.5m ap at dierent spanwise posi-

tions. All the nine positions investigated independently. This investigation shows the

optimal spanwise position for the ap. The load reduction χ on the out-of plane bend-

ing moment can be seen in Table 9.2. The highest reduction with about 14% have the

9 AFC simulation 75

Table 9.1: Radial position of the 9 possible active elements.

AE# r [m] r/R [%] DR [m] DR/R [%]

1 41.75 96.5 1.5 3.52 40.25 93.1 1.5 3.53 38.75 89.6 1.5 3.54 37.25 86.1 1.5 3.55 35.75 82.7 1.5 3.56 34.25 79.2 1.5 3.57 32.75 75.8 1.5 3.58 31.25 72.3 1.5 3.59 29.75 68.8 1.5 3.5

active elements at position AE# 2 and AE# 3. This is around r/R = 92% of the blade

length. The more outer sections have a smaller chord and the more inner elements a

smaller radial velocity, which both lower their inuence on the total blade forces.

Table 9.2: Load reduction for 1.5m ap at dierent radial positions.

AE# σAFMB [kNm] χ [%]

Default 274.6 0.01 239.6 12.72 237.2 13.73 237.4 13.54 238.3 13.25 239.7 12.76 241.3 12.17 242.9 11.68 244.8 10.99 246.8 10.1

Figure 9.6 points out the relation between ap position and load reduction. Com-

pared to the investigation in [2], the results correspond well.

9 AFC simulation 76

9

10

11

12

13

14

65 70 75 80 85 90 95 100

redu

ctio

n in

out

-of-

plan

e be

ndin

g m

omen

t χ [%

]

r/R [%]

Figure 9.6: Load reduction of a single ap with a length of 1.5m at dierent radialpositions.

9.1.2 Flap size

This section investigates the inuence of the ap size. Three sections or more at a time

are modeled as one active element. This yields in a ap length of 4.5m, 9m, 10.5m,

12m and 13.5m respectively. The positions of the aps are varied again, and nally

all sections form AE# 1 − 9 are simulated as one single ap. The reduction in χ on

the out-of plane bending moment is listed in Table 9.3. It can be seen, that the load

reduction gets bigger, with increasing ap length, although the reduction potential is

not linearly growing. For small aps, the reduction is around 28%, the medium sized

aps reach 38%− 44% and the big aps up to 50%. In accordance with the results of

the previous section, an outer position of the ap is preferable.

Finally Figure 9.7 summarizes the results and shows the load reduction of the ap

wise root bending moment for all previous simulations. A comparison with [2] again

shows the correlation between the results. Although the potentials for load reduction

are of the same magnitude, it has to be noted that the denition of the load reduction

given in this document, diers from the one in the cited report.

9 AFC simulation 77

Table 9.3: Load reduction for dierent ap lengths and dierent radial positions.

AE# DR [m] σAFMB [kNm] χ [%]

Default - 274.6 0.0123 4.5 193.7 29.5456 4.5 196.9 28.8789 4.5 205.6 25.1

123456 9.0 152.8 44.4456789 9.0 169.1 38.41234567 10.5 135.2 50.82345678 10.5 136.9 50.13456489 10.5 145.8 46.912345678 12.0 138.4 49.623456789 12.0 137.6 49.9123456789 13.5 131.4 52.1

To have an understanding of how the out-of-plane bending moment is inuenced by

the active elements, Figure 9.8 shows the out-of-plane root bending moments for the

baseline simulations and the one for the single 13.5m ap, with the maximum load

reduction of about 52%.

9 AFC simulation 78

0

10

20

30

40

50

0 2 4 6 8 10 12 14

redu

ctio

n in

out

-of-

plan

e ro

ot b

endi

ng m

omen

t χ [%

]

flap length [m]

Figure 9.7: Load reduction of single aps with dierent lengths and dierent radialpositions.

600

800

1000

1200

1400

1600

1800

2000

2200

2400

0 10 20 30 40 50 60 70 80 90

out-

of-p

lane

ben

ding

mom

ent b

lade

1 [k

Nm

]

time [s]

baseline13.5m flap

Figure 9.8: Out-of-plane bending moment for baseline simulation and the single13.5m aps with maximum load reduction.

9 AFC simulation 79

9.1.3 Actuator speed and range

The software makes it easy as well, to change the operational range and the maximum

speed of the actuator. The standard exible ap described above has a range of ±20

and a speed of 20/s. Generally it can be said, that if a ap has a high range it needs

as well a certain speed, in order to make use of this range. If the ap is too slow, it

is not able to reach it's extreme positions within one rotor revolution. In this case the

actuator speed is the limiting parameter. On the other hand, if the ap is too fast, the

additional gain is low, as the ap range becomes the limiting factor. Figure 9.9 shows

the inuence of dierent actuator speeds for the the 4.5m ap (123) and the 9m ap

(123456). It shows, that for the specic simulation conditions (15rpm), the ap speed

of 20/s is sucient.

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50 60

redu

ctio

n in

out

-of-

plan

e be

ndin

g m

omen

t /S

ymbo

l c

[%]

actuator speed [deg/s]

4.5m flap (123)9m flap (123456)

Figure 9.9: Inuence of dierent actuator speeds on the load reduction for two apcongurations.

The overall ap length has a big inuence on the parameters as well. The bigger the

ap, the the smaller the range of the ap has to be. In order to investigate the inuence

of the two actuator parameters in combination, the 9m ap (123456) was exemplary

modied in the range and speed. Figure 9.10 shows the dependencies of the absolute

ap range (it is assumed that the ap can be equally deected in positive as well as

negative direction) and the ap speed on the load reduction. It can be seen, that a

9 AFC simulation 80

ap with a limited ap range of ±15 can still achieve a reduction of 45% compared

to the maximal possible 49%, but provides a simpler structural design.

0 5 10 15 20 25 30 0 5

10 15

20 25

30 35

40 0 5

10 15 20 25 30 35 40 45 50

χ

efficient flap configuration

actuator speed [deg/s]

abs. actuator range [deg]

χ

Figure 9.10: Inuence of dierent actuator ranges and speeds on the load reductionfor a 9m ap conguration.

The simulation above makes the assumption that the range of deection in positive

and negative direction is the same. In other words, a ap with the proposed absolute

deection of 30, has a deection of ±15. For further structural simplications of

the ap, a non-equal deection range can be taken into account as well. Figure 9.11

indicates, that the ap deects slightly more in positive direction, than in negative

direction.

These results are only valid for the examined case and all the other relevant cases

have to be examined as well, but it shows the potential of the software. The short

computation time of about 10s per simulation makes it possible to investigate sev-

eral congurations and parameters and the software helps to nd an optimal actuator

conguration, where the actuator size is harmonized with the actuator rate and the

actuator speed.

9 AFC simulation 81

-15

-10

-5

0

5

10

15

0 10 20 30 40 50 60 70 80 90

flap

angl

e [d

eg]

time [s]

Figure 9.11: Flap angle for 9m ap over simulation time.

9.1.4 Sensor delay

In all the previous simulations it was assumed that the measured control variable is

processed by the controller without delay and the actuator can respond instantaneously.

The introduction of a delay between sensor data acquisition and the ap response has

a signicant inuence on the potential for load reduction. Dierent time delays were

simulated for the 9m ap with the sections AE# 1−9. Figure 9.12 shows the decrease

of load reduction potential χ with increasing time delay. A delay of only 5ms reduced

the potential by half.

9.1.5 Multiple aps

Until now, only adjacent sections have been apped. The question arises what happens,

if a ap is split into two or more parts. The simulation of multiple and independent

aps on one wing with the PID controller, is not scope of this work. The manual

controller tuning for two and more aps is demanding and using the gains from the

previous simulations did not yield in a higher load reduction than for a single ap.

This is not consistent with the results, which were found in other investigations, like

[2]. With the use of multiple aps, the total length of the apping sections could be

9 AFC simulation 82

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25 30 35 40

redu

ctio

n in

out

-of-

plan

e be

ndin

g m

omen

t χ [%

]

controller delay [ms]

Figure 9.12: Load reduction over controller time delay.

reduced to obtain the same load reduction as for a big single ap.

9.2 Optimization loop

As described above, it is not trivial to nd the optimal controller gains for multiple

aps. Each actuator inuences the blade dependent control parameters individually

(ap rate, ap angle, out-of-plane bending moment) but they are not independent from

each other. A more sophisticated controller or a (intelligent) tuning sweep has to be

used.

Another approach to investigate the advantage of multiple active elements is the use

of the optimization loop. As described in 7.1.1, a local blade element control variable

is used for the controller. In this case it is the local blade element normal force DFN .

The loop does not represent a real controller, as it simply calculates all possible ap

angles for a time step and then chooses the optimal one. Another drawback is, that

keeping the local blade element force constant does not mean, that the uctuation of

the out-of plane bending moment is minimized. On the other hand, a concept of several

independent actuators, which only have local control variables has advantages as well.

9 AFC simulation 83

Multiple actuators with more simple integrated sensor-controller units can be used as

a modular concept for active ow control. The sensor could measure, for example, the

local angle of attack, which couples the local inow velocity directly to a trailing edge

ap angle. Then each ap works for it's own, yielding in a less powerful but also less

complex and modular system.

To show the potential of this concept, a simulation with all sections form AE# 1−9

being individually active is performed. This represents a conguration, in which each

actuator has it's own sensor and controller. The load reduction is 44%, as can be seen

in Table 9.4. In contrary, a single ap including the sections AE# 1− 9 with only one

sensor at section AE# 3, shows the same eect.

Table 9.4: Load reduction for individual sections using the optimization loop.

AE# Ctrl. Type DR [m] σAFMB [kNm] χ [%]

Default - - 274.6 0.0123456789 PID 13.5 131.4 52.1

1-2-3-4-5-6-7-8-9 LOOP (9 sensors) 13.5 155.1 43.6123456789 LOOP (1 sensor @ AE# 3) 13.5 155.5 43.7

In Figure 9.13 the element normal force for active element number AE# 3 is shown

for the three congurations listed above. It can be seen, that the optimization loop

keeps the element force at a constant value of about 2900kNm. Next to the normal

force of the baseline simulation, the element force from the PID controlled single ap

is shown as well. It has to be noted, that the optimization loop does not represent a

real controller. The results have to be seen as ideal. The reason for the uctuation of

the normal force in Figure 9.13 is the step size of the control loop. For the simulation

the step size is set to 1. If the step size of the loop is small enough, there would be

no uctuation at all.

The approach of keeping the local blade element force constant, shows less potential

in the reduction of the blade root bending moment than blade PID control approach.

Additionally, there is not a big gain in using multiple sensors for individually controlled

elements. This result has to be examined in more detailed investigations.

9 AFC simulation 84

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 10 20 30 40 50 60 70 80 90

blad

e el

emen

t nor

mal

forc

e D

FN

of A

E#3

[kN

m]

time [s]

baselinePID: 13.5m flap

LOOP: 9x1.5m flap

Figure 9.13: Local blade element force DFN at AE# 3 for the baseline, the singlePID controlled 13.5m ap and the multiple individually optimizationloop controlled aps.

The parametric investigation presented here shall only demonstrate the potential

and the limitation of the software. It is the initial step for further, more detailed

investigations.

10 Suggestions for future research

The current software provides the possibility to estimate the performance of dierent

active ow control devices on wind turbine blades. Now dierent active ow concepts

have to be investigated in more detail and compared with each other.

In a second step, a better model representation has to be implemented. Especially the

oversimplied structural wind turbine and blade model of YawDyn has to be improved.

The following list gives an overview about possible further steps:

Structural model: To simulate the eects of AFC elements on wind turbines in more

detail, an advanced structural model has to be used. An enhanced structural

binding to AeroDyn is FAST [22]. The medium complexity structural model is as

well provided by NREL and features and the possibility to include wind turbine

operational control. Useful information on FAST can be found in [23], [20] and

[21]. An extension called CurveFAST adds a blade torsional degree of freedom

and is described in [30].

Aerodynamic model: As the implementation of actuators for active ow control

changes the physic eects on the ow eld, the aerodynamic models have to

be adapted accordingly. If trailing edge aps are used, for example, the imple-

mentation of an unsteady aerodynamic model for the attached ow region as

described in [1] or [4] might be necessary.

Control: A more advanced control strategy than the simple PID controller has to be

developed and/or the controller tuning has to be improved in order to simulate

multiple aps on a blade.

Noise: The additional noise, which created by the actuators has to be investigated.

Scope of investigations The whole range of the dierent wind turbine states of op-

eration and load cases has to be investigated.

10 Suggestions for future research 86

Validation: As only few other investigations have been made, the results have to be

validated against experimental data. Until now, there is no possibility to validate

the results properly.

11 Conclusion

A software for the preliminary investigation of dierent active ow concepts for wind

turbines has been developed. It is based on the open-source codes XFLR5 and QBlade.

The user friendly extension QBladeAE features a graphical user interface for the use of

the NREL aeroelastic design codes AeroDyn and YawDyn. The user can easily design

wind turbine blades on which dierent active elements can be positioned. The dier-

ent aerodynamic characteristics of the actuators for active ow control are represented

by their individual two-dimensional airfoil polars. The NREL FORTRAN routines

were modied and two approaches for the control of the dierent active elements were

implemented: a simple PID controller and an optimization loop.

The working principle and the limitation of the software was shown with the simula-

tion of a form exible trailing edge (exible ap). The results show once again the

potential of trailing edge devices for load reduction on wind turbine blades: an ideal

ap of 13.5m length on a 43m long blade reduced the standard deviation of the root

apwise bending moment by 52%. The inuence of dierent ap size, ap positions,

numbers, ap speed and ap range has been investigated as well.

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A Appendix

A.1 QBladeAE input les for YawDynAE

1 AeroDyn . i p t f i l e from QbladeAE . Sim : FINAL . Wing : AD1 . 5 .

2 SI Units f o r input and output [ SI ]

3 STEADY Dynamic s t a l l model [BEDDOES or STEADY]

4 NO_CM Aerodynamic p i t ch ing moment model [USE_CM ro NO_CM]

5 DYNIN Inf low model [DYNIN or EQUIL ]

6 SWIRL Induct ion f a c t o r model [NONE or WAKE or SWIRL]

7 0 .005 Convergence t o l e r an c e f o r induct ion f a c t o r

8 PRAND Tip−l o s s model (EQUIL only ) [ PRANdtl , GTECH, or NONE]

9 PRAND Hub−l o s s model (EQUIL only ) [ PRANdtl or NONE]

10 wind\smaple_w ind f i l e

11 89 .00 Wind r e f e r e n c e (hub) he ight .

12 0 .10 Tower shadow c e n t e r l i n e v e l o c i t y d e f i c i t .

13 3 .00 Tower shadow ha l f width .

14 4 .00 Tower shadow r e f e r e n c e po int .

15 1 .2250 Air dens i ty .

16 1 .53 e−05 KinVisc − Kinematic a i r v i s c o s i t y

17 0.0010 Time i n t e r v a l f o r aerodynamic c a l c u l a t i o n s .

18 5 Number o f a i r f o i l f i l e s used . F i l e s l i s t e d below :

19 " a i r f o i l s \ c i r c l e _360_Polar . txt "

21 [ l e f t out f o r b r ev i ty ]

23 " a i r f o i l s \DU−96−W−180. txt "

24 25 Number o f blade elements per blade

25 RELM Twist DR CHOD F i l e ID Elem Data

27 0 .75 0 .00 1 .50 2 .19 1 PRINT

28 2 .50 0 .00 2 .00 2 .34 1 PRINT


32 42 .40 0 .00 0 .80 0 .68 9 PRINT

33 42.925 0 .00 0 .25 0 .41 9 PRINT

34 43.15 0 .00 0 .20 0 .17 9 PRINT

35 SINGLE

Listing A.1: Example aerodyn.ipt input le for QBladeAE simulation

A Appendix 95

1 NACA 63(3)−218, 360 Polar

2 QBladeAE , Reyn#: 3 .0 e6 Mach#: 0 .1

3 3 Number o f a i r f o i l t ab l e s in t h i s f i l e

4 −10 0 10 Table ID parameter

5 0 .00 No longe r used




9 3 .59 −1.50 −6.46 Zero l i f t ang le o f attack ( deg )

10 6 .35 7 .03 6 .57 Cn s l ope f o r zero l i f t ( d imens i on l e s s )

11 0 .70 −0.55 1 .09 Cn at s t a l l va lue f o r p o s i t i v e angle o f attack

12 −1.02 −0.89 −0.60 Cn at s t a l l va lue f o r negat ive angle o f attack

13 0 .00 0 .00 0 .00 Angle o f attack f o r minimum CD ( deg )

14 0 .008 0 .005 0 .008 Minimum CD value

15 −180.0 −0.04 0 .006 −0.04 0 .001 −0.04 0 .006

16 −177.0 0 .25 0 .010 0 .25 0 .012 0 .25 0 .010


20 6 .5 0 .32 0 .009 0 .93 0 .001 1 .29 0 .016

21 7 .0 0 .37 0 .010 0 .98 0 .012 1 .33 0 .017

22 7 .5 0 .43 0 .010 1 .03 0 .012 1 .36 0 .018


26 179 .0 −0.24 0 .008 −0.24 0 .001 −0.24 0 .008

27 180 .0 −0.04 0 .006 −0.04 0 .001 −0.04 0 .006

Listing A.2: Example airfoil.ipt input le for QBladeAE simulation

1 YawDyn . i p t f i l e from QbladeAE .

2 90 .00 Time durat ion o f the s imula t i on ( sec )

3 1500 Number o f azimuth s e c t o r s used f o r i n t e g r a t i o n

4 5 .00 Decimation f a c t o r f o r output p r i n t i ng

5 0.0100 TOLER, Trim so l u t i on t o l e r an c e ( deg )

6 3 Number o f b lades

7 0 .00 0 .00 0 .00 I n i t i a l p i t ch ang l e s ( deg )

8 −4.00 Rotor hub s l i n g ( d i s t ance from yaw ax i s to hub ; p o s i t i v e downwind ) (m)

9 −4.00 Shaft t i l t ang le ( deg )

10 −2.00 Rotor precone angle ( deg )

11 15 .00 RPM, ro to r speed in r e vo l u t i on s per minute

12 0 .00 Ps i In i t , I n i t i a l r o to r po s i t i o n ( zero f o r Blade 1 down) ( deg )

13 FIXED Yaw Model : FREE or FIXED yaw system

14 2 .00 I n i t i a l yaw angle ( deg )

15 0 .00 I n i t i a l yaw rate ( deg/ sec )

16 0 .00 Mass moment o f i n e r t i a about yaw ax i s ( kg m^2)

17 0 .00 YawSti fstrong += f , s t i f f n e s s o f yaw spr ing (Nm/rad )

18 0 .00 YawDamp, yaw damping c o e f f i c i e n t (Nm sec )

19 0 .00 YawFriction , constant f r i c t i o n moment at yaw ax i s (Nm)

20 HINGE Hub model : HINGE, TEETER or RIGID

21 0 .00 0 .00 0 .00 I n i t i a l f l a p ang l e s ( deg )

22 0 .00 0 .00 0 .00 I n i t i a l f l a p r a t e s ( deg/ sec )

23 1 .2 RHinge , rad ius o f r o to r hub (m)

24 10 .57 RBar , d i s t ance from hinge to blade c . g . (m)

25 9845.00 Mass o f one blade ( kg )

26 1 .89 e6 Mass moment o f i n e r t i a o f blade about hinge ax i s ( kg m^2)

27 2 .20 e7 Tors iona l s t i f f n e s s o f blade root spr ing (Nm/rad )

28 0 .00 Teeter s l i n g d i s t ance o f t e e t e r ax i s upwind o f r o to r apex (m)

29 0 .00 Free t e e t e r ang le ( deg )

30 0 .00 Teeter s t i f f n e s s , f i r s t or l i n e a r c o e f f . (Nm/rad )

31 0 .00 Teeter s t i f f n e s s , c o e f f . o f d e f l e c t i o n (Nm/rad^2)

32 0 .00 Teeter damping c o e f f i c i e n t ( lb f−f t−sec )

33 1 ,2 ,3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ,11 ,12 ,13 ,14 ,15 ,16 ,17 [ l e f t out f o r b r ev i ty ]

Listing A.3: Example yawdyn.ipt input le for QBladeAE simulation

A Appendix 96

1 TurbSim v1.50 Input F i l e . Generated in QBladeAE on Do Dez 16 2010 1 2 : 1 2 : 2 1 . . Wing : S88V3 .

3 −−−−−−−−−Runtime Options−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−4 123456 RandSeed1 − F i r s t random seed (−2147483648 to 2147483647)

5 RANLUX RandSeed2 − Second random seed f o r i n t r i n s i c pRNG, or an a l t e r n a t i v e

6 True WrBHHTP − Output hub−he ight turbu lence parameters in binary form?

7 False WrFHHTP − Output hub−he ight turbu lence parameters in formatted form?

8 False WrADHH − Output hub−he ight time−s e r i e s data in AeroDyn form?

9 False WrADFF − Output f u l l−f i e l d time−s e r i e s data in TurbSim/AeroDyn form?

10 True WrBLFF − Output f u l l−f i e l d time−s e r i e s data in BLADED/AeroDyn form?

11 False WrADTWR − Output tower time−s e r i e s data ?

12 False WrFMTFF − Output f u l l−f i e l d time−s e r i e s data in formatted form?

13 False WrACT − Output coherent turbu lence time s t ep s in AeroDyn form?

14 True Clockwise − Clockwise r o t a t i on look ing downwind?

15 0 ScaleIEC − Sca l e IEC turbu lence models to exact t a r g e t standard dev i a t i on ?

17 −−−−−−−−Turbine/Model Sp e c i f i c a t i o n s−−−−−−−−−−−−−−−−−−−−−−−18 25 NumGrid_Z − Ver t i c a l gr id−point matrix dimension

19 25 NumGrid_Y − Hor i zonta l gr id−point matrix dimension

20 0 .05 TimeStep − Time step [ seconds ]

21 600 .0 AnalysisTime − Length o f ana l y s i s time s e r i e s [ seconds ]

22 90 .0 UsableTime − Usable l ength o f output time s e r i e s [ seconds ]

23 89 .0 HubHt − Hub he ight [m] ( should be > 0.5∗GridHeight )

24 107 .0 GridHeight − Grid he ight [m]

25 107 .0 GridWidth − Grid width [m]

26 0 .00 VFlowAng − Ver t i c a l mean f low ( u p t i l t ) ang le [ degree s ]

27 0 .00 HFlowAng − Hor i zonta l mean f low ( skew ) angle [ degree s ]

29 −−−−−−−−Meteo ro l og i ca l Boundary Condit ions−−−−−−−−−−−−−−−−−−−30 IECKAI TurbModel − Turbulence model

31 1 IECstandard − Number o f IEC 61400−x standard

32 A IECturbc − IEC turbu lence c h a r a c t e r i s t i c

33 NTM IEC_WindType − IEC turbu lence type

34 de f au l t ETMc − IEC Extreme Turbulence Model "c" parameter [m/ s ]

35 d e f au l t WindProf i le − Wind p r o f i l e type

36 89 .00 RefHt − Height o f the r e f e r e n c e wind speed [m]

37 13 .00 URef − Mean ( t o t a l ) wind speed at the r e f e r e n c e he ight [m/ s ]

38 d e f au l t ZJetMax − Jet he ight [m]

39 0 .20 PLExp − Power law exponent [− ]

40 0 .03 Z0 − Sur face roughness l ength [m]

42 −−−−−−−−Non−IEC Meteo ro l og i ca l Boundary Condit ions−−−−−−−−−−−−43 de f au l t Lat i tude − S i t e l a t i t u d e [ degree s ] ( or " d e f au l t ")

44 0.0000 RICH_NO − Gradient Richardson number

45 de f au l t UStar − Fr i c t i on or shear v e l o c i t y [m/ s ] ( or " d e f au l t ")

46 de f au l t ZI − Mixing l ay e r depth [m] ( or " d e f au l t ")

47 de f au l t PC_UW − Hub mean u 'w' Reynolds s t r e s s ( or " d e f au l t ")

48 de f au l t PC_UV − Hub mean u ' v ' Reynolds s t r e s s ( or " d e f au l t ")

49 de f au l t PC_VW − Hub mean v 'w' Reynolds s t r e s s ( or " d e f au l t ")

50 de f au l t IncDec1 − u−component coherence parameters

51 de f au l t IncDec2 − v−component coherence parameters

52 de f au l t IncDec3 − w−component coherence parameters

53 de f au l t CohExp − Coherence exponent ( or " d e f au l t ")

55 −−−−−−−−Coherent Turbulence Sca l i ng Parameters−−−−−−−−−−−−−−−−−−−56 . . . CTEventPath − Name o f the path where event data f i l e s are l o ca t ed

57 LES CTEventFile − Type o f event f i l e s ("LES" , "DNS" , or "RANDOM")

58 true Randomize − Randomize the d i s turbance s c a l e and l o c a t i o n s ? ( t rue / f a l s e )

59 1 .00 Di s tSc l − Disturbance s c a l e ( r a t i o o f wave he ight to ro to r d i sk ) .

60 0 .50 CTLy − Frac t i ona l l o c a t i o n o f tower c e n t e r l i n e from r i gh t Randomize = true . )

61 0 .50 CTLz − Frac t i ona l l o c a t i o n o f hub he ight from the bottom of the datase t .

62 30 .0 CTStartTime − Minimum s t a r t time f o r coherent s t r u c t u r e s in RootName . c t s

64 ==================================================

65 NOTE: Do not add or remove any l i n e s in t h i s f i l e !

66 ==================================================

Listing A.4: Example full eld input le for TurbSim

A Appendix 97

1 Active Element . i p t f i l e from QBladeAE .

2 ON Act ivate a c t i v e s imula t i on

3 PID Def ine c on t r o l e r [ PID or LOOP]

4 25 Number o f blade elements per blade

5 1 Number o f a c t i v e e lements per blade

6 RELM AEID

7 1 0

8 2 0


12 23 1

13 24 0

14 25 2

15 General AE Se t t i ng s

16 1 Step s i z e f o r c on t r o l loop

17 0 Sensor delay

18 AE#1 Set t ing

19 −10 10 Min/Max range o f actuator

20 30 Maximum actuator speed

21 FlapRate Control parameter [LOOP: DFN, DFT; PID : AFMB[kNm] , FlapAngle , FlapRate ]

22 0 Set po int

23 23 Sensor po s i t i o n

24 20 12 5 Kp, Ki , Kd

Listing A.5: Example active.ipt input le for QBladeAE simulation

A Appendix 98

A.2 Geometric blade design

Table A.1: Blade geometric parameters in NREL format used for the simulation.

Radius r[m] Twist [deg] DR [m] Chord c[m] Airfoil

0.75 12.00 1.50 2.19 Cylinder2.50 12.00 2.00 2.34 Cylinder5.00 12.00 3.00 2.79 Morph17.25 11.50 1.50 3.15 Morph29.50 9.97 3.00 3.17 DU-00-W-40112.50 8.06 3.00 3.05 DU-00-W-35015.50 6.59 3.00 2.87 DU-97-W-30018.50 5.32 3.00 2.65 DU-91-W-25021.50 4.18 3.00 2.40 DU-93-W-21024.50 3.17 3.00 2.18 DU-93-W-21027.50 2.34 3.00 1.97 DU-93-W-21029.75 1.70 1.50 1.81 DU-96-W-18031.25 1.41 1.50 1.70 DU-96-W-18032.75 1.16 1.50 1.58 DU-96-W-18034.25 0.91 1.50 1.47 DU-96-W-18035.75 0.69 1.50 1.36 DU-96-W-18037.25 0.47 1.50 1.27 DU-96-W-18038.75 0.25 1.50 1.16 DU-96-W-18040.25 0.04 1.50 1.05 DU-96-W-18041.75 0.02 1.50 0.86 DU-96-W-18042.65 0.00 0.30 0.62 DU-96-W-18042.92 0.00 0.25 0.41 DU-96-W-18043.15 0.00 0.20 0.18 DU-96-W-180

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QBladeAE Thesis