development and application of a simulation tool for vertical and horizontal axis wind turbines

DEVELOPMENT AND APPLICATION OF A SIMULATION TOOL FOR VERTICAL AND HORIZONTAL AXIS WIND TURBINES

David Marten ISTA, TU Berlin Berlin, Germany

Juliane Wendler ISTA, TU Berlin Berlin, Germany

Georgios Pechlivanoglou TU Berlin, SMART BLADE

Berlin, Germany

Christian Navid Nayeri ISTA, TU Berlin Berlin, Germany

Christian Oliver Paschereit ISTA, TU Berlin Berlin, Germany

ABSTRACT A double-multiple-streamtube vertical axis wind turbine

simulation and design module has been integrated within the

open-source wind turbine simulator QBlade. QBlade also

contains the XFOIL airfoil analysis functionalities, which

makes the software a single tool that comprises all functionality

needed for the design and simulation of vertical or horizontal

axis wind turbines. The functionality includes two dimensional

airfoil design and analysis, lift and drag polar extrapolation,

rotor blade design and wind turbine performance simulation.

The QBlade software also inherits a generator module, pitch

and rotational speed controllers, geometry export functionality

and the simulation of rotor characteristics maps. Besides that,

QBlade serves as a tool to compare different blade designs and

their performance and to thoroughly investigate the distribution

of all relevant variables along the rotor in an included post

processor. The benefits of this code will be illustrated with two

different case studies. The first case deals with the effect of stall

delaying vortex generators on a vertical axis wind turbine

rotor. The second case outlines the impact of helical blades and

blade number on the time varying loads of a vertical axis wind

turbine.

NOMENCLATURE A Weibull distribution scale parameter

AEP Annual energy production

AoA Angle of Attack

α Inflow Angle

α 0 Blade twist angle

BEM Blade Element Momentum

CP Power coefficient

Cl, Cd Lift / Drag coefficient

CN, CT Normal / Thrust coefficient

CF_t_rot Tangential force coefficient of the rotor

CF_x_rot Streamwise force coefficient of the rotor

CF_y_rot Crosswise force coefficient of the rotor

c Chord length

δ Angle between blade normal and turbine axis

DMS Double Multiple Streamtube

HAWT Horizontal Axis Wind Turbine

k Weibull distribution shape parameter

N Blade number

r Local radius

Re Reynolds Number

TSR Tip Speed Ratio

θ Azimuthal angle

uup, udown Upwind / downwind interference factor

VG Vortex generator

Vup, Vdown Velocity at upwind / downwind rotor disc

V0 Freestream velocity

Veq Equilibrium velocity between the rotor discs

VAWT Vertical Axis Wind Turbine

W Relative velocity at blade element

INTRODUCTION Recently, fuelled by offshore and urban wind turbine

applications, interest in vertical axis wind turbine (VAWT)

technology is increasing after their development almost ceased

in the mid 90’s [1]. VAWTs offer some distinct advantages in

the aforementioned applications over horizontal axis wind

Proceedings of ASME Turbo Expo 2013: Turbine Technical Conference and Exposition GT2013

June 3-7, 2013, San Antonio, Texas, USA

GT2013-94979

Copyright © 2013 by ASMEV008T44A017-1

Downloaded From: http://proceedings.asmedigitalcollection.asme.org/ on 05/06/2015 Terms of Use: http://asme.org/terms

turbines (HAWT) such as their insensitivity to changes in wind

direction or the possibility to store gearbox and generator below

the rotor, reducing investment costs for offshore applications.

However, compared to their horizontal axis counterparts only

sparse knowledge on VAWT aerodynamics and no freely

distributed simulation or design tools are available. The major

goal of extending QBlade with a simulation module for VAWTs

is to facilitate the research in this area with a new publicly

available code for aerodynamic analysis.

The software QBlade [2] was started in 2010 as a

multiplatform software tool for the aerodynamic design and

simulation of HAWTs without the need to import, convert or

process data from other sources. Another focus was to embed

the code in a convenient graphical user interface to improve

accessibility over comparable simulation tools. In order to ease

research on wind turbines worldwide, the software is distributed

freely under the Gnu Public License (GPL). QBlade has been

downloaded more that 20.000 times during the last two years

and is being applied by universities, businesses and individuals

around the world. A module for the design and simulation of

VAWTs was recently integrated in a new release [3, 4]. The

simulation algorithms are based on a Blade Element Momentum

(BEM) theory algorithm for the simulation of HAWTs and on a

Double Multiple Streamtube (DMS) algorithm for the

simulation of VAWTs. Furthermore, modules for airfoil design

and analysis or lift and drag polar extrapolation to 360° angle of

attack (AoA) are provided inside the simulation tool. In the

following, the basic functionality of QBlade, details of the

newly integrated VAWT simulation module and its application

in two case studies are described.

MODULES WITHIN QBLADE At its current state of development the QBlade software

consists of four major modules, facilitating the design and

simulation of a wind turbine rotor (Fig.1). These modules,

embedded in a graphical user interface, are:

• Airfoil design and analysis (XFOIL / XFLR5)

• Cl and Cd polar extrapolation to 360° AoA

• Rotor blade design and optimization

• Wind turbine setup and simulation

Fig.1 Modules in QBlade

Airfoil Design and Analysis The basic requirement of any blade element based rotor

performance simulation is tabulated data of lift and drag

coefficients, over a range of AoA, for every airfoil geometry

that is used in the rotor design. As a source of these airfoil

coefficients XFOIL [5] can compute the two dimensional flow

around subsonic isolated airfoils by combining a higher order

panel method with a fully coupled viscous/inviscid interaction

method. XFOIL was developed by Drela and Giles at

Massachusetts Institute of Technology (MIT) and is considered

as one of the standard low order analysis tools for airfoils.

In 2003 Depperois [6] wrote a graphical user interface for

XFOIL and expanded its functionality. The current version of

this development, the open source code XFLR5, is integrated

within the QBlade software to design or import airfoil

geometries for rotor blade design, generate or import measured

airfoil performance coefficients for rotor simulations and

manage the airfoil database.

It is well known [7] that blade element momentum balance

coupled wind turbine simulation methods are highly sensitive to

the quality of the lift and drag polar data that is used in a

simulation. The benefits employing the XFOIL algorithm are

the large number of experimental and numerical validations and

the high quality of the simulated airfoil coefficients. Around the

maximum lift coefficient and for immediate post-stall behavior

however, XFOIL is known to give poor predictions. The RFOIL

[8] code, developed at Delft University, improves the prediction

of transition by incorporating three dimensional and rotational

effects in the integral boundary layer equations of XFOIL.

Other helpful features of airfoil design and simulation for wind

turbines with XFOIL / XFLR5 are:

• 4 and 5 digit NACA airfoil generator

• Airfoil coordinate mixing for transition airfoils

• Inverse design of airfoils from pressure distribution

• Forced and free boundary layer transition ( ne

method [5]) to simulate blade roughness effects

Extrapolation of Cl and Cd Coefficients The airfoil analysis with XFOIL is limited to the prediction

of lift and drag coefficients at angles that lie before and just

beyond the stall point. For higher AoA the separation region

increases ad XFOILs assumption, that the viscous flow is

confined to a small area around the airfoil, becomes

increasingly invalid and convergence cannot be obtained

anymore. However, in the root region of a HAWT blade and on

VAWT blades in general AoA that lie beyond the stall point

occur frequently. Therefore, to be used in the BEM or DMS

algorithm and to ensure their smooth operation the Cl and Cd

polars need to be extrapolated to the full range of 360° AoA.

The general approach for this extrapolation is to apply curve fits

to the completely stalled polar curve of a flat plate, under the

assumption that an airfoil at high AoA behaves very much like a

thin plate with a sharp leading edge. In QBlade, any polar that



is contained in the database can be extrapolated via the

Montgomerie (Fig.2) [9] or the Viterna-Corrigan [10] post stall

model.

Fig.2 Polar extrapolation to 360° after Montgomerie

Rotor Blade Design and Optimization The blade design module (Fig.3) in QBlade allows the

efficient and intuitive design of HAWT or VAWT rotor

geometries. A blade geometry is defined by a distribution of

airfoil geometries at selected sections over the length of the

blade. The discretization of a rotor blade during a simulation is

independent of the number of sections that is specified in a

blade design. If an element, during a simulation, is located

between two different airfoil sections a linear interpolation

between the polar data of these two airfoils is performed. Using

XFOILs airfoil coordinate mixing, and computing the

coefficients of the intermediate airfoil, the overall accuracy can

be improved [2]. A HAWT blade is further defined by

specifying:

• Chord length

• Twist Angle

• Edgewise offset

• Flapwise curvature

• Pitch axis

Also, for the distribution of twist angles and chord lengths

shape optimization routines can be applied to the geometry. The

twist can be optimized to yield the highest lift to drag ratio for a

chosen tip speed ratio (TSR). The chord can be optimized after

the theory of Betz (constant circulation) or Schmitz [11]

(including wake rotation) for a chosen TSR and blade number.

For the design of a VAWT blade the following parameters

have to be specified:

• Chord length

• Radial position

• Twist angle

• Azimuthal angle

The radial position of the VAWT blade sections can be

automatically distributed to resemble a Troposkien [12] shape, a

blade shape where the blade stress from centrifugal forces acts

only normal to the blades cross section, or an easier to

manufacture arc line approximation of the Troposkien. During

the blade design three dimensional openGL visualization aids

the design process. The geometry can later be exported as a

cloud of points or into the .stl CAD format.

Fig.3 VAWT blade design module

Wind Turbine Definition and Simulation A wind turbine in QBlade consists of a rotor design and

additional parameters that further describe the turbine

characteristics. The type of power regulation (stall, pitch,

prescribed pitch), rotational speed control (single, two step,

optimal, prescribed), cut in- and cut out velocity and generator

efficiency have to be specified.

A simulation can be performed in three different ways. One

option is a rotor simulation over a range of TSRs. This

simulation results only in dimensionless coefficients and is

particularly useful to compare different rotor geometries

independent of their size. The second option is a multi-

parameter simulation (Fig.4). The simulation is carried out over

a range of wind speeds, rotational speeds and blade pitch angles

simultaneously resulting in a three dimensional rotor

performance matrix. These results can be used to develop

custom wind turbine controller strategies or to investigate the

turbine characteristics for several operational states. The third

option is the turbine simulation that computes the specified

turbines performance over a range of wind speeds and also

yields the annual energy production (AEP) for a selected

Weibull wind speed distribution.

The simulation results can be analyzed in three different

kinds of graphs. Rotor graphs plots the integral values, such as

the power coefficient Cp and the thrust coefficient Ct over the

TSR. The blade graph displays the distribution of blade

variables such as thrust and normal force, lift and drag

coefficients, AoA or relative velocities over the blade. In case of

a VAWT simulation the additional azimuthal graph yields the



distribution of variables depending on the azimuthal position of

the blade during one rotation of the rotor.

Fig.4 Multi parameter simulation module

HAWT AND VAWT SIMULATION ALGORITHMS

The HAWT and VAWT simulation algorithms in QBlade

are both based on blade element theory (to estimate the local

blade forces) coupled with a multiple streamtube momentum

balance (to account for the global flow field) over one (HAWT)

or two (VAWT) rotor discs. The use of these lower order

accuracy performance prediction methods allows for a rapid

development of the aerodynamic rotor shape, based on the

comparison of different rotor designs. These designs can be

studied with more sophisticated CFD techniques in greater

detail after the preliminary shape has been developed with a

BEM based method. The well documented validation of these

engineering methods with experimental and field data, their

computational efficiency and robustness and the long term

experience that exists are the reasons why they are widely used

in industry and research.

Blade Element Momentum Method The HAWT rotor simulation follows the classical blade

element momentum method, as described by Hansen [13], and

in the following will not be discussed in greater detail. The

BEM algorithm assumes a uniform, steady state inflow and

radial independence of the two dimensional blade sections.

Under these assumptions three dimensional effects, that play an

important role in wind turbine aerodynamics, cannot be

accounted for a priori. However, the impact of these effects on

the rotor loads and its performance is included in a simulation

by means of semi-empirical corrections. The corrections that

are included in the BEM algorithm of QBlade are:

• Prandtl tip and hub loss correction [13]

• Shen tip and hub loss correction [14]

• Snel’s correction for blade crossflow [15]

• Buhl’s correction for the turbulent wake state [16]

• Reynolds number drag correction, from Hernandez

and Crespo [17]

The BEM algorithm of QBlade has been validated numerous

times against measured data [2, 18] and compared with different

established and commercial BEM codes, such as Flex5 [18] by

DTU and the GL certified WT_Perf [2, 19] from the National

Renewable Energy Laboratory (NREL).

Double Multiple Streamtube Algorithm

Fig.5 Sketch of the Double Multiple Streamtube model

The VAWT analysis in QBlade is an implementation of the

DMS algorithm, as described by Paraschivoiu, and a detailed

derivation of all equations that follow can be found in [20]. The

turbine is modeled as two separate rotor discs (Fig.5), one for

the upstream and one for the downstream half during one

rotation. The rotor blade is discretized into an arbitrary, user

specified, number of elements. The circular path of each

element is divided into steps of 5°, as proposed in [20].



The rotor extracts kinetic energy from the wind at both, the

upstream and downstream, rotor discs. It is assumed that half of

the decrease in velocity occurs as the flow passes each disc.

Therefore:

0VuV upup = (1)

is the velocity at the upstream rotor disc and uup is the upstream

interference factor

( )012 VuV upeq −= (2)

is the velocity in the equilibrium plane between the two discs,

and

( )012 VuuV updowndown −= (3)

is the velocity in the downstream rotor disc, with udown as the

downstream interference factor. One limitation of the DMS

model is that the theory fails if the upstream interference factor

uup > 0.5. In this case the downstream disc experiences a change

in flow direction and the algorithm fails to converge.

In the current implementation of the DMS algorithm the

interference factors are, for each height position, averaged over

each half circle, resulting in one upstream and one downstream

interference factor for each blade element. The formula for the

upwind interference factor is derived from blade element theory

and the momentum equation over each streamtube:

1

2

22

2

2

cos

sincos

cos

18

−

−

−= ∫ θ

δ

θθ

θ

π

π

π

dV

WCC

Nc

ru

up

TNup (4)

The normal and thrust coefficients are calculated from tabulated

airfoil data:

αα

αα

cossin

sincos

dlT

dlN

CCC

CCC

−=

+= (5)

The AoA depends on the azimuthal angle, blade geometry, the

local TSR and the relative velocity:

−−= −

W

V

V

r up

up

00

1 sinsincoscoscossin αθω

αδθα (6)

with the relative velocity:

δθθω 22

2

coscossin +

−=

up

upV

rVW (7)

For the first iteration an interference factor uup=1 is

assumed to determine the upstream induced velocity Vup, from

which a new uup is computed until convergence. The downwind

interference factors are computed in a similar approach, with

the velocity Vdown and the integration for the downwind

interference factor is performed from 2/π to 2/3π . Once

the interference factors are known, the blade forces and

performance can be obtained by averaging over one revolution.

Analog to the BEM algorithm the DMS algorithm does not

account for three dimensional or unsteady aerodynamic effects

and thus has its limitations. However numerous empirical

corrections for dynamic stall effects or the influence of struts

and the tower exists. Also, more sophisticated model

formulations, that take into account streamtube expansion or

variable influence factors, are available in the literature [20,

21]. So far only the tip-loss correction as described by

Paraschivoiu has been implemented in QBlade but more

corrections, such as a correction for the tower shadow, and

variable influence factors will be integrated in the near future.

Fig.6 Comparison of DMS results for the Sandia 17m

turbine to measured and simulated data

To validate the implemented algorithms the predicted

performance of the 2 bladed Sandia 17 m turbine [22] was

compared to measured and simulated performance data from the

CARDAA [20] code (Fig.6). The comparison shows good

agreement between the two similar codes and the measured

data, the differences between the two codes might be due to

different polar data used during the simulation or small

differences of the implementation, iteration or discretization.

All other resulting simulation variables were compared to

published [20] CARDAA results and show similar distributions.



APPLICATION TO WIND TURBINE SIMULATIONS In the following, the VAWT simulation module is applied

in two different case studies to demonstrate its capabilities to

analyze wind turbine performance and loads. During all

simulations the tip loss correction was activated. All graphs and

blade designs shown are screenshots taken from the QBlade

software.

Investigation of Vortex Generators on a VAWT rotor The effect of vortex generators (VG) on the performance

of a generic 2 bladed Darrieus wind turbine was investigated.

The rotor geometry matches the Sandia 17m turbine while

NACA 63(3)-618 airfoils were used in this investigation. The

rotational speed was at constant 35 rounds per minute (rpm).

The airfoil polar data with and without installed VGs (Fig.7)

originates from wind tunnel measurements [25] taken at the

large wind tunnel of the Technical University of Berlin (TUB).

Fig.7 Lift and drag polar for NACA 63(3)-618 with and

without VG, measured data at Re = 1.1x10^6, from [22]

VGs are passive flow control devices that delay stall by

generating streamwise vortices that transfer momentum to the

boundary layer. The disadvantage of VGs is that their

application introduces an additional parasitic drag, which

results in a decreasing aerodynamic efficiency for moderate

AoA. However for AoA between the stall angle of the baseline

airfoil and the stall angle of the VG outfitted airfoil the

efficiency is increased.

Fig.8 The effect of VG’s on the rotor tangential force

coefficient over the azimuthal angle theta at a TSR of 3

The outer blade section of a Darrieus rotor, close to where

the blade is connected with the tower, generally experiences

very low TSRs. At low TSRs the AoA is changing drastically

during one rotation of a blade. In this case, at a TSR of 2 a

blade section experiences a change in AoA of +-25°, which is

far beyond the stall point of the baseline airfoil. For low TSR a

VG can improve the performance of a VAWT (Fig.8), for high

TSR the performance is decreased.

Fig.9 The effect of VG’s on the power output for three

configurations

When a VAWT is operating at a constant angular frequency

the performance will be increased for high- and decreased for

low wind speeds (Fig.9). If the measure of performance is AEP

it depends on the wind site if VGs can increase the overall

performance. For a wind site described by the Weibull factors

k=2 and A=6.21 the rotor described above was optimized for

AEP by equipping different lengths of the rotor with VGs,

starting from the blade tips. The optimum in AEP was found for



the rotor where both outer 12 % of the blade had VGs installed.

The AEP increased about 0.2 % due to the vortex generators.

Another positive effect of VGs on VAWT rotors is that the

increase in torque at low TSR aids the self starting ability of the

rotor.

Effect of Blade Shape on Time Varying Loads A VAWT has an inherent unsteady aerodynamic behavior

because the AoA is constantly changing during the rotation of

the blade [26]. This results in varying torque and streamwise or

crosswise forces and introduces fatigue loads to the turbines

structure. One way to reduce these loads is to increase the blade

number, but this leads to additional manufacturing costs.

Another option is to employ helical blades that introduce a

phase shift angle between the upper and lower blade forces and

in this way reduce the load variation. The effect of a blade

inclination on the rotor tangential, streamwise and crosswise

force coefficients is investigated for a two bladed rotor with

shift angles of 0°, 60° and 120°. The simulation results are also

compared to a rotor with higher blade number. The generic

rotor design (Fig.10), employed in this investigation has straight

blades, a solidity of 0.25 and NACA 0015 airfoils.

Fig.10 The different rotor configurations: 2 blades: 0° shift,

60° shift, 120° shift; 3 blades: 0° shift; 4 blades: 0° shift

When the blade number is increased the chord length is

reduced to maintain a constant solidity. The Reynolds number

was assumed as constant (Re = 1.000.000) during the

simulations. The CP over TSR curve is identical for all 2 bladed

rotor designs, only the rotors with additional blades have

slightly higher CP values. This is because of smaller tip losses,

due to a higher aspect ratio of the blades. All simulation results

were computed dimensionless over a range of tip speed ratios.

At low tip speed ratios (Fig.11) (TSR < 2 for this design)

the blade inclination is more efficient in reducing the varying

rotor torque compared to an increased blade number. This plays

an important role for the self starting capacity of a VAWT. For

all straight blade configurations there are rotor positions where

no or even negative torque occurs. This disqualifies the rotors

for a self start. Inclined blades however introduce a phase shift

between the upper and lower blade sections so that torque

minima only affect local blade sections, but not the whole

blade.

Fig.11 Comparison of the rotor tangential force coefficient

over the azimuthal angle theta at a TSR of 1

At the design TSR an increased blade number from 2 to 3

very efficiently reduces the tangential (Fig.12) lengthwise and

crosswise (Fig.13) force fluctuations and thus the fatigue loads.

When the blade number is increased from 3 to 4, the effect on

the fluctuations is far less pronounced. Introducing shift angles

to the blade also is an effective means to alleviate the load

fluctuations. Higher shift angles achieve a greater reduction.

Ideally, under uniform inflow and without the tip loss effect, a

shift angle of 180° would reduce the fluctuations to zero for all

operational points of the turbine.

Fig.12 Comparison of the rotor tangential force coefficient

over the azimuthal angle theta at the design TSR of 3



Fig.13 Rotor crosswise force coefficient over streamwise force coefficient for one full rotation at a TSR of 3

CONCLUSION AND FUTURE WORK The current paper presents in brief the functionality and

theory of the wind turbine simulator QBlade. By means of two

case studies the tools potential for wind turbine research and

design was demonstrated. The software in its current state is a

very comprehensive and flexible tool and to the authors

knowledge it is the only code that combines HAWT and VAWT

simulation, blade design and airfoil analysis in one interface.

The free distribution (Fig.14) of the software leads to a broad

application and thorough validation of the software. QBlade

was downloaded more than 20.000 times and has been applied

numerous times for research and teaching [18, 19, 23, 24, 25].

The software also serves as a modular platform for further

implementations and extensions of its functionality such as

genetic algorithms that exploit the combination of parametric

airfoil design and wind turbine simulation. In the near future it

is planned to extend the functionality to unsteady simulations,

include a wind field generator and integrate the open source

aeroelastics code FAST [7, 27] from the National Renewable

Energy Laboratory (NREL).

Fig.14 QBlades webpage can be found at:

qblade.fd.tu-berlin.de

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development and application of a simulation tool for vertical and horizontal axis wind turbines

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