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

1. See discussions, stats, and author profiles for this publication at: http://www.researchgate.net/publication/267645348
Development and Application of a Simulation
Tool for Vertical and Horizontal Axis Wind
Turbines
CONFERENCE PAPER · JUNE 2013
DOI: 10.1115/GT2013-94979
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2. 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
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3. 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 ( n
e
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
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4. 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
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5. 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].
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6. 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
sin
cos
cos
18
−
− 























−= ∫ θ
δ
θ
θ
θ
π
π
π
d
V
W
CC
Nc
r
u
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 αθ
ω
αδθα (6)
with the relative velocity:
δθθ
ω 22
2
coscossin +








−=
up
up
V
r
VW (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.
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7. 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
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8. 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
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9. 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
REFERENCES
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Retrospective of VAWT Technology”, Technical Report
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[2] Marten, D., Pechlivanoglou, G., 2010, “Integration of a
WT blade design tool in XFOIL/XFLR5”, Proceedings of
the DEWEK 2010
[3] Wendler, J., 2012, “Erweiterung einer Simulationssoftware
um Module zur aero- dynamischen Auslegung und
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[11] Gasch, R., Twele, J., 2007, Windkraftanlagen Grundlagen,
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[12] Reis, G.E., Blackwell, B.F., 1975, “Practical
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circular arc segments”, Technical Report SAND74-0100,
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[13] Hansen, M.O.L., 2008, Aerodynamics of Wind Turbines,
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