Design of an Aeroelastically Tailored 10 MW Wind TurbineRotor
Frederik Zahle, Carlo Tibaldi, Christian Pavese, Michael K.McWilliam, Jose P. A. A. Blasques, Morten H. Hansen
Wind Energy DepartmentTechnical University of Denmark
Sandia Wind Turbine Blade Workshop30 August to 1 September 2016
Albuquerque, NM, USA
IntroductionBlade Design Trade-Offs
A key challenge in blade design is the trade-off between aerodynamics (AEP)
on one side vs. mass and loads on the other side.
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IntroductionBlade Design Trade-Offs
Introducing new blade design technologies and design tools can help us push
these barriers, increasing AEP and reducing costs.
2 of 24F Zahle et al.Wind Energy Department · DTU Design of an Aeroelastically Tailored Rotor
IntroductionBlade Design Trade-Offs
Introducing new blade design technologies and design tools can help us push
these barriers, increasing AEP and reducing costs.
2 of 24F Zahle et al.Wind Energy Department · DTU Design of an Aeroelastically Tailored Rotor
IntroductionBlade Design Trade-Offs
Introducing new blade design technologies and design tools can help us push
these barriers, increasing AEP and reducing costs.
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IntroductionBlade Design Trade-Offs
� A blade designer has to
understand the complex
couplings between the design
variables (Dv), the constraints
(Cn) and the objective(s) (Obj).
� Parameter/sensitivity studies can
help understand relations
between key parameters.
� With potentially hundreds of
design variables, it can be
extremely challenging to
understand all the outputs.
� Numerical optimization is
necessary to carry out
multidisciplinary design.
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IntroductionProjects
� DTU Wind Energy participates in many Danish and European researchprojects focused on development of design solutions of future windturbines:
� Stretched Rotor: Collaboration between DTU Wind Energy and LM WindPower to apply multidisciplinary optimization to stretch a 3.2 MW rotorwithout increasing its loads envelope (Sponsored by Danish EnergyAgency).
� INDUFLAP2: Collaboration with Siemens and Rehau to develop anddemonstrate an active flap system for load alleviation (Sponsored by DanishEnergy Agency).
� INNWIND.eu: Development of cost effective design solutions for 10 MW to20 MW offshore turbines (EU sponsored).
� AVATAR: Development and validation of advanced aerodynamic models fornext generation large scale turbines (EU sponsored).
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IntroductionThis Presentation
� This work presents an integrated multidisciplinary wind turbine
optimization framework utilizing state-of-the-art aeroelastic and strutural
tools.
� Simultaneous design of the outer geometry and internal structure of the
blade,
� The framework is utilized to design a 10 MW rotor constrained not to
exceed the design loads and mass of the DTU 10 MW Reference Wind
Turbine.
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Multidisciplinary Design FrameworkHawtOpt2: Tool for Aero-servo-elastic Optimization ofWind Turbines
HawtOpt2 provides workflows that coupleaeroelastic and structural solvers together toallow for:
� Simultaneous and fully coupledoptimization of lofted blade shape and thestructural design of a blade.
� Exploration of the many often conflictingobjectives and constraints in a rotordesign.
� Detailed tailoring of aerodynamic andstructural properties.
� Constraints on specific fatigue damageloads.
� Placement of natural frequencies.
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Multidisciplinary Design FrameworkHawtOpt2: Tool for Aero-servo-elastic Optimization ofWind Turbines
� Underlying framework: OpenMDAO(written in Python),
� Optimization algorithm: IPOPT interfacedthrough PyOptSparse,
� Geometric parameterisation:FUSED-Wind,
� HAWCStab2/HAWC2 interface:
� AEP,
� Frequency placement,
� Fatigue in frequency domain,
� Reduced/full DLB in time domain,
� BECAS interface:
� Calculation of stiffness properties,
� Calculation of material failure andfatigue damage.
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Multidisciplinary Design FrameworkAero-elastic Solvers: HAWCStab2 and HAWC2
� Structural model: geometrically
non-linear Timoshenko finite
beam element model.
� Aerodynamic model: unsteady
BEM including effects of shed
vorticity and dynamic stall and
dynamic inflow.
� HAWCStab2: Analytic
linearization around an
aero-structural steady state
ignoring gravitational forces.
� HAWCStab2: Fatigue damage
calculated in frequency domain
(including effect of ATEFs) based
on the linear model computed by
HAWCStab2.Image from: Sønderby and Hansen, Wind Energy, 2014
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Multidisciplinary Design FrameworkStructural Solver: BECAS (BEam Cross section AnalysisSoftware)
� Finite element based tool for
analysis of the stiffness and
mass properties of beam
cross sections.
� Correctly predicts effects
stemming from material
anisotropy and inhomogeneity
in sections of arbitrary
geometry (e.g., all coupling
terms).
� Detailed stress analysis
based on externally computed
extreme loads.
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Multidisciplinary Design FrameworkEstimation of Extreme loads using HAWC2
� Fully resolved time simulations of all DLCs (+1000 simulations) is time
consuming, and inclusion of turbulence is not suited for gradient based
MDO,
� A reduced DLB has been developed, which requires <40 cases and
omits turbulence inflow.
Image from C Pavese, unpublished
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Multidisciplinary Design FrameworkReduced Design Load Basis
Image from C Pavese, unpublished
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Multidisciplinary Design FrameworkAerostructural Optimization Workflow Diagram
Optimizer Geometric properties Geometric properties Beam propsFrequencies
Fatigue DEL rateExtreme loads
Tower clearanceMaterial failure Objective function
Planform DVs Planform splines
Structural DVs Structural splines
Blade geometry Blade structureCross-sectional FE:
BECAS
WT control DVs Blade geometry Beam propsAeroelastic solver:
HAWCStab2
WT control DVs Blade geometry Beam propsAeroelastic solver:
HAWC2
Blade structure Extreme loadsCross-sectional FE:
BECAS
Mass properties AEP Extreme loads Cost function
Figure: Extended Design Structure Matrix diagram of the workflow of HawtOpt2.
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10 MW Rotor Design StudyProblem Objective
minimizexp, xs, xoper
−AEP({xp, xs, xoper},p)
AEP({0, 0, 0},p)
subject to g(xp) ≤ 0,
hg(xs) ≤ 0,
hs(xs) ≤ 0,
k({xp, xs}) ≤ 0
(1)
where
� xp is the planform variables,
� xs is the structural variables,
� xoper is the wind turbine control variables,
� p is turbine parameters kept constant.
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10 MW Rotor Design StudyProblem Design Variables
Parameter # of DVs Comment
Chord 6 -
Twist 5 Root twist fixed
Relative thickness 3 Root and tip relative thickness fixed
Blade prebend 4 -
Blade precone 1 -Blade length 1 -
Tip-speed ratio 1 -
Trailing edge uniax 2 Lower/Upper side
Trailing edge triax 2 Lower/Upper side
Trailing panel triax 2 Lower/Upper sideSpar cap uniax 4 Lower/Upper side
Leading panel triax 2 Lower/Upper side
Leading edge uniax 2 Lower/Upper side
Leading edge triax 2 Lower/Upper side
DP4, DP5, DP8, DP9 5 Lower/Upper spar cap position/width and web attachment
Total 60
Table: Design variables used in the optimization.
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10 MW Rotor Design StudyProblem Constraints
Constraint Value Comment
max(chord) < 6.2 m Maximum chord limited for transport.
max(prebend) < 6.2 m Maximum prebend limited for transport.
max(rotor cone angle) > −5 deg -
min(relative thickness) > 0.24 Same airfoil series as used on the DTU 10MW RWT.
t/wsparcap > 0.08 Basic constraint to avoid spar cap buckling.
min(tip tower distance) > ref value Simplified DLC1.3 operational case with wsp=15 m/s pitch=0
deg.
Blade root flapwise moments (MxBR) < ref value Simplified DLC1.3 operational case with wsp=15 m/s pitch=0
deg.
Rotor thrust (FyTT) < ref value Simplified DLC1.3 operational case with wsp=15 m/s pitch=0deg.
Rotor torque < ref value Ensure that the rotational speed is high enough below rated to
not exceed generator maximum torque.
Blade mass < 1.01 * ref value Limit increase in blade mass to maintain equivalent production
costs.
Blade mass moment < 1.01 * ref value Limit increase in blade mass moment to minimise edgewise fa-tigue.
Lift coefficient @ r/R = [0.5 − 1.] < 1.35 Limit operational lift coefficient to avoid stall for turbulent inflow
conditions.
Ultimate strain criteria < 1.0 Aggregated material failure in each section for 12 load directions
for DLC1.3, DLC6.x.
Table: Non-linear constraints used in the design process.
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10 MW Rotor Design StudyFinite Difference Gradient Quality
� The accuracy of the gradients is critical to get a rapidly and smoothly
converging optimization,
� Careful scaling of all design variables and correct choice of finite
difference step size,
� Two examples of fully coupled gradients exhibit fairly smooth
convergence,
� All parameters were scaled to match a step size of dx = 0.01,
corresponding to 1% of the representative scale of the parameters
0 1 2 3 4 5DV index
−0.07
−0.06
−0.05
−0.04
−0.03
−0.02
−0.01
0.00
0.01
d_ae
p_d_
chord
dx=1.0e-03dx=2.5e-03dx=5.0e-03dx=7.5e-03dx=1.0e-02dx=2.5e-02dx=5.0e-02dx=7.5e-02dx=1.0e-01
0.0 0.5 1.0 1.5 2.0 2.5 3.0DV index
−12
−10
−8
−6
−4
d_tip
pos_
d_r04u
niax
dx=1.0e-03dx=2.5e-03dx=5.0e-03dx=7.5e-03dx=1.0e-02dx=2.5e-02dx=5.0e-02dx=7.5e-02dx=1.0e-01
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ResultsOptimized Blade Planform
0 20 40 60 80 100Blade running length [m]
0
1
2
3
4
5
6
7
Cho
rd le
ngth [m
]
BaselineNew design
0 20 40 60 80 100Blade running length [m]
−15
−10
−5
0
5
10
Blade
twist [de
g.]
BaselineNew design
0 20 40 60 80 100Blade running length [m]
20
30
40
50
60
70
80
90
100
Relative thickn
ess [%
]
BaselineNew design
0 20 40 60 80 100Blade radial coordiante [m]
7
6
5
4
3
2
1
0
1
Blade out of plane coordinate [-] Baseline
New design
Figure: Optimized blade planform compared to the baseline DTU 10MW RWT.
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ResultsOptimized Blade Internal Structure
0.0 0.2 0.4 0.6 0.8 1.00
20
40
60
80
100
Thickn
ess [m
m]
Trailing edge paneltriaxuniaxbalsauniaxtriax
0.0 0.2 0.4 0.6 0.8 1.00
10
20
30
40
50
60
70
80
Thickn
ess [m
m]
Trailing paneltriaxuniaxbalsauniaxtriax
0.0 0.2 0.4 0.6 0.8 1.00
10
20
30
40
50
60
70
80
Thickn
ess [m
m]
Spar cap
triaxuniaxuniaxtriax
0.0 0.2 0.4 0.6 0.8 1.00
5
10
15
20
25
30
35
40
45
Thickn
ess [m
m]
Leading paneltriaxuniaxbalsauniaxtriax
0.0 0.2 0.4 0.6 0.8 1.00
10
20
30
40
50Th
ickn
ess [m
m]
Leading edge paneltriaxuniaxbalsauniaxtriax
Figure: Material stacking sequence for each region along the optimized blade.
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ResultsOptimized Lofted Blade
� Internal structure optimized with very few geometric constraints,
� Main laminates placed forward in the cross-section and angled,
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ResultsOptimized Load Distributions
0 20 40 60 80 100Blade radial coordinate [m]
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0Lo
cal t
hrus
t coe
ffici
ent [
-]
wsp=7.00wsp=8.00wsp=9.00wsp=10.00wsp=11.00
0 20 40 60 80 100Blade radial coordinate [m]
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Normal force [N/m]
wsp=7.00wsp=8.00wsp=9.00wsp=10.00wsp=11.00
0 20 40 60 80 100Blade radial coordinate [m]
−6
−5
−4
−3
−2
−1
0
1
Blade
torsion [deg
]
wsp=7.00wsp=8.00wsp=9.00wsp=10.00wsp=11.00
0 20 40 60 80 100Blade radial coordinate [m]
0
5
10
15
20
Angle of attack [deg]
wsp=7.00wsp=8.00wsp=9.00wsp=10.00wsp=11.00
Figure: Blade local thrust coefficients and normal forces (upper), and blade torsion andangle of attach (lower) for a range of wind speeds.
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ResultsA closer look at the structural tailoring
� Initial design had very little torsional coupling,
� Bend twist coupling occurs ”hands off” due to the use of a fully coupled
MDO approach,
� The bend-twist coupling was achieved through a forward movement of
the shear center and reduction of torsional stiffness,
� The resulting coupling is referred to as ”shear twist coupling”,
� This coupling does not suffer from loss in flapwise stiffness, making it
more attractive that e.g. material coupling.
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ResultsA closer look at the structural tailoring
� Initial design had very little torsional coupling,
� Bend twist coupling occurs ”hands off” due to the use of a fully coupled
MDO approach,
� The bend-twist coupling was achieved through a forward movement of
the shear center and reduction of torsional stiffness,
� The resulting coupling is referred to as ”shear twist coupling”,
� This coupling does not suffer from loss in flapwise stiffness, making it
more attractive that e.g. material coupling.
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ResultsPower Curve
� Steady state AEP increased by 11.1%,
� Turbulent power curve evaluated at TI=10%, six seeds, AEP gain of
8.7%,
� Aeroelastically tailored blade loses power in turbulent conditions.
0 5 10 15 20 25Wind speed [m/s]
0
2000
4000
6000
8000
10000
12000
Mecha
nical P
ower [kW]
BaselineNew design
4 6 8 10 12 14Wind speed [m/s]
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Pmek_t/Pmek_s [-]
BaselineNew design
Figure: Power curve for the optimized blade compared to the baseline design forsteady state conditions (dotted lines) and turbulent power curve with 10% reference TI(left), and the ratio between the turbulent and steady state power curves (right).
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ResultsUltimate loads
0.20.4
0.60.8
1.01.2
1.4
AEP
Tower Clearance
Blade Root Flap
Blade Root Edge
Blade Root Tors.
Tower Bottom FA
Tower Bottom S2S
Tower Top FA
Tower Top S2S
AEP
Tower Clearance
Blade Root Flap
Blade Root Edge
Blade Root Tors.
Tower Bottom FA
Tower Bottom S2S
Tower Top FA
Tower Top S2S
Baseline Optimized Design
0.20.4
0.60.8
1.01.2
Blade Root Flap
Blade Root Edge
Blade Root Tors.
Tower Bottom FA
Tower Bottom S2S
Tower Top FA
Tower Top S2S
Blade Root Flap
Blade Root Edge
Blade Root Tors.
Tower Bottom FA
Tower Bottom S2S
Tower Top FA
Tower Top S2S
Baseline Optimized Design
Figure: Turbine extreme (left) and lifetime equivalent (right) loads relative to thebaseline DTU 10MW RWT computed using the full design load basis comprising of1800 cases.
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ConclusionsSummary
� Fully coupled aerostructural design tool HAWTOpt2 used to design a 10
MW stretched blade,
� 8.7% increase in AEP compared to the DTU 10MW RWT, probably not a
representative increase compared to commercial blades (!),
� Final blade is the raw design coming out of the optimization, not modified
manually,
� Simultaneous tailoring of the internal structure and the aerodynamic
shape results in a bend-twist coupled blade,
� No previous knowledge of how to achieve the aeroelastic tailoring was
included in the optimization problem,
� Purely a result of an MDO approach!
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ConclusionsFuture Work
� Design for manufacturability,
� More degrees of freedom:
Shear-twist coupling, material
coupling, sweep, all included in the
same optimization problem,
� Improvement of the extreme loads
predictions using multi-fidelity
optimization techniques (AMMF),
� Material based fatigue damage,
� Non-linear panel buckling in BECAS,
� More detailed and robust
cross-sectional meshing with
analytic gradients,
� Analytic gradients in BECAS.
24 of 24F Zahle et al.Wind Energy Department · DTU Design of an Aeroelastically Tailored Rotor