Application of CFD for Simulation of Wind Energy Converters
André Braune ANSYS Continental Europe
Overview • CFD simulations for
wind energy converters – Blade design aspects Profile design • Loads for FSI • Turbulence • Acoustics •
Siting & terrai terrainn modeli modeling ng – Siting – Cooling of generator housing
© Siemens Wind Power
Overview • CFD simulations for
wind energy converters – Blade design aspects Profile design • Loads for FSI • Turbulence • Acoustics •
Siting & terrai terrainn modeli modeling ng – Siting – Cooling of generator housing
© Siemens Wind Power
Computational Fluid Dynamics for Generator design
Aerodynamic blade design
Structural blade design
Wind park design
© Kato Engineering
Terrain modeling
Acoustics
Tower design de sign
Housing & base cooling
Blade Design •
Challenges – Aerodynamic efficiency
across expected wind speeds and wind profiles – Determine integrity of structures made of complex composite materials – Minimize noise – Maximize strength while minimize weight
•
Benefits of simulations – Virtual prototyping of
initial candidate designs – Reduced wind tunnel and full scale testing – Automation of design process – Fewer prototypes & lower design costs – Multi-physics simulations
Aerodynamic Blade Design • •
Main aspects: Design of 2D profiles 3D blades – Advanced turbulence
modeling:
SST turbulence model Laminar to turbulent transition model • Roughness effects • Tip vortices • Scale resolving simulation • •
(LES, SAS …)
– Interaction with upstream
turbines – Design studies & optimization
Photo © José Luis Gutiérrez, graphic courtesy of IMPSA S.A., Argentina
Transition: 2D S809 Airfoil
Laminar flow airfoil for wind turbine applications • Rex = 2 106, = 0° 20° • Experiment: •
– 2D: low-turbulence wind tunnel
@ Delft University of Technology, (Somers, 1989) – 3D: profile for the NREL phase IV full wind turbine experiment, (Simms, 2001) •
ANSYS CFD – Transitional and fully turbulent – Grid: 150 000 elements (2D) – Max. y+ 1
Sommers, D. M., 1989, “Design and Experimental Results for the S908 Airfoil”, Airfoils, Inc., State College, PA Simms, D., Schreck, S., Hand, M, and Fingersh, L.J. (2001). “NREL Unsteady Aerodynamics Experiment in the NASA-Ames Wind Tunnel: A Comparison of Predictions to Measurements”, NREL
Transition: 2D S809 Airfoil Tu Contour Transition
Transition
Transition
Transition: 2D S809 Airfoil Pressure (Cp) Distribution AoA = 1°
AoA = 14°
AoA = 9°
AoA = 20°
Transition: 3D NREL Wind Turbine Separated flow Turbulence production
Stagnation point
Reattachment
NREL 3D – Pressure Side Transitional Turbulence N = 72 rev/min
Transition: 3D NREL Wind Turbine
Turbulent
Arrows indicate flow direction
Transitional
3D Separation on Wind Turbine •
SST-SAS 3D CFD simulation – Combination of scale
resolving model (LES) and statistical model – Resolves larger and medium scales, e.g. 3D shape of separation zones, turbulence structures etc. – Combination with automatic wall treatment, transition & wall roughness possible
NACA 63618, ACA 10 SAS simulation snapshot
© Siemens Wind Power
© J. Laursen, P. Enevoldsen, S. Hjort: 3D CFD rotor computations of a Multi-megawatt HAWT rotor , EW EC 2007
Tip Vortex: NACA 0012 Wing
NACA 0012 with rounded wing tip tip vortex • Re = 4.6 106 • Experiment: •
– Bradshaw et al (1997)
•
ANSYS CFD: – Grid: 5.5 million elements – Max. y+ = 1 (on airfoil)
Tip Vortex: NACA 0012 Wing
Models resolving streamline curvature • Eddy viscosity ratio: •
– Lower turbulence in
vortex core region reduced production of turbulent kinetic energy – Better prediction of swirling velocities and turbulence levels in vortex core
Plane X/C=0.67 SST SST-CC
Structural Blade Design •
Fluid Structure Interaction - FSI – 1-Way Fluid Structure Interaction ANSYS Mechanical ANSYS CFD (deformations) • ANSYS CFD ANSYS Mechanical (pressure •
loads, …)
– 2-Way Fluid Structure Interaction • Full unsteady-state interaction between aerodynamic loads and structural response
1-Way- and 2-Way-FSI
Geometry model Operating points CFD mesh
CSM mesh
CFD calculation
Loads CSM calculation
2-way coupling Stresses, deformations
Aero-Acoustic Simulations •
Challenges – Aero-acoustic noise
based on unsteady-state phenomena – Coupling of different noise sources and transmission processes – Large differences in time and length scales! •
Small sound pressure fluctuations & acoustic energies, compared to aerodynamic pressure differences!
•
Benefits of simulations – All aero-acoustic sources
of noise can be simulated (e.g. inherent turbulent fluctuations quadrupoles) – Different acoustic models allow for balancing between computational efforts & accuracy / details
Aero-Acoustic Source Classification
Monopole (simple source)
Flow
m = m(t)
Dipole
Quadrupole
(2 monopoles)
(2 dipoles)
Flow
psurface = psurface(t)
Flow
= (t)
Unsteady mass injection
Unsteady external forces
Unsteady turbulent shear stresses
Acoustic ~ U 3M Power
Acoustic ~ U 3M 3 Power
Acoustic ~ U 3M 5 Power
Monopole and dipole sources dominant at low Mach numbers
CFD Approaches to Aeroacoustics • •
•
•
•
Steady-state RANS based noise source modeling – Empirical correlations estimate acoustic radiation Modal Analysis – Linearized Navier-Stokes-Equations with super-imposed pertubations – Resonant frequencies and mode shapes Acoustic Analogy modeling – CFD calculate source field – Analytical solution propagate sound from source to receiver location Coupling of CFD and specialized acoustics codes: – Acoustic sources determined with CFD, but acoustic waves not tracked with CFD – Account for external scattering & reflections Direct Computational Aero-Acoustics (CAA) – Resolve the acoustic pressure fluctuations as part of the CFD solution
I n c r e a s i n g a c c u r a c y
I n c r e a s i n g c o m p u t a t i o n a l e f f o r t
Example: Generic Car Mirror
Sensors downstream the mirror: 140
140
140
130
130
130
120
120
120
110
110
110
100
100
100
90
90
] B 80 d [ 70 L P S 60
Sensor 121
50
90
] B 80 d [ 70 L P S 60
Sensor 122
50
40
] B 80 d [ 70 L P S 60
40
30
Freestream Velocity = 140 km/h
20 10
40
30 Freestream Velocity = 140 km/h
20
Experimental data
Experimental data
10
SAS model
0 100 Frequency [Hz]
1000
30 Freestream Velocity = 140 km/h
20
Experimental data
10
SAS model
0 10
Sensor 123
50
SAS model
0 10
100 Frequency [Hz]
1000
10
100 Frequency [Hz]
1000
Turbine Site Selection & Wind Park Modeling •
Challenges – Turbine efficiency and
operation stability depends on turbine placement
Steep terrain, mountains • Off-shore installations •
– Impact of turbine-turbine
wake effects for varying wind directions and speeds
•
Benefits of simulations – Optimize turbine output
and placement
Wind speed & turbulence prediction over complex terrain • Account for wake effects •
– Upfront prediction of
power output as a function of wind speeds and direction
Terrain Modeling
© 2007 swisstopo
Isosurface of high turbulence
Wind Park Modeling Velocity contours showing wake shading effect & turbulence structures ANSYS FLUENT: LES simulation with sliding rotor meshes
© From: Th. Hahm, F2E Fluid & Energy Engineering GmbH & Co. KG
© From: O. Röglin,
Example: Black Law Wind Farm •
Central Scotland – Operated by Scottish Power Renewables – Largest operating wind farm in the UK (Jan 2006) with 54 turbines – Total installed power capacity of 124 MW (2.3 MW each) – Small height variations (170 m) across farm – Measurements available
http://www.bbc.co.uk/britainfromabove/stories/rewinds/blacklaw.shtml
Map Image: Ordnance Survey © Crown Copyright 2008, License number 100048580
Example: Black Law Wind Farm Multiple Wakes Wind speed at hub height, wind direction 210 Without wind turbines
With wind turbines
Wind Park Power Output Estimation
Montavon, C., 1998, „Simulation of atmospheric flows over complex terrain for wind power potential assessm ent‟, PhD thesis no. 1855, EPF
Lausanne, Switzerland.
Housing & Generator Cooling •
Challenges – Ensure effective cooling
under all environmental conditions – Complex geometries & many details – Many parameters Fan positions & number Positions of electrical devices • Air flow blockage • Outside temperature & incoming sun radiation • •
•
Benefits of simulations – Virtual prototyping of
different cooling solutions & layouts • • •
Fan locations & number Air guidance Device positions
– Less trial & error – Reduce thermal peak
loads on generator, gear, transformer, structures etc. •
Pre-identify “problem” regions (hot spots)
Heat Transfer: Aspects •
Turbulence: – Reliable turbulence models – Near wall treatment of boundary layers – Advanced turbulence models (SAS, Transition, …)
CHT: – Coupled simulation of heat transfer in fluid and solid regions • Radiation: – Between surfaces – Sun radiation •
Turbulence Models: Diffuser Flow k- model
No separation SST model
Separation
Turbulence Models: Comparison
Velocity Inlet
•
Constant Heat Flux
Experiment – Baughn et al. (1984)
Example: Cooling in Electric Motor / Generator
Example: Tower Base Cooling •
Simulation procedure: – Geometry import &
simplification – Geometry parameterization for some parts (e.g. fan openings) – Parametric meshing of fluid and solid domains regions – Simulation with fluid & solid regions (heat losses defined by energy sources)
© Nordex
