ansys-cfd for simulation of wind energy converters

© 2009 ANSYS, Inc. All rights reserved. 1 ANSYS, Inc. Proprietary© 2009 ANSYS, Inc. All rights reserved. 1 ANSYS, Inc. Proprietary

Application of CFD for

Simulation of Wind

Energy Converters

André Braune

ANSYS Continental

Europe

© 2009 ANSYS, Inc. All rights reserved. 2 ANSYS, Inc. Proprietary

Overview

• CFD simulations for

wind energy converters

– Blade design aspects

• Profile design

• Loads for FSI

• Turbulence

• Acoustics

– Siting & terrain modeling

– Cooling of generator

housing

© Siemens Wind Power


Computational Fluid Dynamics for

Structural

blade design

Terrain modeling

Wind park

design

Tower design Housing & base

cooling

Acoustics

Aerodynamic

blade design

Generator

design

© Kato Engineering


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 upstreamturbines

– 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+ 1Sommers, 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

Technical report, NREL/TP-500-29494.



Transition

Transition

Tu Contour

Transition



Pressure (Cp) Distribution

AoA = 1°

AoA = 9°

AoA = 14°

AoA = 20°


Transition: 3D NREL Wind Turbine

NREL 3D – Pressure Side

Transitional Turbulence

N = 72 rev/min

Separated flow

Turbulence production

Reattachment

Stagnation point


Transition: 3D NREL Wind Turbine

Arrows indicate flow direction

Turbulent

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

© Siemens Wind Power

© J. Laursen, P. Enevoldsen, S. Hjort: 3D CFD rotor computations of a

Multi-megawatt HAWT rotor , EWEC 2007

NACA 63618, ACA 10

SAS simulation snapshot


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

SST SST-CCPlane X/C=0.67


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

CFD mesh CSM mesh

Operating points

LoadsCFD calculation

CSM calculation

Stresses, deformations

2-way coupling


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!


– 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)

Dipole (2 monopoles)

Quadrupole(2 dipoles)

Unsteady mass

injection

Acoustic ~ U 3M

Power

Unsteady external

forces

Acoustic ~ U 3M 3

Power

Unsteady turbulent

shear stresses

Acoustic ~ U 3M 5

Power

Monopole and dipole sources dominant at low Mach numbers

m = m(t) psurface = psurface(t) = (t)

Flow FlowFlow


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

Incre

asin

g c

om

pu

tatio

na

l effo

rt

Incre

asin

g a

ccu

racy


Sensors

downstream

the mirror:

10 100 1000Frequency [Hz]

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

SP

L [

dB

]

Freestream Velocity = 140 km/h

Experimental data

SAS model

Sensor 121


0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

SP

L [

dB

]


Experimental data

SAS model

Sensor 122


0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

SP

L [

dB

]


Experimental data

SAS model

Sensor 123

Example: Generic Car Mirror


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


– 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 swisstopoIsosurface of

high turbulence


Wind Park Modeling

© From: Th. Hahm,

F2E Fluid & Energy Engineering GmbH & Co. KG

Velocity contours showing wake shading effect &

turbulence structures

ANSYS FLUENT: LES simulation with sliding

rotor meshes

© From: O. Röglin,

TÜV NORD SysTec GmbH & Co. KG


• 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 availablehttp://www.bbc.co.uk/britainfromabove/stories/rewinds/blacklaw.shtml

Example: Black Law Wind Farm

Map Image: Ordnance Survey © Crown Copyright 2008, License number 100048580

http://www.bbc.co.uk/britainfromabove/stories/rewinds/blacklaw.shtml


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 assessment‟, 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

SST model

k- model

SST model

No separation

Separation


Velocity

Inlet

Constant

Heat Flux

Turbulence Models: Comparison

• 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


Computational Fluid Dynamics for

Structural

blade design

Terrain modeling

Wind park

design

Tower design Housing & base

cooling

Acoustics

Aerodynamic

blade design

Generator

design

© Kato Engineering

ansys-cfd for simulation of wind energy converters

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