Wind Turbine Design Origins of Systems Engineering And

Wind Turbine Design Origins of Systems Engineering and

nchen MDAO for Wind Energy Applications M Carlo L. Bottasso Technische Universität Mnchen, Germany nstitute niversität I U nergy E ind echnische T W

WESE Workshop, Pamplona, Spain, 2-3 October 2019 Design Optimization of Wind Turbines ◀ ◀ Multidisciplinarity Multidisciplinarity MDAO tools: tools: MDAO Some technical reasons behind the the behind reasons technical Some Conclusions and outlook outlook and Conclusions architectures, methods, limits and gaps gaps and limits methods, architectures, falling prices of energy from wind wind from energy of prices falling

Outline Outline need for MDAO MDAO for need

and the the and

▶

Design Optimization of Wind Turbines turbines onshore for Data i.e. i.e. size grows more than power rating rating power than more grows size

1MW ≥ Reducing Reducing specific power, power, specific

Design Design Trends Higher

tower Larger swept Larger because of vertical shear of shear vertical because ⇒ higher

area wind wind speed ⇒ larger power Improved capacity capacity Improved capture

(Source: factor factor

⇒ IEA Wind TCP Task 26)

lower CoE CoE Design Optimization of Wind Turbines decrease decrease Since power: Rated More More

푪 time time 푷 퐦퐚퐱 푽 풓

can not be drastically increased, the most effective to effective way the increased, most drastically be not can spent in spent is to reduce reduce is to 푷

풓 =

ퟐ ퟏ 푃 푃 흔푨 푟 region III at full at full III region 푽 Decreasing Decreasing Increasing A Increasing 풓 ퟑ specific power (or power loading) power loading) (or power specific 푪 푷 퐦퐚퐱 푽 풓

Why? Why? Rated wind speed speed wind Rated power, power, 푉 푟

increased capacity factor factor capacity increased

푽 풓

= ퟑ 푉 ퟐ ퟏ 푷 흔 푷 풓 푪 ൗ 풓 푷 ൗ 푨 푨 퐦퐚퐱

Design Optimization of Wind Turbines to avoid cubic law of growth: need for R&D and technological innovation innovation technological and R&D for need growth: of law cubic avoid to can machines Larger

Weight (Cost) (but (but AEP as only size Grows Design Design Challenges Trends &

as as

size 3 not be designed by designed be not

2 ) Size Size innovation innovation Technological

simple upscaling upscaling simple of smaller ones, ones, smaller of

(source: (source: LM Wind Power)

Design Optimization of Wind Turbines Systems engineering engineering Systems with come will innovation Each Some Present Some Present and Future Technological

Innovations Innovations that Enable Upscaling

- the final judge final the

pros pros and and -

: “Nice

cons cons

idea, but it does reduce reduce CoE ?” Design Optimization of Wind Turbines 12 10 10 kW

MW 1970 1970 Hamilton Hamilton How How Did Get We Here? Vestas kW) (30 V10

Standard, Standard, WTS

, 1979 - 4 MW) (4

1982

Boeing, 1987 Boeing, MOD - 5B 5B MW) (3.2

12MW 220m 220m 12MW HALIADE GE 2021 GE 2021

- X Siemens 2017 Siemens SWT (in (in part from G

- 8.0

- 154 . v 2019 2019 an 9.5MW 2017 2017 9.5MW V164 V164

Kuik, Kuik, 8MW 2016 8MW 2016

TUDelft Vestas Vestas

) Just Compare the Blades! Materials Solidity Airfoils Shape Add-ons

12 MW

HALIADE-X SWT-8.0-154 V164 Vestas 12MW 220m Siemens 2017 8MW 2016 GE 2021 9.5MW 2017 Wind Turbines

MOD-5B (3.2 MW) Boeing, 1987

10 kW

V10 (30 kW) 1970 Vestas, 1979 2019 Design Optimization of nchen M

Multidisciplinarity & Couplings nstitute niversität

I and the Need for MDAO U nergy E ind echnische T W - - - drive torque, - … Brakes and yaw Pitch Generator

Design Optimization of Wi nd Turbines (RPM, weight, weight, (RPM, - train, …) train, actuators actuators

- procedures down shut emergencyand normal - limits admissible within - - - conditions wind on depending points set different - Pitch … Handling actuator Keeping to wind Reacting to Reacting Regulating -

torque control laws: control torque

transients

the machine at machine the gusts gusts

turbulence turbulence duty : run

- cycles cycles - up, up, -

GE wind turbine (from inhabitat.com) inhabitat.com) (from GE wind turbine

- - - - … Transportability Noise Production Energy Annual technology, constraints constraints technology, - fiber) carbon of use clever wood, fiberglass, issue, an is quantity shear (but materialscomposite - platforms) moored floating & (off train/tower/foundations rotor/drive among - local modes, buckling of dampingcertain low - harmonics frequencies - deflections - (DELs)Loads Equivalent Damage - IEC (DLCs,Cases Load of Design number large from computed -

( AEP Manufacturing Manufacturing Weight Complex Stability of Placement tip blade Maximum Fatigue Loads - shore: hydro loads, hydro shore: )

: : size, massive envelope envelope (25 year life), life), year (25 : LCOs, flutter,

couplings couplings

wrt

natural natural

rev - 61400)

- - - drive torque, - … Brakes and yaw Pitch Generator

Design Optimization of Wi nd Turbines (RPM, weight, weight, (RPM, - train ac ac

tuators , …) , - procedures down shut emergencyand normal - limits admissible within - - - conditions wind on depending points set different - Pitch Warning: … Handling actuator Keeping to wind Reacting to Reacting Regulating -

torque control laws: control torque

• • transients

the machine at machine the gusts gusts ( Potentially expensive expensive Potentially couplings Strong one load assessment: 10

turbulence turbulence duty : run

- cycles cycles - up, up, -

Ewn ubn fo inhabitat.com) (from turbine wind GE

7 10 - - - - … Transportability Noise Production Energy Annual 8 time steps) time steps) technology, constraints constraints technology, - fiber) carbon of use clever wood, fiberglass, issue, an is quantity shear (but materialscomposite - platforms) moored floating & (off train/tower/foundations rotor/drive among - local modes, buckling of dampingcertain low - harmonics frequencie - deflection - Loads Damage - (DL Cases of number computed -

( AEP Manufacturing Manufacturing Weight Complex Stability Placem Maximu Fatigue Loads - shore: hydro loads, hydro shore: )

(DELs)

: : size, massive ent of ent Equivalent envelope envelope Cs, IEC Cs, m blade tip blade m (25 year life), life), year (25 : s

s from large large from

flutter, LCOs, flutter, Design Load Design couplings couplings

wrt

natural natural

rev - 61400)

Design Optimization of Wind Turbines Dramatic Dramatic 2. 1. Example Follow with structural optimization for minimum weight weight minimum for optimization structural with Follow max(AEP) for optimization aerodynamic purely Perform Aero Aero Optimum : reduction in solidity solidity in reduction INNWIND.EU INNWIND.EU Baseline design by design Baseline

10 MW MW 10 A Simple A Simple Example:

⇨ INNWIND.EU INNWIND.EU ( to improve to leads improve AEP to class 1A, D=178.3, H=119m) H=119m) D=178.3, 1A, class CoE CoE

Chord Chord ≠ Structural ≠ Structural Optimum

increases increases ▼

consortium consortium

(+2.6%) (+2.6%) large increase in weight weight in increase large

Spar Spar cap

▼ Design Optimization of Wind Turbines MDAO will MDAO will Requirements Pre Lengthy loops loops Lengthy requirements/constraints requirements/constraints up up - • • • • MDAO Optimization

design, design, Use Use Account specialists to refine/verify Provide Be

(months)

fast fast fully

to satisfy all to satisfy never replace never replace approach design to approach (hours/days) (on (on (hours/days) solutions solutions

- ab

improves improves integrated for for

initio

multi in in for for all complex couplings (no fixes a posteriori)

all all areas tools tools (no manual intervention)

exploration/knowledge of design space design of exploration/knowledge

disciplinary optimization disciplinary optimization the standard standard hardware -

experienced designer! experienced (aerodynamics, (aerodynamics, structures, Based Based : discipline

!) Design Design of - oriented specialist groups oriented groups specialist Data transfer/compatibility Data transfer/compatibility Different Different

among groups groups among

simulation models simulation tools

controls, controls, sub

but … :

greatly speeds speeds greatly

WTGs WTGs - systems) for for

Design Optimization of Wind Turbines • • • • • tools Integrated Several proprietary tools at various companies companies various at tools proprietary Several POLIMI & POLIMI & NREL DTU: ( FOCUS ECN: ------al. 2016, Bortolotti 2016 al. et 2016, Bortolotti al. Papers First First Begin by New framework design HAWTOPT (

Sandia: WISDEM ( WISDEM Sandia:

conference presentations from 2009 onwards (EACWE onwards 2009 conference presentations from 2008 industry presentations from to

TUM: TUM: in 2007 thanks to grant from TREVI in 2007 TREVI thanks grant from to : Bottasso et al., 2012 : et al., Bottasso

: Duineveld

Dssing

Cp - Max (with A. Croce, P. Bortolotti & many many others) & Bortolotti Max P. Croce, A. (with

, , 2008

2011) 2011)

Literature Literature Dykes et al., 2014 al., Dykes et

) M. M.

McWilliam McWilliam 2015; Croce et al. 2016, Sartori et Sartori 2016, 2015; al. et Croce

- 19

) onwards onwards Energy Energy Spa

2009) 2009) nchen M

What Does a Typical MDAO nstitute niversität

I Tool Look Like? U nergy E ind echnische T W Configurational design Sub-systems Aeroservoelastic multibody parameters • Generator • Pitch model • Nacelle • Brake • Cooling • …

2D FEM sectional model Aerodynamic design parameters Blade and tower beam models Structural design parameters

Control systems

Optimizer Cost model(s) Load & performance analysis: min Cost • DLCs • AEP wrt design variables Constraints: • Campbell subject to constraints • Max tip deflection • Noise • Ultimate & fatigue loads • … • Natural frequencies • Buckling • Manufacturing constraints • Geometric constraints • Noise • … Configurational design Sub-systems Aeroservoelastic multibody parameters • Generator • Pitch model • Nacelle • Brake • Cooling • …

2D FEM sectional model Aerodynamic design parameters Design variables: Blade and tower beam models• Configurational • Sub-systems • Aerodynamic • Controls Structural design parameters • Structural • ... • Materials

Control systems

Optimizer Cost model(s) Load & performance analysis: min Cost • DLCs • AEP wrt design variables Constraints: • Campbell subject to constraints • Max tip deflection • Noise • Ultimate & fatigue loads • … • Natural frequencies • Buckling • Manufacturing constraints • Geometric constraints • Noise • … Design Optimiza• • tion• • • of• Wind flavors: algorithmic possible Various Turbines … opt Global/local models Surrogate of Partitioning Iterative Monolithic

Some design parameters design parameters Some

Problem may be may Problem very minor effects on effects minor very

vars vars

Possibly Possibly each for repeated change in design each Expensive ill - posed posed non

CoE CoE have have performance analysis be has analysis to performance - smooth smooth

load behavior (DLC jump) jump) (DLC behavior load 2D + beam models unable unable models beam + 2D to capture capture to

local 3D effects effects 3D local

variable variable “Coarse” level: 2D FEM & beam models update update

“Fine” level: 3D FEM

Automatic 3D CAD Automatic 3D FEM meshing Analyses: generation - Max tip deflection - Max stress/strain

Constraint/model Constraint/model - Fatigue - Buckling

Verification of design constraints

Joint & laminate analysis Automatic 3D FEM meshing Root 3D CAD model - Bolt preload calculation - Max stress/strain - Fatigue

(Ref.: C.L. Bottasso et al., Multibody System Dynamics, 2014) “Coarse” level: 2D FEM & beam models

A similar fine-level refinement could be used for aerodynamics, but apparently not yet reported in the literature update update

“Fine” level: 3D FEM

Automatic 3D CAD Automatic 3D FEM meshing Analyses: generation - Max tip deflection - Max stress/strain

Constraint/model Constraint/model - Fatigue - Buckling

Verification of design constraints

Joint & laminate analysis Automatic 3D FEM meshing Root 3D CAD model - Bolt preload calculation - Max stress/strain - Fatigue

(Ref.: C.L. Bottasso et al., Multibody System Dynamics, 2014) Design Optimization of Wind Turbines Example: INNWIND.EU INNWIND.EU Example: • • Result • • Idea Optimum is Optimum Redesign ▲ Material Material products market within material existing closest pick designer: turbine Wind material best the Identify D : efine efine :

a parametric composite material model (mechanical (mechanical model material composite parametric a

designer: design new material with optimal properties properties optimal with material new design designer:

of of Combined optimum: Blade mass mass Blade optimum: Combined

between H between spar spar Composite Co

caps caps

10 MW MW 10

laminate laminate

GFRP and CFRP CFRP GFRP and for each component within the model model the within component each for

Optimum is Optimum

Redesign of the the of Redesign between between -

9.3%, blade cost cost blade 9.3%, -

Design

Bx - GFRP and GFRP and shell shell properties vs. cost) cost) vs. properties - 2.9% 2.9% skin skin

laminate laminate - GFRP GFRP

▼ Design Optimization of Wind Turbines Appealing noise for Appealing C constraints: Additional Design L max (margin to stall), stall), to (margin max • •

and/or CFD 2D and/or by aerodynamics Airfoil parameterization Bezier airfoil airfoils together blade with together airfoils

Airfoil Airfoil Free

d aware design aware design

t (Ref. (Ref. Bottasso

O geometry, … geometry,

thi

Xfoil Xfoil

p :

Form

et et al +

s Viterna Viterna

., J. Phys: Conf. Series, 524,

i extrapolation extrapolation

. Co

d -

b Design Design

T f

Integrated approach for aero-structural optimization of of optimization aero-structural for approach Integrated Blades WT 2014, 2014, Automaticappearance of

SciTech 2015 flatback flatback

airfoil! ) Design Optimization of Wind Turbines measuring the measuring Design: beyond Design: Beyond Beyond the Understanding and Understanding wind wind grid integration grid integration farm control farm plant

Some Open Issues: a Personal View View Personal a Open Issues: Some

BEM BEM

turbine: turbine: design, design, inflow inflow

• • • • Co … Airfoils Materials Control systems - design everything design everything Uncertainty quantification quantification Uncertainty Stability analysis analysis Stability

Design Optimization of Wind Turbines F. Campagnolo, Campagnolo, F. Canet, H. Bortolotti, with P. collaboration in at TUM & Work POLIMI MDAO for WTGs: WTGs: MDAO for

gaining acceptance delivering gaining acceptance and results

A. Croce, Croce, A.

only about 10 years old, but but 10 years old, about only

Conclusions Conclusions

L. Sartori Sartori L.

growing strongly, strongly, growing