wind turbine aerodynamics optimization

Name

Technische Universität MünchenInstitute for Flight Propulsion

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Name of presentationPresented by: Low Chee MengSupervisor: Marc Kainz

Development of a Wind TurbineOptimization Tool Using Open-Source Software

Low Chee Meng


Outline

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• Project objectives• The wind environment• Aerodynamics of wind turbines• Structure of the optimization program• Validation of the wind turbine performance code• Results from optimization runs

Development of a Wind Turbine Optimization Tool Using Open-Source Software


Low Chee Meng

Project Objectives

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• Develop and validate a wind turbine blade/rotor aerodynamics performance code

• Develop an optimization algorithm wrapped around the rotor/blade performance code

• Test the accuracy and effectiveness of the code in achieving the optimization objective: Maximize annual power capture at different wind sites

Development of a Wind Turbine Optimization Tool Using Open-Source Software


Low Chee Meng

Windspeed and Energy Density Distribution

*Plotted from shape and scale factors obtained from Christoffer and Ulbricht-Eissing (1989)

Windspeed Distribution

Energy Density Distribution

EnergyDensity (U ∞ )=f (U ∞ )×(12 ρU∞3 )

f (U ∞ )=kc

U ∞

cexp[−( U ∞

c )k

]


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Atmospheric Boundary Layer

• Weibull parameters usually for winds measured at height of 10m → correct windspeed pdf for desired turbine hub height

• Azimuthal variation of windspeed for each revolution → use a correction model proposed by Wagner (2010) to find an azimuthal mean windspeed at hub height

Source:Sørensen and Sørensen (2011)


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Source:<http://www.mstudioblackboard.tudelft.nl/duwind>

Wind Turbine Performance Map

C p=Aerodynamic Power Extracted

Total Wind Power

λ=Blade Tip Tangential Velocity

Incoming Windspeed

For a fixed RPM turbine, High λ = low windspeeds Low λ = high windspeeds

Cp,max

λCp,max

Coefficient of Power:

Tip Speed Ratio:

X


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Source:Staino et al. (2012)

Source:Hansen M. (2008)

Source:http://www.esru.strath.ac.uk

a=U ∞−U d

U ∞

a'=U Ω

2rΩ

Axial Induction Factor: Tangential Induction Factor:

Blade Element Momentum Theory (BEM)

Actuator Disk Theory Blade Element Theory


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C p,max,ideal=0 .593

aCp,max=13

Betz LimitC p=

Pextracted

Pwind

=4a (1−a )2 Derived from actuator disk theory

Lossless conversion of axial momentum of flow to power extracted by the actuator disk


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Corrections to BEM

Source: Bushong. S. (2012)

Source: Hansen (2008)

2) Turbulent Wake State

3) Tip/hub losses

1) Stall delay

Source: Yu et al. (2011)

Source: Lindenberg (2004)


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Rotor Performance Calculations

ChordMetal angle,

βRPM Airfoil

Rotor Design

2D Polars XFOILStall Delay Correction

360° Extrapolation

BEM ProcedureCalculate

Power Curve

Calculate AnnualEnergy Production

WindspeedPDF

P (U cut-in<U <U cut-out)

C p (λ)


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BEM Code ValidationReference Code: QBladeTest Cases: NREL Phase 2 and 6 Rotors

Specification Phase 2 Phase 6

RPM 72 72

Radius (m) 5.05 5.029

Airfoils S809 S809

Max. Power (kW) 20 20

Chord/Metal Angles See plots


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Validation Results

βtip

=3° βtip

=6°

βtip

=12°βtip

=9°


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Genetic Algorithms

• Inspired by biological 'survival-of-the-fittest' processes• Fitter, more highly adapted entities have better chances

of passing on their traits to successive generations• Advantages of GA compared to gradient techniques:

- More robust

- Less likely to be trapped by local minima• Disadvantages

- Requires many more iterations

Source: Beliakov and Lim (2007)


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● Individuals – Rotor design● Genes – Design variables● Genome – Collection of genes that fully describe the rotor design● Population/Generation – A batch of rotor designs● Selection – Process by which designs are chosen for gene sharing● Crossover – Sharing of genes● Mutation – Random change to genes

Nomenclature for GA-based Techniques

Gene

Gene 1 Gene 2 Gene 3

Genome

Genome 1

Genome 2

Genome 3

Genome 4

Population

Initial Population/Generation 1 Generation 2 Generation n...

Evolutionary Progress


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Airfoil combination

RPM

Chord at Blade root, 25%, 50%, 75%, blade tip

Blade metal angle at25%, 50%, 75%, bladetip

111

72

0.7369

0.5511

0.4529

0.3551

0.2435

24.848

11.151

6.210

4.370

3.000

Genes(Design Variables)


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Selection of 2 IndividualsFor gene sharing

Do Crossover /Gene sharing

Do Mutation

Insert new Individuals intonext population

Enough individuals toPopulate next generation?No

Assess fitness of eachIndividual in new generation

Termination criteria met?

Create and AssessInitial population

Yes

End

Yes

No


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Program Structure

Fitness EvaluationBEM Module

GA Module

Encoding

Selection

Crossover Mutation

User Input

Airfoil PolarPreparation

Module

Wind Turbine Rotor Optimizer


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Optimization Test Environments

Windspeed PDF

Energy Density

2 Test Environments:● High Winds – Helgoland● Low Winds - Singapore


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High Winds Environment

Evolution of AEP


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Evolution of RPM Blade Root Bending Moment, MB


● RPM ↑ to re-align flow angle as windspeeds ↑● Windspeeds ↑ thus M

B,max ↑. Increase

should be as low as possible. Ideally, no increase!

MBmax


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Cp↓ V

cutout


● Evolved blade with highest AEP has lower Cp than baseline blade across all tip speed ratios● Power output is lower for all windspeeds but operational windspeed range has widened.


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Vcutout

AccessibleEnergy ↑


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Pareto Trade-off – AEP and Blade Root Bending Moment

● Pareto-frontier delineates the max-AEP↑-for-min-M

Bmax-

penalty line along the AEP-M

Bmax curve

● ParetoBest rotor has 4% lower AEP compared to AEPmax rotor but also lower M

Bmax by 7.6%


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MBmax

Vcutout


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αstall

αstall


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● Compared to AEPmax blade, ParetoBest blade has more evenly distributed power contribution from its radial blade segments● Root region power contribution of ParetoBest blade increases as windspeed ↑

Contribution of root region to power increases significantly as windspeed ↑


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● Compared to AEPmax blade, ParetoBest blade has smaller outboard chord and enlarged inboard chord, this reduces the blade root bending moment

ParetoBest AEPmax


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ParetoBest

AEPmax

Baseline


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Low Winds Environment

● AEPmax design underwent just 1 re-design● RPM decreases → Cp curve shifts to the left● Cp curve shifts up to increase power capture over all tip speed ratios and windspeeds


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Increase in Cp due to combination of: ● Increase in chord lengths for most of the bladespan● Reduction in RPM and blade metal angle near the tip, both increase angle of attack

C(r) ↑

β↓


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● AEPmax rotor higher power capture for entire windspeed range except very close to V

cutout

● Increase in energy capture comes from increase in angle of attack (mostly pre-stall) and blade forces● M

Bmax increases correspondingly and by a large margin of 20%


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Vcutout


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αstall

αstall


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MBmax

remains largely unchanged

● ParetoBest blade has lower MBmax

compared to AEPmax blade mainly

due to enlargement of root region chord and reduction of tip chord


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ParetoBest

AEPmax

Baseline


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Summary of Findings

● Adaptation toward high RPM for high winds, low RPM for low winds

Strategy for high winds● Expand operational windspeed range as much as possible to tap energy available of high winds● Power limitation is a problem. For fixed RPM, non-pitchable blades, solutions may include the following:

- have high β at the root, low (negative) β at the tip- force tip to stall at high winds- enlarge root chord, reduce tip chord- power contribution shifts from tip to root as windspeed↑


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Summary of FindingsSummary of Findings

Strategy for low winds● Maximize energy capture for low winds at the sacrifice of efficiency at high winds (where wind energy density is low)● The following may be the solution:

- reduce RPM- increase metal angle β to increase angle of attack and blade forces- enlarge chord to increase axial induction factor and blade forces- enlarge chord more near root region to reduce blade root bending moment increase


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Conclusion

● An optimization program for wind turbine rotor aerodynamics has been successfully implemented● The GA is able to improve on baseline design and is robust and reliable● Identified possible good 'genes' for rotors at low and high windspeeds

Outlook

● Run the program with variable airfoil combinations● Tune the GA algorithm for a more thorough search of possible designs but more iterations needed● Improve the accuracy and speed of the BEM code● Include structural and noise factors as those are important considerations as well


Low Chee Meng

Danke schön!

Grazie

wind turbine aerodynamics optimization

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