wind turbine aerodynamics optimization
Post on 14-Apr-2015
27 Views
Preview:
TRANSCRIPT
Name
Technische Universität MünchenInstitute for Flight Propulsion
1
Name of presentationPresented by: Low Chee MengSupervisor: Marc Kainz
Development of a Wind TurbineOptimization Tool Using Open-Source Software
Low Chee Meng
Technische Universität MünchenInstitute for Flight Propulsion
Outline
2
• 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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Project Objectives
3
• 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
Technische Universität MünchenInstitute for Flight Propulsion
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
]
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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)
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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)
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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)
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Validation Results
βtip
=3° βtip
=6°
βtip
=12°βtip
=9°
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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)
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
● 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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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)
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Program Structure
Fitness EvaluationBEM Module
GA Module
Encoding
Selection
Crossover Mutation
User Input
Airfoil PolarPreparation
Module
Wind Turbine Rotor Optimizer
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Optimization Test Environments
Windspeed PDF
Energy Density
2 Test Environments:● High Winds – Helgoland● Low Winds - Singapore
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
High Winds Environment
Evolution of AEP
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Evolution of RPM Blade Root Bending Moment, MB
High Winds Environment
● RPM ↑ to re-align flow angle as windspeeds ↑● Windspeeds ↑ thus M
B,max ↑. Increase
should be as low as possible. Ideally, no increase!
MBmax
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Cp↓ V
cutout
High Winds Environment
● 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.
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
High Winds Environment
Vcutout
AccessibleEnergy ↑
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
High Winds Environment
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%
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
MBmax
Vcutout
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
αstall
αstall
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
● 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 ↑
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
● Compared to AEPmax blade, ParetoBest blade has smaller outboard chord and enlarged inboard chord, this reduces the blade root bending moment
ParetoBest AEPmax
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
ParetoBest
AEPmax
Baseline
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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↓
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Low Winds Environment
● 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%
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Low Winds Environment
Vcutout
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
αstall
αstall
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
ParetoBest
AEPmax
Baseline
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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↑
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
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
Technische Universität MünchenInstitute for Flight Propulsion
Low Chee Meng
Danke schön!
Grazie
top related