Cornell University Dynamically Variable Blade Geometry for Wind Energy Greg Meess, Michael Ross Dr. Ephrahim Garcia Laboratory for Intelligent Machine Systems AIAA Regional Student Conference Boston University April 23-24, 2010
Cornell University
Feb 23, 2016
Cornell University. Dynamically Variable Blade Geometry for Wind Energy. Greg Meess , Michael Ross Dr. Ephrahim Garcia Laboratory for Intelligent Machine Systems. AIAA Regional Student Conference Boston University April 23-24, 2010. Goal. - PowerPoint PPT Presentation
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Cornell University
Dynamically Variable Blade Geometry for Wind Energy
Greg Meess, Michael RossDr. Ephrahim Garcia
Laboratory for Intelligent Machine Systems
AIAA Regional Student Conference
Boston UniversityApril 23-24, 2010
Goal
Increase wind turbine energy output by morphing blade shape to match changing wind speeds.
Pitch Chord
TwistCamber
Outline
• Motivation• Problem Parameterization• Airfoil Generation• Turbine Analysis• Parametric Study• Results
– Geometry– Power output
Motivation
• Wind turbines are constantly increasing in size– Power output is proportional to rotor swept area– The largest turbines cannot be built on land
• Blades are designed for higher wind speeds– Maximize rated power– Turbine spends little time operating at rated power
• Little focus on low wind speeds
– Variable Pitch
http://www.terramagnetica.com/2009/08/01/why-are-wind-turbines-getting-bigger/
Problem Parameterization
• Turbine has operating wind regime between 4 m/s and 20 m/s– 4 m/s is lower limit of current
turbines• Fixed speed generator of 60
rpm– Rotations vary from 30 to
120 rpm.• Rayleigh Distribution is used
to assess annual power output
Vestas V90 power output vs. wind speed (www.vestas.com)
Sample wind speed Rayleigh distribution
Turbine Performance Analysis
• Equations based on basic BEM theory1, WT_Perf source code2, and Aerodyn Theory Manual3.– Blade divided into a number of elements– Power of each element is P= 1/2ρAU34a(1-a)
• Power Coefficient Cp = 4a(1-a)– Axial induction factor defined as a = (U1-U2)/U1– Need initial guess for axial induction factor– Axial induction factor calculated using relative wind
angle, coefficients of lift and drag, tip loss factor– Initial axial induction factor updated– Iterate for convergence– Calculate power
Polyamide
Nylon “Kite Wing”
1 Manwell, J.F., et al., Wind Energy Explained, John Wiley & Sons Ltd., 2002.2 Buhl, Marshall, National Renewable Energy Laboratory, 2004.3 Moriarty, Patrick, et. al., Aerodyn Theory Manual, National Renewable Energy Laboratory,
Streamtube around wind turbine rotor, used as basis for BEM theory (Manwell 85).
Blade geometry for analysis of horizontal axis wind turbine (Manwell 108).
Dividing the blade into several elements (Moriarty 2)
Airfoil Generation
• NACA XX12 Series– Leading edge, trailing edge follow
NACA equations– Flexible panels connect to leading
edge, rest on trailing edge– As chord extends/retracts, panels
keep airfoil profile• XFOIL Simulation
– CL, CD data collected for angles of attack between -10° and 45°
NACA 2412 original, fully extended, and fully retracted shapes
Sample data from XFOIL for modified shapes
Parametric Study
• 1-parameter search routines can find ideal value at given wind speed
• Static blade design is generated by optimizing all parameters at a single wind speed (10 m/s)
• Each variable case takes the static blade and changes one parameter to adapt to changing wind conditions.
• For the shape and chord changes, three different cases are studied, depending on the shape used during optimization.
Extending
Dual
Retracting
Static Blade Design
Variable Pitch Results
4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
static
Instantaneous Wind Speed (m/s)
Pow
er C
oeff
icie
nt
5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.00 10.50 11.000
10
20
30
40
50
60
70
80
static
Average Wind Speed (m/s)
Ann
ual O
utpu
t (M
Wh)
Variable Pitch Case
High Speed Shape
Low Speed Shape
Ele
men
t Ang
le (d
egre
es)
Variable Pitch Results
4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
static
variable pitch
Instantaneous Wind Speed (m/s)
Pow
er C
oeff
icie
nt
5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.00 10.50 11.000
10
20
30
40
50
60
70
80
static
variable pitch
Average Wind Speed (m/s)
Ann
ual O
utpu
t (M
Wh)
Variable Camber Case
Low Speed Shape
High Speed Shape
Ele
men
t Ang
le (d
egre
es)
Variable Camber Results
4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
static
variable pitch
variable camber
Instantaneous Wind Speed (m/s)
Pow
er C
oeff
icie
nt
5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.00 10.50 11.000
10
20
30
40
50
60
70
80
static
variable pitch
variable camber
Average Wind Speed (m/s)
Ann
ual O
utpu
t (M
Wh)
Variable Chord Case
Low Speed Shape
High Speed Shape
Ele
men
t Ang
le (d
egre
es)
Variable Chord Results
4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
static
variable pitch
variable camber
variable chord
Instantaneous Wind Speed (m/s)
Pow
er C
oeff
icie
nt
5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.00 10.50 11.000
10
20
30
40
50
60
70
80
static
variable pitch
variable camber
variable chord
Average Wind Speed (m/s)
Ann
ual O
utpu
t (M
Wh)
Variable Twist Case
High Speed Shape
Low Speed Shape
Ele
men
t Ang
le (d
egre
es)
Variable Twist Results
4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
static
variable pitch
variable camber
variable chord
variable twist
Instantaneous Wind Speed (m/s)
Pow
er C
oeff
icie
nt
5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.00 10.50 11.000
10
20
30
40
50
60
70
80
static
variable pitch
variable camber
variable chord
variable twist
Average Wind Speed (m/s)
Ann
ual O
utpu
t (M
Wh)
Conclusions
wind speed
retracting chord
variable pitch
variable twist
variable camber
Fair (6.7 m/s) 18.64% 23.54% 29.26% 21.77%
Good (7.25 m/s) 15.51% 18.45% 23.71% 17.09%
Excellent (7.75 m/s) 13.44% 14.98% 20.14% 13.79%
Outstanding (8.4 m/s) 11.56% 11.60% 16.99% 10.47%
Superb (10.45 m/s) 9.67% 7.51% 14.22% 6.16%
Percent Improvement over Static Blade:
• Angle of attack has the greatest influence on performance.
• Variable twist was the only parameter to show consistent improvement over variable pitch (~5%).
• Shape distribution is close to linear, could be achieved with torque tube.
V-22 Osprey Torque Tube Mechanism
F. Tad Calkins, Boeing’ s Morphing Aerostructures, Boeing Commercial Airplanes
Future Work
• Inclusion of empirical airfoil data
• Addition of changing Reynolds number to the simulation
• Examine effects of time delay in response to rapid wind variation
• Multiple-parameter cases
• Physical wind tunnel testing of prototypes
• Cost & lifetime analysis for comparison with variable-speed turbines
Acknowledgements
Donald J. BarryTranslated the WTPerf FORTRAN source code from Windows to Linux, which was invaluable to debugging our own simulation code.
Sidney LeibovichConsultation, instruction and general advice on wind turbine modeling.
Questions & Comments?
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