Dynamically Variable Blade Geometry for Wind Energy
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Transcript of Dynamically Variable Blade Geometry for Wind Energy
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Cornell UniversityLaboratory for Intelligent Machine Systems
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
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Laboratory for Intelligent Machine Systems
Goal: Increase wind turbine energy output by morphing blade shape to match changing wind speeds.
Pitch Chord
Twist
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Laboratory for Intelligent Machine Systems
Outline• Motivation• Experimental Design• Airfoil Generation• Simulation• Optimization• Results
– Geometry– Power output
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Laboratory for Intelligent Machine Systems
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
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Laboratory for Intelligent Machine Systems
Problem Parameterization • Blade Element Momentum
(BEM) Theory is used• Turbine has operating 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
• Chord, twist, and camber are examined
Vestas V90 power output vs. wind speed
Sample wind speed Rayleigh distribution
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Laboratory for Intelligent Machine Systems
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
Emphasize Panels
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Laboratory for Intelligent Machine Systems
Turbine Performance Analysis• Equations based on basic BEM theory,
WT_Perf source code, and Aerodyne Theory Manual.– Blade divided into a number of elements– Axial induction factor initialized
• a = (U1-U2)/U1– Relative wind angle calculated from
normal/tangential velocities– Lift/drag coefficients interpolated from airfoil
data based on angle of attack– Axial induction factor updated– Iterate for conversion– Element torque/power calculated– Total power calculated
Polyamide
Nylon “Kite Wing”
Add stream tube picture
Citations
Crazy airfoil crosssection diagram
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Laboratory for Intelligent Machine Systems
Parametric Study• Performance of morphing blades compared to that of a fixed blade
– Sample blade from WT_Perf optimized across all parameters at wind speed of 10 m/s
– All morphing blades begin with this shape• Each morphing blade changes one parameter
– Three chord scenarios are examined• Extension only• Extension and retraction• Retraction only
Add arrows
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Laboratory for Intelligent Machine Systems
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Laboratory for Intelligent Machine Systems
Optimization
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Cornell UniversityLaboratory for Intelligent Machine Systems
Morphology Plot
Low Speed Shape
High Speed Shape
Variable Pitch
15°
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Laboratory for Intelligent Machine Systems
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Cornell UniversityLaboratory for Intelligent Machine Systems
Morphology Plot
Variable CamberAdd picture
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Laboratory for Intelligent Machine Systems
Highlight new lines
Annual Output Power Curve
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Cornell UniversityLaboratory for Intelligent Machine Systems
Morphology Surface
Low Speed Shape
High Speed Shape
Variable Chord
Emphasize retraction over others
Define retraction factor
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Laboratory for Intelligent Machine Systems
Highlight new lines
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Cornell UniversityLaboratory for Intelligent Machine Systems
Morphology Surface
Low Speed Shape
High Speed Shape
Variable Twist
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Laboratory for Intelligent Machine Systems
Highlight new lines
Clarification/vertical lines
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Laboratory for Intelligent Machine Systems
Conclusion
• Variable Twist has the most influence on the performance– Consistent 5% improvement over current pitch control scheme– Achievable using torque tube mechanism– Shape distribution close to linear
wind speed (m/s) retracting chord
variable pitch
variable twist
variable camber
Fair (6.7) 18.64% 23.54% 29.26% 21.77%
Good (7.25) 15.51% 18.45% 23.71% 17.09%
Excellent (7.75) 13.44% 14.98% 20.14% 13.79%
Outstanding (8.4) 11.56% 11.60% 16.99% 10.47%
Superb (10.45) 9.67% 7.51% 14.22% 6.16%
Percent Improvement over Static Blade
Find V-22 paper or illustration
Cite “Fair”, “Good”,etc.
Emphasize improvement over pitch
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Laboratory for Intelligent Machine Systems
Future Work
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Laboratory for Intelligent Machine Systems
Acknowledgements
Find official titles
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Laboratory for Intelligent Machine Systems
Questions & Comments?
Laboratory for Intelligent Machine SystemsAcknowledgements: Professor Sidney Leibovich, Donald Barry