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Cornell University

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.

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Cornell University

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  1. 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

  2. Goal Increase wind turbine energy output by morphing blade shape to match changing wind speeds. Pitch Camber Chord Twist

  3. Outline • Motivation • Problem Parameterization • Airfoil Generation • Turbine Analysis • Parametric Study • Results • Geometry • Power output

  4. 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/

  5. 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

  6. 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 Streamtube around wind turbine rotor, used as basis for BEM theory (Manwell 85). Nylon “Kite Wing” Dividing the blade into several elements (Moriarty 2) 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, Blade geometry for analysis of horizontal axis wind turbine (Manwell 108).

  7. 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

  8. 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

  9. Static Blade Design

  10. Variable Pitch Results

  11. Variable Pitch Case Element Angle (degrees) High Speed Shape Low Speed Shape

  12. Variable Pitch Results

  13. Variable Camber Case Element Angle (degrees) Low Speed Shape High Speed Shape

  14. Variable Camber Results

  15. Variable Chord Case Element Angle (degrees) Low Speed Shape High Speed Shape

  16. Variable Chord Results

  17. Variable Twist Case Element Angle (degrees) High Speed Shape Low Speed Shape

  18. Variable Twist Results

  19. Conclusions Percent Improvement over Static Blade: V-22 Osprey Torque Tube Mechanism F. Tad Calkins, Boeing’ s Morphing Aerostructures, Boeing Commercial Airplanes • 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.

  20. 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

  21. Acknowledgements Donald J. Barry Translated the WTPerf FORTRAN source code from Windows to Linux, which was invaluable to debugging our own simulation code. Sidney Leibovich Consultation, instruction and general advice on wind turbine modeling.

  22. Questions & Comments?

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