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Design optimization of a wind turbine blade to reduce the fluctuating unsteady aerodynamic load in turbulent wind

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Abstract

Design optimization of the wind turbine of a NREL 1.5-MW HAWT blade was studied to minimize the fluctuation of the bending moment of the blade in turbulent wind. In order to analyze the unsteady aerodynamic load of a wind turbine, FAST code was used as the analysis code. To consider turbulent wind as the wind input model in FAST, TurbSim was used as a turbulent wind simulator. For effective geometrical representation of the aerodynamic shape of a wind turbine blade, the shape modeling function was used to represent the chord length and twist angle. The fluctuation of the out-of-plane bending moment at the blade root was minimized by maintaining the required power of the wind turbine. Through the redistribution of the section force in the radial direction between both the primary and tip regions, the magnitude of the fluctuation of the out-of-plane bending moment was reduced by about 20%, and the rated power of 1.5-MW was maintained. The local angles of attack for the optimized blade were near the point of the maximum lift-to-drag ratio in the primary and tip regions compared to the baseline blade. The fluctuating unsteady aerodynamic load in the optimized blade was reduced within the operating range of the wind speed. With the optimized blade shape, the wind turbine can be operated with decreased fluctuating aerodynamic loads and have a longer life in turbulent wind.

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References

  1. T. Acjermann and L. Soder, Wind energy technology and current status: a review, Renewable and sustainable energy reviews, 4 (2000) 315–374.

    Article  Google Scholar 

  2. R. Harrison, E. Hau and H. Snel, Large wind turbine: Design and Economics, John Wiley & Sons. Ltd., West Sussex, England (2000) 1–26.

    Google Scholar 

  3. EWEA, Wind Energy — The facts part I: technology, European Wind Energy Association, Brussels, Belgium (2009) 72–79.

    Google Scholar 

  4. M. O. L. Hansen, J. N. Sørensen, S. Voutsinas, N. Sørensen, and H. A. Madsen, State of the art in wind turbine aerodynamics and aeroelasticity, Progress in Aerospace Sciences, 42 (2006) 285–330.

    Article  Google Scholar 

  5. R. van Rooij and W. A. Timmer, Roughness sensitivity considerations for thick rotor blade airfoils, Journal of Solar Energy Engineering, 125 (2003) 468–478.

    Article  Google Scholar 

  6. G. Petrone and G. Nicola, Wind turbine performance analysis under uncertainty, Proc. of 49th AIAA Aerospace Sciences Meeting 2011, Orlando, Florida, USA (2011) 2011–544.

  7. J. G. Leishman, Challenges in modeling the unsteady aerodynamics of wind turbines, Wind Energy, 5 (2002) 85–132.

    Article  Google Scholar 

  8. H. Kim, S. Lee and S. Lee, Influence of blade-tower interaction in upwind-type horizontal axis wind turbines on aerodynamics, Journal of Mechanical Science and Technology, 25(5) (2011) 1351–1360.

    Article  Google Scholar 

  9. J. Li, J. Chen and X. Chen, Aerodynamic response analysis of wind turbines, Journal of Mechanical Science and Technology, 25(1) (2010) 89–95.

    Article  Google Scholar 

  10. C. Masson, A. Smaili and C. Leclerc, Aerodynamic analysis of HAWTs operating in unsteady conditions, Wind Energy, 4 (2001) 1–22.

    Article  Google Scholar 

  11. Y. Hasegawa, H. Imamura, J. Murata, K. Kikuyama, K. Karikomi and N. Yonezawa, Aerodynamic loads on horizontal axis wind turbine rotors exerted by turbulent inflow, Proc. of 2nd International Energy Conversion Engineering Conference, Providence, Rhode Island, USA (2004) 2004–5704.

  12. K. Saranyasoontorn and L. Manuel, A comparison of wind turbine design loads in different environments using inverse reliability techniques, Journal of Solar Energy Engineering, 126 (2004) 1060–1068.

    Article  Google Scholar 

  13. P. J. Moriarty, W. E. Holley and S. Butterfield, Effect of turbulence variation on extreme loads prediction for wind turbines, Journal of Solar Energy Engineering, 124 (2002) 387–395.

    Article  Google Scholar 

  14. T. J. Larsen, H. A. Madsen and K. Thomsen, Active load reduction using individual pitch, based on local blade flow measurements, Wind Energy, 8(1) (2005) 67–80.

    Article  Google Scholar 

  15. H. Sun, Wind turbine airfoil design using response surface method, Journal of Mechanical Science and Technology, 25(5) (2011) 1335–1340.

    Article  Google Scholar 

  16. H. Xuan, Z. Weimin, L. Xiao and L. Jieping, Aerodynamic and aeroacoustic optimization of wind turbine blade by a genetic algorithm, Proc. of 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, USA (2008) 2008–1331.

  17. K. Lee, K. Kim, D. Lee, K. Lee and J. Park, Two-step optimization for wind turbine blade with probability approach, Journal of Solar Energy Engineering, 132 (2010) 034503.

    Article  Google Scholar 

  18. G. B. Eke and J. I. Onyewudiala, Optimization of wind turbine blades using genetic algorithm, Global Journal of Researches in Engineering, 10(7) (2010) 22–26.

    Google Scholar 

  19. P. Fuglsang and H. A. Madsen, Optimization method for wind turbine rotors, Journal of Wind Engineering and Industrial Aerodynamics, 80(1–2) (1999) 191–206.

    Article  Google Scholar 

  20. E. Benini and A. Toffolo, Optimal design of horizontal-axis wind turbines using blade-element theory and evolutionary computation, Journal of Solar Energy Engineering, 124(4) (2002) 357–363.

    Article  Google Scholar 

  21. M. Jureczko, M. Pawlak and A. Mezyk, Optimization of wind turbine blades, Journal of Materials Processing Technology, 167(2–3) (2005) 463–471.

    Article  Google Scholar 

  22. V. D. Casás, F. L. Peña and R. J. Duro, Automatic design and optimization of wind turbine blades, Proc. of the International Conference on Computational Intelligence for Modeling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA’06), Sydney, Australia (2006) 205–210.

  23. S. Joncas, M. J. de Ruiter and F. V. Keulen, Preliminary design of large wind turbine blades using layout optimization techniques, Proc. of 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, USA (2004) 2004–4655.

  24. NWTC design codes (FAST by Jason Jonkman, Ph.D.). http://wind.nrel.gov/designcodes/simulators/fast/ . Last modified 05-November-2010; accessed 05-November-2010.

  25. NWTC design codes (TurbSim by Neil Kelley, Bonnie Jonkman). http://wind.nrel.gov/designcodes/preprocessors/turbsim/ . Last modified 03-February-2011; accessed 03-February-2011.

  26. J. M. Jonkman and M. L. Buhl Jr., FAST user’s guide, National Renewable Energy Laboratory, Golden, Colorado, USA (2005).

    Book  Google Scholar 

  27. P. J. Moriarty and A. C. Hansen, AeroDyn theory manual, National Renewable Energy Laboratory, Golden, Colorado, USA (2005).

    Book  Google Scholar 

  28. J. M. Jonkman and M. L. Buhl Jr., TurbSim user’s guide, National Renewable Energy Laboratory, Golden, Colorado, USA (2007).

    Google Scholar 

  29. International Electrotechnical Commission (TC88), Wind turbine generator systems — Part 1: Safety Requirements, Second Edition, International Electrotechnical Commission, Geneva, Swiss (1999).

    Google Scholar 

  30. B. M. Kulfan and J. E. Bussoletti, Fundamental parametric geometry representations for aircraft component shapes, Proc. of 11th AIAA/ISSMO MAO conference, Portsmouth, Virginia, USA (2006) 2006-6948.

  31. J. Rho, Y. Ku, J. Kee and D. Lee, Development of vehicle modeling function for 3-dimensional shape optimization, Journal of Mechanical Design, 131 (2009) 121004.

    Article  Google Scholar 

  32. D. J. Malcolm and A. C. Hansen, WindPACT turbine rotor design study, National Renewable Energy Laboratory, Golden, Colorado, USA (2002).

    Google Scholar 

  33. PIAnO (Process Integration, Automation and Optimization) User’s Manual Version 2.4, FRAMAX Inc. (2008).

  34. S. Jun, Y. Jeon, J. Rho and D. Lee, Application of collaborative optimization using genetic algorithm and response surface method to an aircraft wing design, Journal of Mechanical Science and Technology, 20(1) (2006) 133–146.

    Article  Google Scholar 

Download references

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Correspondence to Dong-Ho Lee.

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This paper was recommended for publication in revised form by Associate Editor Do Hyung Lee

Jihoon Jeong is a Ph.D candidate in aerospace engineering at Seoul National University. His B.S degree is from Seoul National University. His research interests include multidisciplinary design optimization and reliability-based design optimization for complex systems.

Dong-Ho Lee is a professor in School of Mechanical and Aerospace Engineering at Seoul National University. He is a member of The National Academy of Engineering of Korea. He is interested in computational fluid dynamics, wind tunnel test and multidisciplinary design optimization for large and complex systems (e.g. aircraft, helicopter, high speed train, compressor, and wind turbine).

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Jeong, J., Park, K., Jun, S. et al. Design optimization of a wind turbine blade to reduce the fluctuating unsteady aerodynamic load in turbulent wind. J Mech Sci Technol 26, 827–838 (2012). https://doi.org/10.1007/s12206-011-1106-4

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  • DOI: https://doi.org/10.1007/s12206-011-1106-4

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