skip to main content

An investigation of the Steady-State and Fatigue Problems of a Small Wind Turbine Blade Based on the Interactive Design Approach

1UDERZA Laboratory, University of El Oued, 39000 El Oued, Algeria

2Department of Mechanical Engineering, University of El Oued, 39000 El Oued, Algeria

3College of Technical Engineering, Al-Farahidi University, Baghdad 10005, Iraq

4 University of Warith Al-Anbiyaa, College of Engineering Karbala, Iraq

5 Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Iraq

6 Department of Energy Engineering, University of Baghdad, Iraq

7 Department of Mechanics Al-Farabi Kazakh National University, Kazakhstan, Baghdad, Iraq

8 System Technologies and Engineering Design Methodology, Hamburg University of Technology, Hamburg, Germany

View all affiliations
Received: 29 Aug 2022; Revised: 12 Nov 2022; Accepted: 8 Dec 2022; Available online: 18 Dec 2022; Published: 1 Jan 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

A wind turbine blade is an essential system of wind energy production. During the operation of the blade, it is subjected to loads resulting from the impact of the wind on the surface of the blade. This leads to appear large deflections and high fatigue stresses in the structure of blades. In this paper, a 5 kW horizontal axis wind turbine blade model is designed and optimized using a new MATLAB code based on blade element momentum (BEM) theory.The aerodynamic shape of the blade has been improved compared with the initial design, the wind turbine power has been increased by 7% and the power coefficient has been increased by 8%.  The finite Element Method was used to calculate the loads applied to the blade based on Computational Fluid Dynamics (CFD) and BEM theory.High agreements were obtained between the results of both approaches (CFD and BEM).The ANSYS software was also used to simulate and optimize the structure of the blade by applying variable static loads 3.3, 6, and 8.3 kg and compared the results with the experimental results. It was reduced the maximum deflectionswith 37%, 42.85%, and 42.61% when using CFRP material and 4.5%, 15.45%, and 16.19% for GFRP material that corresponds to the applied forces. Based on the results, the mass of the optimized model decreased by 47.86% for GFRP and 71.24% for CFRP. IEC 61400.2 standard was used to estimate thefatigue loads, damage, blade life prediction, and verify blade safety usinga Simplified Load Model(SLM) and FAST software. It was found that the blade will be safe under extreme wind loads, and the lifetime of the wind blade (GFRP) is 5.5 years and 10.25 years,according to SLM and FAST software, respectively. At the same time, the lifetime of the wind blade (CFRP)is more than 20 years, according to the two applied methods.

Fulltext View|Download
Keywords: damage; composite materials; fatigue life; standard IEC 61400.2; Simplified Load Model; FAST software

Article Metrics:

  1. Ajirlo, K. S., Tari, P. H., Gharali, K., & Zandi, M. (2021). Development of a wind turbine simulator to design and test micro HAWTs. Sustainable Energy Technologies and Assessments, 43100900
  2. Backe, S., & Balle, F. (2018). A novel short-time concept for fatigue life estimation of carbon (CFRP) and metal/carbon fiber reinforced polymer (MCFRP). International Journal of Fatigue, 116317-22 https://doi.org/10.1016/j.ijfatigue.2018.06.044
  3. Bazilevs, Y., Korobenko, A., Deng, X., & Yan, J. (2016). Fluid–structure interaction modeling for fatigue-damage prediction in full-scale wind-turbine blades. Journal of Applied Mechanics, 83(6): https://doi.org/10.1115/1.4033080
  4. Bechmann, A., Sørensen, N. N., & Zahle, F. (2011). CFD simulations of the MEXICO rotor. wind energy, 14(5): 677-89 https://doi.org/10.1002/we.450
  5. Commission, I. E. (2013). IEC 61400-2: 2013-Wind Turbines. part 2. Small Wind Turbines. In. Australia: International Electrotechnical Commission
  6. Costa, M. S., Evans, S. P., Bradney, D. R., & Clausen, P. D. (2017). A method to optimise the materials layout of small wind turbine blades. Renewable Energy and Environmental Sustainability, 219 https://doi.org/10.1051/rees/2017006
  7. Da Costa, M., & Clausen, P. (2020). Structural Analysis of a small wind turbine blade subjected to gyroscopic load. In Journal of Physics: Conference Series, 042006. IOP Publishing https://doi: 10.1088/1742-6596/1618/4/042006
  8. Dervilis, N., Choi, M., Taylor, S., Barthorpe, R., Park, G., Farrar, C., & Worden, K. (2014). On damage diagnosis for a wind turbine blade using pattern recognition. Journal of sound and vibration, 333(6): 1833-50 https://doi.org/10.1016/j.jsv.2013.11.015
  9. Du, Y., Zhou, S., Jing, X., Peng, Y., Wu, H., & Kwok, N. (2020). Damage detection techniques for wind turbine blades: A review. Mechanical Systems and Signal Processing, 141106445 https://doi.org/10.1016/j.ymssp.2019.106445
  10. Evans, S., Dana, S., Clausen, P., & Wood, D. (2021). A simple method for modelling fatigue spectra of small wind turbine blades. Wind Energy, 24(6): 549-57 https://doi.org/10.1002/we.2588
  11. Evans, S. P. (2017). Aeroelastic measurements, simulations, and fatigue predictions for small wind turbines operating in highly turbulent flow, The University of Newcastle, Australia
  12. Hu, W., Park, D., & Choi, D. (2013). Structural optimization procedure of a composite wind turbine blade for reducing both material cost and blade weight. Engineering Optimization, 45(12): 1469-87 https://doi.org/10.1080/0305215X.2012.743533
  13. Jonkman, J. M., & Buhl, M. L. (2005). FAST user's guide (National Renewable Energy Laboratory Golden, CO, USA). https://www.nrel.gov/wind/nwtc/fastv7.html
  14. Kim, D.-M., Kim, D.-H., Park, K.-K., & Kim, Y.-S. (2009). Efficient Super-element Structural Vibration Analyses of a Large Wind-turbine Rotor Blade Considering Rotational and Aerodynamic Load Effects. Transactions of the Korean Society for Noise Vibration Engineering, 19(7): 651-58 https://doi.org/10.5050/KSNVN.2009.19.7.651
  15. Kim, T., Hansen, A. M., & Branner, K. (2013). Development of an anisotropic beam finite element for composite wind turbine blades in multibody system. Renewable Energy, 59172-83
  16. Korkiakoski, S., Brøndsted, P., Sarlin, E., & Saarela, O. (2016). Influence of specimen type and reinforcement on measured tension–tension fatigue life of unidirectional GFRP laminates. International Journal of Fatigue, 85114-29 https://doi.org/10.1016/j.ijfatigue.2015.12.008
  17. Lee, H. G., Kang, M. G., & Park, J. (2015). Fatigue failure of a composite wind turbine blade at its root end. Composite Structures, 133878-85 https://doi.org/10.1016/j.compstruct.2015.08.010
  18. Liu, X., Wang, L., & Tang, X. (2013). Optimized linearization of chord and twist angle profiles for fixed-pitch fixed-speed wind turbine blades. Renewable Energy, 57111-19 https://doi.org/10.1016/j.renene.2013.01.036
  19. Make, M., & Vaz, G. (2015). Analyzing scaling effects on offshore wind turbines using CFD. Renewable Energy, 831326-40 https://doi.org/10.1016/j.renene.2015.05.048
  20. Mandell, J. F., Samborsky, D. D., & Cairns, D. S. (2002). Fatigue of composite materials and substructures for wind turbine blades. In. Albuquerque, California: Sandia National Laboratories
  21. Peeters, M., Santo, G., Degroote, J., & Van Paepegem, W. (2018). Comparison of shell and solid finite element models for the static certification tests of a 43 m wind turbine blade. Energies, 11(6): 1346 https://doi.org/10.3390/en11061346
  22. Pourrajabian, A., Afshar, P. A. N., Ahmadizadeh, M., & Wood, D. (2016). Aero-structural design and optimization of a small wind turbine blade. Renewable energy, 87837-48 https://doi.org/10.1016/j.renene.2015.09.002
  23. Rajadurai, J. S., Christopher, T., Thanigaiyarasu, G., & Rao, B. N. (2008). Finite element analysis with an improved failure criterion for composite wind turbine blades. Forschung im Ingenieurwesen, 72(4): 193-207 http://doi.org/10.1007/s10010-008-0078-8
  24. Rosato, M. A. (2018). Small Wind Turbines for Electricity and Irrigation: Design and Construction (CRC Press: New York, USA)
  25. Rubiella, C., Hessabi, C. A., & Fallah, A. S. (2018). State of the art in fatigue modelling of composite wind turbine blades. International Journal of Fatigue, 117230-45 https://doi.org/10.1016/j.ijfatigue.2018.07.031
  26. Shokrieh, M. M., & Rafiee, R. (2006). Simulation of fatigue failure in a full composite wind turbine blade. Composite Structures, 74(3): 332-42 https://doi.org/10.1016/j.compstruct.2005.04.027
  27. Song, F., Ni, Y., & Tan, Z. (2011). Optimization design, modeling and dynamic analysis for composite wind turbine blade. Procedia Engineering, 16369-75 https://doi.org/10.1016/j.proeng.2011.08.1097
  28. Tenguria, N., Mittal, N., & Ahmed, S. (2010). Investigation of blade performance of horizontal axis wind turbine based on blade element momentum theory (BEMT) using NACA airfoils. International Journal of Engineering, Science and Technology, 2(12): 25-35 https://doi.org/10.1260/1708-5284.12.1.83
  29. Uchida, T., Taniyama, Y., Fukatani, Y., Nakano, M., Bai, Z., Yoshida, T., & Inui, M. (2020). A new wind turbine CFD modeling method based on a porous disk approach for practical wind farm design. Energies, 13(12): 3197 https://doi.org/10.3390/en13123197
  30. Wood, D. (2009). Using the IEC Simple Load Model for Small Wind Turbines. Wind Engineering, 33(2): 139-54 https://doi.org/10.1260/0309-524X.34.3.241
  31. Wu, W. H., & Young, W. B. (2012). Structural analysis and design of the composite wind turbine blade. Applied Composite Materials, 19(3-4): 247-57 https://doi.org/10.1007/s10443-011-9193-z
  32. Zhang, C., Chen, H.-P., & Huang, T.-L. (2018). Fatigue damage assessment of wind turbine composite blades using corrected blade element momentum theory. Measurement, 129102-11 https://doi.org/10.1016/j.measurement.2018.06.045
  33. Zhou, S., & Wu, X. (2019). Fatigue life prediction of composite laminates by fatigue master curves. Journal of Materials Research and Technology, 8(6): 6094-105 https://doi.org/10.1016/j.jmrt.2019.10.003
  34. Zidane, I. F., Swadener, G., Ma, X., Shehadeh, M. F., Salem, M. H., & Saqr, K. M. (2020). Performance of a wind turbine blade in sandstorms using a CFD-BEM based neural network. Journal of Renewable and Sustainable Energy, 12(5): 053310

Last update:

No citation recorded.

Last update: 2024-05-25 12:38:34

No citation recorded.