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Development of fatigue life prediction method and effect of 10-minute mean wind speed distribution on fatigue life of small wind turbine composite blade

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  • Jang, Yun Jung
  • Choi, Chan Woong
  • Lee, Jang Ho
  • Kang, Ki Weon

Abstract

This study aims to develop a fatigue life prediction method and to identify the effect that a 10-minute mean wind speed distribution has on the fatigue life of a small-scale wind turbine composite blade. First, combining the von Karman isotropic turbulence model and the Weibull distribution for a 10-minute mean wind speed provided us with the 1-Hz full wind history for a specific time period. Accordingly, the fatigue stress spectra at the blade's fatigue-critical locations (FCLs) were created by applying a stress tensor, in which the interaction between flapwise and edgewise bending moments was taken into consideration. The fatigue life of a composite blade can be predicted with a reliability R = 95% by applying the P–S–N curve obtained from the constant amplitude fatigue tests and rainflow cycle counting, and cumulative damage rule to the fatigue stress spectra. To acquire the second-order regression equation, nonlinear regression analysis was performed on the fatigue lives, which were simulated by using the proposed fatigue life prediction method. In this equation, the variables were the shape parameter, K, and the scale parameter, C, of the Weibull distribution for a 10-minute mean wind speed. The effects of the Weibull parameters on fatigue life were evaluated through the sensitivity analysis of the equations.

Suggested Citation

  • Jang, Yun Jung & Choi, Chan Woong & Lee, Jang Ho & Kang, Ki Weon, 2015. "Development of fatigue life prediction method and effect of 10-minute mean wind speed distribution on fatigue life of small wind turbine composite blade," Renewable Energy, Elsevier, vol. 79(C), pages 187-198.
    • Handle: RePEc:eee:renene:v:79:y:2015:i:c:p:187-198
    DOI: 10.1016/j.renene.2014.10.006
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    References listed on IDEAS

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    1. Zhou, Wei & Yang, Hongxing & Fang, Zhaohong, 2006. "Wind power potential and characteristic analysis of the Pearl River Delta region, China," Renewable Energy, Elsevier, vol. 31(6), pages 739-753.
    2. Adaramola, M.S. & Oyewola, O.M., 2011. "Evaluating the performance of wind turbines in selected locations in Oyo state, Nigeria," Renewable Energy, Elsevier, vol. 36(12), pages 3297-3304.
    3. Kim, Jae-Hoon & Lee, Seung-Pyo & Jin, Ji-Won & Kang, Ki-Weon, 2013. "Estimation for probabilistic distribution of residual strength of sandwich structure with impact-induced damage," Renewable Energy, Elsevier, vol. 54(C), pages 219-226.
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    Cited by:

    1. Bashirzadeh Tabrizi, Amir & Whale, Jonathan & Lyons, Thomas & Urmee, Tania & Peinke, Joachim, 2017. "Modelling the structural loading of a small wind turbine at a highly turbulent site via modifications to the Kaimal turbulence spectra," Renewable Energy, Elsevier, vol. 105(C), pages 288-300.
    2. Liang Lu & Minyan Zhu & Haijun Wu & Jianzhong Wu, 2022. "A Review and Case Analysis on Biaxial Synchronous Loading Technology and Fast Moment-Matching Methods for Fatigue Tests of Wind Turbine Blades," Energies, MDPI, vol. 15(13), pages 1-34, July.
    3. Evans, S.P. & Bradney, D.R. & Clausen, P.D., 2018. "Assessing the IEC simplified fatigue load equations for small wind turbine blades: How simple is too simple?," Renewable Energy, Elsevier, vol. 127(C), pages 24-31.
    4. Meng, Hang & Lien, Fue-Sang & Li, Li, 2018. "Elastic actuator line modelling for wake-induced fatigue analysis of horizontal axis wind turbine blade," Renewable Energy, Elsevier, vol. 116(PA), pages 423-437.
    5. Lu, Liang & Wu, Haijun & Wu, Jianzhong, 2021. "A case study for the optimization of moment-matching in wind turbine blade fatigue tests with a resonant type exciting approach," Renewable Energy, Elsevier, vol. 174(C), pages 769-785.
    6. Precious Chisom Jumbo-Egwurugwu & Franklin Okoro & Ibe Emmanuel & Obo-Obaa Elera Njiran, 2022. "Technical Evaluation of Cathodic Protection of Subsea Structures," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 9(4), pages 01-05, April.
    7. Antoine Chrétien & Antoine Tahan & Philippe Cambron & Adaiton Oliveira-Filho, 2023. "Operational Wind Turbine Blade Damage Evaluation Based on 10-min SCADA and 1 Hz Data," Energies, MDPI, vol. 16(7), pages 1-18, March.
    8. Menegozzo, L. & Dal Monte, A. & Benini, E. & Benato, A., 2018. "Small wind turbines: A numerical study for aerodynamic performance assessment under gust conditions," Renewable Energy, Elsevier, vol. 121(C), pages 123-132.
    9. Jianxiong Gao & Zongwen An & Haixia Kou, 2018. "Fatigue life prediction of wind turbine rotor blade composites considering the combined effects of stress amplitude and mean stress," Journal of Risk and Reliability, , vol. 232(6), pages 598-606, December.

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