Abstract
This paper presents a method for stochastic deterioration modeling and fatigue damage assessment for composite wind turbine blades operating in offshore environments. The fatigue damage of the composite blades is analyzed and assessed based on the estimates for the applied loads along the blade span, stress analysis, fatigue crack evolution, and lifetime probability of fatigue failure. The complex stress states of the blade are mainly caused by the aerodynamic loads generated by corrected blade element momentum theory, gravity loads, and centrifugal loads. The fatigue of the wind turbine blade is then investigated on the basis of the actual fatigue damage propagation process. The stochastic gamma process is introduced to calculate the probability of fatigue failure of the blade for various critical limits, and these results together with lifecycle cost analysis are employed to determine the optimum maintenance strategy. Finally, a numerical example for a National Renewable Energy Laboratory 5-MW wind turbine blade is adopted to demonstrate the applicability of the proposed method. The numerical results show that the proposed approach can provide a reliable tool for estimating stress states, evaluating fatigue damage, analyzing lifetime fatigue failure probability, and optimizing repair time of the composite wind turbine blade.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The corresponding author is grateful for the financial supports received from the National Key Research and Development Program of China (Grant No. 2019YFE012159), the National Natural Science Foundation of China (Grant No. 51978263), and the Natural Science Key Foundation of Jiangxi Province (Grant No. 20192ACBL20008).
References
Buhl, M. L., Jr. 2005. A new empirical relationship between thrust coefficient and induction factor for the turbulent windmill state. Golden, CO: National Renewable Energy Laboratory.
Chen, H.-P. 2018. Structural health monitoring of large civil engineering structures. New York: Wiley.
Chen, H.-P., and A. M. Alani. 2013. “Optimized maintenance strategy for concrete structures affected by cracking due to reinforcement corrosion.” ACI Struct. J. 110 (2): 229–238. https://doi.org/10.14359/51684403.
Chen, H.-P., C. Zhang, and T.-L. Huang. 2017. “Stochastic modelling fatigue crack evolution and optimum maintenance strategy for composite blades of wind turbines.” Struct. Eng. Mech. 6 (63): 703–712. https://doi.org/10.12989/sem.2017.63.6.703.
Edirisinghe, R., S. Setunge, and G. Zhang. 2013. “Application of gamma process for building deterioration prediction.” J. Perform. Constr. Facil. 27 (6): 763–773. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000358.
Epaarachchi, J. A. J. A., and P. D. P. D. Clausen. 2006. “The development of a fatigue loading spectrum for small wind turbine blades.” J. Wind Eng. Ind. Aerodyn. 94 (4): 207–223. https://doi.org/10.1016/j.jweia.2005.12.007.
Grujicic, M., G. Arakere, E. Subramanian, V. Sellappan, A. Vallejo, and M. Ozen. 2010. “Structural-response analysis, fatigue-life prediction, and material selection for 1 MW horizontal-axis wind-turbine blades.” J. Mater. Eng. Perform. 19 (6): 790–801. https://doi.org/10.1007/s11665-009-9558-8.
Hu, W., K. K. K. Choi, O. Zhupanska, and J. H. J. Buchholz. 2016. “Integrating variable wind load, aerodynamic, and structural analyses towards accurate fatigue life prediction in composite wind turbine blades.” Struct. Multidiscip. Optim. 53 (3): 375–394. https://doi.org/10.1007/s00158-015-1338-5.
Huang, T.-L., H. Zhou, H.-P. Chen, and W.-X. Ren. 2016. “Stochastic modelling and optimum inspection and maintenance strategy for fatigue affected steel bridge members.” Smart Struct. Syst. 18 (3): 569–584. https://doi.org/10.12989/sss.2016.18.3.569.
Jonkman, J., S. Butterfield, W. Musial, and G. Scott. 2009. Definition of a 5-MW reference wind turbine for offshore system development.. Golden, CO: National Renewable Energy Laboratory.
Kensche, C. W. 2006. “Fatigue of composites for wind turbines.” Int. J. Fatigue 28 (10): 1363–1374. https://doi.org/10.1016/j.ijfatigue.2006.02.040.
Kim, B., W. Kim, S. Lee, S. Bae, and Y. Lee. 2013. “Development and verification of a performance based optimal design software for wind turbine blades.” Renewable Energy 54: 166–172. https://doi.org/10.1016/j.renene.2012.08.029.
Koh, W. X. M. M., and E. Y. K. K. Ng. 2016. “Effects of Reynolds number and different tip loss models on the accuracy of BEM applied to tidal turbines as compared to experiments.” Ocean Eng. 111: 104–115. https://doi.org/10.1016/j.oceaneng.2015.10.042.
Kong, C., J. Bang, and Y. Sugiyama. 2005. “Structural investigation of composite wind turbine blade considering various load cases and fatigue life.” Energy 30 (11–12): 2101–2114. https://doi.org/10.1016/j.energy.2004.08.016.
Kong, C., T. Kim, D. Han, and Y. Sugiyama. 2006. “Investigation of fatigue life for a medium scale composite wind turbine blade.” Int. J. Fatigue 28 (10): 1382–1388. https://doi.org/10.1016/j.ijfatigue.2006.02.034.
Mahmoodian, M., and A. Alani. 2014. “Modelling deterioration in concrete pipes as a stochastic gamma process for time-dependent reliability analysis.” J. Pipeline Syst. Eng. Pract. 5 (1): 4013008. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000145.
Mandell, J. F., D. D. D. Samborsky, L. Wang, and N. K. Wahl. 2003. “New fatigue data for wind turbine blade materials.” J. Sol. Energy Eng. 125 (4): 506. https://doi.org/10.1115/1.1624089.
Marín, J. C. C., A. Barroso, F. París, and J. Cañas. 2009. “Study of fatigue damage in wind turbine blades.” Eng. Fail. Anal. 16 (2): 656–668. https://doi.org/10.1016/j.engfailanal.2008.02.005.
Moriarty, P. J., and C. Hansen. 2005. “AeroDyn theory manual.” Renewable Energy 15 (Jan): 500–36313. https://doi.org/10.1146/annurev.fl.15.010183.001255.
Qu, C. X., T. H. Yi, and H. N. Li. 2019. “Mode identification by eigensystem realization algorithm through virtual frequency response function.” Struct. Control Health Monit. 26 (10): 1–19. https://doi.org/10.1002/stc.2429.
Qu, C. X., T. H. Yi, Y. Z. Zhou, H. N. Li, and Y. F. Zhang. 2018. “Frequency identification of practical bridges through higher-order spectrum.” J. Aerosp. Eng. 31 (3): 04018018. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000840.
Resor, B. R. 2013. Definition of a 5 MW/61.5 m wind turbine blade reference model.. Albuquerque, NM: Sandia National Laboratories.
Shen, W. Z., R. Mikkelsen, J. N. Sørensen, and C. Bak. 2005. “Tip loss corrections for wind turbine computations.” Wind Energy 8 (4): 457–475. https://doi.org/10.1002/we.153.
Shokrieh, M. M. M., and R. Rafiee. 2006. “Simulation of fatigue failure in a full composite wind turbine blade.” Compos. Struct. 74 (3): 332–342. https://doi.org/10.1016/j.compstruct.2005.04.027.
Sørensen, J. D. 2009. “Framework for risk-based planning of operation and maintenance for offshore wind turbines.” Wind Energy 12 (5): 493–506. https://doi.org/10.1002/we.344.
Spera, D. A. 1994. Wind turbine technology. New York: ASME.
Strauss, A., R. Wan-Wendner, A. Vidovic, I. Zambon, Q. Yu, D. M. Frangopol, and K. Bergmeister. 2017. “Gamma prediction models for long-term creep deformations of prestressed concrete bridges.” J. Civ. Eng. Manage. 23 (6): 681–698. https://doi.org/10.3846/13923730.2017.1335652.
Toft, H. S., and J. D. Sørensen (2011). “Reliability-based design of wind turbine blades.” Struct. Saf. 33 (6): 333–342. https://doi.org/10.1016/j.strusafe.2011.05.003.
van Noortwijk, J. M. (2009). “A survey of the application of gamma processes in maintenance.” Reliab. Eng. Syst. Saf. 94 (1): 2–21. https://doi.org/10.1016/j.ress.2007.03.019.
Wang, L., X. Liu, and A. Kolios. 2016. “State of the art in the aeroelasticity of wind turbine blades: Aeroelastic modeling.” Renewable Sustainable Energy Rev. 64: 195–210. https://doi.org/10.1016/j.rser.2016.06.007.
Wang, L., X. Liu, N. Renevier, M. Stables, and G. M. G. M. Hall. 2014. “Nonlinear aeroelastic modelling for wind turbine blades based on blade element momentum theory and geometrically exact beam theory.” Energy 76: 487–501. https://doi.org/10.1016/j.energy.2014.08.046.
Wu, F., and W. Yao. 2010. “A fatigue damage model of composite materials.” Int. J. Fatigue 32 (1): 134–138. https://doi.org/10.1016/j.ijfatigue.2009.02.027.
Yang, J., C. Peng, J. Xiao, J. Zeng, S. Xing, J. Jin, and H. Deng. 2013. “Structural investigation of composite wind turbine blade considering structural collapse in full-scale static tests.” Compos. Struct. 97: 15–29. https://doi.org/10.1016/j.compstruct.2012.10.055.
Zhang, C. 2018. “Reliability-based fatigue damage assessment and optimum maintenance strategy of offshore horizontal wind turbine blades.” Ph.D. thesis, Dept. of Engineering Science, Univ. of Greenwich.
Zhang, C., H.-P. Chen, and T.-L. Huang. 2018. “Fatigue damage assessment of wind turbine composite blades using corrected blade element momentum theory.” Measurement 129 (2018): 102–111. https://doi.org/10.1016/j.measurement.2018.06.045.
Zhang, C., and K. F. Tee. 2019. “Application of gamma process and maintenance cost for fatigue damage of wind turbine blade.” Energy Procedia 158: 3729–3734. https://doi.org/10.1016/j.egypro.2019.01.884.
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Published In
Journal of Aerospace Engineering
Volume 34 • Issue 3 • May 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 29, 2020
Accepted: Nov 24, 2020
Published online: Mar 8, 2021
Published in print: May 1, 2021
Discussion open until: Aug 8, 2021
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