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Fatigue reliability analysis and assessment of offshore wind turbine blade adhesive bonding under the coupling effects of multiple environmental stresses

Author

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  • Li, Yongjie
  • Liu, Zheng
  • He, Zhenfeng
  • Tu, Liang
  • Huang, Hong-Zhong

Abstract

Offshore wind turbine blades (OWTBs) serve in harsh environments, such as high temperature, high humidity, salt fog, etc., and the various environmental stresses will have coupling effects on the OWTBs structural performance, in particular greatly accelerating the fatigue properties degradation and failure process of adhesive bonding. Thus, special consideration should be paid to the coupling effects of multiple environmental stresses on the fatigue reliability of adhesives. For this purpose, a fatigue reliability modeling method, which innovatively integrated fatigue analysis with multi-environmental stresses effect analysis, is proposed in this paper. Specifically, the fatigue damage process of adhesive bonding is characterized by Cyclic Cohesive Zone Model (CCZM); a multi-environmental degradation factor model (MEDFM) is built to quantify the coupling effects of environmental stresses on adhesive bonding fatigue lifetime, then the MEDFM is fused into the CCZM to realize the fatigue damage process simulation with the considerations of environmental stress. Finally, the fatigue reliability model considering multi-environmental stress coupling effects is built for adhesive bonding, and the feasibility and accuracy of the proposed method are verified by comparing it with the experimental results. The proposed method can be used for OWTBs fatigue analysis and structural design.

Suggested Citation

  • Li, Yongjie & Liu, Zheng & He, Zhenfeng & Tu, Liang & Huang, Hong-Zhong, 2023. "Fatigue reliability analysis and assessment of offshore wind turbine blade adhesive bonding under the coupling effects of multiple environmental stresses," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:reensy:v:238:y:2023:i:c:s095183202300340x
    DOI: 10.1016/j.ress.2023.109426
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    Cited by:

    1. Wang, Haijie & Li, Bo & Lei, Liming & Xuan, Fuzhen, 2024. "Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Zhang, Ruixing & An, Liqiang & He, Lun & Yang, Xinmeng & Huang, Zenghao, 2024. "Reliability analysis and inverse optimization method for floating wind turbines driven by dual meta-models combining transient-steady responses," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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