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A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower

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  • Meng, Debiao
  • Yang, Shiyuan
  • Jesus, Abílio M.P. de
  • Zhu, Shun-Peng

Abstract

In Reliability-based Multidisciplinary Design Optimization (RBMDO), the key performance functions of wind turbine are usually implicit, which means the performance response can only be obtained through time-consuming Physics Experiment (PE) or Finite Element Analysis (FEA). However, for practical engineering, the computational cost of repeatedly using PE or FEA is prohibitive. To tackle this challenge, in this study, an adaptive Kriging-model-assisted RBMDO strategy is proposed. The novel updated-strategy for performance function in RBMDO is discussed to find effective training samples of active learning for Kriging model. Also, a powerful decoupling strategy of RBMDO is introduced and combined with the proposed method to enhance computational efficiency further. Two case studies, including a mathematic example and a hydraulic turbine rotor bracket design example, are utilized to illustrate the advantage of the given strategy. Finally, the proposed method is applicated into an engineering design of 5 MW offshore wind turbine tower to ensure its reliability and safety.

Suggested Citation

  • Meng, Debiao & Yang, Shiyuan & Jesus, Abílio M.P. de & Zhu, Shun-Peng, 2023. "A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower," Renewable Energy, Elsevier, vol. 203(C), pages 407-420.
    • Handle: RePEc:eee:renene:v:203:y:2023:i:c:p:407-420
    DOI: 10.1016/j.renene.2022.12.062
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    References listed on IDEAS

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    1. Hu, Huakun & Xue, Wendong & Jiang, Peng & Li, Yong, 2022. "Bibliometric analysis for ocean renewable energy: An comprehensive review for hotspots, frontiers, and emerging trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Li, Xiaoke & Zhu, Heng & Chen, Zhenzhong & Ming, Wuyi & Cao, Yang & He, Wenbin & Ma, Jun, 2022. "Limit state Kriging modeling for reliability-based design optimization through classification uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Oh, Ki-Yong & Nam, Woochul & Ryu, Moo Sung & Kim, Ji-Young & Epureanu, Bogdan I., 2018. "A review of foundations of offshore wind energy convertors: Current status and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 16-36.
    4. Chen, Chaohe & Ma, Yuan & Fan, Tianhui, 2022. "Review of model experimental methods focusing on aerodynamic simulation of floating offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    5. Fabian Vorpahl & Holger Schwarze & Tim Fischer & Marc Seidel & Jason Jonkman, 2013. "Offshore wind turbine environment, loads, simulation, and design," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 2(5), pages 548-570, September.
    6. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    7. Clark, Caitlyn E. & DuPont, Bryony, 2018. "Reliability-based design optimization in offshore renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 390-400.
    8. Alexandre Mathern & Christoph von der Haar & Steffen Marx, 2021. "Concrete Support Structures for Offshore Wind Turbines: Current Status, Challenges, and Future Trends," Energies, MDPI, vol. 14(7), pages 1-31, April.
    9. Al-Sanad, Shaikha & Wang, Lin & Parol, Jafarali & Kolios, Athanasios, 2021. "Reliability-based design optimisation framework for wind turbine towers," Renewable Energy, Elsevier, vol. 167(C), pages 942-953.
    10. Zanuttigh, Barbara & Angelelli, Elisa & Kortenhaus, Andreas & Koca, Kaan & Krontira, Yukiko & Koundouri, Phoebe, 2016. "A methodology for multi-criteria design of multi-use offshore platforms for marine renewable energy harvesting," Renewable Energy, Elsevier, vol. 85(C), pages 1271-1289.
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    Cited by:

    1. Shiyuan Yang & Hongtao Wang & Yihe Xu & Yongqiang Guo & Lidong Pan & Jiaming Zhang & Xinkai Guo & Debiao Meng & Jiapeng Wang, 2023. "A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties," Mathematics, MDPI, vol. 11(23), pages 1-26, November.
    2. Jianxiong Gao & Yuanyuan Liu & Yiping Yuan & Fei Heng, 2023. "Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings," Mathematics, MDPI, vol. 11(18), pages 1-24, September.

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