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Comparative Study of Time-Domain Fatigue Assessments for an Offshore Wind Turbine Jacket Substructure by Using Conventional Grid-Based and Monte Carlo Sampling Methods

Author

Listed:
  • Chi-Yu Chian

    (Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 106, Taiwan)

  • Yi-Qing Zhao

    (Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 106, Taiwan)

  • Tsung-Yueh Lin

    (R/D Section, Research Department, CR Classification Society, Taipei 10487, Taiwan)

  • Bryan Nelson

    (R/D Section, Research Department, CR Classification Society, Taipei 10487, Taiwan)

  • Hsin-Haou Huang

    (Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 106, Taiwan)

Abstract

Currently, in the design standards for environmental sampling to assess long-term fatigue damage, the grid-based sampling method is used to scan a rectangular grid of meteorological inputs. However, the required simulation cost increases exponentially with the number of environmental parameters, and considerable time and effort are required to characterise the statistical uncertainty of offshore wind turbine (OWT) systems. In this study, a K-type jacket substructure of an OWT was modelled numerically. Time rather than frequency-domain analyses were conducted because of the high nonlinearity of the OWT system. The Monte Carlo (MC) sampling method is well known for its theoretical convergence, which is independent of dimensionality. Conventional grid-based and MC sampling methods were applied for sampling simulation conditions from the probability distributions of four environmental variables. Approximately 10,000 simulations were conducted to compare the computational efficiencies of the two sampling methods, and the statistical uncertainty of the distribution of fatigue damage was assessed. The uncertainty due to the stochastic processes of the wave and wind loads presented considerable influence on the hot-spot stress of welded tubular joints of the jacket-type substructure. This implies that more simulations for each representative short-term environmental condition are required to derive the characteristic fatigue damage. The characteristic fatigue damage results revealed that the MC sampling method yielded the same error level for Grids 1 and 2 (2443 iterations required for both) after 1437 and 516 iterations for K- and KK-joint cases, respectively. This result indicated that the MC method has the potential for a high convergence rate.

Suggested Citation

  • Chi-Yu Chian & Yi-Qing Zhao & Tsung-Yueh Lin & Bryan Nelson & Hsin-Haou Huang, 2018. "Comparative Study of Time-Domain Fatigue Assessments for an Offshore Wind Turbine Jacket Substructure by Using Conventional Grid-Based and Monte Carlo Sampling Methods," Energies, MDPI, vol. 11(11), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3112-:d:181962
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    References listed on IDEAS

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    1. Li, Kewen & Bian, Huiyuan & Liu, Changwei & Zhang, Danfeng & Yang, Yanan, 2015. "Comparison of geothermal with solar and wind power generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1464-1474.
    2. Dong, Wenbin & Moan, Torgeir & Gao, Zhen, 2012. "Fatigue reliability analysis of the jacket support structure for offshore wind turbine considering the effect of corrosion and inspection," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 11-27.
    3. Tao Pang & Yipeng Yang & Dai Zhao, 2015. "Convergence Studies on Monte Carlo Methods for Pricing Mortgage-Backed Securities," IJFS, MDPI, vol. 3(2), pages 1-15, May.
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    Cited by:

    1. Ju, Shen-Haw, 2022. "Increasing the fatigue life of offshore wind turbine jacket structures using yaw stiffness and damping," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Wang, L. & Kolios, A. & Liu, X. & Venetsanos, D. & Rui, C., 2022. "Reliability of offshore wind turbine support structures: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    3. Song, Yupeng & Sun, Tao & Zhang, Zili, 2023. "Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions," Renewable Energy, Elsevier, vol. 212(C), pages 570-588.
    4. Clemens Hübler & Wout Weijtjens & Cristian G. Gebhardt & Raimund Rolfes & Christof Devriendt, 2019. "Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design," Energies, MDPI, vol. 12(4), pages 1-20, February.
    5. Tsung-Yueh Lin & Yi-Qing Zhao & Hsin-Haou Huang, 2020. "Representative Environmental Condition for Fatigue Analysis of Offshore Jacket Substructure," Energies, MDPI, vol. 13(20), pages 1-20, October.

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