Meta-models for fatigue damage estimation of offshore wind turbines jacket substructures

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Original languageEnglish
Pages (from-to)1158-1163
Number of pages6
JournalProcedia Engineering
Volume199
Publication statusPublished - 12 Sept 2017
Event10th International Conference on Structural Dynamics, EURODYN 2017 - Rome, Italy
Duration: 10 Sept 201713 Sept 2017

Abstract

One of the main structural components of offshore wind turbines is the substructure which bridges the gap between seabed and tower foot. One possible concept employed in intermediate water depths for turbines with high-rated power is the jacket. This structure is excited by several environmental impacts like wind and wave loads or centrifugal loads from the rotor motion. In order to reach competitive costs of energy, it is crucial to minimize the lifetime capital expenses by means of robust and reliability-based design. However, a simulation-based optimization approach on the full scale model requires high numerical capacity. In this work, the problem of numerically expensive fatigue life evaluation is addressed by the utilization of a meta-model approach. The performance of two meta-models solutions, namely Kriging and Interval Predictor Model, is compared. In particular, the different behavior of the probabilistic confidence intervals of the Kriging regression and the interval bounds of the IPM is discussed.

Keywords

    extreme loads, fatigue, imprecise probability, offshore wind turbine, reliability, robust optimization

ASJC Scopus subject areas

Cite this

Meta-models for fatigue damage estimation of offshore wind turbines jacket substructures. / Brandt, Sebastian; Broggi, Matteo; Hafele, Jan et al.
In: Procedia Engineering, Vol. 199, 12.09.2017, p. 1158-1163.

Research output: Contribution to journalConference articleResearchpeer review

Brandt, S, Broggi, M, Hafele, J, Guillermo Gebhardt, C, Rolfes, R & Beer, M 2017, 'Meta-models for fatigue damage estimation of offshore wind turbines jacket substructures', Procedia Engineering, vol. 199, pp. 1158-1163. https://doi.org/10.1016/j.proeng.2017.09.292
Brandt, S., Broggi, M., Hafele, J., Guillermo Gebhardt, C., Rolfes, R., & Beer, M. (2017). Meta-models for fatigue damage estimation of offshore wind turbines jacket substructures. Procedia Engineering, 199, 1158-1163. https://doi.org/10.1016/j.proeng.2017.09.292
Brandt S, Broggi M, Hafele J, Guillermo Gebhardt C, Rolfes R, Beer M. Meta-models for fatigue damage estimation of offshore wind turbines jacket substructures. Procedia Engineering. 2017 Sept 12;199:1158-1163. doi: 10.1016/j.proeng.2017.09.292
Brandt, Sebastian ; Broggi, Matteo ; Hafele, Jan et al. / Meta-models for fatigue damage estimation of offshore wind turbines jacket substructures. In: Procedia Engineering. 2017 ; Vol. 199. pp. 1158-1163.
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abstract = "One of the main structural components of offshore wind turbines is the substructure which bridges the gap between seabed and tower foot. One possible concept employed in intermediate water depths for turbines with high-rated power is the jacket. This structure is excited by several environmental impacts like wind and wave loads or centrifugal loads from the rotor motion. In order to reach competitive costs of energy, it is crucial to minimize the lifetime capital expenses by means of robust and reliability-based design. However, a simulation-based optimization approach on the full scale model requires high numerical capacity. In this work, the problem of numerically expensive fatigue life evaluation is addressed by the utilization of a meta-model approach. The performance of two meta-models solutions, namely Kriging and Interval Predictor Model, is compared. In particular, the different behavior of the probabilistic confidence intervals of the Kriging regression and the interval bounds of the IPM is discussed.",
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AU - Brandt, Sebastian

AU - Broggi, Matteo

AU - Hafele, Jan

AU - Guillermo Gebhardt, Cristian

AU - Rolfes, Raimund

AU - Beer, Michael

N1 - Funding information: This work was supported by the compute cluster which is funded by the Leibniz Universität Hannover, the Lower Saxony Ministry of Science and Culture (MWK), and the German Research Foundation (DFG). We gratefully acknowledge the financial support of the German Federal Ministry for Economic Affairs and Energy (research project Gigawind life, FKZ 0325575A) and the Lower Saxony Ministry of Science and Culture (research project ventus efficiens, FKZ ZN3024) that enabled this work.

PY - 2017/9/12

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N2 - One of the main structural components of offshore wind turbines is the substructure which bridges the gap between seabed and tower foot. One possible concept employed in intermediate water depths for turbines with high-rated power is the jacket. This structure is excited by several environmental impacts like wind and wave loads or centrifugal loads from the rotor motion. In order to reach competitive costs of energy, it is crucial to minimize the lifetime capital expenses by means of robust and reliability-based design. However, a simulation-based optimization approach on the full scale model requires high numerical capacity. In this work, the problem of numerically expensive fatigue life evaluation is addressed by the utilization of a meta-model approach. The performance of two meta-models solutions, namely Kriging and Interval Predictor Model, is compared. In particular, the different behavior of the probabilistic confidence intervals of the Kriging regression and the interval bounds of the IPM is discussed.

AB - One of the main structural components of offshore wind turbines is the substructure which bridges the gap between seabed and tower foot. One possible concept employed in intermediate water depths for turbines with high-rated power is the jacket. This structure is excited by several environmental impacts like wind and wave loads or centrifugal loads from the rotor motion. In order to reach competitive costs of energy, it is crucial to minimize the lifetime capital expenses by means of robust and reliability-based design. However, a simulation-based optimization approach on the full scale model requires high numerical capacity. In this work, the problem of numerically expensive fatigue life evaluation is addressed by the utilization of a meta-model approach. The performance of two meta-models solutions, namely Kriging and Interval Predictor Model, is compared. In particular, the different behavior of the probabilistic confidence intervals of the Kriging regression and the interval bounds of the IPM is discussed.

KW - extreme loads

KW - fatigue

KW - imprecise probability

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