Articles | Volume 3, issue 1
https://doi.org/10.5194/wes-3-149-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-3-149-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Mar 2018
Research article |
| 26 Mar 2018
| 26 Mar 2018
Application of a Monte Carlo procedure for probabilistic fatigue design of floating offshore wind turbines
Kolja Müller
CORRESPONDING AUTHOR
Stuttgart Wind Energy at Institute of Aircraft Design, University of
Stuttgart, Stuttgart, Germany
Po Wen Cheng
Stuttgart Wind Energy at Institute of Aircraft Design, University of
Stuttgart, Stuttgart, Germany
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Cited
21 citations as recorded by crossref.
- Multi-parameter full probabilistic modeling of long-term joint wind-wave actions using multi-source data and applications to fatigue analysis of floating offshore wind turbines Y. Song et al. 10.1016/j.oceaneng.2022.110676
- Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling S. Guth et al. 10.1002/we.2880
- Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions Y. Song et al. 10.1016/j.renene.2023.05.070
- Reducing the number of load cases for fatigue damage assessment of offshore wind turbine support structures using a simple severity-based sampling method L. Stieng & M. Muskulus 10.5194/wes-3-805-2018
- An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks H. Gao et al. 10.1016/j.ress.2020.106819
- Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes D. Singh et al. 10.1088/1742-6596/2265/3/032070
- Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design C. Hübler et al. 10.3390/en12040603
- A review on recent risk assessment methodologies of offshore wind turbine foundations G. Hou et al. 10.1016/j.oceaneng.2022.112469
- Uncertainty models for the structural design of floating offshore wind turbines: A review M. Ramezani et al. 10.1016/j.rser.2023.113610
- From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases N. Dimitrov et al. 10.5194/wes-3-767-2018
- Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering L. Stieng & M. Muskulus 10.1002/we.2382
- Uncertainty in Wave Basin Testing of a Fixed Oscillating Water Column Wave Energy Converter F. Judge et al. 10.1115/1.4051164
- Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data R. Haghi et al. 10.3390/en17020346
- Load condition determination for efficient fatigue analysis of floating offshore wind turbines using a GF-discrepancy-based point selection method Y. Song et al. 10.1016/j.oceaneng.2023.114211
- Aero-structural design and optimization of 50 MW wind turbine with over 250-m blades S. Yao et al. 10.1177/0309524X211027355
- Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions X. Li & W. Zhang 10.1016/j.renene.2020.06.043
- Operational-based annual energy production uncertainty: are its components actually uncorrelated? N. Bodini & M. Optis 10.5194/wes-5-1435-2020
- Effective turbulence and its implications in wind turbine fatigue assessment R. Slot et al. 10.1002/we.2397
- Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling L. Stieng & M. Muskulus 10.5194/wes-5-171-2020
- On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model D. Liu et al. 10.1016/j.renene.2024.120238
- Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature E. Fekhari et al. 10.1017/dce.2023.27
21 citations as recorded by crossref.
- Multi-parameter full probabilistic modeling of long-term joint wind-wave actions using multi-source data and applications to fatigue analysis of floating offshore wind turbines Y. Song et al. 10.1016/j.oceaneng.2022.110676
- Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling S. Guth et al. 10.1002/we.2880
- Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions Y. Song et al. 10.1016/j.renene.2023.05.070
- Reducing the number of load cases for fatigue damage assessment of offshore wind turbine support structures using a simple severity-based sampling method L. Stieng & M. Muskulus 10.5194/wes-3-805-2018
- An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks H. Gao et al. 10.1016/j.ress.2020.106819
- Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes D. Singh et al. 10.1088/1742-6596/2265/3/032070
- Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design C. Hübler et al. 10.3390/en12040603
- A review on recent risk assessment methodologies of offshore wind turbine foundations G. Hou et al. 10.1016/j.oceaneng.2022.112469
- Uncertainty models for the structural design of floating offshore wind turbines: A review M. Ramezani et al. 10.1016/j.rser.2023.113610
- From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases N. Dimitrov et al. 10.5194/wes-3-767-2018
- Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering L. Stieng & M. Muskulus 10.1002/we.2382
- Uncertainty in Wave Basin Testing of a Fixed Oscillating Water Column Wave Energy Converter F. Judge et al. 10.1115/1.4051164
- Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data R. Haghi et al. 10.3390/en17020346
- Load condition determination for efficient fatigue analysis of floating offshore wind turbines using a GF-discrepancy-based point selection method Y. Song et al. 10.1016/j.oceaneng.2023.114211
- Aero-structural design and optimization of 50 MW wind turbine with over 250-m blades S. Yao et al. 10.1177/0309524X211027355
- Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions X. Li & W. Zhang 10.1016/j.renene.2020.06.043
- Operational-based annual energy production uncertainty: are its components actually uncorrelated? N. Bodini & M. Optis 10.5194/wes-5-1435-2020
- Effective turbulence and its implications in wind turbine fatigue assessment R. Slot et al. 10.1002/we.2397
- Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling L. Stieng & M. Muskulus 10.5194/wes-5-171-2020
- On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model D. Liu et al. 10.1016/j.renene.2024.120238
- Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature E. Fekhari et al. 10.1017/dce.2023.27
Latest update: 23 Apr 2024
Short summary
An efficient and accurate Monte Carlo approach is presented to assess the lifetime fatigue loading on a floating offshore wind turbine accurately. This is typically challenging in simulation effort due to the many different combinations of relevant environmental conditions which need to be considered. The applied method uses quasi-random Sobol sequences and shows promising performance with respect to convergence and accuracy.
An efficient and accurate Monte Carlo approach is presented to assess the lifetime fatigue...
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