Supplementary material to "Reducing the number of load cases for fatigue damage assessment of offshore wind turbine support structures by a simple severity-based sampling method"

2018 ◽  
Author(s):  
Lars Einar S. Stieng ◽  
Michael Muskulus
Keyword(s):  
Wind Turbine ◽  
Fatigue Damage ◽  
Damage Assessment ◽  
Sampling Method ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Support Structures ◽  
Supplementary Material

A Fast and Practical Method for Predicting the Fatigue Life of Offshore Wind Turbine Jacket Support Structures

2017 ◽  
Author(s):  
Chaoshuai Han ◽  
Yongliang Ma ◽  
Xianqiang Qu ◽  
Peijiang Qin ◽  
Binbin Qiu
Keyword(s):  
Fatigue Life ◽  
Wind Turbine ◽  
Fatigue Damage ◽  
Time Domain ◽  
Practical Method ◽  
Computational Effort ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Wave Loads ◽  
Support Structures

Fatigue assessment is a very important part in the design process of offshore wind turbine support structures subjected to wind and wave loads. Fully coupled time domain simulations due to wind and wave loads can potentially provide reliable fatigue predictions, however, it will take high computational effort to carry out fatigue analysis of the simultaneous wind and wave response of the support structure in time domain. For convenience and reducing computational efforts, a fast and practical method is proposed for predicting the fatigue life of offshore wind turbine jacket support structures. Wind induced fatigue is calculated in the time domain using ANSYS based on rainflow counting, and wave induced fatigue is computed in frequency domain using SACS based on a linear spectral analysis. Fatigue damage of X-joints and K-joints under combined environmental loads of wind and wave is estimated by using the proposed method. To verify the accuracy of the proposed formula, fatigue damage based on time domain rainflow cycle counting is calculated and can be considered as a reference. It is concluded that the proposed method provides reasonable fatigue damage predictions and can be adopted for evaluating the combined fatigue damage due to wind and wave loads in offshore wind turbine.

scholarly journals Reducing the number of load cases for fatigue damage assessment of offshore wind turbine support structures using a simple severity-based sampling method

2018 ◽  
Vol 3 (2) ◽  
pp. 805-818 ◽  
Author(s):  
Lars Einar S. Stieng ◽  
Michael Muskulus
Keyword(s):  
Wind Turbine ◽  
Design Optimization ◽  
Fatigue Damage ◽  
Simple Procedure ◽  
Computational Effort ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Support Structures ◽  
Fatigue Assessment ◽  
Operational Conditions

Abstract. The large amount of computational effort required for a full fatigue assessment of offshore wind turbine support structures under operational conditions can make these analyses prohibitive, especially for applications like design optimization, for which the analysis would have to be repeated for each iteration of the process. To combat this issue, we present a simple procedure for reducing the number of load cases required for an accurate fatigue assessment. After training on one full fatigue analysis of a base design, the method can be applied to establish a deterministic, reduced sampling set to be used for a family of related designs. The method is based on sorting the load cases by their severity, measured as the product of fatigue damage and probability of occurrence, and then calculating the relative error resulting from using only the most severe load cases to estimate the total fatigue damage. By assuming this error to be approximately constant, one can then estimate the fatigue damage of other designs using just these load cases. The method yields a maximum error of about 6 % when using around 30 load cases (out of 3647) and, for most cases, errors of less than 1 %–2 % can be expected for sample sizes in the range 15–60. One of the main points in favor of the method is its simplicity when compared to more advanced sampling-based approaches. Though there are possibilities for further improvements, the presented version of the method can be used without further modifications and is especially useful for design optimization and preliminary design. We end the paper by noting some possibilities for future work that extend or improve upon the method.

scholarly journals 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 ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3112 ◽  
Author(s):  
Chi-Yu Chian ◽  
Yi-Qing Zhao ◽  
Tsung-Yueh Lin ◽  
Bryan Nelson ◽  
Hsin-Haou Huang
Keyword(s):  
Monte Carlo ◽  
Wind Turbine ◽  
Fatigue Damage ◽  
Sampling Method ◽  
Sampling Methods ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Statistical Uncertainty ◽  
High Convergence Rate ◽  
Grid Based

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.

scholarly journals Reducing the number of load cases for fatigue damage assessment of offshore wind turbine support structures by a simple severity-based sampling method

2018 ◽  
Author(s):  
Lars Einar S. Stieng ◽  
Michael Muskulus
Keyword(s):  
Wind Turbine ◽  
Design Optimization ◽  
Fatigue Damage ◽  
Simple Procedure ◽  
Computational Effort ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Support Structures ◽  
Fatigue Assessment ◽  
Operational Conditions

Abstract. The large amount of computational effort required for a full fatigue assessment of offshore wind turbine support structures under operational conditions can make these analyses prohibitive. Especially for applications like design optimization, where the analysis would have to be repeated for each iteration of the process. To combat this issue, we present a simple procedure for reducing the number of load cases required for an accurate fatigue assessment. After training on one full fatigue analysis of a base design, the method can be applied to establish a deterministic, reduced sampling set to be used for a family of related designs. The method is based on sorting the load cases by their severity, measured as the product of fatigue damage and probability of occurrence, and then calculating the relative error resulting from using only the most severe load cases to estimate the total fatigue damage. By assuming this error to be approximately constant, one can then estimate the fatigue damage of other designs using just these load cases. The method yields a maximum error of about 6 % when using around 30 load cases (out of 3647) and, for most cases, errors of less than 1–2 % can be expected for sample sizes in the range 15–60. One of the main points in favor of the method is its simplicity when compared to more advanced sampling-based approaches. The method as is can be used without further modifications and is especially useful for design optimization and preliminary design. We end the paper by noting a few possibilities for future work that extend or improve upon the method.

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