Fatigue analysis of floating wind turbine support structure applying modified stress transfer function by artificial neural network

Kim, Hyeonjin; Jang, Beom-Seon; Park, Chang-Kyu; Bae, Yoon Hyeok

doi:10.1016/j.oceaneng.2017.12.009

Cited by 24 publications

(12 citation statements)

References 5 publications

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“…Using importance sampling in practice requires some further refinement. If all we were interested in was the total fatigue damage at a single location of the structure, (13) or (14) would suffice. However, one often wants the damage in several locations along the height of the structure.…”

Section: Sampling Distribution For Multiple Locationsmentioning

confidence: 99%

“…The latter study was able to ensure a maximum error of about 10%. Kim et al used a frequency domain approach based on an artificial neural network to modify the stress transfer function. A more general study on selection of subsets of environmental conditions for offshore wind turbines using a maximum dissimilarity algorithm was conducted by Guanche et al Finally, Choe, Byon, and Chen used an extended type of importance sampling to estimate the reliability in a framework of stochastic simulation models.…”

Section: Introductionmentioning

confidence: 99%

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Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering

Stieng

Muskulus

2019

Wind Energy

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A complete fatigue assessment for operational conditions for offshore wind turbines involves simulating thousands of environmental states. For applications such as optimization, where this assessment needs to be repeated many times, that presents a significant computational problem. Here, we propose a novel way of reducing the number of simulated environmental states (load cases) while maintaining an acceptable accuracy. From one full fatigue analysis of a base design, the OC3 monopile (with the NREL 5MW turbine), the distribution of fatigue damage per load case can be used to estimate the lifetime fatigue damage of a range of modified designs. Using importance sampling and a specially adapted two‐stage filtering procedure, we obtain pseudo‐optimal sets of load cases from which the fatigue damage is estimated. This is applied to seven different designs that have been modified to emulate iterations of an optimization loop. For several of these designs, sampling less than 1% of all load cases can give damage estimates with median errors of less than 2%. Even for the most severe cases, using 3% of the environmental states yields a maximum error of 10%. While further refinement is possible, the method is considered viable for applications within design optimization and preliminary design.

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Section: Sampling Distribution For Multiple Locationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering

Stieng

Muskulus

2019

Wind Energy

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“…Something similar could be argued in terms of the methodology, that such a simple approach is only possible in the current setting, but we would still stress the overall simplicity as a major reason why this method would be useful. Especially the avoidance of more advanced statistical and computational procedures (like in Cheng, 2018, andKim et al, 2018) will likely make this approach more appealing for industrial applications. There is also little reliance on software, requiring only the ability to sort the fatigue data and then create sampling sets for which duplicate load cases have been removed.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

“…The approach achieved a maximum error of about 10 % in the fatigue estimates when using reduced load case sets of 200-500 out of a total of 5400. Finally, Kim et al (2018) used an artificial neural network to modify the stress transfer function in order to simplify fatigue assessment in the frequency domain.…”

mentioning

confidence: 99%

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

Stieng

Muskulus

2018

Wind Energ. Sci.

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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.

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“…Finally, Kim et al (2018) used an artificial neural network to modify the stress transfer function in order to simplify fatigue assessment in the frequency domain.…”

mentioning

confidence: 99%

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

Stieng¹,

Muskulus²

2018

Preprint

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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 5 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 10 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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Fatigue analysis of floating wind turbine support structure applying modified stress transfer function by artificial neural network

Cited by 24 publications

References 5 publications

Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering

Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering

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

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

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