Bivariate statistics of floating offshore wind turbine dynamic response under operational conditions

Xu, Xiaosen; Wang, Fang; Gaidai, Oleg; Næss, Arvid; Xing, Yihan; Wang, Junlei

doi:10.1016/j.oceaneng.2022.111657

Cited by 35 publications

(26 citation statements)

References 28 publications

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“…l = 1.5 was arbitrarily picked as the threshold level. Gaidai, 2008;Naess and Gaidai, 2009;Naess and Moan, 2013;Gaidai et al, 2018;Gaidai et al, 2020;Xu et al, 2022). Probability can be estimated approximately as in the average conditional exceedance rate method.…”

Section: System Reliability Approachmentioning

confidence: 99%

A novel multi-dimensional reliability approach for floating wind turbines under power production conditions

Xing

Gaidai

et al. 2022

Front. Mar. Sci.

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Floating offshore wind turbines (FOWT) generate green renewable energy and are a vital part of the modern offshore wind energy industry. Robust predicting extreme offshore loads during FOWT operations is an important safety concern. Excessive structural bending moments may occur during certain sea conditions, posing an operational risk of structural damage. This paper uses the FAST code to analyze offshore wind turbine structural loads due to environmental loads acting on a specific FOWT under actual local environmental conditions. The work proposes a unique Gaidai-Fu-Xing structural reliability approach that is probably best suited for multi-dimensional structural responses that have been simulated or measured over a long period to produce relatively large ergodic time series. In the context of numerical simulation, unlike existing reliability approaches, the novel methodology does not need to re-start simulation again each time the system fails. As shown in this work, an accurate forecast of the probability of system failure can be made using measured structural response. Furthermore, traditional reliability techniques cannot effectively deal with large dimensionality systems and cross-correction across multiple dimensions. The paper aims to establish a state-of-the-art method for extracting essential information concerning extreme responses of the FOWT through simulated time-history data. Three key components of structural loads are analyzed, including the blade-root out-of-plane bending moment, tower fore-aft bending moment, and mooring line tension. The approach suggested in this study allows predicting failure probability efficiently for a non-linear multi-dimensional dynamic system as a whole.

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Section: System Reliability Approachmentioning

confidence: 99%

A novel multi-dimensional reliability approach for floating wind turbines under power production conditions

Xing

Gaidai

et al. 2022

Front. Mar. Sci.

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“…. , η N ) is now introduced with each component η j being either η X , η Y or η Z …, depending on which of X(t) or Y (t) , or Z(t) … corresponds to the current unidimensional response component maxima with the running index j , see [12][13][14][15][16] .…”

Section: P X Maxmentioning

confidence: 99%

Novel methods for wind speeds prediction across multiple locations

Gaidai

Yan

et al. 2022

Sci Rep

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This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.

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“…This study outlines a general-purpose, robust and straightforward multidimensional bio-system reliability method. The method introduced in this study has been previously successfully validated by application to a wide range of engineering models, 30,[40][41][42][43][44][45][46][47][48][49] but only for one and two-dimensional system responses and, in general, very accurate predictions were obtained. Both measured and simulated time series responses can be analysed using the proposed method.…”

Section: Use Casementioning

confidence: 99%

A novel statistical method for long-term coronavirus modelling

et al. 2022

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Background: Novel coronavirus disease has been recently a concern for worldwide public health. To determine epidemic rate probability at any time in any region of interest, one needs efficient bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the multi-dimensionality advantage, that suggested methodology offers, namely dealing efficiently with multiple regions at the same time and accounting for cross-correlations between different regional observations. Methods: Modern multi-dimensional novel statistical method was directly applied to raw clinical data, able to deal with territorial mapping. Novel reliability method based on statistical extreme value theory has been suggested to deal with challenging epidemic forecast. Authors used MATLAB optimization software. Results: This paper described a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. Namely, accurate maximum recorded patient numbers are predicted for the years to come for the analyzed provinces. Conclusions: The suggested method performed well by supplying not only an estimate but 95% confidence interval as well. Note that suggested methodology is not limited to any specific epidemics or any specific terrain, namely its truly general. The only assumption and limitation is bio-system stationarity, alternatively trend analysis should be performed first. The suggested methodology can be used in various public health applications, based on their clinical survey data.

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Bivariate statistics of floating offshore wind turbine dynamic response under operational conditions

Cited by 35 publications

References 28 publications

A novel multi-dimensional reliability approach for floating wind turbines under power production conditions

A novel multi-dimensional reliability approach for floating wind turbines under power production conditions

Novel methods for wind speeds prediction across multiple locations

A novel statistical method for long-term coronavirus modelling

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