SEM-REV offshore energy site wind-wave bivariate statistics by hindcast

Gaidai, Oleg; Xu, Xiaosen; Wang, Junlei; Ye, Renchuan; Cheng, Yong; Karpa, Oleh

doi:10.1016/j.renene.2020.04.113

Cited by 17 publications

(16 citation statements)

References 25 publications

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“…Where 1{A} 1 if A is true, while it is 0 if not. As shown in (Wang, 2001;Bak et al, 2013;Naess and Karpa, 2015a;Naess and Karpa, 2015b;Gaidai et al, 2016;Jian et al, 2018;Gaidai et al, 2019a;Gaidai et al, 2019b;Xu et al, 2019b;Gaidai et al, 2020;Xu et al, 2021)…”

Section: Acer Methodsmentioning

confidence: 88%

“…As the order k increases, the accuracy of Eqn. 7 improves and ACER k (η) functions converge conveniently fast with growing k, see (Gaidai et al, 2020)- (Xu et al, 2019b). Note that by increasing the conditioning level k, possible data clustering effects, e.g.…”

Section: Acer Methodsmentioning

confidence: 99%

“…Where εk (η, m) is the same function as in Eqn. 5 but restricted to a specific sea state with number m. As shown in (Gaidai et al, 2020)- (Xu et al, 2019b), the long-term extreme value distribution of M(T), can then be expressed as follows based on the ACER function of order k:…”

Section: Acer Methodsmentioning

confidence: 99%

“…Weight function ω is defined as ω(η) {lnC + (η) − lnC − (η)} −2 with (C − (η), C + (η)) being a 95% CI, empirically estimated from the measured data. Detailed procedure for further optimising parameters a k , b k , c k , d k has been outlined in (Gaidai et al, 2020)- (Xu et al, 2019b).…”

Section: Acer Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Improving Extreme Anchor Tension Prediction of a 10-MW Floating Semi-Submersible Type Wind Turbine, Using Highly Correlated Surge Motion Record

et al. 2022

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Extreme value prediction of the load-effect responses of complex offshore structures such as the floating wind turbine (FWT) is crucial in ultimate limit state (ULS) design. This paper considers two cases to understand the feasibility of the bivariate correction on the extreme load and motion responses of a 10-MW semi-submersible type FWT. The empirical anchor tension force and surge motion used in this study are obtained from the FAST simulation tool (developed by the National Renewable Energy Laboratory) with the load cases stimulated at under-rated, rated and above rated speeds. Then, the bivariate correction method is applied to model FWT extreme response for a 5-years return period prediction with a 95% confidence interval (CI), based on just 2 min short response record. The proposed methodology permits accurate correction of the bivariate extreme value in case of, for example, corrupted measurement sensor data. Based on the proposed novel method’s performance, it is concluded that the bivariate correction method can offer better robust and precise bivariate predictions of coupled surge motion and anchor tension of the FWT.

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Section: Acer Methodsmentioning

confidence: 88%

Section: Acer Methodsmentioning

confidence: 99%

Section: Acer Methodsmentioning

confidence: 99%

Section: Acer Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Improving Extreme Anchor Tension Prediction of a 10-MW Floating Semi-Submersible Type Wind Turbine, Using Highly Correlated Surge Motion Record

et al. 2022

Self Cite

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show abstract

“…In general, due to the random variables governing the dynamic system and the high degree of freedom, existing theoretical reliability methodologies make it difficult to determine realistic structural system reliability (Ditlevsen and Bjerager, 1986;Ditlevsen and Madsen, 1996;Melchers and Beck, 2018). In theory, either direct Monte Carlo simulation or sufficient measurements are used to accurately estimate the reliability of complex structures (Naess and Gaidai, 2008;Naess and Gaidai, 2009;Naess and Moan, 2013;Gaidai et al, 2018;Xu et al, 2019;Gaidai et al, 2020;Gaidai et al, 2021). However, the computational or experimental cost may be prohibitive for many complicated dynamic systems.…”

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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Energy harvester reliability study by Gaidai reliability method

Gaidai,

Sun,

Wang

2024

Climate Resilience

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This study validates a novel structural reliability method, particularly suitable for high‐dimensional green energy harvesting device dynamic systems, versus a well‐established bivariate statistical method, known to accurately predict two‐dimensional system extreme response contours. Classic reliability methods dealing with time series do not always have an advantage of dealing easily with dynamic system high dimensionality, along with complex cross‐correlations among different system components. Energy harvesters constitute an important part of modern offshore green energy engineering; hence, proper experimental study along with safety and reliability analysis are of practical design and engineering importance. To study the performance of galloping energy harvesters, a series of laboratory wind tunnel tests have been conducted, selecting different wind speeds. This study illustrates the usage of the advocated novel reliability method, by analyzing bivariate statistics of experimental galloping energy harvester's dynamics. The bivariate statistics was extracted from available experimental results, more specifically for the device's voltage‐force dataset. Advantage of the proposed methodology being that relatively short experimental data record may still yield meaningful design results, provided proper statistical methods have been applied. Safety and reliability are important engineering concerns for all kinds of green energy devices. In the case of measured device's structural response, an accurate prediction of system failure or damage probability is possible, as illustrated in this study. Distinctive advantage of advocated novel semi‐analytical reliability methodology being the fact that it can tackle dynamic systems with practically unlimited number of dimensions (or components), along with complex nonlinear cross‐correlations between different system key components.

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SEM-REV offshore energy site wind-wave bivariate statistics by hindcast

Cited by 17 publications

References 25 publications

Improving Extreme Anchor Tension Prediction of a 10-MW Floating Semi-Submersible Type Wind Turbine, Using Highly Correlated Surge Motion Record

Improving Extreme Anchor Tension Prediction of a 10-MW Floating Semi-Submersible Type Wind Turbine, Using Highly Correlated Surge Motion Record

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

Energy harvester reliability study by Gaidai reliability method

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