@article {Maack:2018:0736-2935:93,
title = "Stochastic vibration analysis in wind turbines",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2018",
volume = "257",
number = "1",
publication date ="2018-12-01T00:00:00",
pages = "93-101",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2018/00000257/00000001/art00010",
keyword = "random vibration, wind turbine, uncertain parameter, stochastic FEM",
author = "Maack, Friedrich-Carl and Sepahvand, Kheirollah and Marburg, Steffen",
abstract = "The reliability of wind turbines is affected by randomness in the turbulent flow and uncertainty in parameters. Considering these, the objective of this paper is to present the application of stochastic FEM to a full-scale wind turbine under random wind loads. The turbulent wind fields
are simulated by the data-driven temporal spatial decomposition (TSD) method, which represents the original wind data by a low-dimensionality model. Even though only a few random variables are employed to encode for the wind turbulence, results show that the synthetic TSD representations are
consistent with the original data. The turbulent wind field generates a random drag load on the wind turbine; the resulting random displacement of the rotor is studied using non-intrusive stochastic method. The major advantage of the approach is that it is independent from the considered system
and can therefore be implemented for any computational model, regardless of its complexity. The results of the stochastic analysis are illustrated through the response statistics of the rotor tip with maximum displacement. To validate the method and evaluate its accuracy, the statistics are
compared against benchmark results from the Monte Carlo method and show very good accordance at a significantly lower computational cost.",
}