Wind turbine rotor blade monitoring using digital image correlation: a comparison to aeroelastic simulations of a multi-megawatt wind turbine

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Winstroth, J.; Schoen, L.; Ernst, B.; Seume, J. R.: Wind turbine rotor blade monitoring using digital image correlation: a comparison to aeroelastic simulations of a multi-megawatt wind turbine. In: Journal of Physics Conference Series 524 (2014), 12064. DOI: http://dx.doi.org/10.1088/1742-6596/524/1/012064

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/414

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Sum total of downloads: 480




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Abstract: 
Optical full-field measurement methods such as Digital Image Correlation (DIC) provide a new opportunity for measuring deformations and vibrations with high spatial and temporal resolution. However, application to full-scale wind turbines is not trivial. Elaborate preparation of the experiment is vital and sophisticated post processing of the DIC results essential. In the present study, a rotor blade of a 3.2 MW wind turbine is equipped with a random black-and-white dot pattern at four different radial positions. Two cameras are located in front of the wind turbine and the response of the rotor blade is monitored using DIC for different turbine operations. In addition, a Light Detection and Ranging (LiDAR) system is used in order to measure the wind conditions. Wind fields are created based on the LiDAR measurements and used to perform aeroelastic simulations of the wind turbine by means of advanced multibody codes. The results from the optical DIC system appear plausible when checked against common and expected results. In addition, the comparison of relative out-of-plane blade deflections shows good agreement between DIC results and aeroelastic simulations.
License of this version: CC BY 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2014
Appears in Collections:Fakultät für Maschinenbau

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 356 74.17%
2 image of flag of China China 41 8.54%
3 image of flag of United States United States 27 5.62%
4 image of flag of Japan Japan 4 0.83%
5 image of flag of Brazil Brazil 4 0.83%
6 image of flag of Taiwan Taiwan 3 0.62%
7 image of flag of Europe Europe 3 0.62%
8 image of flag of Canada Canada 3 0.62%
9 image of flag of United Kingdom United Kingdom 2 0.42%
10 image of flag of Spain Spain 2 0.42%
    other countries 35 7.29%

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