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Experimental updating of a segmented wind turbine blade numerical model using the substructure method
During the development of the wind turbine system, blades are considered as the most critical components. The blades’ dynamic characteristics must be investigated during the design process to improve their mechanical performance. This work presents an experimental updating of a segmented wind turbin...
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Published in: | Journal of strain analysis for engineering design 2021-02, Vol.56 (2), p.67-75 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | During the development of the wind turbine system, blades are considered as the most critical components. The blades’ dynamic characteristics must be investigated during the design process to improve their mechanical performance. This work presents an experimental updating of a segmented wind turbine blade numerical model. For this purpose, the blade segments were manufactured using the fused deposition 3D printing technology and assembled using a threaded spar and nut. For different values of the segments assembly load, the natural frequencies and damping ratios were identified using the eigensystem realization algorithm method. The dynamic behavior of the segmented blade was examined numerically using the three-node shell element, taking into consideration the additional stiffness generated by the applied assembly load. In this study, numerical and experimental modal analysis were used to identify the first five eigenfrequencies of the blade. To update the numerical model parameters, through the identified experimental results, an iterative method was developed based on the Craig–Bampton substructure approach. Results show that the blade natural frequencies are proportional to the segments assembly load. The application of the proposed method on the segmented blade numerical model updating shows that the present method is computationally efficient. |
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ISSN: | 0309-3247 2041-3130 |
DOI: | 10.1177/0309324720932786 |