Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 2
Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-623-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-623-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 7, issue 2
Research article
 | 
16 Mar 2022
Research article |  | 16 Mar 2022

Model updating of a wind turbine blade finite element Timoshenko beam model with invertible neural networks

Pablo Noever-Castelos, David Melcher, and Claudio Balzani

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Short summary
In the wind energy industry, a digital twin is fast becoming a key instrument for the monitoring of a wind turbine blade's life cycle. Here, our introduced model updating with invertible neural networks provides an efficient and powerful technique to represent the real blade as built. This method is applied to a full finite element Timoshenko beam model of a blade to successfully update material and layup parameters. The advantage over state-of-the-art methods is the established inverse model.
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