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Keywords

Wind turbine blade, Modal analysis, Debonding, Machine learning, Optimization

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Abstract

The structural stiffness of a blade changes when it is defective, which affects its modal parameters. Consequently, modal parameters can be crucial to investigating defects.

Therefore, developing a model that can accurately analyze the modal parameters of healthy and defective blades is essential.

This study proposes a high-fidelity finite element (FE) model development method based on multiobjective optimization.

Three 10-kW-class wind turbine blades were manufactured, and the modal parameter changes due to debonding damage were measured.

The initial FE model was constructed using structural drawings and considering specific material properties.

Subsequently, the modal analysis results were compared with the experimental results.

The analysis errors of normal and abnormal blades were expressed as cost functions and multiobjective optimization was performed.

Machine learning–based predictive models facilitated time- and cost-effective optimization, enabling modal updating that simultaneously reflects modal parameter changes for various blade models.

Because accurately predicting the dynamic characteristics of blades by various defect locations and sizes is crucial for blade damage detection, the proposed method is significant for damage detection research.

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Turbomachinery, Industrial Equipment, Predictive Modeling, Energy

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