Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

Bruna Pavlack, Jessé Paixão, Samuel Da Silva, Americo Cunha, David García Cava

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

This study aims to investigate the performance of a data-driven methodology for quantifying damage based on the use of a metamodel obtained from the Polynomial Chaos-Kriging method. The investigation seeks to quantify the severity of the damage, described by a specific type of debonding in a wind turbine blade as a function of a damage index. The damage indexes used are computed using a data-driven vibration-based structural health monitoring methodology. The blade’s debonding damage is introduced artificially, and the blade is excited with an electromechanical actuator that introduces a mechanical impulse causing the impact on the blade. The acceleration responses’ vibrations are measured by accelerometers distributed along the trailing and the wind turbine blade. A metamodel is formerly obtained through the Polynomial Chaos-Kriging method based on the damage indexes, trained with the blade’s healthy condition and four damage conditions, and validated with the other two damage conditions. The Polynomial Chaos-Kriging manifests promising results for capturing the proper trend for the severity of the damage as a function of the damage index. This research complements the damage detection analyses previously performed on the same blade.
Original languageEnglish
Article number147592172110079
JournalStructural Health Monitoring
Early online date3 May 2021
Publication statusE-pub ahead of print - 3 May 2021


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