Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Structural Health Monitoring Année : 2022

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

Résumé

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 (PC-Kriging). 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 Structural Health Monitoring (VSHM) 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 PC-Kriging method based on the damage indexes, trained with the blade's healthy condition and four damage conditions, and tested with the other two damage conditions. The PC-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 analyzes previously performed on the same blade.
Fichier principal
Vignette du fichier
SHM-20-0532.pdf (3.73 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03225764 , version 1 (13-05-2021)

Identifiants

Citer

Bruna Pavlack, Jessé Paixão, Samuel da Silva, Americo Cunha Jr, David García Cava. Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades. Structural Health Monitoring, 2022, 21, pp.666-682. ⟨10.1177/14759217211007956⟩. ⟨hal-03225764⟩
20 Consultations
47 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More