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

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.

Automated summary

This study investigates the performance of a data-driven methodology for quantifying damage using a metamodel obtained from the Polynomial Chaos-Kriging method. The severity of debonding damage in a wind turbine blade is quantified as a function of a damage index. The metamodel is validated with different damage conditions and shows promising results in capturing the trend of damage severity.