4.7 Article

Fatigue testing of a 14.3 m composite blade embedded with artificial defects - Damage growth and structural health monitoring

Journal

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.compositesa.2020.106189

Keywords

Wind turbine blade; Delamination; Fatigue test; Damage detection; Structural health monitoring; Defect

Funding

  1. DARWIN Project (Drone Application for pioneering Reporting in Wind turbine blade INspections) from Innovation Fund Denmark [6151-00020B]
  2. Danish Energy Technology Development and Demonstration Program (EUDP) [64016-0023]

Ask authors/readers for more resources

This study presents a comprehensive experimental investigation into fatigue damage growth in composite wind turbine blades, demonstrating the effectiveness of various techniques including Infrared Thermography, Digital Image Correlation, and Acoustic Emission in detecting and monitoring damages. New experimental observations highlight the necessity and complexity of reliable modeling of nonlinear structural behavior on a large scale for predicting local fatigue crack growth.
Understanding fatigue damage growth of composite wind turbine blades is an essential step towards reliable structural health monitoring (SHM) and accurate lifetime prediction. This study presents a comprehensive experimental investigation into damage growth within a full-scale composite wind turbine blade under fatigue loading. The blade has artificial defects embedded to initiate damage growth. The damages are detected and monitored using Infrared (IR) thermography, Digital Image Correlation (DIC), and Acoustic Emission (AE). Steady damage growth and imminent structural failure are identified, demonstrating the effectiveness of these techniques to detect subsurface damages. New experimental observations include cyclic buckling of a trailing edge region and tapping and rubbing between the shear web and spar cap, both damages due to adhesive joint debonds. These observations highlight the necessity and the complexity of reliable modeling of nonlinear structural behavior on a large scale in order to predict local fatigue crack growth.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available