Development of Digital-Image-Correlation Technique for Detecting Internal Defects in Simulated Specimens of Wind Turbine Blades

Abstract

In the performance of a wind turbine system, the blades play a vital role. However, they are susceptible to damage arising from complex and irregular loading (which may even cause catastrophic collapse), and they are expensive to maintain. Therefore, it is very important both to find defects after blade manufacturing is completed and to find damage after the blade is used for a certain period of time. This study provides a new perspective for the detection of internal defects in glass-fiber-and carbon-fiber-reinforced panels, which are used as the main materials in wind turbine blades. A gap or fracture between fiber-reinforced materials, which may occur during blade manufacturing or operation, is simulated by drilling a hole 5 mm in diameter in the middle layer of the laminated material. Then, a digital-image-correlation (DIC) method is used to detect internal defects in the blade. Tensile load is applied to the fabricated specimen using a tensile tester, and the generated changes are recorded and analyzed with the DIC system. In the glass-fiber-reinforced laminated specimen, internal defects were detected from a strain value of 5% until the end of the experiment, while in the case of the carbon-fiber-reinforced laminated specimen, internal defects were detected from 1% onward. It was proved using the DIC system that the defect was detected as a certain level of strain difference developed around the internal defects, according to the material properties.