Effects of defects in composite wind turbine blades – Part 2: Progressive damage modeling of fiberglass-reinforced epoxy composites with manufacturing-induced waves

Abstract

Abstract. Composite wind turbine blades are typically reliable; however, premature failures are often in regions of manufacturing defects. While the use of damage modeling has increased with improved computational capabilities, they are often performed for worst-case scenarios in which damage or defects are replaced with notches or holes. To better understand and predict these effects, an effects-of-defects study has been undertaken. As a portion of this study, various progressive damage modeling approaches were investigated to determine if proven modeling capabilities could be adapted to predict damage progression of composite laminates with typical manufacturing flaws commonly found in wind turbine blades. Models were constructed to match the coupons from, and compare the results to, the characterization and material testing study presented as a companion. Modeling methods were chosen from established methodologies and included continuum damage models (linear elastic with Hashin failure criteria, user-defined failure criteria, nonlinear shear criteria), a discrete damage model (cohesive elements), and a combined damage model (nonlinear shear with cohesive elements). A systematic, combined qualitative–quantitative approach was used to compare consistency, accuracy, and predictive capability for each model to responses found experimentally. Results indicated that the Hashin and combined models were best able to predict material response to be within 10 % of the strain at peak stress and within 10 % of the peak stress. In both cases, the correlation was not as accurate as the wave shapes were changed in the model; correlation was still within 20 % in many cases. The other modeling approaches did not correlate well within the comparative framework. Overall, the results indicate that this combined approach may provide insight into blade performance with known defects when used in conjunction with a probabilistic flaw framework.