A short overview of composite materials for wind turbine applications is presented here. Requirements toward the wind turbine materials, loads, as well as available materials are reviewed. Apart from the traditional composites for wind turbine blades (glass fibers/epoxy matrix composites), natural composites, hybrid and nanoengineered composites are discussed. Manufacturing technologies for wind turbine composites, as well their testing and modelling approaches are reviewed.

Link back to DTU OrbitCitation (APA): Kim, T., Hansen, A. M., & Branner, K. (2013 ABSTRACTIn this paper a new anisotropic beam finite element for composite wind turbine blades is developed and implemented into the aeroelastic nonlinear multibody code, HAWC2, intended to be used to investigate if use of anisotropic material layups in wind turbine blades can be tailored for improved performance such as reduction of loads and/or increased power capture. The element stiffness and mass matrices are first derived based on pre-calculated anisotropic beam properties, and the beam element is subsequently put into a floating frame of reference to enable full rigid body displacement and rotation of the beam. This derivation provides the mass and stiffness properties and the fictitious forces needed for implementation into HAWC2. The implementation is subsequently validated by running three validation cases which all show good agreement with results obtained by other authors. Further, a parametric study is conducted in order to investigate if the given anisotropic effect of the composite blade, bend-twist coupling effect, is able to be examined by the developed beam element in a multibody system or not. Two different coupled examples of bend-twist coupling for the blade of a 5MW fictitious wind turbine are considered. The two cases differ in the amount of bend-twist coupling introduced into the blade so that they produce 0.3deg and 1deg twist at the blade tip (towards feather), respectively, for a 1m flapwise tip deflection towards the tower. It is examined if the current structural model is able to capture the anisotropic effects in a multibody system.

Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision.

The paper proposes a methodology for reliable design and maintenance of wind turbine rotor blades using a condition monitoring approach and a damage tolerance index coupling the material and structure. By improving the understanding of material properties that control damage propagation it will be possible to combine damage tolerant structural design, monitoring systems, inspection techniques and modelling to manage the life cycle of the structures. This will allow an efficient operation of the wind turbine in terms of load alleviation, limited maintenance and repair leading to a more effective exploitation of offshore wind.

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