Abstract
Abstract This work presents the effect of the high curvature to thickness ratio on the characteristics of Lamb Waves propagating over the skins of composite structures. It is accessed how the curvature on composite skins affects the group velocity of the symmetrical (S0) and asymmetrical (A0) wave modes. It is also accessed the gradient of curvature effect, when the wave propagates from a flat to a curved part on the skin. The results are intended to be used for the improvement of structural health monitoring of wings and wind turbine blades. This is accomplished through dynamic explicit linear finite element method simulations of plates with flat and curved parts made of Eglass-epoxy bidirectional laminate. As the skin structures are often designed to withstand torsional loads, the fibers are aligned with 45 degrees from the leading edge (curved region) throughout the analysis. Several skins with different curvature to thickness ratios are generated and simulated. Results and trends are presented and can be used to improve the algorithms for damage detection on those structures. It is shown that the group velocity of the incoming waves change considerably with the presence of curvature, for both main modes of vibration.
Highlights
The end of the 20th century and the beginning of the 21st has seen an exponential growth in composite structures usage
The present work showed the changes in the group velocity of the main Lamb wave modes at and near the bend for a glass fiber/epoxy bi-directional laminate
It was assessed a laminate with a 90 degrees bend using time domain finite element analysis for many values of leading edge radii
Summary
The end of the 20th century and the beginning of the 21st has seen an exponential growth in composite structures usage. The aerospace and marine industries dominated the former era On the latter era, the wind energy industry increased a lot the demand for composite materials and is the main user (Ellabban et al, 2014). On its roadmap that set the framework for the cost reduction and future increase in wind energy usage, the International Energy Agency stated that operation and maintenance costs (O&M) need to reduce drastically (IEA, 2013). That poses additional difficulties and costs, especially for offshore wind turbines, where the access for workers is extremely complex and depends on good weather. Because of those difficulties, among others, the IEA suggested the usage of real time predictive maintenance
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