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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Madsen, Steen Hjelm
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (3/3 displayed)
- 2021Fatigue testing of a 14.3 m composite blade embedded with artificial defects – damage growth and structural health monitoringcitations
- 2019Understanding progressive failure mechanisms of a wind turbine blade trailing edge section through subcomponent tests and nonlinear FE analysiscitations
- 2018Buckling and progressive failure of trailing edge subcomponent of wind turbine blade
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article
Fatigue testing of a 14.3 m composite blade embedded with artificial defects – damage growth and structural health monitoring
Abstract
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.