Add2ReliaBlade

• Cracks can develop in rotor blades when wind turbines are in operation. Repairing them is both time-consuming and expensive.
• The ReliaBlade research project is investigating the extent to which defects in production are responsible for the damage.
• The Add2Reliablade project addition is using these data to improve and validate existing calculation and simulation methods.
• In a subproject, Fraunhofer IWES, in cooperation with Fraunhofer EZRT, is among other things producing high-resolution computed tomography (CT) images to render defects from production visible and to identify the progression of damage.

Despite many years of experience in the development, design, and testing of rotor blades, cracks can develop in these important components when wind turbines are in operation, leading to costly repairs and major yield losses.

One cause of these cracks can be found in the largely manual production of the rotor blades, where inaccuracies and defects in the laminate and bonding occur. The influences of these defects on the service life of rotor blades have yet to be analyzed in full. They are therefore designed with appropriate safety factors, but these have not been able to prevent the damage completely so far and also render the blades heavier and more expensive.

The German-Danish research project ReliaBlade, which has been running since 2018, has set itself the task of closing these knowledge gaps. The Add2ReliaBlade project addition is utilizing data available from ReliaBlade to contribute to a further increase in the structural reliability of rotor blades. The project partners want to achieve this goal by improving and validating existing calculation and simulation methods.

In a subproject, Fraunhofer IWES is responsible among other things for complementing the ReliaBlade results with high-resolution CT images, which reveal possible production failures in the microstructure and also identify the progression of damage during the load history.

To make the very extensive information accessible as intuitively as possible, IWES is working on methods from the field of big data. This includes the creation of a database architecture including evaluation routines and knowledge graphs for the linkage of content and targeted provision of data and their interrelationships via an ontology.