A full-scale composite tidal blade fatigue test using single and multiple actuators

In order to perform fatigue testing on tidal turbine blades, it is necessary to apply cyclic loads that do not match the blade's natural frequency. This is due to the high stiffness of the blades and the thermal challenges associated with testing composite materials at frequencies typically around 18–20 Hz. To overcome this challenge, auxiliary systems are used to load the blades. However, conventional hydraulic systems commonly used for this purpose are known to be energy-intensive and inefficient. In this work, we present results obtained at the FastBlade fatigue testing facility, which utilises a regenerative digital displacement hydraulic pump system to address these issues. This innovative system has proven to be highly efficient, resulting in up to 75% energy savings compared to standard hydraulic systems. To perform these tests, we first performed a series of Reynolds-Averaged Navier–Stokes (RANS) simulations using on-site water velocity data to determine equivalent target hydrodynamic loads. These target loads are applied to the blades using initially a single contact point and, later, three load contact points. The FastBlade facility showcases an effective approach to fatigue testing during these tests. Throughout the testing process, comprehensive measurements are taken to evaluate the response of the blades and the FastBlade test structure itself. These measurements provide valuable insights into the mechanical behaviour of the blades when a single or multi-actuator setup is used to match the root bending moment and contribute to the refinement of testing practices. Notably, the blades successfully endured the equivalent of 20 years of tides in an accelerated fatigue loading test without experiencing catastrophic failure. The data obtained from these tests will enable the identification of improvements in testing procedures, including control strategies, load introduction methods, instrumentation layout, instrument calibration, and test design. This knowledge will lead to enhanced performance and reliability of the FastBlade facility, further advancing the field of tidal turbine blade testing

Using Audio-Data for Anomaly Detection in the Fatigue Test of a Composite Tidal Turbine Blade

FastBlade is a research facility for testing large-scale composite and metal structures. Fatigue tests run on tidal turbine blades measure the mechanical response of a blade subject to the number of loading cycles that mimic the ones it will experience over its lifetime of a subsea deployment. To maximise its throughput by running the facility uninterruptedly, unmanned operation of the site should be possible. One of its key enablers is anomaly detection. Microphones are used as a non-specific and affordable sensing method. Using the audio data, we applied a Fast Continuous Wavelet Transform to extract the patterns recorded during normal and abnormal operations. These outputs are used to train a neural network autoencoder (NNA). The original image is reconstructed from the compressed vector in the latent space (LS) of the NNA, and the loss is computed to detect and quantify anomalies. The study's findings demonstrate the success of using audio data to detect short-lived anomalies despite limited information about the critical assets in the set-up and can be easily extrapolated to other systems