Meandering evolution and width variation, a physics-statistical based modeling approach

Many models have been proposed to simulate and understand the long-term evolution of meandering rivers. These models analyze the hydraulics of the in-channel flow and the river bank movement (erosion \u2013 accretion) process in different ways, but some gap still remain, e.g. the stability of long-term simulations when width variations are accounted for. Here we proposed a physics-statistical based approach to simulate the river bank evolution, that erosion and deposition processes act independently, with a specific shear stress threshold for each of them. In addition, we link the width evolution with a parametric probability distribution (PPD) based on a mean characteristic channel width. We are thus able to obtaining stable long-term simulations with realistic and reasonable spatio-temporal distribution of the along channel width

Temporal and spatial characterisation of tidal blade load variation for structural fatigue testing

To achieve the full potential of tidal stream energy, developers are incentivised to use larger blades on tidal turbines. This requires validation of blade structural designs through full-scale blade fatigue tests to de-risk the engineering process. However, the loading scenarios encountered in testing facilities and those in reality could be significantly different, which induces errors in blade loads and fatigue damage. Here we characterise the unsteady tidal blade load variation through model-scale experiment. It was found that the standard deviations of thrust load range between 200% and 637% of condition without waves. This results in an increase of predicted fatigue damage between 6% and 18%. It was observed that the centre of effort shifts towards the blade root when encountering wave crests of opposing waves, which has not been reported in the literature to date. To reduce errors in fatigue test while the centre of effort is fixed, matching blade shear forces should be sacrificed to match target bending moment at the root. Matching blade shear forces leads to a reduction of predicted fatigue damage ranges from 17% to 25%, which can induce errors in fatigue testing. We anticipate our findings would facilitate the development of fatigue testing of tidal turbine blades

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