Efficient Reconstruction of High-Resolution Tidal Turbine Blade Deflection and Strain Maps Through Sensing Location Optimisation

During fatigue tests of tidal turbine blades, digital image correlation (DIC) is used to collect vital information about the specimen. DIC provides high-resolution displacement and strain maps of selected blade sections; however, continuous operation is hindered by the need to acquire, transfer, and process large volumes of high-resolution images, precluding real-time use during long tests. We address this problem by optimising sparse sensing locations on the blade surface so that full-field maps can be accurately reconstructed from a small subset of pixel measurements. In contrast to most DIC improvements found in the literature, which focus on accelerating the processing stage, this approach circumvents the need to collect high-resolution data. We evaluate this approach in a case study at FastBlade, a dedicated testing facility for tidal turbine blades. With less than 1% of the original pixels measured, the mean relative error evaluated on the dataset is 0.4% and 16% for displacement and strain maps, respectively, with the larger strain error reflecting the higher spatial complexity of strain fields. The optimised layouts outperform random and grid-like arrangements. The framework enables real-time monitoring and, subject to relevant validation, might be applied to reconstruct high-resolution strain maps directly from strain-gauge readings, potentially extending to in-ocean blade monitoring. Given the high accuracy of deflection reconstructions, using them to derive strain fields is suggested as a direction for further study.