Machine Learning Based Approaches for Estimating Rotor Airloads in Hover - Lee, H.; Mortimer, P.; Seshadri, P.; Sirohi, J.; Rauleder, J. (My University, 2023)
The accurate measurement of rotor blade airloads often goes hand-in-hand with the accuracy of validation in numerical models, but obtaining the required data via direct installation of sensors is challenging. This study proposes new machine learning based methodologies to estimate the airloads, specifically thrust, of a two-bladed rotor in hover using non-contact measurements: flow field measurements from Particle Image Velocimetry (PIV) and blade deformation measurements from Digital Image Correlation (DIC). The flow field based approach used two-dimensional, two-component (2D- 2C) PIV measurements to train a Gaussian Process (GP) spatial model. The model was then used to construct a higher-fidelity flow field from which thrust was predicted with momentum conservation principles. The noise removal and filling in any missing vectors by the GP-enhanced flow field yielded accuracy improvements in thrust estimation, from -22.71% to -10.54% percentage difference from the measured thrust in the worst case, and -35.52% to 1.89% in the best case. The blade deformation based approach used the Complexity Pursuit algorithm to extract the first two flap bending modes of the rotor blades from DIC measurements at 0? collective with ambient excitation. The extracted modal information was used to estimate the rotor thrust at two test conditions, a steady 9? collective and a 2? step increase in collective. In both cases, the estimated thrust was within 5% of the measured value.
Keywords: Dynamics