Bayesian Flexural Rigidity Identification of Small Wind Turbine Blades Using an Unscented Kalman Filter

This study presents a comprehensive approach to estimating the flexural rigidity and mechanical behavior of small wind turbine blades through the application of an Unscented Kalman Filter (UKF), making significant contributions to the fields of structural mechanics and material identification. The methodology is based on Euler-Bernoulli beam theory and utilizes the Galerkin-Ritz technique for discretization. Focusing on in-plane bending vibrations while omitting axial vibration, this approach estimates the material properties of interest. To validate the proposed method, both simulation studies and experimental tests are conducted. The simulations involve a uniform cantilever beam, and lab-scale experimental tests are carried out on a non-rotating small-scale wind turbine blade. Experimental modal analysis is used to extract the blade’s modal properties, and a finite element model (FEM) is constructed using the material properties estimated by the UKF. A comparison between the FEM results and experimental data demonstrates the effectiveness and accuracy of the adapted UKF in real-world applications. This integrated experimental-numerical framework not only verifies the UKF's performance but also offers valuable insights for material identification and structural assessment, applicable to both small and large-scale wind turbines. These findings could lead to significant advancements in wind turbine blade optimization for enhanced sustainability and performance.