Dynamic Stall Estimation with Transition Networks

Estimating dynamic stall under in-flight conditions presents significant challenges and requires advanced aerodynamic prediction tools. This study focuses on enhancing dynamic stall estimation by predicting lift for a NACA0015 airfoil experiencing intermittent, highly separated flow conditions at a Reynolds number of 5.5x10⁵. We present a data-driven approach using surface pressure sensor data as input. The airfoil is pitched around a static stall angle of α_ss = 20° with an 8° pitching amplitude. Particle Image Velocimetry (PIV) provides detailed visualization of flow structures, aiding in the refinement of the prediction model. The research covers six experimental cases with reduced frequencies ranging from 0.025 to 0.15. A weighted-average transition network is applied to data from a varying number of surface pressure taps. This study investigates the role of clustering in distinguishing different flow states and how reducing the number of clusters affects dynamic stall lift estimation accuracy. It aims to balance effective state differentiation with cluster reduction. Additionally, it explores how improvements in data clustering and network node reduction impact overall accuracy, phase-averaged performance, and the ability to capture variations between cycles.