Rapid Prediction of Ice Accretion on Swept Wings Based on Proper Orthogonal Decomposition and Surrogate Modelling

Numerical simulations of three-dimensional airfoil icing are computationally intensive, with icing complexities on swept wings surpassing those on straight wings. To enable rapid and accurate ice formation predictions on swept wings, this study proposes a prediction methodology integrating proper orthogonal decomposition (POD) and Kriging surrogate modelling. This approach incorporates key physical parameters influencing ice formation, including flight altitude, flight speed, ambient temperature, liquid water content, and median volume diameter. First, an optimized Latin hypercube sampling method (OLHS) was employed to generate 120 icing conditions under both continuous and intermittent maximum icing scenarios. Numerical simulations were then conducted to establish an icing dataset, which was subsequently transformed into one-dimensional ice height data for various two-dimensional airfoil sections. Next, surrogate models for two-dimensional airfoils were developed using POD and Kriging interpolation to establish relationships between meteorological and flight conditions and the corresponding icing shapes. Finally, three-dimensional ice geometries were reconstructed through uniform interpolation of multiple two-dimensional icing profiles. Validation results demonstrated a strong agreement between surrogate model predictions and numerical simulations, enabling rapid and accurate real-time ice shape estimations across various conditions. The predicted ice shape similarity exceeded 94% for rime ice and 89% for glaze ice. This methodology provides valuable insights for aircraft anti-icing and de-icing design while also contributing to the development of optimized ice-tolerant aerodynamic strategies.