TY  - JOUR
AU  - Xia, Yuhao
AU  - Li, Tingyu
AU  - Yue, Jing
AU  - Peng, Bo
AU  - Yi, Xian
PY  - 2026
TI  - Rapid prediction of water droplet collection coefficients on 3D spheres using POD and KAN
JO  - Acta Aerodynamica Sinica
SN  - 0258-1825
SP  - 66
EP  - 75
VL  - 44
IS  - 5
AB  - The accurate prediction of water droplet collection coefficients is essential for icing analysis and the design of anti- and de-icing systems. Traditional high-fidelity numerical simulation methods, however, are often hindered by their computational complexity and time-intensive nature. Deep learning-based rapid prediction methods present a promising avenue to address these challenges. In this study, we propose a fast prediction approach that leverages proper orthogonal decomposition (POD) and Kolmogorov-Arnold networks (KAN) to accurately predict water droplet collection coefficients on three-dimensional spherical surfaces. Using POD, we extract its dominant intrinsic modes and corresponding fitting coefficients. A KAN-based deep learning model is then developed to map working condition parameters to the fitting coefficients. Experimental results demonstrate that the proposed POD-KAN model is well-suited for predicting water droplet collection coefficients on 3D spheres, delivering high accuracy with an average absolute error of 3.386 × 10−4. Moreover, after model training, the computational efficiency for obtaining the water droplet collection coefficients is improved by nearly 2.7 × 105 times compared with traditional high-fidelity numerical simulations. This method provides efficient and reliable technical support for the rapid iterative optimization design of aircraft anti-icing/de-icing systems, and holds significant engineering application value for improving aviation flight safety.
UR  - https://doi.org/10.7638/kqdlxxb-2024.0215
DO  - 10.7638/kqdlxxb-2024.0215