Rapid prediction of water droplet collection coefficients on 3D spheres using POD and KAN

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.