基于POD和KAN的三维球水滴收集系数快速预测OA

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.