Wing ice accretion prediction based on conditional generation adversarial network

Abstract

The ice accretion on the aircraft's surface under low temperatures and high humidity is crucial for flight safety. With respect to the limitation of traditional icing simulation methods, it is very hard to predict exact ice profiles, which can extremely affect the flight performance of an aircraft. A conditional generative adversarial network (CGAN) is utilized to rapidly predict ice accretion and reconstruct three-dimensional ice patterns along the leading edge of a wing. The CGAN is trained using experimental data obtained from a wing with varying sweep angles. The results indicate that the CGAN achieves a high level of accuracy, specifically 97.5%, in predicting the similarity of ice shapes in the test set. When assessing the sample feature capture and prediction capability of the predictive model, it is shown that the CGAN exhibits superior predictive performance across different sample sizes.