Uncertainty involved drag divergence characteristic predicting method based on VAE
摘要
Effective access to obtain the drag divergence characteristic of an airfoil is crucial for improving the economy, safety, and comfort of the aircraft. Current methods face challenges in providing satisfactory performance concerning efficiency, reliability and generalizability. In this study, an uncertainty-based network is developed, aiming to realize the prediction of the drag characteristic curves and drag divergence characteristics for different supercritical airfoil. To be more specific, a VAE-based network is established, with an encoder to convert the airfoil geometry into low-dimensional latent variables. These latent variables are then simultaneously fed into both the decoder and the drag characteristic prediction network. The decoder is responsible for geometric reconstruction, ensuring that the latent variables maintain a correlation with the geometric features of the airfoil. The prediction network maps the latent variables to the drag characteristic curve. Once trained, the uncertainty distribution of the drag can be realized through sampling from the distribution of latent variables. The analysis of results indicates the model’s outstanding performance, with a mean absolute error of