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Uncertainty involved drag divergence characteristic predicting method based on VAE

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Abstract

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 \(7.407\times 10^{-5}\) for the airfoil geometric reconstruction and a mean absolute error of 0.455 count for the drag coefficient prediction. The mean error of the drag divergence Mach number prediction reaches \(2.805\times 10^{-4}\). The results show that the model has considerable potential for engineering applications.

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Data availability

The datasets generated during the current study are available from the corresponding author upon reasonable request, subject to further review to ensure compliance with company policies.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No.U23A2069).

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Authors and Affiliations

  1. Shanghai Aircraft Design and Research Institute, No. 5188 Jinke Road, Shanghai, 201210, China

    Wei Liu, Hairun Xie, Jixin Xiang, Zenghui Huang & Miao Zhang

  2. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai, 200240, China

    Jing Wang

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  1. Wei Liu
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  2. Hairun Xie
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  3. Jing Wang
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  4. Jixin Xiang
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  5. Zenghui Huang
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Correspondence to Miao Zhang.

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Liu, W., Xie, H., Wang, J. et al. Uncertainty involved drag divergence characteristic predicting method based on VAE. J Membr Comput (2024). https://doi.org/10.1007/s41965-024-00139-y

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