A super-resolution reconstruction method for hypersonic target flow field

Zhou Jun; Bai Lu; Li Yin; Huigang Shi; Jingyu Bai

doi:10.1117/12.3032238

26 July 2024 A super-resolution reconstruction method for hypersonic target flow field

Zhou Jun, Bai Lu, Li Yin, Huigang Shi, Jingyu Bai

Proceedings Volume 13189, Second Conference on Space, Atmosphere, Marine, and Environmental Optics (SAME 2024); 131890E (2024) https://doi.org/10.1117/12.3032238
Event: Second Conference on Space, Atmosphere, Marine, and Environmental Optics (SAME 2024), 2024, Hangzhou, China

Abstract

With the rapid advancement of hypersonic flight technology, the high-precision measurement of hypersonic flow field parameters has become an urgent issue. The computational fluid dynamics (CFD) method for obtaining high-resolution flow field data requires high grid quality and involves complex numerical solving processes, leading to significant computational costs. There is a growing need to quickly obtain high-resolution flow field data with less effort. Leveraging the powerful nonlinear fitting capabilities of neural networks, it is possible to perform fine super-resolution reconstruction of low-resolution flow field data in a data-driven manner. In this study, we propose an improved Enhanced Super-resolution Convolutional Neutral Network (ESRCNN) model tailored for hypersonic target flow fields. This enhanced model is applied to the super-resolution reconstruction of low-resolution flow fields of blunt body aircraft targets and compared with interpolation methods and traditional deep learning methods. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used as evaluation metrics. The results validate the accuracy and superiority of this model in reconstructing hypersonic target flow fields. This method provides an effective approach for the precise measurement and reconstruction of hypersonic flow fields, thereby contributing to the advancement of hypersonic flight technology.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhou Jun, Bai Lu, Li Yin, Huigang Shi, and Jingyu Bai "A super-resolution reconstruction method for hypersonic target flow field", Proc. SPIE 13189, Second Conference on Space, Atmosphere, Marine, and Environmental Optics (SAME 2024), 131890E (26 July 2024); https://doi.org/10.1117/12.3032238

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KEYWORDS

Super resolution

Image restoration

Convolutional neural networks

Feature extraction

Data acquisition

Data modeling

Deep learning

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