KEYWORDS: Super resolution, Image restoration, Feature extraction, Convolutional neural networks, Deep learning, Data modeling, Data acquisition, Target recognition, Signal to noise ratio, Image quality
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.
The non-equilibrium ultraviolet radiation characteristics of plume and shock are closely related to flight parameters such as vibration-rotation temperature, where temperature is the most important thermodynamic quantity for calculating non-equilibrium radiation. CN is the main product of aircraft reentry into the atmosphere and the ablation of carbon-based composite materials, and has very high emission efficiency, making it one of the best molecules for high-temperature gas temperature measurement. In this paper, the two-temperature model and the line-by-line method are used to calculate the CN ultraviolet spectral radiance, and the spectral structure of the violet band is analyzed. The effects of vibration temperature and rotation temperature on the spectral intensity and spectral shape of ultraviolet radiation under thermodynamic non-equilibrium conditions are discussed. According to the relationship between the band tail of CN violet band v=0 peak and the vibration temperature and rotation temperature, a method to invert the rotation temperature using the the slope of CN spectral relative intensity
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