A super-resolution reconstruction method for hypersonic target flow field

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.