Super-resolution is an innovative technique that upscales the resolution of an image or a video and thus enables us to reconstruct high-fidelity images from low-resolution data. This study performs super-resolution analysis on turbulent flow fields spatially and temporally using various state-of-the-art machine learning techniques like ESPCN, ESRGAN and TecoGAN to reconstruct high-resolution flow fields from low-resolution flow field data, especially keeping in mind the need for low resource consumption and rapid results production/verification. The dataset used for this study is extracted from the 'isotropic 1024 coarse' dataset which is a part of Johns Hopkins Turbulence Databases (JHTDB). We have utilized pre-trained models and fine tuned them to our needs, so as to minimize the computational resources and the time required for the implementation of the super-resolution models. The advantages presented by this method far exceed the expectations and the outcomes of regular single structure models. The results obtained through these models are then compared using MSE, PSNR, SAM, VIF and SCC metrics in order to evaluate the upscaled results, find the balance between computational power and output quality, and then identify the most accurate and efficient model for spatial and temporal super-resolution of turbulent flow fields.
翻译:超分辨率是一种创新技术,它提升了图像或视频的分辨率,从而使我们能够从低分辨率数据中重建高不忠图像。本研究利用ESPCN、ESRGAN和TecoGAN等最先进的机器学习技术,在空间上和时间上对动荡流场进行超分辨率分析,以便从低分辨率流场数据中重建高分辨率流场,特别是铭记需要低资源消耗和快速生成/核实结果。本研究所用的数据集取自“isotorocic 1024 coarse”数据集,该数据集是约翰·霍普金斯海啸数据库的一部分。我们利用了预先培训的模型,并根据我们的需求对其进行了微调,以便最大限度地减少计算资源和实施超分辨率模型所需的时间。这种方法提供的优势远远超过了对常规单一结构模型的期望和结果。然后,通过这些模型获得的结果用MSE、PSNR、SAM、VIF和SCC测量数据集来比较,这是约翰·霍普金斯海啸数据库(JHTDB)的一部分。我们利用了预先培训过的模型,并微调调整了这些模型,以便评估最高空间流流流流流和空间流流压的模型,从而确定最高流流流流流流流流流数据。