
The dataset contains 100,000 images with a resolution of 1000 x 600 pixels of the test case of a flow around a cylinder at Reynolds number 3900, where the flow field is characterized by a Kármán vortex street developing in the wake of the cylinder. The vortex street consists of a characteristic coherent vortex system in which the rotational axes of the individual vortices are aligned with the cylinder axis. The data set of gray-scale images was generated by post-processing the transient Large Eddy Simulation (LES) velocity field data using a projection mapping in the sense that the system remains ergodic on a reduced state space. The data was originally used to train and validate generative models. The results of these studies are presented in the article Drygala, C., Winhart, B., di Mare, F., & Gottschalk, H. (2022). Generative modeling of turbulence. Physics of Fluids, 34(3), where a detailed description of the numerical setup can be found.
Machine learning, Deep learning, Computational fluid dynamics
Machine learning, Deep learning, Computational fluid dynamics
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