TURB-Rot. A large database of 3d and 2d snapshots from turbulent rotating flows
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Abstract (arXiv)
We present TURB-Rot, a new open database of 3d and 2d snapshots of turbulent velocity fields, obtained by Direct Numerical Simulations (DNS) of the original Navier-Stokes equations in the presence of rotation. The aim is to provide the community interested in data-assimilation and/or computer vision with a new testing-ground made of roughly 300K complex images and fields. TURB-Rot data are characterized by multi-scales strongly non-Gaussian features and rough, non-differentiable, fields over almost two decades of scales. In addition, coming from fully resolved numerical simulations of the original partial differential equations, they offer the possibility to apply a wide range of approaches, from equation-free to physics-based models. TURB-Rot data are reachable at http://smart-turb.roma2.infn.it
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 1.
4 papers.
- 2022Multi-scale data reconstruction of turbulent rotating flows with Gappy POD, Extended POD and Generative Adversarial Networkscited by 8
- 2023Generative Adversarial Networks to infer velocity components in rotating turbulent flowscited by 5
- 2022Classifying Turbulent Environments via Machine Learningcited by 0
- 2025CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamicscited by 0
- 2025CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics30 Jul 2025
- 2023Generative Adversarial Networks to infer velocity components in rotating turbulent flows18 Jan 2023
- 2022Multi-scale data reconstruction of turbulent rotating flows with Gappy POD, Extended POD and Generative Adversarial Networks21 Oct 2022
- 2022Classifying Turbulent Environments via Machine Learning3 Jan 2022