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TURB-Rot. A large database of 3d and 2d snapshots from turbulent rotating flows

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arxiv 2006.07469 v1 pith:SMM6PDT4 submitted 2020-06-14 physics.flu-dyn cs.CVphysics.data-anphysics.geo-phstat.ML

L. Biferale , F. Bonaccorso , M. Buzzicotti , P. Clark di Leoni This is my paper · ORCID
classification physics.flu-dyncs.CVphysics.data-anphysics.geo-phstat.ML
keywords turb-rotfieldsdatadatabaseequationsnumericaloriginalsimulations
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TURB-Smoke. A database of Lagrangian pollutants emitted from point-sources and dispersed in turbulent flows

    physics.flu-dyn 2025-07 conditional novelty 5.0 of 10

    New public database: DNS-generated Lagrangian particle trajectories and 2D/3D concentration fields from five point sources in turbulence at five wind strengths, for source-tracking and olfactory search benchmarks.

  2. CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics

    cs.LG 2025-07 conditional novelty 4.0 of 10

    Adding a Fourier compressed-sensing denoising step and an SNR-weighted loss to SHRED improves reconstruction of spatiotemporal fields from subsampled sensor data in the paper's four test cases.

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