CC BY 4.0Free to use and share, including commercially, as long as you credit the creators.
Size
35.5 GB
How to cite
Vishwasrao, A., Vinuesa, R., Vanderwel, C., & Lim, H. D. (2026). Data for Diff-SPORT: Diffusion-based sensor placement optimization and reconstruction of turbulent flows in urban environments [Data set]. Zenodo. https://doi.org/10.5281/zenodo.23090303
Please also cite the publication below that describes it.
Dataset of microscale atmospheric flow simulations around randomly generated urban geometries for several stability classes. Useful for training and testing ML surrogates of urban flow.
Time and space data from direct numerical simulations of turbulent flow over regular cuboid arrays (DIPLOS geometry). Useful for urban canopy turbulence studies.
LES velocity fields over urban canopies with inputs and U-net predicted fields, including perturbed cases. Useful for training and testing ML surrogates of urban flow.
Code for diffusion-based sensor placement and reconstruction of turbulent flows in urban environments. Useful for flow reconstruction and sensor design.
224 large-eddy simulations of flow over idealised building-block arrays. They systematically vary plan-area density (8 values, 0.0625 to 0.64), aligned and staggered layouts, 7 building-height distributions, and perpendicular or oblique wind. A parameter sweep for canopy parameterisation and model validation.
From Zenodo, added 2026-11-07.
Listed there as “Data for Diff-SPORT: Diffusion-based sensor placement optimization and reconstruction of turbulent flows in urban environments”. Tags proposed by AI and not yet checked by hand. Spotted a mistake? Tell the portal contacts.