Forced Homogeneous Isotropic Turbulence DNS

Image 1

Description

These snapshots are derived from a Direct Numerical Simulation (DNS) of forced homogeneous isotropic turbulence, which is publicly available through the Johns Hopkins Turbulence Database (JHTDB). The original simulation was performed on a triply periodic cubic domain of size $2\pi \times 2\pi \times 2\pi$, discretized on a grid of $1024 \times 1024 \times 1024$ nodes, with periodic boundary conditions applied in all three directions. The incompressible Navier–Stokes equations are solved using a pseudo-spectral method. Statistical stationarity is maintained by a large-scale forcing that holds the total energy constant within the low-wavenumber shells for which $|\mathbf{k}| \le 2$, so that energy injected at large scales cascades to and is dissipated at the smallest resolved scales. The turbulence is characterized by a Taylor-scale Reynolds number that fluctuates around $Re_\lambda \approx 433$.

For this machine-learning-oriented release, we subsampled the original JHTDB forced isotropic turbulence fields and re-saved them in a new, ML-friendly format. Each snapshot retains the complete three-dimensional flow field with all three velocity components (u, v, w) and pressure. Four versions of the dataset are provided:

  • Sequential version — 20 consecutive snapshots sampled at the stored time interval,
  • Random version — 10 snapshots drawn at random (non-consecutive) times from the statistically stationary regime. This version is intended for tasks that benefit from diverse, decorrelated samples, such as single-frame reconstruction, statistical modeling, or generative learning.

For each of the sequential and random versions, there are two sub-versions of the data, snapshots separated by DNS simulation time step (fine) and 10 DNS time-steps (coarse).

Quick Info

  • Contributors: Charles Meneveau
  • Nɸ = 4
  • Nx = 1024, Ny = 1024, Nz = 1024, Nɸ = 4
  • DOI
  • .bib
  • Download.sh
ID Re$_{\lambda}$ Description Size (GB) Links
0 433 Sequential Snapshots (coarse) 321
1 433 Collection of snapshots at different time (coarse) 161
2 433 Sequential Snapshots (fine) 321
3 433 Collection of snapshots at different time (fine) 161

Updated: