tmarkmann/dataset-rbc-fno

Rayleigh-Benard Convection (RBC) Dataset

This dataset contains Direct Numerical Simulation (DNS) data of Rayleigh-Benard convection in two and three dimensions, generated using Oceananigans.jl. It accompanies the paper Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh-Benard convection, published in Neurocomputing (2026).

Dataset Description

Rayleigh-Benard Convection describes convection in a fluid layer heated from below and cooled from above. The dataset provides time-series trajectories of the full flow state (velocity, temperature, pressure) at different Rayleigh numbers, suitable for training data-driven surrogate models.

Dataset Structure

2D/
  train/   # 50 episodes per Ra (7 Ra values)
  val/     # 10 episodes per Ra
  test/    # 20 episodes per Ra
3D/
  train/   # 50 episodes at Ra=2500
  val/     # 20 episodes
  test/    # 20 episodes

Each file is named ra{value}.h5 and stored in HDF5 format with the following datasets per episode i:

Key	2D Shape	3D Shape	Description
states{i}	(1000, 5, 64, 96)	(1000, 6, 32, 48, 48)	Flow state: velocity + temperature + pressure
actions{i}	(1000, 12)	(1000, 8, 8)	Boundary heating actions
nusselts{i}	(1000,)	(1000,)	Nusselt number (convective heat flux)

State channels

• 2D (5 channels): u, v, T, p_hyd, p_non (horizontal velocity, vertical velocity, temperature, hydrostatic pressure, non-hydrostatic pressure)
• 3D (6 channels): u, v, w, T, p_hyd, p_non (3 velocity components, temperature, hydrostatic pressure, non-hydrostatic pressure)

The last two channels (pressure) are typically excluded during surrogate model training.

Simulation Parameters

Parameter	2D	3D
Domain D	2pi x 2	4pi x 4pi x 2
Grid N	96 x 64	48 x 48 x 32
Ra	{1e4, 3e4, 1e5, 3e5, 1e6, 3e6, 1e7}	2500
Pr	0.7	0.7
(T_C, T_H)	(1, 2)	(0, 1)
dt	0.03	0.01
Timesteps per episode	1000	1000

Usage

import h5py

with h5py.File("2D/train/ra300000.h5", "r") as f:
    # Load episode 0 states: (1000, 5, 64, 96)
    states = f["states0"][:]

    # Velocity and temperature (exclude pressure)
    uvT = states[:, :3]  # (1000, 3, 64, 96)

For use with the training code, see the RBC-FNO-Surrogate repository.

Citation

@article{MARKMANN2026133201,
  title = {Fourier neural operators as data-driven surrogates
  for two- and three-dimensional Rayleigh-Benard convection},
  journal = {Neurocomputing},
  volume = {679},
  pages = {133201},
  year = {2026},
  issn = {0925-2312},
  doi = {https://doi.org/10.1016/j.neucom.2026.133201},
  author = {Thorben Markmann and Michiel Straat and Sebastian
  Peitz and Barbara Hammer},
}