Datasets:

SM-Bello
/

rde-72-dataset

language: en
license: mit
tags:
  - aerospace
  - propulsion
  - cfd
  - detonation
  - machine-learning
  - dataset
  - surrogate-modeling
size_categories: 1K&lt;n&lt;&lt;10K

RDE-72: Rotating Detonation Engine Spatiotemporal Dataset

Overview

RDE-72 is the first open dataset of full 2D spatial fields from Rotating Detonation Engine (RDE) CFD simulations. It combines 12 high-fidelity OpenFOAM reactingFoam cases with 60 synthetic cases generated via a Conditional Variational Autoencoder (CVAE), enabling neural surrogate modeling and operating envelope mapping.

Key feature: Unlike prior RDE datasets that provide only bulk statistics (mean pressure, temperature), RDE-72 contains complete 2D field sequences showing detonation wave structure, shock fronts, and reaction zones.

Dataset Specifications

Total Cases: 72 (12 real OpenFOAM + 60 CVAE synthetic)
Spatial Resolution: 150 × 300 (45,000 cells)
Temporal Resolution: 20 timesteps per case (50μs intervals, 0.05–1.0 ms)
Channels: 6 per timestep
1. p: Pressure [Pa]
2. T: Temperature [K]
3. Ux: Axial Velocity [m/s]
4. Uy: Transverse Velocity [m/s]
5. H2: Hydrogen Mass Fraction
6. O2: Oxygen Mass Fraction
Data Format: .npz (NumPy compressed archive)
Size: 1.48 GB

Data Structure

import numpy as np

data = np.load('rde_full_dataset_72cases.npz')
fields = data['fields']        # (72, 20, 6, 150, 300)
is_synthetic = data['is_synthetic']  # bool[72], False=real, True=synthetic

## Citation
@dataset{rde72_2026,
  author = {Bello, S. M.},
  year = {2026},
  title = {RDE-72: Spatiotemporal Dataset for Rotating Detonation Engine CFD},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/SM-Bello/rde-72-dataset}
}