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

```python
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}
}