Aerospace · high-lift aerodynamics
HiLiftAeroML
1,800 WMLES cases spanning 180 NASA CRM-HL variants and ten angles of attack.
- Domain
- Aerospace
- Cases
- 1,800 cases
- Method
- Explicit WMLES
- Grid resolution
- 300–500 million cells per case
- Stored size
- About 66.9 TB stored
- Licence
- CC BY 4.0
01 · Overview
Dataset description
HiLiftAeroML comprises 1,800 simulations: 180 variants of the NASA Common Research Model in high-lift configuration, each evaluated at ten angles of attack from 4° to 22°. The dataset supports surrogate modelling of complex three-dimensional aircraft flows.
The simulations use GPU-accelerated explicit wall-modelled LES on solution-adapted grids containing 300–500 million cells. In addition to geometry, surface and volume fields and integrated coefficients, the repository provides deterministic training, validation and test splits and native-surface quadrature weights for reproducible, area-weighted evaluation.
Potential research applications
Surrogate modelling of complete aircraft in high-lift configuration.
Evaluation of interpolation and out-of-distribution generalisation.
Assessment on unseen geometries, angles of attack, deflections and stall conditions.
Area-weighted evaluation of native-surface predictions.
02 · Contents
Available data products
| Data group | Description | Formats |
|---|---|---|
| Geometry | Tessellated and CAD definitions for each aircraft variant | STL · STEP |
| Surface fields | Compressed time-averaged boundary data | VTU.TGZ |
| Surface quadrature | Barycentric dual-area weights aligned with native boundary points | NPY |
| Volume fields | Compressed three-dimensional time-averaged solution | VTU.TGZ |
| Integrated data | Forces, moments, geometry values and flow reference values | CSV |
| Evaluation assets | Deterministic split families, manifests, validation records and plots | JSON · CSV · PNG |
Native-point surface dual areas
Each sidecar contains one little-endian float32 barycentric dual-area weight in square inches per native boundary point, in the exact point order of the corresponding raw VTU.
| Property | Value |
|---|---|
| File pattern | geo_LHCi_AoA_j/boundary_dual_area_geo_LHCi_AoA_j.npy |
| Association | PointData · native points |
| Coverage | 1,800/1,800 cases |
| Units | in² |
| Sidecar payload | 1.02 TB |
03 · Data access
Repository access and file selection
The repositories are hosted on Hugging Face. The client can select individual data groups and estimate the transfer before files are retrieved.
pip install -U huggingface_hub hf_xet
hf auth loginFile selection
Select data groups
--dry-run enabled Estimate the complete repository
hf download nvidia/HiLiftAeroML \
--type dataset \
--local-dir ./hiliftaeroml_data \
--dry-run
04 · Citation
Citation and persistent identifiers
Users of HiLiftAeroML should cite the corresponding publication and include the dataset DOI where one is available.
@article{ashton2026hiliftaeroml,
title={HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics},
author={Ashton, Neil and Clark, Adam and Heidt, Liam and Ivey, Christopher and Bose, Sanjeeb and Agrawal, Rahul and Goc, Konrad and Ranade, Rishi and Adams, Corey and Sharpe, Peter and Nidhan, Sheel and Akkurt, Semit and Leibovici, Daniel and Kossaifi, Jean},
journal={arXiv preprint arXiv:2605.19565},
year={2026},
url={https://arxiv.org/abs/2605.19565}
}
05 · Provenance
Provenance, licence and limitations
Contributors
- Created by collaborators from NVIDIA, Cadence Design Systems and The Boeing Company
- Computing support from Cadence, NVIDIA, TACC, CSCS and Oak Ridge Leadership Computing Facility
Licence
HiLiftAeroML is available under CC BY 4.0. Review the repository licence before redistribution or adaptation.
Dataset catalogue