HiLiftAeroML: A High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
physics.flu-dyn, cs.LG
Submitted: 2026-05-19
Updated: 2026-09-25
Comments: 70 pages. v2: expanded with GeoTransolver and Transolver baselines, native-support evaluation, updated CFD and data-quality documentation, computational-cost analysis, and public score-reproduction artifacts and checkpoints (https://doi.org/10.6084/m9.figshare.33993865)
License: http://creativecommons.org/licenses/by/4.0/
The gist: HiLiftAeroML is, to our knowledge, the first open high-fidelity computational fluid dynamics dataset dedicated to high-lift aircraft aerodynamics.
Terminology
Abstract
HiLiftAeroML is, to our knowledge, the first open high-fidelity computational fluid dynamics dataset dedicated to high-lift aircraft aerodynamics. It contains 1,800 simulations spanning 180 variants of the NASA Common Research Model high-lift configuration and ten angles of attack from 4 to 22. Each case was generated with a GPU-accelerated explicit wall-modeled large-eddy simulation approach on solution-adapted grids of 300--500 million cells, covering attached, separated, and post-stall flow conditions. Comparisons with wind-tunnel measurements for reference landing configurations show good agreement in integrated loads and sectional pressures, with grid adaptation substantially improving drag and pitching-moment predictions. The CC-BY-4.0 release includes geometries, time-averaged surface and volume fields, integrated loads, validation material, and deterministic benchmark splits. Initial GeoTransolver and Transolver baselines, evaluated on the complete native surface and volume support of every held-out case rather than on a sampled subset, reconstruct the fields well for several interpolation and held-out-geometry tests, while high-angle separated flow, limited-data training, and out-of-distribution flow-regime shifts remain substantially harder. The dataset and baselines provide a common resource for developing and assessing data-driven models for realistic high-lift aerodynamics.
Sources
- AB-UPT for Automotive and Aerospace Applications
- AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics
- Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics
- AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers
- DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations
- BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft
- X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation
- Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes
- Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains
- SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design
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