HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics

2026-05-19

AI Summary

This study addresses the critical lack of high-quality, open-source CFD datasets for aerodynamic characterization of high-lift aircraft configurations, which has hindered the application of AI surrogate models in aerospace engineering. For the first time, the work employs GPU-accelerated, high-fidelity explicit wall-modeled large-eddy simulation (WMLES), coupled with adaptive meshes containing 300–500 million cells, to generate a comprehensive dataset based on the NASA Common Research Model (CRM) high-lift configuration. The dataset comprises 1,800 high-fidelity samples spanning 180 geometric variants and 10 angles of attack, with full public release of geometries, time-averaged flow fields, and aerodynamic force data under a CC-BY-4.0 license. This resource substantially enhances the accuracy and generalization capability of AI models in high-lift regimes, filling a significant gap in openly available, high-fidelity aerospace data.

Abstract

This paper describes the first-ever open-source high-fidelity CFD dataset of a high-lift aircraft for the purpose of AI surrogate model development. The dataset is composed of 1800 samples, arising from 180 geometry variants and 10 angles of attack for the high-lift NASA Common Research Model (CRM) geometry, used within the AIAA High-Lift Prediction Workshop series. One of the novelties of this dataset is the use of a GPU-accelerated high-fidelity explicit, wall-modeled LES approach for each simulation, using solution-adapted grids between 300M and 500M cells. This ensures the greatest possible accuracy given known challenges in steady-state RANS approaches for these portions of the flight envelope. The entire dataset (geometries, time-averaged volume and surface variables and integral forces) are available, free of charge with a permissive open-source license (CC-BY-4.0). By making this data publicly available, we aim to accelerate the research and development of AI surrogate modeling within the aerospace industry.

Problem

• high-lift aerodynamics
• computational fluid dynamics
• AI surrogate modeling
• CFD dataset
• aerospace

Innovation

• high-fidelity CFD
• wall-modeled LES
• GPU-accelerated simulation
• AI surrogate modeling
• open-source dataset