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

Neil Ashton; Adam Clark; Liam Heidt; Christopher Ivey; Sanjeeb Bose; Rahul Agrawal; Konrad Goc; Rishi Ranade; Corey Adams; Peter Sharpe; Sheel Nidhan; Semit Akkurt; Daniel Leibovici; Jean Kossaifi

arXiv · 2605.19565

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

Neil Ashton·Adam Clark·Liam Heidt·Christopher Ivey·Sanjeeb Bose·Rahul Agrawal·Konrad Goc·Rishi Ranade·Corey Adams·Peter Sharpe·Sheel Nidhan·Semit Akkurt·Daniel Leibovici·Jean Kossaifi

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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.

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Neil Ashton, Adam Clark, Liam Heidt, Christopher Ivey, Sanjeeb Bose, Rahul Agrawal, Konrad Goc, Rishi Ranade, Corey Adams, Peter Sharpe, Sheel Nidhan, Semit Akkurt, Daniel Leibovici, Jean Kossaifi. 2026-05-19. HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics. https://arxiv.org/abs/2605.19565

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