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HiLiftAeroML: High-Fidelity Computational Fluid Dynamics ...
Neil Ashton, · 2026-05-20 · via cs.LG updates on arXiv.org

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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.
Subjects: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
Cite as: arXiv:2605.19565 [physics.flu-dyn]
  (or arXiv:2605.19565v1 [physics.flu-dyn] for this version)
  https://doi.org/10.48550/arXiv.2605.19565

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Neil Ashton [view email]
[v1] Tue, 19 May 2026 09:12:10 UTC (29,871 KB)