
















Authors: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
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) |
From: Neil Ashton [view email]
[v1]
Tue, 19 May 2026 09:12:10 UTC (29,871 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。