首个开源高升力飞机高精度仿真数据集，助力航空AI模型研发。
HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
- 基于GPU加速的壁面模化大涡模拟，每例仿真网格达3亿至5亿单元。
- 包含180种机翼构型、10个迎角，共1800组高精度气动数据。
- 数据完全开源，适合航空AI建模、气动设计优化的研究者使用。
本文发布首个面向人工智能代理模型开发的高升力飞机高保真计算流体动力学（CFD）开源数据集。数据集涵盖180种高升力NASA通用研究模型（CRM）构型，每个构型在10个迎角下进行模拟，共计1800个样本。所有仿真均采用GPU加速的显式壁面模化大涡模拟（LES）方法，使用3亿至5亿单元的自适应网格，确保在传统稳态RANS方法存在挑战的飞行包线区域实现最高精度。数据集包含几何信息、时均体变量与表面变量以及积分力，全部免费开放，遵循宽松的CC-BY-4.0开源许可。通过公开该数据集，旨在加速航空航天领域人工智能代理模型的研发进程。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。