用神经网络预测翼型气动性能，比传统方法更快更准。
Benchmarking machine learning models for predicting aerofoil performance
- 用四种神经网络在25个迎角下预测翼型升力系数。
- PointNet和MLP表现最佳，其中PointNet升力计算更准。
- 为风能领域提供了可快速高精度预测的替代方案。
本文研究了神经网络（NNs）作为风能与潮汐能行业所用翼型性能分析的替代方法的能力。当前评估升力与阻力系数的方法包括计算流体动力学（CFD）、薄翼型理论和面元法，均存在计算速度与精度之间的权衡。为此，本文基于美国国家可再生能源实验室（NREL）发布的windAI_bench数据集进行基准测试，并以AirfRANSdataset作为对比起点。研究评估了四种神经网络（MLP、PointNet、GraphSAGE、GUNet）在25个迎角（4°至20°）下对翼型气流进行预测并利用面元法计算升力系数（C_L）。结果显示，GraphSAGE与GUNet在训练阶段表现良好，但在测试阶段表现不佳。最终确定PointNet与MLP为表现最优的两个模型：虽然MLP对流场行为预测更普遍准确，但PointNet在计算升力系数方面更具精度。
原文摘要 · Abstract (English)
This paper investigates the capability of Neural Networks (NNs) as alternatives to the traditional methods to analyse the performance of aerofoils used in the wind and tidal energy industry. The current methods used to assess the characteristic lift and drag coefficients include Computational Fluid Dynamics (CFD), thin aerofoil and panel methods, all face trade-offs between computational speed and the accuracy of the results and as such NNs have been investigated as an alternative with the aim that it would perform both quickly and accurately. As such, this paper provides a benchmark for the windAI_bench dataset published by the National Renewable Energy Laboratory (NREL) in the USA. In order to validate the methodology of the benchmarking, the AirfRANSdataset benchmark is used as both a starting point and a point of comparison. This study evaluates four neural networks (MLP, PointNet, GraphSAGE, GUNet) trained on a range of aerofoils at 25 angles of attack (4$^\circ$ to 20$^\circ$) to predict fluid flow and calculate lift coefficients ($C_L$) via the panel method. GraphSAGE and GUNet performed well during the training phase, but underperformed during testing. Accordingly, this paper has identified PointNet and MLP as the two strongest models tested, however whilst the results from MLP are more commonly correct for predicting the behaviour of the fluid, the results from PointNet provide the more accurate results for calculating $C_L$.
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