用跨声速机翼气动快速预测，仅需少量数据即可达到高精度。
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning
- 利用剖面气动模型预训练，再通过迁移学习微调机翼三维流场
- 数据量减半时误差仍低于非迁移框架，且引入斜率理论再降9%误差
- 适合需要快速设计迭代的航空航天气动优化场景
基于机器学习的模型可快速获取跨声速后掠机翼流场，但构建训练数据集计算成本高。本文提出一种嵌入物理知识的迁移学习框架：将三维机翼流场分解为展向各截面二维剖面流场进行分析。先用剖面样本预训练气动预测模型，再用少量机翼样本微调，基于各截面二维结果预测三维流场。在确定对应剖面几何与工况时嵌入斜率理论，并对比评估低精度涡格法与数据驱动方法以获取截面升力系数。相比非迁移模型，引入预训练使误差降低30%，再引入斜率理论进一步降低9%。当减少数据集规模时，所需机翼训练样本不足原框架的一半即可达到相同误差水平，显著降低建模难度。
原文摘要 · Abstract (English)
Machine learning-based models provide a promising way to rapidly acquire transonic swept wing flow fields but suffer from large computational costs in establishing training datasets. Here, we propose a physics-embedded transfer learning framework to efficiently train the model by leveraging the idea that a three-dimensional flow field around wings can be analyzed with two-dimensional flow fields around cross-sectional airfoils. An airfoil aerodynamics prediction model is pretrained with airfoil samples. Then, an airfoil-to-wing transfer model is fine-tuned with a few wing samples to predict three-dimensional flow fields based on two-dimensional results on each spanwise cross section. Sweep theory is embedded when determining the corresponding airfoil geometry and operating conditions, and to obtain the sectional airfoil lift coefficient, which is one of the operating conditions, the low-fidelity vortex lattice method and data-driven methods are proposed and evaluated. Compared to a nontransfer model, introducing the pretrained model reduces the error by 30%, while introducing sweep theory further reduces the error by 9%. When reducing the dataset size, less than half of the wing training samples are need to reach the same error level as the nontransfer framework, which makes establishing the model much easier.
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