Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly refined meshes may ensure precision, they come with high computational costs. Similarly, adaptive remeshing techniques require multiple simulations and come at a great computational cost. This means that the meshing process is reliant upon expert knowledge and years of experience. Automating mesh generation can save significant time and effort and lead to a faster and more efficient design process. This paper presents a machine learning-based scheme that utilizes Graph Neural Networks (GNN) and expert guidance to automatically generate CFD meshes for aircraft models. In this work, we introduce a new 3D segmentation algorithm that outperforms two state-of-the-art models, PointNet++ and PointMLP, for surface classification. We also present a novel approach to project predictions from 3D mesh segmentation models to CAD surfaces using the conformal predictions method, which provides marginal statistical guarantees and robust uncertainty quantification and handling. We demonstrate that the addition of conformal predictions effectively enables the model to avoid under-refinement, hence failure, in CFD meshing even for weak and less accurate models. Finally, we demonstrate the efficacy of our approach through a real-world case study that demonstrates that our automatically generated mesh is comparable in quality to expert-generated meshes and enables the solver to converge and produce accurate results. Furthermore, we compare our approach to the alternative of adaptive remeshing in the same case study and find that our method is 5 times faster in the overall process of simulation. The code and data for this project are made publicly available at https://github.com/ahnobari/AutoSurf.

翻译：计算流体动力学(CFD)广泛应用于不同工程领域，但精确模拟依赖于对模拟域进行合适的网格划分。虽然高度精细的网格能确保精度，但会带来高昂的计算成本。同样，自适应网格重构技术需要多次模拟，计算代价巨大。这意味着网格划分过程依赖于专家知识和多年经验。自动化网格生成可以节省大量时间和精力，并实现更快速、更高效的设计流程。本文提出一种基于机器学习的方案，利用图神经网络(GNN)和专家引导自动生成飞行器模型的CFD网格。在本工作中，我们引入了一种新型三维分割算法，该算法在表面分类任务上优于两种最先进模型PointNet++和PointMLP。我们还提出了一种新颖方法，利用共形预测方法将三维网格分割模型的预测结果投影到CAD曲面上，该方法提供了边际统计保证和稳健的不确定性量化与处理。我们证明，即使对于能力较弱的非精确模型，添加共形预测也能有效使模型避免网格细化不足，从而防止CFD网格划分失败。最后，通过真实案例研究验证了方法的有效性：我们自动生成的网格在质量上与专家生成的网格相当，并能支持求解器收敛并产生精确结果。此外，在相同案例中我们将方法与自适应网格重构方案进行对比，发现在整个模拟流程中，我们的方法速度提升了5倍。本项目的代码和数据已在 https://github.com/ahnobari/AutoSurf 公开。