The article presents the application of inductive graph machine learning surrogate models for accurate and efficient prediction of 3D flow for industrial geometries, explicitly focusing here on external aerodynamics for a motorsport case. The final aim is to build a surrogate model that can provide quick predictions, bypassing in this way the unfeasible computational burden of traditional computational fluid dynamics (CFD) simulations. We investigate in this contribution the usage of graph neural networks, given their ability to smoothly deal with unstructured data, which is the typical context for industrial simulations. We integrate an efficient subgraph-sampling approach with our model, specifically tailored for large dataset training. REV-GNN is the chosen graph machine learning model, that stands out for its capacity to extract deeper insights from neighboring graph regions. Additionally, its unique feature lies in its reversible architecture, which allows keeping the memory usage constant while increasing the number of network layers. We tested the methodology by applying it to a parametric Navier–Stokes problem, where the parameters control the surface shape of the industrial artifact at hand, here a motorbike.

中文翻译：

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用于外部空气动力学 3D 流场预测的大规模图机器学习替代模型

本文介绍了归纳图机器学习代理模型的应用，用于准确有效地预测工业几何形状的 3D 流动，其中明确关注赛车运动案例的外部空气动力学。最终目标是建立一个可以提供快速预测的替代模型，从而绕过传统计算流体动力学（CFD）模拟的不可行的计算负担。我们在这篇文章中研究了图神经网络的使用，因为它们能够顺利处理非结构化数据，这是工业模拟的典型环境。我们将高效的子图采样方法与我们的模型相结合，专为大型数据集训练而定制。 REV-GNN 是所选的图机器学习模型，它因其从相邻图区域提取更深入见解的能力而脱颖而出。此外，其独特之处在于其可逆架构，可以在增加网络层数的同时保持内存使用量恒定。我们通过将其应用于参数纳维-斯托克斯问题来测试该方法，其中参数控制手头的工业工件（这里是摩托车）的表面形状。