Mesh-based numerical solvers are an important part in many design tool chains. However, accurate simulations like computational fluid dynamics are time and resource consuming which is why surrogate models are employed to speed-up the solution process. Machine Learning based surrogate models on the other hand are fast in predicting approximate solutions but often lack accuracy. Thus, the development of the predictor in a predictor-corrector approach is the focus here, where the surrogate model predicts a flow field and the numerical solver corrects it. This paper scales a state-of-the-art surrogate model from the domain of graph-based machine learning to industry-relevant mesh sizes of a numerical flow simulation. The approach partitions and distributes the flow domain to multiple GPUs and provides halo exchange between these partitions during training. The utilized graph neural network operates directly on the numerical mesh and is able to preserve complex geometries as well as all other properties of the mesh. The proposed surrogate model is evaluated with an application on a three dimensional turbomachinery setup and compared to a traditionally trained distributed model. The results show that the traditional approach produces superior predictions and outperforms the proposed surrogate model. Possible explanations, improvements and future directions are outlined.

翻译：基于网格的数值求解器是许多设计工具链中的重要组成部分。然而，诸如计算流体动力学之类的精确仿真耗时且资源密集，因此采用代理模型来加速求解过程。另一方面，基于机器学习的代理模型虽然能快速预测近似解，但往往缺乏精度。因此，预测-校正方法中预测器的开发成为本文的重点，其中代理模型预测流场，数值求解器对其进行校正。本文将基于图机器学习领域的最新代理模型扩展到工业相关规模的数值流模拟网格上。该方法将流域分区并分布到多个GPU上，并在训练期间在这些分区之间提供光环交换。所使用的图神经网络直接操作于数值网格，能够保留网格的复杂几何形状及所有其他属性。所提出的代理模型在三维涡轮机械设置上进行了应用评估，并与传统训练的分布式模型进行了比较。结果表明，传统方法能产生更优的预测，且性能优于所提出的代理模型。文中还概述了可能的解释、改进方向及未来工作。