The computational cost of computational fluid dynamics (CFD) simulation is relatively high due to its computational complexity. To reduce the computing time required by CFD, researchers have proposed various methods, including efficient time advancement methods, correction methods for discrete control equations, multigrid methods, reasonable initial field setting methods, and parallel methods. Among these methods, the initial field setting method can provide significant performance improvements, but there is little work on it. Existing CFD industrial software typically uses inflow conditions for the initial flow field or applies empirical methods, which can cause instability in the CFD calculation process and make convergence difficult. With the rapid development of deep learning, researchers are increasingly attempting to replace CFD simulations with deep neural networks and have achieved significant performance improvements. However, these methods still face some challenges. First, they can only predict the computational flow field on regular grids. They cannot directly make predictions for irregular grids such as multi-block grids and unstructured grids, so the final flow field can only be obtained through interpolation and similar methods. Second, although these methods have been claimed to provide high accuracy, there is still a significant gap in performance with CFD and they cannot yet be applied to real scenarios. To address these issues, we propose a Residual Graph Convolutional Network for Initial Flow Field Setting (RGCN-IFS) in CFD simulations. This method converts the grid into a graph structure and uses an improved graph neural network to predict the flow field. In this way, we can predict the flow field on any type of grid. More importantly, this method does not directly replace CFD simulations, but it rather serves in an auxiliary role, providing appropriate initial flow fields for the CFD calculations, improving the convergence efficiency while ensuring calculation accuracy, and directly bridging the accuracy gap between intelligent surrogate models and CFD simulations.

中文翻译：

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用于在计算流体动力学模拟中设置初始流场的残差图卷积网络

由于计算复杂性，计算流体动力学（CFD）模拟的计算成本相对较高。为了减少CFD所需的计算时间，研究人员提出了多种方法，包括高效的时间推进方法、离散控制方程的修正方法、多重网格方法、合理的初始场设置方法和并行方法。在这些方法中，初始字段设置方法可以提供显着的性能改进，但目前在这方面的工作很少。现有CFD工业软件通常采用流入条件作为初始流场或采用经验方法，这会导致CFD计算过程不稳定，收敛困难。随着深度学习的快速发展，研究人员越来越多地尝试用深度神经网络代替CFD模拟，并取得了显着的性能提升。然而，这些方法仍然面临一些挑战。首先，他们只能预测规则网格上的计算流场。它们无法直接对多块网格和非结构化网格等不规则网格进行预测，因此只能通过插值等类似方法获得最终的流场。其次，虽然这些方法声称能够提供高精度，但其性能与 CFD 仍有很大差距，尚无法应用于实际场景。为了解决这些问题，我们提出了一种用于 CFD 模拟中初始流场设置的残差图卷积网络 (RGCN-IFS)。该方法将网格转换为图结构，并使用改进的图神经网络来预测流场。这样，我们就可以预测任何类型网格上的流场。更重要的是，该方法并不是直接取代CFD模拟，而是起辅助作用，为CFD计算提供合适的初始流场，在保证计算精度的同时提高收敛效率，直接弥合智能代理模型之间的精度差距和 CFD 模拟。