基于机器学习的计算流体力学网格优化生成
Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics
摘要：
计算流体动力学（CFD）是工程学的一个主要子领域。相应的流动模拟的典型特征在于大量的计算资源需求。通常，需要非常精细和复杂的网格来以适当的方式解决物理效果。由于所有CFD算法至少与底层网格离散化的大小成线性比例，因此找到最佳网格是计算效率的关键。一种用于找到最佳网格的方法是面向目标的自适应网格细化。然而，这通常在计算上要求很高，并且仅在有限数量的工具中可用。在这方面的贡献，我们采用了机器学习的方法来确定最佳的网格密度。我们使用经典的方法生成优化的网格，并提出训练一个卷积网络预测任意几何形状的最佳网格密度。所提出的概念是沿着二维风洞模拟验证超过60，000模拟。使用20，000个模拟的训练集，我们实现了超过98.7%的准确率。最佳网格的相应预测可用作任何网格生成和CFD工具的输入。因此，无需复杂的计算，任何CFD工程师都可以从高质量的网格开始预测。
英文摘要：
Computational Fluid Dynamics (CFD) is a major sub-field of engineering. Corresponding flow simulations are typically characterized by heavy computational resource requirements. Often, very fine and complex meshes are required to resolve physical effects in an appropriate manner. Since all CFD algorithms scale at least linearly with the size of the underlying mesh discretization, finding an optimal mesh is key for computational efficiency. One methodology used to find optimal meshes is goal-oriented adaptive mesh refinement. However, this is typically computationally demanding and only available in a limited number of tools. Within this contribution, we adopt a machine learning approach to identify optimal mesh densities. We generate optimized meshes using classical methodologies and propose to train a convolutional network predicting optimal mesh densities given arbitrary geometries. The proposed concept is validated along 2d wind tunnel simulations with more than 60,000 simulations. Using a training set of 20,000 simulations we achieve accuracies of more than 98.7%. Corresponding predictions of optimal meshes can be used as input for any mesh generation and CFD tool. Thus without complex computations, any CFD engineer can start his predictions from a high quality mesh.
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