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Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2021-02-25 , DOI: arxiv-2102.12923
Keefe Huang, Moritz Krügener, Alistair Brown, Friedrich Menhorn, Hans-Joachim Bungartz, Dirk Hartmann

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.

中文翻译:

计算流体力学中基于机器学习的最佳网格生成

计算流体动力学(CFD)是工程学的一个主要子领域。相应的流量模拟通常以大量的计算资源需求为特征。通常,需要非常细而复杂的网格以适当的方式解决物理影响。由于所有CFD算法至少与基础网格离散化的大小成线性比例关系,因此找到最佳网格对于计算效率至关重要。用于找到最佳网格的一种方法是面向目标的自适应网格细化。但是,这通常对计算要求很高,并且仅在数量有限的工具中可用。在此贡献范围内,我们采用机器学习方法来确定最佳网格密度。我们使用经典方法生成优化的网格,并提出训练卷积网络,以预测在给定任意几何形状的情况下的最佳网格密度。所提出的概念已在2d风洞模拟中进行了超过60,000次模拟验证。使用20,000个模拟训练集,我们可以达到98.7%以上的准确度。最佳网格的相应预测可以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网格开始其预测。最佳网格的相应预测可以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网格开始其预测。最佳网格的相应预测可以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网格开始其预测。
更新日期:2021-02-26
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