SuperMeshing: Boosting the Mesh Density of Stress Field in Plane-Strain Problems Using Deep Learning Method
SuperMeshing: Boosting the Mesh Density of Stress Field in Plane-Strain Problems Using Deep Learning Method
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SuperMeshing：使用深度学习方法提高平面应变问题中应力场的网格密度
DOI：
10.1115/1.4054687
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Xin
中科院分区：
文献类型：
--
作者：
Handing Xu;Zhenguo Nie;Qingfeng Xu;Yaguan Li;F. Xie;Xin
数值计算中空间分辨率的提高往往会导致计算效率相对于网格密度的约束而降低。针对这种无法进行数值计算的问题，我们提出了一种在二维域内提高有限元网格密度的新方法。该方法以有限元计算的二维平面应变问题的von Mose应力场为基础，利用深度神经网络SMNet学习应力场从低网格密度到高网格密度的非线性映射，同时实现了数值计算精度和效率的提高。我们将剩余的稠密块引入到SMNet中，以提取丰富的局部特征，增强预测能力。结果表明，在网格密度为2 ×、3 ×和4 ×时，SMNet能有效地提高应力场的空间分辨率。与指标相比，SMNet的相对误差为1.67%，表现出比其他许多方法更好的性能。SMNet可以作为基于网格的数值方法中二维物理场的增强型网格密度增强模型。
The increase of the spatial resolution in numerical computation always leads to a decrease in computing efficiency with respect to the constraint of mesh density. In response to this problem of inability to perform numerical computation, we propose a novel method to boost the mesh-density in finite element method (FEM) within 2D domains. Running on the von Mises stress fields of the 2D plane-strain problems computed by FEM, the proposed method utilizes a deep neural network named SMNet to learn a non-linear mapping from low mesh-density to high mesh-density in stress fields, and realizes the improvement of numerical computation accuracy and efficiency simultaneously. We use the residual dense blocks into SMNet to extract abundant local features and enhance the prediction capacity. The result indicates that SMNet can effectively increase the spatial resolution of stress fields under multiple scaling factors in mesh-density: 2 ×, 3 × and 4 ×. Compared with the targets, the relative error of SMNet is 1.67%, showing better performance than many other methods. SMNet can be generically used as an enhanced mesh-density boosting model of 2D physical fields for mesh-based numerical methods.