论文标题
一种在代表体积元素中加速相位场骨折模拟的多种学习方法
A Manifold Learning Approach to Accelerate Phase Field Fracture Simulations in the Representative Volume Element
论文作者
论文摘要
由于复合材料的广泛应用,异质材料的多尺度模拟在固体力学和材料科学中是一个流行而重要的主题。但是,经典FE2(有限元2)方案可能会昂贵,尤其是当微问题是非线性的时候。在本文中,我们考虑了微问题是断裂相位场公式的情况。我们采用局部线性嵌入(LLE)流形学习方法,一种非线性尺寸降低的方法,以提取包含代表体积元素(RVE)中相位场代表的初始微裂纹模式集合的歧管。然后,可以使用最小计算的学习歧管准确地重建与任何其他微裂料模式相对应的输出数据,例如,在固定载荷下进化的相位场。该方法具有两个功能:该方案的最小参数数量,一个特定于输入的错误栏。后一个功能可以针对是否使用提出的,较便宜的重建或使用准确但昂贵的高保真计算的任何新输入具有自适应策略。
The multiscale simulation of heterogeneous materials is a popular and important subject in solid mechanics and materials science due to the wide application of composite materials. However, the classical FE2 (finite element2) scheme can be costly, especially when the microproblem is nonlinear. In this paper, we consider the case when the microproblem is the phase field formulation for fracture. We adopt the locally linear embedding (LLE) manifold learning approach, a method for non-linear dimension reduction, to extract the manifold that contains a collection of phase-field-represented initial microcrack patterns in the representative volume element (RVE). Then the output data corresponding to any other microcrack pattern, e.g., the evolved phase field at a fixed load, can be accurately reconstructed using the learned manifold with minimum computation. The method has two features: a minimum number of parameters for the scheme, and an input-specific error bar. The latter feature enables an adaptive strategy for any new input on whether to use the proposed, less expensive reconstruction, or to use an accurate but costly high-fidelity computation instead.