论文标题
错位微观结构的数据挖掘：内部能量粗粒的概念
Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies
论文作者
论文摘要
脱位可塑性的连续模型需要组成式闭合假设，例如，通过将错位微结构的细节与能量密度联系起来。当前，没有系统的方法来从参考模拟中得出或提取此类信息，例如离散脱位动力学或分子动力学。在这里，提出了一种新型的数据挖掘方法，可以通过该方法提取来自离散位错系统的能量密度数据。我们的方法依赖于系统的和受控的粗粒过程，因此与感兴趣的长度规模一致。对于数据挖掘，使用了一系列由2D和3D离散脱位动力学模拟产生的不同脱位微结构。数据集的分析导致能量公式作为各种位错密度场的函数。所提出的方法解决了位错微结构的粗粒子期间体素尺寸依赖能量计算的长期问题。因此，这对于任何连续脱位动力学模拟至关重要。
Continuum models of dislocation plasticity require constitutive closure assumptions, e.g., by relating details of the dislocation microstructure to energy densities. Currently, there is no systematic way for deriving or extracting such information from reference simulations, such as discrete dislocation dynamics or molecular dynamics. Here, a novel data-mining approach is proposed through which energy density data from systems of discrete dislocations can be extracted. Our approach relies on a systematic and controlled coarse-graining process and thereby is consistent with the length scale of interest. For data-mining, a range of different dislocation microstructures that were generated from 2D and 3D discrete dislocation dynamics simulations, are used. The analyses of the data sets result in energy density formulations as function of various dislocation density fields. The proposed approach solves the long-standing problem of voxel-size dependent energy calculation during coarse graining of dislocation microstructures. Thus, it is crucial for any continuum dislocation dynamics simulation.
