arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.14815cond-mat.mtrl-scicond-mat.mes-hall
收藏

位错微观结构的数据挖掘:内能粗粒化的概念

Data-mining of dislocation microstructures: concepts for coarse-graining of internal energies

Hengxu Song, Nina Gunkelmann, Giacomo Po, Stefan Sandfeld

摘要:

位错塑性的连续体模型需要本构闭合假设,例如,通过将位错微观结构的细节与能量密度相关联。目前,还没有系统的方法从参考模拟(例如离散位错动力学或分子动力学)中导出或提取这样的信息。在这里,提出了一种新的数据挖掘方法,通过该方法可以从离散位错系统中提取能量密度数据。我们的方法依赖于一个系统的和受控的粗粒化过程,从而与感兴趣的长度尺度一致。对于数据挖掘,使用了从2D和3D离散位错动力学模拟生成的一系列不同的位错微观结构。数据集的分析结果在能量密度配方作为各种位错密度场的函数。该方法解决了位错微结构粗晶化过程中体素尺寸相关能量计算的长期问题。因此,它是任何连续位错动力学模拟的关键。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑