Analytics for microstructure datasets produced by phase-field simulations
Analytics for microstructure datasets produced by phase-field simulations
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DOI：
10.1016/j.actamat.2015.09.047
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发表时间：
2016-01
期刊：
影响因子：
9.4
通讯作者：
P. Steinmetz;Yuksel C. Yabansu;J. Hötzer;Marcus Jainta;B. Nestler;S. Kalidindi
中科院分区：
文献类型：
--
作者：
P. Steinmetz;Yuksel C. Yabansu;J. Hötzer;Marcus Jainta;B. Nestler;S. Kalidindi
相场模拟已成为探索加工参数对材料内部结构演化影响的重要工具，具有广泛的应用前景。在高性能计算机上进行的模拟使相对较大的材料体积得以分辨，并产生了大数据集。尽管这样的计算忠实地捕捉到了在相应实验中观察到的许多特征，但还没有一个广泛采用的框架来对预测的材料结构进行定量分析，并将它们与实验进行严格的比较。本文证明，最近开发的基于两点空间相关性和主成分分析(PCA)概念的材料结构量化框架可以满足这一关键需求。进一步证明，采用严格的结构量化框架有助于客观地确定许多模拟参数和在模拟中做出的选择。
Phase-field simulations have become valuable tools in explorations of the effects of the processing parameters on the internal structure evolution of materials in a broad range of advanced materials. Simulations conducted on high performance computers have enabled the resolution of relatively large material volumes and have generated big datasets. Although such computations have captured faithfully the many features observed in the corresponding experiments, there has not yet been a broadly adopted framework to quantitatively analyze the predicted material structures and compare them rigorously with experiments. This paper demonstrates that the recently developed framework for the quantification of the material structure, based on the concepts of 2-point spatial correlations and principal component analyses (PCA), can address this critical need. It is further demonstrated that the adoption of a rigorous framework for structure quantification can help to establish objectively many of the modeling parameters and choices made in the simulations.