Phillips Carolyn L, Peterka Tom, Karpeyev Dmitry, Glatz Andreas
Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA.
Materials Science Division, Argonne National Laboratory, Argonne, Illinois 60439, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Feb;91(2):023311. doi: 10.1103/PhysRevE.91.023311. Epub 2015 Feb 20.
In type II superconductors, the dynamics of superconducting vortices determine their transport properties. In the Ginzburg-Landau theory, vortices correspond to topological defects in the complex order parameter. Extracting their precise positions and motion from discretized numerical simulation data is an important, but challenging, task. In the past, vortices have mostly been detected by analyzing the magnitude of the complex scalar field representing the order parameter and visualized by corresponding contour plots and isosurfaces. However, these methods, primarily used for small-scale simulations, blur the fine details of the vortices, scale poorly to large-scale simulations, and do not easily enable isolating and tracking individual vortices. Here we present a method for exactly finding the vortex core lines from a complex order parameter field. With this method, vortices can be easily described at a resolution even finer than the mesh itself. The precise determination of the vortex cores allows the interplay of the vortices inside a model superconductor to be visualized in higher resolution than has previously been possible. By representing the field as the set of vortices, this method also massively reduces the data footprint of the simulations and provides the data structures for further analysis and feature tracking.
在II型超导体中,超导涡旋的动力学决定了它们的输运性质。在金兹堡-朗道理论中,涡旋对应于复序参量中的拓扑缺陷。从离散的数值模拟数据中提取它们的精确位置和运动是一项重要但具有挑战性的任务。过去,涡旋大多是通过分析表示序参量的复标量场的大小来检测的,并通过相应的等高线图和等值面进行可视化。然而,这些主要用于小规模模拟的方法会模糊涡旋的精细细节,在大规模模拟中扩展性较差,并且不容易实现对单个涡旋的分离和跟踪。在这里,我们提出了一种从复序参量场中精确找到涡旋中心线的方法。使用这种方法,可以以比网格本身更精细的分辨率轻松描述涡旋。涡旋核心的精确确定使得模型超导体内部涡旋的相互作用能够以比以前更高的分辨率可视化。通过将场表示为涡旋集,这种方法还大幅减少了模拟的数据量,并为进一步分析和特征跟踪提供了数据结构。