Jamming and Disorder in Hard-Particle Packings

硬颗粒填料中的堵塞和紊乱

基本信息

批准号：
0312067
负责人：
Salvatore Torquato
金额：
--
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2003
资助国家：
美国
起止时间：
2003-07-01 至 2008-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312067&HistoricalAwards=false
关键词：
Jamming Disorder Hard Particle Packings

项目摘要

ABSTRACTA research program by Salvatore Torquato, the principal investigator, and his colleagues in the Dept. of Chemistry & Princeton Materials Institute is aimed at studying the statistical geometry of jammed hard-particle packings. Efficient and novel algorithms are developed to generate monodisperse and polydisperse packings of hard spheres, hard ellipsoids, and hard spherocylinders in two and three dimensions. In particular, efforts are directed toward improving the Lubachevsky-Stillinger and Zinchenko packing algorithms. A randomized linear-programming algorithm will be devised to test for ``jamming'' categories in the generated hard-particle systems, as well as extensions to deal with nonlinearities in the impenetrability constraints. The generated jammed packings will be statistically characterized by using high-quality scalar order metrics and by collecting Voronoi tessellation statistics. Finally, the aforementioned tools and algorithms will be employed to investigate the space of jammed structures, including the identification of the maximally random jammed (MRJ) state and low-density jammed packings.Important scientific advances and outcomes are likely to emerge from the proposed research. This multidisciplinary project joins the statistical physics, materials science, geometry, applied mathematics, and high-performance computing communities. Unifying these different perspectives and goals is a challenging task, but one that offers great rewards to the computational-science community in general. Because particle packings are widely used as simple models for granular materials, glasses, liquids, and other random media, the proposed research will have important implications for the development of novel materials as well as nanotechnology.

首席研究员Salvatore Torquato的摘要研究计划及其在化学与普林斯顿材料研究所的同事旨在研究堵塞硬粒子包装的统计几何形状。开发了有效且新颖的算法，以在两个和三个维度中生成硬球，硬椭圆形和硬晶状体夹具的单分散和多分散堆。特别是，为改善Lubachevsky-Stillinger和Zinchenko包装算法而致力于改善。将设计一种随机线性编程算法，以测试生成的硬粒子系统中的``干扰''类别，以及在无法穿透性约束中处理非线性的扩展。通过使用高质量的标量订单指标和收集Voronoi Tessellation统计数据，生成的堵塞包装将在统计上进行统计学特征。最后，将采用上述工具和算法来研究堵塞结构的空间，包括鉴定最大随机堵塞（MRJ）国家和低密度堵塞的包装。重要的科学进展和结果可能会从拟议的研究中得出。这个多学科项目与统计物理，材料科学，几何，应用数学和高性能计算社区相连。统一这些不同的观点和目标是一项具有挑战性的任务，但总体上为计算科学社区提供了丰厚的回报。由于粒子包装被广泛用作颗粒材料，眼镜，液体和其他随机培养基的简单模型，因此拟议的研究将对新型材料和纳米技术的开发具有重要意义。