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.

摘要：化学系和普林斯顿材料研究所首席研究员 Salvatore Torquato 及其同事的研究项目旨在研究堵塞硬颗粒填料的统计几何形状。开发了高效且新颖的算法来生成二维和三维硬球、硬椭球和硬球柱的单分散和多分散堆积。特别是，我们致力于改进 Lubachevsky-Stillinger 和 Zinchenko 打包算法。将设计一种随机线性编程算法来测试生成的硬粒子系统中的“干扰”类别，以及处理不可穿透性约束中的非线性的扩展。生成的堵塞堆积将通过使用高质量标量阶度量并收集 Voronoi 曲面细分统计数据进行统计表征。最后，上述工具和算法将用于研究堵塞结构的空间，包括识别最大随机堵塞（MRJ）状态和低密度堵塞堆积。拟议的研究可能会产生重要的科学进展和成果。这个多学科项目结合了统计物理学、材料科学、几何学、应用数学和高性能计算社区。统一这些不同的观点和目标是一项具有挑战性的任务，但它为整个计算科学界带来了巨大的回报。由于颗粒堆积被广泛用作颗粒材料、玻璃、液体和其他随机介质的简单模型，因此这项研究将对新型材料和纳米技术的发展产生重要影响。