Jamming and Disorder in Hard-Particle Packings

硬颗粒填料中的堵塞和紊乱

• 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

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：首席研究员萨尔瓦多·托夸托和他在该部门的同事们进行的一项研究计划。普林斯顿材料研究所的化学研究所，旨在研究堵塞的硬颗粒填料的统计几何学。 高效和新颖的算法开发，以产生单分散和多分散的硬球，硬椭球，硬spherecylinders在二维和三维包装。 特别是，努力是针对改进的Lubachevsky-Stillinger和Zinchenko包装算法。 将设计一个随机线性规划算法来测试所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。 所产生的堵塞包装将通过使用高质量的标量顺序度量和收集Voronoi镶嵌统计的统计特征。 最后，本文将利用上述工具和算法研究堵塞结构空间，包括最大随机堵塞（MRJ）状态和低密度堵塞填充的识别，并有望取得重要的科学进展和成果。这个多学科项目加入了统计物理，材料科学，几何，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，但这对计算科学界来说是一个巨大的回报。 由于颗粒填充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，因此所提出的研究将对新型材料和纳米技术的发展具有重要意义。