二维/三维中代表性粉末颗粒堆积的生成：哪种工具适用于哪种应用？
Generation of representative powder particle packing in 2D/3D: which tool for which application?
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中文总结 AI 辅助
该研究系统基准测试四种开源粉末颗粒堆积生成工具，对比其在不同构型下的PSD保真度、相对密度及计算效率，为不同应用场景的工具选择提供指导。
中文摘要 AI 辅助
尽管致密球体堆积是粉末冶金、增材制造和颗粒物理模拟的初始状态，但堆积生成器的选择很少有系统基准指导。代表性堆积必须满足：（i）-（ii）物理上可接受（无重叠颗粒，处于重力平衡）；（iii）忠实于目标粒度分布（PSD）；（iv）相对密度Φ具有代表性；（v）计算成本可承受。本文对四种开源工具进行了基准测试，这些工具从结构上满足（i）-（ii）：顺序DR（下落-滚动）及其致密变体DR-ME，以及离散元法（DEM）代码LAMMPS（重力法）和dp3D（等静压压缩法）。在四种构型（二维/三维对数正态分布、三维双峰分布及三维域尺寸研究）中，将它们与工业MIM级粉末进行比较，PSD保真度由与箱宽无关的Hellinger距离衡量，Φ则与原料固相装载量（通过阿基米德法测得φ_exp=0.62）对比。三维中，DEM代码生成的堆积最致密，但运行速度慢三个数量级以上：对于约20000个颗粒，DR的φ比dp3D低9%，但运行速度快1800倍。这些理想化模型堆积为应用驱动的工具选择提供了指导方针。
英文摘要
Although dense sphere packings serve as the initial state for simulations in powder metallurgy, additive manufacturing and granular physics, the choice of a packing generator is rarely guided by a systematic benchmark. A representative packing must be (i)-(ii) physically admissible (non-overlapping particles in gravitational equilibrium); (iii) faithful to the target particle size distribution (PSD); (iv) representative in relative density Phi; and (v) computationally affordable. Four open-source tools have been benchmarked, meeting (i)-(ii) by construction: the sequential DR (dropping-and-rolling) and its densified variant DR-ME, and the discrete element method (DEM) codes LAMMPS (gravity) and dp3D (isostatic compression). Across four configurations (2D/3D lognormal, 3D bimodal, and a 3D domain-size study), they are compared against an industrial MIM-grade powder, with PSD fidelity measured by the bin-width-independent Hellinger distance and Phi against the feedstock solid loading (phi_exp = 0.62, by Archimedes' method). In 3D, the DEM codes reach the densest packings but run more than three orders of magnitude slower: for approximatively 20 000 particles, DR shows a 9% phi shortfall relative to dp3D while running 1800x faster. These idealised model packings yield application-driven tool-selection guidelines.
