期刊
POWDER TECHNOLOGY
卷 386, 期 -, 页码 176-192出版社
ELSEVIER
DOI: 10.1016/j.powtec.2021.03.032
关键词
Particle morphology reconstruction; X-ray micro-computed tomography; Spherical harmonic analysis; Principal component analysis; Discrete element method; Gene mutation
资金
- Research Grant Council of theHong Kong SAR [CityU 11201020, CityU 11213517]
- National Natural Science Foundation of China [51779213]
- Hong Kong PhD fellowship scheme (HKPFS)
In this paper, an additive virtual particle reconstruction technique based on spherical harmonic-based principal component analysis (SH-PCA) is proposed, allowing reconstruction to any specific relative length scale (RLS). By introducing scaling factors of different scale ranges, gene-mutated virtual particles can be readily generated. These gene-mutated virtual particles were then used to investigate the initial packing properties of granular soils in discrete element modelling.
Many techniques are developed to reconstruct virtual particles of granular soils. However, the generation of realistic virtual particles involving gene mutations of particle morphology, particularly those at specified relative length scale (RLS), has not fully realised. In this paper, with the spherical harmonic-based principal component analysis (SH-PCA), an additive virtual particle reconstruction technique is proposed. The additive feature of this proposed technique stems from the variation matrix after SH-PCA, in which different principal components correspond to different scale ranges. Based on this additive feature, particle morphology can be reconstructed to any specific RLS. Besides, by introducing scaling factors of different scale ranges, gene-mutated virtual particles can be readily generated, either with a uniform mutation at all RLS or with a singular mutation at any specific RLS. Furthermore, these resulted gene-mutated virtual particles were incorporated into discrete element modelling to investigate the initial packing properties of granular soils. (c) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据