Particle weight spatial distribution and force chain network evolution in flat-bottomed silos using the discrete element method

In industrial-scale powder storage facilities, abnormal stress distribution on silo walls can lead to severe safety accidents, including structural deformation and collapses. The powder weight distribution in three-dimensional flat-bottomed silos was studied from the force chain network rearrangement of the micro-mechanism to the wall stress distribution of the macro-behavior. The effects of particle size, silo diameter, and powder type were considered using the discrete element method. At a constant filling height-to-diameter ratio (Hfilling/D), increasing particle size slightly raises the bottom load-bearing percentage, while maintaining a constant filling height significantly increases this percentage as silo diameter increases. The size effect notably affects load-bearing distribution and pressure accumulation on the silo wall. The bottom effect weakens the sidewall load-bearing capacity near the bottom. The average particle–wall friction coefficient in static packing is lower than the predefined value, indicating insufficient wall friction mobilization. Shallow silos primarily support powder weight from the bottom. As Hfilling/D increases, strong force chains develop, enhancing force transmission and uniformity, while enhancing the sidewall load-bearing capacity.

Authors

Institutions

Publication Details

Journal
Physics of Fluids
Published
2025-02-01
DOI
https://doi.org/10.1063/5.0253684
Citations
3
Primary Topic
Granular flow and fluidized beds
Type
article
Field-Weighted Citation Impact
4.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Particle weight spatial distribution and force chain network evolution in flat-bottomed silos using the discrete element method

Minghao You, Xiaoping Chen, Jiliang Ma, Xin Wang et al.
3 citations
Physics of Fluids
Granular flow and fluidized beds
4.00
article

Particle weight spatial distribution and force chain network evolution in flat-bottomed silos using the discrete element method

Minghao You, Xiaoping Chen, Jiliang Ma, Xin Wang, Daoyin Liu
article en
3 citations

Abstract

In industrial-scale powder storage facilities, abnormal stress distribution on silo walls can lead to severe safety accidents, including structural deformation and collapses. The powder weight distribution in three-dimensional flat-bottomed silos was studied from the force chain network rearrangement of the micro-mechanism to the wall stress distribution of the macro-behavior. The effects of particle size, silo diameter, and powder type were considered using the discrete element method. At a constant filling height-to-diameter ratio (Hfilling/D), increasing particle size slightly raises the bottom load-bearing percentage, while maintaining a constant filling height significantly increases this percentage as silo diameter increases. The size effect notably affects load-bearing distribution and pressure accumulation on the silo wall. The bottom effect weakens the sidewall load-bearing capacity near the bottom. The average particle–wall friction coefficient in static packing is lower than the predefined value, indicating insufficient wall friction mobilization. Shallow silos primarily support powder weight from the bottom. As Hfilling/D increases, strong force chains develop, enhancing force transmission and uniformity, while enhancing the sidewall load-bearing capacity.

Physics of FluidsVol. 37(2)
Southeast University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 9%
Granular flow and fluidized beds
4.00
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.