An interpretation of the influence of particle shape on the mechanical behavior of granular material

2019 ◽  
Vol 21 (3) ◽  
Author(s):  
Debdeep Sarkar ◽  
Meisam Goudarzy ◽  
Diethard König
Keyword(s):  
Granular Material ◽  
Mechanical Behavior ◽  
Particle Shape

scholarly journals EFFECT OF PARTICLE SHAPE ON STRENGTH AND DEFORMATION OF GRANULAR MATERIAL IN DEM ANALYSIS

2005 ◽  
pp. 51-63 ◽  
Author(s):  
Satoshi NONAMI ◽  
Shoji KATO ◽  
Yuji YOSHIMURA ◽  
Kenichi MAEDA ◽  
Shuichi YAMAMOTO
Keyword(s):  
Granular Material ◽  
Particle Shape ◽  
Dem Analysis ◽  
Strength And Deformation

scholarly journals Effect of filler particle shape on plastic-elastic mechanical behavior of high density poly(ethylene)/mica and poly(ethylene)/wollastonite composites

2018 ◽  
Vol 141 ◽  
pp. 92-99 ◽  
Author(s):  
Lubomír Lapčík ◽  
David Maňas ◽  
Barbora Lapčíková ◽  
Martin Vašina ◽  
Michal Staněk ◽  
...  
Keyword(s):  
Mechanical Behavior ◽  
Particle Shape ◽  
Filler Particle ◽  
High Density ◽  
Poly Ethylene

scholarly journals Porosity Prediction of Granular Materials through Discrete Element Method and Back Propagation Neural Network Algorithm

2020 ◽  
Vol 10 (5) ◽  
pp. 1693
Author(s):  
Yu Liu ◽  
Miaomiao Li ◽  
Peifeng Su ◽  
Biao Ma ◽  
Zhanping You
Keyword(s):  
Neural Network ◽  
Granular Material ◽  
Granular Materials ◽  
Particle Shape ◽  
Back Propagation ◽  
Discrete Element ◽  
Back Propagation Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bpnn Model

Granular materials are used directly or as the primary ingredients of the mixtures in industrial manufacturing, agricultural production and civil engineering. It has been a challenging task to compute the porosity of a granular material which contains a wide range of particle sizes or shapes. Against this background, this paper presents a newly developed method for the porosity prediction of granular materials through Discrete Element Modeling (DEM) and the Back Propagation Neural Network algorithm (BPNN). In DEM, ball elements were used to simulate particles in granular materials. According to the Chinese specifications, a total of 400 specimens in different gradations were built and compacted under the static pressure of 600 kPa. The porosity values of those specimens were recorded and applied to train the BPNN model. The primary parameters of the BPNN model were recommended for predicting the porosity of a granular material. Verification was performed by a self-designed experimental test and it was found that the prediction accuracy could reach 98%. Meanwhile, considering the influence of particle shape, a shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape.

scholarly journals Discrete Element Method for Modeling the Mechanical Behavior of Unsaturated Granular Material

2016 ◽  
Vol 35 (1) ◽  
pp. 157-181
Author(s):  
K. Tourani ◽  
. R. Mahboubi ◽  
E. Seyedi Hosseininia ◽  
◽  
◽  
...  
Keyword(s):  
Discrete Element Method ◽  
Granular Material ◽  
Mechanical Behavior ◽  
Discrete Element ◽  
Element Method
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