The importance of parameter-dependent coefficient of restitution in discrete element method simulations

Yurata, Tarabordin; Gidaspow, Dimitri; Piumsomboon, Pornpote; Chalermsinsuwan, Benjapon

doi:10.1016/j.apt.2021.02.005

Cited by 6 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Relevant parameters were collected from previous research papers and laboratory tests [38][39][40][41][42][43][44]. The initial analysis used fractional two-level factors, with the parameter ranges shown in Table 2.…”

Section: 1 Identification Of Micro Parameter For Upscaling Dem Modelmentioning

confidence: 99%

Calibrating polypropylene particle model parameters with upscaling and repose surface method

Sudsawat,

Chongchitpaisan,

Arunyanart

2023

Eureka: PE

View full text Add to dashboard Cite

The discrete element method (DEM) is a computational technique extensively utilized for simulating particles on a large scale, specifically focusing on granular materials. Nonetheless, its implementation requires a substantial amount of computational power and accurate material properties. Consequently, this study delves into an alternative approach referred to as volume-based scaled-up modeling, aiming to simulate polypropylene particles using DEM while mitigating the computational burden and regenerating new material properties. This novel method aims to reduce the CPU time required for the simulation process and represent both the macro mechanical behavior and micro material properties of polypropylene particles. To accomplish this, the dimensions of the polypropylene particles in the DEM simulation were magnified by a factor of two compared to the original size of the prolate spheroid particles. In order to determine the virtual micro material properties of the polypropylene particles, a calibration method incorporating the design of experiments (DOE) and repose surface methodology was employed. The predicted bulk angle of repose (AOR) derived from the upscaled DEM parameters exhibited a remarkably close agreement with the empirical AOR test, demonstrating a small relative error of merely 1.69 %. Moreover, the CPU time required for the upscaled particle model proved to be less than 71 % of that necessary for the actual-scale model of polypropylene particles. These compelling results confirm the effectiveness of enlarging the particle volume used to calibrate micro-material properties in the Discrete Element Method (DEM) through the DOE technique. This approach proves to be a reliable and efficient method

show abstract