Experimental, continuum- and DEM-based velocities in a flat-bottomed bin

Balevičius, Robertas; Maknickas, Algirdas; Sielamowicz, Irena

doi:10.1016/j.powtec.2020.08.071

Cited by 2 publications

(3 citation statements)

References 39 publications

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“…Lethe-DEM predicts the trends and velocity magnitudes with acceptable accuracy. DEM results in the same work [41] have similar error values. Errors are attributed to instantaneous experimental measurements.…”

Section: Flat-bottomed Silomentioning

confidence: 60%

“…Balevivcius et al [41] measured the particle velocity using particle image velocimetry and DEM in a flat-bottomed silo with n p = 18, 000 particles. Table 5-a shows the geometry of the silo and reports the physical properties of the simulation [41]. An animation (silo1.mp4) of this simulation is available in the supplementary materials.…”

Section: Flat-bottomed Silomentioning

confidence: 99%

“…Figure12: Lateral distributions of (a) vertical, and (b) lateral components of particles velocity in a flat-bottomed silo, obtained from Lethe-DEM (lines) and experiments (markers) using PIV[41].…”

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confidence: 99%

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Lethe-DEM : An open-source parallel discrete element solver with load balancing

Golshan¹,

Münch²,

Gassmöller³

et al. 2021

Preprint

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Approximately 75% of the raw material and 50% of the products in the chemical industry are granular materials. The Discrete Element Method (DEM) provides detailed insights of phenomena at particle scale and it is therefore often used for modeling granular materials. However, because DEM tracks the motion and contact of individual particles separately, its computational cost increases nonlinearly O(n p log(n p )) -O(n 2 p ) depending on the algorithm) with the number of particles (n p ). In this article, we introduce a new open-source parallel DEM software with load balancing: Lethe-DEM. Lethe-DEM, a module of Lethe, consists of solvers for two-dimensional and three-dimensional DEM simulations. Loadbalancing allows Lethe-DEM to significantly increase the parallel efficiency by ≈ 25 − 70% depending on the granular simulation. We explain the fundamental modules of Lethe-DEM, its software architecture, and the governing equations. Furthermore, we verify Lethe-DEM with several tests including analytical solutions and comparison with other software. Comparisons with experiments in a flat-bottomed silo, wedge-shaped silo, and rotating drum validate Lethe-DEM. We investigate the strong and weak scaling of Lethe-DEM with 1 ≤ n c ≤ 192 and 32 ≤ n c ≤ 320 processes, respectively, with and without load-balancing. The strong-scaling analysis is performed on the wedge-shaped silo and rotating drum simulations, while for the weak-scaling analysis, we use a dam break simulation. The best scalability of Lethe-DEM is obtained in the range of 5000 ≤ n p /n c ≤ 15 000. Finally, we demonstrate that large scale simulations can be carried out with Lethe-DEM using the simulation of a three-dimensional

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Section: Flat-bottomed Silomentioning

confidence: 60%

Section: Flat-bottomed Silomentioning

confidence: 99%