A GPU-based discrete element modeling code and its application in die filling

Yue, Xiaoqiang; Zhang, Hao; Ke, Chunhai; Luo, Congshu; Shu, Shi; Tan, Yuanqiang; Feng, Chunsheng

doi:10.1016/j.compfluid.2014.11.020

Cited by 13 publications

(4 citation statements)

References 32 publications

(38 reference statements)

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“…This is especially critical because a number of DEM simulations are performed without using a supercomputer and the calculation time increases significantly on a single personal computer when an excessive number of particles are used. This problem cannot be solved even with the latest multicore processors (Liu et al, 2014;Yue et al, 2014). Many industries require application of the DEM to large-scale powder systems on a single personal computer.…”

Section: Large-scale Discrete Element Simulation 21 Existing Problemsmentioning

confidence: 99%

“…It has been applied in various engineering fields and many DEM applications exist. With respect to single flow such as granular flow, the DEM has been applied to solid mixing (Radeke et al, 2010;Chandratilleke et al, 2012;Halidan et al, 2014;Eitzlmayr et al, 2014;Huang and Kuo, 2014), die filling (Guo et al, 2009;Wu, 2008;Mateo-Ortiz et al, 2014;Yue et al, 2014), powder transportation (Tsuji et al, 1992;Mio et al, 2009;Kuang et al, 2013), and granulation (Washino et al, 2013), among others. Since the DEM-CFD method was developed, the DEM has been applied to new fields along with fluidized beds and cyclones.…”

Section: Introductionmentioning

confidence: 99%

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How Should the Discrete Element Method Be Applied in Industrial Systems?: A Review

Sakai

2016

KONA

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In this paper, we describe an industrial application of the discrete element method (DEM). The DEM has been applied to various powder systems thus far and therefore appears to be an established approach. However, it cannot be applied to many industrial systems because of several critical problems such as modeling of large-scale simulations, complexly shaped wall boundaries and free surface fluid flow. To solve these problems, novel models were developed by our group. A coarse-grain DEM model was developed for large-scale simulations, a signed distance function-based wall boundary model was developed for complexly shaped walls and a DEM-moving particle semi-implicit method was developed for solid-liquid flow involving a free surface. The adequacy of these models was demonstrated through verification and validation tests. Our approach shows promise in industrial applications.

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Section: Large-scale Discrete Element Simulation 21 Existing Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Should the Discrete Element Method Be Applied in Industrial Systems?: A Review

Sakai

2016

KONA

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“…Efficient GPU implementations of the practical DEM models are more challenging, because of the increase in the computing time and the required memory, which can reduce the number of the simulated particles. Yue et al [26] have made a GPU version of the Trubal code and demonstrated its application in die filling. In 3D simulations, containing 20 000 particles, an average speedup of 19.66 has been achieved on NVIDIA Tesla K40c card.…”

Section: Introductionmentioning

confidence: 99%

The Performance Analysis of the Thermal Discrete Element Method Computations on the GPU

Pacevič

Kačeniauskas

2022

cai

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The paper presents a GPU implementation of the thermal discrete element method (TDEM) and the comparative analysis of its performance. Several discrete element models for granular flows, the bonded particle model and the TDEM are considered for quantitative comparison of computational performance. The performance measured on NVIDIA ® Tesla™ P100 GPU is compared with that attained by running the same OpenCL code on Intel ® Xeon™ E5-2630 CPU with 20 cores. The presented GPU implementation of the TDEM increases the computing time of the bonded particle model only up to 30.6 % of the computing time of the simplest DEM model, which is an acceptable decrease in the performance required for solving coupled thermomechanical problems.

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“…For three dimensional IB-LBM simulations, the grid number is very large and the efficiency of computations on CPUs is relatively low. In this paper, the in-house code is implemented based on the CPU-GPU heterogeneous architecture [45]. In the CPU-GPU heterogeneous computer system, the GPU cooperates with the CPU in the complicated calculation progress.…”

Section: Cuda Introduction and Parameters Of Platformsmentioning

confidence: 99%

On the drag coefficient and averaged Nusselt number of an ellipsoidal particle in a fluid

Shu

Zhang

et al. 2018

Powder Technology

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The paper aims to improve existing correlations for the drag coefficient and averaged Nusselt number of an ellipsoidal particle in a fluid by additionally considering its orientation. To do so, three dimensional Immersed Boundary-Lattice Boltzmman Method (IB-LBM) simulations were carried out on a classical problem where a hot stationary ellipsoidal particle was passed by continuous cold fluid flows. By changing the shape (0.25 ≤ Ar ≤ 2.5) and incident angle (0 • ≤ θ ≤ 90 •) of the solid particle as well as the Reynolds number (10 ≤ Re ≤ 200), the momentum and heat transfer between the solid and fluid phases was quantitatively evaluated and the drag coefficient and averaged Nusselt number were numerically quantified. Then, based on the obtained data, correlations for the drag coefficient and averaged Nusselt number were established by considering Ar, θ and Re as the key influencing factors. Proposed correlations were proven to hold promising prediction capabilities and would be useful to be enclosed in those complex multiphase coupling calculations.

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A GPU-based discrete element modeling code and its application in die filling

Cited by 13 publications

References 32 publications

How Should the Discrete Element Method Be Applied in Industrial Systems?: A Review

How Should the Discrete Element Method Be Applied in Industrial Systems?: A Review

The Performance Analysis of the Thermal Discrete Element Method Computations on the GPU

On the drag coefficient and averaged Nusselt number of an ellipsoidal particle in a fluid

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