Experimental and Numerical Investigation on the Discharge of Wood Pellets From a Hopper With the Discrete Element Method

2012 ◽  
Author(s):  
Dominik Höhner ◽  
Siegmar Wirtz ◽  
Viktor Scherer
Keyword(s):  
Discrete Element Method ◽  
Bulk Density ◽  
Particle Shape ◽  
Discharge Rate ◽  
Discrete Element ◽  
Wall Friction ◽  
Approximation Schemes ◽  
Wood Pellets ◽  
Hopper Discharge ◽  
Element Method

In this study hopper discharge experiments with wood pellets were conducted. The experimental bulk density, flow behavior and discharge rate were compared to corresponding 3-dimensional discrete element simulations with both multi-sphere and polyhedral approximations of the pellet geometry. Additionally a numerical sensitivity analysis for the particle-wall friction was made in order to evaluate the influence of this parameter on hopper discharge in the context of different particle geometries. In the past comparisons of experimentally and numerically obtained results demonstrated the adequacy of the discrete element method for predicting the general discharge behavior of a hopper. Nevertheless, in this study, comparing two different particle shape-approximations, significant differences in terms of bulk density, discharge rate, flow profile and dependency on the particle-wall friction coefficient between both investigated particle-shape approximation schemes could be observed. As a result, particle shape-representation must be considered a significant parameter in DEM-simulations.

scholarly journals Estimation of Bulk Density Distribution in Particle Charging Process Using Discrete Element Method Considering Particle Shape

2008 ◽  
Vol 48 (11) ◽  
pp. 1500-1506 ◽  
Author(s):  
Masatoshi Akashi ◽  
Hiroshi Mio ◽  
Atsuko Shimosaka ◽  
Yoshiyuki Shirakawa ◽  
Jusuke Hidaka ◽  
...  
Keyword(s):  
Discrete Element Method ◽  
Density Distribution ◽  
Bulk Density ◽  
Particle Shape ◽  
Discrete Element ◽  
Particle Charging ◽  
Element Method

2716 New method for considering particle shape in Discrete Element Method

2007 ◽  
Vol 2007.20 (0) ◽  
pp. 621-622
Author(s):  
Masatoshi AKASHI ◽  
Hiroshi MIO ◽  
Atsuko SHIMOSAKA ◽  
Yoshiyuki SHIRAKAWA ◽  
Jusuke HIDAKA ◽  
...  
Keyword(s):  
Discrete Element Method ◽  
Particle Shape ◽  
Discrete Element ◽  
New Method ◽  
Element Method

scholarly journals Comparison between O(n2) and O(n) neighbor search algorithm and its influence on superlinear speedup in parallel discrete element method (DEM) for complex-shaped particles

2018 ◽  
Vol 35 (6) ◽  
pp. 2327-2348 ◽  
Author(s):  
Beichuan Yan ◽  
Richard Regueiro
Keyword(s):  
Discrete Element Method ◽  
Particle Shape ◽  
Search Algorithm ◽  
Discrete Element ◽  
Search Algorithms ◽  
Content Type ◽  
Neighbor Search ◽  
Shape Complexity ◽  
Superlinear Speedup ◽  
Element Method

Purpose This paper aims to present performance comparison between O(n2) and O(n) neighbor search algorithms, studies their effects for different particle shape complexity and computational granularity (CG) and investigates the influence on superlinear speedup of 3D discrete element method (DEM) for complex-shaped particles. In particular, it aims to answer the question: O(n2) or O(n) neighbor search algorithm, which performs better in parallel 3D DEM computational practice? Design/methodology/approach The O(n2) and O(n) neighbor search algorithms are carefully implemented in the code paraEllip3d, which is executed on the Department of Defense supercomputers across five orders of magnitude of simulation scale (2,500; 12,000; 150,000; 1 million and 10 million particles) to evaluate and compare the performance, using both strong and weak scaling measurements. Findings The more complex the particle shapes (from sphere to ellipsoid to poly-ellipsoid), the smaller the neighbor search fraction (NSF); and the lower is the CG, the smaller is the NSF. In both serial and parallel computing of complex-shaped 3D DEM, the O(n2) algorithm is inefficient at coarse CG; however, it executes faster than O(n) algorithm at fine CGs that are mostly used in computational practice to achieve the best performance. This means that O(n2) algorithm outperforms O(n) in parallel 3D DEM generally. Practical implications Taking for granted that O(n) outperforms O(n2) unconditionally, complex-shaped 3D DEM is a misconception commonly encountered in the computational engineering and science literature. Originality/value The paper clarifies that performance of O(n2) and O(n) neighbor search algorithms for complex-shaped 3D DEM is affected by particle shape complexity and CG. In particular, the O(n2) algorithm outperforms the O(n) algorithm in large-scale parallel 3D DEM simulations generally, even though this outperformance is counterintuitive.

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