Yusuke Shigeto

Numerical Study on Coarse Grain Modeling of the DEM for Industrial Scale Gas-Solid Flow Systems

The Discrete Element Method (DEM) is widely used in various numerical simulations related to granular media. The DEM is a Lagrangian approach where individual particle is calculated based on the Newton’s second law of motion. Therefore, the DEM enables us to investigate the granular flow characteristics at the particle level. On the other side, the DEM has a difficulty to be applied in large-scale powder systems because the calculation cost becomes too expensive when the number of particles is huge. To solve this issue, we have developed a coarse grain modeling as a large scale model of the DEM. The coarse grain particle represents a group of original particles. The coarse grain model was used in typical gas-solid and solid-liquid two phase flows so far, where the particle size was relatively large, namely, cohesive force did not act between the solid particles. In the present study, the coarse grain model is evolved to simulate fine particles by considering the interparticle van der Waals force. The adequacy of the coarse grain model is proved by comparing the simulation results of original particle system. Through this study, the coarse grain model is shown to simulate the cohesive particle behavior precisely.Copyright

Study on the Particle Behavior in a Screw Conveyer System by the Discrete Element Method

Large amount of particles are used in the industrial systems. Numerical analyses of these systems are expected to reduce designing cost. However the numerical analysis of powder is not used practically, because it requires high calculation cost which grows up with the number of particles. Besides, there are memory consumption problem which is required for calculation space. In this paper, the parallel simulation techniques of the Discrete Element Method (DEM) on multi-core processors are described. In the present study, it is shown that the algorithm enables all the processes of the DEM to be executed parallel. Moreover, a new algorithm which makes the memory space usage effectively and accelerates the calculation speed is proposed for multi-thread parallel computing of the DEM. In the present study, the memory space usage is shown to be reduced drastically by introducing this algorithm. In addition, the coarse grain model which emulates original particles with less calculation particles is applied in order to reduce calculation cost. For the practical usage of the DEM in industries, the simulation is performed in a large-scale powder system which possesses a complicated drive unit. In the current study, it is shown that the large scale DEM simulation of practical systems is enabled to be executed by our proposing algorithms.Copyright