Issue |
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|
|
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Article Number | 04026 | |
Number of page(s) | 7 | |
Section | Monte Carlo Transport | |
DOI | https://doi.org/10.1051/epjconf/202124704026 | |
Published online | 22 February 2021 |
https://doi.org/10.1051/epjconf/202124704026
AN IMPROVED DISTINCT ELEMENT METHOD FOR HIGH PACKING FRACTION STOCHASTIC MEDIA MODELING
Department of Engineering Physics, Tsinghua University, Beijing 100084, China
fengzy17@mails.tsinghua.edu.cn
Published online: 22 February 2021
Due to the generality and flexibility of Monte Carlo method in geometric modeling, Monte Carlo method plays an important role in accurate simulation of random media. At present, rand om sequential addition method (RSA) and distinct element method (DEM) are more accurate and mature explicit modeling methods. The former approach has the problem of upper limit of packing fraction, which is suitable for stochastic geometry with lower filling rate. DEM method can fill random medium model with packing fraction higher than 60%, but DEM is not suitable for non-contact dispersed particles based on the interaction between particles. There fore, an improved DEM method is proposed to solve the problem of modeling non-contact p articles dispersed in the stochastic media with high packing fraction. The virtual surfaces are constructed outside of the outer layer of particles to make them in contact with each other. Thus, the particle system is suitable for DEM method. The construction of virtual surface does not affect the neutron transport process. The correctness of the improved DEM is verified by comparing the total filling particle number and calculation results of keff with RSA method. At the same time, according to the distribution of filling particles, the improved DEM method fills the particles more uniformly.
Key words: distinct element method / virtual surfaces / stochastic media / Monte Carlo method
© The Authors, published by EDP Sciences, 2021
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