Six-directional sampling method and mean mixing indices for solids blending performance analysis of DEM simulations

In the present study, a six-directional sampling method (SDSM) and six-directional sampling mean mixing indices (SDS-mMI) [i.e., SDS-mLMI, SDS-mRSD, and SDS-mSMI] based on SDSM are proposed for the first time by considering Lacey mixing index (LMI), relative standard deviation (RSD) and sub-domain mixing index (SMI) as basis to assess the solids blending performance in DEM simulations. The proposed approach consists of dividing the blender into small grids, and accumulating the mass and mass contributions of each type of particle by moving in six different directions to generate predetermined number of samples, finding the mixing index in each direction, and finding the mean value of the index over all the six directions. The proposed approach is evaluated for two typical blenders with cohesive/cohesion-less, mono/bi-dispersed particles systems. The proposed mixing indices based on the sampling approach are compared with the same mixing indices based on BCSM, and also with other non-sampling indices.(c) 2021 Elsevier B.V. All rights reserved.

Automated summary

In this study, a six-directional sampling method (SDSM) and corresponding mixing indices (SDS-mMI) based on SDSM were proposed for assessing solids blending performance in DEM simulations. The proposed method divided the blender into small grids and accumulated the mass and mass contributions of each particle type by moving in six different directions. The evaluation of the proposed approach was conducted on two typical blenders and compared with other indices.