GPU-based DEM simulation for scale-up of bladed mixers

GPU-based DEM is used to study large-scale particle mixing in bladed mixers. A bladed mixer is scaled-up to three different sizes by maintaining the geometric similarity. Four Froude numbers are selected as the main operating conditions of bladed mixers with different sizes. The results demonstrated that the mixing quality across different mixer sizes is similar at the same Froude number, but it requires a longer mixing time to achieve similar mixing performances as the mixer becomes larger. Correlations to predict the mixing rate, average particle velocity, average total forces, average contact forces and average blade torque as functions of the scale-up ratio and Froude number (or rotation speed) are proposed. A similarity study shows that maintaining the dynamic or kinematic similarity does not produce a similar mixing performance, while maintaining the mixing rate produces a similar mixing performance across all mixers. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study utilizes GPU-based DEM to investigate particle mixing in bladed mixers at different scales. Results indicate that maintaining geometric similarity while scaling up mixers results in similar mixing quality at the same Froude number, although larger mixers require longer mixing times. Correlations are proposed to predict various parameters as functions of scale-up ratio and Froude number, and a similarity study suggests that maintaining mixing rate produces consistent mixing performance across all mixer sizes.