Predictive modeling of mixing time for super-ellipsoid particles in a four-bladed mixer: A DEM-based approach

Numerical simulations of super-ellipsoid particles in a four-bladed mixer using the discrete element method is presented. The effect of number of particles, blade rake angle and impeller rotation speed, on the performance of the mixer was examined. Mixing mechanism and flow pattern of particles were studied through velocity profiles, particle trajectories and granular temperature. It was found that the spherical particles mix faster than the prolate and the oblate particles. In case of using acute blade rake angles, the mixing is slower than obtuse angle. It was found that the granular temperature is higher in the vicinity of the wall for all particle shapes. The spherical particles have the highest granular temperature than the prolate and the oblate particles, which have same granular temperatures. To predict the mixing time, regression models were employed. The voting regressor model was shown to be successful at predicting the mixing time.

Automated summary

This study presents numerical simulations of super-ellipsoid particles in a four-bladed mixer using the discrete element method. The effect of several factors on the mixer's performance, including the number of particles, blade rake angle, and impeller rotation speed, was examined. Mixing mechanism and flow pattern were analyzed through various parameters such as velocity profiles, particle trajectories, and granular temperature. The study revealed that spherical particles have a faster mixing rate compared to prolate and oblate particles, and acute blade rake angles result in slower mixing than obtuse angles. Regression models were employed to predict mixing time, and the voting regressor model showed success in this regard.