Novel designs of blade mixer impellers from the discrete element method and topology optimization

Granular mixers are essential for manufacturing products that require bulk materials as precursors. The modeling of mixers is aided by the discrete element method (DEM), which can simulate the flow of bulk material. DEM is applied in mixer design studies by varying a few design parameters and analyzing the predicted performance, but systematic design via optimization methods has yet to be performed. In this work, we utilized a combined DEM and topology optimization approach to design blade mixer impellers for improved mixing and reduced energy consumption. A DEM model was formulated from a blade mixer setup and validated with experimental particle flow data. The computational cost of the model was then reduced by employing larger particle sizes in the simulations. A binary genetic optimization algorithm was used afterwards to find the tilt angle, number, and shape of impeller blades that would improve mixing or reduce the required driving power. The high mixing impellers have four blades that each have cavities at the center and the tip. The increased blade count promotes convective mixing, while the cavities enhance shear mixing. Meanwhile, the low energy impellers have two blades that both have cavities at the center and less material at the blade tips. These minimize particle-impeller collisions, which in turn reduce the power required to drive the impeller. This work demonstrates the use of optimization methods to design the shape of impeller blades - a variable often overlooked in conventional DEM studies. Our approach may be used to systematically design other mixer types as well.