Experimental and Numerical Analysis of an Interactive Motion Drum for Enhanced Mixing and Segregation of Mixtures

Abstract

Rotating drums are widely used in particle processing, where internal obstacles play a crucial role in controlling particle mixing and segregation dynamics. Traditional designs often struggle with stagnant zones and inconsistent mixing dynamics, particularly under varying rotational conditions. This study addresses this gap by proposing an innovative interactive motion design between the drum and obstacles, creating reverse rolling waves that disrupt stagnant zones and enhance mixing efficiency. By employing counter-rotation (where the drum and obstacles rotate in opposite directions), the design effectively disrupts stagnant zones, enhances convection and diffusion, and significantly improves mixing efficiency across different speeds. At higher rotational speeds, cascading effects intensify, reducing particle clusters while maintaining high mixing efficiency. Conversely, synchronous rotation (drum and obstacles rotating in the same direction) favors segregation, with obstacles disrupting particle flow, leading to localized velocity reductions and eventual separation. To quantitatively assess these effects, machine learning algorithms, including Segment Anything, were employed to analyze experimental and simulation results, providing robust validation of particle dynamics. Additionally, a 3D-printed, self-designed rotating drum was utilized to experimentally validate the optimized flow patterns predicted through simulations. This study offers new insights into designing advanced rotating drum systems with enhanced control over mixing and segregation dynamics, bridging the gap between computational modeling and experimental validation for improved particle processing performance.