GPU and CPU based Discrete Element Method (DEM) model for Parametric Analysis of Paddle Mixers

Description

Presentation at the 8th International Conference on Particle-Based Methods (Particles 2023), Milan, Italy, 9-11 October 2023

This work presents a study aimed at enhancing the understanding of the mixing performance in double shaft, batch-type paddle mixers. To achieve this, we employed the discrete element method (DEM) on GPU and CPUs in conjunction with a Plackett-Burman design of experiments simulation plan [1]. Our objective was to identify the significance of various factors on the mixing performance of the system. We examined the effects of multiple factors, including three material properties (particle size, particle density, and composition), three operational conditions (initial filling pattern, fill level, and impeller rotational speed), and three geometric parameters (paddle size, paddle angle, and paddle number). The investigation utilized the relative standard deviation (RSD) as a quantitative measure. Four key performance indicators (KPIs) were defined to evaluate the mixing performance of the double paddle mixer: mixing quality, mixing time, average mixing power, and energy required to achieve a steady state. Our findings indicate that while material properties have an effect, the operational conditions and geometric parameters exhibit greater significance in influencing the mixer's performance. Notably, the geometric parameters were observed to significantly impact energy consumption without affecting mixing quality and mixing time, suggesting their potential for designing more sustainable mixers. Additionally, our analysis of granular temperature revealed that the central area between the two paddles exhibits high diffusivity, which correlates with the mixing time. This insight contributes to a deeper understanding of the mixing process. Overall, this study sheds light on the factors influencing the mixing performance of double shaft, batch-type paddle mixers, providing valuable insights for improving mixer design and operation. REFERENCES [1] Emmerink, J.; Hadi, A.; Jovanova, J.; Cleven, C.; Schott, D.L. Parametric Analysis of a Double Shaft, Batch-Type Paddle Mixer Using the Discrete Element Method (DEM). Processes 2023, 11, 738.

Period	9 Oct 2023
Event title	Particles 2023: 8th International Conference on Particle-Based Methods
Event type	Conference
Location	Milan, ItalyShow on map