Effect of Young's modulus on DEM results regarding transverse mixing of particles within a rotating drum

Abstract

The use of a Young's modulus lower than its true value is commonly adopted in numerical studies based on the discrete element method (DEM), in order to reduce the computational costs. However, the sensitivity of the DEM results to the value of Young's modulus is poorly understood. In this paper, we present a study on the relationship between the Young's modulus and DEM results, where the transverse mixing of particles within a rotating drum in rolling regime was considered. The values of E0, 0.01E0, 0.001E0, 0.0007E0, 0.0005E0, and 0.0001E0 were employed in the model, where E0 was the true value of the Young's modulus of the particle. The results reveal that Young's modulus makes considerable effects on the motion behaviour of individual particle during collision. However, these effects have little contribution to the mixing of a granular bed which is the cumulative effect of the motion of all individual particles. The explanation for this is that the mixing contribution from self-diffusion owing to the random collisions of particles is negligible compared to the one from the shearing effect of particles. For Young's modulus ranging from E0 to 0.001E0, the simulated mixing results are comparable to each other, and all agree well with the experimental results. However, a significant reduction occurs to the mixing rate of the granular bed with E ranging from 0.0007E0 to 0.0001E0 owing to the decrease in the shearing effects of particles, and this change is transitional rather than developmental. The effects of the drum rotation speed and filling level on the relationship between Young's modulus and particle mixing is not observed. It is proposed that there is a critical value between 0.001E0 and 0.0007E0, above which the effect of Young's modulus on the mixing of the bed is ignorable and reliable simulation results can be obtained. Therefore, there exists a lower limit for using adjusted Young's modulus to reduce the computational costs.