Performance of single and six-bladed powder mixers

Laurent, B.F.C.; Bridgwater, J.

doi:10.1016/s0009-2509(02)00052-0

Cited by 50 publications

(26 citation statements)

References 17 publications

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“…[40,41] Granular diffusion coefficients have been used to characterize the rate of mixing in numerous studies. [4,[42][43][44][45][46][47][48][49][50]53] In particular, the axial diffusion coefficient is compared in the current study with predictions from the analytical solution of the one-dimensional advection-diffusion equation. [4,47,[49][50][51][52] …”

Section: The Discrete Element Methods (Dem)mentioning

confidence: 99%

“…Using the dispersion index, local mixing maps can be produced showing regions of good and poor mixing to qualitatively assess blender performance. [40] The dispersion index can be computed for systems where particle trajectories can be determined, using either PEPT, [39,[42][43][44] DEM, [45,46] or video-graphic tracking techniques. [47,48] However, the dispersion index is not independent of the sample size.…”

Section: Performance Characterization Of Continuous Blendersmentioning

confidence: 99%

“…The quantity D ij is commonly referred to either as the diffusion or dispersion tensor in the granular materials literature (diffusion; [4,[45][46][47][48] dispersion [42][43][44]49,50] ). The term diffusion will be used in the present work.…”

Section: Performance Characterization Of Continuous Blendersmentioning

confidence: 99%

“…The diffusion equation or advection-diffusion equation, utilizing the values of the diffusion coefficients, has been used in a number of studies to model axial [4,[49][50][51][52] and transverse [47] mixing behavior, and quantify axial mixing rates. [42][43][44] Researchers have also computed the full diffusion coefficient tensor for shear flows [46] and vertical channel flows, [48] and have found granular diffusion to be an anisotropic process. Some studies have made comparisons of the diffusion coefficients with predictions from kinetic theory for granular flows.…”

Section: Performance Characterization Of Continuous Blendersmentioning

confidence: 99%

“…[40] From the parametric studies performed by Sarkar and Wassgren, [40,41] three data sets are available: fill level vs. impeller speed (data set I), [40] particle cohesion vs. impeller speed (data set II), [41] and particle cohesion vs. fill level (data set III). [42] Simulation parameters and invariants for the three data sets have been summarized in Table 3. The dimensionless bulk fill volume fraction n is given by…”

Section: Dem Modeling Approach Ii: Periodic Slice Simulationsmentioning

confidence: 99%

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Computational Approaches for Studying the Granular Dynamics of Continuous Blending Processes, 1 – DEM Based Methods

Dubey

Sarkar

Ierapetritou

et al. 2011

Macro Materials & Eng

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Computational methods using mechanistic modeling with a specific application in the area of continuous blending are presented. These methods complement experimental designs and aim to reduce the amount of time, effort, and material required to characterize a device or a process. The discrete element method is applied to the specific case of continuous mixing using two approaches. The first approach models an entire blender and studies the impact of changing impeller speed on residence time distribution (RTD), number of blade passes experienced by the powder, and mean centered variance of the particle residence time. The mean centered variance and the number of blade passes exhibit a maximum with increasing impeller speed, indicating optimal mixing behavior at an intermediate speed range. The second DEM approach utilizes a periodic slice of the full blender in order to explore in detail the effect of speed, fill level, and cohesion on mixing performance. The results show that the transverse mixing rates were generally higher than the corresponding axial mixing rates. Transverse diffusion was highest for large impeller speeds at larger fills, and axial diffusion was highest for large impeller speeds at smaller fills. Variations in particle–particle cohesion within the investigated range do not affect diffusion values significantly. The axial diffusion coefficient can be reasonably predicted from the residence time distribution using the 1D advection‐diffusion model. magnified image

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