Challenges of DEM: I. Competing bottlenecks in parallelization of gas–solid flows

Liu, Peiyuan; Hrenya, Christine M.

doi:10.1016/j.powtec.2014.04.095

Cited by 25 publications

(11 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The situation worsens for smaller particles, since the time step is 4 proportional to particle size. For example, our recent study on fine particles (Liu and Hrenya, 2014) showed that even for a pseudo-2D fluidized bed (bed with thickness of 1 particle diameter), the bed width attainable by DEM-CFD is limited to ~ 1 cm, much smaller than typical experiments with bed width and thickness > 10 cm. To overcome this computational challenge, it is desirable to increase the simulation time step or have a scale-independent parameter to compare with experiments.…”

Section: Introductionmentioning

confidence: 92%

Fine-particle defluidization: Interaction between cohesion, Young׳s modulus and static bed height

Liu

LaMarche

Kellogg

et al. 2016

Chemical Engineering Science

Self Cite

View full text Add to dashboard Cite

The defluidization of fine (mildly cohesive) glass particles with mean size 69 m is studied by DEM-CFD simulations and experiments for quantitative model validation. Cohesion arising from van der Waals forces between particles is explicitly incorporated into DEM-CFD, along with the corresponding effects of surface roughness. Due to computational limitations, DEM-CFD simulations often use artificially soft particles and systems much smaller than experiments, making the direct comparison between simulation and experimental results questionable. This computational obstacle was recently overcome for coarse (non-cohesive) particles via the identification of a system-size-independent measurement, thereby allowing for the direct comparison between small-scale simulations and largescale experiments (LaMarche et al., 2015b). Here, we assess the applicability of the same measurement, namely the defluidization curve, to cohesive systems. Unlike non-cohesive systems, the simulation results exhibit a sensitivity to Young's modulus and (low) static bed heights. These observations are explained by the enhanced cohesive effect at lower Young's modulus and decreasing bed porosity with increasing static bed height. Nonetheless, by using the true material Young's modulus in the DEM-CFD simulations and sufficiently large static bed height, a system-size-independent defluidization measurement is achieved that compares well with experimental data from a much larger system. This ability to directly compare small-scale simulations and large-scale experiments via a system-size independent measurement (defluidization) allows for the quantitative validation of the particle-level models in DEM-CFD without resorting to particle-level measurements and/or unrealistically small experimental systems.

show abstract

Section: Introductionmentioning

confidence: 92%

Fine-particle defluidization: Interaction between cohesion, Young׳s modulus and static bed height

Liu

LaMarche

Kellogg

et al. 2016

Chemical Engineering Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…In DEM, 1HZWRQ ¶V HTXDWLRQV RI PRWLRQ DUH VROYHG IRU every particle in the domain, and therefore adjusting the particle properties for each particle is straightforward [4]. However, tracking the positions of every particle in the domain requires a large computational overhead, and consequently the number of particles, or size of the system, that can be simulated with DEM is limited to orders of magnitude smaller than realistic processes [5]. In TFM, the particle phase and fluid (liquid or gas) phase are treated as interpenetrating, interacting continua, hence the particle phase is treated as a fluid (i.e., individual particles are not considered), and mass and momentum balances are solved for each phase.…”

Section: Introductionmentioning

confidence: 99%

Two-fluid modeling of cratering in a particle bed by a subsonic turbulent jet

LaMarche

Benavides-Morán

Wachem³

et al. 2017

Powder Technology

View full text Add to dashboard Cite

The Two-Fluid Method is capable of modeling large-scale (i.e., lab scale or larger) multiphase (particle-fluid) flows by treating both the fluid and particle phase as interpenetrating continua and solving mass and momentum balances for each phase. To solve for the flow of the solids phase the momentum balance requires constitutive relations in the form of normal and shear stresses ± i.e., pressures and viscosities. However, the stresses that account for frictional contacts in dense particle systems, and are relevant to this work, are empirically based. A study of the effects of adjusting the frictional model formulation (the empirical parameters of the model), by changing the overall frictional stress magnitude and the relative magnitude of the frictional viscosity to the frictional pressure, on the behavior of the bed is presented here. It was found that the magnitude of the frictional viscosity relative to the frictional pressure affects the crater growth prediction almost as much as the magnitude of the overall frictional stress. Additionally, a frictional model formulation is validated for sand particles, and predictions are compared with existing experimental data for the crater formation of a sand bed under a vertical, impinging jet of gas (Metzger et al. J Areo Eng (2008) v22, p24-32). In the low jet velocity regime (subsonic, turbulent jet), the model predicts the salient features previously measured for the growth rate of the crater of time, the profile of the crater, and the response of the crater to turning the jet off. In the high jet velocity regime (compressible, near sonic jet flow) the prediction agrees qualitatively with prior experimental observations.

show abstract

“…Since its initial release, MFIX has been successfully employed to model a variety of multiphase flow problems relevant to industrial and energy-related applications [13,14,15,16,17,18,19,20,21]. Due to the inherent complexity and diverse range of spatial/temporal scales involved in multiphase flow simulations, several initiatives were carried out to reduce the time-to-solution through performance improvements [22,23,24,25,26,27,28,29,30].…”

Section: Department Of Energymentioning

confidence: 99%

Enhancing the physical modeling capability of open-source MFIX-DEM software for handling particle size polydispersity: Implementation and validation

et al. 2017

View full text Add to dashboard Cite

Multiphase flows are ubiquitous in many industrial processes. The inherent coupling of different phases poses many unique challenges in predicting and effectively controlling these processes. Hence, computational modeling and simulation offers a viable approach to overcome these challenges. In this study, we present recent development efforts for enhancing the physical modeling capabilities of an open-source computational modeling tool for real life industrial multiphase processes by enabling particle-size polydispersity and demonstrating with an associated validation study. The proposed implementation was performed in MFIX open-source framework due to its unique feature of tightly integrated computational fluid dynamics and discrete element method solvers for simulating coupled continuum fluid and granular flows. We have implemented the polydispersity feature in a minimally invasive way and provided means to allow easy specification of an arbitrary particle size distribution function, which also * Authors contribute equally to this work.

show abstract

Challenges of DEM: I. Competing bottlenecks in parallelization of gas–solid flows

Cited by 25 publications

References 25 publications

Fine-particle defluidization: Interaction between cohesion, Young׳s modulus and static bed height

Fine-particle defluidization: Interaction between cohesion, Young׳s modulus and static bed height

Two-fluid modeling of cratering in a particle bed by a subsonic turbulent jet

Enhancing the physical modeling capability of open-source MFIX-DEM software for handling particle size polydispersity: Implementation and validation

Contact Info

Product

Resources

About