Coarse Graining for Large-scale DEM Simulations of Particle Flow – An Investigation on Contact and Cohesion Models

Nasato, Daniel Schiochet; Goniva, Christoph; Pirker, Stefan; Kloss, Christoph

doi:10.1016/j.proeng.2015.01.282

Cited by 70 publications

(33 citation statements)

References 5 publications

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“…This contact model uses a parameter called surface energy (γ) to quantify the attractive nature of the particles. When surface energy is set to zero, the JKR model reduces to the Hertz contact model, a nonlinear spring-and-dashpot model based on the Hertz theory of elastic contacts [36]. The normal contact force between two spherical particles of radius R 1 and R 2 with a normal overlap, δ N is given by [37]:…”

Section: Methods and Simulation Setupsmentioning

confidence: 99%

Calibration of Discrete-Element-Method Parameters for Cohesive Materials Using Dynamic-Yield-Strength and Shear-Cell Experiments

et al. 2019

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This study tested the effectiveness of using dynamic yield strength (DYS) and shear-cell experiments to calibrate the following discrete-element-method (DEM) parameters: surface energy, and the coefficients of sliding and rolling friction. These experiments were carried out on cohesive granules, and DEM models were developed for these experiment setups using the JKR cohesion contact model. Parameter-sensitivity analysis on the DYS model showed that the DYS results in the simulations were highly sensitive to surface energy and were also impacted by the values of the two friction coefficients. These results indicated that the DYS model could be used to calibrate the surface energy parameter once the friction coefficients were fixed. Shear-cell sensitivity analysis study found that the influence of surface energy on the critical-state shear value cannot be neglected. It was inferred that the shear-cell model has to be used together with the DYS model to identify the right set of friction parameters. Next, surface energy was calibrated using DYS simulations for a chosen set of friction parameters. Calibrations were successfully conducted for simulations involving experimentally sized particles, scaled-up particles, a different shear modulus, and a different set of friction parameters. In all these cases, the simulation DYS results were found to be linearly correlated with surface energy and were within 5% of the experimental DYS result. Shear-cell simulations were then used to compare calibrated surface-energy values for the scaled-up particles with the experimentally sized particles. Both the simulations resulted in similar critical-state shear values. Finally, it was demonstrated that a combination of DYS and shear-cell simulations could be used to compare two sets of friction parameters and their corresponding calibrated surface energy values to identify the set of parameters that better represent the flow behavior demonstrated by the experimental system.

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Section: Methods and Simulation Setupsmentioning

confidence: 99%

Calibration of Discrete-Element-Method Parameters for Cohesive Materials Using Dynamic-Yield-Strength and Shear-Cell Experiments

et al. 2019

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“…The investigated problems include fluidized and non-fluidized particle systems [46][47][48][49][50][51][52][53][54]. Additional physical phenomena like heat transfer [48], adhesive interparticle forces [55,56], chemical reactions [57], and particle size distributions [46,54,58] have also been incorporated. Nevertheless, many different scaling rules for particle-particle and fluid-particle interaction forces have been reported in the literature, including the incorporation of sub-grid drag models.…”

Section: -1)mentioning

confidence: 99%

Impact of Contact Scaling and Drag Calculation on the Accuracy of Coarse‐Grained Discrete Element Method

et al. 2020

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The accuracy of coarse‐grained discrete element method (CGDEM) relies on appropriate scaling rules for contact and fluid‐particle interaction forces. For fluidized bed applications, different scaling rules are used and compared with DEM results. The results indicated that in terms of averaged values as mean particle position and voidage profile, the coupling of computational fluid dynamics and CGDEM leads to accurate results for low scaling factors. Regarding the particle dynamics, the approach leads to an underestimation of RMS values of particle position indicating a loss of particle dynamics in the system due to coarse graining. The impact of cell cluster size on drag force calculation is studied. The use of energy minimization multiscale drag correction is investigated, and a reduced mesh dependency and good accuracy are observed.

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“…'Coarse graining' may be used to reduce the computational requirements of a simulation. The real particles are replaced by fewer, larger coarse particles, each of which represents many real particles [10,11,12]. Thus, none of the input parameters for a coarse-grained simulation may necessarily be obtainable by laboratory testing.…”

Section: Dem Materials Parameter Identificationmentioning

confidence: 99%

Efficient Calibration of Discrete Element Material Model Parameters Using Latin Hypercube Sampling and Kriging

Rackl¹,

Görnig²,

Hanley³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Abstract. Material model parameter identification for discrete element models (DEM) is typically done using a trial-and-error approach and its outcome depends largely on the experience of the DEM user. This paper describes a work flow which facilitates the efficient and systematic calibration of discrete element material models against experimental data. The described workflow comprises three steps. In the first step, an approach based on the design and analysis of computer experiments (DACE) is adopted in which data is generated for the parametrisation of Kriging models based on Latin hypercube sampling. In the second step, multi-objective optimisation is applied to the Kriging models. This study introduces an additional cost criterion, which includes the Rayleigh time step, in order to reduce the solution set size to one element. In the third step, the optimisation procedure is repeated with the actual DEM models, using the optimal parameter set from the Kriging models as the start value. This final step with the full DEM models refines the parameter set against experimental data. Since DEM material model calibration is time-consuming, the workflow is implemented into an automated process chain. In this paper, the methodology is described in detail and results are shown which illustrate the usefulness and effectiveness of this approach. Initial verification simulations run using the calibrated parameters give good agreement with experimental results.

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Coarse Graining for Large-scale DEM Simulations of Particle Flow – An Investigation on Contact and Cohesion Models

Cited by 70 publications

References 5 publications

Calibration of Discrete-Element-Method Parameters for Cohesive Materials Using Dynamic-Yield-Strength and Shear-Cell Experiments

Calibration of Discrete-Element-Method Parameters for Cohesive Materials Using Dynamic-Yield-Strength and Shear-Cell Experiments

Impact of Contact Scaling and Drag Calculation on the Accuracy of Coarse‐Grained Discrete Element Method

Efficient Calibration of Discrete Element Material Model Parameters Using Latin Hypercube Sampling and Kriging

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