Establishing predictive capabilities of DEM – Verification and validation for complex granular processes

Ooi, Jin Y.

doi:10.1063/1.4811861

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…Macroscopic bulk behaviours during hopper discharge have been well investigated by other researchers by looking at pressure distribution [50], FR [49,51] and contact force chains [49,52,53]. In this study, FR, repose angles and particle velocity at the orifice exit were considered as possible features that could be easily measured experimentally for the model validation.…”

Section: Bulk Materials Behaviour During Dischargementioning

confidence: 99%

Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis

et al. 2015

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Selection or calibration of particle property input parameters is one of the key problematic aspects for the implementation of the discrete element method (DEM). In the current study, a parametric multi-level sensitivity method is employed to understand the impact of the DEM input particle properties on the bulk responses for a given simple system: discharge of particles from a flat bottom cylindrical container onto a plate. In this case study, particle properties, such as Young's modulus, friction parameters and coefficient of restitution were systematically changed in order to assess their effect on material repose angles and particle flow rate (FR). It was shown that inter-particle static friction plays a primary role in determining both final angle of repose and FR, followed by the role of inter-particle rolling friction coefficient. The particle restitution coefficient and Young's modulus were found to have insignificant impacts and were strongly cross correlated. The proposed approach provides a systematic method that can be used to show the importance of specific DEM input parameters for a given system and then potentially facilitates their selection or calibration. It is concluded that shortening the process for input parameters selection and calibration can help in the implementation of DEM.Keywords Discrete element method · DEM input parameters · DEM calibration · Parameter sensitivity analysis Electronic supplementary material The online version of this article (

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Section: Bulk Materials Behaviour During Dischargementioning

confidence: 99%

Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis

et al. 2015

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“…Finally, it is worth mentioning that all the bond models can provide reasonably good results with right sets of input parameters, i.e., by providing enough resolution and considering the right beam slenderness ratios that are likely to be encountered in the study phenomena. Therefore, appropriate calibration, verification and validation are vital before applying DEM in solving real-world problems [62].…”

Section: Discussionmentioning

confidence: 99%

A comparative assessment and unification of bond models in DEM simulations

et al. 2021

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Bonded contact models have been increasingly used in the discrete element method (DEM) to study cemented and sintered particulate materials in recent years. Several popular DEM bond models have been proposed in the literature; thus it is beneficial to assess the similarities and differences between the different bond models before they are used in simulations. This paper identifies and discusses two fundamental types of bond models: the Spring Bond Model where two bonded particles are joined by a set of uniform elastic springs on the bond’s cross-section, and the Beam Bond Model in which a beam is used to connect the centres of two particles. A series of cantilever beam bending simulation cases were carried out to verify the findings and assess the strength and weakness of the bond models. Despite the numerous bond models described in the literature, they can all be considered as a variation of these two fundamental model types. The comparative evaluation in this paper also shows that all the bond models investigated can be unified to a general form given at a predefined contact point location.

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“…Our calibration is performed at bulk level, using the mass per dose as the calibration reference parameter. Other studies have shown that calibration can also be performed at contact level [55,42,56], however, since we use mesoparticles, the contact properties of primary particles do not enter the model.…”

Section: Calibration and Sensitivity Studiesmentioning

confidence: 99%

Reprint of "Experiments and discrete element simulation of the dosing of cohesive powders in a simplified geometry"

Imole¹,

Krijgsman²,

Weinhart³

et al. 2016

Powder Technology

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Establishing predictive capabilities of DEM – Verification and validation for complex granular processes

Cited by 7 publications

References 32 publications

Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis

Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis

A comparative assessment and unification of bond models in DEM simulations

Reprint of "Experiments and discrete element simulation of the dosing of cohesive powders in a simplified geometry"

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