Development and validation of calibration methods for discrete element modelling

Grima, Andrew P; Wypych, Peter W

doi:10.1007/s10035-010-0197-4

Cited by 102 publications

(48 citation statements)

References 5 publications

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“…The implementation of the rolling/twisting governed by CL and HM contact laws, at the 60th data assimilation step scheme for the CL contact law, on the other hand, calculates the rolling stiffness from the radii of the contacting grains, the tangential contact stiffness and dimensionless factor β m . Nevertheless, the agreement between the posterior PDFs using the same contact law is generally good, and in line with most large-scale DEM simulations that use scaled grain size distributions to represent granular soils [6,14,20]. The agreement of the posterior distributions in Fig.…”

Section: Posterior Pdfs Of Micromechanical Parameterssupporting

confidence: 81%

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

et al. 2018

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The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress-strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes' rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro-macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.

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Section: Posterior Pdfs Of Micromechanical Parameterssupporting

confidence: 81%

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

et al. 2018

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“…The COR can be measured directly [6] or rarely be obtained via calibration due to the dependence on the numerical code used in the calibration process [7][8][9][10]. The inter-grain coefficient of restitution (e p ) was obtained based on a double pendulum [11].…”

Section: Introductionmentioning

confidence: 99%

Experimental determination of parameter effects on the coefficient of restitution of differently shaped maize in three-dimensions

et al. 2015

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“…The implications of scale effects being incorrectly captured by smaller models are clear, with designs based on models that do not properly account for scale effects potentially being dangerous. Similarly, the identification and mitigation of scale effects in industrial contexts can result in more efficient systems (Grima and Wypych 2011), higher profits, and improved safety as scale effects can be taken into account during design and upscaling.…”

Section: Introductionmentioning

confidence: 99%

A laboratory-numerical approach for modelling scale effects in dry granular slides

2018

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Granular slides are omnipresent in both natural and industrial contexts. Scale effects are changes in physical behaviour of a phenomenon at different geometric scales, such as between a laboratory experiment and a corresponding larger event observed in nature. These scale effects can be significant and can render models of small size inaccurate by underpredicting key characteristics such as flow velocity or runout distance. Although scale effects are highly relevant to granular slides due to the multiplicity of length and time scales in the flow, they are currently not well understood. A laboratory setup under Froude similarity has been developed, allowing dry granular slides to be investigated at a variety of scales, with a channel width configurable between 0.25 and 1.00 m. Maximum estimated grain Reynolds numbers, which quantify whether the drag force between a particle and the surrounding air act in a turbulent or viscous manner, are found in the range 10 2 − 10 3 . A discrete element method (DEM) simulation has also been developed, validated against an axisymmetric column collapse and a granular slide experiment of Hutter et al. (Acta Mech 109:127-165, 1995), before being used to model the present laboratory experiments and to examine a granular slide of significantly larger scale. This article discusses the details of this laboratory-numerical approach, with the main aim of examining scale effects related to the grain Reynolds number. Increasing dust formation with increasing scale may also exert influence on laboratory experiments. Overall, significant scale effects have been identified for characteristics such as flow velocity and runout distance in the physical experiments. While the numerical modelling shows good general agreement at the medium scale, it does not capture differences in behaviour seen at the smaller scale, highlighting the importance of physical models in capturing these scale effects.

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Development and validation of calibration methods for discrete element modelling

Cited by 102 publications

References 5 publications

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

Experimental determination of parameter effects on the coefficient of restitution of differently shaped maize in three-dimensions

A laboratory-numerical approach for modelling scale effects in dry granular slides

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