An Adaptive Reduced-Dimensional Discrete Element Model for Dynamic Responses of Granular Materials With High-Frequency Noises

Zhong, Xinran; Sun, WaiChing

doi:10.1615/intjmultcompeng.2018026895

Cited by 11 publications

(7 citation statements)

References 50 publications

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“…The damping values of α and β for those simulations that best matched with the experimental tests described in Figures 10,11,14,and 15 were plotted in Figure 16. These best-matched values of α and β were observed to follow the same trend as the results reported in Figure 4A (included in Figure 16) from matching MP to SP damping simulations following the DeJong 43 method (see Section 2.2) to assign β, without including the simulations that were strongly influenced by undamped bouncing.…”

Section: Using Mp Damping To Approximate Correct Use Of Sp Dampingmentioning

confidence: 86%

“…11,13 Alternatively, when the phenomena to be modelled imply high-frequency oscillations, the SP component of Rayleigh damping is desirable in order to avoid non-realistic behaviour. 14 However, the computational time of certain problems may then become impractically long because the application of SP damping requires a time step that is smaller than that required by the conditionally stable explicit scheme. Another factor that makes for lengthy computation time when using SP damping is the required number and size of the discrete elements.…”

Section: Introductionmentioning

confidence: 99%

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Damping considerations for rocking block dynamics using the discrete element method

Gálvez

Sorrentino

Dizhur

et al. 2022

Earthq Engng Struct Dyn

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It is well established that unreinforced masonry (URM) buildings develop damage-forming collapse mechanisms during high-intensity earthquakes, with these mechanisms exhibiting large rocking displacements before collapsing. The Discrete Element Method (DEM) of analysis can realistically capture phenomena that involve large movements of elements, resulting in the technique being ideal for simulating the collapse of URM building elements. Consequently, extensive research using DEM to analyse the seismic response of URM buildings and building components has recently been published. However, the variety of reported damping approaches that have apparently led to DEM results that successfully replicate physical observations underscores the need for consistent guidance related to the assignment of damping factors. The Rayleigh damping distribution model implemented in the DEM software 3DEC was used to study the differences between mass proportional (MP) and stiffness proportional (SP) damping configurations. After reviewing phenomena that need to be damped and previous works where damping was implemented, the capabilities and drawbacks of the time-efficient MP damping configuration were studied and the results compared to simulations with SP damping. When considering numerical simulations that incorporated MP damping and led to results that were seemingly well-matched to experimental tests, it was found that the apparent robustness of decisions pertaining to the adopted input parameters was deceptive in most cases. Consequently, SP damping was recommended for all DEM rocking simulations, even though MP damping could be used with satisfactory accuracy in certain situations discussed herein. A pragmatic relationship between both damping strategies was proposed.

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Section: Using Mp Damping To Approximate Correct Use Of Sp Dampingmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Damping considerations for rocking block dynamics using the discrete element method

Gálvez

Sorrentino

Dizhur

et al. 2022

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

show abstract

“…The current paper focuses on using a quasistationary velocity field for the reduced DEM. It is a compelling idea, seemingly feasible, to extend this to viscoelastic deformations using the method of Zhong and Sun [25] for predicting the velocity field.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Zhong and Sun [25], with inspiration from [24] and [11], investigated a reduced-order model for granular materials under small viscoelastic deformations, with fix connectivity between the particles, using proper orthogonal decomposition (POD) of the displacement field.…”

Section: Previous Workmentioning

confidence: 99%

Data-driven model order reduction for granular media

Wallin

Servin

2021

Comp. Part. Mech.

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We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distribution and system control signals. Rapid model inference of particle velocities replaces the intense process of computing contact forces and velocity updates. In coupled DEM and multibody system simulation, the predictor model can be trained to output the interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the media in domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particle motion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regression predictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured accuracy is about 90% and 65%, respectively, and the speed-up range between 10 and 60.

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“…Recently, Zhong and Sun [24] investigated a reduced-order model for granular materials under small viscoelastic deformations, with fix connectivity between the particles, using proper orthogonal decomposition (POD) of the displacement field. Inspi-ration for that work was found in [23] and [10].…”

Section: Previous Workmentioning

confidence: 99%

Data-driven model order reduction for granular media

Wallin,

Servin

2020

Preprint

View full text Add to dashboard Cite

We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we run many offline simulations in advance and train a model to predict the velocity field from the mass distribution and system control signals. Rapid model inference of particle velocities replaces the intense process of computing contact forces and velocity updates. In coupled DEM and multibody system simulation the predictor model can be trained to output the interfacial reaction forces as well. An adaptive model order reduction technique is investigated, decomposing the media in domains of solid, liquid, and gaseous state. The model reduction is applied to solid and liquid domains where the particle motion is strongly correlated with the mean flow, while resolved DEM is used for gaseous domains. Using a ridge regression predictor, the performance is tested on simulations of a pile discharge and bulldozing. The measured precision is about 90% and 65%, respectively, and the speed-up range between 10 and 60.

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An Adaptive Reduced-Dimensional Discrete Element Model for Dynamic Responses of Granular Materials With High-Frequency Noises

Cited by 11 publications

References 50 publications

Damping considerations for rocking block dynamics using the discrete element method

Damping considerations for rocking block dynamics using the discrete element method

Data-driven model order reduction for granular media

Data-driven model order reduction for granular media

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