Fast, flexible particle simulations — An introduction to MercuryDPM Article Swipe
Thomas Weinhart
,
Luca Orefice
,
Mitchel Post
,
Marnix P. van Schrojenstein Lantman
,
Irana F.C. Denissen
,
Deepak R. Tunuguntla
,
Jonathan Michael Foonlan Tsang
,
Hongyang Cheng
,
Mohamad Yousef Shaheen
,
Hao Shi
,
Paolo Rapino
,
Elena Grannonio
,
Nunzio Losacco
,
João Manuel de Oliveira Barbosa
,
Lü Jing
,
Juan E. Álvarez Naranjo
,
Sudeshna Roy
,
Wouter K. den Otter
,
Anthony R. Thornton
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1016/j.cpc.2019.107129
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1016/j.cpc.2019.107129
We introduce the open-source package MercuryDPM, which we have been developing over the last few years. MercuryDPM is a code for discrete particle simulations. It simulates the motion of particles by applying forces and torques that stem either from external body forces, (gravity, magnetic fields, etc.) or particle interactions. The code has been developed extensively for granular applications, and in this case these are typically (elastic, plastic, viscous, frictional) contact forces or (adhesive) short-range forces. However, it could be adapted to include long-range (molecular, self-gravity) interactions as well. MercuryDPM is an object-oriented algorithm with an easy-to-use user interface and a flexible core, allowing developers to quickly add new features. It is parallelised using MPI and released under the BSD 3-clause licence. Its open-source developers’ community has developed many features, including moving and curved walls; state-of-the-art granular contact models; specialised classes for common geometries; non-spherical particles; general interfaces; restarting; visualisation; a large self-test suite; extensive documentation; and numerous tutorials and demos. In addition, MercuryDPM has three major components that were originally invented and developed by its team: an advanced contact detection method, which allows for the first time large simulations with wide size distributions; curved (non-triangulated) walls; and multicomponent, spatial and temporal coarse-graining, a novel way to extract continuum fields from discrete particle systems. We illustrate these tools and a selection of other MercuryDPM features via various applications, including size-driven segregation down inclined planes, rotating drums, and dosing silos. Program summary: Program Title: MercuryDPM Program Files doi: http://dx.doi.org/10.17632/n7jmdrdc52.1 Licensing provisions: BSD 3-Clause Programming language: C++, Fortran Supplementary material: http://mercurydpm.org Nature of problem: Simulation of granular materials, i.e. conglomerations of discrete, macroscopic particles. The interaction between individual grains is characterised by a loss of energy, making the behaviour of granular materials distinct from atomistic materials, i.e. solids, liquids and gases. Solution method: MercuryDPM (Thornton et al., 2013, 2019; Weinhart et al., 2016, 2017, 2019) is an implementation of the Discrete Particle Method (DPM), also known as the Discrete Element Method (DEM) (Cundall and Strack, 1979). It simulates the motion of individual particles by applying forces and torques that stem either from external forces (gravity, magnetic fields, etc.) or from particle-pair and particle–wall interactions (typically elastic, plastic, dissipative, frictional, and adhesive contact forces). DPM simulations have been successfully used to understand the many unique granular phenomena – sudden phase transitions, jamming, force localisation, etc. – that cannot be explained without considering the granular microstructure. Unusual features: MercuryDPM was designed ab initio with the aim of allowing the simulation of realistic geometries and materials found in industrial and geotechnical applications. It thus contains several bespoke features invented by the MercuryDPM team: (i) a neighbourhood detection algorithm (Krijgsman et al., 2014) that can efficiently simulate highly polydisperse packings, which are common in industry; (ii) curved walls (Weinhart et al., 2016) making it possible to model real industrial geometries exactly, without triangulation errors; and (iii) MercuryCG (Weinhart et al., 2012, 2013, 2016; Tunuguntla et al., 2016), a state-of-the-art analysis tool that extracts local continuum fields, providing accurate analytical/rheological information often not available from experiments or pilot plants. It further contains a large range of contact models to simulate complex interactions such as elasto-plastic deformation (Luding, 2008), sintering (Fuchs et al., 2017), melting (Weinhart et al., 2019), breaking, wet and dry cohesion (Roy et al., 2016, 2017), and liquid migration (Roy et al., 2018), all of which have important industrial applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.cpc.2019.107129
- OA Status
- hybrid
- Cited By
- 107
- References
- 58
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2969764023Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.cpc.2019.107129Digital Object Identifier
- Title
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Fast, flexible particle simulations — An introduction to MercuryDPMWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-12-27Full publication date if available
- Authors
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Thomas Weinhart, Luca Orefice, Mitchel Post, Marnix P. van Schrojenstein Lantman, Irana F.C. Denissen, Deepak R. Tunuguntla, Jonathan Michael Foonlan Tsang, Hongyang Cheng, Mohamad Yousef Shaheen, Hao Shi, Paolo Rapino, Elena Grannonio, Nunzio Losacco, João Manuel de Oliveira Barbosa, Lü Jing, Juan E. Álvarez Naranjo, Sudeshna Roy, Wouter K. den Otter, Anthony R. ThorntonList of authors in order
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https://doi.org/10.1016/j.cpc.2019.107129Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.cpc.2019.107129Direct OA link when available
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Computer science, Particle (ecology), Statistical physics, Particle method, Computational science, Physics, Nuclear physics, Geology, Oceanography, PlasmaTop concepts (fields/topics) attached by OpenAlex
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107Total citation count in OpenAlex
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2025: 21, 2024: 25, 2023: 21, 2022: 7, 2021: 26Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.It | 24, 109, 333, 426, 511 |
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| abstract_inverted_index.BSD | 118, 249 |
| abstract_inverted_index.DPM | 370 |
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| abstract_inverted_index.MPI | 113 |
| abstract_inverted_index.The | 49, 271 |
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| abstract_inverted_index.can | 447 |
| abstract_inverted_index.dry | 543 |
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| abstract_inverted_index.the | 2, 12, 26, 117, 184, 284, 316, 324, 335, 378, 398, 409, 413, 434 |
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| abstract_inverted_index.way | 204 |
| abstract_inverted_index.wet | 541 |
| abstract_inverted_index.– | 383, 391 |
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| abstract_inverted_index.body | 40 |
| abstract_inverted_index.case | 61 |
| abstract_inverted_index.code | 19, 50 |
| abstract_inverted_index.doi: | 245 |
| abstract_inverted_index.down | 230 |
| abstract_inverted_index.etc. | 390 |
| abstract_inverted_index.from | 38, 209, 290, 348, 356, 506 |
| abstract_inverted_index.have | 8, 372, 560 |
| abstract_inverted_index.i.e. | 265, 293 |
| abstract_inverted_index.last | 13 |
| abstract_inverted_index.loss | 280 |
| abstract_inverted_index.many | 127, 379 |
| abstract_inverted_index.over | 11 |
| abstract_inverted_index.real | 470 |
| abstract_inverted_index.size | 191 |
| abstract_inverted_index.stem | 36, 346 |
| abstract_inverted_index.such | 524 |
| abstract_inverted_index.that | 35, 167, 345, 392, 446, 494 |
| abstract_inverted_index.this | 60 |
| abstract_inverted_index.thus | 427 |
| abstract_inverted_index.time | 186 |
| abstract_inverted_index.tool | 493 |
| abstract_inverted_index.used | 375 |
| abstract_inverted_index.user | 96 |
| abstract_inverted_index.were | 168 |
| abstract_inverted_index.wide | 190 |
| abstract_inverted_index.with | 93, 189, 408 |
| abstract_inverted_index.(DEM) | 328 |
| abstract_inverted_index.(iii) | 478 |
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| abstract_inverted_index.2013, | 304, 484 |
| abstract_inverted_index.2014) | 445 |
| abstract_inverted_index.2016) | 464 |
| abstract_inverted_index.2016, | 309, 548 |
| abstract_inverted_index.2016; | 485 |
| abstract_inverted_index.2017, | 310 |
| abstract_inverted_index.2019) | 311 |
| abstract_inverted_index.2019; | 305 |
| abstract_inverted_index.Files | 244 |
| abstract_inverted_index.core, | 101 |
| abstract_inverted_index.could | 77 |
| abstract_inverted_index.etc.) | 45, 354 |
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| abstract_inverted_index.pilot | 509 |
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| abstract_inverted_index.team: | 175, 436 |
| abstract_inverted_index.these | 62, 215 |
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| abstract_inverted_index.tools | 216 |
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| abstract_inverted_index.walls | 460 |
| abstract_inverted_index.well. | 87 |
| abstract_inverted_index.which | 6, 181, 453, 559 |
| abstract_inverted_index.(DPM), | 320 |
| abstract_inverted_index.(Fuchs | 531 |
| abstract_inverted_index.1979). | 332 |
| abstract_inverted_index.2008), | 529 |
| abstract_inverted_index.2016), | 489 |
| abstract_inverted_index.2017), | 534, 549 |
| abstract_inverted_index.2018), | 556 |
| abstract_inverted_index.2019), | 539 |
| abstract_inverted_index.Method | 319, 327 |
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| abstract_inverted_index.either | 37, 347 |
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