Skip to main content
Log in

Metaball based discrete element method for general shaped particles with round features

  • Original Paper
  • Published:
Computational Mechanics Aims and scope Submit manuscript

Abstract

Discrete element method (DEM) has achieved considerable success on simulating complex granular material behaviours. One of the key challenges of DEM simulations is how to describe particles with realistic geometries. Many shape description methods have been developed including sphere-clustering, polyhedrons, sphero-polyhedrons, superquadric particles to name a few. However, to model general shaped particles with round features , these techniques are either introducing artificial surface roughness or are limited to a few regular shapes. Here we proposed a metaball based DEM where the metaball equation is used to describe particle shapes. Because of its flexibility on choosing control points in the metaball equation, many complex shaped particles can be modelled within this framework. The particle collision is handled by solving an optimization problem. A Newton–Raphson method based algorithm of finding the closest points for metaball DEM is developed accordingly. Using 3D printed particles, the proposed scheme is validated by comparing the simulated ran-out distance with granular column collapses experimental results. The model is further applied to study shape effects on vibration induced segregations. It is shown that the proposed metaball DEM can capture shape influence which may crucial in many engineering and science applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Donzé FV, Richefeu V, Magnier S-A (2009) Advances in discrete element method applied to soil, rock and concrete mechanics. Electron J Geotech Eng 8(1):44

    Google Scholar 

  2. O’Sullivan C (2011) Particulate discrete element modelling, vol 574. CRC Press, London

    Book  Google Scholar 

  3. Krabbenhoft K, Lyamin A, Huang J, da Silva MV (2012) Granular contact dynamics using mathematical programming methods. Comput Geotech 43:165–176

    Article  Google Scholar 

  4. Galindo-Torres S, Pedroso D, Williams D, Li L (2012) Breaking processes in three-dimensional bonded granular materials with general shapes. Comput Phys Commun 183(2):266–277

    Article  Google Scholar 

  5. Boon C, Houlsby G, Utili S (2013) A new contact detection algorithm for three-dimensional non-spherical particles. Powder Technol 248:94–102

    Article  Google Scholar 

  6. Mollon G, Zhao J (2014) 3d generation of realistic granular samples based on random fields theory and Fourier shape descriptors. Comput Methods Appl Mech Eng 279:46–65

    Article  Google Scholar 

  7. Servin M, Wang D, Lacoursière C, Bodin K (2014) Examining the smooth and nonsmooth discrete element approaches to granular matter. Int J Numer Meth Eng 97(12):878–902

    Article  MathSciNet  Google Scholar 

  8. Das N (2007) Modeling three-dimensional shape of sand grains using discrete element method, vol 149. PhD thesis

  9. Bowman ET, Soga K, Drummond W (2001) Particle shape characterisation using Fourier descriptor analysis. Geotechnique 51(6):545–554

  10. Kruggel-Emden H, Rickelt S, Wirtz S, Scherer V (2008) A study on the validity of the multi-sphere discrete element method. Powder Technol 188(2):153–165

    Article  Google Scholar 

  11. Mollon G, Zhao J (2012) Fourier–Voronoi-based generation of realistic samples for discrete modelling of granular materials. Granular Matter 14(5):621–638

    Article  Google Scholar 

  12. Garboczi EJ, Bullard JW (2013) Contact function, uniform-thickness shell volume, and convexity measure for 3d star-shaped random particles. Powder Technol 237:191–201

    Article  Google Scholar 

  13. Fu P, Walton OR, Harvey JT (2012) Polyarc discrete element for efficiently simulating arbitrarily shaped 2d particles. Int J Numer Meth Eng 89(5):599–617

    Article  MathSciNet  Google Scholar 

  14. Podlozhnyuk A, Pirker S, Kloss C (2017) Efficient implementation of superquadric particles in discrete element method within an open-source framework. Comput Part Mech 4(1):101–118

    Article  Google Scholar 

  15. Cai R, Xiao H, Zheng J, Zhao Y (2019) Diffusion of size bidisperse spheres in dense granular shear flow. Phys Rev E 99(3):032902

    Article  Google Scholar 

  16. Jing L, Kwok C, Leung Y (2017) Micromechanical origin of particle size segregation. Phys Rev Lett 118(11):118001

    Article  Google Scholar 

  17. Descantes Y, Tricoire F, Richard P (2019) Classical contact detection algorithms for 3d dem simulations: drawbacks and solutions. Comput Geotech 114:103134

    Article  Google Scholar 

  18. Seelen L, Padding J, Kuipers J (2018) A granular discrete element method for arbitrary convex particle shapes: method and packing generation. Chem Eng Sci 189:84–101

    Article  Google Scholar 

  19. Cleary PW, Sawley ML (2002) Dem modelling of industrial granular flows: 3d case studies and the effect of particle shape on hopper discharge. Appl Math Model 26(2):89–111

    Article  Google Scholar 

  20. Wu C-Y, Cocks AC (2006) Numerical and experimental investigations of the flow of powder into a confined space. Mech Mater 38(4):304–324

    Article  Google Scholar 

  21. Mack S, Langston P, Webb C, York T (2011) Experimental validation of polyhedral discrete element model. Powder Technol 214(3):431–442

    Article  Google Scholar 

  22. Höhner D, Wirtz S, Kruggel-Emden H, Scherer V (2011) Comparison of the multi-sphere and polyhedral approach to simulate non-spherical particles within the discrete element method: Influence on temporal force evolution for multiple contacts. Powder Technol 208(3):643–656

    Article  Google Scholar 

  23. Garcia X, Latham J-P, Xiang J-S, Harrison J (2009) A clustered overlapping sphere algorithm to represent real particles in discrete element modelling. Geotechnique 59(9):779–784

    Article  Google Scholar 

  24. Ashmawy AK, Hoang VV,  Sukumaran B et al (2003) Evaluating the influence of particle shape on liquefaction behavior using discrete element modeling. In: The thirteenth international offshore and polar engineering conference, international society of offshore and polar engineers

  25. Alonso-Marroquin F (2008) Spheropolygons: a new method to simulate conservative and dissipative interactions between 2d complex-shaped rigid bodies. EPL (Europhys Lett) 83(1):14001

    Article  Google Scholar 

  26. Alonso-Marroquín F, Wang Y (2009) An efficient algorithm for granular dynamics simulations with complex-shaped objects. Granul Matter 11(5):317–329

    Article  Google Scholar 

  27. Galindo-Torres S, Muñoz J, Alonso-Marroquin F (2010) Minkowski–Voronoi diagrams as a method to generate random packings of spheropolygons for the simulation of soils. Phys Rev E 82(5):056713

    Article  Google Scholar 

  28. Galindo-Torres S, Alonso-Marroquín F, Wang Y, Pedroso D, Castano JM (2009) Molecular dynamics simulation of complex particles in three dimensions and the study of friction due to nonconvexity. Phys Rev E 79(6):060301

    Article  Google Scholar 

  29. Williams JR, Pentland AP (1992) Superquadrics and modal dynamics for discrete elements in interactive design. Eng Comput 9:115–127

  30. Barr AH (1981) Superquadrics and angle-preserving transformations. IEEE Comput Graph Appl 1(1):11–23

    Article  MathSciNet  Google Scholar 

  31. Zhao S, Zhao J (2019) A poly-superellipsoid-based approach on particle morphology for DEM modeling of granular media. Int J Numer Anal Methods Geomech 43(13):2147–2169

    Article  Google Scholar 

  32. Houlsby G (2009) Potential particles: a method for modelling non-circular particles in DEM. Comput Geotech 36(6):953–959

    Article  Google Scholar 

  33. Andrade JE, Lim K-W, Avila CF, Vlahinić I (2012) Granular element method for computational particle mechanics. Comput Methods Appl Mech Eng 241:262–274

    Article  Google Scholar 

  34. Lai Z, Chen Q, Huang L (2020) Fourier series-based discrete element method for computational mechanics of irregular-shaped particles. Comput Methods Appl Mech Eng 362:112873

    Article  MathSciNet  Google Scholar 

  35. Luding S (2008) Introduction to discrete element methods: basic of contact force models and how to perform the micro-macro transition to continuum theory. Eur J Environ Civ Eng 12(7–8):785–826

    Article  Google Scholar 

  36. Solov’yov IA, Sushko G, Solov’yov AV (2017) Mbn explorer users’ guide

  37. Cundall PA, Strack OD (1979) A discrete numerical model for granular assemblies. Geotechnique 29(1):47–65

    Article  Google Scholar 

  38. Luding S (2008) Cohesive, frictional powders: contact models for tension. Granul Matter 10(4):235

    Article  MathSciNet  Google Scholar 

  39. Galindo-Torres S (2013) A coupled discrete element lattice Boltzmann method for the simulation of fluid–solid interaction with particles of general shapes. Comput Methods Appl Mech Eng 265:107–119

    Article  MathSciNet  Google Scholar 

  40. Zhang P, Galindo-Torres S, Tang H, Jin G, Scheuermann A, Li L (2017) An efficient discrete element lattice Boltzmann model for simulation of particle–fluid, particle–particle interactions. Comput Fluids 147:63–71

    Article  MathSciNet  Google Scholar 

  41. Zhang P, Galindo-Torres S, Tang H, Jin G, Scheuermann A, Li L (2019) Velocity interpolation based bounce-back scheme for non-slip boundary condition in lattice Boltzmann method. arXiv preprint arXiv:1903.01111

  42. Zhang P, Galindo-Torres S, Tang H, Jin G, Scheuermann A, Li L (2016) Lattice Boltzmann simulations of settling behaviors of irregularly shaped particles. Phys Rev E 93(6):062612

    Article  Google Scholar 

  43. Trujillo-Vela MG, Galindo-Torres SA, Zhang X, Ramos-Cañón AM, Escobar-Vargas JA (2020) Smooth particle hydrodynamics and discrete element method coupling scheme for the simulation of debris flows. Comput Geotech 125:103669

    Article  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the funding from The Belt and Road Special Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (2019491511). We thank Westlake University Supercomputer Center for computational resource and related assistance. The software used for all the simulations presented in this paper is based on the open source library ComFluSoM (URL: https://github.com/peizhang-cn/ComFluSoM) developed by the first author.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. A. Galindo-Torres.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, P., Dong, Y., Galindo-Torres, S.A. et al. Metaball based discrete element method for general shaped particles with round features. Comput Mech 67, 1243–1254 (2021). https://doi.org/10.1007/s00466-021-02001-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00466-021-02001-9

Keywords

Navigation