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Reduced order modeling for realtime simulation with granular materials
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)ORCID iD: 0000-0002-0787-4988
Umeå University, Faculty of Science and Technology, Department of Physics. (Digital Physics)
2019 (English)Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

The discrete element method (DEM) is a versatile but computationally intense method for simulation of granular materials. It is therefore rarely used in applications that require realtime performance, e.g, interactive simulaions with a human operator or hardware in the loop.

We investigate the use of reduced order modeling for achieving realtime performance in coupled discrete element and rigid multibody simulations. First, a large data set is produced from a series of simulations that cover a selected state-space. The particle data is coarse-grained into discrete field variables, representing mass density, velocity, strain and stress. A reduced order representation of the state-space is identified. Different methods for predicting the fields are explored, given certain observations and assumptions about the state of the simulation e.g., motion of boundaries, rigid bodies or control signals. The particle positions and velocities can then be advanced in time using the predicted fields plus a model for particle diffusion [4] and a local incompressibility constraint [1]. The resulting method can be seen as an extension to the one in [5], by extending the reduced space from rigid body motion of particle aggregates to a low-dimensional space of flow fields [2, 3].

The precision and computational performance of the reduced order simulation method is analyzed on simple test systems, including silo flow and a blade cutting a granular bed. Finally, coupled simulation of an articulated rigid multibody system and a reduced order granular system is demonstrated.

REFERENCES

[1] K. Bodin, C. Lacoursiere, and M. Servin. Constraint fluids. IEEE Transactions on Visualization and Computer Graphics, 18(3):516–526, 2012.

[2] F. Boukouvala, Y. Gao, F. Muzzio, and M. Ierapetritou. Reduced-order discrete element method modeling. Chemical Engineering Science, 95:12–26, 2013.

[3] A. Rogers and M. Ierapetritou. Discrete element reduced-order modeling of dynamicparticulate systems. AIChE Journal, 60:3184–94, 2014.

[4] P. Salamon, D. Fernandez-Garcia, and J. G ` omez-Hern ´ andez. A review and numerical assessment of the random walk particle tracking method. Journal of Contaminant Hydrology, 87(3):277 – 305, 2006.

[5] M. Servin and D. Wang. Adaptive model reduction for nonsmooth discrete element simulation. Computational Particle Mechanics, 3(1):107–121, 2016.

Place, publisher, year, edition, pages
2019.
National Category
Physical Sciences
Research subject
Physics
Identifiers
URN: urn:nbn:se:umu:diva-159553OAI: oai:DiVA.org:umu-159553DiVA, id: diva2:1319374
Conference
VIth International Conference on Particle-Based Methods - Fundamentals and Applications - PARTICLES 2019
Available from: 2019-05-31 Created: 2019-05-31 Last updated: 2019-05-31

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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