Project Description

(Stop animation)

For the simulation of granular media, i.e. multi-particle systems, canonical methods such as the Finite Element Method or the Finite Volume Method cannot be used, since the medium to be simulated lacks continuousness. For that reason methods that are not based on meshes are employed, which are based on data structures that completely lack adjacency. The so-called Discrete Element Method (DEM), which is one of these methods, is used to carry out this simulation. The goal of this research is to link the simulation of a multi-particle system and the simulation of a surrounding fluid in order to simulate dispersions. Dispersions are mixtures of solid particles and fluids, i.e. gases and liquids.

As computational fluid dynamics is very CPU-expensive and therefore time-consuming for large scale problems, it is usually carried out as a parallel computation on supercomputers or clusters. During such a computation the simulation domain is divided up into subdomains which are processed by the different nodes in parallel. Therefore it stands to reason to structure the particle simulation in the same manner. So at the beginning the work is focused on developing and implementing an efficient particle simulation program. As the positions of the particles and thus their distribution in the system can change, an adaptive domain subdivision is necessary to follow changes in the structure of the system. This provides for an optimal and even work distribution among the different processors. One flexible approach uses recursive domain subdivision that yields data structures with tree-based topology.

Even though parallelization reduces computation times, its influence on a program's performance is often overestimated compared to optimizing the structure of the program itself: A disadvantageously implemented program can be made to run only twice as fast if it is parallelized on two machines, whereas optimizing the data layout and bundling operations upon them can achieve a reduction of the computation time to less than one tenth of the original run time. To evaluate the effects of these optimization strategies profiling programs are used that provide an insight into the behaviour of the program, especially its use of cache memory. To additionally reduce computation time a mechanism controlling the size of the time steps can be used that allows for the size of these time steps to be varied depending on the state of the system.

The demand for flexibility and extensibility of the simulation program suggests an implementation in C++, thus enabling the application of object oriented structures. Through the use of expression template functions for the numerical calculations a computational efficiency is achieved that can hold its own compared to the efficiency of Fortran77, the language that has dominated numerical applications so far, while at the same time making it a lot easier to read the emerging code.

Discrete Element Method

• Periodic boundary conditions are an important tool for the simulation of space with unlimited extend that is continuously filled with matter of a specific type. The example shows a simple simulation with 3D periodic boundary conditions and Pasimodo's contact model for non-convex polyhedral geometries. For a better perception of the periodicity of the simulation domain, it can be useful to concentrate on the red torus while viewing the video in a loop.

  Implementation and model creation: Florian Fleißner



• Bonding of particles with beam-elements. The deformation is computed from the relative positions and orientations of the connected particles. Model development and implementation: Martin Obermayr (Fraunhofer ITWM)


• The contact point between ellipsoids is computed by an optimization procedure. The frames indicate the current closest points on both particles. The optimization loop is interupted in case of no contact. Model development and implementation: Martin Obermayr (Fraunhofer ITWM)


• 1152 hard cubes are falling on a rigid support. Simulation including sticking and slipping friciton. Model creation: Frank Nägele (student research project), Florian Fleißner


• Particles can be arbitrarily assembled to compounds. Model creation: Florian Fleißner


• Granular flow through a hopper with different particle coloring. Model creation: Florian Fleißner


• Particle flow with rigid obstacles represented by 3D-surface meshes. Note the very different behavior of the medium with (left) and without (right) the consideration of sticking, slipping and drilling friction and the rolling resistance. Model creation: Florian Fleißner


• Arbitrary non-convex CAD-geometries as e.g. the shop cart geometry below, can be imported e.g. as STL-files. Model creation: Florian Fleißner


• Identification of the piling angle of glass beads. To allow for a better perception of the rotation, the beads are colored with a checkered color pattern. The simulations consider sticking friction, slipping friction and drilling friction and, moreover, the rolling resistance of the glass beads. Model creation: Florian Fleißner


• Simulation of a laboratory test to determine the performance of a flow obstacle. The performance is defined as the obstacle's capability to reduce the flow momentum. Model creation: Florian Fleißner


• Interactions between rigid macro-bodies and particles. To obtain a steady flow, particles are created and removed via particle sources and sinks during the simulation. Model creation: Florian Fleißner


• Particle sources can move freely. Model creation: Florian Fleißner


• Examples for different ways of flow visualization for particle systems. Bounding boxes with velocity glyphs, streamlines, surface reconstruction and transparent surface with particles. Model creation: Florian Fleißner


• Particle driven water wheel. Model creation: Florian Fleißner


• Simulation of a tumble sieving machine. Model creation: Christian Ergenzinger (diploma thesis), Florian Fleißner


• Simulations of a double lane change maneuver of a single-compartment tank truck carrying a granular cargo. The simulations were performed as co-simulations between Pasimodo (granulate) and Simpack (multibody system of the truck) with a data exchange via Matlab/Simulink and TCP/IP connections. The maneuver is depicted with a track velocity of v=17.5 m/s on the left and a track velocity of v=20 m/s on the right. Model creation: Vincenzo D'Alessandro (master thesis), Florian Fleißner

Hybrid Discrete Element Method

• Simulation of an orthogonal cutting process. Model creation: Timo Gaugele, in the framework of the DFG Priority Program SPP 1180


• Simulation of a tensile test with a highly elastic polysiloxane specimen. Model creation: Christian Ergenzinger


• Ballast made from bonded particles is subjected to different loadings:
  1. Cyclic compression.
  2. Oedometric compression. (Firstly, all particles are shown in the movie. Subsequently, only those particles are displayed, which are involved in fracture processes.)
  3. A sleeper is pressed into a ballast bed.

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  Model creation: Christian Ergenzinger in the framework of the DFG SFB 716


• Simulation of a multiaxial copression test with a rock specimem (breakage color coded). Model creation: Celine Geiger (student research project), Christian Ergenzinger


• Oblique rebound of an elastic sphere from a rigid plane. Model creation: Florian Fleißner


• Simulation of a nearly limp membrane falling on an obstacle. Model creation: Florian Fleißner


• Torus falling on a membrane. The membrane consists of bonded spherical particles. Only the bonds are displayed and color coded with respect to the tensile force in the bonds. Model creation: Florian Fleißner


• Simulation of a plastic string dangling under gravity. Model creation: Timo Gaugele


• Simulation of wattling with five threads, modelled as beaded spherical particles. The spheres are bonded by linear-elastic force elements. Model creation: Florian Fleißner

Links

Contact

• Dr.-Ing. Florian Fleißner