Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

DEM/CFD-DEM Modelling of Non-spherical Particulate Systems: Theoretical Developments and Applications

Binder jet 3D printing—Process parameters, materials, properties, modeling, and challenges

We present a multi–purpose CFD–DEM framework to simulate coupled fluid–granular systems. The motion of the particles is resolved by means of the Discrete Element Method (DEM), and the Computational Fluid Dynamics (CFD) method is used to calculate the interstitial fluid flow. We first give a short overview over the DEM and CFD–DEM codes and implementations, followed by elaborating on the numerical schemes and implementation of the CFD–DEM coupling approach, which comprises two fundamentally different approaches, the unresolved CFD–DEM and the resolved CFD–DEM using an Immersed Boundary (IB) method. Both the DEM and the CFD–DEM approach are successfully tested against analytics as well as experimental data.

Models, algorithms and validation for opensource DEM and CFD-DEM

The Discrete Element Method is widely used to simulate systems of granular par- ticles approximating particles by one sphere per particle. However w.r.t. their shape many particles widely differ from spheres. Thus it is hard to accurately simulate granular behavior using spherical particles. LIGGGHTS (6), an open source discrete element particle simulation software, allows handling of non-spherical particles by clumping multiple spheres together and integrating them as one rigid body, this is called multi-sphere method. Available surface data of real-world objects from laser-scans motivated a coupling of these two capabilities, i.e. the approximation of non-convex objects, given by stereo-lithography files, by a given number of spheres. In this paper we present a novel method to approximate closed objects by a finite number of spheres. This method is grid based and consists of four parts: First a grid is created and data is calculated for each grid-point. Based upon this data spheres are added subsequently until a given number of spheres is reached. After that the volume of the approximation is calculated and the approximation is scaled such that its volume equals the volume of the object. In order to enable the users to compare different approximations a measure of quality is defined. Finally, sample results obtained by the presented algorithm are given.

Approximation of objects by spheres for multisphere simulations in dem

Metallurgical processes like the blast furnace or the Siemens Corex® process are widely used for the production of hot metal. Main raw materials include coke and iron ore for the blast furnace process and coal and iron ore for the Corex® process. Typically, these materials are charged into the furnace through chutes, and the process is subsequently subject to particle segregation at different stages of the process: (a) segregation that takes place as the bulk solids are added/charged into the furnace (b) segregation that takes place in the bed, induced by the granular flow pattern (c) segregation induced by gas-solid interaction, e.g. near the so-called raceway (a zone of partial fluidization due to gas injection). Segregation will lead to different segregation patterns and thus different grain size distributions. The grain size distribution is a relevant parameter as it has major influence on the process stability and efficiency by means of pressure drop and homogeneity of the chemical reactions. Numerical Discrete Element Method (DEM) simulations were performed to show that different ways of adding the granular material into the vessel (‘charging recipes’) lead to different segregation patterns and grain size distributions.

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Stefan Amberger

Papers

Models, algorithms and validation for opensource DEM and CFD-DEM

Approximation of objects by spheres for multisphere simulations in dem

Simulation of particle segregation in metallurgical furnaces for iron production

Link quality parameters in VSAT networks for effective operation and for weather monitoring

Strategies for simulation software quality assurance applied to open source DEM