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MercuryCG

MercuryDPM contains a unique toolbox for analysing particle data, the MercuryCG toolbox:

Three documentation pages exist for this tool:

The first page describes the mathematical formulae used to extract continuum fields from particle data. The second page tells you how to apply the analysis tool in practice, i.e. which command to use and what options to set. The third page tells you how to apply analytical output to a running MercuryDPM simulation.

Here are a few examples of the coarse-graining method applied:

  1. Coarse-graining applied to silo flow [Weinhart et al, Powder Tech., 2016]

    The figure on the left shows a 3D particle simulation of granular flow through a thin, rectangular silo. One can observe three regions, a static zone near the side walls, a shear band, and core flow. To properly define those regions, we use coarse graining to define the velocity v(x,z), averaged over depth y and time t, then plot the normalised pure shear, shown in the right figure. One can clearly see that the pure shear is large in the shear band. By fitting the pure shear values with a Gaussian, the shear band centre and width can be clearly defined.

  2. Coarse-graining applied to segregation problems [Weinhart et al, AIP Conf. Proc. 1542, 2013].
    The centre image shows the flow of a mixture of particles of two different sizes down an inclined, rough surface. The simulation is periodic in x- and y-direction and evolved until the flow is steady, uniform. One can clearly see that the mixture segregates, with large particles on the top and small particles on the bottom. To find out what causes the segregation, we compute the continuum fields of the small and large particles separately and look for differences between the two mixture components. We observe that the contribution of the small particles to the granular temperature (or kinetic stress) is overproportionally large (i.e. larger than their volume fraction), shown in the top right of the figure. This suggests that segregation is driven by the kinetic stress differences between the components (and not the total stress differences, as was suggested in previous work). Note that coarse-graining clearly defines how much each mixture component contributes to the total stress, and it was only due to that clear definition of partial stresses in the coarse-graining formulation that this effect could be observed.
  3. Atomistic fluids [Hartkamp et al, J. Chem. Phys. 137, 044711 (2012)]

    Coarse-graining is also applicable to molecular dynamics simulations, in this case atomistic flow through a nanochannel. Near the channel wall, we observed oscillations in the flow density, which indicates that the flow is slightly ordered. To resolve this phenomenon, a high spatial resolution is required, which can easily be obtained in coarse-graining by using a small length scale