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The European Physical Journal Special Topics

, Volume 227, Issue 14, pp 1515–1527 | Cite as

Developing coarse-grained models for agglomerate growth

  • Milena SmiljanicEmail author
  • Rudolf Weeber
  • Dirk Pflüger
  • Christian Holm
  • Andreas KronenburgEmail author
Regular Article
  • 11 Downloads
Part of the following topical collections:
  1. Particle Methods in Natural Science and Engineering

Abstract

In this paper we present a coarse-graining (CG) approach for the agglomeration of nano-particles and clusters. In the current context, coarse-graining involves the replacement of fractal-like clusters by "representative" spherical particles. This simplification reduces significantly the number of degrees of freedom and allows for the computation of much larger systems and for better collision statistics of larger clusters. However, detailed information on the cluster shape is lost, but it is exactly this detailed shape that determines collision frequencies between fractal clusters and thus the agglomerates' growth. Therefore, additional properties need to be "inherited" by the coarsegrained particle that ensure similar collision frequencies. We generate collision probabilities as functions of the minimum passing distance between the clusters and provide these as additional function to the CG particle. This allows for partial overlap between CG particles, and the collision/sticking event is triggered with a specific probability only. We compare collision frequencies of CG simulations with equivalent Langevin dynamics simulations where all primary particles are tracked, and we observe decent agreement between cluster growth predicted by CG and the detailed Langevin dynamics simulations. Remaining differences may stem from differences in cluster transport.

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References

  1. 1.
    T.T. Kodas, M. Hampden-Smith, Aerosol processing of materials (New York, Wiley, 1999)Google Scholar
  2. 2.
    J. Gregory, Particles in water: properties and processes (CRC Press, 2005)Google Scholar
  3. 3.
    C.A. Grant, P.C. Twigg, R. Baker, D.J. Tobin, Beilstein J. Nanotechnol. 6, 1183 (2015)CrossRefGoogle Scholar
  4. 4.
    I. Schreiver, B. Hesse, C. Seim, H. Castillo-Michel, J. Villanova, P. Laux, N. Dreiack, R. Penning, R. Tucoulou, M. Cotte, A. Luch, Sci. Rep. 7, 11395 (2017)ADSCrossRefGoogle Scholar
  5. 5.
    L. Isella, Y. Drossinos, Phys. Rev. E 82, 011404 (2010)ADSCrossRefGoogle Scholar
  6. 6.
    G.A. Adebayo, B.C. Anusionwu, A.N. Njah, O.J. Adeniran, B. Mathew, R.S. Sunmonu, Pramana 75, 523 (2010)ADSCrossRefGoogle Scholar
  7. 7.
    G. Inci, A. Kronenburg, R. Weeber, D. Pflüger, Flow Turbul. Combust. 98, 1 (2017)CrossRefGoogle Scholar
  8. 8.
    S. Hirschmann, M. Brunn, M. Lahnert, M.W Glass, M. Mehl, D. Pflüger, Load balancing with p4est for short-range molecular dynamics with ESPResSo, in Advances in parallel computing (IOS Press, Amsterdam, 2017), Vol. 32, p. 455Google Scholar
  9. 9.
    S.K. Friedlander, Smoke, dust and haze, 2nd edn. (Oxford University Press, Oxford, New York, 2000)Google Scholar
  10. 10.
    S.E. Pratsinis, J. Colloid Interface Sci. 124, 416 (1988)ADSCrossRefGoogle Scholar
  11. 11.
    M. Levitt, A. Warshel, Nature 253, 694 (1975)ADSCrossRefGoogle Scholar
  12. 12.
    S. Izvekov, A. Violi, J. Chem. Theory Comput. 2, 504 (2006)CrossRefGoogle Scholar
  13. 13.
    C.J. Meyer, D.A. Deglon, Miner. Eng. 24, 719 (2011)CrossRefGoogle Scholar
  14. 14.
    A. Arnold, O. Lenz, S. Kesselheim, R. Weeber, F. Fahrenberger, D. Roehm, P. Kosovan, C. HolmESPResSo 3.1: molecular dynamics software for coarse-grained models, in Meshfree methods for partial differential equations VI , , edited by M. Griebel, M.A. Schweitzer (Springer, Berlin, Heidelberg, 2013), Vol. 89, p. 12Google Scholar
  15. 15.
    H.J. Limbach, A. Arnold, B.A. Mann, C. Holm, Comput. Phys. Commun. 174, 704 (2006)ADSCrossRefGoogle Scholar
  16. 16.
    R.M. Kerr, J. Fluid Mech. 153, 31 (1985)ADSCrossRefGoogle Scholar
  17. 17.
    G. Inci, A. Arnold, A. Kronenburg, R. Weeber, Aerosol Sci. Technol. 48, 842 (2014)ADSCrossRefGoogle Scholar
  18. 18.
    D.C. Richardson, K.J. Walsh, N. Murdoch, P. Michel, Icarus 222, 427 (2011)ADSCrossRefGoogle Scholar

Copyright information

© EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Combustion Technology, University of StuttgartStuttgartGermany
  2. 2.Institute for Computational Physics, University of StuttgartStuttgartGermany
  3. 3.Institute for Parallel and Distributed Systems, University of Stuttgart, Universitätsstr. 38StuttgartGermany

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