A Parallelised High Performance Monte Carlo Simulation Approach for Complex Polymerisation Kinetics

Authors

  • Hugh Chaffey-Millar,

    1. Center for Advanced Macromolecular Design, School of Chemical Sciences and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
    Search for more papers by this author
  • Don Stewart,

    1. Programming Languages and Systems, School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
    Search for more papers by this author
  • Manuel M. T. Chakravarty,

    1. Programming Languages and Systems, School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
    Search for more papers by this author
  • Gabriele Keller,

    Corresponding author
    1. Programming Languages and Systems, School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
    • Programming Languages and Systems, School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.
    Search for more papers by this author
  • Christopher Barner-Kowollik

    Corresponding author
    1. Center for Advanced Macromolecular Design, School of Chemical Sciences and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
    • Center for Advanced Macromolecular Design, School of Chemical Sciences and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.
    Search for more papers by this author

Abstract

A novel, parallelised approach to Monte Carlo simulations for the computation of full molecular weight distributions (MWDs) arising from complex polymerisation reactions is presented. The parallel Monte Carlo method constitutes perhaps the most comprehensive route to the simulation of full MWDs of multiple chain length polymer entities and can also provide detailed microstructural information. New fundamental insights have been developed with regard to the Monte Carlo process in at least three key areas: (i) an insufficient system size is demonstrated to create inaccuracies via poor representation of the most improbable events and least numerous species; (ii) advanced algorithmic principles and compiler technology known to computer science have been used to provide speed improvements and (iii) the parallelisability of the algorithm has been explored and excellent scalability demonstrated. At present, the parallel Monte Carlo method presented herein compares very favourably in speed with the latest developments in the h-p Galerkin method-based PREDICI software package while providing significantly more detailed microstructural information. It seems viable to fuse parallel Monte Carlo methods with those based on the h-p Galerkin methods to achieve an optimum of information depths for the modelling of complex macromolecular kinetics and the resulting microstructural information.

original image

Ancillary