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Simulating the flow of entangled polymers.

Yuichi Masubuchi1

  • 1Institute for Chemical Research, Kyoto University, Gokasho Uji-City, Japan 611-0011;

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|February 7, 2014
PubMed
Summary
This summary is machine-generated.

Simulating entangled polymer flow requires linking molecular structure to macroscopic behavior. Mesoscopic models bridge atomistic and fluid dynamics, enabling better polymer processing predictions.

Keywords:
coarse-grainingmesoscopic modelingmultiscalepolymer dynamicspolymer processingrheology

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Area of Science:

  • Polymer Science
  • Materials Science
  • Computational Chemistry

Background:

  • Industrial polymer processing relies on fluid dynamics simulations using constitutive equations.
  • Current methods lack the ability to derive constitutive relationships directly from molecular structure.
  • Atomistic molecular dynamics is computationally prohibitive for large-scale polymer systems.

Purpose of the Study:

  • To develop methods for obtaining polymer constitutive relationships from molecular structure.
  • To bridge the gap between atomistic, mesoscopic, and macroscopic simulation scales.
  • To enable integrated simulations for predicting polymer processing behavior.

Main Methods:

  • Development and application of coarse-grained polymer models with reduced degrees of freedom.
  • Utilizing mesoscopic models, such as tube models, designed to capture entangled polymer dynamics.
  • Establishing links between coarse-grained models and both atomistic and macroscopic simulations.

Main Results:

  • Mesoscopic models show significant success in reproducing entangled polymer dynamics.
  • Coarse-grained models offer a practical alternative to atomistic simulations for larger systems.
  • Progress has been made in connecting different simulation scales.

Conclusions:

  • Integrated simulations linking materials chemistry to polymer processing are becoming feasible.
  • Mesoscopic models serve as crucial intermediaries between atomistic detail and macroscopic flow.
  • Advances in coarse-grained and mesoscopic modeling are key to optimizing polymer processing automation.