An artificial neural network-based multiscale method for hybrid atomistic-continuum simulations

Nikolaos Asproulis, Dimitris Drikakis

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

This paper presents an artificial neural network-based multiscale method for coupling continuum and molecular simulations. Molecular dynamics modelling is employed as a local "high resolution" refinement of computational data required by the continuum computational fluid dynamics solver. The coupling between atomistic and continuum simulations is obtained by an artificial neural network (ANN) methodology. The ANN aims to optimise the transfer of information through minimisation of (1) the computational cost by avoiding repetitive atomistic simulations of nearly identical states, and (2) the fluctuation strength of the atomistic outputs that are fed back to the continuum solver. Results are presented for prototype flows such as the isothermal Couette flow with slip boundary conditions and the slip Couette flow with heat transfer.

Original languageEnglish
Pages (from-to)559-574
Number of pages16
JournalMicrofluidics and Nanofluidics
Volume15
Issue number4
DOIs
Publication statusPublished - 1 Oct 2013

Keywords

  • Artificial neural networks
  • Continuum fluid dynamics
  • Hybrid methods
  • Molecular dynamics
  • Multiscale modelling

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