Enhancing nano-scale computational fluid dynamics with molecular pre-simulations: unsteady problems and design optimisation

David M. Holland, Matthew K. Borg, Duncan A. Lockerby*, Jason M. Reese

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We demonstrate that a computational fluid dynamics (CFD) model enhanced with molecular-level information can accurately predict unsteady nano-scale flows in non-trivial geometries, while being efficient enough to be used for design optimisation. We first consider a converging-diverging nano-scale channel driven by a time-varying body force. The time-dependent mass flow rate predicted by our enhanced CFD agrees well with a full molecular dynamics (MD) simulation of the same configuration, and is achieved at a fraction of the computational cost. Conventional CFD predictions of the same case are wholly inadequate. We then demonstrate the application of enhanced CFD as a design optimisation tool on a bifurcating two-dimensional channel, with the target of maximising mass flow rate for a fixed total volume and applied pressure. At macro scales the optimised geometry agrees well with Murray's Law for optimal branching of vascular networks; however, at nanoscales, the optimum result deviates from Murray's Law, and a corrected equation is presented.

Original languageEnglish
Pages (from-to)46-53
Number of pages8
JournalComputers and Fluids
Volume115
DOIs
Publication statusPublished - 22 Jul 2015

Keywords

  • Nanofluidics
  • Computational fluid dynamics
  • Molecular dynamics
  • Hybrid methods
  • Design optimisation
  • Murray's Law
  • Arbitrary geometries
  • Murrays Law
  • Networks
  • Flow
  • Presimulation

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