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Capturing Knudsen layer phenomena using a lattice Boltzmann model.

Yong-Hao Zhang1, Xiao-Jun Gu, Robert W Barber

  • 1Centre for Microfluidics and Microsystems Modelling, Computational Science and Engineering Department, Council for the Central Laboratory of the Research Councils, Daresbury Laboratory, Warrington WA4 4AD, United Kingdom. Y.Zhang@dl.ac.uk

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 13, 2006
PubMed
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This study introduces an effective mean-free path for lattice Boltzmann methods to accurately simulate rarefied gas flows, improving near-wall accuracy for micro/nanoscale devices beyond current slip-flow limitations.

Area of Science:

  • Computational fluid dynamics
  • Rarefied gas dynamics
  • Micro/nanoscale transport phenomena

Background:

  • Lattice Boltzmann methods (LBM) are computationally efficient for rarefied gas flows.
  • Existing LBM struggle with the Knudsen layer's nonlinear shear stress-strain rate relationship.
  • Current models are limited to slip-flow solutions of Navier-Stokes equations.

Purpose of the Study:

  • To extend LBM capabilities beyond the slip-flow regime.
  • To accurately capture Knudsen layer effects in rarefied gas flows.
  • To improve near-wall accuracy in micro/nanoscale simulations.

Main Methods:

  • Proposed an effective mean-free path model.
  • Applied the model to shear-driven and pressure-driven flows between parallel plates.

Related Experiment Videos

  • Investigated flows across a range of Knudsen numbers (0.01 to 1).

    • Main Results:

    • The proposed mean-free path effectively addresses Knudsen layer effects.
    • Significantly improved near-wall accuracy of LBM simulations.
    • Demonstrated computational economy over a wide Knudsen number range.

    Conclusions:

    • The developed LBM approach extends simulation capabilities for rarefied gas flows.
    • Offers a computationally efficient and accurate method for micro/nanoscale device analysis.
    • Overcomes limitations of traditional slip-flow models.