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Updated: Jun 25, 2026
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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
Published on: September 26, 2016
Lattice Boltzmann modeling of multicomponent diffusion in narrow channels
Seung Hyun Kim1, Heinz Pitsch, Iain D Boyd
1Department of Mechanical Engineering, Stanford University, California 94305-3035, USA. shkcomb@stanford.edu
Summary
Lattice Boltzmann (LB) methods accurately model multicomponent diffusion, even at finite Knudsen numbers. Higher-order LB methods capture diffusion slip and kinetic boundary layer phenomena observed in Direct Simulation Monte Carlo (DSMC) simulations.
Area of Science:
- Multiphysics simulation
- Computational fluid dynamics
- Statistical mechanics
Background:
- Accurate modeling of multicomponent diffusion is crucial for various engineering applications.
- Finite Knudsen numbers introduce complexities, necessitating advanced simulation techniques.
- Existing methods may not fully capture phenomena like diffusion slip.
Purpose of the Study:
- To investigate Lattice Boltzmann (LB) modeling for multicomponent diffusion at finite Knudsen numbers.
- To derive and validate analytic solutions for binary diffusion in narrow channels using LB methods.
- To compare LB method performance against Direct Simulation Monte Carlo (DSMC).
Main Methods:
- Developed analytic solutions for binary diffusion using standard and higher-order LB methods.
- Employed the Direct Simulation Monte Carlo (DSMC) method for validation.
- Simulated diffusion in narrow channels with finite Knudsen numbers.
Main Results:
- LB methods successfully reproduce diffusion slip phenomena.
- Higher-order LB methods accurately capture kinetic boundary layer effects.
- Analytic LB solutions align well with DSMC simulation results.
Conclusions:
- Lattice Boltzmann methods are effective for simulating multicomponent diffusion, including slip phenomena.
- Higher-order LB methods provide a more accurate representation of complex kinetic behaviors.
- The study validates LB methods against DSMC for diffusion modeling.
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