Reversible Diffusion by Thermal Fluctuations

@article{Donev2013ReversibleDB,
  title={Reversible Diffusion by Thermal Fluctuations},
  author={Aleksandar Donev and Thomas G. Fai and and E. Vanden-Eijnden},
  journal={arXiv: Statistical Mechanics},
  year={2013}
}
A model for diffusion in liquids that couples the dynamics of tracer particles to a fluctuating Stokes equation for the fluid is investigated in the limit of large Schmidt number. In this limit, the concentration of tracers is shown to satisfy a closed-form stochastic advection-diffusion equation that is used to investigate the collective diffusion of hydrodynamically-correlated tracers through a combination of Eulerian and Lagrangian numerical methods. This analysis indicates that transport in… 

Figures from this paper

A reversible mesoscopic model of diffusion in liquids: from giant fluctuations to Fick’s law

We study diffusive mixing in the presence of thermal fluctuations under the assumption of large Schmidt number. In this regime we obtain a limiting equation that contains a diffusive stochastic drift

msp LOW MACH NUMBER FLUCTUATING HYDRODYNAMICS OF DIFFUSIVELY MIXING FLUIDS

We formulate low Mach number fluctuating hydrodynamic equations appropriate for modeling diffusive mixing in isothermal mixtures of fluids with different density and transport coefficients. These

Low Mach Number Fluctuating Hydrodynamics of Diffusively Mixing Fluids

We formulate low Mach number fluctuating hydrodynamic equations appropriate for modeling diffusive mixing in isothermal mixtures of fluids with different density and transport coefficients. These

Brownian dynamics without Green's functions.

Numerically demonstrate the ability of the FIB method to accurately capture both the static (equilibrium) and dynamic properties of interacting particles in flow, and propose a random finite difference approach to approximating the stochastic drift proportional to the divergence of the configuration-dependent mobility matrix.

References

SHOWING 1-10 OF 28 REFERENCES

Nonlinearity

Real systems are nonlinear. Before discussing nonlinear theory, I review three reasons why the core theory of control focuses on linear analysis. First, feedback compensates for model uncertainty.

Nature Communications

  • Itor Ial
  • Education
    Nature Cell Biology
  • 2010
Peer review, at its best, should aim to provide authors and editors with rigorous and constructive feedback resulting in an improved study, and there is clearly room for improvement, the current system is not broken.

Physical Chemistry Chemical Physics

COMMUNICATION Radom et al. Accurate quantum chemical energies for tetrapeptide conformations: why MP2 data with an insuffi cient basis set should be handled with caution

I and J

Communications in Applied Mathematics and Computational Science

Physics Reports

  • Education

J. of Statistical Mechanics: Theory and Experiment

  • J. of Statistical Mechanics: Theory and Experiment
  • 2011

Here w ∇c and w · ∇c are short-hand notations for k (φ k · ∇c) @BULLET dB k /dt and k (φ k · ∇c) dB k /dt

  • Here w ∇c and w · ∇c are short-hand notations for k (φ k · ∇c) @BULLET dB k /dt and k (φ k · ∇c) dB k /dt