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Stochastic rotation dynamics: a Galilean-invariant mesoscopic model for fluid flow
1Supercomputing Institute, University of Minnesota, 1200 Washington Avenue South, Minneapolis, Minnesota 55415, USA.
Summary
This study enhances a stochastic fluid dynamics model, achieving Galilean-invariance and deriving analytic expressions for viscosity and diffusion. Simulations confirm these findings, revealing long-time tails in autocorrelation functions.
Area of Science:
- Computational fluid dynamics
- Statistical physics
Background:
- Stochastic models are crucial for simulating complex fluid behaviors.
- Achieving Galilean-invariance in these models is essential for physical accuracy.
Purpose of the Study:
- To investigate a stochastic fluid dynamics model with continuous velocities and multiparticle collisions.
- To demonstrate the achievement of full Galilean-invariance for arbitrary Mach numbers.
- To derive and validate analytic expressions for transport coefficients.
Main Methods:
- Analysis of a recently introduced stochastic model.
- Derivation of analytic expressions for viscosity and diffusion.
- Comparison of derived expressions with simulation data.
- Measurement of long-time tails in velocity and stress autocorrelation functions.
Main Results:
- Full Galilean-invariance is achieved for arbitrary Mach numbers within the model.
- Analytic expressions for viscosity and diffusion constant were successfully derived.
- Simulation results show good agreement with the derived analytic expressions.
- Long-time tails were observed and measured in velocity and stress autocorrelation functions.
Conclusions:
- The investigated stochastic model provides a robust framework for fluid dynamics simulations.
- The model's Galilean-invariance and accurate transport coefficients enhance its physical realism.
- The presence of long-time tails suggests important collective effects in the simulated fluid system.