Algorithm refinement for stochastic partial differential equations: II. Correlated systems
- CCS-3, Los Alamos National Laboratory, MS-B256, Los Alamos, NM 87545 (United States)
- Department of Physics, San Jose State University, San Jose, CA 95192-0106 (United States)
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States)
We analyze a hybrid particle/continuum algorithm for a hydrodynamic system with long ranged correlations. Specifically, we consider the so-called train model for viscous transport in gases, which is based on a generalization of the random walk process for the diffusion of momentum. This discrete model is coupled with its continuous counterpart, given by a pair of stochastic partial differential equations. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass and momentum conservation. This methodology is an extension of our stochastic Algorithm Refinement (AR) hybrid for simple diffusion [F. Alexander, A. Garcia, D. Tartakovsky, Algorithm refinement for stochastic partial differential equations: I. Linear diffusion, J. Comput. Phys. 182 (2002) 47-66]. Results from a variety of numerical experiments are presented for steady-state scenarios. In all cases the mean and variance of density and velocity are captured correctly by the stochastic hybrid algorithm. For a non-stochastic version (i.e., using only deterministic continuum fluxes) the long-range correlations of velocity fluctuations are qualitatively preserved but at reduced magnitude.
- OSTI ID:
- 20687250
- Journal Information:
- Journal of Computational Physics, Vol. 207, Issue 2; Other Information: DOI: 10.1016/j.jcp.2005.02.004; PII: S0021-9991(05)00050-1; Copyright (c) 2005 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA); ISSN 0021-9991
- Country of Publication:
- United States
- Language:
- English
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