Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-30T23:12:03.975Z Has data issue: false hasContentIssue false
  • English
  • Français

Employing Per-Component Time Step in DSMC Simulations of Disparate Mass and Cross-Section Gas Mixtures

Published online by Cambridge University Press:  03 June 2015

Roman V. Maltsev
Roman V. Maltsev*
Affiliation:
8-22, Koltsovo, Novosibirsk, 630559, Russia
*
*Corresponding author.Email:[email protected]
Get access

Abstract

A new approach to simulation of stationary flows by Direct Simulation Monte Carlo method is proposed. The idea is to specify an individual time step for each component of a gas mixture. The approach consists of modifications mainly to collision phase simulation and recommendations on choosing time step ratios. It allows lowering the demands on the computational resources for cases of disparate collision diameters of molecules and/or disparate molecular masses. These are cases important e.g., in vacuum deposition technologies. Few tests of the new approach are made. Finally, the usage of new approach is demonstrated on a problem of silver nanocluster diffusion in argon carrier gas under conditions of silver deposition experiments.

Keywords

65C0576M28Direct Simulation Monte Carlotime stepdisparate massesgas mixturenanoclusters
Type
Research Article
Information
Communications in Computational Physics , Volume 14 , Issue 3 , September 2013 , pp. 703 - 721
Copyright
Copyright © Global Science Press Limited 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Bird, G. A., Molecular Gas Dynamics and the Direct Simulation of Gas Flows, Clarendon Press, 1994Google Scholar
[2]Fan, J. and Shen, C., Statistical simulation of low-speed unidirectional flows in transition regime, Proc. 21st Intern. Symp. On Rarefied Gas Dynamics, ed. Brun, R.et al., Toulouse, France, Cepadues-Editions, 2 (1998), 445.Google Scholar
[3]Sun, Q., Information Preservation Methods for Modeling Micro-Scale Gas Flows, PhD dissertation, Aerospace Engineering; The University of Michigan, 2003.Google Scholar
[4]Baker, L. L. and Hadjiconstantinou, N. G., Variance reduction for Monte Carlo solutions of the Boltzmann equation, Phys. Fluids, 17(5) (2005), 051703.CrossRefGoogle Scholar
[5]Homolle, T. M. M. and Hadjiconstantinou, N. G., A low-variance deviational simulation Monte Carlo for the Boltzmann equation, J. Comput. Phys., 226 (2007), 2341.CrossRefGoogle Scholar
[6]Huang, J.-C., Xu, K. and Yu, P., A unified gas-kinetic scheme for continuum and rarefied flows II: multi-dimensional cases, Commun. Comput. Phys., 12 (2012), 662.CrossRefGoogle Scholar
[7]Titarev, V. A., Efficient deterministic modelling of three-dimensional rarefied gas flows, Commun. Comput. Phys., 12 (2012), 162.Google Scholar
[8]Alexander, F. J., Garcia, A. L. and Alder, B. J., Cell size dependence of transport coefficients in stochastic particle algorithms, Phys. Fluids, 10(1998), 1540; Erratum, Phys. Fluids, 12(3) (2000), 731.Google Scholar
[9]Hadjiconstantinou, N. G., Analysis of discretization in the direct simulation Monte Carlo, Phys. Fluids, 12(10) (2000), 2634.Google Scholar
[10]Maltsev, R. V., On the Selection of the Number of Model Particles in DSMC Computations, in Proc. 27th Intern. Symp. on Rarefied Gas Dynamics, AIP Conf. Proc. 1333 (2010), 289.Google Scholar
[11]Burt, J. M. and Boyd, I. D., Convergence detection in direct simulation Monte Carlo calculations for steady state flows, Commun. Comput. Phys., 10 (2011), 807.Google Scholar
[12]Ivanov, M. S. and Rogasinsky, S. V., Analysis of numerical techniques of the direct simulation Monte Carlo method in the rarefied gas dynamics, Sov. J. Numer. Anal. Math. Model, 2(6) (1988), 453.Google Scholar
[13]Rader, D. J., Gallis, M. A., Torczynski, J. R. and Wagner, W., Direct simulation Monte Carlo convergence behavior of the hard-sphere-gas thermal conductivity for Fourier heat flow, Phys. Fluids, 18(7) (2006), 077102.Google Scholar
[14]Bird, G. A., Molecular Gas Dynamics, Oxford Univ. Press, 1976.Google Scholar
[15]Morozov, A. A. and Yu, M.Plotnikov, Analysis of efficiency of some approaches of solving problems by the DSMC method, in Rarefied Gas Dynamics, Proc. 21st Intern. Symp. (Marseille, France), eds. Brun, R., Campargue, R., Gatignol, R. and Lengrand, J.-C., CEPADE-EDITION, 2 (1999), 133.Google Scholar
[16]Kannenberg, K. C., Computational Methods for the Direct Simulation Monte Carlo Technique with Application to Plume Impingement, Ph.D. thesis, Cornell University, Ithaca, New York, 1998.Google Scholar
[17]Shevyrin, A.A., Bondar, Ye. A. and Ivanov, M. S., Analysis of Repeated Collisions in the DSMC Method, in Proc. 24th International Symposium on Rarefied Gas Dynamics, AIP Conf. Proc. 762 (2005), 565.Google Scholar
[18]De Angelis, F., Toccoli, T. and Pallaoro, A., et al., SuMBE based organic thin film transistors, Synthetic Metals, 146 (2004), 291.Google Scholar
[19]Maltsev, R. V., Inertia effects in the compressed layer in front of a flat plate, in Proc. 27th Intern. Symp. on Rarefied Gas Dynamics, AIP Conf. Proc. 1333 (2010), 295.Google Scholar
[20]Rebrov, A. K., Safonov, A. I. and Timoshenko, N. I., et al., Gas-jet synthesis of silver-polymer films, J. Appl. Mech. Tech. Phys., 51(4) (2010), 598.Google Scholar