Statistical Simulation of Low-Speed Rarefied Gas Flows

✍ Scribed by Jing Fan; Ching Shen

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 154 KB
• Volume: 167
• Category: Article
• ISSN: 0021-9991
• DOI: 10.1006/jcph.2000.6681

✦ Synopsis

Molecular-based numerical schemes, such as the direct simulation Monte Carlo (DSMC) method, are more physically appropriate for rarefied gas flows in microelectromechanical systems (MEMS). It is difficult for them to be statistically convergent, however, because the statistical fluctuation becomes insurmountably large at the low Mach numbers that are characteristic of MEMS. An information preservation (IP) technique is proposed to address this issue. This technique assigns each simulated molecule in the DSMC method two velocities. One is the molecular velocity used to compute the molecular motion following the same steps as the DSMC method. The other is called information velocity. It corresponds to the collective velocity of an enormous number of real molecules that the simulated molecule represents. Using the information velocity to compute macroscopic velocity and shear stress may remove the statistical fluctuation source inherent in the DSMC method that results from the randomness of the thermal velocity. The IP technique has been applied to benchmark problems, namely Couette, Poiseuille, and Rayleigh flows, in the entire Knudsen regime. The characteristic velocities in these flows range from 0.01 to 1 m/s, much smaller than the thermal velocity of about 340 m/s at room temperature. The meaningful results are obtained at a sample size of 10 3 -10 4 , in comparison with a sample size of 10 8 or more required for the DSMC method at such a range of flow velocity. This results in a tremendous gain in CPU time. The velocity distributions, surface shear stress, and mass flux given by the IP calculations compare quite well with exact solutions at the continuum and free molecular limits, and with the numerical solutions of the linearized Boltzmann equation and experimental data in the transition regime.