2005
DOI: 10.2514/1.12542
|View full text |Cite
|
Sign up to set email alerts
|

Evaluation of Macroscopic Properties in the Direct Simulation Monte Carlo Method

Abstract: General expressions for evaluating macroscopic properties in the direct simulation Monte Carlo (DSMC) method are examined using numerical examples. DSMC simulations show that the evaluations (both statistically dependent and independent) generally follow Gaussian distributions except that the temperature evaluations follow chi-square distributions, whereas their statistical errors mainly depend on the sample size. To reduce the statistical errors for macroscopic properties required during DSMC simulations, a s… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
35
0

Year Published

2005
2005
2024
2024

Publication Types

Select...
6
2
1

Relationship

2
7

Authors

Journals

citations
Cited by 51 publications
(35 citation statements)
references
References 17 publications
0
35
0
Order By: Relevance
“…For instance, Rader et al [21] showed that the numerical error in thermal conductivity for Fourier heat flow of hard sphere gas was about 0.040 5 (Δx/λ) 2 due to the cell size, 0.028 7 (Δt/τ) 2 due to the time step, and 0.083/N c due to the number of particles per cell, where λ is the mean free path and τ is the mean collision time. The statistical error due to a limited sample size is proportional to 1/ √ N s for flow properties such as velocity, density, and temperature [22], where N s can be enlarged using ensemble average.…”
Section: Dsmc Methodsmentioning
confidence: 99%
“…For instance, Rader et al [21] showed that the numerical error in thermal conductivity for Fourier heat flow of hard sphere gas was about 0.040 5 (Δx/λ) 2 due to the cell size, 0.028 7 (Δt/τ) 2 due to the time step, and 0.083/N c due to the number of particles per cell, where λ is the mean free path and τ is the mean collision time. The statistical error due to a limited sample size is proportional to 1/ √ N s for flow properties such as velocity, density, and temperature [22], where N s can be enlarged using ensemble average.…”
Section: Dsmc Methodsmentioning
confidence: 99%
“…As this progression and relaxation occurs, DSMC and NS regions are loosely coupled and interface locations are periodically updated throughout the simulation. Before the NS portions of the hybrid numerical cycle, the macroscopic state variables are set in each NS boundary cell using a subrelaxation average [33] of the molecular properties in each corresponding DSMC cell. As an example, the final steady-state interface locations for the hollow-cylinder-flare simulation are shown at the top of Fig.…”
Section: Modular Particle-continuum Numerical Methods a Problem mentioning
confidence: 99%
“…Note that the amount of scatter associated with a given θ value is the same as the scatter resulting from averaging over 1 θ time-steps. 21 Figure 5 shows the success of this averaging technique applied to the DSMC velocity variation. Although when using θ = 0.01 the average follows the DSMC variation with almost no lag, the scatter is far too large to use this average as a NS boundary condition.…”
Section: Reducing Scatter In Ns Boundary Conditionsmentioning
confidence: 99%