2000
DOI: 10.1063/1.1289691
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Time step truncation error in direct simulation Monte Carlo

Abstract: The time step truncation error in direct simulation Monte Carlo calculations is found to be O(⌬t 2 ) for a variety of simple flows, both transient and steady state. The measured errors in the transport coefficients ͑viscosity, thermal conductivity, and self-diffusion͒ are in good agreement with predictions from Green-Kubo analysis ͓N. Hadjiconstantinou, Phys. Fluids 12, 2634 ͑2000͔͒.

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Cited by 121 publications
(64 citation statements)
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“…It should also be noted that our numerical experiments have shown that the numerical error associated with VRDSMC is not strongly affected by other discretization parameters (e.g. Δy) and thus provided ε remains small, much like DSMC [1,13,15], accurate solutions can be obtained with fairly coarse grids and thus total numbers of particles that are not excessively large. Furthermore, this moderate increase in N cell is only practically limiting in applications characterized by Kn 1, while most transition regime flows can be described to engineering accuracy with N cell = O(100), which is not substantially higher than the number of particles per cell required by DSMC.…”
Section: Approximation Error and Limitationsmentioning
confidence: 99%
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“…It should also be noted that our numerical experiments have shown that the numerical error associated with VRDSMC is not strongly affected by other discretization parameters (e.g. Δy) and thus provided ε remains small, much like DSMC [1,13,15], accurate solutions can be obtained with fairly coarse grids and thus total numbers of particles that are not excessively large. Furthermore, this moderate increase in N cell is only practically limiting in applications characterized by Kn 1, while most transition regime flows can be described to engineering accuracy with N cell = O(100), which is not substantially higher than the number of particles per cell required by DSMC.…”
Section: Approximation Error and Limitationsmentioning
confidence: 99%
“…A convergence proof for this algorithm can be found in [27]; an analysis of the error associated with the timestep discretization can be found in [13,15]. Here, we use a form of the collision integral that is convenient for discussing particle methods [4,5,19].…”
Section: Variance Reduction Using Importance Weights: Vrdsmcmentioning
confidence: 99%
“…The linear dimensions of the cells should be small in comparison with the length scale of the macroscopic §ow gradients normal to the streamwise directions, which means that the cell dimensions should be of the order or smaller than the local mean free path [27,28]. The time step should be chosen to be su©ciently small in comparison with the local mean collision time [29,30]. A very small time step results in an ine©cient advancement of the solution and accumulation of statistics.…”
Section: Free-stream and Flow Conditionsmentioning
confidence: 99%
“…It has been shown [10], [11] that the error in the transport coefficients is proportional to the square of the timestep, with a proportionality constant such that for timesteps of the order of one mean free time the error is of the order of 5%. In our simulations, ∆t < λ/(7c o ), thus making the error negligible.…”
Section: B Simulationsmentioning
confidence: 99%