Implementation of unsteady sampling procedures for the parallel direct simulation Monte Carlo method

Authors:
H. M. Cave;K. -C. Tseng;J. -S. Wu;M. C. Jermy;J. -C. Huang;S. P. Krumdieck
Affiliations:
Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand;National Space Organisation, 8F, 9 Zhan-Ye 1st Road, Hsinchu Science Park, Hsinchu, Taiwan;Department of Mechanical Engineering, National Chiao-Tung University, 1001 Ta-Hsueh Road, Hsinchu 30050, Taiwan;Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand;Department of Merchant Marine, National Taiwan Ocean University, 2 Pei-Ning Road, Keelung 20224, Taiwan;Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
Venue:
Journal of Computational Physics
Year:
2008

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Abstract

An unsteady sampling routine for a general parallel direct simulation Monte Carlo method called PDSC is introduced, allowing the simulation of time-dependent flow problems in the near continuum range. A post-processing procedure called DSMC rapid ensemble averaging method (DREAM) is developed to improve the statistical scatter in the results while minimising both memory and simulation time. This method builds an ensemble average of repeated runs over small number of sampling intervals prior to the sampling point of interest by restarting the flow using either a Maxwellian distribution based on macroscopic properties for near equilibrium flows (DREAM-I) or output instantaneous particle data obtained by the original unsteady sampling of PDSC for strongly non-equilibrium flows (DREAM-II). The method is validated by simulating shock tube flow and the development of simple Couette flow. Unsteady PDSC is found to accurately predict the flow field in both cases with significantly reduced run-times over single processor code and DREAM greatly reduces the statistical scatter in the results while maintaining accurate particle velocity distributions. Simulations are then conducted of two applications involving the interaction of shocks over wedges. The results of these simulations are compared to experimental data and simulations from the literature where there these are available. In general, it was found that 10 ensembled runs of DREAM processing could reduce the statistical uncertainty in the raw PDSC data by 2.5-3.3 times, based on the limited number of cases in the present study.