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首页> 外文期刊>Journal of Computational Physics >Implementation of unsteady sampling procedures for the parallel direct simulation Monte Carlo method
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Implementation of unsteady sampling procedures for the parallel direct simulation Monte Carlo method

机译：并行直接模拟蒙特卡洛方法的非定常采样程序的实现

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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. (c) 2008 Published by Elsevier Inc.
机译：介绍了一种用于通用并行直接模拟蒙特卡罗方法（PDSC）的非稳定采样例程，该方法可以模拟近连续范围内随时间变化的流动问题。开发了一种称为DSMC快速集成平均方法（DREAM）的后处理程序，以改善结果的统计分散性，同时最大程度地减少内存和仿真时间。此方法通过使用基于近似平衡流的宏观特性的麦克斯韦分布来重新启动流量或输出获得的瞬时粒子数据，从而在感兴趣的采样点之前，在少量采样间隔内建立重复运行的总体平均值通过PDSC最初针对非平衡流的非稳​​定采样（DREAM-II）。通过模拟激波管流动和简单的库埃特流动的发展来验证该方法。发现非稳态PDSC可以在两种情况下通过单个处理器代码显着减少运行时间来准确预测流场，而DREAM可以大大减少结果的统计分散，同时保持精确的粒子速度分布。然后对两个应用程序进行了仿真，这些应用程序涉及楔上的冲击相互作用。将这些模拟的结果与实验数据和来自文献的模拟（如果有）进行比较。通常，基于本研究的有限案例，发现10次DREAM处理的组合运行可以将原始PDSC数据的统计不确定性降低2.5-3.3倍。 （c）2008年，Elsevier Inc.发行。

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