@Article{CiCP-20-5,
author = {},
title = {A Probabilistic Automatic Steady State Detection Method for the Direct Simulation Monte Carlo},
journal = {Communications in Computational Physics},
year = {2016},
volume = {20},
number = {5},
pages = {1183--1209},
abstract = {<p style="text-align: justify;">The statistical error associated with sampling in the DSMC method can be
categorized as type I and II, which are caused by the incorrect rejection and acceptance
of the null hypothesis, respectively. In this study, robust global and local automatic
steady state detection methods were developed based on an ingenious method based
purely on the statistics and kinetics of particles. The key concept is built upon probabilistic
automatic reset sampling (PARS) to minimize the type II error caused by incorrect
acceptance of the samples that do not belong to the steady state. The global
steady state method is based on a relative standard variation of collisional invariants,
while the local steady state method is based on local variations in the distribution
function of particles at each cell. In order to verify the capability of the new methods,
two benchmark cases&nbsp;–&nbsp;the one-dimensional shear-driven Couette flow and the
two-dimensional high speed flow past a vertical wall&nbsp;–&nbsp;were extensively investigated.
Owing to the combined effects of the automatic detection and local reset sampling, the
local steady state detection method yielded a substantial gain of 30-36% in computational
cost for the problem studied. Moreover, the local reset feature outperformed the
automatic detection feature in overall computational savings.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.080815.240316a},
url = {https://global-sci.com/article/80232/a-probabilistic-automatic-steady-state-detection-method-for-the-direct-simulation-monte-carlo}
}