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@ARTICLE{Peravali:611460,
author = {Peravali, Surya Kiran and Jafari, Vahid and Samanta, Amit
K. and Küpper, Jochen and Amin, Muhamed and Neumann,
Philipp and Breuer, Michael},
title = {{A}ccuracy and {P}erformance {E}valuation of {L}ow
{D}ensity {I}nternal and {E}xternal {F}low {P}redictions
using {CFD} and {DSMC}},
reportid = {PUBDB-2024-04925, arXiv:2401.13344},
year = {2024},
abstract = {The Direct Simulation Monte Carlo (DSMC) method was widely
used to simulate low density gas flows with large Knudsen
numbers. However, DSMC encounters limitations in the regime
of lower Knudsen numbers (Kn<0.1). In such cases, approaches
from classical computational fluid dynamics (CFD) relying on
the continuum assumption are preferred, offering accurate
solutions at acceptable computational costs. In experiments
aimed at imaging aerosolized nanoparticles in vacuo a wide
range of Knudsen numbers occur, which motivated the present
study on the analysis of the advantages and drawbacks of
DSMC and CFD simulations of rarefied flows in terms of
accuracy and computational effort. Furthermore, the
potential of hybrid methods is evaluated. For this purpose,
DSMC and CFD simulations of the flow inside a
convergent-divergent nozzle (internal expanding flow) and
the flow around a conical body (external shock generating
flow) were carried out. CFD simulations utilize the software
OpenFOAM and the DSMC solution is obtained using the
software SPARTA. The results of these simulation techniques
are evaluated by comparing them with experimental data (1),
evaluating the time-to-solution (2) and the energy
consumption (3), and assessing the feasibility of hybrid
CFD-DSMC approaches (4). Keywords: DSMC; SPARTA; Continuum
assumption; Transition regime; Rarefied flow;
high-performance computing},
cin = {FS-CFEL-CMI / HSU / UNI/CUI / UNI/EXP},
cid = {I:(DE-H253)FS-CFEL-CMI-20220405 / I:(DE-H253)HSU-20230616 /
$I:(DE-H253)UNI_CUI-20121230$ /
$I:(DE-H253)UNI_EXP-20120731$},
pnm = {631 - Matter – Dynamics, Mechanisms and Control
(POF4-631) / HIDSS-0002 - DASHH: Data Science in Hamburg -
Helmholtz Graduate School for the Structure of Matter
$(2019_IVF-HIDSS-0002)$},
pid = {G:(DE-HGF)POF4-631 / $G:(DE-HGF)2019_IVF-HIDSS-0002$},
experiment = {EXP:(DE-MLZ)NOSPEC-20140101},
typ = {PUB:(DE-HGF)25},
eprint = {2401.13344},
howpublished = {arXiv:2401.13344},
archivePrefix = {arXiv},
SLACcitation = {$\%\%CITATION$ = $arXiv:2401.13344;\%\%$},
doi = {10.3204/PUBDB-2024-04925},
url = {https://bib-pubdb1.desy.de/record/611460},
}