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@INPROCEEDINGS{Hundshagen:874549,
author = {Hundshagen, Markus and Casimir, Nicolas and Pesch, Andreas
and Skoda, Romuald},
title = {{H}igh-{P}erformance {F}low {S}imulation and
{S}cale-{A}daptive {T}urbulence {M}odelling of {C}entrifugal
{P}umps},
volume = {50},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
reportid = {FZJ-2020-01503},
series = {Publication Series of the John von Neumann Institute for
Computing (NIC) NIC Series},
pages = {367 - 378},
year = {2020},
comment = {NIC Symposium 2020},
booktitle = {NIC Symposium 2020},
abstract = {While for the design point operation of centrifugal pumps,
where an essentially steady flow field is present and
statistical turbulence models yield an appropriate
prediction of the characteristics, the flow field gets
increasingly unsteady towards off-design operation. Special
designs as e. g. sewage pumps are characterised by a
single-blade impeller and show significantly unsteady
characteristics even in the design point. For such
highly-unsteady and turbulent flow fields, statistical
models tend to fail. On the other hand, Large-Eddy
Simulation models, where the large-vortex part of the
turbulent spectrum is directly resolved, show a much better
flow prediction. However, the spatial resolution and thus
computational effort are too high for engineering real pump
applications. Therefore, we provide an assessment of
scale-adaptive turbulence simulation (SAS) models that
recover a statistical flow solution in regions of low
unsteadiness and – like Large-Eddy Simulation – resolve
a part of the turbulent spectrum down to the available grid
resolution for highly unsteady flow regions. After a
thorough validation on standard turbulence test cases e. g.
the periodic hill case, it is shown that with a moderately
higher computational effort than statistical models, the SAS
yields a considerable improvement of the prediction of the
turbulence field in part load operation of a centrifugal
pump while the mean flow field could be well predicted even
with a well-established statistical model.},
month = {Feb},
date = {2020-02-27},
organization = {NIC Symposium 2020, Jülich (Germany),
27 Feb 2020 - 28 Feb 2020},
cin = {NIC},
cid = {I:(DE-Juel1)NIC-20090406},
pnm = {899 - ohne Topic (POF3-899)},
pid = {G:(DE-HGF)POF3-899},
typ = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
url = {https://juser.fz-juelich.de/record/874549},
}