001     874549
005     20210130004719.0
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037 _ _ |a FZJ-2020-01503
041 _ _ |a English
100 1 _ |a Hundshagen, Markus
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111 2 _ |a NIC Symposium 2020
|c Jülich
|d 2020-02-27 - 2020-02-28
|w Germany
245 _ _ |a High-Performance Flow Simulation and Scale-Adaptive Turbulence Modelling of Centrifugal Pumps
260 _ _ |a Jülich
|c 2020
|b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
295 1 0 |a NIC Symposium 2020
300 _ _ |a 367 - 378
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a Contribution to a book
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490 0 _ |a Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series
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520 _ _ |a 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.
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700 1 _ |a Casimir, Nicolas
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700 1 _ |a Pesch, Andreas
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700 1 _ |a Skoda, Romuald
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|e Corresponding author
787 0 _ |i IsPartOf
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856 4 _ |y OpenAccess
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910 1 _ |a Ruhr University Bochum
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910 1 _ |a Ruhr University Bochum
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910 1 _ |a Ruhr University Bochum
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910 1 _ |a Ruhr University Bochum
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914 1 _ |y 2020
915 _ _ |a OpenAccess
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915 _ _ |a Creative Commons Attribution CC BY 4.0
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920 1 _ |0 I:(DE-Juel1)NIC-20090406
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