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000874549 037__ $$aFZJ-2020-01503
000874549 041__ $$aEnglish
000874549 1001_ $$0P:(DE-HGF)0$$aHundshagen, Markus$$b0
000874549 1112_ $$aNIC Symposium 2020$$cJülich$$d2020-02-27 - 2020-02-28$$wGermany
000874549 245__ $$aHigh-Performance Flow Simulation and Scale-Adaptive Turbulence Modelling of Centrifugal Pumps
000874549 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2020
000874549 29510 $$aNIC Symposium 2020
000874549 300__ $$a367 - 378
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000874549 4900_ $$aPublication Series of the John von Neumann Institute for Computing (NIC) NIC Series$$v50
000874549 520__ $$aWhile 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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000874549 7001_ $$0P:(DE-HGF)0$$aCasimir, Nicolas$$b1
000874549 7001_ $$0P:(DE-HGF)0$$aPesch, Andreas$$b2
000874549 7001_ $$0P:(DE-HGF)0$$aSkoda, Romuald$$b3$$eCorresponding author
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000874549 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aRuhr University Bochum$$b0
000874549 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aRuhr University Bochum$$b1
000874549 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aRuhr University Bochum$$b2
000874549 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aRuhr University Bochum$$b3
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000874549 9201_ $$0I:(DE-Juel1)NIC-20090406$$kNIC$$lJohn von Neumann - Institut für Computing$$x0
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