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000874553 1112_ $$aNIC Symposium 2020$$cJülich$$d2020-02-27 - 2020-02-28$$wGermany
000874553 245__ $$aSub-Grid Scale Modelling at Scale with Deep Learning and up to 60 Billion Degrees of Freedom
000874553 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2020
000874553 29510 $$aNIC Symposium 2020
000874553 300__ $$a379 - 388
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000874553 4900_ $$aPublication Series of the John von Neumann Institute for Computing (NIC) NIC Series$$v50
000874553 520__ $$aThis work presents fully resolved direct numerical simulations (DNSs) of a turbulent reactive planar temporally non-premixed jet configuration with up to 60 billion degrees of freedom. As scalar mixing is of utmost importance for this kind of configuration, a novel deep learning (DL) approach in the context of large-eddy simulation is presented which results in predictive mixing statistics on underresolved grids. The usability of the mixing model is approved by applying it to the DNS data. Furthermore, node performance measurements for the training of the DL networks are shown for different computing clusters.
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000874553 7001_ $$0P:(DE-HGF)0$$aDenker, Dominik$$b1
000874553 7001_ $$0P:(DE-Juel1)158080$$aJitsev, Jenia$$b2$$eCorresponding author
000874553 7001_ $$0P:(DE-HGF)0$$aPitsch, Heinz$$b3
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