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000874547 037__ $$aFZJ-2020-01502
000874547 041__ $$aEnglish
000874547 1001_ $$0P:(DE-HGF)0$$aKräuter, Robert$$b0
000874547 1112_ $$aNIC Symposium 2020$$cJülich$$d2020-02-27 - 2020-02-28$$wGermany
000874547 245__ $$aMachine Learning Applications in Convective Turbulence
000874547 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2020
000874547 29510 $$aNIC Symposium 2020
000874547 300__ $$a357 - 366
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000874547 4900_ $$aPublication Series of the John von Neumann Institute for Computing (NIC) NIC Series$$v50
000874547 520__ $$aTurbulent convection flows are ubiquitous in natural systems such as in the atmosphere or in stellar interiors as well as in technological applications such as cooling or energy storage devices. Their physical complexity and vast number of degrees of freedom prevents often an access by direct numerical simulations that resolve all flow scales from the smallest to the largest plumes and vortices in the system and requires a simplified modelling of the flow itself and the resulting turbulent transport behaviour. The following article summarises some examples that aim at a reduction of the flow complexity and thus of the number of degrees of freedom of convective turbulence by machine learning approaches. We therefore apply unsupervised and supervised machine learning methods to direct numerical simulation data of a Rayleigh-Bénard convection flow which serves as a paradigm of the examples mentioned at the beginning.
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000874547 7001_ $$0P:(DE-HGF)0$$aKrasnov, Dmitry$$b1
000874547 7001_ $$0P:(DE-HGF)0$$aPandey, Ambrish$$b2
000874547 7001_ $$0P:(DE-HGF)0$$aSchneide, Christiane$$b3
000874547 7001_ $$0P:(DE-HGF)0$$aPadberg-Gehle, Kathrin$$b4
000874547 7001_ $$0P:(DE-HGF)0$$aGiannakis, Dimitrios$$b5
000874547 7001_ $$0P:(DE-HGF)0$$aSreenivasan, Katepalli R.$$b6
000874547 7001_ $$0P:(DE-HGF)0$$aSchumacher, Jörg$$b7$$eCorresponding author
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000874547 8564_ $$uhttps://juser.fz-juelich.de/record/874547/files/NIC_2020_Schumacher.pdf$$yOpenAccess
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000874547 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aLeuphana Universität Lüneburg$$b3
000874547 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aLeuphana Universität Lüneburg$$b4
000874547 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aNew York University$$b5
000874547 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aNew York University$$b6
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000874547 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aNew York University$$b7
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