TY  - CONF
AU  - Kräuter, Robert
AU  - Krasnov, Dmitry
AU  - Pandey, Ambrish
AU  - Schneide, Christiane
AU  - Padberg-Gehle, Kathrin
AU  - Giannakis, Dimitrios
AU  - Sreenivasan, Katepalli R.
AU  - Schumacher, Jörg
TI  - Machine Learning Applications in Convective Turbulence
VL  - 50
CY  - Jülich
PB  - Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
M1  - FZJ-2020-01502
T2  - Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series
SP  - 357 - 366
PY  - 2020
AB  - Turbulent 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.
T2  - NIC Symposium 2020
CY  - 27 Feb 2020 - 28 Feb 2020, Jülich (Germany)
Y2  - 27 Feb 2020 - 28 Feb 2020
M2  - Jülich, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
UR  - https://juser.fz-juelich.de/record/874547
ER  -