Machine Learning Applications in Convective Turbulence
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung
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Machine Learning Applications in Convective Turbulence. / Kräuter, Robert; Krasnov, Dmitry; Pandey, Ambrish et al.
NIC Symposium 2020: 27 − 28 February 2020, Jülich, Germany, Proceedings. Hrsg. / M. Müller; K. Binder; A. Trauntmann. Jülich : Forschungszentrum Jülich , 2020. S. 357-366 (Publication Series of the John von Neumann Institute for Computing (NIC); Band 50).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung
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TY - CHAP
T1 - Machine Learning Applications in Convective Turbulence
AU - Kräuter, Robert
AU - Krasnov, Dmitry
AU - Pandey, Ambrish
AU - Schneide, Christiane
AU - Padberg-Gehle, Kathrin
AU - Giannakis, Dimitrios
AU - Sreenivasan, Katepelli R.
AU - Schumacher, Jörg
N1 - Conference code: 10
PY - 2020
Y1 - 2020
N2 - 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.
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.
KW - Mathematics
UR - http://juser.fz-juelich.de/record/874547?ln=de
UR - https://juser.fz-juelich.de/record/874262/files/NIC_Series_50.pdf
M3 - Article in conference proceedings
SN - 978-3-95806-443-0
T3 - Publication Series of the John von Neumann Institute for Computing (NIC)
SP - 357
EP - 366
BT - NIC Symposium 2020
A2 - Müller, M.
A2 - Binder, K.
A2 - Trauntmann, A.
PB - Forschungszentrum Jülich
CY - Jülich
T2 - 10th John von Neumann Institute for Computing Symposium - 2020
Y2 - 27 February 2020 through 28 February 2020
ER -