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MSMO2: Structural approaches for turbulent flows

Mar 01, 2021 | 11:00 AM - 04:30 PM

Chairs: Nikki Vercauteren, Peter Koltai, Abhishek Parswarar Harikrishnan 

Phenomenological modelling has been long since used to describe turbulent flow dynamics. Most modelling approaches used in fluid mechanics and in atmospheric sciences rely on statistical physics ideas applied to close the equations used to represent the evolution of large-scale flow structures. Structures with certain degrees of coherence however exist and have been observed at all scales – examples include vortical structures, convective cells or hurricanes. Theoretical understanding of the dynamics of structures on multiple scales could lead to different ways of closing the turbulent flow equations when all scales cannot be resolved numerically.

Additionally, new data-driven approaches enable to automatically uncover structures of dynamical relevance at a multitude of scales – a two-in-one modelling approach through which closure schemes can be learned from detailed simulations of turbulent flows. This mini symposium aims at gathering contributions in these theoretical and data-driven areas to advance understanding of turbulent and atmospheric flows.

Find abstracts for all talks here or linked individually below.

11:00

Kathrin Padberg-Gehle (Leuphana University Lueneburg, Germany)

Transport analysis in turbulent convection by Lagrangian trajectories

11:30

Oliver Junge (TU Munich)

Linear response for the dynamic Laplacian and finite-time coherent sets

 12:00

Federica Gugole (CWI, Amsterdam, The Netherlands) 

Detecting turbulence structure via Dynamic Mode Decomposition

 12:30 Open Discussion

 13:00 

Lunch Break 

14:30

Berengere Dubrulle (CNRS-CEA, Saclay, France)

Can we simulate climate change? The lessons from a laboratory experiment

15:00

Tom Doerffel (FU Berlin)

Modelling of diabatic heating in tropical cyclones

 15:30

Qi (Rose) Yu (UC San Diego, USA)

Physics-Guided Deep Learning for Turbulent Flow Prediction

16:00

Open Discussion