Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session A26: Focus Session: Fluid Dynamics of Atmospheric and Oceanic Extreme Events
8:00 AM–9:57 AM,
Sunday, November 18, 2018
Georgia World Congress Center
Room: B314
Sponsoring
Unit:
GPC
Chair: Pedram Hassanzadeh, Rice University; Mohammad Farazmand, MIT
Abstract ID: BAPS.2018.DFD.A26.4
Abstract: A26.00004 : Data-driven forecasting of high-dimensional 3D Rayleigh-Benard turbulence using Hankel-DMD
8:39 AM–8:52 AM
Presenter:
Mohammad Amin Khodkar
(Rice University)
Authors:
Mohammad Amin Khodkar
(Rice University)
Pedram Hassanzadeh
(Rice University)
Athanasios Antoulas
(Rice University)
Dynamic Mode Decomposition (DMD) with Takens’ delay-embedding (Hankel-DMD) is used to forecast the spatio-temporal evolution of the fully turbulent flow in a high-dimensional 3D Rayleigh-Benard convection system at the Rayleigh number of one million. A long dataset is produced using DNS, which is then employed to build a reduced-order model for the horizontally averaged temperature anomaly (which is a function of height z and time t) using Hankel-DMD, which is data-driven and model-free. Compared with the DNS data, the reduced model can fairly accurately predict the spatio-temporal evolution of the temperature anomaly and the leading principal components for a few hundred advective time scale before the prediction diverges from DNS and decays to zero. The impact of the length of the dataset used to build the reduced model, the embedded delay, and the reduced dimension on the prediction accuracy and prediction limit are investigated in detail to find the optimal range for these parameters and understand the capabilities and limitations of this approach.
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.A26.4
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