Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session J26: Flow Instability: Rayleigh-Taylor II
4:35 PM–6:32 PM,
Sunday, November 19, 2023
Room: 151A
Chair: Balu Nadiga, LANL
Abstract: J26.00003 : Data-Driven Low-Order Modeling of the Rayleigh-Taylor Transition to Turbulence
5:01 PM–5:14 PM
Presenter:
Balu Nadiga
(LANL)
Authors:
Balu Nadiga
(LANL)
Sébastien Thévenin
(CEA)
Gilles Kluth
(CEA)
Benoit-joseph Gréa
(CEA de Bruyeres-le-Chatel)
A related talk in this session presents results on a Bayesian approach to inferring the initial conditions (IC) using a neural network trained to map IC and time to a handful of domain-averaged scalars that characterize the instantaneous state of the system---length of the mixing zone, turbulent kinetic energy and dissipation and others.
In this talk, we pursue the analysis further by considering data-driven modeling of the dynamical evolution and transition to turbulence in the suite of RT simulations.
While we are ultimately interested in developing improved mix-models, here we present preliminary results on modeling the dynamical evolution of the same set of domain-averaged scalars using a variety of methods ranging from neural-ODEs to attention mechanisms. We expect that further data-driven modeling of the residual with respect to state of the art RANS models will lead to such improved mix models.
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