Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers

Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers

We formulate a data-driven, physics-constrained closure method for coarse-scale numerical simulations of turbulent fluid flows. Our approach involves a closure scheme that is non-local both in space and time, i.e. the closure terms are parametrized in terms of the spatial neighborhood of the resolve...

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Bibliographic Details
Main Authors: Charalampopoulos, Alexis-Tzianni G., Sapsis, Themistoklis P.
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering
Format: Article
Language:English
Published: American Physical Society 2024
Subjects:
Fluid Flow and Transfer Processes
Modeling and Simulation
Computational Mechanics
Online Access:https://hdl.handle.net/1721.1/154257

Internet

https://hdl.handle.net/1721.1/154257

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