4D large scale variational data assimilation of a turbulent flow with a dynamics error model

DOI10.1016/j.jcp.2020.109446zbMath1436.76010OpenAlexW3014668091MaRDI QIDQ776712

Pranav Chandramouli, Dominique Heitz, Etienne Mémin

Publication date: 13 July 2020

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-02547763/file/jcp.pdf

zbMATH Keywords

4D variation assimilationadjoint-optimisationdynamics error modelstochastic flow dynamicsturbulent wake flow

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Statistical turbulence modeling (76F55) Variational methods applied to problems in fluid mechanics (76M30)

Related Items (12)

Nudging-based data assimilation of the turbulent flow around a square cylinder ⋮ A multigrid/ensemble Kalman filter strategy for assimilation of unsteady flows ⋮ What is observable from wall data in turbulent channel flow? ⋮ A novel estimation method for microstructural evolution based on data assimilation and phase field crystal model ⋮ A consistent stochastic large-scale representation of the Navier-Stokes equations ⋮ Combining direct and indirect sparse data for learning generalizable turbulence models ⋮ Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction ⋮ Analytical properties for a stochastic rotating shallow water model under location uncertainty ⋮ Assimilation of disparate data for enhanced reconstruction of turbulent mean flows ⋮ Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation ⋮ Stochastic linear modes in a turbulent channel flow ⋮ State estimation in turbulent channel flow from limited observations

Uses Software

• L-BFGS
• TAPENADE

Cites Work

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