Enhanced ensemble-based 4DVar scheme for data assimilation

DOI10.1016/j.compfluid.2015.03.025OpenAlexW2009311406MaRDI QIDQ1645762

Cordelia Robinson, Yin Yang, Etienne Mémin, Dominique Heitz

Publication date: 22 June 2018

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-01144923/file/Yang-CF.pdf

zbMATH Keywords

optimal controldata assimilationensemble methodsshallow water modeldepth sensor

Mathematics Subject Classification ID

Water waves, gravity waves; dispersion and scattering, nonlinear interaction (76B15)

Related Items

Shape optimisation for a stochastic two-dimensional cylinder wake using ensemble variation, Reconstruction of unsteady viscous flows using data assimilation schemes, Coarse large-eddy simulations in a transitional wake flow with flow models under location uncertainty, Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes, Finite‐difference‐based simulation and adjoint optimization of gas networks, Kriging-enhanced ensemble variational data assimilation for scalar-source identification in turbulent environments, Assimilation of disparate data for enhanced reconstruction of turbulent mean flows, Nonlinearly most dangerous disturbance for high-speed boundary-layer transition, 4D large scale variational data assimilation of a turbulent flow with a dynamics error model

Uses Software

• TAPENADE

Cites Work

• Inflow and initial conditions for direct numerical simulation based on adjoint data assimilation
• Conditioning and preconditioning of the variational data assimilation problem
• The Tapenade automatic differentiation tool
• Fluid flow dynamics under location uncertainty