Dynamic estimation for reduced Galerkin models of fluid flows

Reduced Galerkin models of fluid flows are traditionally obtained by the Galerkin projection of a low-dimensional flow expansion onto the Navier-Stokes equation. A new approach in this application domain is the a posteriori model parameter estimation to correct for distortions due to the low dimensional compression. Preserving model structure, this leads to considerable improvement in dynamic prediction the cylinder wake benchmark.