DPIV-driven flow simulation: a new computational paradigm

Abstract

We present a new approach to simulating unsteady fluid flows, with only very few degrees of freedom, by employing directly eigenmodes extracted from digital particle image velocimetry experimental data. In particular, we formulate standard Galerkin and nonlinear Galerkin approximations of the incompressible Navier–Stokes equations using hierarchical empirical eigenfunctions extracted from an ensemble of flow snapshots. We demonstrate that standard Galerkin approaches produce simulations capable of capturing the short–term dynamics of the flow, but nonlinear Galerkin projections are more effective in capturing both the short– and long–term dynamics, leading to bounded solutions. These findings are documented by applying these approaches to flow past a stationary circular cylinder at Reynolds number 610.