  We develop a Reduced Order Model (ROM) for a Large Eddy Simulation (LES)
approach that combines a three-step algorithm called Evolve-Filter-Relax (EFR)
with a computationally efficient finite volume method. The main novelty of our
ROM lies in the use within the EFR algorithm of a nonlinear,
deconvolution-based indicator function that identifies the regions of the
domain where the flow needs regularization. The ROM we propose is a hybrid
projection/data-driven strategy: a classical Proper Orthogonal Decomposition
Galerkin projection approach for the reconstruction of the velocity and the
pressure fields and a data-driven reduction method to approximate the indicator
function used by the nonlinear differential filter. This data-driven technique
is based on interpolation with Radial Basis Functions. We test the performance
of our ROM approach on two benchmark problems: 2D and 3D unsteady flow past a
cylinder at Reynolds number 0 <= Re <= 100. The accuracy of the ROM is assessed
against results obtained with the full order model for velocity, pressure,
indicator function and time evolution of the aerodynamics coefficients.
