  A new approach to model order reduction of the Navier-Stokes equations at
high Reynolds number is proposed. Unlike traditional approaches, this method
does not rely on empirical turbulence modeling or modification of the
Navier-Stokes equations. It provides spatial basis functions different from the
usual proper orthogonal decomposition basis function in that, in addition to
optimally representing the training data set, the new basis functions also
provide stable and accurate reduced-order models. The proposed approach is
illustrated with two test cases: two-dimensional flow inside a square
lid-driven cavity and a two-dimensional mixing layer.
