  A quantitative evaluation method for a reduced-order model of the flow field
around an NACA0015 airfoil based on particle image velocimetry (PIV) data is
proposed in the present paper. The velocity field data obtained by the
time-resolved PIV measurement were decomposed into significant modes by a
proper orthogonal decomposition (POD) technique, and a linear reduced-order
model was then constructed by the linear regression of the time advancement of
the POD modes or the sparsity promoting dynamic mode decomposition (DMD). The
present evaluation method can be used for the evaluation of the estimation
error and the model predictability. The model was constructed using different
numbers of POD or DMD modes for order reduction of the fluid data and different
methods of estimating the linear coefficients, and the effects of these
conditions on the model performance were quantitatively evaluated. The results
illustrates that forward (standard) model works the best with two to ten
significant DMD modes selected by sparsity promoting DMD.
