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A quantitative evaluation method for a reduced-order model of the flow fieldaround an NACA0015 airfoil based on particle image velocimetry (PIV) data isproposed in the present paper. The velocity field data obtained by thetime-resolved PIV measurement were decomposed into significant modes by aproper orthogonal decomposition (POD) technique, and a linear reduced-ordermodel was then constructed by the linear regression of the time advancement ofthe POD modes or the sparsity promoting dynamic mode decomposition (DMD). Thepresent evaluation method can be used for the evaluation of the estimationerror and the model predictability. The model was constructed using differentnumbers of POD or DMD modes for order reduction of the fluid data and differentmethods of estimating the linear coefficients, and the effects of theseconditions on the model performance were quantitatively evaluated. The resultsillustrates that forward (standard) model works the best with two to tensignificant DMD modes selected by sparsity promoting DMD.

Quantitative Evaluation of a Linear Reduced-order Model based on Particle-image-velocimetry Data of Separated Flow Field around Airfoil