In this study, a time-dependent surrogate approach is presented to generate the training data for identifying the reduced-order\nmodel of an unsteady aerodynamic system with the variation of mean angle of attack and Mach number in a transonic flight\nregime. For such a purpose, a finite set of flight samples are selected to cover the flight range of concern at first. Subsequently,\nthe unsteady aerodynamic outputs of the system under given inputs of filtered white Gaussian noise at these flight samples are\nsimulated via CFD technique which solves Euler equations. The unsteady aerodynamic outputs, which are viewed as a timedependent\nfunction of flight parameters, can be approximated via the Kriging technique at each time step. By this way, the\ntraining data for any combination of flight parameters in the range of concern can be obtained without performing any further\nCFD simulations. To illustrate the accuracy and validity of the training data generated via the proposed approach, the\nconstructed data are used to identify the reduced-order aerodynamic models of a NACA 64A010 airfoil via a robust subspace\nidentification algorithm. The unsteady aerodynamics and aeroelastic responses under various flight conditions in a transonic\nflight regime are computed. The results agree well with those obtained by using the training data of CFD technique.
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