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This paper compares the performance of two unsteady aerodynamic reduced-ordermodels (ROMs), namely linear Volterra series and the autoregressive withexogenous input (ARX) model, on modeling dynamically linear aerodynamicbehaviors. The difference between these two methods is that the latter modelhas an autoregressive term while the former model has only the input-relatedterm. The first system is a plunging cylinder in a low-Reynolds number flow,where the flow stable (Re < 47). Although the training data can be fitted wellwith both methods, the linear Volterra method requires a higher model orderthan the ARX model for the same accuracy. Comparison of the frequency responseindicates that the ARX model approximates the frequency response more closely,while the frequency response at high Reynolds number is over-fitted by Volterraseries. The second aerodynamic system is a flow over a pitching NACA0012airfoil, including subsonic and transonic states. The convergence of the modelwith respect to delay orders, at different Mach numbers and mean angles ofattack, is studied in detail. As the Mach number or the mean angle of attackincreases, the required delay order will increase. But the ARX model stillmodels this system with a small number of terms at the same level of accuracy.All results indicate that the ARX model outperforms the linear Volterra seriesin most of cases, especially when the flow is close to the unstable state.

Comparisons of Two Reduced-Order Models for Linearized Unsteady Aerodynamic Identification