Multioutput Autoregressive Aeroelastic System Identification and Flutter Prediction

The paper presents a methodology for reduced-order modeling of aeroelastic systems identification and flutter prediction. The aeroelastic system is modeled as a multioutput autoregressive process, and the model parameters are identified based on aeroelastic responses simulated in a computational fluid dynamics (CFD) code. The aeroelastic system is identified at a few subcritical (preflutter) dynamic pressure values. Flutter onset is determined from a stability parameter that is computed for the two dominant aeroelastic modes. The methodology is demonstrated on three test cases: a linear two-dimensional airfoil, transonic two-dimensional wing with reference to a wind-tunnel test, and a generic transport aircraft model. The paper compares the multioutput autoregressive to the single-output autoregressive model and shows that the former is more straightforward to implement, is robust, and requires shorter identification data. Overall, the methodology is simple to implement because it only requires an aeroelastic simulation capability and basic processing. It is computationally efficient and is therefore suitable for CFD-based flutter prediction. Other than predicting the flutter onset dynamic pressure, the method can be used to determine whether the aeroelastic system is stable at a specific dynamic pressure value without resorting to a lengthy CFD aeroelastic simulation.