Flutter Prediction Using Data-Based Aeroelastic Parameter Varying Models

A parameter-varying estimation (PVE) framework is proposed to increase the efficiency of flight testing programs using short and long duration flight-data s ets. This approach generates a set of tools that will rapidly evaluate parameters that are required to certify an aircraft during flight envelope expansion programs. An aeroelastic parameter-varying (APV) framework was devised to consider the variations of the aircraft parameters due to flight condi tions. The core algorithms are based on the FFT-based PolyMax system identification method as well a s the linear fractional transformation (LFT) algebra and µ analysis for aeroelastic/aeroservoelastic systems. This blended approach proved to be robust enough to tolerate real flight testing environme nts using short and long-duration waypoint flight-test data. Two application cases were used to success fully predict the flutter onset using the algorithms developed during this work. The first case study inv olved a generic pitch and plunge linear aeroelastic wind-tunnel model whereas flight test data from the Aerostructure Test Wing (ATW) program was used to demonstrate the feasible application of the PVE core algorithms using data coming from actual flight-test programs.