A folding wing morphing aircraft should complete the folding and unfolding process of its wings while in flight. Calculating the hinge moments during the morphing process is a critical aspect of a folding wing design. Most previous studies on this problem have adopted steady-state or quasi-steady-state methods, which do not simulate the free-flying morphing process. In this study, we construct an aeroelastic flight simulation platform based on the secondary development of ADAMS software to simulate the flight-folding process of a folding wing aircraft. A flexible multibody dynamic model of the folding wing structure is established in ADAMS using modal neutral files, and the doublet lattice method is developed to generate aerodynamic influence coefficient matrices that are suitable for the flight-folding process. The user subroutine is utilized, aerodynamic loading is realized in ADAMS, and an aeroelastic flight simulation platform of a folding wing aircraft is built. On the basis of this platform, the flight-folding process of the aircraft is simulated, the hinge moments of the folding wings are calculated, and the influences of the folding rate and the aircraft’s center of gravity (c.g.) position on the results are investigated. Results show that the steady-state method is applicable to the slow folding process. For the fast folding process, the steady-state simulation errors of the hinge moments are substantially large, and a transient method is required to simulate the flight-folding process. In addition, the c.g. position considerably affects the hinge moments during the folding process. Given that the c.g. position moves aft, the maximum hinge moments of the inner and outer wings constantly increase.

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This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

Calculating hinge moments during the morphing process is a critical aspect in the folding wing design. The deficiencies of the traditional flat-plate aerodynamic model in the calculation are expounded in this work, and a flight simulation platform based on a high-order panel method is established. On the basis of the platform, a typical flight-folding process of the aircraft is simulated, and the results of different aerodynamic models are compared. Results show that airfoil thickness has a great influence on the aerodynamic loading distribution of wing surfaces and thus affects the hinge moments during the folding process. The flat-plate method, which ignores the influence of the airfoil thickness, shows a great simulation error in hinge moment, whereas the high-order panel method can effectively describe the thickness effect and obtain reliable simulation results.