NONLINEAR AEROELASTIC ANALYSIS OF A REGIONAL AIRLINER WING VIA A NEURAL-NETWORK BASED REDUCED ORDER MODEL FOR AERODYNAMICS

This paper investigates the use of a neural network based reduced order model to solve the nonlinear gust response of a regional aircraft wing characterised by high angle of attack and gust intensity. Dynamic aerodynamic stall is generally able to provide a natural load mitigation, which is generally not considered in aircraft design. The neural network is combined with strip theory, thus requiring the training of a single airfoil model. Given the difficulties in obtaining CFD based aerodynamic data due to the excessive time consuming simulations, the approach has started the training phase with data obtained from the Beddoes-Leishman unsteady aerodynamic model. The wing response is then evaluated by a reduced-order model implemented in the Simulink environment, based on Nastran structural data and strip theory, enhanced by a number of neural networks in parallel describing the nonlinear transient behavior.