Research on load prediction of carrier-based aircraft landing test based on measured data

Abstract As one of the most important parts in the ground (carrier) landing process of carrier-based aircraft, undercarriage is directly related to the safety of the aircraft, the crew, and even the landing platform with its strength and stiffness. The landing test to evaluate the strength and stiffness of the undercarriage of the carrier-based aircraft has strict regulations on the sinking velocity of the aircraft during landing. Still, in consideration of safety, it is difficult for the aircraft to reach the sinking velocity specified in the test under normal conditions. In this thesis, based on a large number of measured flight data, the artificial neural network was used to reasonably predict the undercarriage landing load of carrier-based aircraft and improve the landing test points, thus providing a new idea for the research on undercarriage load strength.