The folding wing mechanism is widely used in aircraft design. Whether the folding wing surface can unfold smoothly determines whether the aircraft can fly normally. Therefore, studying the aerodynamic loads and structural deformations during the unfolding process of folded wing surfaces is very important. The motion process of a folded wing mechanism is a typical fluid-structure interaction (FSI) process. During deployment, the wing surface moves under the combined action of the actuator’s pull and the aerodynamic loads from the incoming flow, while the large deformation of the wing surface during its movement, in turn, affects the aerodynamic loads on the mechanism from the flow field.
Considering the FSI effects during the unfolded motion process of the folded wing, simulation was conducted using the ALE algorithm in LS-DYNA to obtain the kinematic and dynamic parameters in the unfolded motion process, and also to get the aerodynamic torque on the wing under different angles and angular velocities. In practical engineering applications, the actuation force of the deployment mechanism can vary due to factors such as the amount and performance of the pyrotechnic material. Consequently, the final velocity and the whole motion process of the wing mechanism will also change. For the calculation of aerodynamic external loads under multiple operating conditions, using the ALE algorithm will consume a large amount of computational time and cost. Given the high computational cost and long computation time of finite element simulations, a BP neural network was established to calculate the aerodynamic loads on the wing surface under different actuation forces. This allows for a rapid assessment of whether significant deformation or damage will occur to the folding mechanism or nearby components during the deployment process.