In recent years, the air-drop launch technology of near-space UAVs has attracted much attention. Between downfall from the carrier and the flight control system’s initiation, the UAV presents free-fall movement. This free-fall process is very important for the control effect of the flight control system and is also crucial for the safety of the UAV and the carrier. Focus is required on two important dynamic parameters of the UAV: the moment of inertia and the center of mass position. In this paper, we used a quasi-steady model proposed by predecessors to address the flat-plate falling problem with modifications to describe the freely falling motion of the wing. Computational fluid dynamics (CFD) were used to simulate the free-fall movement of the wing with various parameters, and the wing release behavior was analyzed to check the quasi-steady model. Research shows that the movement characteristics of the falling wing are mostly reflected in the longitudinal plane, and the developed quasi-steady analytical model can more accurately describe the dynamic behavior of free-fall to some extent. By using CFD methods, we further investigated the aerodynamic performance of the free-fall wing. The results show that the wing mainly presents tumbling and fluttering motion. Changing the moment of inertia around the tumbling axis changes the tumbling frequency and the time point as the wing enters tumbling. In contrast, changing the position of the center of mass significantly changes the form of falling and makes the free-fall motion more complex. Therefore, it is necessary to carefully configure the center of mass in the UAV design process.

Based on the research background of high-performance distributed electric propulsion (DEP) fixed-wing unmanned aerial vehicles (UAVs), a data-driven aerodynamic modeling method is developed to address the design challenges of propulsion/aerodynamic coupling in the distributed propulsion wing (DPW) configuration which fully integrates the distributed propulsors and wing surfaces. To overcome the issues of limited data and insufficient understanding of physical mechanisms in practical engineering, physical prior knowledge is introduced, and a physics-informed grey-box aerodynamic model, named Focus-VI, is established based on extremely tiny datasets utilizing variational inference and attention mechanisms. Compared with the fully black-box multi-layer perceptron (MLP), Focus-VI demonstrates advantages in higher accuracy, stronger generalization ability, and certain interpretability, and can be effectively applied to the modeling, analysis, and optimization design of DPW.