The integrated ducted structure significantly enhances the aerodynamic performance of the duct–rotor system, reducing noise and enhancing rotor safety. However, in the duct–rotor system, the coupled parameters of the rotor and the duct are crucial to its aerodynamic characteristics. This study employs a Computational Fluid Dynamics numerical analysis to investigate the aerodynamic performance of the duct–rotor, focusing on coupled parameters related to the rotor blade’s relative position and tip gap. Additionally, our research delves into the fundamental mechanisms of key coupled parameters, such as the blade tip gap, elucidating their role in optimizing the duct–rotor’s aerodynamics. The results emphasize the critical role of the tip gap in influencing the aerodynamic performance of the duct–rotor. Notably, when the tip gap exceeds 3.0% of the duct’s radius, the aerodynamic advantage provided by the duct is insufficient to offset the loss in the rotor’s performance. As the tip gap exceeds 7.0% of the duct’s radius, the aerodynamic deterioration caused by the duct gradually stabilizes. Furthermore, the influence of rotor blade position on aerodynamic performance is relatively minor. However, placing the rotor at the duct exit position proves advantageous for improving the aerodynamic distribution characteristics of the system.

The precise regulation of the hinge moment and pitch angle driven by the pitch-regulated mechanism is crucial for modulating thrust requirements and ensuring stable attitude control in Martian coaxial rotorcraft. Nonetheless, the aerodynamic hinge moment in rotorcraft presents time-dependent dynamic properties, posing significant challenges for accurate measurement and assessment for such characteristics. In this study, we delve into the detailed aerodynamic hinge moment characteristics associated with the pitch-regulated mechanism of Mars rotorcraft under a spectrum of control strategies. A robust computational fluid dynamics model was developed to simulate the rotor’s aerodynamic loads, accompanied by a quantitative hinge moment characterization that takes into account the effects of varying rotor speeds and pitch angles. Our investigation yielded a thorough understanding of the interplay between aerodynamic load behavior and rotor surface pressure distributions, leading to the creation of an empirical mapping model for hinge moments. To validate our findings, we engineered a specialized test apparatus capable of measuring the hinge moments of the pitch-regulated mechanism, facilitating empirical assessments under replicated atmospheric conditions of both Earth and Mars. The result indicates aerodynamic hinge moments depend nonlinearly on rotational speed, peaking at a 0° pitch angle and showing minimal sensitivity to pitch under 0°. Above 0°, hinge moments decrease, reaching a minimum at 15° before rising again. Simulation and experimental comparisons demonstrate that under Earth conditions, the aerodynamic performance and hinge moment errors are within 8.54% and 24.90%, respectively. For Mars conditions, errors remain below 11.62%, proving the CFD model’s reliability. This supports its application in the design and optimization of Mars rotorcraft systems, enhancing their flight control through the accurate prediction of aerodynamic hinge moments across various pitch angles and speeds.

This study investigates the impact of rotor spacing on the aerodynamic performance of a coaxialcopter and promotes an innovative regulated control strategy for the coaxial drone. The present research introduces a coaxialcopter with variable rotor spacing, and employing finite element numerical simulations, we assess the aerodynamic behavior of this novel configuration. Through comprehensive measurements and analysis of its aerodynamic performance across varying rotor spacings from 0.1 R to 1 R, we validate the effectiveness of a rotor-spacing control strategy for enhancing takeoff maneuvers. The numerical simulation and experiment results reveal that the performance characteristics of both the upper and lower rotors converge toward that of a single rotor as the space ratio increases, along with a reduction in their thrust fluctuations and aerodynamic performance periodicity. Considering stable power consumption patterns and endurance performance, we analyzed the interrelations binding the pitch distance of the rotors, rotational speed, and pitch angle, vis à vis the thrust coefficient and power coefficient. Through the parameter optimization method, we demonstrate that adjusting rotor spacing offers a practical means to enhance payload capacity without increasing the power input, thereby improving efficiency, which validates the practicality and efficacy of the parameter optimization approach. Furthermore, optimizing rotor spacing for specific operational scenarios enhances overall aerodynamic performance, suggesting a viable flight control strategy for takeoff and landing conditions for coaxial drones.

The accurate calibration of snow parameters is necessary to establish an accurate simulation model of snow, which is generally used to study tire–snow interaction. In this paper, an innovative parameter inversion method based on in situ test results is proposed to calibrate the snow parameters, which avoids the damage to the mechanical properties of snow when making test samples using traditional test methods. A coupled Eulerian–Lagrangian (CEL) model of plate loading in snow was established; the sensitivity of snow parameters to the macroscopic load–sinkage relationship was studied; a plate-loading experiment was carried out; and the parameters of snow at the experimental site were inverted. The parameter inversion results from the snow model were verified by the experimental test results of different snow depths and different plate sizes. The results show the following: (1) The material cohesive, angle of friction, and hardening law of snow have great influence on the load–sinkage relationship of snow, the elastic modulus has a great influence on the unloading/reloading stiffness of snow, and the influence of density and Poisson’s ratio on the load–sinkage relationship can be ignored. (2) The correlation coefficient between the inversion result and the matching test data is 0.979, which is 0.304 higher than that of the initial inversion curve. (3) The load–sinkage relationship of snow with different snow depths and plate diameters was simulated by using the model parameter of inversion, and the results were compared with the experimental results. The minimum correlation coefficient was 0.87, indicating that the snow parameter inversion method in this paper can calibrate the snow parameters of the test site accurately.

As unmanned aerial vehicle (UAV) systems evolve from automated execution toward autonomous decision-making, multirotor UAVs increasingly face complex dynamics, uncertain sensing conditions, and task-level autonomy demands. Reinforcement learning (RL) has emerged as a promising learning-based paradigm for addressing these challenges. Existing surveys on RL-based UAV control predominantly classify methods from an algorithmic or learning-paradigm perspective, while relatively little attention has been paid to the functional roles of RL policies within the control loop. This often leads to an unclear correspondence between algorithmic characteristics and the requirements of different control layers. To address this gap, this review proposes a biomimetic “spinal cord–cerebellum–cerebrum” framework, organizing existing RL studies into low-level dynamic stabilization, mid-level perception–action coordination, and high-level task planning and decision-making. The proposed hierarchy emphasizes the functional role and intervention depth of RL policies within the control architecture, further supporting a layer-wise analysis of sim-to-real challenges. This review aims to provide a structured understanding of the roles of reinforcement learning in hierarchical UAV control and to highlight future research directions toward robust real-world deployment.