In the design of electric vertical takeoff and landing (eVTOL) vehicles, coaxial rotors have garnered significant attention due to their superior space usage and aerodynamic efficiency compared to standard rotors. However, it is challenging to study the flow field near the rotors due to the blade–vortex interface (BVI) and vortex–vortex contact between two rotors. Using sliding mesh technology and Reynolds-averaged Navier–Stokes (RANS) solvers, a numerical method was established to simulate the flow field of a coaxial rotor in hover, which was verified by experiments. Using this method, this paper analyzes the relationship between position and intensity of the tip vortex of the upper rotor, the axial velocity of induced flow and the load distribution on the blades at the azimuth when the BVI phenomenon occurs with a difference in rotational speed and rotor spacing. The results indicate that, when the BVI phenomenon appears, the blade-tip vortex of the top rotor rapidly dissipates, and the load distribution of the lower blade changes due to the induced flow of the vortex. When the rotational speed increases, the spanwise thrust coefficient of each rotor changes slightly. The vortex–vortex interaction becomes stronger, which leads to vortex pairing. When the distance between the rotors decreases, the BVI phenomenon occurs at an earlier azimuth and the location of the BVI moves towards the tip of the lower blade. The vortex–vortex interaction is also enhanced, which leads to vortex pairing and vortex merging.

Fixed-wing aircraft with vertical takeoff and landing capabilities need a lower speed and a higher lift during transition. To meet these needs, a tandem-channel wing layout has been developed, including a FLR (front wing lower than rear wing) configuration and a FUR (front wing upper than rear wing) configuration, which differ in height differences between the front and rear wings. Numerical simulations have been performed to investigate the aerodynamic characteristics of the two configurations. The results show that a significant increase in lift coefficient occurs when the propeller rotational speed and the angle of attack increase. The lift at a small angle of attack increases by more than 50% at a high propeller rotational speed, and the stall angle of attack increases by more than 10 degrees. For the FLR configuration, the downwash effect of the front wing impacts the rear wing, decreasing the local angle of attack and delaying airflow separation on the top surface. For the FUR configuration, the up surface of the rear wing is induced by the wake flow of the front wing propeller at a high propeller rotational speed, which increases the lift and the stall angle of attack but makes the aircraft have static instability.

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization.

The missions of modern aircraft require multiple abilities, such as highly efficient taking-off and landing, fast arrival, and long-endurance hovering. It is difficult to achieve all technical objectives using traditional aircraft design technology. The active flow control technology using the concept of a co-flow jet (CFJ) is a flow control method without a mass source that does not require air from the engine. It has strong flow control ability in low-speed flow, can greatly improve the stall angle of the aircraft, and can obtain large lift enhancement. At transonic conditions, it can lead to a larger lift–drag ratio with a small expense. CFJ technology has great application potential for aircraft due to its flexible control strategy and remarkable control effect. In this paper, the concept of a combination of CFJ and variable camber technology is proposed which realizes the change of airfoil camber to meet different task requirements with the movable droop head. By using the built-in ducted fan, air is blown and sucked in the jet channel so as to realize CFJ flow control. In a state of high-speed flight, complete geometric restoration is achieved by closing the channel and retracting the droop head. In this paper, the design and aerodynamic analysis of a CFJ device with variable camber based on a supercritical airfoil with small camber and a small leading-edge radius are carried out using the computational fluid dynamics (CFD) method. Comparative studies are conducted for different schemes on the taking off and landing performances, and discussions are had on core technical parameters such as power consumption. The results indicate that by utilizing the CFJ technology with more than 10 degrees of droop device, the maximum lift coefficient of a supercritical airfoil with a small camber and leading-edge radius, which is suitable for transonic flight, can be increased to a value larger than 4.0.