Search Results (3)

Recently, the world’s first plasma-propelled drone was successfully flown, demonstrating that plasma propulsion technology is suitable for drone flight. The research on plasma propulsion drones has sparked a surge of interest. This study utilized a proxy model and the NSGA-II multi-objective genetic algorithm to optimize the geometric parameters based on staggered thrusters that affect the performance of electroaerodynamics (EAD) thrusters used for solid-state plasma aircraft. This can help address key issues, such as the thrust density and the thrust-to-power ratio of solid-state plasma aircraft, promoting the widespread application of plasma propulsion drones. An appropriate sample set was established using Latin hypercube sampling, and the thrust and current data were collected using a customized experimental setup. The proxy model employed a genetically optimized Bayesian regularization backpropagation neural network, which was trained to predict the effects of variations in the geometric parameters of the electrode assembly on the performance parameters of the plasma aircraft. Based on this information, the maximum achievable value for a given performance parameter and its corresponding geometric parameters were determined, showing a significant increase compared to the sample data. Finally, the optimal parameter combination was determined by using the NSGA-II multi-objective genetic algorithm and the Analytic Hierarchy Process. These findings can serve as a basis for future researchers in the design of EAD thrusters, helping them produce plasma propulsion drones that better meet specific requirements. Full article

The resurgence in interest in aircraft electro-aerodynamic (EAD) propulsion has been sparked due to recent advancements in EAD thrusters, which generate thrust by employing a plasma generated through electrical discharge. With potentially quieter propulsion that could contribute to the generation of lift or the control of attitude, it is important to determine the feasibility of an EAD-propelled airplane. First, the main propulsive characteristics (thrust generation and power consumption) of EAD thrusters were drawn from the literature and compared with existing technologies. Second, an algorithm was developed to couple standard equations of flight with EAD propulsion performance and treat the first-order interactions. It fairly replicated the performance of the only available autonomous EAD-propelled drone. A test case based on an existing commercial UAV of 10 kg equipped with current-generation EAD thrusters anticipated a flight of less than 10 min, lower than 30 m in height, and below 8 m · s ⁻¹ in velocity. Achieving over 2 h of flight at 30 m of height at 10 m · s ⁻¹ requires the current EAD thrust to be doubled without altering the power consumption. For the same flight performance as the baseline UAV, the prediction asked for a tenfold increase in the thrust at the same power consumption. Full article

Electroaerodynamic unmanned aerial vehicles (EAD-UAVs) are innovative UAVs that use high-voltage asymmetric electrodes to ionize air molecules and Coulomb force to push these ions to produce thrust. Unlike fixed-wing and rotor UAVs, EAD-UAVs contain no moving surfaces and have the advantages of very low noise, low mechanical fatigue, and no carbon emissions. This paper proposes an EAD-UAV configuration with an orthogonal arrangement of multiple EAD thrusters to adjust the EAD-UAV attitude and flight trajectory through voltage distribution control alone. Based on a one-dimensional dynamic model of an EAD thruster, the attitude–path coupling dynamics of the EAD-UAV were derived. To achieve EAD-UAV flight control for a specified target, the Bezier shaping approach (BSA) was implemented to realize rapid trajectory optimization considering the coupling dynamic constraints. The numerical simulation results indicate that the BSA can quickly procure an optimized flight trajectory that satisfies the dynamic and boundary constraints. Compared with the Gaussian pseudospectral method (GPM), the BSA changes the optimization index of the objective function by nearly 1.14% but demands only nearly 1.95% of the computational time on average. Hence, the improved integrative Bezier shaping approach (IBSA) can overcome the poor convergence issue of the BSA under the continuous acceleration constraint of multi-target flight trajectories. Full article