“Urban air vehicles” have been hailed as the next revolution in aviation. Prototypes of various sizes have been flown to demonstrate basic flight (hover and climb), but in most cases there is no demonstration of full flight capability, for example conversion from vertical to level flight (conversion corridor). There are proposals for vehicles in a wide range of scales: from drones specifically designed to deliver goods, to full size vehicles for manned transportation. Most of the concepts proposed include full electric propulsion, multiple (often convertible) rotors (ducted or un-ducted, counter-rotating), and widespread use of composite materials. Start-up companies are seeking funding with high-profile demonstrations in front of the media, but many unresolved technical problems are not been solved. Large aerospace companies have joined the fray. These initiatives are fuelling expectations that achieving the next milestone is within easy reach. This paper aims to fill some gaps in understanding and curb optimism. It takes a holistic view in order to establish a scientific basis for design, manufacturing, operations.

To improve the endurance performance of long-endurance Unmanned Aerial Vehicles (UAVs), a smart morphing method to adjust the UAV and flight mode continuously during flight is proposed. Using this method as a starting point, a smart morphing long-endurance UAV design is conducted and the resulting improvement in the endurance performance studied. Firstly, the initial overall design of the smart morphing long-endurance UAV is carried out, then the morphing form is designed and various control parameters are selected. Secondly, based on multi-agent theory, an architecture for the smart morphing control system is built and the workflow of the smart morphing control system is planned. The morphing decision method is designed in detail based on the particle swarm optimisation algorithm. Finally, a simulation of the smart morphing approach in the climb and cruise stages is carried out to quantitatively verify the improvement in the endurance performance. The simulation results show that the smart morphing method can improve the cruise time by 4.1% with the same fuel consumption.

Electric-powered disposable unmanned aerial vehicles (UAVs) have wide applications due to their advantages in terms of long time flight and load capacity. Thus, improving their endurance has become an important task to enhance the performance of these UAVs. To achieve this, we investigated a battery dumping strategy which splits the battery into several packs that are used and dumped in sequence to reduce the dead weight. The Peukert effect is also considered. In this paper, the sensitivity analysis method was employed to analyse the endurance benefits for different battery weight ratios, Peukert constants and capacities, quantitatively. The results show that the endurance benefits are significantly affected by all three parameters. For ideal batteries, the endurance can be improved by 20% and 28% respectively when employing a double-pack or triple-pack battery strategy (for a battery weight ratio of 0.4), but these benefits will fall rapidly if the Peukert constant exceeds 1.0 or the battery weight declines. Besides, the endurance will be 10% longer if the lift coefficient rather than the velocity remains constant after the battery packs are dumped at a Peukert constant of 1.2.

Dynamic soaring improves the endurance of Unmanned Aerial Vehicles (UAVs) by obtaining energy from the horizontal wind shear gradient. The use of dynamic soaring in small solar UAVs can mitigate the trade-off between energy capacity and battery weight to achieve continuous all-day flight. The goal of this study is to determine the optimal energy acquisition methods for small solar UAVs using dynamic soaring and to decrease the battery weight to achieve all-day flight. A dynamic soaring UAV model that considers the influence of the wind shear gradient and a solar power energy model are established. The conditions to obtain a closed-loop energy system during daytime and nighttime flights are discussed, and the minimum mass of the energy system required for these conditions is determined. Simulations of single-cycle circular flights and a 72-h continuous flight of a small solar UAV are performed. The analyses and simulation results show that: (1) the combination of dynamic soaring and solar technology significantly reduces the energy consumption and reduces the required battery weight, (2) the flight speed and flight attitude angles have significant effects on the optimal total energy acquisition and (3) wind fields with a large horizontal gradient and strong solar illumination provide energy and load advantages.

Accurate navigation is required in many Unmanned Aerial Vehicle (UAV) applications. In recent years, GNSS Precise Point Positioning (PPP) has been recognised as an efficient approach for providing precise positioning services. In contrast to the widely used Real-Time Kinematic (RTK), PPP is independent of reference stations, which greatly broadens its scope of application. However, the accuracy and reliability of PPP can be significantly decreased by poor GNSS satellite geometry and outage. In response, a real-time four-constellation GNSS PPP is applied to improve the geometry in this work, and PPP is tightly coupled with an Inertial Measurement Unit (IMU) to smooth the position and velocity output, thus improving the robustness of the navigation solution. Experimental flight tests are carried out using a UAV in an open-sky area, and GNSS-challenged environments are simulated. The results show that the four-constellation GNSS PPP/IMU integration reduces the Root-Mean-Square (RMS) Three-Dimensional (3D) positioning and velocity error by 76.4% and 67.1%, respectively, in open sky with respect to the one-GNSS PPP. Under scenarios where GNSS measurements are insufficient, the coupled system can still provide continuous solutions. Moreover, the coupled PPP/IMU system can also maintain the convergence of PPP during GNSS-challenged periods and can greatly shorten the re-convergence period of PPP when the UAV returns to the open sky.

This work aims to describe a mathematical model and a numerical method to simulate a thin anisotropic membrane moving and deforming in 3D space under a dynamic load of an arbitrary time and space profile. The anisotropic continuum medium model described in the article can be used to model a membrane made of composite material using its effective elastic parameters. The model and the method allow the consideration of problems when the quasi-static approximation is not valid and elastic waves caused by the impact should be calculated. The model and the method can be used for numerical study of different processes in thin composite layers, such as shock load, ultrasound propagation, non-destructive testing procedures and vibrations. The thin membrane is considered as a 2D object in 3D space, an approach that allows a reduction in the computational time compared with full 3D models, while still having an arbitrary material rheology and load profile.

Visualising the loads that a structure can tolerate provides a key insight into the structural design process, especially for materials and structures that are governed by complex failure criteria. This paper proposes a general method for efficient construction of performance envelopes in load space, and demonstrates the approach with two examples. The performance envelope identifies all possible failure modes, all the redundant and non-redundant structural constraints, and the limiting failure mode in a particular direction in load space. Once the envelope has been constructed, the structural reserve factors can be calculated extremely quickly. In design such envelopes are most useful for structural analysis processes which involve a very large number of load cases, and where the cost of constructing an envelope for a given feature is relatively modest.

Microwaves are a form of electromagnetic radiation commonly used for telecommunications, navigation and food processing. More recently microwave technologies have found applications in fibre-reinforced polymer composites, which are increasingly used in aircraft structures. Microwave energy can be applied with low power (up to milliwatts) for non-destructive testing and high power (up to kilowatts) for heating/curing purposes. The state-of-the-art applications at high power include curing, three-dimensional (3D) printing, joining and recycling, whereas low-power microwave techniques can provide quality checks, strain sensing and damage inspection. Low-power microwave testing has the advantage of being non-contact, there is no need for surface transducers or couplants, it is operator friendly and relatively inexpensive; high-power microwave energy can offer volumetric heating, reduced processing time and energy saving with no ionising hazards. In this paper the recent research progress is reviewed, identifying achievements and challenges. First, the critical electromagnetic properties of composites that are closely related to the heating and sensing performance are discussed. Then, representative case studies are presented. Finally, the trends are outlined, including intelligent/automated inspection and solid-state heating.

This paper presents the design, manufacturing and experimental assessment of a morphing element consisting of a composite corrugated panel that hosts a diffused actuation system based on Shape Memory Alloy (SMA) actuators. The characterisation of the SMA actuators is reported and the system performance is predicted through an analytical model and finite element analyses. Two versions of the actuated system are proposed, with different methods for the physical integration of the SMA wires into the composite part. Manufacturing and testing of specimens with different wire densities are reported. Correlation with experiments validates the analytical and numerical approaches adopted for the design and analyses. The results confirm the potential of the concept proposed for developing corrugated panels that can be contracted in a predefined direction by a load-bearing actuation system, but still retain high stiffness and strength properties in other directions.

This study investigates and proposes a fire detection and suppression system for a smart air cargo container. A series of smoke spread and fire evolution numerical models are executed to assess the performance of container-based fire detection in various fire scenarios. This is to identify the worst case and optimise the location and threshold setting of fire detection sensors, achieving the shortest detection time. It is found that the fire detection threshold (reduction in light transmission = 12%/ft) for a container-based system can be set at three times the standard activation threshold for a cargo-based fire detection system, which can reduce the number of false alarms by three orders of magnitude. Moreover, effectiveness analysis of passive fire protection for the glass fibre-reinforced polymer-made smart container indicates an allowable leakage size of 0.01m2. The risk of internal overpressure has been found to be negligible for the leakage size required by aircraft pressure equalisation.

Multi-dimensional aerodynamic database technology is widely used, but its model often has the curse of dimensionality. In order to solve this problem, we need projection to reduce the dimension. In addition, due to the lack of traditional method, we have improved the traditional flow field reconstruction method based on artificial neural networks, and we proposed an array neural network method.

In this paper, a set of flow field data for the target problem of the fixed Mach number is obtained by the existing CFD method. Then we arrange all the sampled flow field data into a matrix and use proper orthogonal decomposition (POD) to reduce the dimension, whose size is determined by the first few modals of energy. Therefore, significantly reduced data are obtained. Then we use an arrayed neural network to map the flow field data of simplified target problem and the flow field characteristics. Finally, the unknown flow field data can be effectively predicted through the flow field characteristic and the trained array neural network.

At the end of this paper, the effectiveness of the method is verified by airfoil flow fields. The calculation results show that the array neural network can reconstruct the flow field of the target problem more accurately than the traditional method, and its convergence speed is significantly faster. In addition, for the case of high angle flow field, the array neural network also performs well. There are no obvious jumps, and huge errors are found in results. In general, the proposed method is better than the traditional method.

This paper provides a solution to the active vibration control of a microsatellite with two solar panels. At first, the microsatellite is processed as a finite element model containing a rigid body and two flexible bodies, according to the principles of mechanics, and that the dynamic characteristics are solved by modal analysis. Secondly, the equation involving vibration control is established according to the finite element calculation results. There are several actuators composed of macro fibre composite on the two solar panels for outputting control force. Furthermore, the control voltage for driving actuator is calculated by using fuzzy algorithm. It is clear that the smart structure consists of the flexible bodies and actuators. Finally, the closed-loop control simulation for suppressing harmful vibration is established. The simulation results illustrate that the responses to the external excitation are decreased significantly after adopting fuzzy control.