Near space airships have gained widespread attention owing to the unique characteristics of long endurance at high altitudes; because of this characteristic, such airships have great potential for use in the fields of communication relay, early warning, earth observation etc. Near space airships can substitute near-earth geostationary satellites owing to the advantages of high load and long endurance at altitude exceeding 20 km. Buoyancy against gravity is generated by the airship hull, which is completely filled with helium, while the near space airship is equipped with an electrical propulsion system powered by solar energy. Unlike conventional aircraft, nearspace airships are a new type of vehicles with less design experience; coupled multi-disciplinary problems related to aerodynamics, structure, energy, control, etc. exist. With the aim of finding a solution for optimal near-space airship design, this study proposed a design using a multidisciplinary design optimization method.

The stratospheric airship is a type of aerostat that uses solar energy as its power source and can fly continuously for months or even years in near space. The rapid and accurate prediction of the output power of its solar array is the key to maintaining energy balance and extending flight time. This paper establishes an online learning model for predicting the output power of the solar array of stratospheric airships. The readings of radiometers arranged on the surface of the airship are used as features for training the model. The parameters of the model can be updated in real-time during the flight process without retraining the entire model. The effect of radiometer placement on the model accuracy was also analyzed. The results show that for the continuous flight of 40 days, the online learning model can achieve an accuracy of 88% after training with 10 days of flight data and the accuracy basically reaches its highest level after 20 days. In addition, placing the radiometers at the four corners of the array can achieve a higher prediction accuracy of 95%. The online model can also accurately identify and reflect the effect of module efficiency attenuation or damage and maintain high accuracy.

Intelligent bearing fault diagnosis based on deep learning has immense potential. However, improving the noise immunity, generality, and accuracy of fault diagnosis methods is still challenging. This paper proposes a novel adaptive hybrid attention mechanism deep residual threshold network (AHA-RTN) for bearing fault diagnosis under various noise conditions. First, channel-wise and spatial attention were both integrated into residual blocks to capture multiscale information. Hybrid attention was obtained using the proposed adaptive attention module, which computes adjustment coefficients of each attentional mechanism. Next, a novel noise reduction activation function based on soft thresholding was incorporated to suppress noise. Finally, the method was validated on two distinct bearing datasets under various noise conditions. The results show that the proposed AHA-RTN has better noise immunity and accuracy than the other advanced multiscale convolutional neural networks.

Meta-aircraft, in High-Altitude, Long-Endurance (HALE) unmanned aerial vehicle (UAV) applications, utilize a strategy of formation flying in the stratosphere and aerial docking in the troposphere to enhance flight range and gust resistance. This paper explores an aerial docking strategy for unmanned meta-aircraft using a surrogate model based on aerodynamic data. The study begins with an analysis of the aerodynamic characteristics and the establishment of a dynamic model, followed by the development of a surrogate model using the vortex lattice method and a BP neural network. This model accurately simulates aerodynamic changes near the wingtip. Optimization of the docking process, focusing on impulse and moment of impulse, is achieved using a greedy algorithm. The results show a reduction in drag impulse and rolling moment by 10.89% and 15.76%, respectively, thereby easing the burden on the control system of UAVs.