Airship shape is crucial to the design of stratosphere airships. In this paper, multidisciplinary design optimization (MDO) technology is introduced into the design of airship shape. We devise a composite objective function, based on this technology, which takes account of various factors which influence airship performance, including aerodynamics, structures, energy and weight to determine the optimal airship shape. A shape generation algorithm is proposed and appropriate mathematical models are constructed. Simulation results show that the optimized shape gives an improvement in the value of the composite objective function compared with a reference shape.

In this technical note, we investigate the agent-based finite-time leader-following consensus for Earth-observation systems with multiple stratosphere airships, in which the high-order nonlinear dynamic and external disturbances are considered for multi-agent systems. Based on the distributed observer, which is used to acquire the state of the leader airship for the following airships, the dynamic cascade control is designed to realize the finite-time leader-following consensus for Earth-observation systems. It is mathematically proven that the convergence is finite for the stratosphere airship earth-observation system. Simulation results are given to illustrate the effectiveness of the proposed scheme.

Recently, an effective yet easy-to-use strain measurement method, which is based on the Savitzky–Golay filter, was proposed for full-field strain measurement using the digital image correlation technique (DIC). The Savitzky–Golay filter-based method is derived based on a given polynomial. When the order of the deformation is not the same as that of the employed polynomial, the mismatched problem may lead to strain measurement errors. However, in real experiments, the order of the deformation is unknown. In general, the order of the real deformation is higher than that of the employed Savitzky–Golay filter-based method. So, in this paper, strain measurement errors due to the matched or undermatched Savitzky–Golay filter-based method are carefully examined using simulated and real experimental patterns. Four different orders of strain deformations are employed to investigate the matched or undermatched problems. The influence of the strain window size and grid space on the strain measurement are also studied in the paper.

A modified digital image correlation (DIC) method is presented to balance the influence of subset size choice, improve the calculation efficiency, and enhance the measurement accuracy. The relations between pixels and the central pixel are investigated. A relatively large subset size is selected in the method and all the pixels in a subset are treated nonuniformly. Weight is assigned to each pixel dependent on the significance of the pixel to track a subset from deformed images, and weights are computed by Butterworth function based on the distances between the pixels and the central pixel in the subset. Compared with classical DIC, the proposed method is a compromise between a small and a large subset. The choice of subset size for classical DIC is displaced by choice of parameters of Butterworth function in the presented method. The influence of subset size is reduced, and the calculation efficiency is improved with enhanced measurement accuracy by the modified method. Computer-simulated speckle patterns and real experiments are applied to verify the proposed method.

Stratospheric airships have much potential in military and commercial applications. Design, analysis and optimization of stratospheric airships involves complex trade-off of different disciplines, and hence a multidisciplinary approach is essential. This paper describes a methodology coupling several disciplines and involving seven design variables to obtain the optimal design of a stratospheric airship powered by solar arrays. A numerical method is established to calculate the output power of the solar array in the optimization process. The optimal solutions are obtained using hybrid algorithms. The methodology can obtain the optimal envelope shape, solar array layout and other general configurations of subsystems. Results show that the methodology was able to achieve a solution with a 19.2% reduction in airship volume compared to the value being part of an arbitrary initial set of airship parameters. In addition, a comparative study is carried out to highlight the importance of considerations of solar array layouts and array circumferential location. Furthermore, detailed sensitivity analysis shows that operating parameters of latitudes, heading angles and average resisting wind speeds have significant effects on the airship design and solar array layouts.

The healthy operation of aircraft engines is crucial for flight safety, and accurate Remaining Useful Life prediction is one of the core technologies involved in aircraft engine prognosis and health management. In recent years, deep learning-based predictive methods within data-driven approaches have shown promising performance. However, for engines experiencing a single fault, such as a High-Pressure Compressor fault, existing deep learning-based predictive methods often face accuracy challenges due to the coupling relationship between different fault modes in the training dataset that includes a mixture of multiple fault modes. In this paper, we propose the FC-AMSLSTM method, a novel approach for Remaining Useful Life prediction specifically targeting High-Pressure Compressor degradation faults. The proposed method effectively addresses the limitations of previous approaches by fault classification and decoupling fault modes from multiple operating conditions using a decline index. Then, attention mechanisms and multi-scale convolutional neural networks are employed to extract spatiotemporal features. The long short-term memory network is then utilized to model RUL estimation. The experiments are conducted using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. The results demonstrate that compared to other prediction models, the FC-AMSLSTM method effectively reduces RUL prediction error for HPC degradation faults under multiple operating conditions.