Foldable wings are designed for tube-launched unmanned aerial vehicles (UAVs), aiming to improve portability and meet launch platform requirements. However, conventional tube-launched UAVs cannot operate across the wide speed ranges required for the performance of multiple missions, due to the fixed configuration of their wings after launch. This study therefore proposes a tube-launched UAV which can change wing-sweep angle to expand the flight speed range and enhance the UAV’s agility. A computational aerodynamics method is employed to assess the transient aerodynamic performance of the UAV during the sweep morphing process. The simulation results indicate that the transient aerodynamic forces generate a dynamic hysteresis loop around the quasi-steady data. The lift and drag coefficients exhibit maximum relative deviations of 18.5% and 12.7% from the quasi-steady data for the sweep morphing period of 0.5 s. The hysteresis effect of the flow structure, rather than the additional velocity resulting from wing-sweep morphing, is the major contributor to the aerodynamic hysteresis loop. Compared to the conventional tube-launched UAVs, the proposed tube-launched UAV with a variable-sweep wing shows a wider flight speed range, from 22.59 to 90.12 m/s, and achieves an 82.84% increase in loitering speed. To verify the effectiveness of the wing-sweeping concept, a prototype was developed, and a flight test was carried out. The test data obtained from flight control system agree well with the simulation data, which demonstrates the feasibility and effectiveness of the variable-sweep wing in widening the speed range for tube-launched UAVs. This work can provide a reference for the design of tube-launched UAVs for wide speed range flight.

To analyze the deformation behavior of short glass fiber-reinforced poly-ether-ether-ketone (SGFR-PEEK) under various conditions through numerical simulations, it is crucial to construct a constitutive model that can describe its stress–strain behavior over a wide range of strain rates and temperatures. In this study, quasi-static compression tests were conducted on SGFR-PEEK composites with varying mass fractions, and dynamic tests were performed using a Split Hopkinson Pressure Bar to acquire the material's compressive stress–strain response under quasi-static and dynamic conditions. The results indicate that, under compression, the yield stress of SGFR-PEEK composites increases with an augmentation in glass fiber content, rises with increasing strain rate, and decreases with elevated temperature. Based on experimental findings, a modified Johnson–Cook constitutive model was established to characterize the mechanical performance of SGFR-PEEK. In comparison to the traditional Johnson–Cook intrinsic structure model, the modified model takes into account the glass fiber mass fraction as comprehensively as possible and better predicts the material's flow behavior at high strain rates. Finally, this modified constitutive model was implemented in the ABAQUS software using the user-defined subroutine VUMAT to simulate the compression behavior of SGFR-PEEK composites under different loading conditions, and the model was validated. This research provides valuable insights for the practical application of SGFR-PEEK composites in engineering.

The random error in micro-electro-mechanical systems (MEMS) gyroscopes is one of the major aspects that limit measurement accuracy. In order to address the inaccurate extraction of noise and trend during the signal preprocessing, as well as the subjectivity in autoregressive moving average (ARMA) model ordering, this paper proposes a method based on interval empirical mode decomposition and ARMA model. In the proposed method, the original signal is decomposed into a series of intrinsic mode functions (IMFs) and a residual through empirical mode decomposition (EMD). Based on the Hellinger distance and autocorrelation function, IMFs are then classified into noise IMFs, hybrid IMFs, and signal IMFs. The improved sand cat swarm optimization is utilized to optimize the ordering process of the ARMA model. The improved adaptive filter is adopted to compensate the random error, and the compensated signal is reconstructed with the signal IMFs and residual to obtain the final output. Experiments show that under static conditions, the proposed method could reduce the root mean square error (RMSE) by 52.6% and 33.3%, respectively, compared with the traditional EMD and ARMA methods. Under dynamic conditions, the proposed method could reduce the RMSE by 51.1% and 37.1%, respectively, compared with the traditional EMD and ARMA methods. The proposed method could effectively compensate the random error and improve the measurement accuracy of MEMS gyroscopes.