Aerospace Systems最新文献

Long-distance space systems generate enormous amounts of bigdata. These bigdata can be used to generate intelligent that can help us better understand the behavior of space systems. There is currently no such tool for precisely understanding and predicting the behavior of aerospace systems. In this study, three different aerospace systems are analyzed to build the respective artificial intelligence (AI) models to understand and predict their space behavior using the deep learning (DL) ecosystem. We studied the pulsed plasma thruster (PPT), an electric space propulsion system; the ARTEMIS-P1 spacecraft sensor array; and the UAV battery system. Three sets of comparative analyses are carried out to assess the model accuracy. A number of tests are utilized to assess and predict the exact physical behavior. The comparison and test results show that DL-based artificial models are capable enough (> 99%) to mimic the exact system behaviors. This DL-based approach provides a novel means of understanding and predicting the real behavior of the aerospace systems.

Convolution neural network (CNN) is widely used in rotating machinery fault diagnosis. However, in real industries, the rotating machinery often operates under changing speed and heavy background noise conditions. As a result, the fault-related information from collected signals is submerged by interference pulse, and most existing CNN-based diagnosis methods can hardly extract enough discriminative features. To tackle the above issues, this paper proposes a feature enhancement multiscale network (FEMN) for health state prediction. First, the convolution local attention mechanism is introduced to adaptively extract discriminative features. Next, to fully utilize features from intermediate layers, the ADD module is leveraged to intelligently integrate the feature information from each two CLAMs. Besides, the multiscale feature enhancement module is used to filter the noise interference and extract multiscale features, and the boundary feature enhancement module is applied to focalize the distribution of fault-related features. Finally, the FEMM is constructed based on the above contributions. Experimental results on the motor and bearing dataset under nonstationary conditions demonstrate the FEMN outperforms five state-of-the-art methods.

Composite structures often experience various types of defects and damages during manufacturing, assembly, and service. In order to effectively restore the strength of damaged structures without compromising their original aerodynamic shape, adhesive repair is commonly employed. This paper investigates the tensile behavior of composite laminate. Initial tests include intact specimens, damaged specimens, and baseline scarf repair specimens. The load-carrying capacity and stiffness of the baseline repair specimens were both improved. Numerical analysis is developed based on the dimensions of the specimens. Numerical analysis model was established based on the dimensions of the specimens, employing continuum shell elements and cohesive elements to simulate the adhesive between the patch and the parent structure. The simulation results closely matched the experimental results, confirming the reliability of the simulation approach. Using this model as a basis, a parametric study is conducted on the patch repair parameters, including the scarf angle, the number of extra plies, and the overlapping width of extra plies. It is found that increasing the scarf angle and the overlap width of extra plies enhances the ultimate load capacity of the specimens, while increasing the number of extra plies improves the tensile stiffness. Subsequently, a scarf repair configuration with an angle of 1:50, an overlap width of 12.7 mm, and two extra plies is selected for the repair. Optimized scarf repair specimens are obtained and subjected to tensile testing. The results demonstrate that the optimized specimens exhibit excellent tensile performance, with an ultimate load reaching 93% of the intact specimens and a tensile stiffness in the linear range reaching 97% of the intact specimens.

The stability of a rocket during flight is the one of the most crucial factors from the perspective of a design engineer. Without stability, a rocket is equivalent to an uncontrolled and unpredictable, high-speed projectile. Passive control can stabilize flight in one of two ways: by shifting the center of pressure (CP) behind the center of gravity (CG); or by producing a spin along the axis of flight. This study aims to induce this spin or rotation through the design of fins. This study is a synergistic application of few of the many engineering practices and processes. It has generated airfoil profiles for rotation inducing fins using NACA database; developed a software model using SolidWorks to run analysis using commercial FEA, CFD and stability analysis software; and additively manufactured a prototype model for experimental testing in a subsonic wind tunnel. Pressure, which is responsible for spin, was measured experimentally at different locations across the length of the model and was found to have comparable values as those obtained for CFD study. The experiment also displayed a longitudinally stable spin of the model.

Micro-aerial vehicles (MAVs) find it extremely difficult to navigate in GNSS-denied indoor staircase environments with obstructed Global navigation satellite system (GNSS) signals. To avoid hitting both static and moving obstacles, MAV must estimate its position and heading in the staircase indoor scenes. In order to detect vanishing points and estimate heading for MAV navigation in a staircase environment, five different input colour space image frames—namely RGB image into a grayscale image and RGB image into hyper-opponent colour space—O1, O2, O3, and Sobel R channel image frames—have been used in this work. To determine the position and direction of the MAV, the Hough transform technique and K-means clustering algorithm have been incorporated for line and vanishing point recognition in the staircase image frames. The position of the vanishing point detected in the staircase image frames indicates the position of the MAV (Centre, left or right) in the staircase. In addition, to compute the heading of MAV, the Euclidean distance between the staircase picture centre, mid-pixel coordinates at the image’s last row, and the detected vanishing point pixel coordinates in the succeeding staircase image frames are used. The position and heading measurement can be utilised to send the MAV a suitable control signal and align it at the centre of the staircase when it deviates from the centre. The integrated Hough transform technique and K-means clustering-based vanishing point detection are suitable for real-time MAV heading measurement using the O2 channel staircase image frames for indoor MAVs with a high accuracy of ± 0.15° when compared to the state-of-the-art grid-based vanishing point detection method heading accuracy of ± 1.5°.

This paper proposes a finite-time stable chattering-free output feedback control method for rigid satellites equipped with single gimbal control moment gyro (SGCMG) actuators, considering dynamic uncertainties and external disturbances. The dynamics of a rigid satellite are first represented using the modified Rodrigues parameter (MRP) explanation, and then transformed into Lagrangian state space affine form. Because of cost or technical restrictions, angular velocity data are not always accessible for practical application. So angular velocity is considered to be unmeasurable. In order to avoid increasing mathematical calculations and designing separate observers to estimate external disturbances and system states with finite time convergence, a fast third-order sliding mode state observer has been used to simultaneously estimate disturbances and system states. The main part of the proposed controller is also composed of the fast non-singular terminal sliding mode method, which is a combination of linear sliding mode and terminal sliding mode and guarantees finite-time stability and elimination of chattering phenomenon. For the computation of inverse of Jacobian matrix, off-diagonal singularity robust steering algorithm has been used that capable of escaping any kind of singularities. The stability of the proposed method and the simulation results of the proposed method have been presented and compared with the results of the methods available in the literature, which shows the efficiency of the method proposed.

The electrical actuator is usually used in the navigation and control system of the hypersonic aircraft, and it can be described by a multi-body dynamical system, which contains brushless motor, gear pairs, ball screw, folk, rudder, etc. For such a complex multi-body system, it may contain clearance between the mating components, such as the gear pairs, the nut of the ball screw and the folk. Additionally, the discontinuous friction force is introduced due to the friction sheet between the folk and the rudder shaft. Since the working temperature of the electrical actuator for the hypersonic aircraft can be extremely high and time-varying, the stiffness, clearance and friction coefficient will also change during the maneuvering flight of the hypersonic aircraft. In this paper, the ordinary differential equations of each subsystem of the electrical actuation system for the hypersonic aircraft will be developed. The continuous and discontinuous interaction forces between the mating components will be derived. The temperature effects will be considered such that the stiffness, clearance and the friction coefficient of such an actuation system are in the function of the working temperature. The dynamic responses of such an electrical actuation system for different working temperatures will be compared based on the numerical simulations, which shows the evidence that the temperature can reduce the transmission ratio of such a system, as well as affecting the system flutter behavior, through changing the contact position of the adjacent meshing components.

This paper puts forth a new approach for reducing the weight of a fuel cell (FC) powered fixed-wing unmanned aerial vehicle (UAV). The key innovation combines concurrent optimization of the FC and battery sizes along with their power management strategy. A particle swarm optimization (PSO) algorithm is leveraged to perform this concurrent optimization. Through these optimizations, reductions in weight are achieved for both the power sources and fuel tank, while maintaining optimized power output profiles. The optimization results demonstrate significant system weight reductions of 66.87% and 47.72%, for two distinct power profiles that were analyzed. Profile I corresponds to a smooth, continuous power demand over time, while Profile II is a fluctuant profile. In addition to weight savings, the power management optimization reveals an important interplay between the power profile demanded, control strategy, and sizing of the power sources. It was found that the FC is best sized to match the longest duration high power segment of the mission. This power-matched sizing results in stable, efficient operation of the FC over time. Conversely, the battery is sized sufficiently large to meet peak instantaneous power demands that exceed the FC capability. These findings showcase the potential of the proposed optimization approach to facilitate improved performance for electric fixed-wing UAVs. Moving forward, a series of numerical simulations validate the proposed methodology and confirm the deduced results.

Motivated by the difficulty of accurately determining the inflow parameters in icing wind tunnels and flight tests, the Markov Chain Monte Carlo (MCMC) method, a commonly used Bayesian inference method, is explored to solve the inverse problem with the help of an icing software. The icing software, SJTUICE, is used to produce ice shapes for inversion and serves as the prediction tool in the iterations of the inversion problem. The influence of prior estimation of different icing parameters on the convergence and accuracy of the inversion problem is discussed. The feasibility of the MCMC method in inferring the inflow condition in terms of rime ice and glaze ice is assessed. Generally, fast convergence and good accuracy in terms of single inflow parameter inversion can be easily achieved. However, the number of iterations required increases rapidly with the number of inflow parameters in the MCMC method.

The development of flight software for Unmanned Aerial Systems (UAS) is challenging due to the absence of an established development process defined by aerospace certification authorities. This research paper outlines our methods and tools for analyzing flight-critical UAS control software on the target hardware. We present our toolchain and methodology for evaluating the flight control computer stack, runtime memory, and timing characteristics. Additionally, we compare the performance of the flight control computer under various hardware and cache settings to justify, which hardware features should be enabled. The tools and processes employed in this research are deployable to any other development environment and are not restricted to the specific target hardware used in this paper.