Aerospace Systems最新文献

The bleed air system is an important part of the aircraft, and the normal operation of the bleed air system has an important impact on the safety and comfort of the aircraft. A deep learning-based method was proposed for the fault diagnosis of the precooler and pressure regulating valve (PRV) in the aircraft bleed air system. This method used long short-term memory network (LSTM) and Informer as prediction models. It also used the mean square error of the predicted and actual values as an anomaly detection indicator. The QAR data of the Airbus A320 series aircraft were used for experimental verification, and the model was evaluated and analyzed from the aspects of prediction performance, fault detection rate, false alarm rate, miss rate, etc. The results showed that the accuracy of our method reached more than 92%, and compared with LSTM, the accuracy of informer increased by 0.5%, the false alarm rate decreased by 0.4%, and the miss rate decreased by 6.7%, proving the effectiveness and superiority of the method of this paper.

The present article considers a modified optimal control model (MOCM) of the pilot. The main idea behind the proposed modification is moving the time delay element, initially located at the input of the model, to its output. Such relocation eliminates the need for introducing a “predictor” block to the model’s structure, which simplifies the algorithm for calculating the parameters of the optimal model. Besides a description of the modified algorithm, the paper assesses the agreement between the performance achieved using the algorithm and the results of experimental investigations. In addition, the results of modeling are compared to the results obtained using a structural model. The comparison is carried out for a model of a second-generation supersonic transport for this vehicle. The MOCM’s potential in evaluating alternative flight control system laws is discussed as well.

Accidental mechanical damage resulting from impact on the structure of the aircraft during its operation can lead to both easily detectable (visible impact damage—VID) and non-detectable damage during visual inspection production or operational damage (barely visible impact damage—BVID). At the same time, for each category of damage, strength from the required loads must be provided, so, for example, for damage of the first category (BVID), static strength from the design load must be provided throughout the entire service life. The provision of this requirement is carried out by experimental and computational methods. To carry out numerical calculations using the finite element method (FEM), it is necessary to use the PCM model, which will allow reproducing the damage resulting from the impact with high accuracy. Currently, monolayer strength criteria based on the implementation of a plane stress–strain state (PSSS), which takes into account only the components of the stress tensor in the plane of the layer, are widely used. But in case of impact, the direction from the layer also plays an important role. The scientific novelty of the proposed mathematical model of polymer composite material (PCM) is the addition of a monolayer strength criterion for volumetric FE, taking into account the direction from the plane of the layer. In this paper, a comparative assessment of the strength criteria for PCM in the simulation of impact was carried out Hu et al. (Polymers 14: 2946, 2022), Wang et al. (3D Progressive damage modeling for laminated composite based on crack band theory and continuum damage mechanics, 2015). Models with layer-by-layer modeling of a PCM sample with a cohesive interface between layers were developed to account for delaminations arising from impact Falcó et al. (Composite Structures 190: 137-159, 2018).

Polymer composite materials (PCM) are increasingly used in various fields of technology, primarily in the aviation industry, due to their high specific characteristics of structural properties and high manufacturability of molding methods for products of various functionality and undeniable advantages over traditional metal materials. The volume of PCM application in the airframe design of a number of modern and promising passenger aircraft is currently beginning to exceed 40% by weight and 80% by the area of the external contour of the aircraft. The development of automated technologies for laying out prepregs determines global trends in this direction, which consist in replacing traditional, manual methods of forming packages with automated methods of laying out. This gives significant advantages, such as a significant increase in the speed and accuracy of the process of laying out a semi-finished composite. The high demand for automation is due to increased requirements for the mechanical and precision characteristics of products. The key place in the article is given to determining the economic efficiency of methods of manufacturing composite parts for civil aviation equipment. The results of a comparative analysis of the technological cost of manufacturing parts from composite materials are presented, including assessment of the labor intensity of manufacturing work piece, material usage coefficients, share of manual labor, and degree of automation for polymer products through manual and automated technological calculations. It is shown that the use of automated technologies in the production of standard large-sized PCM panels makes it possible to reduce the consumption of basic materials by 15%, the labor intensity of manufacturing by 36% and the total manufacturing time by 28% compared to the existing level of pilot production. The key indicator of waste-free technological process (material utilization coefficient (MUC)) reaches values of 0.85–0.95.

Due to the excellent performance of carbon fiber-reinforced polymer (CFRP), they are widely used in the world's aircraft manufacturing industry, including aeroengine blades. During aircraft service, engine blades are often impacted by foreign objects such as breakstone, seriously affecting the airworthiness and safety of aircraft. Therefore, studying the impact resistance of carbon fiber composite materials is crucial for improving aircraft safety. In this paper, ABAQUS is used to establish a simulation model for impacting composite blades with breakstone. The VUMAT user subroutine is compiled to predict the damage of inner layer elements based on 3D-Hashin failure criterion and stiffness reduction scheme; cohesive elements based on the bilinear model are inserted between adjacent laminas to predict the delamination damage of the composite material. The damage initiation of cohesive elements is judged by QUADS criterion, and the damage evolution is performed using the B–K criterion of the energy method. Finally, based on the simulation results, the impact force, failure mode, and energy transformation during the impact process are analyzed.

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°.