Journal of Aerospace Engineering

The application of a spaceborne gradiometer to satellite orbit determination has been proposed in recent years. The advantages lie in its full autonomy and its immunity against spoofing attacks. The objective of this paper is to study the observability of the spaceborne gradiometer-based orbit determination (SGOD) system. To address the problem of unknown biases in spaceborne gradiometer measurements, the joint estimation of satellite orbital states and gradiometer biases is adopted. Then, the system observability matrices are formulated based on the theory of nonlinear observability. The local weak observability is assessed numerically by singular values and condition numbers of the observability matrices. A special unobservable case is demonstrated, which is the circular orbit in the Keplerian two-body model. Different orbital parameters, attitude control modes, and gravity models are furtherly employed to analyze their effects on system observability. The observability analysis shows that low altitudes, large eccentricities, large inclinations, attitude maneuverings, and the $J_2$ perturbation can improve the system observability. Furthermore, a numerical simulation using the extended Kalman filter (EKF) is developed to obtain accuracies and convergence rates of the SGOD system. Effects of orbital parameters, attitude control modes, and the $J_2$ perturbation on orbit determination results are also studied. It is shown that the simulation results agree with the observability analysis. Using a spaceborne gradiometer with 0.1 E ($1\mathrm{E}={10}^{-9}{\mathrm{s}}^{-2}$) accuracy, position accuracy of tens of meters is achieved for a low Earth orbit.

The single-expansion ramp nozzle (SERN) has a compact structure and adaptability under off-design conditions, which is one of the nozzle configurations that the combined cycle engine gives priority to. Flow separation is likely to occur when the nozzle operates at low-pressure ratios. Subsonic regions, such as separation bubbles, recirculation zone, and boundary layer, also exist in the overexpanded flow field. At this point, the varying external flow field can affect the flow field inside the nozzle, which may lead to the transition of the flow separation pattern. The majority of relevant studies are conducted in a static environment because of experimental conditions limitations. This paper numerically simulates the transitions of separation patterns during the acceleration and deceleration processes of a short flap SERN. The separation transitions mechanism is analyzed, as well as the performance impact of the transitions. Current results show that the separation pattern transition happens during both the acceleration and deceleration processes, and free shock separation is a relatively stable state. The separation pattern transition occurs as a function of the external flow field, recirculation zone, boundary layer, and separation shock wave coupling. Compared with previous studies on the long flap SERN, the coupling mechanisms of the internal and external flow fields of the two nozzles were found to be different. The shock wave structure of the short flap SERN’s flow field is stable during the transition processes, and the nozzle performance does not exhibit a step change. The hysteresis of the short flap SERN is also weaker.

This paper innovatively designs a surrogate model for the performance of aeroengine high-temperature rising combustor based on cubic polynomials. According to the comparison of the one-dimensional calculation method, the three-dimensional computational fluid dynamics (CFD) numerical simulation, and the experiment, the feasibility of the one-dimensional (1D) calculation method and the reliability of the sample data calculated by this method are proved. The predictions of the metamodel were compared with those calculated by using the 1D program, and the root-mean square errors of the two target parameters were 0.000077% and 0.001725%. Based on this, sets of test data were used to verify the accuracy of using the surrogate model. The test results were compared with those of an artificial neural network (ANN) model and the 1D program. The root-mean square error (RMSE) of the cubic polynomial model and the ANN model in terms of combustion efficiency and total pressure loss were 0.0150% and 0.0107%, and 0.1628% and 0.3032%, respectively. A global sensitivity analysis identified the oil-to-gas ratio and the total inlet pressure as the most important factors affecting the combustion efficiency and total pressure loss. Particle swarm optimization (PSO) was used to obtain the optimal nondominant Pareto solution set. It provides physical insights for the optimization design of aeroengine combustor.

The ignition process in a thin strut equipped supersonic combustor fueled with liquid kerosene was observed to deepen the understanding of the ignition characteristics. A large number of experiments were carried out under the conditions of $T_t=\mathrm{1,680}\mathrm{K}$, $P_t=1.87\mathrm{MPa}$, and $Ma=2.8$. The combustion establishment process and the flame characteristics were captured and reproduced by a high speed camera. Results show that the flame is first established near the igniter flame in the lower half of the combustor under the action of the recirculation zone and oxygen supplement at the end of the strut. Then, the flame spreads to the main flow of the combustor. The ignition process under different equivalence ratios was compared and studied. The results indicate that the ignition delay time and the flame distribution of the ignition process are affected by the equivalence ratio. At the same time, it was found that there are two modes in the flame establishment process, namely, the upstream propagation mode and the downstream propagation mode. The flame propagation speeds in different directions under these two propagation modes are different. The results presented in this paper are valuable for the future optimization of ignition performance in a scramjet combustor using kerosene fuel injection.

This study used a combination of the instantaneous baseline method and a probability-based diagnostic imaging (PDI) algorithm for damage identification in a stiffener-reinforced aluminium plate. Selecting an appropriate weight distribution function plays a significant role in the PDI algorithm by expressing the probability of damage existence around a sensing path. Weight distribution functions are highly dependent on the weight distribution coefficient ($\beta$). This parameter ($\beta$) normally is estimated by trial and error, which is incorrect. In addition, determining this parameter is quite costly and time-demanding. To address these issues thoroughly, a new approach is proposed for modelling the weight distribution coefficient based on the physics of wave propagation and the actuator–sensor distance. An appropriate value for $\beta$ is estimated for each sensing path between actuators and sensors in a specific structure based on the proposed model, making the results more accurate and reasonable. The proposed model was validated by identifying damages at three different locations of a structure for five scenarios (three single-damage and two multiple-damage scenarios). Moreover, the results obtained from proposed model using linear and Gaussian weight distributions were compared with the results extracted from an existing approach. The acceptable localization results clearly indicate the effectiveness of proposed approach.

Abstract

Soft-core sandwich structures subjected to coplanar compression loading might exhibit wrinkling behaviors, which will reduce significantly the support capability of the structure. In order to prevent the wrinkling behaviors of sandwich structures, this work attempts to improve stiffness of face sheets reinforced by carbon nanotubes (CNTs). To achieve such a goal, the following work is to be required. First, an equivalent single-layer model will be proposed to accurately analyze the wrinkling behaviors of sandwich structures because only layerwise models possess the capability to accurately predict wrinkling behaviors of sandwich structures, but these models are often computationally expensive. Second, the influence of distributions of the CNTs through thickness on the buckling and wrinkling behaviors for the sandwich beams will be investigated in detail. Moreover, the translation mechanism between the buckling deformation and the wrinkling deformation has been also explored. The proposed model has been verified by using the quasi three-dimensional (3D) elasticity solutions and the 3D finite-element results (3D-FEM). Third, by selecting the proper distributions of the CNTs through the thickness of sandwich structures, the wrinkling behaviors of sandwich structures can be effectively prevented. Thus, a scheme resisting wrinkling deformation of sandwich structures has been proposed, which can provide a reference for other investigators.

Practical Applications

Sandwich structures are extensively used in many engineering applications due to their high strength to weight ratios and ease of manufacture. However, sandwich structures subjected to in-plane compressive loads might encounter wrinkling failure. In practical applications, this phenomenon is so common, but when it occurs, the sandwich structures will become unstable and cannot withstand higher loads. Therefore, it is crucial to take effective measures to avoid the occurrence of such unfavorable phenomena. To this end, a strategy was presented, where carbon nanotubes are proposed to be added to the face-sheet of sandwich structures. In order to verify the effectiveness of this method, a computational model was also proposed to analyze the wrinkling behaviors. By comparing with the 3D-FEM results of computer simulations, it was proven that the proposed model is correct and effective. In addition, it was found that different volume fractions and distributions of carbon nanotubes have the different influences resisting wrinkling deformation. In practical applications, structural engineers might get some inspirations to avoid the instability of sandwich structures from the research results of this paper.

Superior mechanical properties such as ultrahigh stiffness and strength along with amazing physical properties have led to the use of nano graphene platelets (GPLs) as a reinforcement in polymer composite structures such as sandwich plates. In this research, free vibration analysis of rectangular sandwich plates with functionally graded (FG) GPL-reinforced face sheets and a soft core is investigated. A high-order three-layer accurate sandwich plate theory is employed to model the soft-core sandwich structure. Third order shear deformable plate theory is applied for functionally graded graphene nano-composite face sheets while second and third order polynomials are used for displacements of the soft core. In this theory, the transverse flexibility of the core as well as the continuity conditions of transverse shear stresses are assumed. Hamilton’s principle is applied to extract the motion equations and the corresponding analytical solutions are presented. The effects of materials properties and geometrical parameters on the natural frequencies of the sandwich plates are studied. A comparison of the present results in particular cases with those of published studies confirms the accuracy of the present analysis. Results show that the natural frequencies of the sandwich plates are enhanced by reinforcing the face sheets with GPLs.

Aeroengine fault detection is extremely significant to ensure flight safety. However, it still poses a vast challenge due to the complexity of aeroengine systems. In this paper, an unsupervised deep autoencoder (DAE) with dimension fusion function (DFF) method (DFF-DAE) was proposed to effectively detect aeroengine faults based on multivariate time-series data. The proposed method does not rely on mathematical models and expert experience to automatically extract the hierarchical representation features of raw data. The DFF-DAE has a three-layer autoencoder with its first encoding layer incorporating a DFF, which is developed for multivariate time-series fusion before encoding. A case study of aeroengine rolling bearing locking fault was conducted on the turbofan engine ground test data composed of vibration and rotational speed time-series data in variable working states. The results indicated that DFF-DAE succeeds in efficiently employing multivariate time-series data to perform accurate fault detection. Compared with other state-of-the-art methods, DFF-DAE yields superior predictive ability and detection accuracy. Furthermore, DFF-DAE provides application potential for online fault detection because it is effective in variable working states of the aeroengine.

The paper presents tests of wind tunnel guide vanes with glass-fiber wool for reducing the airflow pressure loss and noise between the intake tower of a vertical suction wind tunnel and the corner of its section. After installing a double-arc airfoil guide vane with glass-fiber wool inside at the corner and determining how the guide vane influences the wind tunnel airflow through computer simulation, the numerical results showed that with seven double-arc airfoil guide vanes, the airflow pressure loss was reduced by 62%, the corner vortex was eliminated, and the flow field uniformity was improved. Furthermore, the loss of noise transmission in the wind tunnel with glass-fiber wool was simulated with different software. The results showed that the maximum noise absorption was 57.8 dB. Additionally, the average noise absorption was 31.9 dB when the windward side of the guide vane was a 5-mm-diameter perforated plate with a 0.3 porosity rate, and the internal filling with glass-fiber wool has a flow resistance of $\mathrm{4,896}\mathrm{Pa}·\mathrm{s}/{\mathrm{m}}^2$. Finally, a simulation of the perforated guide vanes was carried out, and the results showed that the perforated guide vanes improved the aerodynamic performance of the wind tunnel by 18.6% compared with the wind tunnel without the guide vanes. The research results can provide a reference for the multifunctional design of wind tunnel guide vanes.

As the key starting component of supersonic flight, identification of the unstarted state of the supersonic inlet is the premise of restarting the inlet and an important link of engine protection control. In order to explore the characteristics of the dynamic pressure inside the inlet, the selection of a pressure sensor configuration scheme, that is, how to obtain more abundant pressure information with fewer sensors, is also an urgent problem to be solved. This paper combines time-frequency analysis and deep learning for flow pattern recognition of a supersonic inlet and proposes a strength-based Pareto evolutionary algorithm combined with improved extinction and migration strategies (EI-SPEA2). The signals of all sensors were preliminarily processed using continuous wavelet transform, and the dynamic signals of all sensors were transformed into two-dimensional (2D) time-frequency spectrograms, which were then used as inputs by convolutional neural network to determine the inlet flow patterns from the dynamic sensor signals. A multiobjective optimization algorithm, EI-SPEA2, was adopted, which takes the minimum classification error, minimum number of selected sensors, and optimal sensor distribution as the optimization objectives. Through the continuous iteration of the population, identification of the unstarted state and optimal configuration of sensors was carried out simultaneously. By introducing the improved EI strategy at the end of iteration, SPEA2 can get rid of the local optimum and achieve convergence faster. A distance index for measuring sensor distribution and two indexes for measuring sensor information differentiation are proposed to improve sensor configuration scheme. The experimental results demonstrated that compared with competitive methods, EI-SPEA2 has better performance on both dual-objective optimization problems and three-objective optimization problems. Moreover, three indexes proposed to measure the distribution of the sensor are beneficial to improving the sensor configuration scheme, and the distance-based index is more suitable.

In this paper, phase change paraffin microcapsule and aerated concrete are combined to construct a new type phase change aerated concrete. Four groups of specimens with 0%, 2.5%, 5%, and 7.5% of microcapsule phase change material (MPCM) were manufactured. Comprehensive tests, including microstructure characterization, dry density, compressive strength, and thermal parameter tests were conducted to evaluate the influence of MPCM on mechanical and thermal properties of the phase change aerated concrete. Scanning electron microscope diagrams show that the structure of MPCM is complete in the cement substrate without damage. The test results show that with the increase of the content of MPCM: (1) density, peak strain, specific heat capacity, and heat storage coefficient increase; (2) elastic modulus and thermal conductivity decrease; and (3) the cubic compressive strength and the ultimate prismatic strength increase first and then decrease. Based on this study, the optimal content of MPCM is 2.5%, which performs better than non-autoclaved aerated concrete without MPCM in terms of both mechanical and thermal properties.