Due to the limit of the installation location and the electrode number of the electrostatic sensor array (ESA), the linear independent measurement information of the aeroengine fault prognostics and health management (PHM) system is rare. Aimed at this problem, this paper proposes an electrostatic cross-correlation (CC) sensitivity weighting based exhaust debris monitoring method. With the same electrode number, the CC focus method is applied to effectively enhance the number of measurement information that can characterize different sensitive regions. On this basis, the 8-electrode ESA is designed, and the CC sensitivity distribution of different electrode pairs is established and weighted by 16 correlation velocities. The results can reflect the velocity and location information of charged debris. The effectiveness of the method is validated by experiment results of the belt-style electrostatic induction experimental facility and the vertical gravity experimental device. The average correlation coefficients between the monitoring results of single particles and multiple particles and the actual distribution reached 0.668 and 0.652, respectively, which enhanced monitor information and stability of PHM system.

In order to meet the requirements of large and slender structural components, a new type of 5-DOF hybrid robot is proposed, and the new large rotation angle 2T2R parallel mechanism module in the robot is analyzed and studied. Firstly, the degrees of freedom of 2-UPS & (2-RPR)R parallel mechanism are calculated by using the screw theory, and the modified G-K formula is used for verification. Secondly, the closed vector equation is used to analyze the kinematics of the mechanism, the forword kinematics model and inverse kinematics model are established and the Jacobian matrix is calculated. Then, the workspace of the mechanism is plotted with the constraint conditions of the mechanism. Then, the dexterity of the mechanism is analyzed with linear velocity isotropic index and angular velocity isotropic index. Finally, the kinematic simulation is carried out by the given trajectory. Through analysis, the feasibility and practical value of the mechanism are verified, which lays a foundation for the application of the new 5-DOF hybrid robot.

To assess aircraft carbon dioxide emission levels and verify airworthiness, the latest ICAO certification process for carbon dioxide emissions has been studied. The comprehensive index CO₂ metric consisting of the specific air range (SAR) and the reference geometry factor (RGF) is used as a measure of airworthiness. The test flight measurement point is selected according to the maximum takeoff weight (MTOM) value of the aircraft, and the corresponding aircraft carbon dioxide metric value limit line is determined. Based on this, a fast evaluation method of aircraft CO₂ metric based on quick access recorder (QAR) data is proposed. Using the QAR data of the new serving aircraft, the corrected relationship between fuel consumption rate and thrust and that between airspeed and thrust are established by multi-parameter support vector machine regression (SVR) method. The total weight of the aircraft is used to solve the SAR values and the CO₂ metric under the three flight test points. The QAR data of a new serving B777-200 aircraft was used for case analysis. The calculation results show that the CO₂ metric of the model is 1.598 7, which exceeds the limit line by 20.5%. The proposed calculation method of CO₂ metric based on QAR data can be used as an effective way to complete the rapid assessment of aircraft carbon dioxide emission levels.

With the successful application and rapid development of deep learning in many fields, the integration of deep learning with traditional structural analysis has become a new research direction. In terms of solving the finite element stiffness matrix problem, the application of convolutional neural network in structural analysis is studied. Taking the quadrilateral plane stress element as an example, based on the convolutional neural network, a neural network model for solving the finite element global stiffness matrix is proposed. Moreover, the relationship between the learning effect of the network and the number of network convolution kernels and the number of training samples is analyzed. The calculation example shows that, within a certain range, the learning ability of the network increases with the number of convolution kernels and the number of training samples. In practical applications, the corresponding convolutional neural network can be set according to specific accuracy requirements. After the convolutional network training is completed, the calculation of the element stiffness matrix is real-time, and the accuracy meets the engineering requirements.

Loose particles such as metal fragments and wires may be left in the control circuit of the aerobat during the process of manufacture, which will cause potential danger like short circuits. To solve this problem, a method of identifying material of loose particles in the aerobat based on particle impact noise detection (PIND) is proposed. This method firstly uses short-time autocorrelation function to obtain the pulse part of PIND signal, and then extracts various statistic features in time domain and frequency domain, which is combined with Mel frequency cepstral coefficient (MFCC) feature, and finally trains support vector machine model for material classification. In order to verify the effectiveness of the proposed method, loose particles' PIND signals with three different types of material are acquired and used for model training and tests. Test results show that the accuracy of identification can reach 98% which is better than related papers' results, verifying the effectiveness of the proposed method.

Small joined-wing UAVs always face the disturbance caused by simple runway conditions and the influence of their own nonlinear factors. The existing control methods do not pay enough attention to the rolling control of the UAV during takeoff taxiing. In order to solve these problems, this paper analyzes the problems faced by the takeoff taxiing and the characteristics of joined-wing layout from the example of a certain type of joined-wing UAV taxiing test, and then designs a joined-wing UAV takeoff taxiing controller based on the backstepping control method. The control method takes full account of the interference caused by the takeoff conditions and the influence of UAV nonlinear factors, and effectively controls the yaw and roll of the UAV's takeoff taxiing. Simulation and UAV taxiing test results show that the proposed design method is effective.

For aircraft fuel tanks with continuously varying cross section in aircraft fuel system design, a method for calculating fuel mass properties with cross-section adaptability is proposed. Firstly, the fuel level normal vector is calculated using flight attitude and overload to determine the location of the lowest fuel level. Secondly, the split step is determined based on equivalent fuel entity to perform the first segmentation, and errors will be compensated through subsequent segmentations according to the cross-sectional area changes until the segmentation accuracy reaches preset accuracy threshold. Finally, fuel mass properties are measured and calculated for the segmented fuel entities. The simulation carried out for different types of fuel tanks with continuously varying cross section in fuel system of a certain type of UAVs shows that this method has the cross-section adaptability and can improve calculation efficiency by 71.43%-92.31% compared with conjugate gradient optimization algorithm, while ensuring calculation accuracy. It is suitable for quick and accurate determination of feasibility of design schemes in terms of gravity center by calculating fuel mass properties.

The aerodynamic shape of the blended wing body (BWB) aircraft is largely determined by the cabin layout, and thus designing a reasonable cabin layout is very important in the BWB aircraft conceptual design. In order to improve the efficiency of cabin layout design for BWB aircraft, a prototype for rapid generation of cabin layout was developed based on knowledge based engineering (KBE) method by integrating the knowledge and specifications of cabin layout into the process of geometric model generation of the cabin layout. The cabin layout designs for the BWB passenger aircraft with 300 seats and 400 seats were used as the examples to illustrate effectiveness of the prototype. The results show that the cabin layout schemes can be automatically generated using the prototype once the requirements and key parameters of the cabin layouts are given. The prototype can be used as a rapid tool for the cabin layout of BWB aircraft.

By the use of a muffle, the static response of the yttria-stabilized zirconia (YSZ) flame sensor to temperature was measured in the range of 873-1 523 K, and the static calibration curve and response characteristics of the sensor were obtained and analyzed. The results show that the linearity of the YSZ flame sensor is 12.88%; with 24 V excitation voltage, the average sensitivity is 10.02 mV/K; the indexes of hysteresis and repeatability are 2.13% and 2.22%, respectively; the interchangeability between different YSZ flame sensors is 1.22%; the static calibration curve of the sensor can be accurately fitted by the Boltzmann function with an error of less than±3.5%. The YSZ flame sensor has significant nonlinearity, high sensitivity, good precision and interchangeability, and the overall performance is good. Compared with thermocouples and ion-based flame sensors commonly used for flame detection, the output signal of the YSZ flame sensor in response to the flame temperature is steady and robust, which can effectively improve the accuracy and reliability of flame detection.

To numerically solve the two-dimensional Euler equations, discontinuous Galerkin method and backward difference formula (BDF) are used as spatial and temporal discretization, respectively. The Newton-Raphson method is taken to solve the nonlinear equations arising from the implicit time integration. The Jacobia matrix is constructed. Owing to the high-order, sparsity and non-symmetry of the matrix, the preconditioned generalized minimal residual (GMRES) method is chosen in every time step for solving the linear equations. The preconditioner is constructed using incomplete lower-upper (ILU) decomposition method. The bandwidth reduction technique is applied to the solution of the linear equations. Without extra storage cost, the application narrows the difference between the preconditioner and the coefficient matrix, thus accelerating the convergence of GMRES method and increasing the available time step size for temporal integration. Typical aerodynamic problems are solved to test the effectiveness of the application.

In conventional aerodynamic force mapping methods, conservative interpolation method can ensure the equivalence of the integral load, but the calculation is complex. While the non-conservative interpolation method is simple to implement with consistency of the distribution of aerodynamic pressure, but no conservation of the overall load. In this paper, equality constraints are used to ensure that the integral loads are equal, with the least squares based on point interpolation method using radical basis function (RPIM) to maintain the distribution characteristics of the original aerodynamic pressure, thus transforming the aerodynamic force mapping problem into a quadratic programming problem with equality constraints. This operation builds up a new conservative method on an original non-conservative method, which has preserved the advantages of each method. Mapping the aerodynamic force from a three-dimensional mesh to low-order panel mesh of a high-aspect-ratio wing is taken as an example. The results show that the method proposed in this paper can not only strictly guarantee the equality of integral loads, but also inherit the high efficiency of non-conservative interpolation method, despite the loss of some accuracy in the distribution of aerodynamic force. Besides, it is suitable for fast equivalent mapping of various complex external aerodynamic data, and similar data mapping problem in other disciplines.

In the distributed complex network environment, collecting the large number of users' behavioral data along with the website data during browsing accurately and comprehensively, efficiently storing them are the basis of user behavior analysis. In order to solve the problems of diversity of data types and storage differences, improve the efficiency of data retrieval, and provide support for the analysis of user behavior for the individual needs of enterprises, a white box mode of user trace collection and storage system is designed in this paper. The users visit the Web server and processes the data of interaction/transaction and user operations, such as pictures, video, description of goods and other types of files. These interfaces and data are called user browsing traces, and operation sequences are the actual user behaviors in order. User data and operation sequence analysis can accurately reflect user characteristics. The collection system is modeled by the interface window tree, providing a unified access interface for data, which is stored in different locations according to the data types. The applications input parameters to specify the storage location to create the database. Through the access interface, the user data can be accessed according to the different file types and requirements. The model solves the problem of capturing, storing, and retrieving traces of Internet-oriented user interaction, and has good accuracy and integrity.

Aimed at the problem that the infrared sensor has a large error, in this paper, based on the combination of spherical coordinates and Cartesian coordinates, a mathematical model of cooperative location is established, and the reason for the large positioning error when the distance between the two machines is close is obtained by analyzing the mathematical model. Then, the ridge regression algorithm is used to solve the target position estimation value and the positioning error covariance matrix of the two sets of measurement subsets with higher positioning accuracy. Finally, the two sets of measurement subsets are fused and positioned by the weighted least squares algorithm to derive the cooperative location optimization algorithm. The simulation analysis shows that the proposed algorithm can significantly improve the positioning accuracy in the whole detection area, and can maintain high positioning accuracy when the two machines are close.

In view of the three-petal high-speed floating-ring seal (TFRS) applied in liquid rocket engine, the leakage and wear characteristics of the seal under complex and changeable conditions are revealed, and the sensitive parameters affecting the sealing characteristics are obtained, which promotes the development of high-speed seal technology. According to the key parameters of pressure, rotational speed and circumferential spring specific pressure of the seal, the leakage rate of TFRS was simulated numerically, and the leakage rate and wear rate of TFRS were measured experimentally. The analysis results show that the leakage rate and wear rate increase with the increase of pressure. Speed is sensitive to TFRS wear rate and insensitive to TFRS leakage rate. With the increase of speed, wear rate increases rapidly and leakage rate decreases slowly. With the increase of spring specific pressure of the seal, the earlier spring specific pressure of the seal are the sensitive parameters affecting the leakage rate. At this time, the leakage rate changes greatly and the wear rate changes slightly. The later spring specific pressure of the seal are the sensitive parameters affecting the wear rate. At this time, the wear rate changes greatly and the leakage rate changes slightly. The leakage rate and wear rate are smaller in the appropriate range of spring specific pressure of the seal. The research results provide a basis for the design, application and in-depth study of TFRS for liquid rocket engines.

In order to improve the capability of the glide reentry vehicle to respond to dynamic missions and improve the robustness of its guidance system, an online solving method for hypersonic glide re-entry trajectory planning problem is established. The method approximates the original problem by a series of convex subproblems. By choosing arc length as the independent variable and introducing logarithmic velocity as the state instead of velocity, the nonlinearity of the dynamic equation is greatly reduced, and the dynamic pressure and heat flow constraints completely become linear constraints. No-fly zone constraints are treated by using a cutting plane method like mixed integer programming to avoid unnecessary calculation as much as possible. By taking the product of aerodynamic coefficient and atmospheric density as the control quantity directly, the pseudo-linear control model is constructed to further weaken the nonlinearity. The non-convex constraint is relaxed appropriately to ensure the feasibility of the subproblem. In order to avoid excessive relaxation, the upper and lower boundaries of the given off-line height and speed are used to estimate the corresponding parameters in order to accelerate convergence. The X-33 re-entry task is taken as an example to verify the effectiveness of the method. The method can take simple constant function as initial value and converge after several iterations.

For the numerical simulation of fluid-structure interaction with moving boundary, a local Cartesian mesh adaptation method coupling flow field features and geometric features is developed based on immersed finite element method. This method overcomes the inaccuracy of simulating solid motion with a single adaptive indicator. In the coupling adaptation, the vorticity is used as the adaptive indicator factor for flow field, and the solid position is used as the indicator for the geometric feature to drive mesh adaptation. The advantages of the coupling adaptive strategy are verified by a numerical example, disk entrained in a lid-driven cavity flow, with volume conservation of the disk and some points' motion trajectory on disks. The computational results show that the volume conservation of the disk cannot be well guaranteed only by the adaptation based on flow characteristics; the trajectory tracking of the disk cannot be effectively achieved only by the geometry-based adaptation; but the coupling adaptation strategy in this paper can ensure the accuracy of the two indexes at the same time. When the overall computational degrees of freedom remain constant, the 2-norm of divergence of velocity can be reduced by one order of magnitude and the trajectory error 2-norm of the disk is reduced by two orders of magnitude.

Aimed at the orbit correction problem of space-based laser interferometric gravitational wave detector, a spacecraft orbit correction method based on virtual formation configuration design is proposed. The detector is composed of three spacecraft forming an equilateral triangle configuration. The configuration of the detector is unstable due to the orbit error and perturbation. It is assumed that an ideal spacecraft is running in nominal orbit, and the real spacecraft in actual orbit forms a virtual formation with the ideal spacecraft. The three spacecraft of detector form three virtual formations with their ideal spacecraft. Considering the stability requirement of detector configuration and the effect of perturbation, the configuration of the virtual formation is designed to solve the correction value of mean orbital elements of spacecraft. The orbit correction value is less than the orbit deviation value, and orbit correction is realized by four-pulse control. The numerical simulation results show that the method meets the stability requirements of the detector configuration through partial orbit correction, and has the potential to reduce fuel consumption and prolong mission life.

Aiming at the coupling problem between the inlet and the engine, the integrated control of inlet/engine based on auxiliary door at low speed and high angle of attack is studied. Firstly, a real-time inlet model is established, which is related to the flight conditions, angle of attack, auxiliary door and the total pressure recovery coefficient and flow rate at the outlet. Then, the inlet mass flow is matched with the engine mass flow, and the control simulation platform of the integrated inlet/engine real-time model is established. Secondly, in order to solve the problem of insufficient inlet flow and uneven pressure in the process of large maneuver, an integrated control method of inlet/engine with auxiliary door regulation is proposed. That is, the control of total pressure recovery coefficient at the outlet of inlet is realized by adjusting the auxiliary door. Under the condition of ensuring the stability of inlet, the control method is based on robust control. Under the condition of stable performance of inlet exit, the control of engine speed and pressure ratio is realized based on H_∞ robust control method. The results of research show that during the whole maneuvering process, the proposed integrated inlet/engine control can keep the engine performance stable. Under typical mission conditions, the thrust increases by 16%, and the fuel consumption decreases by 6%.

The advantages of DS (Dempster-Shafer) evidence theory in dealing with uncertain information have been widely used in various fields. This paper proposes an improved DS evidence theory algorithm for the existence of evidence conflicts in traditional DS. Firstly, combined with the correlation limitation of Pearson correlation coefficient and the correction of zero factor of fusion process, the weight of distribution and the overall unrelated evidence body is greatly reduced, and the overall importance of the evidence body is corrected. Secondly, the DS combination rule calculation is performed to corrected basic probability assignment (BPA) to obtain the fusion result. Compared with the performance of other improved DS theory algorithms in solving common conflict evidence and the number of evidence body fusion, the proposed algorithm has faster convergence rate and higher fusion BPA on credible proposition, which proves the effectiveness of the proposed algorithm.

To identify weaknesses in diesel engine systems under strong electromagnetic pulse, a method to study the identification of weak links in diesel engine system is proposed by combined weighted fault tree and hierarchical Bayesian network. This method takes into account the correlation of the failure of the same layer element, and the local application of the weighted fault tree solves the problem of obtaining partial conditional transfer probability tables. First, based on Bayesian network two-way reasoning function, the sensitivity threshold and electromagnetic stress data of each components were obtained by the diesel engine irradiation test and electromagnetic simulation software, the prior failure probability of component to the system level is calculated under strong electromagnetic pulse. Then, Bayesian probability formula is used to calculate the posterior probability of the components' failure under the condition of engine failure. The weak links of diesel engine system are identified according to the sequence of the components posterior probability, which may provide a reference for the design of electromagnetic protection scheme. Taking wide band high-power microwave (WBHPM) illumination as an example, the parameter acquisition and probability calculation process of the hierarchical Bayesian network fault model for diesel engines are illustrated. The results show that the actuator, camshaft and crankshaft sensors are not only important parts of diesel engine system, that but also is weak links, which need to be protected.

The lightweight neural network embedded on artificial intelligence (AI) chips can realize the onboard automatic detection of vehicle objects in unmanned aerial vehicle (UAV) videos, which is important in practical applications. In this paper, a vehicle object detection algorithm in UAV videos is proposed, and then deployed and tested on AI chips. For the proposed detection algorithm, firstly, the MobileNet-SSD network is clipped based on the range of vehicle objects' size in UAV images to construct a lightweight single-frame object detector. Secondly, the interframe motion estimation was introduced to improve the poor detection performance which is usually caused by small object characteristics and lightweight network. Thirdly, the position range of missing objects in the current frame is predicted according to the information of adjacent frames. Finally, the predicted position is corrected by detection results, and the recall of lost objects is realized. Additionally, a high-quality UAV image vehicle dataset was built by fusion and automatic supplementary annotation of multiple datasets. The proposed algorithm is verified on the embedded development platform based on RK3399 chip. The results show that the network with the proposed algorithm can significantly reduce the occupation of storage resources with the lightweight characteristics. Compared to the traditional single-image detection algorithm, the proposed algorithm can effectively improve the detection accuracy. Moreover, detection speed can be as low as 125.3 ms per frame on the AI chip.

In the process of jamming the target radar, the radiated signal of the phased array-based jammer will also be captured by the enemy's passive detection radar. Based on FDA jammer, this paper proposes an angle deception method for the adjacent amplitude comparison direction finding system. First, the half-power beamwidth of the FDA radar is calculated based on the FDA array model. Then, the half-power beamwidth is substituted into the adjacent amplitude comparison direction finding system. Finally, through simulation, the angle deception effect and error influence of the FDA jammer on the direction finding system are analyzed. The simulation results show that, under far field conditions, the FDA jammer has a good angle deception effect on the direction finding system.