Aircraft ground frost has an impact on flight safety and causes flight delays. An accurate frost forecast is an essential basis for the generation of airport snow removal parameters. Frost formation on aircraft ground is influenced by many factors such as weather, body surface structure and flight time, and airports have special forecast indexes for aircraft frost formation. Therefore, frost formation on aircraft ground is divided into the nucleation stage and the frost layer growth stage. A frost formation temperature model based on classical nucleation theory is established during the nucleation stage, and the frost formation temperature is defined as the temperature at which the coverage rate of frozen liquid nuclei approaches the threshold. The surface characteristics of aircraft and the influence of the external environment on the frosting temperature were studied. During the growth stage, the Euler multiphase flow-based frost layer growth model is developed, the frost layer growth identification criterion is added from the frost layer formation principle, and the impact of various working conditions on the frost layer properties of the aircraft surface is investigated. The results of simulation and field experiments show that the average error of the frost formation temperature model is 0.65 ℃. The average error of the frost growth model is 5.99%. The results demonstrate that the proposed phased modeling method can provide theoretical support for predicting ground aircraft frosting.

An essential component of the intelligent interpretation of remote sensing images is the use of semantic segmentation algorithms to assign feature class labels to individual pixels. Aiming at the problem of low segmentation accuracy of deep neural networks for small-scale objects caused by the large scale difference between different categories of objects in high-resolution remote sensing images, a U-Net++ guided by dual attention semantic segmentation model is proposed in this paper. In the encoding stage of the network, a dual parallel backbone network is constructed to extract features, and mutual attention is utilized to capture the dependencies between pixels of feature maps of different scales, adaptively fusing features of different scales with the same network depth to enhance the attention to small-scale objects. To address the issue of fine segmentation in complex scenes, a spatial and channel hybrid attention mechanism is introduced in the network’s decoding stage to reduce the semantic gap between the outputs of various depth sub-decoders while fusing the semantic information and spatial location representations at various levels therein. The proposed algorithm achieves notable performance metrics, with the mean intersection over union (mIoU) values of 86.77% and 82.73% on the Potsdam dataset and Vaihingen dataset, respectively, accompanied by the mean F₁-score of 92.32% and 90.79%. These results underscore the algorithm’s efficacy in delivering comprehensive segmentation of small scale objects, surpassing the performance of other state-of-the-art semantic segmentation algorithms such as U-Net++, FarSeg, DMAU-Net, and SAPNet.

In the active sidestick system, there is a problem of surplus force, which causes problems such as ‘jigging rod beater’, poor situational awareness, and easy to induce flight accidents under special circumstances. The parameters associated with the surplus force of the system were first identified through theoretical analysis. The influence of each parameter on the surplus force of the system was then thoroughly examined through Monte Carlo simulation. Based on the results of the Monte Carlo simulation, the spectrum analysis of the surplus force and the sensitivity analysis of the influence of the surplus force of each parameter were conducted in order to better understand the mechanism of the surplus force and determine the influence mechanism and law of the system parameters on the surplus force. The driver’s control frequency, the equivalent inertia of the sidestick, the equivalent damping of the sidestick, the signal bias of the speed sensor, and the signal bias of the torque sensor are among the factors that have the biggest effects on the surplus force of the system. The degree to which each parameter influences the surplus force is determined through qualitative and quantitative analysis, which serves as a guide for the design and compliance verification method of the surplus force suppression method of the active sidestick system.

Rolling bearings are commonly used components in mechanical equipment, and the effective prediction of the remaining useful life (RUL) of bearings plays an important role in formulating a reasonable maintenance plan, avoiding sudden downtime of mechanical equipment, and ensuring the safety of equipment. Traditional deep learning methods are difficult to extract multi-dimensional and multi-scale degradation features, which reduces the accuracy of RUL prediction. Meanwhile, there are uncertainties such as noise and model parameters, which make it difficult to meet the maintenance requirements for point prediction of RUL. In this paper, we propose an RUL prediction model called EEMD-AFP-FSBLformer, which integrates the FSBLformer network, attention feature pyramid (AFP), discrete wavelet transform (DWT), and ensemble empirical mode decomposition (EEMD). The low-frequency modal functions are firstly processed by EEMD decomposition with DWT noise reduction and bearing time-domain degradation features with the noise reduction processed high-frequency modal functions in order to produce more representative degradation features; then the degradation features are inputted into the AFP network in order to extract the multi-scale features; and finally, these degradation features are used as inputs to the FSBLformer model. The FSBLformer model’s encoder incorporates the self-attention and feature attention mechanisms, while the decoder employs the bidirectional long short-term memory (BiLSTM) network, which improves the model’s performance in time prediction and feature extraction. The experiments are conducted in different working conditions of the PHM2012 dataset and XJTU-SY dataset, and the comparative experimental analysis shows that the model has a high coefficient of determination of more than 94%, which can effectively extract the multi-scale degradation features of bearings and predict their RUL.

In response to the demands of educational modernization, the scenario-based application of campus data provides new opportunities for the digital transformation of higher education. In order to identify spatiotemporal patterns of campus behavior, this study uses trajectory reconstruction, semantic mapping, and pattern mining on three months’ worth of Wi-Fi logs and point of interest (POI) data from thirty thousand students and faculty. Innovatively introducing the Dirichlet multinomial regression (DMR) model and spatio-temporal routine mining on mobile phone data (STRMM) model for student behavior analysis, the DMR model effectively integrates dynamic trajectories and static landmark data to identify 10 categories of campus functional areas. The STRMM model enhances the ability to capture periodic and uncertain behaviors, categorizing undergraduate daily activities into 10 typical patterns, including standard teaching-oriented and focused research-oriented types. Additionally, grade-level dynamics in student behavior were shown by identifying six types of undergraduate groups with distinct evolutionary behavioral patterns. These groups demonstrated a shift from course-dominated activities in lower grades to self-directed research and flexible schedules in higher grades. The study confirms that Wi-Fi data-based behavioral analysis can effectively identify functional areas and student behavior characteristics, providing data support for precise management, resource optimization, and the practice of ‘Three-Comprehensive Education’ with important practical reference value for promoting the digital transformation of higher education.

This paper suggests a perception-enhanced and multi-scale fusion-based algorithm for small target vehicle detection in order to address the problem of inadequate information and weak feature expression ability of small targets carried by vehicles, which leads to low detection accuracy and missed detections in current algorithms. Firstly, a spatial local feature enhancement backbone network SLFPB-ST is designed to solve the problem of severe loss of feature information for small targets during the feature extraction process. Secondly, a multi-scale information fusion network (MSIFN) is proposed to fuse features at multiple scales by allocating weights to focus on more detailed information. In order to restrict the characteristics of large objects while maintaining the feature representation of small targets, MSIFN also has a large objects restriction block (LRB). Finally, an anchor-free mechanism is adopted to reduce missed detections of small targets and improve detection accuracy. Experimental results on the UA-DETRAC dataset and Vehicle dataset demonstrate that compared with the Swin Transformer algorithm, our algorithm achieves an improvement of 5.15% in mAP, 9.35% in AP50, and 4.35% in AP75 with an increase in parameter size by 14 MB and a decrease in detection speed by 0.4 frames per second. This validates that our algorithm exhibits good robustness and applicability.

To remedy the problem of aerial imagery, including the high proportion of small objects and the poor feature extraction effect, a small object detection algorithm with relatively balanced accuracy and storage resource consumption is proposed. To improve the capacity to extract fine-grained features and produce adaptable features without background information, a lightweight adaptive compound convolutional (LACC) module is first suggested. Then, a multi-scale feature fusion network based on LACC is designed to further reduce the missing rate of small targets. Secondly, the sub-branches of the spatial context pyramid (SCP) are used to replace the spatial pyramid pooling-fast (SPPF) module, which can reduce information confusion and redundancy, and adapt to small target detection scenarios. In order for the network to successfully increase the detection and positioning capabilities of occlusion-overlapping targets, a WiseIou-V3-NMS non-maximum suppression method is then built, taking into account that the detection frame contains objects but is erased. Finally, a lightweight shared convolutional GN detection head is proposed to keep the sensitivity to multi-scale feature information while reducing the number of parameters and computation. On the VisDrone2019 public dataset, the proposed algorithm achieves a mean accuracy MAP0.5 of 0.466, which is 0.077 higher than the baseline algorithm YOLOv8s, the number of network parameters is reduced by 21.6%, and the model size is reduced by 18.7%.

To address the challenges of significant object scale variation, a high proportion of small-sized objects, and severe background noise interference in images captured from unmanned aerial vehicle (UAV) viewpoint images, a novel object detection algorithm based on feature information enhancement and complementation is proposed. First, to leverage high-level semantic information for capturing richer multi-scale features, a multivariate fusion spatial pyramid pooling-fast (MFSPPF) method is introduced. Second, in order to avoid losing important information during feature fusion, a multi-branch semantic enhancement (MBSE) module is created to extract rich multi-scale features over several branches and create links between these features. In addition, the detailed feature complementation (DFC) module is proposed to extract and refine the low-level feature information to obtain rich, fine-grained feature information and achieve the complementation of the detailed information in the high-level features after feature fusion. Experiments conducted on the VisDrone2021 dataset demonstrate that the proposed algorithm outperforms the baseline YOLOv8m method, with average precision (AP), AP50, AP75, AP(s), AP(m), and AP(l) improving by 3.7%, 5.4%, 3.9%, 3.1%, 4.0%, and 7.9%, respectively. Furthermore, the proposed method is applicable to other YOLOv8 models. The proposed algorithm is appropriate for UAV perspective image detection jobs because it maintains a fast detection speed while achieving better detection performance across a range of intersection over union (IOU) thresholds and object sizes.

A feature visualization convolutional neural network-based surrogate model is suggested to achieve the quick prediction of solid divert and attitude control motor performance and assess the influence of various parameters. After obtaining a number of simulation datasets for training from numerical simulation modeling of the solid divert and attitude control motor, a feature visualization convolutional neural network simulation surrogate model with fast inference characteristics is established, which can realize fast performance prediction. The results show that for pressure and thrust of the solid divert and attitude control motor, the model prediction error is 0.2% and 6%, respectively. For multi-parameters input problem of the solid divert and attitude control motor, the proposed method can identify the influence of parameters on performance. Pressure changes have a significant impact on the prediction results for solid divert and attitude control motor gas valve pressure during operation; for gas valve thrust, the pintle stroke has a greater impact on the thrust prediction result when the valve opening is less than 3.5 mm, while the inlet pressure has a greater impact when the valve opening is greater than 3.5 mm. The method has good generalizability and can directly evaluate the key parameters at the algorithmic level, while eliminating the cumbersome comparison of multiple sets of simulation experiments, which can greatly improve the research efficiency.

In order to accurately measure the remaining fuel in the aircraft fuel tank, the aircraft fuel quantity measurement algorithm based on fluid unit mass force (AFUMF) is proposed. The unit normal vector of the fuel surface in the aircraft-body coordinate frame can be directly determined using the three-axis accelerometer’s output signals as the equivalent of fluid unit mass force. The fuel quantity database can then be created and utilized for the fuel volume calculation by combing the point coordinates obtained from the fuel probes' height signal to determine the directed distance from origin as the fuel surface height. A CATIA secondary development program is developed to evaluate the error distribution of least mean square (LMS) and AFUMF by using the Monte-Carlo analysis method. The result shows AFUMF will alleviate the fuel probes’ error and only 1 valid probe is needed, on the contrary, LMS will amplify the fuel probes’ error and at least 3 valid probes are required. Using a six-degree-of-freedom platform, a fuel tank test item was constructed to validate AFUMF. The test findings indicate that, under steady conditions, the maximum error is ±0.5% full fuel tank quantity.

Ion thrusters have been widely used in attitude and orbit control missions of long-duration spacecraft because of their outstanding advantages of high specific impulse and long life. A comprehensive experimental study was carried out to investigate the effect of the accelerator grid compensation coefficient on the performance of the continuous variable thrust ion thruster to achieve a large range and high precision thrust regulation. The mechanism and influencing factors of accelerator grid compensation design were analyzed. Based on 10cm diameter continuous variable thrust ion thrusters, two types of grid systems with different accelerator grid compensation coefficients were designed. The effects of accelerator grid compensation coefficients on a wide range of discharge characteristics of the ion thruster plasma in the discharge chamber, electron backflow limited voltage and acceleration current, ion beam distribution and ion sputtering etching effect were studied under different thrust modes. Two types of grid systems with varying accelerator grid compensation coefficients were devised based on continuous variable thrust ion thrusters with a diameter of 10 cm. The result shows that under constant total acceleration voltage, changing the accelerator grid compensation coefficient can adjust the basic performance of the discharge chamber and the density distribution of the ion beam. And the smaller the thrust, the more pronounced the effect of the compensation coefficient variation. At a wide range of variable-thrust operating points, the accelerator grid compensation coefficient variation has a significant effect on the ion sputtering erosion effect of the ion beam on the grid system, but it has little effect on the electron backflow limited voltage of the grid system. It can be improved by adjusting the total acceleration voltage. The above research will provide technical support for the matching design of the grid system of a prototype ion thruster and optimization of high-performance variable-thrust control strategies, facilitating the illustration and implementation of such systems.

For space stations to operate safely in the space environment, astronauts' extravehicular maintenance tasks are essential. Therefore, it is essential to quantitatively assess the time required for these operations to ensure the success of extravehicular activities and the safety of astronauts. In this study, we present a quantitative technique for evaluating astronaut operational time during extravehicular maintenance activities, with the goal of designing and analyzing the duration of astronaut extravehicular operations to fulfill extravehicular time requirements. The proposed method takes into consideration the influence of the microgravity environment and the reference motion time of the joint in the literature data, which was modified by the modular arrangement of predetermined time standard (MOD) method, and the uncertainty range of the reference motion time of the joint was obtained by fitting the literature data with the least square method. For the working conditions of different astronauts and different extravehicular tasks, a comparative analysis with publicly available videos of extravehicular activities was conducted to verify the accuracy of the assessment method and provide a feasible basis for the time required for astronauts' extravehicular maintenance operations.

In order to prevent the occurrence of tail striking and optimize the take-off rotation process, this paper established a dynamic model of rotation to calculate the performance parameters such as pitch angle of the aircraft and tail ground clearance, taking into account the change of the tail striking angle during the rotation process. Using the Boeing 737-800 as an example, this study examined how the rotation rate affected the rotation distance, contrasted the tail striking dynamic limit angle with the tail striking fixed limit angle, and examined the benefits of the latter. The results show that in most cases, the optimal rotation rate ranges from 2.5 to 3.0 (°)/s. In comparison to the tail striking fixed limit angle, the rotation distance and lift-to-lift-off time are reduced by approximately 10% and 10%, respectively, with the tail striking dynamic limit angle. Finally, it is suggested that the ratio of the length of the rear fuselage to the length of the landing gear should be about 5.0, and that the ratio should be increased slightly if the tail striking dynamic limit angle is used. This study can provide pilots with a reference, improve the take-off performance of the aircraft, and increase the load of the aircraft while preventing tail striking.

To reduce carbon emissions during the departure taxiing process of outbound aircraft, this paper proposes a type of optimization model for aircraft towing operations based on a green and low-carbon approach. The flexible launch buffer time is defined, the dynamic traction launch conditions are quantified, the aircraft traction scheduling mixed integer programming model taking carbon emissions into account, and the dynamic adjustment mechanism of flight launch time is established based on the operation rules of the aircraft apron based on the taxiing state and delay of the aircraft surface. The carbon emission level of surface taxiing is reduced by combining the optimization of the traction roll-out motor-vehicle collaborative scheduling plan. The model integrates the optimization of aircraft-towing dispatch plans to reduce carbon emissions during apron taxiing. Considering the complexity and non-linearity of the model, a two-stage algorithm based on linear iteration is proposed for solving. Tianjin Binhai International Airport data is used for case studies, and the model's viability is confirmed and important model parameters are calibrated. The results of the case study indicate that, compared to the current situation, optimizing the towing departure plan reduces emissions during apron operations by 7.98%. The research findings provide decision support for achieving low-carbon operations in the airport apron area and optimizing aircraft-towing dispatch plans.

There are issues with the current analog and analog-digital hybrid chip in radio frequency (RF) conduction sensitivity modeling method models of integrated circuits for RF integrated circuit immunity model-conducted immunity modeling (ICIM-CI). These issues include the model's lack of nonlinear characteristics, significant discrepancies between the prediction and the actual circuits' performance under particular interference conditions, and the inability to perform cascade quantization sensitivity simulation. The study proposes an improved model, integrated circuit immunity model-conducted immunity modeling-nonlinear immunity behavior (ICIM-CI-NIB), an improved modeling method for ICIM-CI with analog and analog-digital hybrid chip conduction sensitivity based on the theory of multi-harmonic distortion, which significantly improves the nonlinear characteristics of the conduction sensitivity model by introducing the multi-harmonic nonlinear parameter into the immunity behavior（IB）module, which improves the ICIM-CI model from the binary judgment result model to the cascaded quantized sensitivity simulation with nonlinear quantized output response of the cascade simulation model. By adopting the ICIM-CI-NIB method, it can quickly generate the frequency domain broadband model of the chip's conduction sensitivity, have a high-precision conduction sensitivity simulation model under the wide-band large-power interference injection, and support the cascade simulation. The observed results on a common analog and analog-digital hybrid chip demonstrate that this method reduces the modeling time by approximately 98% while improving the normalized mean square error (NMSE) by 18.5 dB when compared to the ICIM-CI method.

In a complex tracking environment, existing trackers mainly face the problems of deep convolutional feature redundancy and a lack of positive samples in the target tracking process. In order to remove redundant features in deep convolutional networks, an attention mechanism model based on the fusion of the spatial domain and channel domain, which includes three continuous sub-modules: spatial self-attention, channel attention and spatial attention. On this basis, a deep convolutional network suitable for various vision algorithms is constructed. A feature enhancement technique based on feature modification is meant to extract target deep convolutional features in order to address the issues of negative feedback of redundant features and lack of positive samples. The experimental results show that in the mainstream residual networks (ResNet) object classification tasks, the addition of the feature modification module can significantly reduce the Top-1 and Top-5 error rates, and will not cause additional calculation or network structure adjustment, achieving lightweight insertion. Several tracking methods are combined with the feature modification module to improve tracking performance and address the discriminator’s over-fitting issue.

In order to optimize the scale of the tracker's output, this paper focuses on how to fully utilize the semantic information of the target obtained by image semantic segmentation in complex scenes. It also designs an image semantic segmentation network based on the optimization of the attention mechanism to optimize the target tracker's output and the input of the features, which can realize plug-and-play for various algorithms. The image semantic segmentation mask is used to obtain the rotating frame boundary of the target, and the denoising optimization of the features in the input phase of the target is carried out according to the rotating and non-rotating frame boundaries of the target to attenuate the influence of the background noise on the discriminator of the tracker. The structure of the designed network, training, calibration of the target's rotating frame, and denoising of the tracker's input features are discussed, respectively. The correctness of the target motion model in resolving the scale calibration of the target during target tracking is verified through experimental comparison analysis on public datasets OTB100, VOT2016 and VOT2018. This enhances the accuracy and resilience of target tracking.

This paper uses the simulation model verified by scale test to conduct the simulation research of 69 working conditions with different explosion point positions and different equivalent sizes, given the lack of experimental verification, weak regularity, and challenging application of the dynamic response research of concrete gravity dam under the action of contact explosion load. The time history curves of three parameters of dam bottom curtain vibration velocity, dam acceleration and damaged dam ratio of gravity dam under contact explosion load are investigated, and the law of dynamic response of dam body is preliminarily summarized. On this basis, the dimensionless numbers B and C that can correlate the explosion load parameters with the dynamic response are further proposed. The variation law and trend of the dynamic reaction of the gravity dam based on the analysis of explosion conditions are derived by integrating the parameter values change information of all simulation results. The dimensionless dynamic response law of the dam body can effectively correlate the physical damage and functional damage of the dam body, so as to support the vulnerability analysis of gravity dams, and provide a reference for the study of the damage mechanism of other engineering targets.

The UV femtosecond laser (343 nm, 260 fs) processing threshold and morphology characteristics of Kevlar 49/4211 aramid fiber/epoxy resin composite materials were studied. The area epitaxy method was used to calculate the laser removal threshold and threshold incubation effect of resin matrix, reinforced fibers, and the overall composite material in aramid fiber reinforced polymer (AFRP). The single pulse removal thresholds of the three were predicted; the impact of incident energy flux and equivalent number of input laser pulses on the quality of material processing edges was examined. The results showed that the UV femtosecond laser removal threshold and threshold incubation coefficient of Kevlar 49 aramid fiber were lower than those of 4211 epoxy resin under different pulse numbers, and the overall equivalent removal threshold of AFRP was between resin and aramid fiber. A processing morphology with uniform inlet width, neat edges, and quasi-homogeneous material removal can be achieved by choosing a UV femtosecond laser with an incident flux several times the threshold of 4211 epoxy resin and more than 100 equivalent pulse numbers and employing in-situ tapping or cyclic scanning cutting techniques. This effectively improves the processing accuracy and quality of aerospace AFRP products and satisfies their high-performance manufacturing needs.

Addressing the challenge of the lack of practical engineering methods for grid uncertainty assessment in computational fluid dynamics (CFD) simulations of hypersonic vehicles, this paper proposes a CFD data uncertainty assessment method that is convenient for practical engineering applications. The method utilizes Richardson interpolation to quantitatively assess grid uncertainty for hypersonic vehicles, and establishes a grid drawing specification for hypersonic vehicle simulations. Subsequently, grid uncertainty assessment and analysis are conducted on aerodynamic results obtained from four sets of grid calculations. The analysis results indicate that the proposed method performs well in assessing grid uncertainty for aerodynamic coefficients of waverider vehicles within a small angle of attack (below 8°) at Mach numbers of 4.95 and 7.95. Additionally, the turbulence model has an insignificant impact on this grid uncertainty assessment method. Therefore, the CFD grid uncertainty assessment technology used for hypersonic vehicles exhibits good engineering applicability and provides valuable reference and guidance for grid uncertainty assessment in other aircraft.

High-stealth flying wing layouts must meet the requirements of wideband stealth design. Traditional aerodynamic shape stealth design mainly targets the high-frequency optical range, and there is relatively little research on aerodynamic shape stealth design for wing profiles in the resonance range, making it incapable of providing effective guidance for wideband stealth design of flying wing aerodynamic shapes. To address these issues, research on wideband stealth design of wing layouts was conducted. Firstly, the basic effects method was used to study the sensitivity of wing profile parameters in different frequency bands, and it was found that the requirements for low-frequency stealth design and aerodynamic design have different sensitivity areas to the aerodynamic shape of the airfoil, and there are complex contradictions in stealth design in different frequency bands. This is the basis for a wideband stealth design model for wing layout, and NACA65,3-018 airfoils are used for aerodynamic optimization design that takes wideband stealth into account. The results show that the established wideband stealth design model can improve the aerodynamic performance of wing layout airfoils, simultaneously achieving wideband high stealth characteristics.

How to shorten the design period of aircraft is a difficult problem to be solved urgently in the field of aerospace. Aerodynamic design is the key link of aircraft design. Rapid and accurate acquisition of aircraft aerodynamic characteristics can effectively accelerate the iterative improvement of conceptual design schemes. The deep neural network modeling algorithm of “1+N” mode is used to build the mapping relationship between flow parameters, aerodynamic profile, and surface pressure distribution data based on the China Aerodynamics Research and Development Center’s current aerodynamic database. In order to improve the prediction accuracy, the point cloud segmentation PointNet++ algorithm was improved by combining the original surface grid division information of the pressure distribution data to accurately identify different components of the missile and automatically add label features of different components. The test case shows that by using the improved point cloud segmentation algorithm and the “1+N” mode deep neural network algorithm, the mean relative error (MRE) of the pressure distribution prediction of the tactical missile is basically controlled within 10%. The aforementioned approach has a high modeling efficiency, is appropriate for predicting the pressure distribution of a variety of intricate aircraft forms, and has a promising engineering application.

A graphical analysis-based search algorithm for the transfer orbit state sequence is presented to address the problem of high manual involvement when designing the transfer orbit for the Callisto orbiting mission using graphical analysis approaches. Through the analysis of graphical properties and resonance gravity assists, the variation pattern of orbit states is summarized, and an iterative search algorithm for solving the state transition sequence is proposed. The algorithm can obtain multiple transfer sequences rapidly and efficiently, as demonstrated by the simulation of the transfer orbit for the Callisto orbiting mission. The solution of the corresponding transfer orbits for the obtained sequences shows that the spacecraft can achieve the transfer from a Jovian highly elliptical orbit to the Callisto orbiting orbit within 2.13 years by consuming a velocity increment of 2.108 km/s, saving approximately 100-200 m/s of velocity increment and about 1 year of transfer time compared to other methods. The method proposed in this paper solves the problem of high manual involvement when using graphical analysis methods.

The real-time kinematic (RTK) technique has attracted extensive attention in the field of autonomous driving for its high precision and high dynamic characteristics. As a result, the quantitative assessment of RTK integrity becomes a pressing issue that needs to be resolved in order to ascertain whether it can satisfy the safety standards in this industry. However, most current RTK integrity studies focus on the improvement and optimization of user-end algorithms rather than the analysis and evaluation of overall risks from system perspectives. Therefore, in this paper, an RTK integrity evaluation method based on risk probability decomposition is proposed, offering a theoretical calculation formula of integrity risks. To verify the rationality of this conclusion, observation data from multiple observation stations with different receiver types in typical regions of mid and low latitudes are used for RTK integrity evaluation. The findings demonstrate that, in middle-latitude regions, positioning integrity changes considerably with receiver data quality; nevertheless, in low-latitude regions, ionosphere anomalies become the primary factor influencing integrity. According to the evaluation results in the paper, the integrity risk probability of RTK positioning is between 10⁻⁴ and 10⁻² based on single frequency, single constellation and medium-length baseline. The results of this paper are consistent with the general conclusion of RTK data processing, which proves that the proposed method is reasonable and can evaluate the quantitative RTK integrity effectively. Furthermore, the results of the quantitative evaluation of RTK integrity can be used as an important basis for the design of an integrity monitoring scheme.

In order to realize the integration of high-precision measurement and control of attitude vibration of a flexure spacecraft, an integrated method of attitude vibration measurement and control of a flexible spacecraft based on the distributed mounting of a magnetically suspended control sensitive gyroscope (MSCSG) and a radial basis function (RBF) neural network is proposed. Firstly, a spacecraft dynamics model with distributed mounted MSCSG on the flexible attachment is established, and the mechanism of vibration measurement and control of the flexible attachment by MSCSG is analyzed. Based on this, a neural network robust adaptive state feedback controller is designed, and the control torque output from MSCSG is realized suppression of low-frequency vibration of the flexible attachment while also measuring the deformation of the flexible attachment. The neural network approximates the nonlinear terms of the system and external perturbations brought about by distributed mounted MSCSG. The MSCSG cluster installed on the rigid body simultaneously realizes the measurement and control of spacecraft attitude. Simulation results show that the attitude control accuracy and vibration suppression effect of the proposed method are greatly improved compared with the traditional adaptive control method based on a modal observer.

Distributed array radar (DAR) has the benefits including flexible deployment and excellent spatial resolution. However, the independent clock and oscillator configurations of each radar unit, along with the instability introduced by trigger signals transmitted over the feeder link, introduce errors in time and phase synchronization, thereby reducing the accuracy of coherent synthesis in a DAR. This paper proposes a time and phase synchronization error estimation method based on space-time spectral entropy. The distance-angle space-time two-dimensional spectrum is first constructed by establishing a space-time covariance matrix with time and phase synchronization faults. Then, based on entropy theory, the correspondence between synchronization errors and space-time spectral shape uncertainty is established. The time and phase synchronization errors are estimated by optimizing the space-time spectral entropy to minimize its value. Simulation experiments validate the accuracy of the proposed method, particularly exhibiting good estimation performance under low signal-to-noise ratio.

Microstructured materials are distinguished by their lightweight and multifunctional characteristics, which have a wide variety of applications in the aerospace field. However, the stiffness properties of a single microstructural material are not adaptable to the further weight reduction requirements of an airplane. This paper presents a multiscale aeroelastic optimization method for wing structure and materials based on multiple microstructural configurations. The method is designed for macro/microstructural configurations of wings with large aspect ratios. Homogenization is used to quantify the mechanical properties of microstructured materials, and the density of macroscopic structural elements is combined to determine the total stiffness. The elastic aerodynamic force and displacement are obtained by solving the aeroelastic balance equation. Co-design of the wing structure and material is achieved based on sensitivity information. The optimization results indicate that the overall structural compliance based on multiple microstructured materials is reduced by 18.6% compared to a single microstructured material under the same load. Compared to the optimization results obtained under rigid aerodynamic forces, the effect of wing elasticity has a lesser impact on the microstructure topology configuration, while the outer segments of the macrostructure are strengthened. Furthermore, an acceptable distribution of stiffness throughout the wing may be achieved and the wing structure can be efficiently optimized under complex boundary conditions using the method described in this study.

The modern equipment development cycle is short, and health service requirements are high; and traditional design methods can no longer meet the needs of equipment development, especially the changes in the health service status of equipment, performance degradation, and the impact of external interference and uncertainty. In order to solve the above problems, this paper focuses on equipment health service and proposes a digital twin-based equipment health service framework, which aims to efficiently use the whole life cycle data to achieve rapid iteration. In order to accurately detect the equipment’s current status in real time, the framework can update the digital twin with operating condition data at various times over the equipment’s whole service life cycle. This digital twin-based approach not only simulates the service status of equipment in the model, but also enables real-time assessment of the health status of the equipment. Through this method, it is possible to better meet the needs of the development and service of modern equipment. To demonstrate the efficacy and viability of the suggested approach, this study uses the permanent magnet synchronous motor, a widely used electromechanical drive device in the aerospace industry, as an example.

The gust load is the primary source of fatigue damage for air-to-ground missiles carried on the fuselage of bomber-type aircraft. To assess the lives of these missiles, it is necessary to develop the gust load spectrum for air-to-ground missile launches. This study provided an explanation of the characteristics of air-to-ground missiles and analyzed the gust load environment during their service. Based on the typical mission profiles of air-to-ground missiles, discrete gust speed exceedance curves were collected from measured data. A discrete gust model was applied to calculate gust load responses, resulting in a family of gust vertical acceleration cumulative exceedance curves applicable to a wide range of altitudes for air-to-ground missile spectrum development. A representative curve for typical usage situations was obtained by statistical analysis. A flight-by-flight spectrum for air-to-ground missile launch was created from a 5-by-5 spectrum of gust vertical acceleration based on mission segments. This spectrum presented herein serves as an indicative reflection of the anticipated gust environment for air-to-ground missiles. This study addresses the challenge of formulating the gust spectrum for air-to-ground missiles during the design phase, particularly in situations where measured loads are lacking.

The aviation plunger pump is the core of the aviation hydraulic system, and excessive overturning of its internal parts may cause the performance of the whole system to degrade and fail. How to improve the stability of the aviation plunger pump and reduce the overturning of the parts has been the key issue in the research of its high-pressure and high-speed development. The impact of outlet pressure and plunger pump speed on cylinder overturning was investigated in this article, which created a data-driven high-fidelity dynamics model from the viewpoint of cylinder vibration displacement. Then, an improvement measure to reduce the waist groove size of the distribution plate was proposed and verified through numerical simulation. The results show that reducing the size of the waist groove on the distribution plate can improve the overturning of the cylinder body. The revised model can reflect the actual tilting state, and the relative error with the experimental measurement results is within 5%. The research findings offer theoretical and experimental support for the reliable manufacturing of batch production, as well as research suggestions for the simulation and stability studies of aeronautical piston pumps.

The evaporation of fuel droplet groups in the combustion chamber of aero engines is frequently influenced by droplet interactions; yet, it is challenging to describe the evaporation state of droplet groups based on the evaporation characteristics of individual droplets. Therefore, it is necessary to carry out experimental research on multi-droplet evaporation to explore the evaporation mechanism of multi-droplets. Firstly, an experimental platform was built and the evaporation experiment of two droplets and three droplets of aviation kerosene/glycerin arranged in a straight line was carried out by the hanging drop method. Then, combined with the theoretical and experimental data of droplet evaporation, the droplet evaporation model was established. The findings demonstrate that the rate at which numerous droplets evaporate increases as droplet spacing increases, approaching the rate at which single droplets evaporate. The evaporation rate and droplet spacing have an exponential relationship, and the overall accuracy of the model is high. In conclusion, the experimental data obtained from the experiment and the proposed droplet evaporation model effectively supplement the research content of multi-droplet evaporation characteristics,which has positive significance for the study of droplet group evaporation characteristics and the design of aero engine combustion chambers.

A collision risk assessment method based on the square Mahalanobis distance was proposed to increase the reliability of collision risk prediction results in order to address the issue that probability dilution in space target collision risk prediction reduces the reliability of collision probability. Firstly, the threshold of the square Mahalanobis distance was obtained according to the $ 3\sigma $ rule to determine whether an event has collision risk. Then, the factors such as combined hard-body radius, position error, the aspect ratio of the error ellipse, and the rotation angle were modeled and analyzed, and the model parameters of the square Mahalanobis distance were optimized to improve the computational efficiency. Finally, a collision risk prediction and evaluation model based on the square Mahalanobis distance was developed to solve the probability dilution problem. The outcome of the simulation demonstrates that the suggested approach can successfully lower the high-risk event missed detection rate.

In complex urban environments, tall buildings cause Global Navigation Satellite System (GNSS) signals to suffer from Non-Line-of-Sight (NLOS) propagation and Multipath Interference (MI), which degrade the positioning accuracy of intelligent transportation systems. The existing two-dimensional grid-based multipath modeling method has shortcomings including insufficient precision in the height direction and an overly simplistic adjustment strategy for measurement noise covariance. This article proposes a GNSS/inertial measurement unit (IMU) robust adaptive filter algorithm based on 3D grid error modeling. By dividing the height space on the basis of the existing 2D grid, fine modeling can be further achieved. In the stage of multipath error prediction, the grid-center-matching method is used to alleviate the model prediction error caused by incorrect matching. Then, we propose a filtering model selection strategy based on the multipath error predictions. Moreover, according to the robust theory, we propose a robust threshold dynamic adjustment strategy to update the measurement noise covariance adaptively. The positioning performance of GNSS/IMU integrated navigation in complex urban environments can be significantly improved by the proposed algorithm. The 3D positioning accuracy of the suggested algorithm has improved by 27.57% when compared to the 2D grid assisted GNSS/IMU robust adaptive algorithm and by 48.44% and 31.51%, respectively, when compared to the traditional GNSS/IMU tight combination algorithm and the traditional GNSS/IMU robust adaptive algorithm, according to the results of urban environment vehicle experiments.

Due to uncertainties in the design and assembly process, nonlinear factors such as clearances often exist in multiple sectors of the aerospace electro-mechanical actuator transmission system, significantly affecting the working quality of the electro-mechanical actuator. Therefore, it is essential to accurately establish a nonlinear dynamic model for the electro-mechanical actuator accurately. Moreover, electro-mechanical actuators involve multiple disciplines such as servo control, motor transmission, reduction mechanism, structural elasticity, etc., making the modeling process highly challenging. The research object in this paper is a standard ball screw-type electro-mechanical actuator. Considering the elastic stiffness of actuator transmission components and the nonlinearity of clearances at gear pairs, supporting bearings, and fork pairs, the motor dynamics and servo controllers are finely modeled. The electro-mechanical actuator system is represented by a high-fidelity multidisciplinary dynamic model that can account for nonlinearities in different sector clearances. Furthermore, simulation analysis of the electro-mechanical actuator system performance is conducted. The simulation results are compared with ground vibration test results of the actuator-rudder system. The results indicate that the multidisciplinary coupled dynamic model of the electro-mechanical actuator established in this paper can reflect the influence and degree of influence of the clearances in different parts of the mechanism. The simulation and experimental results are in good agreement, with a maximum error of less than 10%. This research lays the foundation for the digital twin study of complex aerospace electric servo systems.

In this paper, a multiple-input/multiple-output control law design method for active flutter suppression of flying-wing aircraft is proposed. The input/output relationship of the plant is used to estimate and adjust the “unknown perturbation” caused by the uncertain factors of the controlled plant. The output feedback control law is then designed. In order to validate the effectiveness of the proposed control law on flutter suppression, a high aspect ratio flying-wing layout aircraft was chosen as the research object of this paper. For the controller synthesis, the inboard ailerons served as the control surfaces, and the rigid-body pitch rate combined with the wing tip motion velocity provided the feedback. A high aspect ratio flying-wing layout aircraft was selected as the research object for this paper in order to verify the efficacy of the suggested control law on flutter suppression. The two aileron rudder surfaces on the inside of the fuselage were used as control inputs, and the rigid body pitch speed and wing tip motion speed were used as feedback signals to design the control input. The relation between velocity and damping of the closed-loop system and the closed-loop time-domain state equation is carried out. The results show that the control law design method can effectively improve the critical flutter velocity of the aircraft.

Aiming at the problem of the dynamic elastic lever arm effect caused by the forced vibration of the airborne photoelectric pod and the dynamic change of frame angle, an integrated navigation filtering method for online compensation of elastic multi lever arm error is proposed. Firstly, the dynamic model of the linear and angular vibrations of the pod as well as the elastic outer arm model induced by the vibration of the pod are established by examining the primary forms of the forced vibration of the pod. Secondly, based on the multi-arm error model, the velocity, position error and vibration angle error caused by the vibration of the pod are deduced. The elastic outer arm error model and the vibration angle error model are established and introduced into the state vector. The required requirements for each state vector’s observability are found by examining the observability of the system state vectors under various maneuvering states. This provides an adaptive adjustment foundation based on the observability for the planning maneuver scheme. The simulation results show that the root mean square error of the velocity error is reduced by 73.4% and the root mean square error of the position error is reduced by 77.4% after the elastic multi-arm error compensation, compared with the rigid arm error compensation, which effectively improves the accuracy of the pod integrated navigation.

Due to the long experimental period and complex operating conditions, it is necessary to study the precise prediction of membrane performance using limited data in airborne hollow fiber membrane separation performance experiments. An artificial neural network-based prediction model for hollow fiber membrane separation performance has been developed. Error back propagation (BP) neural networks, Elman neural networks, and genetic algorithm optimized BP (GA-BP) neural networks have been constructed to investigate the variation of airborne hollow fiber membrane separation performance with bleed pressure, flight altitude, and bleed flow rate. Compare and predict the performance of hollow fiber membranes under different algorithms. The research results indicate that the three network algorithms constructed can accurately predict the separation characteristics of hollow fiber membranes, with an mean absolute percentage error (MAPE) of less than 1%. With an average prediction accuracy 1.43% greater than the other two, GA-BP neural networks typically produce the best prediction results when selecting 5%, 10%, and 20% of the complete dataset. Based on the research results, suitable algorithms can be selected in practical work to process membrane experimental data and efficiently analyze performance.

To address the issues of a low number of levels and poor torque ripple suppression effect in existing asymmetric bridge power converters for switched reluctance motors (SRM), a direct instantaneous torque control (DITC) based on a novel multi-level power converter is proposed. Firstly, a bridge arm shared five-level power conversion topology was derived for the SRM. Control flexibility was increased by achieving multiple-level combinations with the least number of power devices by providing shared bridge arms both vertically and horizontally. Secondly, an improved DITC strategy with limited switching states is developed to eliminate the switching state disorder in the commutation overlap zone caused by shared bridge arms. On this basis, various factors such as torque error, rotor position, and capacitor voltage balance are comprehensively considered to formulate conduction rules for each phase to minimize motor torque ripple. The suggested bridge arm shared multilevel torque control not only lowers current and torque ripple but also enhances motor copper loss and operating efficiency, according to comparative simulation and experimental results with traditional asymmetrical bridge power converters.

Aiming at the imbalance between the accuracy and speed of fire-and-smoke detection in real-world scenarios, this paper proposes a fire-and-smoke detection algorithm that strengthens spatial feature extraction and multi-scale feature fusion. We improved the extraction of high-level semantic information from the backbone network by embedding the receptive field convolutional attention module into it, enhancing the model’s feature extraction capability. Additionally, we introduced an enhanced strong feature fusion network to further strengthen the fusion of low-level spatial information and high-level semantic information, thereby improving model accuracy. In order to satisfy real-time needs, we additionally used the partial convolution (PConv) module to lightweightly enhance the detection head and backbone network, lowering the model’s parameter count and memory access without compromising accuracy. Furthermore, we adjusted the regression loss function to enhance the model's detection capabilities. According to experimental results, the suggested algorithm improves the mean average precision at IoU threshold 0.5 (mAP50) and 0.5:0.95 (mAP50:95) by 2.1% and 2.9%, respectively, demonstrating its superiority in the field of fire-and-smoke detection. Additionally, the mAP50 and mAP50:95 on the Pascal VOC 07+12 public dataset are increased by 1.4% and 2.4%, respectively, demonstrating the algorithm’s good generalization performance.

This study starts by examining the interconnection topology of integrated modular avionics (IMA) and two dynamic reconfiguration mechanisms: resource backup and resource preemption, in order to address the challenge of modeling and evaluating the dependability of IMA under dynamic reconfiguration. Subsequently, based on the operational principles of IMA and dynamic reconfiguration mechanisms, this paper extends a multi-state system with common bus performance sharing while considering the performance allocation problem for IMA under dynamic reconfiguration. Simultaneously, an algorithm about IMA reliability assessment under dynamic reconfiguration mechanisms based on the universal generating function (UGF) is proposed to quantitatively evaluate the reliability of IMA under different dynamic reconfiguration mechanisms. In order to confirm the viability of the suggested extended model and reliability assessment method, a case study is finally carried out on the IMA in the helicopter, examining the effects of variations in the number of standby processing units on the system’s dependability and suggesting an ideal design solution.