Browse Topic: Electrical, Electronics, and Avionics

Items (60,536)

A Spacecraft Targeting Method Based on 3D Imaging and Point Cloud Alignment

2026-99-2301

To be published on 10/30/2026

Spacecraft are now required to possess all-weather, zero-window, and support-independent launch capabilities. Consequently, research on spacecraft targeting must continue to focus on improving convenience and efficiency. In this paper, a spacecraft targeting method based on point cloud alignment is proposed. Firstly, external Triple Self-targeting Inertial Measurement Units are utilized to complete self-targeting. Secondly, the 3D point cloud of the spacecraft is obtained by binocular imaging technology, and an attitude transformation matrix is obtained by aligning the measured point cloud with the standard point cloud. Finally, the installation relationships between the binocular camera and the Triple Self-targeting Inertial Measurement Units, as well as between the guidance system and the spacecraft body, are determined. Combining these installation relationships with the attitude transformation matrix, the targeting results from the Triple Self-targeting Inertial Measurement Units are transferred to the spacecraft guidance system. In the simulation experiment, the maximum standard deviation of the three directions after multiple targetings is 9′9″, which can meet the requirements of spacecraft targeting. The method proposed in this paper fully retains the self-targeting advantages of the Triple Self-targeting Inertial Measurement Units without external support, while making spacecraft targeting more flexible and rapid. If applied to actual equipment, it will effectively enhance the adaptability of spacecraft equipment to environments.

Wu, Bingjie, Wang, Xiaozi, Zhang, Wei, Li, Mingrun

Practical Predefined-Time Observer for Hypersonic Vehicle Attitude Control

2026-99-2328

To be published on 10/30/2026

This article proposes a practical predefined-time observer for hypersonic vehicle attitude control. To improve the convergence performance, a practical predefined-time condition is introduced into the observer. This design provides high flexibility and allows for a precise prediction of the maximum settling time, which does not depend on initial conditions. Moreover, the actual settling time can be adjusted by modifying the preset parameters. Numerical simulations are carried out to confirm the theoretical properties and to illustrate the observer’s effectiveness.

Zhang, Yufei, Yang, Hua, Chen, Weifang

Geosynchronous Orbit Satellite Maneuver Detection Method Based on Dual-Model Parallel GLRT

2026-99-2367

To be published on 10/30/2026

Ground-based radar can obtain high-precision radial velocity measurements through coherent integration Doppler processing, providing an important data source for geostationary orbit (GEO) satellite maneuver detection. An orbital maneuver causes a persistent mean shift in radial velocity residuals, whereas attitude motion or tumbling due to malfunction induces velocity scintillation, which manifests as a sudden variance amplification. Since both anomalies cause velocity residuals to deviate significantly from normal levels, a single detection model can easily misclassify one as the other. To address this problem, a maneuver detection method is proposed that combines precise orbit determination with a dual-model parallel Generalized Likelihood Ratio Test (GLRT). A high-accuracy initial state is first obtained through the Gibbs method and weighted least squares. Two detection models are then constructed based on distinct residual characteristics: mean shift for maneuver detection and variance amplification for scintillation detection. An ε-contaminated Gaussian mixture GLRT detector is introduced to suppress the influence of outliers. Combined with Monte Carlo threshold calibration and a maneuver-priority competitive decision strategy, the method effectively distinguishes between the two anomaly types. Simulation results demonstrate that under strict false alarm constraints, the detection probability for both maneuvers and velocity scintillation exceeds 0.9, effectively eliminating mutual misclassification. The method achieves stable detection of weak maneuvers lasting tens of seconds with radial velocity increments of approximately 0.03 m/s, demonstrating strong engineering application potential.

Chen, Xiaoyi, Tang, Fujie, Zhang, Xianbo, Cong, Yang, Gao, Kaili, Chen, Defeng

Target Positioning Method for Unmanned Aerial Vehicle Tracking Based on Beidou

2026-99-2353

To be published on 10/30/2026

In order to support successful tracking and monitoring tasks in different environments, drones need to quickly and accurately identify targets. This paper studies a drone combat target positioning method, with a focus on the BeiDou satellite navigation system and its performance under different environmental conditions. A complete system has been designed, including flight control system, environmental sensors, data transmission and processing system, aiming to achieve precise positioning and target tracking of unmanned aerial vehicles. This paper uses on-site testing to explain the coverage range and stability positioning accuracy of ranging signals in different environments. Experimental data shows that the BDS system has good positioning error performance for unmanned aerial vehicles, with an average error of 46.52 cm. BDS positioning time is usually short, with a maximum positioning time of 780ms and a minimum positioning time of 224ms. However, the BDS system still needs improvement in terms of dynamic environmental adaptability, multi-target tracking, anti-interference ability, and adaptability to harsh environmental conditions. Future work will focus on algorithm optimization, hardware technology improvement, and data security to enhance system performance.

Tao, Zhiping, Zhang, Chenghao, Hou, Bo, Song, Juan, Yuan, Peng, Liu, Xianyang

Optimization Method for UAV Scheduling Based on Beidou

2026-99-2352

To be published on 10/30/2026

Currently, UAVs are increasingly used in remote sensing monitoring, logistics and distribution, emergency rescue and recreational aerial photography, etc., which puts higher requirements on wireless networks, i.e., how to allocate limited wireless resources in real-time, efficiently and fairly to ensure that a large number of UAVs can work together in complex environments and maintain high-quality communication services. This paper investigates an optimisation method for UAV scheduling based on the BeiDou satellite navigation system, which improves the accuracy and efficiency of UAV mission execution by utilising the high-precision positioning and time synchronisation services provided by the BeiDou system. The results of the experiment showed that as the number of missions increased from 10 to 50, the average mission completion time increased from 8.3 to 12.5 minutes, the average speed increased from 30 to 34 km/h, the average energy consumption increased from 0.8 to 1.2 kWh, the average distance flown increased from 12 to 22 km, the communication success rate decreased slightly from 99.2 to 98.2%, the resource utilization rate increased from 85.1% to 87.5%. These results show that the method performs well in terms of task completion time, energy consumption, communication success rate and resource utilisation, which provides strong support for the application of UAVs in the fields of logistics and distribution, emergency rescue, and so on.

Ji, Hongbin, Jiang, Kexin, Zhang, Dongxue, Bai, Yanhui, Liu, He, Zhang, Shuangna

Modeling and Supervisory Reconfiguration of a Dissimilar Redundant Flight Control System under Common-Mode Failures

2026-99-2358

To be published on 10/30/2026

Common mode failures may simultaneously impair multiple redundant channels, threatening redundancy independence and flight safety. Existing studies have mainly focused on safety architecture analysis, analytical redundancy design, and reliability allocation, while systematic research on failure propagation in closed loop flight control systems remains limited. In particular, insufficient attention has been given to channel degradation divergence and unified modeling for online reconfiguration. To address these issues, a unified framework for common mode failure propagation is established. On this basis, a dissimilar redundant control architecture is constructed by combining a state feedback channel and a proportional integral derivative channel. A supervisory reconfiguration strategy is developed by integrating multi source degradation assessment, credibility based control allocation, and hysteresis based hybrid mode switching. Simulation results from a longitudinal closed loop model show that the proposed strategy captures the divergent degradation of heterogeneous channels and triggers coordinated reconfiguration during fault propagation. As degradation increases, the control system transitions from baseline operation to weighted fusion and then to a safety preserving degraded mode, with control authority shifted toward the more credible channel. Compared with the case without reconfiguration, the proposed method yields smoother control responses and improved tracking performance.

Peng, Cheng, Guo, Jianwei, Xu, Youping

Adaptive Regulation of Micro Closed-Loop Space Station Ecosystems Using Proximal Policy Optimization

2026-99-2338

To be published on 10/30/2026

Long-term orbital missions require highly independent life-support systems, maintaining ecological stability under limited external support and subsystem attenuation. Conventional strategies are typically based on fixed threshold rules or deterministic mass-balance models and cannot adapt to disturbances or multi-objective trade-offs between resource safety and environmental load. This paper proposes a reinforcement learning framework for managing micro closed-loop ecological systems under reliability decay and environmental constraints. A reinforcement learning controller based on Proximal Policy Optimization (PPO) is trained to learn stable regulation policies with random disturbances. A dynamic state model is constructed to describe the coupled and time-sequential elements, ranging from water storage, oxygen concentration, energy reserve, subsystem reliability to ecological load. Degradation propagation is integrated into the transition dynamics through reliability-dependent recycling efficiency. Simulation experiments demonstrate that the proposed policy maintains all resource variables within the safety margins while reducing ecological imbalance compared with traditional strategies. Consequently, the proposed framework provides an expandable control architecture for independent regulation of space station life-support systems.

Zhang, Heyang, Wu, Meixin, Xie, Junze

A Two-Time-Scale Model Predictive Control Framework for Precise Strain Control of Shape Memory Alloys

2026-99-2307

To be published on 10/30/2026

Precise strain control of Shape Memory Alloys (SMAs) is challenging due to their strong nonlinearity, hysteresis, and thermomechanical coupling. To address the needs of real-time control system design, this paper proposes a two-time-scale decoupling control framework based on Model Predictive Control (MPC). First, a transient physics-based model considering one-dimensional heat conduction and latent heat under electrical excitation is established and then reduced to a lumped-parameter model suitable for controller design. To balance prediction accuracy and computational efficiency posed by the model nonlinearity, an innovative two-time-scale MPC strategy is designed: a fast inner-loop temperature MPC is used for efficient thermal management outside the phase transformation plateau, while it switches to an outer-loop strain MPC for precise trajectory tracking and hysteresis compensation during the critical phase transformation period. Simulations demonstrate that this strategy significantly reduces computational burden while maintaining accuracy. Compared to a single-time-scale MPC, the proposed method reduces the settling time by 26%, the root mean square error by 16%, and the single-step computation time by 19.6% for a step reference strain. This work provides an effective and engineering-friendly solution for high-performance, real-time control of SMA-based actuators.

Ding, Shaozhe, Liu, Longbin, Zhang, Shifeng

PINNs-Based Parameter Estimation for Gradient Descent

2026-99-2303

To be published on 10/30/2026

To address the joint estimation of state and parameters for fixed-wing aircraft in wind gradient environments, this paper proposes a high-precision identification method based on a physics-informed neural network. By integrating wind gradient models with flight dynamics equations, a multi-objective optimization framework combining data-driven approaches with physical constraints is established. Employing polynomial time embedding and a dual-branch network architecture, coupled with a dynamic training strategy, simultaneous estimation of flight state and wind gradient parameters is achieved. Experimental results demonstrate that this method achieves approximately 7% parameter estimation error while accurately reconstructing unobserved attitude information. Requiring only four-dimensional observation data, this approach provides an effective solution for wind gradient glide navigation in low-cost aircraft.

Wu, Ting, Bai, Zhuoyue, Gao, Xianzhong

Research on Failure Mode Verification of Civil Aircraft Based on Reliability Data

2026-99-2314

To be published on 10/30/2026

To verify the accuracy and completeness of FMEA and FTA, this paper proposes a comprehensive failure mode verification method based on reliability data. This method collects multi-source reliability data and identify actual failure modes occurring in equipment during operation firstly, thereby compiling a list of actual failure modes. Next, this list is compared with the FMEA report from the design phase to verify the failure modes themselves. A deeper qualitative verification of the causes, mechanisms, and impacts of the failures is then conducted, leading to the correction and updating of the FTA logical structure to ensure consistency between the safety analysis model and the actual failure logic.Building upon the qualitative verification, the method proceeds to quantitative verification. A comparative analysis is conducted between the actual failure rates of the failure modes and the estimated values in the FMEA. Using the validated failure rate data, the probability of the FTA top event is recalculated and compared against the safety target probability specified in airworthiness regulations to achieve quantitative verification of the failure condition probability. As a core innovation and enhancement of the method, this paper introduces sensitivity analysis. Through tools such as calculating Birnbaum importance and plotting tornado plots, it quantitatively assesses the sensitivity and impact of uncertainties in the failure rate parameters of each bottom event on the probability of failure condition, thereby identifying the key failure modes. The research findings provide theoretical support and a practical engineering foundation for evaluating the actual reliability level of the aircraft fleet, improving the accuracy of safety analysis during the design phase, and guiding preventive maintenance and design improvements.

Han, Bing, Li, Haiwei

Configuration Design and Implementation of the SuperView Neo 2 Satellite System

2026-99-2370

To be published on 10/30/2026

With the rapid advancement of spaceborne Synthetic Aperture Radar (SAR) technology, distributed Interferometric SAR (InSAR) has emerged as the definitive method for high-precision global topographic mapping. This study focuses on the SuperView Neo 2 (SVN2) satellite system, developed by China Siwei Surveying and Mapping Technology Co., Ltd., detailing its technical configuration design and operational implementation within the next-generation commercial remote sensing sector. Equipped with X-band high-performance phased-array SAR payloads, the SVN2 system is engineered for the high-efficiency production of global 1:25,000-scale Digital Surface Models (DSM) while providing millimeter-level surface deformation monitoring through Differential InSAR (DInSAR) capabilities. The paper emphasizes a systematic design approach that integrates four coupled elements: mission objectives, system performance, orbit design, and formation geometry. To satisfy the rigorous requirements of interferometric mapping, the system adopts a sun-synchronous, strictly repeating frozen orbit with a 200-meter radius “pipeline” control strategy. By implementing flexible formation configurations for high and low latitudes, SVN2 achieves the triple synchronization of time, space, and phase necessary for sub-meter resolution and high vertical accuracy. Using the SVN2-03/04 cluster as a primary case study, the results demonstrate that through a sequence of over 40 precise orbital maneuvers, the dual-satellite system successfully established a close-range fly-around formation within the 100-meter-level pipeline just seven days after launch. This operational success represents three major technical breakthroughs for Chinese commercial radar satellites: high-precision strictly repeating orbit maintenance, stable close-range formation flying, and fully autonomous inter-satellite cooperative control. The SVN2 constellation provides a robust, all-weather mapping guarantee for the global geographic information framework and supports diverse applications in geological hazard detection and social economic planning.

Tang, Ke, Qiao, Yishi, Huang, Tao, Zhang, Guoliang, Zhao, Di, Zhang, Zhongze

Hybrid TDOA/AOA Localization and Tracking Method for Multi-Station Cooperative Passive UAV Detection

2026-99-2362

To be published on 10/30/2026

To address geometric precision degradation and tracking divergence in single TDOA or AOA systems, this paper proposes a multi-station UAV tracking method integrating TDOA/AOA hybrid localization with nonlinear filtering to overcome precision degradation and tracking divergence in single-sensor systems. A Weighted Least Squares (WLS) method via pseudo-linearization provides reliable initial coordinate estimations, followed by an Unscented Kalman Filter (UKF) for continuous trajectory tracking. Simulations demonstrate that the proposed architecture eliminates network blind zones and robustly handles abrupt target maneuvers. Compared to the standard EKF, it rapidly suppresses peak RMSE to ~22 m and achieves a steady-state error of 10-15 m. This approach offers a high-precision, rapidly converging passive tracking solution for urban low-altitude defense.

Shen, Huaming, Wang, Yiting, Lv, Zhenbin, Zhou, Haosu, Lu, Wenbin

A Novel Sliding Mode Control Strategy for an Electro-Hydrostatic Actuator Based on Singular Perturbation Theory

2026-99-2355

To be published on 10/30/2026

The Electro-Hydrostatic Actuator (EHA) is a high-order nonlinear system with notable parameter uncertainties and coupled electro-hydraulic dynamics. To achieve high-precision position control, this paper proposes a novel sliding mode control strategy based on singular perturbation theory (SPT-SMC). The singular perturbation theory is applied to achieve a rational order reduction of the EHA system, simplifying controller design while mitigating unmatched disturbances. A sliding mode controller incorporating a new reaching law is then designed based on the reduced-order system. Finally, simulations studies are executed to assess the SPT-SMC. The results demonstrate that, compared with widely used control strategies, the proposed approach achieves faster response while ensuring stability.

Wang, Junyi, Wang, Wenshan, Hao, Juan, Bai, Liangliang, Du, Shihui

ME-YOLO: Aircraft Corrosion Defect Detection Model Based on Multi-Directional Edge Perception and Multi-Scale Feature Fusion

2026-99-2321

To be published on 10/30/2026

Detecting surface defects on aircraft is critical to safety in the aerospace field. The current detection methods suffer from low efficiency, high false positive rate and poor adaptability to complex environment. Although the existing target detection algorithms have achieved notable performance in this field, at present, many models in this field still face considerable challenges. For example, due to the constraints of hardware platforms, machines that can be deployed on edge devices such as UAVs and robots need lightweight and easy-to-deploy models, but lightweight designs often a certain degree of accuracy degradation. At the same time, most of the surface defects of the aircraft are small targets, which impose higher demands on the model. In order to solve these limitations, this paper proposes an improved YOLO model ME-YOLO (multi directional edge fusion YOLO) based on edge enhancement and multi-scale fusion. Firstly, the EdgePyramid module is designed, which uses the rich edge responses extracted by the Multi Orientation Attention Sobel (MOAS) module and through the three-layer cascade pool structure to construct the three-scale edge features aligned with the backbone network. Then, the EdgeEnhance module is introduced to adaptively fuse the conventional convolution features and edge features at the corresponding resolution, effectively enhancing the sensitivity of the model to the corrosion boundary. Experimental results show that, compared with the baseline YOLOv11, ME-YOLO improves mAP@0.5 by 2.7% and simultaneously increases Recall by 2.3%, which helps reduce missed detections of corrosion targets. Although the introduction of multi-directional edge priors brings a slight increase in parameters (+3.66M) and a minor decrease in inference speed (--8.6%), the model still maintains a real-time detection capability of 97.54 FPS, meeting the deployment requirements for edge devices such as UAVs. This strategy of trading a modest computational overhead for improved detection rates in safety-critical tasks offers a potentially practical and robust alternative for intelligent operation and maintenance of aviation equipment.

Zhou, Xing, Li, Yuan

Vibration–Acoustic Emission Fusion and STFT–MFDCNN for Fault Diagnosis of Bearings in the Aero-Engine

2026-99-2315

To be published on 10/30/2026

Rolling bearings are key components of aero-engine rotor systems, and their degradation can trigger abnormal dynamics and safety risks. Unplanned bearing failures increase downtime and inspection cost; early detection enables condition-based maintenance and reduces conservative replacement. Early faults are weak, intermittent, and often masked by operating variability, so diagnosis based on a single sensing modality is usually incomplete. Vibration signals describe global dynamic behavior and resonance responses, whereas acoustic emission (AE) captures micro-damage processes and short impulsive events. Given the differences in bandwidth and noise sources, combining vibration and AE can provide a fuller description of bearing states. This study proposes an STFT–MFDCNN framework for bearing fault diagnosis using fused vibration–AE information. First, synchronized vibration and AE signals are converted into time– frequency images via the short-time Fourier transform (STFT), preserving spectral content and temporal non-stationarity in a unified representation. Second, a dual-branch multi-scale feature dense convolutional neural network (MFDCNN) is constructed. In each branch, parallel convolutions with different kernel sizes capture fault patterns across receptive fields, and dense concatenation connects all preceding feature maps to improve feature reuse and gradient flow. Third, global average pooling generates compact feature vectors for both branches, which are concatenated in a fusion head and classified by a Softmax layer. Experiments on a vibration–AE dataset built in this work show that the proposed method reaches 97.78% identification accuracy. Compared with dual-branch fusion baselines based on AlexNet, ResNet, and a shallow CNN, accuracy increases by 3.34%, 4.45%, and 5.00%, respectively. Compared with single- modality models (Only-AE and Only-Vib), accuracy improves by 14.71% and 6.77%.

Li, Zhichuang, Wang, Xingfeng, Zhang, Yanpu, Yang, Guoan

Synergistic Enhancement of Impact Resistance in SiCw-Polymer Composite

2026-99-2325

To be published on 10/30/2026

Addressing the performance degradation bottleneck of conventional impact-resistant materials under complex operating conditions, and the limitation of existing research focusing primarily on enhancing single properties while neglecting material equilibrium, this study employs silicon carbide whiskers (SiCw) as the reinforcing phase. Through surface modification techniques, SiCw/celluloid and SiCw/polyimide dual-polymer composite systems were constructed and systematically investigated. Surface modification of SiCw was achieved using titanate and silane coupling agents. Through mechanical testing and X-ray photoelectron spectroscopy (XPS) characterisation, the effects of SiCw loading and modification treatments on composite mechanical properties and interfacial bonding were analysed. Results indicate that SiCw introduction significantly enhances the tensile, flexural, and impact strength of the polymer matrix, with optimal addition ratios identified: 6% for the celluloid system and 1.0% for the polyimide system. Surface modification further optimises toughening effects by reducing surface oxide layers and impurities on SiCw particles while strengthening interfacial bonding. This study provides practical guidance for the system design of high-performance impact-resistant composites. The resulting materials hold broad application prospects in sectors demanding high structural impact resistance, such as aerospace and transportation.

Xue, Kaiming

Research on Fault Diagnosis of Civil Aircraft Engine Thrust Reverser Deployment Overspeed with Small Samples Based on Semi-Supervised Learning

2026-99-2319

To be published on 10/30/2026

Civil aircraft engine thrust reversers are easy to have overspeed faults when they are being deployed, therefore this condition is very important for flight safety and maintenance work efficiency. The low sampling frequency, about 1Hz, of onboard data recording systems, for example WQAR, together with external environment interferences such as sudden strong winds, therefore causes a quite large number of wrong and missed alarms in fault data. Therefore, the labeled data that can be obtained are not only few in number but also have low quality. In this article, a fault diagnosis framework which is based on semi-supervised learning is put forward to solve the problems of high labeling expenses and fault diagnosis under the condition of small samples. This framework makes use of a small amount of labeled data and a large amount of unlabeled data. It uses Semi-Supervised Support Vector Machines, Semi-Supervised Neural Networks, and Semi-Supervised Random Forests for dynamic self- training to realize classification of three kinds of states: “Normal State,” “Low-Speed Thrust Reverser Deployment Exceedance,” and “Medium-Speed Thrust Reverser Deployment Exceedance”. The experiment outcomes show that the Semi-Supervised Neural Network model which uses self-training method has the highest classification accuracy on the test set, achieving 94.8%. Further robustness analysis, such as label noise and feature noise disturbance experiments, indicates that this model displays the smallest performance lowering under noisy environments, hence it represents the best overall performance. This research gives an effective method for the diagnosis of engine thrust reverser function problems in an effective and strong way.

Yuan, Xiaojiang, Liu, Nan

Feature Fusion and CNN-Based Acoustic-Emission Diagnosis for Aero-Engine Initial Damage

2026-99-2316

To be published on 10/30/2026

Acoustic emission (AE) signals generated by incipient aero-engine damage are usually weak, intermittent, and highly dependent on sensor position, which makes diagnosis based on a single channel unreliable. To address this issue, this paper develops a multi-sensor AE diagnosis framework that combines entropy-guided feature fusion with a convolutional neural network (CNN). Time-domain and frequency-domain descriptors are first extracted from AE waveforms collected by multiple sensors. The consistency of each feature type across channels is then evaluated using feature entropy. Features with lower entropy are considered more informative for damage discrimination and are therefore retained for fusion. Based on the normalized channel contributions of these selected features, an adaptive weighted fusion matrix is constructed and used as the input to the CNN. The method is validated on a self-built aero-engine structural test rig equipped with 12 AE sensors. Fifteen representative initial-damage cases are designed, including signal excitation, crack-like excitation simulated by pencil-lead break (PLB), and impact-induced micro-deformation cases. Experimental results show that the proposed framework achieves a best average classification accuracy of 96.80% and provides more balanced class-wise performance than conventional machine-learning and deep-learning baselines. Additional noise-injection tests further indicate that the method maintains good robustness over a wide signal-to-noise-ratio range. These results suggest that entropy-guided multi-sensor AE fusion is a practical solution for intelligent diagnosis of aero-engine initial damage.

Jin, Yucheng, Han, Cong, Zhang, Leixuan, Yang, Guoan

High-Temperature Sealed Disc Spring Compression Load-Holding Performance Testing and Material Parameter Calibration

2026-99-2322

To be published on 10/30/2026

In high-temperature sealed assemblies, disc springs provide sustained preload to suppress leakage. However, at elevated temperatures, material creep and stress relaxation may reduce elastic recovery and accumulate permanent deformation, thereby degrading sealing reliability. Because the material properties of commercially procured disc springs vary with suppliers and production batches, they are difficult to apply directly in the analysis of high-temperature components. This study investigates Inconel-800 disc springs. Sustained compression tests were conducted at 600–900 °C to systematically obtain load–time responses and the temperature dependence of permanent deformation. The results show that, as temperature increases, the rate of force decay accelerates significantly and the residual load decreases markedly. At 800 °C and 900 °C, the permanent deformation approaches the compression limit, indicating that time-dependent material deformation becomes the dominant factor. Based on these results, a finite-element model consistent with the test conditions was established. Material creep parameters were calibrated from component-level compression data by minimizing the discrepancy between simulated and experimental curves. The identified parameters were validated under different temperature conditions and loading cases. The simulation results agree well overall with the experiments, and the errors remain within engineeringly acceptable limits. This study provides baseline data and an engineering method for obtaining material parameters to support material selection and structural optimization of high-temperature sealed disc springs.

Yang, Yuyao, Cai, Xianhui

Trajectory Optimization for Non-Cooperative Target Spacecraft Based on Sequential Convex Programming

2026-99-2330

To be published on 10/30/2026

Autonomous operations near space targets that do not cooperate present great challenges because position and attitude planning must be linked. A target of this type lacks navigation aids or communication systems, which makes relative motion planning much more difficult. Although methods based on convex optimization work well for problems with three degrees of freedom, like planetary landing, their use in integrated operations with six degrees of freedom is limited by constraints that are not convex. To solve this problem, this paper proposes a new framework for servicing spacecraft using sequential convex programming. The primary contribution is the extension of convex methods to high dimensional trajectory planning while adding a robust trust region constraint based on trajectory deviation. This mechanism reduces the chance of finding no solution during convex approximations. It improves reliability and convergence when solving complex problems. The approach first creates a continuous optimal control problem with an index that balances energy use and the angle of the line of sight. Nonlinear dynamics are simplified using first order Taylor expansions. Constraints for the field of view and collision avoidance are modeled using spherical and ellipsoidal regions. The non convex problem is changed into a sequence of discrete convex subproblems and solved through iterations. Numerical simulations show that the servicing spacecraft can safely approach the target with stable attitude while meeting all physical limits. The method using sequential convex programming with a trust region demonstrates great potential in optimizing computational efficiency alongside strong numerical stability, laying a solid foundation for future real-time autonomous trajectory generation in complex orbital environments.

Wang, Yuzhi, Yao, Zihao, Wang, Qian, Han, Ziwen, Zhang, Chao, Zhang, Yumei, Li, Shunli

A Study on Energy Management Strategies for Range-Extended UAVs Based on A-ECMS

2026-99-2333

To be published on 10/30/2026

An adaptive equivalent consumption minimization strategy (A-ECMS) is proposed for a hydrogen fuel-cell range-extended UAV hybrid power system composed of a PEM fuel cell, a lithium-ion battery and a supercapacitor. The equivalent factor is updated online by a PID-based SOC feedback mechanism to improve the adaptability of conventional ECMS under variable flight loads. Under the same mission profile and the same initial and terminal SOC constraints, A-ECMS consumes 4.6277 g of hydrogen, reducing hydrogen consumption by 46.90%, 67.51% and 58.42% compared with PID control, conventional ECMS and state-machine control, respectively. Fuel-cell power fluctuation and battery SOC deviation are also reduced.

Li, Mengda, Liu, Zitong

Multiscale MD-FEA Investigation on Large-Deformation Mechanical Behaviors of Hydrogels Modified with Ionic Liquids

2026-99-2306

To be published on 10/30/2026

Hydrogels incorporated with ionic liquids (ILs) demonstrate broad application prospects in the fields of wearable flexible sensors and human physiological monitoring. Nevertheless, the intrinsic correlation between microscale intermolecular interactions and macroscale nonlinear mechanical responses under large tensile deformation has not yet been fully clarified. In this work, a coupled numerical framework integrating molecular dynamics (MD) and finite element method (FEM) was adopted to explore the mechanical behaviors of poly(acrylic acid)/N,N’-bis(acryloyl)cystamine/ionic liquid (PAA/NB/IL) hydrogels under large deformation. A series of molecular models with graded IL doping levels were established to characterize the structural rearrangement and energy change characteristics during uniaxial tension. Numerical results from MD simulations reveal that the addition of ILs strengthens non-covalent interactions such as van der Waals forces and electrostatic attractions, and further drives covalent bond stretching and angular deformation to bear external loads under high-strain conditions. Meanwhile, a generalized Maxwell constitutive model, whose Prony-series parameters were fitted by experimental stress-relaxation data, was introduced into FEM numerical calculations. The simulated quasi-static tensile properties are highly consistent with experimental measurements. Comprehensive analysis demonstrates that the mechanical enhancement induced by ILs stems from the synergistic effect of covalent structural deformation and the reconstruction of non-covalent interactions, which offers valuable theoretical guidance for the development of hydrogel materials applied in large-deformation engineering scenarios.

Li, Sitan

Study on Interface Design and Functional Tolerance of Structured Composites for Zero Thermal Expansion Performance

2026-99-2323

To be published on 10/30/2026

Structured zero thermal expansion composites demonstrate significant application potential in thermal deformation control for high-end equipment, particularly aerospace precision instruments, due to their unique ultra-low thermal expansion properties. However, during the transition from theoretical design models to manufacturing models, uncertainties at connection interfaces and manufacturing errors alter the thermal deformation coordination between heterogeneous components within the material, leading to significant degradation of its zero thermal expansion performance. To address this issue, this study focuses on lattice-structured zero thermal expansion composites derived from topology optimization. First, the connection interface design between heterogeneous lattice components was completed. Subsequently, two typical manufacturing errors—dimensional errors and assembly tolerances—were incorporated into the manufacturability model. Using Monte Carlo random sampling, a mapping relationship was established between the random and discrete manufacturing errors and the composite's equivalent thermal expansion performance. Building upon this foundation, a yield rate metric based on functional tolerance concepts was introduced. This metric was used to evaluate how different interface configurations affect the stability of zero thermal expansion performance. The results demonstrate that, under identical manufacturing precision conditions, different interface configurations exhibit significantly varying thermal expansion stability in response to manufacturing errors characterized by structural parameter dispersion. This research provides valuable insights for translating theoretical designs of such functional materials into manufacturable solutions that meet thermal expansion performance requirements.

Yan, Shaojie, Yang, Zihao, Zhang, Yongcun

Research on Dynamic Force Formation in Aerospace Search and Rescue Missions Based on an Improved Contract Net Algorithm

2026-99-2310

To be published on 10/30/2026

hierarchical biddingjoint evaluation

Lv, Nan, Liu, Ruirui, Li, Donghao, Liu, Xin

Research on Trajectory Planning Based on High-Precision Improved C-W Equation

2026-99-2329

To be published on 10/30/2026

In today’s space environment, the number of tumbling objects such as abandoned satellites, uncontrolled spacecraft, and space debris has increased dramatically. Therefore, in-orbit services such as maintenance, recovery, and capture of rolling targets is an urgent topic that needs to be explored. In this paper, for the mission of spacecraft approaching the tumbling target at close range, the trajectory planning method is studied by using the high-precision relative equation of motion as the orbital dynamic model. Firstly, the error of the relative equation of motion of the center of mass is analyzed, and a method to improve the accuracy of the C-W (Clohessy-Wiltshire) equation is considered to construct a higher-precision orbital dynamics model. The constraints such as attitude dynamics, line-of-sight range, control ability and keep-out zone are mathematically described, and the optimal control problem of pose synchronization is established. On this basis, this paper takes the traditional C-W equation and the improved C-W equation as the relative motion models of the centroids, and, based on the adaptive pseudospectral method and sequential convex optimization, solves the position and attitude synchronization planning problem. This article focuses on analyzing the errors of the C-W equation before and after improvement, as well as the impact of linearization on these errors, studies the accuracy improvement brought by the improved equation and the safe obstacle avoidance of position and attitude trajectories, and compares the advantages and disadvantages of the two methods. Simulations verify the accuracy and efficiency of this trajectory optimization algorithm and confirm the accuracy improvement of the improved C-W equation. The improved C-W equation has significant application potential in trajectory planning tasks in complex orbital environments with high-precision requirements.

Zhang, Chao, Yao, Zihao, Gou, Jingwen, Zhang, Yumei, Wang, Yuzhi, Li, Shunli

Design Consideration for Field-Loadable Software of Civil Aircraft

2026-99-2312

To be published on 10/30/2026

With the increasingly widespread application of airborne software in civil aircraft and the continuous growth of its functional complexity, the frequency of software modifications has risen significantly. The traditional factory-return loading mode leads to high operating costs and long maintenance cycles. Focusing on the design of field-loadable software for civil aircraft, this paper proposes a decision-making strategy for determining whether airborne software should be designed as field-loadable software. This strategy classifies airborne software into three categories, platform basic software, functional software, and database software. It puts forward the design principle that field-loadable design is mandatory for database software, preferred for functional software, and not mandatory for platform basic software. In addition, a multi-dimensional design deviation assessment model is constructed for software that cannot meet the above requirements. Functional software that is not designed as field-loadable and has not yet been installed on aircraft shall be evaluated from five dimensions: interconnection complexity, software characteristics, airline operation, commercial off-the-shelf (COTS) attributes, and competitor aircraft models. For functional software that is not designed as field-loadable but has already been installed on aircraft, three additional evaluation dimensions shall be included: the possibility of software modification, difficulty of optimization, and cost of optimization. Meanwhile, this paper analyses the airworthiness requirements to be considered in the design of field-loadable software, as well as the design considerations required to address problems arising in the delivery process, such as configuration identification Issue, granularity issue of field-loadable software packages and format issue of field-loadable software packages. The research provides a systematic reference for the design decision-making, airworthiness compliance and efficient delivery of civil aircraft field-loadable software, helping to shorten aircraft downtime and reduce airline operation costs.

Feng, Chen, Fan, Hui, Luo, Jialu

A Study on Gust Alleviation Based on Distributed Electric Propulsion

2026-99-2304

To be published on 10/30/2026

In a distributed electric-propulsion aircraft, multi-motor drive propellers distributed across the wings to provide the aircraft’s primary thrust. The electric control system enables rapid adjustment of propeller speed to control the overall attitude and flight path of the distributed electric propulsion aircraft. Furthermore, by utilizing the wake-induced acceleration effect generated by the propellers to alter the aerodynamic force distribution on the wing surfaces, it achieves gust attenuation. When encountering gusts, the PID control system adjusts the propeller slip to increase lift, thereby mitigating the effects of gusts on distributed electric propulsion aircraft. This paper employs PID control to regulate the steady-state performance of a distributed electric-propulsion aircraft under gusty conditions, with the objective of achieving the fastest return to a steady state. When encountering a headwind and a downdraft, the aircraft’s flight quality are severely disrupted; while airspeed, angle of attack, pitch angle, and rate of climb return to their original states or tend to stabilize, the effect of these forces causes the aircraft to lose height. After PID control is applied, under the influence of a tailwind disturbance, the aircraft’s descent under PID control is significantly less than under open-loop control, and it quickly returns to its original height and remains stable; Under the influence of downdraft turbulence, the aircraft under open-loop control is unable to return to its original height, whereas the aircraft under PID control drops slightly before returning to its original height.

Yang, Wenshuo, Yang, Youxu, Fu, Junquan, Shen, Junhui

Experimental Study on Two-Phase Flow and Heat Transfer Characteristics in Rectangular Enhanced Channels

2026-99-2326

To be published on 10/30/2026

Shao, Yawei, Li, Benke, Li, Li, Qian, Jiyu, Wei, Tao

HER-DETR: Lightweight UAV Object Detection Based on High-Frequency Enhancement and Multi-Scale Sparse Fusion

2026-99-2336

To be published on 10/30/2026

The rapid growth of the low-altitude economy has accelerated the deployment of unmanned aerial vehicle (UAV) technology in a wide range of applications, including power infrastructure inspection and disaster relief operations. These scenarios impose stringent real-time processing demands on onboard vision systems. However, aerial images are characterized by a high density of small and distant targets, which significantly weakens feature representation quality, and current state-of-the-art detection models carry substantial computational overhead that conflicts with the limited hardware resources available on embedded UAV platforms. This paper addresses both challenges simultaneously by proposing a novel lightweight, end-to-end detection framework designated the High-frequency Enhancement Retentive Detection Transformer (HER-DETR). The framework introduces three coordinated technical contributions. First, a lightweight backbone network termed the Cross Stage Partial High-frequency Enhancement Network is constructed. This backbone integrates a cross-stage high-frequency residual enhancement mechanism to alleviate feature attenuation commonly observed in small-target scenarios. Second, a Depthwise Intrascale High-Low Feature Interaction block is designed for intra-scale feature interaction at the highest semantic level, strengthening fine-grained representation through a dual-frequency decoupling strategy. Third, a Retention Feature Fusion module is developed as the core cross-layer feature fusion component, enabling bidirectional multi-scale feature integration via a lightweight sparse attention mechanism. Comprehensive experiments on the VisDrone2019 and HIT-UAV benchmark datasets confirm that HER-DETR achieves state-of-the-art performance. Compared with the RT-DETR-r18 baseline, the proposed framework improves mean average precision at a 0.50 intersection-over-union threshold by 2.0 percentage points on VisDrone2019 and by 1.6 percentage points on HIT-UAV, while simultaneously reducing model parameter count and computational floating-point operations by 41.6% and 35.3%, respectively. These results validate the framework’s capability to balance detection accuracy and computational efficiency for real-time UAV deployment.

Jiao, Minghai, Liang, Yinghan, Zhang, Ende, Kan, Shuang

Prediction of Aero-Engine Exhaust Gas Temperature Using iTransformer

2026-99-2335

To be published on 10/30/2026

Exhaust gas temperature (EGT) is one of the key parameters for evaluating the thermal state of an aero-engine during flight operations. Precise EGT predictions are important in both managing engine health and optimizing performance. However, the nonlinear interactions among multiple operating parameters pose challenges for conventional prediction methods. An approach to EGT prediction presented in this paper is based on the iTransformer architecture. Unlike conventional time-series models that process temporal observations as tokens, this algorithm works with every single variable like a unique token and encodes the entire historical sequence of each variable. This design enables the model to capture inter-variable dependencies directly, thus increasing its power to capture multivariate time series. Two different datasets were used to validate the proposed approach and compared to various commonly used prediction models. The results indicate that iTransformer gives smaller prediction errors when the same conditions are met with MAE values of 0.5525°R and 0.4539K both on the two datasets. Despite the overall satisfactory performance, some prediction mismatches can be observed under extreme fast changing conditions of the engine operation. These results demonstrate that the proposed method can effectively characterize the complex interactions among engine variables and has potential for practical EGT prediction applications.

Hao, Mengfu, He, Yongbo, Li, Yifan

Study on the High-Temperature Creep Behavior of Ti-1100 Alloy

2026-99-2344

To be published on 10/30/2026

This study investigates the creep response of the high-temperature titanium alloy Ti-1100 over a temperature range of 550-700 °C under applied stresses varying from 150 to 450 MPa. Under different combinations of stress and temperature, the creep rate, stress exponent, and activation energy for creep were determined using the Arrhenius relationship, while the corresponding creep characteristics and microstructural changes were examined through optical microscopy (OM), electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM). The findings indicate that an alloy is highly sensitive to temperatures and stresses when subjected to long-term creep. At the temperature of 550-700 °C, the Ti-1100 alloy has relatively high creep activation energy when subjected to an applied stress of 300 MPa. For the Ti-1100 alloy, the stress exponent was determined to be 5.33 within the stress interval of 150-450 MPa at 600 °C. When considered alongside the observed microstructural characteristics, this result indicates that dislocation climb serves as the primary mechanism controlling creep during prolonged exposure. Within the investigated temperature and stress window, creep deformation in Ti-1100 gives rise to localized regions with elevated residual stress, and this tendency becomes more pronounced as both temperature and applied stress increase. These areas are likely to grow and evolve into microcracks, thus contributing to the microstructural instability of the alloy.

Yongquan, Yang, Lihui, Yang, Feng, Du, Baohui, Zhu, Xiaochen, Hu, Lihua, Shen, Jianfeng, Li, Shaofu, Kang

Self-Regulation of Force in the Space Truss-Type Deployable Structure Based on Magnetic-Electric Material

2026-99-2341

To be published on 10/30/2026

Under on-orbit disturbances, large space truss-type deployable structures readily undergo vibration. Such vibration may degrade pointing accuracy and positioning precision. As a countermeasure, an internal-force self-regulation method is proposed using a magnetoelectric self-sensing actuator. This actuator combines Terfenol-D with piezoelectric material, thereby realizing force output and self-sensing. It can be placed at critical truss joints. By exploiting the ΔE and Villari effects of magnetostrictive materials, a resonant-force self-sensing mechanism is established. The actuator model is then integrated into the deployable truss’s dynamic model, and an LQR-based active vibration suppression strategy is derived. Simulation results show that the actuator detects static and dynamic output forces. Over a specific magnetic field range, sensing behavior exhibits an approximately linear dependence on the output force. Control outcomes confirm effective suppression of structural vibration, reduced response amplitudes at key nodes, and enhanced structural stability. Therefore, a novel method for vibration suppression in large deployable truss-type space structures is provided by these findings.

Xie, Dongjian, Ni, Xiaofeng, Cheng, Lei, Cui, Qifeng, Zhang, Yahui

An Improved ResU-Net for Thermally Grown Oxide Layer Segmentation in Thermal Barrier Coatings

2026-99-2361

To be published on 10/30/2026

Thermal barrier coatings (TBCs) serve as the critical protective layer for gas turbine components. In complex service environments, the thermally grown oxide (TGO) layer formed between the top coat (TC) and the bond coat (BC), along with the evolution of its microscopic morphology, is a key factor determining the service life of TBCs. To address the complex morphology of TGO layers that challenges conventional segmentation methods, this paper proposes MSF-SA ResU-Net, a multi-scale feature fusion self-attention model based on the ResU-Net architecture, for the image segmentation of thermally grown oxide layers. Building upon residual connections, this model incorporates two key enhancement modules at the bottleneck layer: Multi-Scale Fusion (MSF) and Self-Attention (SA). During the feature processing stage, the MSF module effectively captures the multi-scale morphological features of the TGO layer through receptive fields of different sizes, solving the difficulty of extracting long-strip and irregular morphologies. Subsequently, the SA module recalibrates the fused features by calculating the global dependencies between them, enhancing the network's attention to TGO boundaries and micro-defects. Comprehensive experiments on an in-house dataset of 3,000 SEM images demonstrate that MSF-SA ResU-Net outperforms standard U-Net and contemporary segmentation architectures. Furthermore, due to the feature extraction capability of the ResNet backbone network, the model maintains high accuracy and exhibiting good convergence performance. This makes the architecture an effective solution for the microscopic analysis of thermal barrier coatings.

She, Weicen, Song, Yan

Layout Design and Verification of Deployable Radiators for Large-Scale Phased Array Antennas on High-Orbit Satellites

2026-99-2354

To be published on 10/30/2026

Active phased array antennas are widely deployed on various spacecraft platforms. As the demand for higher communication and data transmission capacity of spacecraft continues to grow, active phased array antennas have become increasingly large in scale, leading to a sharp rise in overall heat generation. Traditional thermal control schemes, which transfer heat to the satellite main structure via heat pipes, can no longer satisfy the heat dissipation requirements of large- scale array structures. To address the aforementioned technical bottlenecks, this paper proposes a layout design of deployable thermal radiators applicable to large phased array antennas for high-orbit communication satellites. Corresponding ground tests and on-orbit flight verifications are completed in this study. This work provides a feasible technical approach for realizing high-efficiency heat dissipation of large-scale space-borne phased array antennas operating in high-orbit environments.

Wu, Ruilan, Zou, Hengguang, Liu, Chang, Zhao, Feng, Wang, Ming, Lv, Lucang, Liu, Kun, Xing, Boyan

Evaluation of Low-Altitude Aircraft Operation Status Based on Fuzzy Reasoning and D-S Evidence Theory

2026-99-2302

To be published on 10/30/2026

To provide auxiliary verification for the technical evaluation of low-altitude aircraft operation status and address the limited effectiveness of uncertainty handling, this paper proposes an evaluation method based on fuzzy reasoning and D-S evidence theory. Firstly, the relevant knowledge of D-S evidence theory and fuzzy reasoning is introduced. Secondly, the aircraft operation status levels are constructed, and an intuitionistic fuzzy reasoning system is established based on Gaussian membership functions. The initial basic probability assignments (BPA) are obtained through defuzzification using the intuitionistic fuzzy centroid method. The conflict between evidences is measured by the weighted average of Jensen-Shannon (J-S) evidence distance and conflict coefficient k, followed by multi-cycle discount correction and fusion. Finally, experimental verification proves the rationality of the proposed method, which can provide a reference for related research.

Niu, Xiaowei, Shen, Di, Yu, Fuping, Guo, Yiduo, An, Xiangyu, Xuan, Haoli

Research on an EPHM Methodology Based on Gas Path Performance Parameters

2026-99-2305

To be published on 10/30/2026

Gas path faults in aero-engines are a major factor affecting flight safety and maintenance costs. To achieve the transition from time-based maintenance to condition-based maintenance and enhance fault prediction and health management capabilities, this paper conducts research on intelligent diagnostic methods based on performance parameters for engine gas path components. Firstly, a component-level nonlinear steady-state model of a dual-spool turbofan engine is established, employing a variable specific heat capacity method to improve computational accuracy and provide a healthy baseline for diagnosis. Secondly, addressing the strong nonlinearity and measurement underdeterminacy in fault diagnosis, a diagnostic framework integrating a multi-population genetic algorithm and multi-operating-point analysis is proposed. The multi-population genetic algorithm effectively enhances global optimization capability and solution stability through parallel evolution, migration operators, and an elite population strategy; multi-operating-point data increases information content and strengthens fault isolation capability. Finally, simulation experiments under ground test conditions are conducted to validate the diagnosis of single-component faults in the low-pressure compressor, high-pressure compressor, high-pressure turbine, and low-pressure turbine. The results show that the proposed method can accurately invert component performance degradation, with a high fault identification accuracy rate, verifying the effectiveness of the proposed model and algorithm. This research provides a feasible technical solution for intelligent condition monitoring and precise diagnosis of aero-engines.

Cai, Na, Li, Ming, Li, BianJiang, Yu, GuangHui, Ma, ZhongYuan

Deriving Representative Head-Impact Profiles in Contact Sports: A Shape-Based Clustering Approach

09-14-03-0018

10/26/2026

Instrumented mouthguards (iMGs) capture thousands of head acceleration events in contact sports, though processing the data from these into meaningful information remains a challenge. To bridge this gap, we developed a principled clustering pipeline to extract concise kinematics that are representative of real-world impact scenarios. We evaluated six multivariate time-series distance metrics using k-medoids clustering and a dataset of 5149 impacts across three sports: mixed martial arts, boxing, and rugby union. Model performance was evaluated using a Coverage Quality Score (CQS), prioritizing reconstruction coverage and cluster separation. Joint Shape-Based Distance (SBD) significantly outperformed all other metrics, including Dynamic Time Warping (DTW), driven primarily by superior reconstruction coverage. The methodology yielded two complementary archetypes. First, a 36-impact canonical set provides distinct medoids representing core kinematic mechanisms; however, distributional checks confirm these medoids alone do not reproduce cohort severity distributions. To resolve this, we generated a second, extended impact set sampled proportionally within clusters along specific severity axes (e.g., peak acceleration and whole-brain strain). This targeted sampling recovers full severity-distribution fidelity using fewer than 150 impacts per dataset. Ultimately, this framework successfully distills large iMG datasets into manageable cohorts providing a scalable solution for assessment, experimental targets, and finite element simulations.

Koumantou, Eleni, Chan, Emily Yik Kwan, Jones, Chris M., Austin, Kieran, Loosemore, Mike, Ghajari, Mazdak

Associations between Vehicle Safety Systems and Pilon Fracture Severity Are Revealed by Using a Novel Objective Quantitative Fracture Energy Metric

09-14-03-0013

10/26/2026

Pilon fractures are a severe lower-extremity injury sustained in motor vehicle crashes, characterized by high-axial loading that drives the talus into the tibial plafond and frequently results in poor long-term functional outcomes and post-traumatic osteoarthritis (PTOA). Despite advances in surgical management, clinical complications remain frequent in 20–50% of cases in literature, which highlights the need to identify modifiable upstream risk factors in the motor vehicle crash environment. The Abbreviated Injury Scale (AIS) is the predominant tool for crash injury severity analysis, and it classifies all closed pilon fractures uniformly as moderate (AIS 2) regardless of comminution or imparted energy and poorly predicts long-term functional outcomes. An existing fracture energy severity metric quantifies the acute mechanical energy delivered to bone during fracture and has been linked to PTOA development. This study represents one of the first multivariate applications of the fracture energy metric to crash data, examining how occupant demographics, crash kinematics, and vehicle safety systems relate to pilon fracture severity. Fifty belted occupants with Orthopaedic Trauma Association (OTA) Type B or Type C pilon fractures were identified through the Crash Injury Research and Engineering Network (CIREN) database. Fracture fragment surface models were generated from clinical computed tomography (CT) and used to compute fracture energy severity through a custom MATLAB script. A generalized linear model with a gamma log link was fit using seven predictors: age, delta-v, knee bolster airbag (KBAB) deployment, fracture loading mechanism, biological sex, osteoporosis, and body mass index (BMI). KBAB deployment and pure axial compression loading were both significantly associated with higher fracture energy (p = 0.011 and p = 0.002), and age approached an inverse association (p = 0.086). These findings establish the fracture energy metric as a viable tool for crash injury analysis and suggest that certain vehicle safety system characteristics warrant further investigation as modifiable factors influencing pilon fracture severity.

Northrup, Samuel, Bangert, L. Garrett, Armstrong, William, Stitzel, Joel D., Martin, R. Shayn, Dibbern, Kevin, Anderson, Donald D., Weaver, Ashley A., Collins, Caitlyn J.

Multi-Objective Optimization of Face Milling Parameters for DIN 2714 Hardened Steel: Hybrid Taguchi–Analysis of Variance and Artificial Intelligence-Based System Modeling

2026-01-5070

10/23/2026

The face milling process for hardened steels used in die and mold manufacturing involves precise parameter settings to achieve high-quality surfaces with high productivity. However, due to the nonlinear relationship between the machining parameters, it becomes challenging to optimize the machining process. This paper focuses on optimizing the face milling parameters for the DIN 2714 hardened steel by employing experimental approaches and AI-based modeling techniques. Taguchi’s L9 orthogonal array-based design of experiments with machining parameters of depth of cut, cutting speed, feed rate, and step-over is employed for the optimization. The material removal rate (MRR) and surface roughness (Ra) were considered as output responses. The results showed that surface roughness is significantly affected by cutting speed, while depth of cut primarily controls MRR. To improve the precision in predicting the output responses, two artificial intelligence (AI) based intelligent models were developed using hybrid modeling techniques—namely, PSO-FIS and GA-FIS. The experimental results demonstrated that the GA-FIS model was accurate in predicting the output response for surface roughness (Ra), with a lower error value (0.0162 μm), while the PSO-FIS model was accurate in predicting MRR with a high R2 value (0.9997), proving that fuzzy modeling techniques can be effectively used to optimize machining operations with high precision.

Tripathy, Chitta Ranjan, Moudgil, Prince Kumar, Katyayn, Arvind, Kumar, Aman

Vehicle Attitude Control via Load Resistance Regulation of a Hydraulic–Electric Energy-Harvesting Suspension

10-11-01-0007

10/21/2026

Vehicle dynamics during autonomous driving can cause passenger discomfort as well as motion sickness. This article introduces an energy-efficient vehicle attitude control method through load resistance control in a hydraulic–electric energy-harvesting suspension. A thorough study on how external load resistance affects a vehicle’s pitch, roll, and vertical acceleration has been conducted without requiring additional energy input. To analyze the effects that the variation of load resistance has on vehicle attitude, a 10 DOF vehicle model and a hydraulic–electric energy-harvesting suspension have been developed. The relationship between load resistance and vehicle attitude is defined and experimentally demonstrated in slalom tests under real-world conditions. The results demonstrate that reducing the load resistance from 70 Ω to 30 Ω reduces peak roll angle by 33.9% and peak pitch angle by 30.4%, while the RMS values are reduced by 46.2%, 28.6%, and 28.6% for roll angle, pitch angle, and vertical acceleration, respectively. A frequency-domain analysis shows that these improvements are achieved without shifting vibrational energy into the 4–8 Hz range most perceptible to humans. This systematic approach provides an energy-efficient way to improve vehicle stability and ride comfort, with potential implications for reducing motion sickness in autonomous driving systems. The quantitative relationship between load resistance and vehicle attitude provides a new understanding of the dynamics of a vehicle with energy-harvesting suspension systems.

Fan, Zhengwu, Liu, Wenbo, He, Mengfan, Xu, Guangzhao, Wu, Zhifei

Suppression of Traffic Oscillations by Vehicular Platoons with Periodic Control Configuration

12-10-01-0003

10/21/2026

Traffic oscillations degrade the operational efficiency and safety of vehicular platoons. This study proposes a predecessor-following platoon with periodically assigned feedback gains and investigates its frequency-selective disturbance attenuation mechanism. An auxiliary second-order undamped periodic model is first established, and the Bloch–Floquet formulation is used to derive the nominal dispersion relation and bandgap boundaries. Closed-loop disturbance attenuation is then evaluated separately using spacing-error transfer matrices that retain positive velocity feedback and actuator-response lag. Frequency-domain transmission analysis and time-domain simulations show that, under nominal homogeneous and fully controllable connected and automated vehicle (CAV) assumptions, the periodic gain configuration creates tunable attenuation bands and provides stronger downstream attenuation than a homogeneous-gain configuration. The results clarify the relationship among periodic gain assignment, bandgap formation, periodic-cell string stability, and finite-length traffic-wave suppression. The effects of vehicle heterogeneity, sensing delay, and mixed traffic remain topics for future investigation.

Yang, Xiujian, Fan, Yanwen, Zhuang, Qingyuan, Qiao, Feiyu

Fine-tuned Large Language Model Enhanced Nonlinear Model Predictive Control for Spacecraft Attitude Stabilization

2026-99-2966

10/19/2026

Guo, Zili, Gao, Sihong, Liu, Yuhan, Wang, Pengyu, Guo, Yanning

Acceleration Factor Derivation Using Test Bed and Field Failure Data of Automotive Engines

2026-01-5077

10/19/2026

Reliability testing and validation are essential parts of any automobile development project. These tests mainly depend on two things: how much of the product’s target life is covered and whether enough samples are tested to capture variations. Today, most projects face tight timelines and cost constraints, which makes it important to design an optimized reliability test plan. Accelerated testing is widely used to reduce testing time, but finding the correct acceleration factor for a complex system like an automotive engine is difficult. Existing studies describe methods such as simple stress tests, step-stress tests, and degradation tests to shorten test duration. However, these methods need more samples at different stress levels to establish a clear relationship between stress and life and then calculate the acceleration factor. While such models work well at the component level, they become much more complicated at the system level because different components can have different acceleration behaviors. In this paper, a method is proposed to derive the acceleration factor by correlating engine test bed data with field failure and usage data. Test engineers often use standard accelerated test cycles for engines used in similar applications. As a result, organizations usually have historical test data and field data from earlier, similar products. The proposed approach models accelerated test data and field failure data separately and then links them to determine the acceleration factor. This derived factor can later be used in future engine development projects to convert test cycles into equivalent field mileage coverage. Weibull analysis and regression techniques are used to support the case studies presented in this work.

Koulage, Dasharath Baliram, Mondal, Kanchan

An Integrated Fault Diagnosis and Prediction Method for Planetary Gears Based on Ensemble Learning

2026-99-2944

10/19/2026

Planetary gears are key transmission components in spacecraft drive systems and are prone to failures under long-term operation in harsh environments. To address the problem that fault diagnosis and life prediction are treated separately in existing research, this paper proposes an integrated diagnosis-prediction method. First, order tracking and time synchronous averaging are used to preprocess vibration signals, eliminating speed fluctuations and noise interference. Then, fault features are extracted via VMD/EEMD decomposition, and the gear health condition is identified through a deep convolutional neural network. Finally, ensemble learning prediction models are established separately for different fault states to realize life prediction. Experimental results show that the VMD+CNN method achieves higher diagnostic accuracy than traditional methods for gears in healthy, cracked, spalled, and pitted conditions. The ensemble prediction models matched with different states can further reduce the life prediction error. This method provides effective technical support for condition-based maintenance of spacecraft planetary gears.

Liu, Yanyan, Gao, Tongxin, Zhu, Yangsong

Coking Inhibition Characteristics of Dimethyl Disulfide during Supercritical Pyrolysis of n-Decane

2026-99-2993

10/19/2026

Inhibition of coking from pyrolysis of hydrocarbon fuel is critical for ensuring the safe operation of high-speed aircraft. In this work, the inhibitory effects of dimethyl disulfide (DMDS) on coke during the supercritical pyrolysis of n-decane were investigated. Experiments using an electrically heated micro-reactor tube were conducted to study the influences of presulfidation and cofeeding methods with varying DMDS concentrations. The pyrolysis products were analyzed via gas chromatography, while pressure drop and coke mass were measured. The composition and morphology of coke were further characterized by energy-dispersive spectroscopy and scanning electron microscopy. The results show that the influence of DMDS on coke formation depends on its application method and concentration. At a DMDS concentration of 500 ppm, the coke inhibition rate reached 85.5% with presulfidation and 81.7% with cofeeding. However, with increasing DMDS concentration beyond 500 ppm, the amount of coke formation increases. Coke formation during supercritical pyrolysis of n-decane primarily involves metal-catalyzed coking and bulk-phase coking processes. The H2S and HS radicals generated from pyrolysis of DMDS react with transition metals such as Fe and Ni, thereby inhibiting the formation of metal-catalyzed filamentous coke. The presulfidation process forms a sulfide layer earlier than the cofeeding process, more effectively suppressing the generation of filamentous coke. However, excessively high DMDS concentrations can lead to its decomposition into sulfur-containing radicals such as thiol radicals (CH3S·), which incorporate into the coke matrix, enhance its reactivity, and promote coke formation. This work offers valuable insights for the application of DMDS as a coke inhibitor in endothermic hydrocarbon fuels.

Liao, Yan, Yao, Yixuan, Yang, Can, Huang, Qin, Chen, Yu

An Adaptive Cloud Prediction Network for Supporting Space-Ground Laser Communication Links

2026-99-2982

10/19/2026

Space-ground laser communication links are highly susceptible to interruptions caused by cloud cover. Reliable dynamic scheduling of ground stations and seamless link handover, therefore, requires accurate short-term cloud-layer forecasts. However, cloud evolution is inherently chaotic, and atmospheric conditions exhibit evident seasonal non-stationarity. These factors weaken the prediction stability of existing models and make it difficult to maintain consistent accuracy over extended forecasting horizons. To address these challenges, this paper proposes KFMNet, an adaptive cloud prediction network that integrates Koopman theory with frequency-domain Mamba modeling. Unlike conventional frameworks that mainly process cloud features in the spatial domain, KFMNet embeds a linear state-space modeling mechanism into the frequency-domain feature evolution process. Specifically, an adaptive spectral gating mechanism is used to capture global cloud distribution patterns in the frequency domain, thereby reducing the computational burden associated with high-resolution image sequences. Meanwhile, a Koopman seasonal gating module introduces monthly prior knowledge into the network, enabling the model to adapt to atmospheric drift across seasonal transitions. Within the encoder-decoder backbone, KFMNet jointly incorporates temporal modeling, frequency-domain representation, and physical consistency constraints, providing a more stable framework for short-term cloud prediction. We evaluate KFMNet on multi-season cloud images collected over Nanjing and Beijing. The climate profiles of these two cities are markedly different. Under these conditions, KFMNet achieves competitive accuracy in cloud segmentation and shows a better ability to handle seasonal and regional shifts than several reference models. In essence, our method adapts well to changing atmospheric conditions and provides a new cloud prediction approach for space-ground laser link assurance. This method combines physical interpretability with the actual needs of real-world deployment.

Wu, Xibing, Meng, Xiangxu, Zhong, Yun, Li, Wenfeng, Zhao, Kanglian

Deep Reinforcement Learning for Downlink User Scheduling in LEO Satellite Massive MIMO Systems

2026-99-2990

10/19/2026

Low earth orbit (LEO) satellite communication system is expected to play a key role in the 6G wireless networks. Due to the limited communication resource and inter-user interference (IUI), the user equipments (UEs) must be well scheduled to maximize spectral efficiency. However, rapid channel variations and high mobility in LEO system make the conventional user scheduling strategies relying on instantaneous channel state information impractical. To address this challenge, this paper proposes a deep reinforcement learning (DRL) framework based on batch normalization in CrossQ algorithm to optimize the downlink user scheduling in a LEO satellite massive multi-input multi-output (MIMO) system. The scheduling task is modeled as a Markov decision process (MDP), where the observation space integrates high-dimensional features including large-scale fading (LSF) coefficients, channel matrix, and temporal features capturing the orbital progress of the satellite. Unlike traditional DRL methods, the CrossQ-based algorithm applies batch normalization (BN) within the critic networks to eliminate the need for slowly-moving target networks, fostering training stability and sample efficiency. A continuous priority score vector is mapped to discrete scheduling decisions through a top-z selection logic, allowing the agent to effectively identify spatially orthogonal user subsets to suppress the IUI. To guide the agent toward the true optimization direction, a differential reward mechanism is designed. Simulation results demonstrate that the proposed CrossQ-based scheduler consistently achieves nearly optimal performance at different signal-to-noise ratios (SNR), reaching at least 91% and 94% of the Monte Carlo search ceiling under different user densities. Moreover, the trained model provides scheduling strategy in millisecond inference time, making it a practical and robust solution for future 6G non-terrestrial networks.

Deng, Chaojun, Zhong, Yun, Meng, Xiangxu, Li, Wenfeng, Zhao, Kanglian

Emergence and Regulation of Scale-Free Correlation in UAV Swarm under Communication Energy Constraints

2026-99-2983

10/19/2026

This paper studies how scale-free correlations, a key feature for efficient group response, emerge in collective motion under communication energy constraints. A two-dimensional Boids model integrated with a point-to-point wireless communication energy consumption model is used. The model dynamically determines, based on a limited communication power budget, an individual’s interactive neighbors, where neighbors are selected based on two different mechanisms: distance-based and probability-based, until the total communication cost reaches the budget. Simulations of flock sizes ranging from 30 to 230 individuals confirm that scale-free correlation persists, even under strict power constraints, which is shown by a correlation length ξ proportional to the flock’s linear size L. Notably, the ratio ξ/L, which reflects the robustness of collective correlation, displays a non-monotonic dependence on the available communication power. Initially, increasing power enhances ξ/L by expanding effective interaction ranges and improving information transmission. However, beyond a threshold, further power increase causes a slow drop in ξ/L, likely due to signal interference from excessively many or distant connections. This finding demonstrates that communication power is a critical, tunable engineering parameter regulating collective coordination, analogous to interaction range or neighbor number in biological models. The results imply that for engineered swarms, maximizing communication resources is not always beneficial; instead, an optimal power allocation exists to maximize coordination performance, balancing connectivity with efficient, localized interactions.

Zhao, Shiping, Gan, Qingzhong, Liu, Jinglei, Liu, Zhihao, Zhang, Liang

LASERAgent: Toward Highly Automated and Reliable Reproduction of LEO Networking Experiments

2026-99-2991

10/19/2026

It is still difficult to reproduce the low-Earth-orbit (LEO) networking experiments since the experimental settings are scattered and the Large Language Model (LLM) assisted workflow is laborious and needs to be refined by manually crafting the prompt, transferring the code and debugging. Existing retrieval-assisted pipelines still demand substantial human effort for parameter inference and code repair, particularly given the precise alignment required for LEO topology, constellation, and simulator configurations. This paper presents LASERAgent, a highly automated agent system for LEO networking experiment reproduction. Unlike retrieval-assisted pipelines that mainly support evidence retrieval and code generation, LASERAgent organizes the downstream workflow into a bounded pipeline of retrieve - extract - generate - check - execute, integrating structured parameter extraction, template-guided generation, deterministic validation, controlled execution, and limited retry. Preliminary results show that LASERAgent reduces required human interventions by roughly half while improving result consistency over prior retrieval-assisted workflows, and that the validation stage plays an important role in reliable automation.

Tian, Siyu, Wei, Guangyu, He, Xiongwen, Liu, Donghua, Ma, Yue, Zhan, Yuheng, Xia, Lang

A Deep Learning Approach for Space Target Pose Estimation Based on Multi-Task Joint Training

2026-99-2981

10/19/2026

The growing number of space objects raises collision risks and demand for on-orbit maintenance. Since most servicing tasks require observing the target, relative pose estimation is key for debris removal and satellite servicing. The complex orbital environment poses significant challenges for conventional feature extraction networks.. To address these challenges, this paper presents a multi-task convolutional neural network for space target pose estimation. The network uses a MobileNetV3-based encoder to extract image features and an Adaptively Spatial Feature Fusion (ASFF) module for adaptive multi-scale feature fusion. Although MobileNetV3 and ASFF have rarely been combined in the aerospace field, their combination offers significant advantages in low parameter count and reduced computational load, showing strong potential for spacecraft pose estimation. Two parallel prediction heads are employed: an EfficientPose head consisting of classification, bounding box regression, rotation matrix regression, and translation vector regression, together with a Heatmap head. This design leads to more robust and accurate pose estimation. The loss function follows a multi-prediction-head joint approach, where each component is weighted to form the final loss for multi-task optimization. The network is trained, validated, and tested on a simulation dataset of the DAWN satellite built with Unity. The dataset includes illumination variations, large distance changes, and multi-view target appearances, thus capturing the complex scenes typical of vision-based recognition tasks. Experimental results show that after 40 training epochs, the model achieves an average position error of about 0.05 m and a rotation error of about 0.8° on the test set. These results demonstrate the effectiveness and accuracy of the proposed network for space target pose estimation.

Wang, Wenzhi, Zhang, Zexu, Zhang, Fan, Song, Zhuo

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