Browse Topic: Data acquisition and handling

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Synergistic Effect of Matrix Porosity and Grouting Material Type on the Performance of Grouted Composite Pavement2026-99-04058/28/2026
The grouted composite pavement combines the advantages of flexibility and rigidity through the composite structure of organic-inorganic materials, but the optimisation of its performance is affected by the complexity of the matrix asphalt mixture void ratio and grouting material type. This study has revealed the influence of matrix asphalt mixture porosity and grouting material type on the grouting effect and road performance of grouted composite asphalt pavement. The results showed that the increase of matrix porosity could significantly improve the grouting rate and resistance to high-temperature rutting of the mortar, but the high porosity led to a decrease of low temperature cracking resistance of the materials. CA mortar enhanced the flexible deformation capacity by optimising the interfacial bond, and its low-temperature cracking resistance was better than that of ordinary cement mortar, but the grouting efficiency and high-temperature performance were slightly lower. In addition, ordinary cement mortar demonstrated better performance regarding high-temperature stability and resistance to water damage.
He, Mu, Wang, Yan, Ye, Ming, Yu, Chao, Ye, Xiao
Synthesis and Performance Study of a High-Workability Polycarboxylate Superplasticizer2026-99-04038/28/2026
This study used hexacarbon polyether (EPEG), acrylic acid (AA), polyethylene glycol maleate (MAPG), and vinyl acetate (VA) as the main raw materials to synthesize a highly workable polycarboxylate superplasticizer (CE-02) under the action of an initiator. The structure of the target product was characterized by FTIR and GPC. Tests showed that under conditions of low water dosage (150 kg), low cementitious material content (220 kg of cement), and poor aggregate gradation, the concrete mixed with CE-02 exhibited an initial slump flow increase of 25 mm, a bleeding rate of 0.6%, no stone exposure, and excellent workability.
Chen, Wenhong, Deng, Lei, Jiang, Yu, Zhang, Bo
Numerical and Experimental Investigation of the Impact of Close-Range Construction of Large-Section Tunnels on Existing Subway2026-99-04258/28/2026
Large-section tunnel construction using the mining method can significantly affect the operational safety of existing metro lines and ground stability, while their non-uniform settlement remains challenging to monitor comprehensively. In this study, the post-station section of a metro project in Chengdu was investigated to elucidate the vertical displacement and ground settlement behavior induced by a large-section tunnel undercrossing an existing metro line. A displacement reconstruction method integrating sparse-point monitoring with a radial basis function neural network (RBFNN) was developed to fit the full-field settlement distribution of both the existing line and the ground surface. A finite element model incorporating the existing shield tunnels, station structures, and the newly constructed mined tunnel was established, and the construction process was simulated. The numerical results indicated maximum settlements of 4.42 mm for the existing line and 4.37 mm for the ground surface, with fitting errors below 5.8% and 6.1%, respectively. Physical model tests further validated the approach, yielding maximum settlements of 0.86 mm and 1.01 mm for the existing line and ground surface, respectively, and an average fitting error below 3.5%. Both numerical and experimental findings confirmed that the induced displacements were within a controllable range and that the surrounding strata remained stable. The proposed method enables accurate and intuitive reconstruction of displacement distribution during under-crossing tunnel construction, reducing the number of required monitoring points while maintaining high fitting accuracy.
Wang, Rui, Chen, Jian, Cheng, Tao, Du, Lin, Li, Ruixiao
Simulation of Gas Diffusion in Nanoscale Microchannels at the Parallel Seam-Weld Interface2026-99-04198/28/2026
Driven by the stringent service conditions of aviation, aerospace, and military equipment, parallel seam welding, as an advanced resistance-welding packaging process, has been widely applied in ceramic-metal packages that require high hermeticity, owing to its excellent sealing performance and reliability. In this study, targeting the hermeticity failures that appear in parallel seam-welded ceramic packages after temperature cycling, molecular dynamics simulation is used to systematically investigate helium diffusion in nanoscale interfacial microchannels and its effect on hermeticity. On the LAMMPS platform, a three-region model is constructed that includes a helium-charging region, a wall-channel region composed of Fe, Ni, and Au, and a vacuum leak region. The Lennard-Jones potential is used to describe interatomic interactions, and a thermal-cycling environment conforming to MIL-STD-883, with a temperature range from -50°C to +125°C, is simulated to represent actual service conditions. The simulation results show that when the channel diameter is less than or equal to 1.2 nanometers, the number of leaked helium atoms remains constant at approximately 22 and is not affected by temperature; when the diameter is greater than or equal to 1.6 nanometers, the leakage exhibits significant temperature dependence. For example, in a 2.6-nanometer channel, 212 atoms leak at 423 K and 176 atoms at 223 K. Both leakage flux and leak rate increase markedly with channel size. OVITO analysis confirms that helium diffusion exhibits molecular-flow characteristics; at very small apertures, atomic escape efficiency is limited by the frequency of collisions with the wall. These findings provide insight for improving hermetic packaging and reliability of critical electronics used in aviation, aerospace, and military equipment.
Li, Xiangyang, Gong, Yubing, Zheng, Xianling
A Study on the Dynamic Characteristics of Long-Span Single Conductors Using Meshfree Methods2026-99-04248/28/2026
The free vibration characteristics of long-span transmission conductors form the fundamental basis for vibration control design, as their natural frequencies and mode shapes directly affect line safety and the selection of vibration suppression devices. In this study, the three-dimensional linear free vibration governing equations were derived through functional integration of the kinetic and potential energies by using Hamilton’s variational principle. Compared with the conventional integral transform method, an improved meshfree discretization strategy is proposed: the shape functions are constructed using the moving least squares (MLS) method, while the boundary conditions are treated with a fully transformed approach, thereby converting the partial differential equations into ordinary differential equations. Subsequently, a corresponding eigenvalue problem is solved to calculate the first few frequencies of the system, and the effect of conductor natural parameters on these frequencies for the transmission conductor is investigated. The results indicate that the natural frequency decreases when the conductor length becomes larger, and the rate of decrease becomes more gradual as the length increases; it decreases with increasing cross-sectional diameter; it decreases linearly with increasing material density; and it increases linearly with increasing elastic modulus. These findings demonstrate that conductor length, cross-sectional diameter, material density, and elastic modulus all have significant effects on the natural frequency. Among them, length and diameter affect the frequency by altering the conductor’s inertia and structural characteristics, whereas density and elastic modulus govern the frequency from the perspectives of inertia and stiffness, respectively.
Li, Chen, Cheng, Yongfeng, Li, Danyu, Qiu, Gang
Vinylene-Linked Covalent Organic Frameworks as Superior Photocatalysts for Hydrogen Evolution over Imine-Linked Counterparts2026-99-04148/28/2026
The scheme of photocatalysis of water, a way of hydrogen generation as a clean, high-efficiency fuel source for aircraft and long-range transport systems has received considerable interest. The development of the covalent organic framework (COF) - derived materials for hydrogen evolution reaction (HER) has since become a research highlight. Compared to traditional methods, photocatalytic hydrogen evolution systems based on COFs can provide ways of generating hydrogen gas without depending upon noble metal catalysts, thereby enhancing the sustainability and prospects of this technology for future aerospace energy applications.In this work, two covalent organic frameworks (COFs) with distinct linkages—a vinylene-linked COF A (via Knoevenagel condensation) and an imine-linked COF B (via Schiff-base reaction)—were designed and synthesized to compare their performance in the photocatalystic hydrogen evolution reaction (HER). Structural and electrochemical characterizations confirmed that, despite lower crystallinity and specific surface area due to pore blockage, COF A exhibited a suitable band structure for photocatalysis and achieved an HER rate of 56 μmol h^–1 g^–1 under simulated sunlight. In contrast, COF B was ineffective. This study experimentally validates the superior photocatalytic potential of vinylene-linked COFs over imine-linked counterparts for HER, highlighting their potential as non-noble-metal catalysts for aerospace and transport-oriented fuel generation.
Cao, Yijie, Luo, Xin
Resilient Trajectory Tracking Static Output-Feedback Control for Automated Vehicles under Denial-of-Service Attacks10-11-01-00028/24/2026
The safety and reliability of autonomous vehicles are critically linked to network communication quality. Consequently, threats such as Denial-of-Service attacks pose significant risks by disrupting communication between essential components and jeopardizing driving safety. This article presents a robust trajectory tracking control method resilient to such attacks. Utilizing a Linear Parameter–Varying control framework, the method effectively addresses time-varying vehicle speeds and incorporates a norm-bounded strategy to manage tire behavior uncertainties. By employing a Static Output-Feedback scheme, it avoids the need of costly sensors while maintaining a straightforward control structure suitable for real-time implementation. Using Lyapunov’s method, we analyze the system stability under attack conditions, ensuring the controller remains robust against disturbances. The design of the resilient controller is formulated as a convex optimization problem with Linear Matrix Inequalities constraints. The effectiveness of the proposed controller is validated through co-simulation in MATLAB/CarSim®, where it outperforms several state-of-the-art controllers across different driving scenarios, maintaining consistent tracking performance despite varying attack severity levels.
Meléndez-Useros, Miguel, Viadero-Monasterio, Fernando, Nguyen, Anh-Tu, López-Boada, María Jesús
Design of Adaptive Overtorsion Sliding Mode Observer Based on Logarithmic Gain Law and Estimation of Satellite Disturbance Torque2026-99-03158/14/2026
The vigorous rate of new spacecraft being launched has made the accurate estimation of in-orbit environmental disturbances torques paramount to reducing attitude control performance corrosion. Leveraging telemetry from an asset in low-earth-orbit, we present a novel Adaptive Super-Twisting Sliding-Mode Observer, which interlinks three techniques heretofore decoupled: 1) saturation-constrained angular-acceleration adaptation; 2) Kalman-filter preconditioning of angular velocity; and 3) state-weighted logarithmic gain with dual leakage. Denoising of raw Euler angle sequences and detection of quasi-steady epochs are achieved with a customized Kalman update, while an adaptive band-pass stage isolates the torque-related acceleration signature. Casting these filtered data into the super-twisting form, we update the log gain on-the-fly, and twin leakage terms remove excess energy with accompanying chatter rejection—without compromising bandwidth. Head-to-head telemetry tests show a positive margin headroom on noise attenuation that has to be compared with the power-gain type counterpart and that increases with the signal roughness, thereby validating the fact that this technique refines environment torque estimates and hence strengthens robustness design envelopes in next-generation attitude-control systems.
Yin, Xu, Deng, Yuhui, Chi, Dongxiang
Research on the Fault Diagnosis Method for Key Components of Inertial Friction Welding Equipment2026-99-03098/14/2026
Inertial Friction Welding (IFW) equipment is essential for the welding process of aircraft engine shaft components. However, the absence of comprehensive fault-handling standards for domestically produced inertial friction welding equipment has hindered its further development. This study focuses on the connecting rod and motor of the 30T-IFW equipment, employing a model-based fault detection method. Through simulation, the deformation of the connecting rod and the frequency response of motor vibration acceleration under different working conditions are obtained. Additionally, a monitoring platform is proposed to collect real-time data on connecting rod deformation and motor vibration from actual welding equipment. By establishing a quantitative correlation model of connecting rod deformation-force and revealing the coupling mechanism between motor eccentricity faults and modal frequency vibrations, a hybrid diagnostic framework that combines simulation of primitive warning and measurement of calibration is proposed. At last, the simulation and experimental results verify the effectiveness of the fault diagnosis method proposed in this paper.
Yang, Haifeng, Yuan, Mingqiang, Sun, Tao, Liang, Wu, Gong, Maolin, An, Xingyi, Wang, Qisong, Liu, Dan
Study on the Impact of Vehicle Fixation Methods on the Range Testing of Electric Vehicles2026-99-03268/14/2026
The range energy consumption testing of electric vehicles is usually completed in an environment where the environmental chamber and chassis dynamometer are built. The vehicle is bound to the chassis dynamometer to simulate the range performance on a real road, and the vehicle's fixing method is particularly important, as it even affects the test results. In order to investigate the impact of vehicle fixation as a key testing factor on the range test results of electric vehicles, this study conducted comparative experiments using rigid fixation test vehicles at different positions. By relying on a chassis dynamometer to simulate road resistance and following the Chinese Light Vehicle Test Code (CLTC-P), a range test is conducted on the same electric vehicle under strictly controlled environmental conditions. The experiment collected data on endurance mileage, total energy consumption, and segmented energy consumption. By comparing the differences in simulated resistance and electric energy change trends of chassis dynamometer under different binding methods of test vehicles during the test process, the comprehensive energy consumption results were different. The results showed that the rigid fixation at different positions significantly affected the sliding results of the test vehicle chassis dynamometer, leading to differences in the comprehensive endurance energy consumption results. The comprehensive endurance mileage difference reached 24 kilometers, and the comprehensive energy consumption difference reached 4Wh/km. This study reveals potential sources of system bias in laboratory testing and analyzes the impact of vehicle fixation methods on comprehensive range energy consumption results. The research conclusions can provide a theoretical basis and empirical reference for improving the current standards for energy consumption and range testing of electric vehicles, and enhancing the accuracy and reproducibility of test results.
Zhou, Meng, Jiang, Zhijie, Geng, Peilin
Numerical Simulation Study on Fire Prevention in Aero-Engine Fuel Tube2026-99-03038/14/2026
Transient gas-liquid two-phase flow in aero-engine fuel pipelines was examined using numerical simulations, focusing on the influence of flow rate on phase change behavior. Under low-flow conditions, phase change occurred repeatedly near the pipe wall, where vapor layers formed and collapsed in an intermittent manner. These processes introduced noticeable unsteadiness in the local mass flow and pressure fields. When the flow rate was increased, vapor generation was largely confined to a narrow region adjacent to the wall, and the overall flow exhibited a more stable character. The results suggest that flow-rate-dependent phase change plays an important role in determining the stability of fuel transport and should be considered in the fire safety assessment of aircraft fuel systems.
Wu, Bin, Xin, Bo, Zeng, Tai, Su, Zhengliang
Research on Optimizing Mars Atmospheric Capture Approach Trajectories Based on the Adaptive Differential Evolution Algorithm2026-99-03078/14/2026
To strictly balance orbital insertion precision with engineering constraints during Mars aerocapture, we present an angle-of-attack (AoA) trajectory optimization framework based on adaptive differential evolution. First, a three-degree-of-freedom flight dynamics model was established utilizing the Mars-GRAM 2024 atmospheric standard. Subsequently, we formulated a penalty function centered on apoapsis altitude deviation to enable constraint-oriented dynamic optimization. Within this framework, we introduced an adaptive, direction-guided mutation strategy that integrates global optimal individuals with elite solutions. Furthermore, a parameter update mechanism driven by mutation success rates was developed to significantly enhance algorithmic robustness and computational efficiency. The AoA command sequence for the capture phase was parameterized using a piecewise constant formulation. Comparative simulations under ±30% atmospheric uncertainty demonstrate that, within critical velocity ranges, our improved algorithm elevates the trajectory altitude by approximately 36 km compared to fixed AoA methods. Notably, it reduces convergence time by 50% while strictly adhering to spacecraft physical performance boundaries. These results underscore the method's capability to provide robust, high-precision orbital adjustment support for aerocapture missions in uncertain atmospheric environments.
Tao, Kemeng
Vibration Simulation of Guide Mechanism Based on Rigid-Flexible Coupling Dynamics2026-99-03108/14/2026
For vibration issues induced by coupling effects between flexible barrel guide mechanisms and moving bodies in high-speed dynamic systems, this study investigated their interaction mechanism using flexible multibody dynamics principles. A solid model was developed in 3D CAD software. The modal neutral file (MNF) of the guide mechanism was generated in ABAQUS, and its contact dynamics with the moving body were simulated in ADAMS via flexible contact theory and the modal superposition method. Comparative simulations revealed that incorporating structural flexibility yielded smoother fluctuations in the moving body’s axis inclination angle, providing more accurate system behaviour characterization. Exit velocity and spin rate errors remained below 5% against theoretical values, demonstrating model reliability.
Zhu, Qing, Cheng, Zixiang, Zhuo, Changfei
RBF Neural Network-Based Compound Control for High-Speed Maglev Trains2026-99-03228/14/2026
Aiming at the inherent instability, strong nonlinearity, and high dynamic characteristics of normal-conducting maglev suspension systems, this paper adopts a composite supervisory control scheme integrating PD control and an RBF neural network. First, a high-speed maglev train-track coupled dynamics model considering track elasticity is established. On this basis, a phased control strategy is designed: the initial phase employs a PD controller to ensure system stability, after which control is seamlessly handed over to an RBF neural network. The weights of this network are continuously refined online via a gradient descent algorithm, enabling progressive enhancement of control precision. Simulation results validate the effectiveness of this approach, confirming its superior performance in both precise suspension gap regulation and robust disturbance rejection. Consequently, the proposed method not only underpins the stable operation of maglev trains but also constitutes a reliable intelligent control framework for high-speed maglev systems.
Yu, Yong, Zhang, Jie, Wang, Yu, Liang, Shi
Graph Neural Networks as Surrogate Models for Aerodynamic Prediction: A Review2026-01-50688/13/2026
Machine Learning and more specifically Deep Learning has successfully erupted into a vast number of engineering fields in the recent years, specially leaping traditional simulation approaches by leveraging data usage. Even though the potential is huge, the delicate selection of an adequate Machine Learning architecture for a specific problem determines the success of its implementation. This is essential for non-Euclidean datasets, like the ones found in social networks, molecule structures, manifolds, and others. In those datasets, the distance between two points does not correspond to the Euclidean distance, but to the path along the edges (either weighted or unweighted). This is the case of Computational Fluid Dynamic (CFD) meshes. In all these fields, the fitting of Graph Neural Networks (GNNs) for this type of datasets have made them gain popularity in the recent times. Specially as aerodynamic predictors they have had a remarkable dominance during the last few years, as not only there is a strong academic research trend toward these architectures, but many “AI-consulting engineering companies” offer them as the surrogate model of choice. In this survey, a brief introduction to GNNs is presented. More importantly, and different from other GNN surveys, this review paper focuses on their current application as aerodynamic coefficients and flow field predictors (academic and industrial), with emphasis on their specific architecture. Nineteen publications have been selected for this review, focusing, but not exclusively, on external aerodynamics.
Lazaro Prat, Aleix, Schütz, Thomas, Gau, Holger
Research on the Intelligent Design Method of Roof Frame Based on the Middle Layer and Component Technology2026-99-09078/7/2026
This study proposes an intelligent automotive roof frame design method based on the middle layer and component technology on CATIA. It aims to solve core roof modeling issues: determining geometric input quantity but uncertain attributes (tangent vectors, normal vectors, number of curve segments, number of surface patches, and boundaries), high manual interaction dependence, and poor knowledge reuse, to realize efficient design knowledge reuse. Methodologically, it builds a feature-driven parametric template, develops a knowledge rule-embedded componentized UDF library (reducing repeated modeling and geometric reconstruction needs), and integrates knowledge engineering for geometric input verification and operation direction control, eliminating curve/surface attribute uncertainty impacts. Verification shows the template stably generates roof crossbeams under simple/complex inputs, improving model robustness and reuse rate, reducing design workload, shortening verification cycles, and providing an extensible solution for white body design.
Jin, Chunning, Fu, Xinyu, Hou, Wenbin
683 Guidance for Electronic Shop Reports in Aviation MaintenanceARINC683 (Current)8/4/2026
The purpose of this standard is to provide guidance for the content, format and two-way electronic exchange of data between a Service Provider and the Customer. The standard is focused on the production of an electronic Shop Findings Report by the Service Provider, and to promote automated electronic data exchange.
Aviation Maintenance Committee
Wirtgen demos digital technologies in roadbuilding workflow26TOFHP08_098/1/2026
Wirtgen Group hosted select media at its training and technology center near Nashville to demonstrate the full road-building workflow - from milling and paving to compaction - and how connected machines, automation and real-time data are helping crews to work more efficiently. As part of John Deere's construction equipment portfolio, Wirtgen Group's specialized machinery combines with Deere's TechStack and digital fleet management technology to improve productivity, efficiency, safety and pavement quality. Wirtgen (rehabilitation), Vögele (paving) and Hamm (compaction) machines were in action for the roadbuilding demo. Other Group brands not demoed include Kleeman for crushers and screening plants for processing, and Benninghoven, which is not part of the portfolio in the U.S., for mixing and recycling plants.
Gehm, Ryan
Optimization of Pneumatic Conveying Elbows to Reduce Grain Particle Normal Contact Forces via CFD-DEM Coupling Method2026-99-01167/31/2026
This study presents a refined design for pneumatic conveying pipelines, featuring a grooved structure at the bend aimed at reducing particle breakage during transportation. Using soybean particles as a focus, the research employs a gas-solid two-phase flow approach to explore how different groove depths and widths influence the breakage rate. We used CFD-DEM simulation techniques, combining fluid mechanics with discrete element modeling to achieve a more accurate representation of particle motion and collision forces during expressing. Based on these simulations, we identified the most effective combination of groove width and spacing. Experimental results showed that a groove width of 4.5 mm coupled with a 40 mm spacing could decrease impact forces on particles by approximately 5% to 10% at expressing speeds of 15 m/s and 20 m/s. Throughout all measured time intervals, the impact forces remained stable, with turbulence exerting minimal influence on the particle forces.
Luo, Xinhao, Yang, Tiancheng, Huang, Bo, Mao, Genwu, Dong, Deliang, Shi, Heng, Li, Xiaoliang, He, Bo
A Data-Driven Method for Predicting The Operational Status of The Aircraft Assembly System2026-99-01117/31/2026
Aircraft assembly systems, as a critical phase in aerospace manufacturing, face significant challenges in maintaining production efficiency and ensuring product quality. This complex manufacturing system exhibits two distinct characteristics: (1) tightly coupled interactions among manufacturing elements involving process sequences, material flows, and equipment utilization; and (2) dynamic resource allocation and material distribution plans. The inherent variability in production element configurations often leads to operational instability and schedule deviations, which may result in abnormal production states. To address these challenges, this study proposes a data-driven predictive framework that integrates Long Short-Term Memory (LSTM) neural networks with multi-criteria evaluation. The developed LSTM-based model effectively forecasts two critical production indicators of cycle time and balance rate, achieving temporal prediction through historical operational data analysis. The proposed methodology facilitates timely anomaly detection and early warning, allowing proactive risk mitigation and ensuring sustained production system stability. This research contributes to advancing intelligent monitoring and control strategies for aircraft assembly operations within data-driven manufacturing environments.
Chen, Bolin, Wu, Junjie, Sun, Jinfeng, Wang, Kai
Attention and Intrinsic Curiosity-Enhanced Deep Reinforcement Learning for Path Planning in Dynamic Environments2026-01-50567/31/2026
Efficient and reliable path planning remains a core challenge for autonomous vehicles operating in dynamic and crowded environments. Although Deep Reinforcement Learning (DRL) has shown considerable potential in autonomous decision-making, it still faces challenges such as insufficient feature extraction, sparse rewards, and low obstacle avoidance efficiency in complex scenarios. To address these issues, this paper proposes an end-to-end path planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization (PPO) algorithm, the framework incorporates a dual-channel attention convolutional neural network module (Attention-CNN) to enhance spatial and semantic understanding of dynamic obstacles, and introduces an Intrinsic Curiosity Module (ICM) to promote active exploration in sparse-reward settings. Furthermore, a reactive avoidance reward function based on velocity-obstacle theory is designed and embedded to achieve real-time proactive collision avoidance in highly dynamic environments. Experiments are conducted in a semi-structured dynamic crowd scenario constructed on the GAZEBO simulation platform. The results demonstrate that PPO-ICM-Attn achieves significant improvements in key metrics such as path success rate, travel time, and path efficiency compared to baseline methods like A*+DWA and standard DRL. Although the gap remains in path efficiency compared to A*+DWA, the proposed method exhibits superior robustness and navigation performance overall, validating its effectiveness in complex dynamic environments.
Shen, Shiquan, Liu, Jiahao, Chen, Zheng, Li, Zongdian, Zhao, Yuting, Wu, Minggong, Zhao, Jie, Qin, Zongquan, Wang, Yanfeng
Advanced Damping Force Modeling Using Machine Learning for Next-Generation Electric Vehicle Suspensions2026-01-50537/31/2026
The rapid evolution of electric vehicles (EVs) has led to the development of innovative approaches to optimize ride comfort, handling, and the overall suspension performance. EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics, unlike their conventional internal combustion engine (ICE) counterparts. This paper outlines an advanced damping force modeling methodology using machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The analysis is based on data-driven ML algorithms, i.e., Gradient Boosting, Random Forest, and Neural Networks, to simulate the nonlinear and frequency-dependent phenomenon of dampers in different operating conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. Additionally, this research evaluates the impact of machine-learned damping force predictions on critical ride and handling metrics, including ride comfort, road-holding ability, and energy efficiency. The results demonstrate that the ML models can enhance the iterative design process considerably and help to create the adaptive suspension systems that will address the particular requirements of EVs. This paper contributes to advancing the state-of-the-art of the suspension modeling, incorporating the ML-based insights in the development cycle. It highlights the possibility of artificial intelligence to transform suspension design, paving the way for superior ride quality and vehicle performance in electric mobility.
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
Application of a Novel Noise-Reduction Material to Reduce Squeak Noise in Automotive Instrument Panels2026-01-50657/31/2026
In recent years, driven by increasing consumer demands for vehicle aesthetics and perceived quality, automotive instrument panels (IPs) have extensively adopted materials with poor friction compatibility, such as chrome-plated strips and synthetic leather. Concurrently, the engineering requirement for tighter matching gaps between components has significantly escalated the risk of friction noise. Traditional mitigation strategies—such as material substitution, increasing gap clearances, or applying physical isolation—are often difficult to implement due to design constraints, rendering the IP a critical high-risk zone for abnormal noise. This paper proposes a methodology to mitigate squeak noise between polycarbonate/acrylonitrile butadiene styrene (PC/ABS) and its mating counterparts by modifying the viscoelastic characteristics of the PC/ABS base material through the addition of a specialized polymer. Furthermore, a neural network model was established to objectively determine the noise compatibility of these materials. Evaluations of the material compatibility before and after modification demonstrate that adding a specific proportion of the special polymer to PC/ABS significantly improves its friction compatibility with materials such as polyvinyl chloride (PVC) skin. The efficacy of this solution was confirmed through application and verification in a mass-production vehicle.
Liu, Zubin, Cao, Chunyu, Hou, Hangsheng
Test and Simulation Analysis of Edgewise Compressive Performance of Marine Composite Sandwich Structures with Delamination Defects2026-99-02007/31/2026
In this paper, PTFE membranes were used to preform delamination defects, and VARI technology was employed to prepare marine composite sandwich structures with such defects. The cohesive zone model was used to emulate the interfacial bonding characteristics, thereby establishing a simulation analysis model to assess the edgewise compressive behavior of marine composite sandwich structures with delamination discontinuities. By combining experimental data with simulation results, the edgewise compressive resistance of marine composite sandwich structures was evaluated. Additionally, various parameters including the size, depth, quantity, and geometry of the delamination defects were studied to investigate their effects on the edgewise compressive performance of the marine laminated structures. The research results indicate that as the number of delamination defects increases, the edgewise compressive strength of the sandwich structure gradually decreases. Particularly, when the diameter of the layering defect is less than 30 millimeters, the influence of the defect on the edgewise compressive strength of the sandwich structure can be negligible. Conversely, when the diameter of the defect exceeds 30 millimeters, the rate of decrease in edgewise compressive strength increases significantly with the increase in the diameter of the defect, thereby greatly exacerbating the adverse effects of the delamination defects and ultimately resulting in a 10.77% reduction in the edgewise compressive strength. Furthermore, it was observed that the delamination defects located at the interface between the two panels and the core material on both sides of the sandwich structure do not affect each other, and to a certain extent, improve the compressive stability of the specimen. The degree of edgewise compressive strength reduction caused by elliptical delamination defects with the same area and long axis length is less than that of corresponding circular delamination defects, indicating that using circular delamination defects in the analysis of composite material structures with delamination defects is safer.
Zhang, Yao, Xu, Mingcai, Bian, Tianya, Zhou, Songqiang, Ji, Bing, Cheng, Jiahuan, Zhuang, Ya, Li, Xiang
Development and Experiment of Rolling Friction and Wear Testing Machine Based on LabVIEW2026-99-02457/31/2026
To investigate the friction and wear characteristics of rolling bearings under various operating conditions, this paper develops a ring-block type rolling bearing friction and wear testing machine based on LabVIEW. The device achieves rotational friction by regulating the speed with a motor and applies the test load using a lever and weight loading method to simulate the actual working conditions of rolling bearings. The testing machine integrates a high-frequency response force sensor with a high-sampling rate data acquisition system, and combines with the LabVIEW platform to achieve real-time collection, processing, and display of friction force. Using a rolling bearing with a diameter of 50mm as the test object, continuous testing was conducted for 100 s under the conditions of a speed of 500r/min and a load of 50N. The experimental results show that the friction coefficient fluctuated significantly in the early stage of the test (the first 20s), and then stabilized. After stabilization, the average friction coefficient measured was approximately 0.005, which is highly consistent with the theoretical value, verifying the accuracy and reliability of this machine. The testing machine has a compact structure and is easy to operate. It is suitable for friction performance testing of various types of rolling bearings and provides an effective experimental means for the tribological research of rolling bearings.
Xiao, Supeng, Hu, Rui, Xu, Chunxia, Liu, Yang, Chen, Binhua
Engineering Practice of Sealing Design and Testing Technology for the Solar Array Drive Assembly of Tianhe Core Module2026-99-02437/31/2026
The Core Module of the space station is the first module of China’s Space Station, responsible for controlling the key parameters such as orbit, speed, and pressure of the entire space station, and serving as the control center of the assembly. The Solar Array Drive Assembly is a part of the Core Module. It needs to participate in the functional requirements of the whole cabin sealing of the cabin body, so it adopts the design scheme of semi-sealed. By adjusting the compression ratio and volume fraction of the sealing ring, and the roughness of the sealing surface, the overall sealing performance is improved. A small cavity leak detection hole is added to realize the sealing effect of detecting the second-layer seal separately. The real leakage rate of the drive mechanism is detected effectively by using multiple calibration schemes in the leak detection process, and the semi-sealing technology of the Solar Array Drive Assembly is verified, which has guidance and reference significance for the subsequent spacecraft design requiring a sealing function.
Dai, Fei, Zhu, Jiahao, Huang, Mengzhe, Qian, Zhiyuan
Construction of a Digital Management Platform for Intelligent Workshops in the Electronics Industry2026-99-01177/31/2026
Under China’s intelligent manufacturing strategy, manufacturing enterprises are expected to achieve digital and networked operations by 2025, with full digital transformation by 2030. Intelligent factories, the core of this transformation, rely on interconnected, integrated, and data-fused systems. This paper focuses on the micro-assembly intelligent workshop at the Nanjing Research Institute of Electronics Technology, which produces micro-circuit modules for large-scale complex electronic systems. The workshop combines discrete and process manufacturing modes, presenting unique challenges for digital management. A digital management platform based on a five-layer architecture (device, network, data, application, and decision layers) is proposed to address multi-dimensional business needs, including production scheduling, logistics, execution, and decision optimization. A hierarchical workflow structure of the workshop, consisting of a main workflow and several sub-processes, is in-depth studied and designed. The platform is constructed based on requirements analysis and workflow design of the workshop and integrates systems such as MES, APS, WMS, and SCADA, supported by AI-driven big data analytics. This study offers a practical framework for advancing digital transformation in the electronics industry.
Zhang, Jian, Wang, Jiafeng, Guo, Yongzhao
Research on Data-Driven Intelligent Monitoring and Prediction System for Fatigue Damage in Metallurgical Cranes2026-99-02167/31/2026
Metallurgical cranes have a high risk of structural fatigue damage and failure under complex working conditions such as high temperature, heavy load, and strong electromagnetic interference. This article proposes a data-driven structural fatigue damage health monitoring system. This system integrates fiber Bragg grating sensing technology, rigid flexible coupling multi-body dynamics simulation, and big data analysis methods to construct a sensor optimization layout strategy based on rigid flexible coupling virtual prototype simulation, achieving real-time perception of stress states in key parts such as the mid span and end beam corners of the main beam. Develop a visualization system that integrates health monitoring, damage diagnosis, and life prediction. This system can dynamically evaluate the structural health status of metallurgical cranes and predict the remaining life of the structure based on a nonlinear cumulative damage model. On site engineering applications have shown that the monitoring and prediction visualization system can effectively improve the intelligent and safe operation and maintenance level of metallurgical cranes, providing a data foundation and possibility for their predictive maintenance.
Chen, Li, Zhang, Xu, Ding, Keqin
Research on the Erosion and Wear Characteristics of Gathering and Transportation Pipelines by Cyclone Abrasive Process Parameters2026-99-01847/31/2026
Cyclone abrasive pigging technology, with advantages like environmental friendliness, easy construction, and low destructiveness, has broad application prospects. Studying how the process parameters affect the erosion-wear characteristics of gathering pipelines is crucial for improving pigging efficiency and effectiveness. This study adopted numerical simulations based on gas-solid two-phase flow erosion theory to explore such effects and verified the simulations via a self-designed experimental platform. Results showed that within the given parameter range, erosion rate rose significantly with velocity, especially at 20-30 m/s, peaking at 60 m/s; 0.6 mm abrasives and 0.25 kg/s mass flow rate led to higher erosion rates. Experimental data matched simulations with <10% error, confirming accuracy. Thus, cyclone abrasive process parameters significantly influence pigging performance, and the findings can guide practical operations within the studied range.
Wang, Haoran, Zhou, Xianjun, Li, Long, Song, Huifang, Zhang, Jin, Jv, Xiaolong
Research and Application of Low-Cost Aerospace Chip Burn-in Systems2026-99-01587/31/2026
In view of the key problems—low chip burn-in efficiency and high burn-in costs—caused by high R&D costs and a limited number of veneer stations in the traditional burn-in system used in the military aerospace field, this project has carried out a series of innovative research. Through systematic scheme optimization design and strict cost control measures, a new burn-in system with significant cost advantages and supporting multi-station parallel processing has been successfully developed for the aerospace field. The core technical breakthroughs of the system are mainly reflected in three aspects: first, through architectural reconstruction, the number of single incubator stations has been increased by leaps and bounds from the traditional 60 to 720; secondly, the use of intelligent monitoring technology can expand the scale of the workstation while using the display for process monitoring and data collection; Finally, the modular design concept is innovatively introduced, which greatly reduces the construction cost per workstation. Actual tests have verified that the processing efficiency of the AD1120 chip burn-in system has achieved a significant improvement of 1100%, which is equivalent to increasing the processing capacity of a single batch by 11 times. Up to now, the system has completed the 160-hour continuous burn-in test of 5,000 AD1120 chips, during which the system operation is stable and reliable, and there is no abnormality in the use of the test chip manufacturers. This breakthrough performance improvement not only significantly shortens the product development cycle but, more importantly, provides a practical technical solution for batch screening of high-reliability chips. Subsequent promotion and application can meet the mass production needs of a variety of chips in the aerospace industry, and provide a way to reduce costs and increase efficiency for the same type of unit.
Gu, Zucheng, Kang, Xiao, Jiang, Shang
Surrogate Model Study for Curvilinear Fibre Variable Stiffness Composite Laminates: Based on Dynamic Hybrid Sampling and Adaptive Surrogate Model Hyper-Parameters Co-Optimization Strategy2026-99-02417/31/2026
Variable stiffness composite laminates with curvilinear fibres have demonstrated significant capability in lightweight structural design, particularly regarding buckling resistance and stiffness enhancement. However, directly applying optimization algorithms often faces challenges such as high computational cost and slow convergence during the optimization design process. Consequently, the incorporation of surrogate models prior to employing optimization algorithms is necessary to simplify computations and accelerate convergence. Manual testing is a conventional approach for hyper-parameter (HP) tuning and continues to be widely used in research. However, manual tuning is suboptimal and time-consuming for many problems. Additionally, the effectiveness of these surrogate models largely depends on the training samples. Therefore, a dynamic hybrid sampling and adaptive surrogate model HP co-optimization strategy is proposed for the optimization design of the variable stiffness composite laminate with curvilinear fibre. In the numerical results, the performance of different surrogate models, comprising Support Vector Regression (SVR), Radial Basis Function Neural Networks (RBFNN), and Back Propagation Neural Networks (BPNN), is systematically compared under varying sample set sizes. Neural results show significant differences in accuracy and efficiency among these three models under varying sample set sizes. SVR demonstrates optimal generalization ability in small sample scenarios, RBFNN strikes a balance between accuracy and efficiency with medium sample size, while BPNN exhibits superior overall predictive performance under large sample condition. The proposed cooptimization strategy overcomes the limitations of traditional single strategy through the closed-loop interaction between dynamic sampling and Bayesian hyper-parameter optimization (HPO). This approach not only significantly improves the predictive accuracy of surrogate models but also greatly reduces the computational cost during the optimization process, making it suitable for computational mechanics problems with high nonlinearity and high-dimensional features. This study provides theoretical foundations and practical guidance for the selection and application of surrogate models in composite material structural optimization, contributing to improved design process efficiency and reliability.
Chen, Dengnuo, Zou, Rui, Chen, Binqi
Desulfurization and Acid Resistance of Multifunctional Protective Coatings in Severe Environment2026-99-02387/31/2026
Desulfurization equipment in electric power industry is in a multi-field coupled corrosion environment with high temperature, high humidity, strong acid and solid-containing slurry. The annual direct economic loss of corrosion exceeds 5 billion yuan, and the equipment replacement cycle is only 1.5-2 years. Traditional protective coatings are difficult to meet the needs. The concept of “bionic barrier-intelligent response-in-situ purification” is proposed to construct multifunctional protective coatings: The Langmuir-Blodgett technique was used to alternately assemble MXene nanosheets and polysilazane. Ti-O-Si covalent bonds enhanced the interface bonding, resulting in a coating hardness of 4H and an elongation of 200%. After 1500 hours of extreme environment test, the coating has low weight loss rate, high self-repair and antibacterial rate, and its service life is extended by 8 times. The engineering application makes the maintenance period of desulfurization tower of a 660MW unit extended from 8 months to 6 years, saving 1.2 million yuan annually, and increasing 200,000 yuan annually by recovering H ˇ SO 2. It provides a cross-scale scheme for electric power corrosion protection.
Nie, Pengfei, Gao, Jiangyu, Chen, Wei
An Automatic Cleaning System for Copper Bar Chain of Cigarette Cutting Machine2026-99-01317/31/2026
The cutting machine is a critical component in the cigarette processing line, and its cutting quality depends on the operational condition of the copper bar chain. The grooves on the surface of the copper bar chain accumulate dust during the operation of the machine, which often causes unstable conveyance of raw materials, significant width variations, and a high defect rate. To address this issue, this study developed a linear reciprocating automatic cleaning system for copper bar chains to remove dust from the groove surface and hinge joints. Experiments have verified that the system improved the cutting qualification rate, increased the operational stability of the cutting machine, reduced manual cleaning workloads, and reached higher cleaning efficiency. This innovative system is also expected to provide a valuable reference for similar equipment manufacturers and advance technological innovation in the cigarette processing industry.
Li, Haitao, Zhang, Chunyuan, Fang, Junqing, Su, Lin, Chen, Peng, Guo, Zhiwei, Xing, Dongdong
A New Ternary Phase Diagram Prediction Descriptor Combined with Machine Learning Research2026-99-01807/31/2026
The morphological characteristics of ternary phase diagrams play a pivotal role in optimizing material properties and facilitating the design of novel alloys. In this study, machine learning (ML) is used to predict the number of phases in ternary alloy systems. A new feature descriptor for phase diagram prediction is proposed in ML, which includes the characteristics of element properties, thermodynamic properties of materials and CALPHAD parameters. Initially, this study constructed a dataset comprising various feature descriptors and validated their correctness employing ML models such as LRC, SVM, RFC, Bagging and GBDT. Subsequently, comparing the performance of different models, and the better-performing models Bagging and GBDT were selected for further prediction studies. The models were fine-tuned using grid search and random search methods to optimize their predictive performance. Ultimately, by predicting phase diagram data for multiple ternary systems at different temperatures, the accuracy rate near the temperature range of the given experimental data was approximately 82%. This demonstrates phase diagram descriptors in conjunction with machine learning to predict ternary phase diagram proposed in this study is practicable. The predicted data also provide guidance for experimental determination of phase diagrams and lay the foundation for future material design and optimization.
Fan, Hanchao, Su, Yu, Jin, Zongxiao, Li, Jun, Lee, Soowohn, Tang, Jianguo, Fu, Huaqing, Du, Zhi
Machine Learning Approach for Bidirectional Modeling and Optimization of Inconel 625 Deposition in Wire Arc Additive Manufacturing05-19-04-00307/30/2026
Machine learning (ML) techniques are increasingly being applied to establish correlations between input parameters and key process responses in the wire arc additive manufacturing (WAAM) process. Despite their potential, there remains limited understanding of how to develop an integrated ML framework that simultaneously considers both the dataset characteristics and the modeling approach to ensure accurate and reliable predictions. The present study addresses this gap by developing an integrated ML framework to predict the deposition behavior of Inconel 625 in WAAM. To capture nonlinear system behavior, three ML methods, namely artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS), were developed and systematically evaluated for predictive modeling and process optimization, considering deposited geometry, area, and efficiency as the key output characteristics. The input parameters, i.e., voltage, wire feed rate, torch travel speed, and shielding gas flow rate, were identified as critical factors influencing the deposition process. The datasets were preprocessed to remove noise and analyzed to extract relevant features that captured the intrinsic physical behavior of the process. Performances of the ML models were evaluated using a separate test dataset, and predictions were assessed through mean absolute percentage deviation (MAPD). Results demonstrated that integrated ML framework could accurately represent intricate interdependencies among process parameters on deposition outcomes, providing a robust method of predictive modeling and parametric process optimization for Inconel 625 deposition by WAAM process. The ANN model demonstrated satisfactory performance for forward modeling with MAPD values of 12.24, 14.87, and 11.91 for deposition geometry, deposition area, and deposition efficiency, respectively. For inverse modeling, the ANN accurately predicted key inputs from outputs, with MAPD values of 1.39, 18.91, 12.25, and 19.36 for voltage, wire feed rate, torch speed, and shielding gas flow rate, respectively. Bidirectional predictive modeling keeps to set operating conditions to achieve desired depositions and process automations.
Samanta, Avishek, Maji, Kuntal
Research on Welding Deformation of the Car Body Based on 3D Laser Scanning2026-99-08217/30/2026
This study aims to verify the accuracy and stability of a system used for measuring and analyzing the welding deformation of vehicle bodies under different welding parameters. A 3D laser scanner was employed to capture the surface topography data of the vehicle’s front deck before and after welding. In order to determine the welding deformation, PolyWorks software was utilized for deformation analysis, which processed the 3D scanning data and compared the post-welding data set. A dedicated vehicle body welding deformation measurement system was developed, including hardware configuration and software development. The BP neural network algorithm was adopted to predict the welding deformation, and the results indicated that the deviation between the predicted values and the average experimental measurements was less than 10%. This confirmed the practicality of the BP neural network in predicting welding deformation and highlighted its effectiveness in technical support for the optimization of welding parameters and deformation control in automotive manufacturing.
Li, Lina, Zhang, Yiqi, Sun, Hongchang, Wei, Xiezhen
Analysis of the Hot Forming Behavior of AZ91 Magnesium Alloy2026-99-08387/30/2026
This study systematically discussed the high-temperature flow behavior of the Mg-Al-Zn based AZ91 alloy, which has significant application potential in modern aviation and automotive industries. The study was carried out in the temperature range of 250°C-450°C and the strain rate range of 0.001 s^−1 -0.1 s^−1, which met the typical industrial hot processing environment. The analysis of high-temperature flow behavior shows that the flow stress is inversely proportional to the deformation temperature and is proportional to the strain rate. An important finding is that the constitutive model parameters are significantly sensitive to strain, so the strain-compensated Arrhenius constitutive model is developed. The model shows high accuracy in predicting the thermal flow stress of AZ91, and provides a valuable calculation tool for the simulation and optimization of forming processes in aerospace parts manufacturing. The results show that the extruded original microstructure presents slender fine grains, while the deformed sample shows a temperature dependent transformation: the low-temperature bimodal structure evolves into uniform fine grains at intermediate temperature, and the grains begin to coarsen at high temperature. At constant high temperature, low strain rate promotes grain growth and twin formation, while high strain rate refines grains and inhibits twins, and dislocation slip is the dominant deformation mechanism. These findings provide vital guidance and support for optimizing hot working parameters of AZ91, and are particularly important for manufacturing lightweight components in aircraft structures and automotive systems. The established process performance relationship is helpful to develop energy-saving manufacturing strategies for transportation equipment, and supports the goal of reducing weight and improving performance in the industrial field.
Li, Jusen, Chang, Ming, Zhu, Wenyu, Sun, Haoran, Chen, Kaida, Yang, Xiaoyin, Zheng, Zhenhao, Zhao, Shengdun
Design of a Digital Twin Interactive System for a 3D Printer2026-99-08597/30/2026
This paper takes a 3D Printer proposed by the project team in the early stage as the research object, constructs a digital twin entity including 3D models and data models in order to develop a digital twin interactive software. By activating the real-time correlation between 3D models and data models, valuable data exchange can be achieved between the digital twin entity and the physical entity, and valuable data can be used to drive both to refresh their operating status.
Li, Qi, Wu, WenKai, Lang, ZhiQi, Jiao, HongCheng, Jing, Tao, Zhao, HanTao, Dong, Shen, Shi, Lei
Research on Integrated Design and Optimization Method of High Speed Shuttle UUV Rudder and Propeller2026-99-08647/30/2026
Along with the advancement of the maritime power strategy, the research, development, and application of deep-sea space stations are becoming increasingly important. However, since deep-sea space stations mainly rely on acoustic communication, they cannot exchange information with ground stations quickly and accurately. To improve data transmission efficiency, this paper proposes using a high-speed shuttle UUV instead of acoustic communication. In this context, an efficient propulsion system is critical as it enables the UUV to achieve high speed and maintain stability. A propeller meeting the 110.9 N thrust requirement is designed using the chart design method, and the 110BL230-630 brushless DC motor is selected based on motor–propeller matching. This motor has a rated speed of 3000 rpm, rated power of 3000 W, and torque of 9.6 Nm. The performance curve of the NACA0012 airfoil is analyzed to select an appropriate rudder surface. The rudder area (4067 mm^2) is designed in accordance with DNV rules, with the following parameters: tip chord length 40 mm, root chord length 40 mm, and half-span 70 mm. CFD analysis is conducted on the designed propeller and the UUV equipped with the integrated propulsion system. The predicted performance of the P4119 propeller (hydrodynamic parameter deviation ≤ 1%) and the SUBOFF hull (resistance relative error ≤ 3.04%) confirms the accuracy of the CFD method for calculating propeller open-water performance and UUV drag. Through comparative analysis, the optimal rudder–propeller spacing is determined to be 60 mm, as this spacing yields the highest propulsion efficiency.
Wei, Jiaguang, Feng, Xiaowei, Zhao, Fuchen, Wang, Xingke, Xu, Shanzhi, He, Wenxuan
Design and Experiment of a Stable Seed Supply Control System for an Elevating Type Sugarcane Seed Supply Device2026-99-08197/30/2026
Aiming at the problems of seed cane pile-up and unstable seed supply efficiency in the sugarcane seed production line caused by the seed supply device, a stable seed supply control system was designed, which consists of a seed collection box, an elastic seed-clearing plate and an electrical control system, etc. The EDEM-RecurDyn coupling simulation was adopted to analyze the seed supply process, and the optimal elastic seed-clearing plate structure was designed. Using the single factor test and Box–Behnken experimental design analyzed the effects of the seed supply belt speed, the speed of the first conveyor belt, the number of sugarcane seeds in the collection box and the seed cutting efficiency on the supply efficiency. Establish a quadratic regression model for the efficiency of seed supply and determine the optimal parameter combination: the seed supply belt speed of 0.097 m/s, first conveyor belt speed of 1.639 m/s, and the number of sugarcane seeds is 14. Using the number of sugarcane seeds as the input quantity for the controller, the real-time data is fed back by the TOF sensor. The controller automatically adjusts the seed-cutting efficiency to maintain the continuity and stability of the seed supply process of the seed supply device. The test results show that after applying this system, the seed supply efficiency reached 1.77 setts/s, which was 6% higher than that of the fixed-parameter system. This research can provide technical support for the stable seed supply of integrated equipment for sugarcane seed production.
Li, Shangping, Xu, Hechang, Ouyang, Runhong, Li, Kaihua
Research on a Rocket Projectile Impact Point Prediction Model Based on Improved Proportional Navigation Guidance Law2026-99-16027/24/2026
Rocket projectiles are a type of ammunition that get their power from rocket engines. Long-range guided rockets, in particular, hold great significance as they seem to mark the way forward in modern warfare. These guided projectiles take full advantage of the considerable range that long-range rockets offer and, at the same time, manage to achieve improved accuracy. This paper delves into a model that is used for predicting the impact point of rocket projectiles, with the application of the proportional navigation guidance law. It also undertakes an analysis of both the strengths and the weaknesses of this model. Through the formulation of equations related to the dynamics of the center of mass and some other supplementary equations, a rather comprehensive trajectory equation was worked out. When this trajectory was simulated, it brought about the creation of a firing table, which is of help in predicting the initial trajectory inclination angle.
Tao, Wenwen, Wang, Ru, Zhang, Liang, Pi, Runge
Influence of Modified Fly Ash Content on the Properties of Resin-Based Friction Materials2026-99-16577/24/2026
In this study, five resin-based brake pad samples with modified fly ash contents of 0%, 4%, 8%, 12%, and 16% were prepared to investigate the influence of fly ash content on the comprehensive performance of the friction materials. The tribological properties of all samples were evaluated under temperature conditions ranging from 100 °C to 350 °C, and their overall performance was assessed using five evaluation indices. Based on the AHP-MOORA algorithm, sample F12 exhibited the highest comprehensive weighted score of 0.11, followed by samples F0 and F8 with scores of 0.10 and 0.09, respectively, indicating a slight decline. In contrast, the comprehensive weighted scores of F4 and F16 were relatively low, at 0.05 and −0.01, respectively. Among the five composites, F12 demonstrated the best overall performance, with F0 and F8 ranking next, while F4 and F16 performed poorly. These results suggest that, within a certain range, increasing the fly ash content can enhance the comprehensive properties of the material. However, excessive addition of fly ash may lead to the detachment of harder particles during wear, thereby increasing wear thickness and wear rate.
Li, Xiaobiao, He, Kang, Zhao, Zhuanzhe, Wu, Bo, Sun, Fei
Hybrid Particle Swarm Optimization and Machine Learning Framework for Enhanced Prediction of Ground Settlement during Rectangular Pipe Jacking2026-99-16477/24/2026
Accurate prediction of ground settlement induced by rectangular pipe jacking, a prevalent trenchless technology in urban infrastructure development, remains a significant challenge. This study addresses this by developing and evaluating a robust machine learning (ML) framework. Leveraging 104 sets of field monitoring data from the Liuye Avenue West Extension rectangular pipe jacking project in Hunan, China, key construction parameters including jacking force, advance rate, and grouting pressure were utilized as inputs to predict ground settlement. A Particle Swarm Optimization (PSO) algorithm was integrated for automated hyperparameter tuning of six distinct ML models: standalone Least Squares Support Vector Machine (LSSVM), Backpropagation Neural Network (BPNN), Random Forest (RF), and their respective PSO-optimized counterparts. Comprehensive performance evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R^2) revealed that the PSO-LSSVM hybrid model achieved superior predictive accuracy and generalization capability. Specifically, on the test dataset, the PSO-LSSVM model yielded an MSE of 0.367, MAE of 0.424, and an R^2 of 0.941. These findings demonstrate that the proposed PSO-enhanced LSSVM model significantly outperforms baseline models, offering a highly effective and reliable tool for predicting ground deformation in similar complex pipe jacking projects.
Hu, Shiwei, Hu, Rong, Zhang, Hong, Chen, Yi, Hu, Da
Fault Diagnosis of Packaging Machine Bearings Based on EEMD-CNN-BiLSTM2026-99-16017/24/2026
During the high-speed operation of packaging machines, if the abnormal components evolve into faults, the packaging machines often stop for inspection or even damage, causing production stagnation and huge economic losses. If key variables are predicted and faults are identified before the evolution of packaging machine failures, it is of great significance to ensure equipment safety and reduce maintenance costs and losses for enterprises. The purpose of fault prediction is to use the information modeling of equipment historical data to output the changes in key features before component failures in the future. Firstly, for the redundant data of multiple measurement points of the same variable in the packaging machine process variables, Pearson correlation analysis is used to obtain more accurate variable data. We reuse adaptive empirical mode decomposition (EEMD) for signal processing and feature extraction, reduce redundant information, use convolutional neural network (CNN) models for spatial feature learning, and then use bidirectional long short-term memory models to capture temporal dependencies of CNN information for capturing time series data. A model is established on the normal training set to fit the normal state of the packaging machine, identify different types and degrees of equipment fault characteristics through normal test set data, and send the predicted results of the equipment state to the fault classifier for judgment to determine whether to issue a fault warning. The results indicate that this article has validated the effectiveness of the model in fault feature extraction and high-precision fault classification through training on equipment status data.
Wu, Aimin, Liu, Shixian, Zhao, Lihui, Liu, Zhao, Wu, Tao, Li, Lianbing
Alignment Control of an Aerial Wireless Power Transmission Device2026-99-16227/24/2026
With the development of battery technology and wireless power transmission technology, their applications in the aviation field have broad prospects. Alignment control is the key to achieving wireless power transmission in the air. This paper first establishes a mathematical model for the proposed wireless power transmission device, decomposes and simplifies it to obtain a controlled object model that is more suitable for the algorithm in this paper. Aiming at the accuracy of alignment control in the task, a fused dynamic inverse algorithm with feedforward optimization was designed step by step and verified through simulation. In the simulation, typical application scenarios were designed in combination with the requirements of wireless power transmission technology. The results show that compared with the general dynamic inversion algorithm and differential feedback-fused dynamic inverse algorithm, better alignment control effects have been achieved, and this algorithm can achieve alignment within the expected error range, further verifying its effectiveness and having certain application prospects.
Tan, Xudong, Hei, Wenjing, Lei, Yidi
Design and Application of A Pressure Measurement System Based on Spacecraft Chemical Propellant Engine Plume Test2026-99-16237/24/2026
Spacecraft with chemical propellant engines, especially spacecraft for exploring extraterrestrial objects, need to carry out plume tests on the ground in order to determine the influence of engine plumes on spacecraft. An important purpose of the plume test is to accurately measure the pressure field in key parts of the spacecraft. In this paper, according to the pressure measurement requirements of the spacecraft plume test, the design of a pressure measurement system is carried out, which mainly includes a pressure measurement sensor, a pressure difference measurement sensor, a pipeline, a cable, a measuring instrument, a data acquisition instrument, upper measurement software, and so on. The designed pressure measurement system was successfully applied to the plume impact test of Chang'e VII, which provided important technical support for the development of the spacecraft.
Wu, Yue, Guo, Qinliang, Wu, Dongliang, Liu, Xiaoning, Tao, Dongxing, Lin, Boying, Xie, Zheng, Wei, Xi, Niu, Tong
Parameter Identification of Projectiles Based on an Improved Butterfly Algorithm Optimizing a Mixed Kernel Extreme Learning Machine2026-99-16167/24/2026
Accurate projectile dynamic modelling requires identifying aerodynamic parameters. The traditional methods for identifying aerodynamic parameters of missiles suffer from significant modeling errors. Therefore, this study proposes an improved butterfly-shaped optimization hybrid extreme learning machine algorithm. It combines the butterfly algorithm with a hybrid extreme learning machine, Cauchy mutation, and adaptive weight. The search ability of the Butterfly algorithm is enhanced by introducing the Cauchy distribution function and adaptive weighting factors. In addition, to balance the weights of searches and to optimize the regularization coefficients and kernel function parameters, the dynamic switching probability p is introduced. The identification accuracy of four different algorithms was compared under noise-free conditions. The feasibility of the improved butterfly-optimized hybrid extreme learning machine was verified. When there is noise, the strength of the algorithm is confirmed by comparing the effect of different noise levels on how well it can identify things. The simulation results show that the improved butterfly optimization hybrid extreme learning machine algorithm has higher accuracy and better robustness in identifying projectile aerodynamic parameters. The simulation results show that the improved butterfly optimization hybrid extreme learning machine algorithm has higher accuracy and better robustness in identifying projectile aerodynamic parameters.
Wang, Qianqian, Wang, Kangjian, Jiao, Wenjie, Yi, Wenjun, Chen, Jintong
Research on a Real-Time Pilot-Induced Oscillation Detector Based on a Convolutional Neural Network2026-99-16137/24/2026
This study aimed to develop a real-time Pilot-Induced Oscillation (PIO) detector by utilizing a Convolutional Neural Network (CNN) and applying it to flight test monitoring. For data training, a database of PIO samples is established. CNN has the advantage of lower complexity and automatic feature learning; it is chosen to develop the PIO detector. The results showed that the accuracy of the trained model has reached 93.71%, where the recall rate reaches 94.74%. To better assist the monitoring team, a simple software is designed based on the trained model for PIO detection, and it has been applied in flight test monitoring. In conclusion, this research demonstrates that the CNN Algorithm can be utilized in PIO discrimination and improve the monitoring capacity for flight testing.
Han, Yiwen, Liu, Chaoqiang
Analysis and Experimental Research on The Impact of the BangBang Valve Switch on the Flow Rate of the Storage and Supply System2026-99-16157/24/2026
This article focuses on a wide range of high-precision storage and supply systems. Under the rated flow rate of 2.928 mg / s of the proportional flow controller, the instantaneous flow fluctuation range of the BangBang valve reaches 2.963 mg / s, exceeding the control accuracy requirement of 1% for the proportional flow controller. By establishing mathematical models of the BangBang valve, proportional valve, and proportional flow controller for simulation analysis, the trend of the simulation results is consistent with the experimental results. Furthermore, considering the spatial layout and weight of the storage and supply system, this paper proposes a method to improve the accuracy of flow output by adding 180 mL of air capacity between the proportional valve and the proportional flow controller. Ultimately, the maximum flow fluctuation of the proportional flow controller at the moment of the BangBang valve opening and closing is 2.941 mg / s, which meets the control accuracy of the proportional flow controller. Moreover, the error between the output flow rate of the proportional flow controller and the rated working flow rate is minor after increasing the air capacity.
Li, Zhong, Yan, Zelong, Huang, Tiankun
Optimization of Turbine Discrete Noise by Non-Uniformly Distributed Blade Design2026-99-16177/24/2026
The Mellin non-uniformly distributed moving blade method was adopted to conduct CFD numerical analysis and sample experimental tests on the axial-flow turbines before and after optimization using the uniformly distributed and non-uniformly distributed design methods, respectively. In the original design, five blades were evenly distributed in the 360° circumferential direction, and the non-uniformly distributed angles were 46°, 102°, 46°, 83°, and 83°. CFD numerical analysis shows that due to the low rotational speed of the turbine and the absence of a sealing structure at the blade tip, factors such as tip noise leakage and backflow have little impact, and the flow field pulsation is mainly caused by the blades themselves. The non-uniformly distributed design can significantly enhance the work-doing capacity of the blades. At 90% of the blade height, the torque can be increased by up to 60%, but at the same time, the axial force on the blades also increases accordingly. Near 80% - 90% of the blade height, the axial force increases by 33%. The flow rate performance of the non-uniformly distributed design is slightly inferior to that of the uniformly distributed design, but the overall noise is better than that of the uniformly distributed design, with maximum optimization of 0.48 dB (A); the maximum values of the first three orders of discrete noise are significantly improved, with a maximum improvement of 0.75 dB (A), and the discrete noise orders of the non-uniformly distributed turbine can avoid blade - related factors and disperse the energy to nearby orders.
Wu, Aiping, Ma, Tianli, Wang, Shiming, Ding, Chengling
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