Browse Topic: Data acquisition and handling

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Short-Term Load Forecasting of Sustainable Power Systems Based on Newton–Raphson-Based Optimizer–Extreme Gradient Boosting under Extreme Weather Events13-07-02-00089/17/2026
With continuous advancements in load-side resources such as distributed photovoltaic systems, electric vehicles, and virtual power plants, the low-carbon and sustainable development attributes of power systems have been significantly enhanced. Meanwhile, the coupling intensity between sustainable power systems and meteorological conditions has been further consolidated. Considerable impacts are exerted by weather variations, particularly extreme weather events, on the dispatching and operation of sustainable power systems. Accurate load forecasting is critical for enabling sustainable power systems operators to optimize power generation strategy, ensuring supply stability and resilience against extreme weather-induced disruptions. However, the intrinsic non-stationarity and volatility of extreme weather events present significant challenges to conventional forecasting approaches. Herein, we introduce a hybrid algorithm integrating Newton–Raphson-based optimizer (NRBO) with extreme gradient boosting (XGBoost) to enhance short-term load predictions under such conditions. The model uses optimally selected meteorological and load features as inputs, while NRBO systematically tunes XGBoost’s hyper-parameters to maximize performance. Evaluated on an Irish dataset, the proposed framework is quantitatively compared against five baseline models, including traditional decision trees and neural networks. The case studies show that the mean absolute percentage error (MAPE) of the proposed model is 2.57%, which is the lowest among these decision tree and neural network algorithms.
Wang, Yi, Zhou, Jian, Wu, Gang, Ma, Tiannan, Ma, Ruiguang, He, Chuan, Zhu, Huixian
Physics-Informed Machine Learning Digital Twin of Electro-Mechanical Brakes for Clamp-Force and Friction Estimation2026-01-08069/14/2026
Recently, there has been a drastic shift in the industry towards wire architectures like steer-by-wire and brake-by-wire. For safe and accurate force control, diagnostics, and consistent performance over the operating envelope, accurate plant modeling of the Electro-Mechanical Brake (EMB) is important. Classical approaches involved linearized dynamic EMB models and the use of the characteristic stiffness curve for calibration at the operating points. These methods often perform poorly over regions where hysteresis, compliance, and friction are strongly nonlinear. Prior research on state or force estimation for EMB has focused on pad contact detection, thermal adaptation, and hysteresis-aware clamp force estimation. However, there are still accuracy gaps in practical applications during transients and under shifting friction regimes. In this work, a digital twin based on Physics-Informed Machine Learning is introduced, following the governing dynamics of the actuator-caliper assembly of EMB while learning (i) a physically significant parameter—system damping (Bsys) and (ii) a non-linear friction term constrained as a function of the actuator motion states and operating conditions. Non-linear friction is captured through gray-box friction formulation and learning unmodeled residual dynamics such as hysteresis and backlash. An EMB test stand is used to collect steps, ramps, holds/engagements, APRBS, and swept-sine excitations, with signals including time-aligned force command, motor torque/current, actuator position/velocity, and pad force measurement from a force sensor for model training. Results demonstrate a decrease in pad-force prediction error, along with non-linear and residual friction estimation. The resulting digital twin can enable sensor-less force estimation, friction compensation design, predictive analytics, and health monitoring through tracking parameter drift and friction signatures.
Rai, Prakhar, Gadhvi, Tirth
Bridging Human Expertise and Machine Intelligence in Chassis System Teardown Evaluation2026-01-08059/14/2026
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
Divakaruni, Saikiran, Vaibhav, Veer, Hansen, Scott, Hood, Trevor, Agrawal, Rahul
Further Investigation of the Variables Affecting Brake Squeal: Dual-Piston and Single-Piston Calipers with NAO Pads2026-01-08259/14/2026
An earlier publication reported that brake squeal occurrence increases with increasing (inboard/outboard) pads wear rate difference in the case of a front dual-piston (twin-piston) caliper for a GVW vehicle of 2,510 kg fitted with Lowmet pads of straight chamfers and diamond chamfers. The current investigation was undertaken to find out if a front dual-piston caliper for a heavier vehicle (GVW 3,200 kg) fitted with NAO pads of straight chamfers, and a lighter single-piston caliper (GVW 2,100 kg) fitted with NAO pads of straight chamfers behave the same or not, using the SAE J2521 and Los Angeles City Traffic simulation procedures. In all cases, brake squeal is found to increase with increasing (inboard/outboard) pads wear rate differences (wear differentials); increasing pad radial taper is associated with increasing (I/O) pads wear differential; pad tangential taper lowers the (I/O) pads wear differential. Increasing friction coefficients do not relate to increasing squeal occurrences. To minimize brake squeal occurrence, caliper should be designed to minimize (I/O) pads wear differential.
Sriwiboon, Meechai, Rhee, Seong Kwan, Sukultanasorn, Jittrathep, Khathinhorm, Nicha, Kunthong, Jitpanu
Predicting Wear of NAO Brake Pad Using Scaled-Down FMVSS 135 Testing and Machine Learning2026-01-08089/14/2026
Brake pad wear is a major and growing source of non-exhaust particulate emissions, projected to reach 1.3 million tons annually by 2030 and contributing up to roughly 55% by mass of non-exhaust traffic-related PM10 in urban environments, underscoring the need for improved durability and material optimization. This study investigates a three-stage eXtreme Gradient Boosting (XGBoost) ensemble paired with a residual Fully Connected Neural Network (FCNN) corrector to predict brake pad wear rate and support formulation optimization. Experiments used a simplified FMVSS 135 protocol on a Universal Mechanical Tester (UMT) simulating realistic braking across eight friction regimes. Wear rate was the sole machine-learning prediction target, while coefficient of friction (CoF) was retained as an input feature rather than a target. Despite a limited but high-quality 280-cycle dataset, regime-aware stratified splitting, sample reweighting, and hyperparameter optimization enabled robust generalization. The three-stage XGBoost ensemble with residual FCNN correction achieved a global held-out test R2 of 0.976 for wear rate prediction. A Taguchi L8 design of experiments defined the brake pad compositions, reducing experimental time and material consumption compared to conventional approaches. The framework demonstrated strong agreement between measurements and predictions for the dominant low-severity regime, while per-regime analysis identified the high-severity minority regimes as the priority for additional data collection, since within-regime R2 remains negative for every regime given current sample sizes. A sequence-aware mean absolute scaled error (MASE) analysis further shows that, despite the high global R2, none of the four pipeline stages currently outperforms a naive one-cycle persistence forecast on absolute error, a distinction reported here for transparency. The scalable architecture enables straightforward integration of additional material and process parameters, supporting iterative brake formulation development in industrial settings and, by reducing empirical testing requirements, sustainable brake material development with reduced replacement frequency and associated emissions.
Katakam, Abhishek, Eslamiat, Hossein, Kancharla, Sai Krishna, Filip, Peter
Research on the Stability of Three-Axis Vehicles with Coordinated Steering under High-Speed Low-Adhesion Conditions2026-99-11369/11/2026
Three-axle vehicles are widely used in engineering, transportation, and other heavy-duty applications, but they are prone to lateral instability at high speeds or on low-adhesion road conditions, which severely degrades handling stability. To enhance their dynamic performance under extreme operating conditions, this paper proposes a direct yaw-moment control (DYC) strategy based on an incremental linear quadratic regulator (ILQR) for a distributed-drive three-axle vehicle equipped with active front-wheel steering (AFS) and differential drive assist steering (DDAS), thereby improving the accuracy and responsiveness of lateral stability control. Furthermore, to mitigate the mutual coupling and interference among multiple control subsystems, a coordinated steering strategy based on phase-plane analysis is proposed to achieve effective integration and dynamic coordination of AFS, DDAS, and DYC. Co-simulation studies conducted in Matlab/Simulink and TruckSim reveal that the proposed coordinated steering strategy substantially diminishes the peak yaw rate and vehicle sideslip angle across diverse driving conditions, thereby considerably enhancing the lateral stability of the three-axle vehicle during extreme maneuvers.
Hu, Jiadong, Wang, Tie
Train Delay Reduction Approach based on a Virtually Coupled Trains Following Model with Timetable Recovery Process: A Case of High-Speed Rail2026-99-15169/11/2026
One challenge in railway operation is how to achieve high levels of punctuality and reliability. However, especially in peak hour operation, a high volume of train traffic will affect timetables, which are more sensitive to the increase in travel time. The concept of virtual coupling has been introduced for controlling train movement mainly to increase capacity. As the operation under virtual coupling requires a short separation distance between trains, it might be applied to reduce the delay and recover the train timetable. However, there is no approach proposed detailing how the coupling is applied to reduce delay. In this paper, the virtual coupling state movement approach based on a vehicle following model with the coupling conditions determined to couple a group of trains for reducing or preventing secondary delay is proposed. The train operation under the proposed approach is simulated in MATLAB software, then applied to the hypothetical case, High-speed line, Bangkok - Nakhon Ratchasima, Thailand. The delay analysis is performed, and the waiting probability is determined to prove the effectiveness of the proposed approach. The simulation results show that trains will be virtually coupled with their front train as a form of train convoy when they cannot proceed at the ideal speed. Thus, operating train movement based on the proposed approach can reduce secondary delay and bring a train to arrive on time compared to the operation under the moving block control.
Chansong, Sukanya, Ketphat, Naphat
Research on Passenger Flow Prediction of Urban Rail Transit Based on Grey Improvement Model2026-99-15649/11/2026
In recent years, China's urban rail transit sector has undergone rapid expansion, with passenger demand consistently increasing. Accurate passenger flow forecasting is essential for ensuring efficient and safe metro operations. This paper takes Nantong Metro Line 1 as a case study and applies an optimized forecasting approach that integrates a grey metabolism model with the Holt double-parameter exponential smoothing method. Based on an analysis of Automated Fare Collection (AFC) data from March 2023 to February 2024, passenger flow on this line demonstrates a clear linear growth trend, which aligns well with the assumptions of the grey metabolism model. The results indicate that the optimized grey metabolism model not only significantly enhances prediction accuracy but also greatly reduces the variance ratio, demonstrating high reliability in forecasting outcomes. This improved methodology provides a more robust tool for metro operators in planning services, managing capacity, and optimizing resource allocation.
Fan, Fan, Zhao, Zeheng, Zhang, Jin, Ma, Junhao, Qian, Beiyue
Research on the Application of Multi-Source Data in Bus Route Network Optimization2026-99-15669/11/2026
This research aims to optimize the bus route network in Shiyan City using bus Origin-Destination (OD) data. By integrating multi-source data, including GDP, population, and bus OD big data from 2018 to 2022, short-term and long-term indicators are forecasted by time-series methods. Shiyan city is divided into 77 Traffic Analysis Zones (TAZs) considering its mountainous terrain, population-industry distribution, and urban planning. A conventional four-stage traffic demand model is applied, calibrated with bus OD data in 2021. The investigation reveals peak-hour bus passenger travel demand of 29,700 short-term and 31,800 long-term person-trips in the city center and key corridors. Bus passenger travel forms a four-vertical and five-horizontal layout in the short term, evolving to a five-vertical, five-horizontal, and two wings pattern in the long term with eastward urban expansion. Accordingly, an optimized and upgraded bus route improvement strategy is devised. In the short term, there are seventy existing bus routes that are adjusted, creating a five-layer bus route network with diverse functions. Long-term plans involve optimizing twenty bus routes and adding eight new routes to align with urban development. This research not only aids an integrated bus route network optimization framework using bus OD data in a time-consuming way, but also provides a sample of bus route network adjustment for a typical mountainous city.
Ye, Qian, Chang, Sheng, Shen, Yucan, Li, Tanfeng, Tian, He, Tian, Shimo, Cen, Jian
Convoy Size Effects in Virtual Coupling: Analysis of How the Number of Trains in a Convoy Influences Capacity and Delay2026-99-15219/11/2026
Rail transportation capacity is related to the number of trains that can be grouped within a convoy under a virtual coupling (VC) system. Grouping trains into larger convoys allows them to operate as a single train, reducing headway and increasing track usage compared with classic signaling systems. However, grouping trains has limitations, such as increased communication requirements between trains and stricter safety conditions. On the other hand, smaller convoys may offer less capacity increase but give more flexibility and operational resilience. Therefore, convoy size is an important parameter in balancing system performance. The research investigates the operational impacts of varying convoy sizes on route capacity and delay using the UIC 406 standard methodology. Simulation results indicated that larger convoys increase route capacity, particularly as trains transition into convoy formation faster. In contrast, there are increased delays. These conclusions demonstrate that convoy size optimization is important for increasing capacity and managing delays, revealing the practical benefits of a convoy management system.
Prompianpong, Nammont, Ketphat, Naphat
Analysis of Traffic Capacity at the Intersection of Xiyou Road and Qianshan Road in Hefei City2026-99-15519/11/2026
With the advancement of urbanization and the popularization of automobiles, the traffic load on urban roads is becoming increasingly heavy, resulting in many traffic problems. Road intersections serve as crucial linchpins in the urban transportation grid, wielding considerable influence over the overall traffic capacity of a city’s road network. Enhancing intersection efficiency and cutting down on delays stand at the heart of tackling urban congestion challenges. This study zeroes in on the crossroads where Xiyou Road intersects with Qianshan Road in Hefei City. Employing hands-on observation and photographic documentation, the research examines traffic flow and signal configurations during the peak demand period (7:30-8:30). The analysis evaluates traffic capacity and utilization rates for through, left-turn, and right-turn lanes at this intersection. Findings reveal that the right-turn lane at the southern entrance and the left-turn lanes at both northern and eastern entries show relatively low saturation levels, while the saturation of other lanes is greater than or close to 1. Therefore, this intersection does not have sufficient capacity. The actual traffic operation at the intersection, particularly during peak traffic times, is analyzed to identify the reasons for congestion Finally, improvement plans for optimizing traffic organization at intersections are proposed, such as optimizing signal timing schemes and transforming traffic channelization. Simulation analysis using VISSIM shows a 9.34% reduction in total intersection parking time, a 34.26% decrease in average queue length, and an 8.12% reduction in average vehicle delay. These results provide a reference for future optimization work, including intersection signal timing and channelization.
Wang, Yanmei, Wang, Chen, Fu, Ziyue, Meng, Xianglong
Structural Airworthiness and Mechanical Characteristics Analysis of Real-Time Flight Data Acquisition Equipment2026-99-14119/11/2026
The rising complexity of civil aviation and recent incidents, including Sichuan Airlines “14 May” and China Eastern Airlines “21 March,” highlight the urgent need for real-time flight data acquisition systems to support timely safety monitoring and early warning. Current methods rely mainly on post-flight data, while real-time monitoring remains constrained by limited bandwidth and insufficient device capabilities. This study presents a comprehensive structural and mechanical analysis of a real-time flight data acquisition device designed according to ARINC-600 standards and airworthiness regulations (ED112A, DO-160G). The device integrates data acquisition, storage, processing, and power modules, utilizing a Xilinx Zynq UltraScale™ platform for hardware-software co-processing, enabling high-speed data parsing, time synchronization, encryption, redundant storage, and secure transmission. Finite element modal analysis was performed to evaluate the structural airworthiness and vibration characteristics. The first ten natural frequencies range from 246.41 Hz to 1109.2 Hz, significantly exceeding typical aerospace excitation frequencies, effectively preventing resonance under operational conditions. Mode shape analysis indicates an evolution from low-order global bending and torsion to high-order local complex deformations, revealing relative stiffness weaknesses at panels and connection points, providing guidance for structural optimization. The study establishes a closed-loop framework connecting mechanical characterization, data security, and equipment reliability. Combined simulation and experimental validation ensures accurate assessment of dynamic performance, supporting operational robustness and airworthiness. The findings not only advance the development of high-performance real-time flight data acquisition systems but also enhance risk identification, early warning, and overall flight safety management in civil aviation.
Xu, Xiaodong, Gao, Jianwei, Guo, Qiang, Zhang, Yun, Kong, Xiangjun
Effect Evaluation of Emergency Barrier-free Blocking Methods in Highway Tunnels Based on Visual/Driving Behavior2026-99-15319/11/2026
To address the lack of safe and effective on-site vehicle blocking and control methods in the event of fires or other emergencies in extra-long tunnels — which can significantly reduce traffic safety risks and prevent secondary accidents — this study proposes a novel barrier-free light–smoke curtain interception method. The method integrates conventional traffic safety warning facilities (gantry-mounted variable message signs and audio–visual alarms) with two light–smoke curtain interception images to form a composite early-warning and interception system. Driving simulation experiments were conducted to comprehensively evaluate its warning effectiveness, interception performance, and operational safety in comparison with methods employing only traditional warning facilities or light curtain images. Furthermore, field drills were performed to validate its real-world applicability and interception effectiveness under both daytime and nighttime conditions. The main findings are as follows: 1) The fixation ratio and interception success rate associated with the proposed method were significantly higher than those of the other two methods, demonstrating enhanced visual attention and superior warning and interception performance. 2) The maximum deceleration observed with the proposed method was lower than that of the light curtain–only method and did not trigger emergency braking, thereby indicating high operational stability and driver comfort. 3) In field drills, after activation of the interception equipment, only one and two vehicles entered the tunnel under daytime and nighttime conditions, respectively, and full control of on-site vehicles was achieved within two minutes without any traffic accidents, verifying the system’s rapid response and effective safety assurance.
Shi, Mingjun, Li, Shicao, Wang, Haohuan, He, Qifei, Che, Zhengzhang, Li, Yanbo
Research on Heavy-Haul Train Operation Adjustment Considering the Degree of Chaos in Train Timetable2026-99-15229/11/2026
Heavy-haul railway development is as important a strategic direction for China’s railway sector as high-speed railway development. With the continuous expansion of heavy-haul railway operational mileage and the growing transportation demand, the complexity of operational adjustment in heavy-haul railways continues to escalate. The operational adjustment for heavy-haul trains subjected to speed restrictions following maintenance windows, aimed at maximizing the restoration of timetable regularity and guaranteeing freight volume, constitutes a critical and urgent research problem. Grounding on the operational principle of heavy-haul railways that prioritizes freight volume preservation over strict punctuality, this paper proposes a rescheduling model that incorporates the timetable disruption level. By adopting the order entropy theory, the model is formulated to minimize the total system disruption level and freight time cost, thereby framing the rescheduling problem as an order entropy optimization problem. The train disruption level is a metric that quantifies the perturbation intensity of a rescheduled timetable relative to the original operational plan within a given railway section. By initializing the entropy value of the pristine operational order to zero, any subsequent entropy variation directly facilitates a systematic investigation into the evolution of train operational order. Effective timetable adjustment strategies are subsequently derived, contingent upon the available buffer time and freight volume constraints. Guided by actual operational timetables and sectional line characteristics, this research designs a case study. The Gurobi solver is employed to obtain a rescheduled heavy-haul train timetable that successfully restores the original freight volume. The proposed methodology contributes to the enrichment of railway transportation organization theory and provides robust decision-making support for the strategic deployment of heavy-haul train services.
Fu, Lu, Lin, Li, Xu, Shichao, Ji, Guanggang, Li, Zheng
Real-time Vehicle Speed Detection Based on YOLOv11 and DeepSORT2026-99-15959/11/2026
The technology of real-time and effective vehicle speed detection is considered a key technology to improve traffic monitoring efficiency and traffic safety management grade. To address the limitations of traditional speed detection schemes—including reliance on dedicated hardware, poor environmental adaptability, and high construction and maintenance costs—this paper proposes a r10eal-time vehicle speed detection system based on YOLOv11 and the DeepSORT algorithm. The proposed system uses the YOLOv11 target detection algorithm as its primary model. DeepSORT multi-target tracking technology is integrated to enhance tracking performance. Speed measurement is implemented using a virtual detection line. This approach enables accurate vehicle detection, continuous tracking, and real-time speed measurement within video frames. Through the experiments, the result shows that the improved YOLOv11n model reaches mAP@0.5 of 0.982 and a recall rate of 0.956 in the test set, higher than the YOLOv8n and YOLOv5s models. The speed detection error can be restricted to 3 km/h, satisfying the real-time detection need. There is no need for road surface modification, and the detection system has a flexible layout, providing a dynamic basis for traffic law enforcement and traffic data support for road construction and traffic control optimization.
Jin, Guowei, Ma, Wenlong, Jiang, Dali, Li, Nan
Study on Low-Velocity Impact Damage of Glass Fiber Reinforced Composite Pipeline2026-99-14169/11/2026
Through low-velocity impact testing, the effects of punch shape (conical, hemispherical, and cylindrical) and impact energy (5, 10, and 15 J) on damage characteristics in glass fiber composite pipes were investigated. Ultrasonic A-scan inspection was employed to detect internal delamination damage at the impact points within the composite pipes. Test results indicate that the contact area between the punch and the pipe is a key factor influencing the severity of pipe damage. A smaller contact area results in a higher energy absorption rate, greater punch displacement, larger area under the load-displacement curve, and longer contact time, leading to more severe damage characteristics. When the conical punch delivered 15 J of impact energy, the energy absorption rate of the glass fiber composite pipe reached 91.6%, exhibiting multiple damage characteristics, including pitting, penetration, and cross-shaped cracks. As impact energy increases, the area of internal delamination damage caused by the three punch shapes exhibits near-linear growth. The conical punch induces severe damage characteristics in the thickness direction but results in the smallest delamination area. Blunt-shaped punches (hemispherical and cylindrical) disperse impact energy over a wider region, leading to increased delamination damage area.
Wang, Xuan, Cao, Yanzhen
Research on Torque Allocation Control of Multi-Articulated Vehicle2026-99-11239/11/2026
The multi-articulated vehicle uses distributed drive mode. Due to its large degree of freedom of movement and the large number of driving shafts, different torque distribution methods affect the operational stability of the vehicle, how to coordinate and distribute the torque of each driving motor has become an urgent problem to be solved. To improve drive stability of the multi-articulated vehicles, propose a layered torque allocation control strategy. The upper-layer sliding mode controller determines the required additional yaw moments of each car body based on the linear reference model, the controller is characterized by swift response and a strong ability to resist interference. The lower-level allocation module comprehensively considers the torque output limitations of the electric hub motors, the prevailing road adhesion state, and the corrective yaw moment constraints given by the upper layer, and constructs an optimization objective function centered on the uniformity and stability of tire load. The optimal distribution of driving forces for each wheel is completed by solving this function dynamically. To validate the strategy's effectiveness, a vehicle dynamics model is built in the multi-body dynamics software ADAMS/View. Using a joint simulation framework integrating ADAMS/View and MATLAB®/Simulink, the effect of the layered control strategy is evaluated in comparative simulation with uncontrolled situation under U-turn and single lane change conditions. The simulation outcomes demonstrate that, compared to uncontrolled situation, the yaw rate deviation of each car body under the torque layered control are significantly reduced, and the adhesion utilization rate of tire is also effectively controlled, thereby the driving stability is improved.
An, Guanbo, Zhang, Liwei
Research on a Brake Energy Recovery Strategy Based on Improved ANFIS2026-99-11349/11/2026
This paper addresses the issue of regenerative braking energy recovery in new energy vehicles and designs and optimizes a braking force distribution strategy. The strategy uses an ANFIS controller to dynamically optimize the proportion of front-axle regenerative braking force. The introduction of a pruning algorithm reduces computational complexity, thereby enabling a significant increase in mileage while maintaining stable driving performance. Co- simulations integrating Simulink and AVL Cruise, alongside Hardware-in-the-Loop (HiL) tests, the proof is that this strategy can still maintain excellent stability under different braking intensities. Moreover, it exhibits significantly higher energy recovery efficiency compared to benchmark strategies, while its effectiveness and real- time performance are successfully validated.
Lin, Hui, Zhao, Xuezhan, Tian, Jiahao
Spatial Sight Distance Value for Curved Tunnel of Mountainous Highway Based on Driving Behaviour2026-99-15539/11/2026
In order to conduct more in-depth research on the driving sight distance of curved tunnels in mountainous highways, a systematic theoretical calculation model of spatial sight distance of curved tunnels based on three-dimensional characteristics is established, and the spatial sight distance value of curved tunnels in mountainous highways is recommended in combination with the changes of driving behaviour under different spatial sight distances. Firstly, the concept of spatial sight distance of curved tunnels is proposed, the theoretical calculation model of spatial sight distance of curved tunnels is established, and the model is verified by a multi-scale neural network; Secondly, five UC-win/road simulation models of curved tunnel with different spatial sight distances are established, and the simulation experiments are carried out in combination with mp160 multi-channel physiological recorder and SMI etgtm eye tracker; Finally, the mathematical statistics method and SPSS software are used to analyse the operation behaviour, psychological behaviour and eye movement behaviour of drivers in curved tunnels with different spatial sight distances, and to verify the different effects of the critical value of spatial sight distance on driving behaviour in the theoretical calculation. Furthermore, by taking the spatial sight distance as the independent variable, the regression model is established with the average speed, trajectory offset, heart rate change rate, and pupil diameter change rate as the dependent variables. Based on the driver’s behaviour threshold, the recommended spatial sight distance of a curved tunnel is proposed. The results show that the recommended range of spatial sight distance of the curved tunnel of mountainous highway with a design speed of 80 km/h is 125 m to 140 m, the limit value is 110 m, and the appropriate value is 155 m. There is a critical value between two-dimensional sight distance and spatial sight distance, which has a significant impact on the change of driving behaviour in a curved tunnel.
Tang, Xie, Zheng, LiWen, Lin, GuoJin, Gao, YanYang, Lan, FuAn
DisReID: Single Domain Generalizable Person Re-ID Method for Highway Construction & Operation2026-99-15309/11/2026
With the advancement of computer vision technologies and the widespread deployment of video surveillance systems, traffic safety and the development of intelligent highways have been significantly enhanced. As a key component of the intelligent video analysis module in smart highways, person re-identification (re-ID) addresses critical challenges, including cross-segment tracking of pedestrians illegally using emergency lanes, multi-camera joint searches for lost persons in service areas, and trajectory tracing of individuals involved in traffic accidents. These functions directly support the core goals of "safety assurance and efficient service" for smart highways. However, due to the complexity of the application scene, its generalization to unseen environments remains a core challenge. This problem is formally studied under the setting of Single-Domain Generalizable Person Re-identification (SDG re-ID), which aims to train a model on a single source domain that can perform well on arbitrary unseen target domains. To handle this issue, this paper proposes a novel Disentangled Augmentation re-ID Framework (DisReID) that disentangles and augments both structure and style. Specifically, DisReID consists of two modules: Structure-aware Viewpoint Simulation (SVS), a novel pre-processing technique that simulates cross-camera perspective changes by perspective transformation, diversifying geometric structure without harming identity semantics; and Style-Dominant Frequency Perturbation (SFP), which selectively focuses on the style-dominant frequencies and applies perturbation to enable controllable style augmentation while preserving structure cues. Furthermore, to alleviate the BN-induced domain bias, we introduce a simple yet effective test-time adaptation strategy, termed Cluster Fine-tuning (CF), that performs unsupervised clustering on target-domain features to assign pseudo-labels and subsequently fine-tunes the model, enhancing adaptability to unseen domains. Extensive experimental results on four public datasets demonstrate that our DisReID achieves superior generalization performance compared to the state-of-the-art methods. This work provides key technical support for the large-scale application of re-ID in smart highways, advancing the goal of "full-domain perception and intelligent collaboration".
Pan, Hong, Yu, Fangying
Research on the Synergetic Coupling Evaluation between the Yangtze River Delta Airport Cluster and the Regional Economy2026-99-15429/11/2026
To explore the coordinated development status between the Yangtze River Delta (YRD) airport cluster and the regional economy, this study takes the period from 2015 to 2023 as the research timeframe. It constructs an evaluation index system covering two dimensions: regional economy (including scale, structure, and benefit) and airport cluster development (including transportation scale, operation efficiency, among others). The Gini coefficient method and Pearson correlation coefficient method are used to screen indicators, while the entropy weight-standard deviation combined weighting method is adopted to calculate weights. Additionally, the coupling coordination model and geographical detector are integrated for in-depth analysis. The results show that the coupling coordination degree of the Yangtze River Delta region as a whole and its internal provinces and cities has rapidly recovered from the severe imbalance during the COVID-19 pandemic, featuring an inherent characteristic of “gradient catch-up and coordinated upgrading”. Factors such as the growth rate of passenger throughput and local fiscal general budget revenue have been identified as core influencing factors, and the interaction among these factors presents trends of two-factor enhancement and nonlinear enhancement. This study provides a theoretical basis and practical reference for promoting the integrated and coordinated development of the Yangtze River Delta airport cluster and the regional economy.
You, Zihao, Li, Yanwei
Assessment Model for Fitness for Work of Construction Personnel in Expressway Bridge Engineering Projects2026-99-15099/11/2026
To mitigate safety risks inherent in highway bridge construction, this research establishes a practical framework for assessing workers’ fitness for work. Using grounded theory, we analyzed interview records and documented accident cases through systematic coding, identifying critical indicators spanning physiological states, safety training effectiveness, and atypical behavioral markers. Rather than relying on single-method approaches, we combined Delphi expert consultation with entropy weighting to capture both professional judgment and data-driven variance, thereby reducing bias while preserving information richness. The resulting assessment protocol enables quantifiable classification of workers into distinct risk tiers. Implementation at the Zhangjinggao Yangtze River Bridge demonstrated the system's discriminatory power through field data collection and direct behavioral monitoring, successfully segmenting the workforce into low-, medium-, and high-risk categories. Results suggest the tool functions effectively as a pre-employment screening mechanism, allowing project managers to intercept potentially unfit workers before they enter hazardous work zones, consequently lowering the incidence of human-factor accidents.
Wu, Zhongguang, Dai, Junping, Ruan, Jing, Shi, Yonglong, Yuan, Zhenzhong, Hao, Jiatian
Analysis of Latency-Influencing Factors in Vehicle-Road-Cloud Integrated Systems Based on Fault Tree Analysis and Analytic Hierarchy Process2026-99-15439/11/2026
Vehicle–road–cloud integrated systems have great potential in terms of improving traffic efficiency and achieving intelligent automatic driving through the integration of on–board terminals, roadside facilities, and cloud computing. However, their operational capabilities are heavily reliant on ultra-low-latency collaborative communication. This paper constructs a latency fault tree model to comprehensively analyze multi-source triggering paths of computation delay and reveals the formation mechanism of the delay path from “germination–induction–evolution”. On this basis, the Analytic Hierarchy Process is used to construct a three-level evaluation framework, and the influencing factors are quantitatively evaluated using NS-3 simulation data of the 004-V2X Communication Performance Testing Dataset. The result shows that the weight value of the network communication layer is the largest, 0.498, which shows that the bottleneck of performance in network communication is the wireless link quality. The cloud processing layer is second 0.327, which is dominated by the computational complexity and resource allocation policy. The impact of the onboard terminal layer is the smallest, 0.175. The FTA–AHP framework supported by empirical data can find the key factors affecting delay, which can help engineering optimization. It is noted that the AHP consistency check (CR) just checks the inner transitivity of expert judgment (i.e., the matrix consistency), while it cannot assure the objectivity and the bias elimination. We reduce the subjectivity by combining multiple experts, anchoring judgment with the simulation data, and performing a sensitivity check on the perturbation of the weights.
Xu, Yunchuan, Wang, Xiaomeng, Wang, Yan
A Three-Dimensional Safety Domain Method for LNG Vessels in Ports Based on Human Factors and Speed Characteristics2026-99-15239/11/2026
To accurately assess the navigation safety status of LNG vessels in port waters and balance safety control with waterway capacity efficiency, this study constructs a 3D dynamic safety domain model for port LNG vessels, integrating human–ship–environment multi-factors. The model introduces the Weibull function to quantify the impact of drivers’ knowledge, skills, and physiological-psychological states on safety boundaries, combines a ship motion mathematical model to establish a 2D safety domain boundary equation, and incorporates hull subsidence to build a vertical dimension, forming a complete 3D model. Longitudinally, the safety distance is calculated using the car-following braking theory, while laterally, boundaries are determined by controlling the ratio of inter-vessel interference force to navigation resistance. Through static scenario analysis and dynamic simulation verification, results show that the safety domain scale is dominated by ship speed and environmental conditions, and its shape tends to shrink as the driver’s state improves, making it more suitable for actual port scenarios than traditional models. Verified with a specific LNG hub port as a case, the safety distance calculated by the model is significantly reduced compared with current specifications, while the delay impact rate and average delay time on other vessels are decreased. The research results establish a quantifiable framework for dynamic safety assessment, providing maritime administrations and on-board pilots with a scientifically-grounded tool to determine real-time safe navigation boundaries in complex port environments, balancing safety control with operational efficiency.
Wang, Yangang, Jia, Changsheng, Zhu, Jinshan
Johnson-Cook Model Calibration of 10# Steel and Dynamic Mechanical Behavior Analysis under High Strain Rates2026-99-12019/4/2026
As a typical material for fragmentation warheads, the mechanical behavior and ballistic penetration performance of 10# steel are critical for assessing warhead lethality. To characterize the dynamic response of 10# steel, systematic experiments were conducted, including quasi-static tensile tests, split-Hopkinson tensile bar tests, and thermal softening measurements. A = 505.46 MPa, B = 292.84 MPa, n = 0.335, C = 0.0343, and m = 1.213 are the calibrated Johnson–Cook parameters. Bridgman-corrected notched tensile tests determined damage parameters D1 to D4: 0.065, 0.746, −0.646, and 0.031). A study of its constitutive behavior shows that the strength of 10# steel increases with stress triaxiality and strain rate, whereas increasing temperature enhances ductility and reduces strength. Finite element software was updated to include the calibrated parameters to develop a material model for ballistic impact simulation. When compared with the ballistic penetration test results obtained using a 14.5 mm projectile, the simulated residual velocities show less than 5% deviation from the measured values. 3D scanning reveals that fragment sizes in experimental data differ by under 10% from simulation predictions. This work enables precise numerical simulations for warhead fragmentation prediction and lightweight armor design.
Tian, Yumo, Zhang, Longhui, An, Fengjiang, Feng, Bo
Molecular Dynamics Simulations of Microstructural Evolution in Pure Titanium under Laser Shock Peening2026-99-12859/4/2026
Impacts of laser shock peening (LSP) on the evolution characteristics of microstructure in commercially pure α-phase titanium (α-Ti) are explored by molecular dynamics (MD) simulations of high strain-rate compression. The EAM potential (Zhou potential) is selected for its ability to capture the evolution of microstructures. Considering the LSP-induced peak plasma pressure, the strain rate during the simulated shock compression process is set at 10^9 s-1 to replicate the LSP process. The stress-strain curve of the α-Ti under high strain-rate compression is obtained. The maximum equivalent stress reaches 3.6 GPa, consistent with the theoretically calculated value. The simulation results reveal that mechanical twins (MTs) are activated at a strain of 3%. The number of mechanical twins increases and eventually stabilizes, forming a network structure throughout the grains. In the meantime, numerous partial dislocations are generated adjacent to the grain boundaries. The dislocation density also increases with strain and dislocation reactions occur. Moreover, grain refinement is identified. The grain size is refined from the initial ~ 8 nm to ~ 4 nm in the polycrystalline α-Ti. Twinning, together with dislocation-mediated plasticity, drives the refinement of grain size. Gradients of twin density, dislocation density, and grain size density are induced by LSP on the surface of α-Ti. This study comprehensively investigates how LSP influences the evolution of microstructures by MD simulations. It develops an innovative numerical strategy that offers a foundation for elucidating the underlying mechanisms of LSP.
Zhao, Congshan, Zhang, Linbing, Xu, Yidi, He, Jianye, Fang, Jing, Li, Zezhou, Ruestes, Carlos J., Cheng, Xingwang
W60(FeCrNi2.5) Alloy with High Strength and High Adiabatic Shear Band Sensitivity2026-99-12439/4/2026
To meet the high-performance requirement of tungsten heavy alloys in kinetic energy penetrators under extreme dynamic loading conditions, high strength and high adiabatic shear band (ASB) sensitivity are essential. The formation and evolution of ASB during the penetration process directly dominate penetration capability of tungsten heavy alloys (WHAs). However, traditional WHA (93W) exhibits relatively low strength and adiabatic shear band insensitivity, which limits its applications in advanced kinetic energy penetrators. This study prepared W60(FeCrNi2.5) alloy by means of spark plasma sintering with 1~3 μm powders. The sintered alloy exhibits outstanding mechanical properties at quasi-static (0.001 s-1) and dynamic (4000 s-1) strain rates. Its yield strengths reach 1.5 GPa and 2.7 GPa respectively, manifesting a notable strain rate strengthening behavior. Dynamic compression tests indicate that the alloy generates ASB with a width of ~8 μm. Within the ASB, the body-centered cubic (BCC) phase is elongated to nanofibers under shear stress, and fine W particles are generated as a result of grain debonding in nanofibers. Meanwhile, the grains of the face-centered cubic (FCC) phase are disintegrated into subgrains due to dislocation pile-ups at subgrain boundaries, and new equiaxed grains are formed through subgrain boundaries rotation. The calculated adiabatic temperature elevation inside the ASB of this alloy reaches a maximum of 1315 K under 4000 s-1. Notably, its ASB sensitivity coefficient reaches 20.8, while that of the 93W alloy is 1.02. Thus, it achieves a favorable combination of high strength and high adiabatic shear band sensitivity, which offers meaningful references for advanced kinetic energy penetrator materials.
Lin, Jingchen, He, Jianye, Wang, Qiang, Wu, Shanghao, Zhang, Linbing, Ruestes, Carlos J., Li, Zezhou, Zhang, Zhaohui, Zhang, Fan, Wang, Lin, Cheng, Xingwang
A Novel Physics-Informed Graph Convolutional Reduced-Order Model for Fluid Flow on Unstructured Meshes2026-99-12459/4/2026
This study proposes a physics-informed graph convolutional reduced-order model, namely Phys-GCN, for high-fidelity and computationally efficient prediction of steady incompressible flow fields. In Phys-GCN, the incompressible Navier–Stokes equations are embedded into the loss function via residual constraints, such that the spatial feature extraction of graph convolutional networks is integrated with the physics-constrained learning strategy of physics-informed neural networks. This mixed design enables the model to capture complex nonlinear flow features while maintaining a clear level of physical interpretability. Benefiting from the node-edge encoding inherent to graph neural networks, Phys-GCN operates directly on unstructured CFD meshes to learn flow features from graph representations constructed using node attributes and adjacency relationships. In doing so, Phys-GCN dispenses with voxelization or SDF preprocessing and fully preserves the local geometric and topological characteristics of the flow domain. The proposed model is systematically evaluated on steady flows past circular and elliptical cylinders, where the predicted velocity and pressure fields are compared against reference CFD solutions in both interpolation and extrapolation scenarios. Results show that, for all physical quantities, the reconstructed steady flow fields achieve mean relative errors below 5%, exhibiting excellent agreement with the CFD benchmark solutions. After offline training, Phys-GCN achieves inference times that are several orders of magnitude faster than conventional CFD solvers, while maintaining comparable predictive accuracy. These findings demonstrate that Phys-GCN provides an accurate and efficient graph-based and physics-informed surrogate for steady flow-field reconstruction on non-uniform, unstructured meshes, thereby laying a solid foundation for future extensions to more complex three-dimensional and compressible flow configurations.
Xie, Haoran, Zhou, Hao, Yu, Changhao, Li, Qiang, Liu, Tianyu, Peng, Jiangzhou
Aerodynamic Load Calculation and Prediction Method for Folded Wing Mechanism2026-99-12899/4/2026
The folding wing mechanism is widely used in aircraft design. Whether the folding wing surface can unfold smoothly determines whether the aircraft can fly normally. Therefore, studying the aerodynamic loads and structural deformations during the unfolding process of folded wing surfaces is very important. The motion process of a folded wing mechanism is a typical fluid-structure interaction (FSI) process. During deployment, the wing surface moves under the combined action of the actuator’s pull and the aerodynamic loads from the incoming flow, while the large deformation of the wing surface during its movement, in turn, affects the aerodynamic loads on the mechanism from the flow field. Considering the FSI effects during the unfolded motion process of the folded wing, simulation was conducted using the ALE algorithm in LS-DYNA to obtain the kinematic and dynamic parameters in the unfolded motion process, and also to get the aerodynamic torque on the wing under different angles and angular velocities. In practical engineering applications, the actuation force of the deployment mechanism can vary due to factors such as the amount and performance of the pyrotechnic material. Consequently, the final velocity and the whole motion process of the wing mechanism will also change. For the calculation of aerodynamic external loads under multiple operating conditions, using the ALE algorithm will consume a large amount of computational time and cost. Given the high computational cost and long computation time of finite element simulations, a BP neural network was established to calculate the aerodynamic loads on the wing surface under different actuation forces. This allows for a rapid assessment of whether significant deformation or damage will occur to the folding mechanism or nearby components during the deployment process.
Wei, Ting, Li, Naitian, Tong, Zongkai
Experimental Study on Heat Transfer Performance of Gravity Heat Pipes in Green Highway Construction2026-99-12399/4/2026
Gravity heat pipe technology offers an innovative solution for utilizing shallow geothermal energy to melt pavement snow and ice in winter, aligning with the requirements of green highway construction. By leveraging the evaporation and condensation of internal working fluids, these heat pipes efficiently transfer underground thermal energy to the ground surface, delivering a continuous and stable heat supply for road pavements in cold weather. To explore the factors affecting heat transfer efficiency, this study built an indoor environmental simulation platform and systematically examined the impacts of heat pipe shape, working fluid type (R-134a, R245fa), heating temperature (15°C–25°C), and working fluid filling rate (15%–30%). A winter pavement snow- melting simulation experiment was conducted to quantify key indicators such as pipe wall temperature and heat transfer power under medium-low temperature conditions. Experimental results show that R-134a heat pipes outperform R245fa counterparts in heat transfer power under simulated shallow geothermal snow-melting conditions. Low filling volumes tend to induce temperature gradients in the condensation section of L-shaped heat pipes, reducing overall efficiency. Straight heat pipes work best at a 15% filling rate, while L-shaped models achieve optimal performance at 25%. Comparative experimental analysis yielded parameter-effect diagrams for heat transfer power and thermal conductivity, which clarify the variation rules of heat pipe performance and provide engineering guidance for gravity heat pipe applications in green highway construction.
Wang, Zhen-kun, Yuan, Zhi-ming, Wang, Kang, Zhang, Wen-jun, Wu, Xiang-song, Liu, Guang-bo
Ultrasonic Guided Wave Thickness Measurement of Cortical Bone with Soft Tissue2026-99-12209/4/2026
Early diagnosis of osteoporosis is crucial for preventing fractures and improving the quality of life of patients. In clinical practice, the mainstream diagnostic methods, such as dual-energy X-ray absorptiometry (DXA), are limited by high equipment costs and ionizing radiation, resulting in a low coverage rate of large-scale early screening. Only less than one-third of brittle fracture patients have received a DXA assessment. To address this issue, this study proposes an innovative diagnostic method based on ultrasonic guided wave technology. This technology is cost-effective and portable, and it overcomes the limitations of X-ray detection in terms of its unsuitability for large-scale early diagnosis. The Young’s modulus and Poisson’s ratio of water are similar to those of soft tissue, so this method utilizes water coupling to simulate the environment of soft tissue around the bone and combines the transverse isotropy of cortical bone, which is an important characteristic that most existing models ignore, to analyze the propagation of guided waves in anisotropic cortical bone. Through the processing of ultrasonic signals using two-dimensional short-time Fourier transform (2D-STFT), the local thickness of cortical bone can be inverted. By establishing a fluid-coupled orthotropic anisotropic plate model, deriving the dispersion equation, solving the theoretical method for the dispersion curve, using the bovine long plate to construct a water-coupled detection platform, and obtaining experimental data to invert the thickness of the bone plate, the local thickness of the bone plate was obtained, proving that this method can effectively reconstruct the thickness changes of anisotropic and variable cross-section cortical bone under simulated soft tissue conditions, with an average relative error of 13%. This lays the foundation for subsequent in vivo experiments and provides a reliable solution for large-scale early osteoporosis screening.
Nong, Kexin, Li, Bing
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
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
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
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
Evaluating the Applicability of Advanced Physical Vapor Deposition Coatings through Characterization and Dry Machining05-19-04-00318/18/2026
This study investigates the characterization and dry machining performance of advanced physical vapor deposition (PVD) aluminum titanium nitride (AlTiN) and aluminum chromium titanium nitride (AlCrTiN) coatings deposited using three techniques: cathodic arc evaporation (CAE), high-power impulse magnetron sputtering (HiPIMS), and scalable pulse power plasma (S3p). The coatings were evaluated for thickness, microstructure, surface roughness, coefficient of friction (CoF), adhesion strength, and microhardness. Among the tested coatings, the S3p-deposited AlCrTiN showed the best performance, exhibiting the highest microhardness (40 GPa), the strongest adhesion (108 N), and the lowest CoF (0.25), along with a defect-free microstructure. Under the selected dry turning condition of 150 m/min cutting speed, 0.15 mm/rev feed rate, and 0.7 mm depth of cut, the S3p-deposited AlCrTiN coating achieved a maximum tool life of 10,800 mm, nearly three times higher than the CAE-deposited AlTiN coating. In contrast, CAE coatings showed comparatively lower hardness and weaker adhesion, with minimum values of 25 GPa and 68 N for C1, along with higher CoF values of 0.58–0.60. Furthermore, AlCrTiN coatings produced by HiPIMS and S3p provided 20–30% longer tool life than AlTiN coatings under identical cutting conditions, highlighting the importance of deposition technique.
Sonawane, Gaurav Dinkar
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
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
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
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
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
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