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Acta Mechanica Sinica 2024年第4期40卷 125-135页

作者：巩绪安马兴宇姜楠 Department of Mechanics School of Mechanical EngineeringTianjin UniversityTianjin300354China National Key Laboratory of Aerodynamic Noise Control China Aerodynamics Research and Development CenterSichuan621000China National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of AeronauticsNorthwestern Polytechnical UniversityXi’an710072China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu610031China

本文在二维机翼上安装了仿生的覆羽结构,在5.0×105的雷诺数下研究它们控制前缘流动分离的效果.覆羽是指覆盖在鸟类翅膀上的多排短而柔软的带有锯齿状尖端的羽毛.它们覆盖在长而硬的飞羽边缘,使鸟的翅膀保持流线型.当流动分离发生时... 详细信息

本文在二维机翼上安装了仿生的覆羽结构,在5.0×105的雷诺数下研究它们控制前缘流动分离的效果.覆羽是指覆盖在鸟类翅膀上的多排短而柔软的带有锯齿状尖端的羽毛.它们覆盖在长而硬的飞羽边缘,使鸟的翅膀保持流线型.当流动分离发生时,这些柔性锯齿形的人工覆羽在来流的激励下能够自适应地变形和颤振.在风洞实验中,本文使用高时间分辨率的粒子图像测速仪(PIV)来测量机翼上表面的速度场,并使用热线风速仪来测量尾流中的高频湍流脉动.安装在20%弦长处的人工覆羽在前缘分离流的激励下会自适应地大幅翻转.安装在80%弦长处的覆羽则会被尾缘卷起的脱落涡向上抬起,并在准平衡状态下轻微摆动.这两类人工覆羽均能够有效地控制流动分离,并减小尾流的湍流脉动强度.

关键词：流动分离风洞实验分离流湍流脉动高时间分辨率热线风速仪高雷诺数锯齿形

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Cooperative control Based on Distributed Model Predictive control Method for Urban Rail Transit

Cooperative Control Based on Distributed Model Predictive Co...

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IEEE International Conference on Networking, Sensing and control

作者： Jiahui Lv Jidong Lv Hongjie Liu Zhengwei Luo Bingyan Zhou Qianying Ma School of Automation and Intelligence (Beijing Jiaotong University) Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Focused on the challenge of train's high-density tracking control for urban rail transit, a cooperative control method based on Distributed Model Predictive control (DMPC) is studied. Firstly, the force of the train is analyzed and the state space model of train operation is established. Upon this foundational framework, the control objectives of the train control system and corresponding control strategies are proposed. Secondly, to ensure the safety of train operations, the DMPC is used to design a cooperative train consistency control algorithm that considers the dynamic parameters such as the position, speed, and acceleration of the front train. The stability of the train control system is analyzed and verified by the Lyapunov stability theorem. Finally, combined with the west section of Beijing Metro Line No. 11, the proposed method is simulated and verified. The experimental results show that the proposed train cooperative control method can ensure the safe operation of trains, and make the state of trains consistent and have good synchronization. Compared with the following control method, the cooperative control method can reduce the train spacing and speed difference, increasing by 85% and 76% respectively.

关键词： Rails State feedback Heuristic algorithms Simulation Stability analysis Sensors Safety Synchronization Predictive control Lyapunov methods

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Fault Diagnosis Method for On-Board Interface Equipment of CTCS-3 Based on Temporal Knowledge Graph Completion

Fault Diagnosis Method for On-Board Interface Equipment of C...

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IEEE International Conference on Networking, Sensing and control

作者： Bingyan Zhou Daqian Zhang Jidong Lv Hongjie Liu Zixu Zhao Zhengwei Luo School of Automation and Intelligence Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Chinese Train control system level 3 (CTCS-3) train control onboard equipment plays a crucial role in ensuring train safety and improving operational efficiency. And the on-board interface equipment enables the interaction between the on-board Automatic Train Protection (ATP), ground equipment, drivers, and trains. While its fault accounts for a relatively high proportion of faults, therefore, this paper proposed a fault diagnosis method for on-board interface equipment based on temporal knowledge graph completion. Firstly, this method introduces temporal series to integrate travel logs and fault statistics, which extract fault phenomena and perform entity alignment, construct the temporal knowledge graph. Secondly, on this basis, we constructed a fault diagnosis network based on knowledge graph completion, which incorporates the Temporal-Translating Embedding (T - TransE) vectorization algorithm, Bidirectional Long Short-Term Memory (Bi-LSTM), and Self-Attention (SA) mechanism for temporal feature extraction. Finally, the T - TransE vectorization model was pretrained using the on-board interface equipment fault data from a railroad station in recent years for selecting the most effective temporal integration method. In order to validate the superiority of the proposed fault diagnosis method and the effectiveness of the data combination method, the diagnostic network without data combination and temporal relationship introduction, and other common fault diagnostic networks, were tested using the on -board fault data. Compared with the following fault diagnosis methods, the model proposed in this paper achieved the highest fault diagnostic accuracy of 96.8%.

关键词： Fault diagnosis Accuracy Knowledge graphs Feature extraction control systems Data models Sensors Protection Long short term memory

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Formal Verification of Key Function of a New Type of Train control system Based on Timed Automata

Formal Verification of Key Function of a New Type of Train C...

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International Conference on Intelligent Transportation

作者： Aiying Li Yu Liu Lei Yuan Guodong Wei National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Due to the harsh environment and rare human presence in the western region, compared with existing train control systems, a new type of train control system(CTCSn) no longer has interval trackside circuits and changes the traditional structure of “ground equipment as the core”. In order to verify the correctness of CTCS-n specifications and verify whether the system meets reliability requirements, this paper proposes a modeling and validation method based on SysML and timed automata(TA). Firstly, the newly added train integrity inspection function in CTCS-n is selected as the research object, and the SysML sequence diagram and activity diagram are used for modeling based on its scene characteristics. According to the conversion rules, the SysML model is converted into the corresponding timed automata network model, which is simulated and verified by using the UPPAAL verification tool. Then the faults of the train integrity function are categorized, and each type of faults is injected into its scenario model to verify its scenario function failure. And the integrity loss fault is injected into the level conversion scenario to verify the impact of the faults on the scenario realization. The results show that the system functional requirements and fault handling meet the system specification requirements.

关键词： Analytical models Automata Inspection control systems Circuit faults Integrated circuit reliability Integrated circuit modeling Intelligent transportation systems Formal verification

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A Quasi-Parallel Resonant Switched Capacitor DC-DC Converter with Wide Input Voltage Range Regulation and Enhanced Dynamic Performances

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Qi, Jingjing Wu, Xuezhi Sun, Kai Tsinghua University State Key Laboratory of Power System Operation and Control Department of Electrical Engineering Beijing100084 China Beijing Jiaotong University National Active Distribution Network Technology Research Center Beijing100044 China

Wide-range and high-efficiency DC-DC converters are in high demand for various applications, such as distributed systems, electric vehicles, and DC buildings. Resonant switched capacitor converters (ReSCC) can achieve soft switching, have small passive device capacity, and exhibit low voltage stress, but the voltage ratio is difficult to be regulated continuously. In this paper, a quasi-parallel resonant switched capacitor DC-DC converter is proposed to satisfy the design requirements, which incorporates a ReSCC with soft-switching and variable voltage ratio, as well as a Buck converter. In addition, a simplified optimal trajectory controller for ReSCC is investigated, which can be combined with PI controller of Buck converter to enhance the load transient of the proposed converter. Moreover, the input voltage feedforward control is implemented with the optimized feedforward coefficient to suppress the input voltage disturbance. The proposed converter can achieve high efficiency and rapid dynamic response over a wide conversion range. The correctness of the theoretical analyses is verified by a 36V∼60V-5V/20A prototype. © 2013 IEEE.

关键词： Capacitor bank

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Intelligent Train Tracking control Based on Monte Carlo Tree Method

Intelligent Train Tracking Control Based on Monte Carlo Tree...

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International Conference on Intelligent Transportation

作者： Zixu Zhao Jidong Lv Boyuan Zhou Wanli Lu Ming Chai Hongjie Liu National Engineering Research Center Of Rail Transportation Operation And Control System Beijing JiaoTong University Beijing China Institute of Communication and Signalling The Center of National Railway lntelligent Transportation System Engineering and Technology Chinese Academy of Railway Science CARS Beijing China

Train tracking distance control is critical to ensure safety and enhance the transportation capacity of urban rail transit. The widely used control methods relying on the dynamics model of trains may not achieve ideal performance because of the inaccuracy of the dynamics model and delay of control commands execution, especially in the Urban Rail Transit scenarios. To overcome these limitations, this paper presents an intelligent tracking control method NN-MCTS combining a deep learning model with Monte Carlo Tree Search (MCTS) to cope with the tracking distance minimization. The deep learning model adopts a neural network (NN) to characterize the relationship between control command and actual train dynamic, while MCTS regards the control problem as a decision-making process leveraging the trajectory output of NN to obtain the reward of the corresponding control sequence. We extracted 20000 actual state trajectory segments of trains of Chengdu Metro Line 8 to train the deep-learning model, and additionally continuous trajectories as the known state of the train ahead to verify the effectiveness of the search algorithm. Experiments show the advantages of the proposed method in accuracy and stability compared with PID and real-time compared with MPC method.

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Low-Earth Orbital Lifetime Prediction Based on Parameter Sensitivity Analysis and Deep Learning

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Chen, Shun-Yi Yang, Zhen Wang, Hua Lu, Lin State Key Laboratory of Space System Operation and Control Changsha410073 China National University of Defense Technology College of Aerospace Science and Engineering Changsha410073 China China Astronaut Research and Training Center Beijing100094 China

This article proposes an intelligent prediction method for the orbital lifetime of resident space objects (RSOs) in low Earth orbit. This method is intended to satisfy the computational efficiency and accuracy requirements when predicting the large-scale RSOs. It helps to analyze the distribution and uncertainty of RSOs' lifetime. First, based on the semi-analytical orbit prediction model, a parameter sensitivity analysis method for the orbital lifetime is proposed, and the parameters that affect the orbital lifetime are investigated. Second, using a fully connected neural network, an orbital lifetime prediction model is established based on deep learning. The hyperparameters of the deep learning model are optimized using the black-winged kite algorithm. Finally, by combining the sensitivity analysis with the deep learning model, an intelligent prediction method is proposed for the rapid prediction of orbital lifetime. Simulation results show that the proposed intelligent prediction method accurately determines the orbital lifetime of RSOs and has higher prediction efficiency than the traditional prediction method. The research results provide an effective calculation and analysis tool for predicting the orbital lifetime of large-scale RSOs in the future. © 2025 IEEE.

关键词： Prediction models

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Abatement of CH4 and NOx emissions in marine LNG engine using a plasma-catalyst reactor: An integrated experimental and computational study

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Separation and Purification Technology 2025年 373卷

作者： Xiang, Chuang Lyu, Lin Liang, Junjie Yang, He Zhu, Neng School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Wuhan430063 China National Engineering Research Center of Ship & Shipping Control System Shanghai200135 China School of Automotive and Transportation Engineering Wuhan University of Science and Technology Wuhan430081 China

To investigate the CH4 and NOx abatement performance of a plasma-catalyst hybrid system in liquefied natural gas (LNG) engine exhaust treatment, we conducted bench-scale experiments to analyze the coupled effects of flow dynamics, thermal distribution, and concentration gradients on pollutant removal. The study systematically evaluated CH4, CO, and NOx removal efficiencies across varying engine operating conditions and specific energy inputs (SEIs), with particular emphasis on distinguishing the individual contributions of plasma and catalytic processes. A validated 2D plasma-catalyst reactor model was developed through a 0D-2D coupling methodology. This computational framework enabled detailed analysis of velocity fields, temperature profiles, and species distributions under different operating scenarios, providing mechanistic insights into CH4 and NOx removal pathways. Simulated results revealed uniform spatial distributions of velocity and thermal characteristics across multiple plasma discharge channels. Increasing SEI induced a progressive temperature elevation along the flow axis, accompanied by concomitant decreases in CH4 concentration (36% maximum reduction) and increases in CO and NOx concentrations through plasma-driven chemical conversion. Within the catalytic module, velocity profiles maintained uniformity while temperature distributions exhibited centerline maxima due to boundary heat dissipation. Elevated SEI enhanced catalytic reduction efficiencies, demonstrating progressive decreases in NO, CH4, and CO concentrations along the flow direction with contraction of high-consumption zones. Notably, NO2 concentrations followed a non-monotonic trend, initially increasing through plasma-generated NO oxidation before subsequent catalytic reduction. The integrated system achieved maximum removal efficiencies of 90.1% for CH4, 89.7% for CO, and 61.6% for NOx through synergistic plasma-catalyst interactions. Critical performance analysis quantified the plasma'

关键词： Velocity distribution

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Screw thermal characteristics analysis and error prediction considering the two-dimensional heat transfer structure

Research Square

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research Square 2021年

作者： Chen, Yu Chen, Jihong Xu, Guangda National Numerical Control System Engineering Research Center Huazhong University of Science and Technology Wuhan430074 China

Thermal error is a common problem in machine tool processing. It is usually proposed to take the screw as one-dimensional rod heat transfer system to solve the thermal error of screw by establishing thermal characteristic equation. This method regards the temperature on the nut as the temperature of the screw contact surface without considering the heat transfer distance between screw and the mounting point on the nut. To solve this problem, this paper takes the screw-nut feed system as a two-dimensional heat transfer structure considering both screw and nut. In addition, to solve the low convergence speed and easy fall into local optimum problem in optimal parameter identification. The improved particle swarm optimization algorithm (IPSOA) is proposed to identify the boundary parameters of thermal characteristics equations. Finally, the temperature and thermal error of the screw-nut feed system is calculated by the identified results. Experiments under different working condition show that the maximum prediction residual error of the two-dimensional method (TDM) is less than 7.2 um and the simulating prediction accuracy can reach 86.2%. Besides, compared with one-dimensional model (ODM), TDM has a higher predict accuracy which verifies the effectiveness of the proposed method. © 2021, CC BY.

关键词： Particle swarm optimization (PSO)

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Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine

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Chinese Journal of Electronics 2020年第4期29卷 786-792页

作者： SUN Yongkui CAO Yuan XIE Guo WEN Tao School of Electronic and Information Engineering Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Railway point machines(RPMS) are one of the key equipments in the railway system to switch different routes for the *** monitoring for RPMs is a vital measure to keep train operation safe and *** convenience and low cost into consideration, a novel intelligent condition monitoring method for RPMs based on sound analysis is ***-domain and frequency-domain features are obtained,and normalized using z-score standardization method to eliminate the influences of different *** particle swarm optimization(BPSO) is utilized to select the most significant discrimination feature *** effects of the selected optimal features are verified using Support vector machine(SVM), 1-Nearest neighbor(1 NN), Random forest(RF), and Naive Bayes(NB).Experiment results indicate SVM performs best on identification accuracy and computing cost compared with the other three *** identification accuracies on normal switching and reverse switching processes reach100% and 99.67%, respectively, indicating the feasibility of the proposed method.

关键词： Condition monitoring Sound analysis Railway point machines(RPMs) Binary particle swarm optimization(BPSO) Support vector machine(SVM)

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