Search Results (80)

Maglev transportation has emerged as a new option for long-distance travel between cities with the rapid development of transportation infrastructure. The fatigue issues of the maglev train–track–bridge coupling system, induced by increased train speeds, have garnered considerable attention. This study focuses on the continuous girder bridge of low-to-medium-speed maglev dedicated lines. A multi-vehicle coupling model and a refined vehicle–track–bridge system were constructed. These were based on the maglev equivalent stiffness-damping theory. Dynamic stress is solved using the modal superposition method. Fatigue performance under multiple working conditions is then evaluated. This evaluation uses the rainflow counting method and Miner’s linear damage theory. Dynamic stress is solved using the modal superposition method, and fatigue performance under multiple working conditions is evaluated based on the rainflow counting method and Miner’s linear damage theory. Key findings include the following: Dynamic stress peaks in the track structure reach 29.4 MPa at high-strength bolts and 20.1 MPa at bridge fasteners, significantly exceeding those in the bridge, identifying these as fatigue-sensitive zones. During a single train passage, the stress amplitudes are mainly concentrated in the low-stress amplitude range, yet annual accumulated damage at the critical node track tie and bridge fastener junction reaches 4.99 × 10⁻⁴. Increasing the train speed to 160 km/h amplifies total damage at the track tie and bridge fastener junction by 365%, with nonlinear growth in fastener damage. This research provides theoretical insights for optimizing speed-up strategies and maintenance protocols in low-to-medium-speed maglev systems. Full article

The suspension system of high-speed maglev trains has composite time-delay factors, such as inductance delay and control circuit latency, which lead to a decrease in the tracking and robustness of the current control loop. Based on the study of electromagnetic characteristics of suspension systems, this paper proposes a composite anti-time-delay current loop based on adaptive parameter optimization. First, a finite element analysis model of the suspension electromagnet is constructed to analyze the changes in suspension force and inductance of the suspension electromagnet. A self-tuning PI current loop is constructed to achieve time-varying parameter matching. Second, to tackle the inherent time delays and disturbances in the control loop, a predictive PI control algorithm combined with an extended state observer (ESO) is introduced, which effectively estimates and compensates for disturbances and phase lags. Furthermore, a parameter optimization strategy based on the adaptive differential evolution (ADE) algorithm is proposed to address the difficulties in current loop tuning. The results demonstrate that compared to traditional current loop strategies, the dynamic performance of the designed composite anti-time-delay current loop is significantly improved, enhancing the current following control capability of the suspension system under complex operating conditions. Full article

The dynamic performance of medium- and low-speed maglev vehicle–track coupling systems, as well as the dynamic response of the vehicle body and suspension frame under suspension electromagnet failure, is of great significance for the safe operation of maglev tracks. Based on vehicle–track coupling dynamics theory, and considering the spatial dynamic magnetic rail relationship in combination with the suspension control system, a dynamic vehicle–track model incorporating suspension electromagnet failure is established. The effect of such failures on electromagnet suspension force and overall vehicle performance are analyzed. The results indicate that the theoretically calculated electromagnetic force differs significantly from the actual force. Under four electromagnet operating conditions, lateral displacement has the greatest influence on suspension force. By considering the magnetic saturation of ferromagnetic materials and the leakage effect of suspension gaps, a spatial dynamic magnetic orbit relationship is established. A single-pole suspension electromagnet fault has little effect on overall vehicle performance. When the suspension electromagnet on one side fails, the suspension frame tilts toward that side and is supported and operated by a sled. When three suspension points fail, the entire suspension frame loses its suspension state and operates fully under sled support. When a suspension frame electromagnet becomes stuck, severe fluctuations in suspension force and vehicle vibration acceleration occur. These fluctuations increase with vehicle operating speed, seriously endangering operational performance. The findings provide a fundamental theoretical basis for the safe operation and maintenance of medium- and low-speed maglev vehicles under fault conditions. Full article

Spatial efficiency drives the adoption of integrated station–bridge structures in maglev transit, yet the rigid coupling between track and station poses inherent challenges to vibration serviceability. This study isolates the impact of support constraints, specifically contrasting rigid connections with pinned supports, on the dynamic performance of a five-story maglev station. Using a unified, high-fidelity 3D coupled model that incorporates electromagnetic suspension nonlinearity, we evaluated structural responses under train speeds of 60–120 km/h. Simulations identify a critical operational threshold: while the waiting hall remains compliant with standard comfort criteria (DIN 4150-3), the platform floor exceeds the 1.5% g acceleration limit during dual-track operations at speeds ≥ 100 km/h. Beyond standard safety checks, the main scientific innovation of this study is revealing the mechanical transmission paths of structure-borne vibrations at the track-frame interface. The results demonstrate that rigid connections create full mechanical coupling, directly passing train-induced bending moments into the station frame. Conversely, pinned supports release the rotational degrees of freedom, which physically cuts off the primary energy transmission route. By explaining this structural decoupling mechanism, this work moves beyond a specific engineering case study to provide a fundamental theoretical framework for vibration control in complex maglev hubs. Full article

To minimize the construction cost of track beam and enhance the dynamic performance of the magnetic suspension system, a simplified coupled vibration model of the electromagnet-track beam-vehicle body was established. Initially, by defining a Lyapunov function to represent the quadratic performance index of the maglev system under parameter perturbation, the controller design problem for the closed-loop system was transformed into an existence problem of linear matrix inequality (LMI) solutions. Consequently, a state-feedback cost-preserving robust controller for a flexible track beam was designed. Subsequently, the impact of the suspension controller on the dynamic characteristics of the flexible track beam, both with and without considering parameter perturbation, was compared and analyzed. Furthermore, the robustness, high-frequency suppression, and low-frequency following characteristics of the LMI-based controller were evaluated. Finally, the influence of two distinct state feedback controllers on the dynamic characteristics of the flexible track beam across different frequency bands was analyzed. Correlation analysis revealed that accounting for parameter perturbation can improve the suspension and dynamic characteristics of the suspension controller. When the system experiences parameter perturbation, the LMI-based suspension controller can achieve stable suspension on the flexible track beam while demonstrating strong robustness, high-frequency suppression, and low-frequency following capabilities. On the flexible beam, controllers with different state feedback exhibit varying dynamic characteristics in different vibration frequency segments of the track beam. Specifically, reducing the feedback state variables in the low-frequency band and increasing them in the high-frequency band can appropriately improve the system’s dynamic characteristics. Full article

The high-speed Electromagnetic Suspension (EMS) maglev guidance system exhibits inherent characteristics of strong nonlinearity, parameter time-variation, and complex external disturbances. To further optimize and improve the control performance of the guidance system for high-speed maglev trains, a novel intelligent control strategy that integrates the Deep Deterministic Policy Gradient (DDPG) algorithm with Super-Twisting Sliding Mode Control (STSMC) is proposed. Focusing on a single-ended guidance unit with differential control of dual electromagnets, an STSMC controller is first designed based on a cascaded control framework. To overcome the limitation of offline parameter tuning in dynamic operational conditions, a reinforcement learning optimization framework employing DDPG is introduced. A multi-objective hybrid reward function is formulated, incorporating error convergence, sliding mode stability, and chattering suppression, thereby realizing the online self-tuning of core STSMC parameters via real-time interaction between the agent and the environment. Numerical simulations under typical disturbance conditions verify that the proposed DDPG-STSMC controller significantly reduces the amplitude of guidance gap variation and accelerates dynamic recovery compared to conventional PID control. Its superior performance in disturbance rejection, control accuracy, and operational adaptability is validated. This study, conducted through high-fidelity numerical simulations based on actual system parameters, provides a robust theoretical foundation for subsequent hardware-in-the-loop (HIL) experimentation. Full article

Affine Frequency Division Multiplexing (AFDM) has been proposed for future high-mobility communication scenarios. However, existing AFDM channel estimation methods suffer significant performance degradation under fractional Doppler conditions due to path energy dispersion. To address this issue, we propose a deep learning network that adaptively learns path energy dispersion through a 1D processing module and a Transformer block, based on the diagonal reconstruction of the AFDM effective channel matrix. 1D processing module employs convolutions with different kernel sizes to extract pilot features, and Transformer block models vary energy dispersion patterns. The proposed method does not require prior knowledge of the number of paths and the assumption of distinct path delays. Simulation results demonstrate that at a Signal-to-Noise Ratio (SNR) of 25 dB, the proposed method achieves up to a 4 dB gain in Normalized Mean Square Error (NMSE) and an 6 dB improvement in Bit Error Rate (BER) over existing traditional methods under fractional Doppler conditions. Full article

Superconducting electrodynamic suspension (EDS) maglev technology has strong potential for ultra-high-speed transportation, with advantages such as self-stability and a large suspension gap. The magneto-electric force relationship between the onboard superconducting magnet and figure-eight null-flux coils is the key to improving system performance. This article shows a novel study on the impact of the shape of null-flux coils on the superconducting EDS maglev system, which has not been systematically studied before. A 3D model of the suspension system of EDS maglev was built using the finite element method (FEM) to study the impact of the null-flux coils’ shape. The electromagnetic forces generated by the system were calculated and compared with those in the literature to validate the model. The results showed that rectangular and circular coils displayed different influences on the components of the electromagnetic force. New results and analysis from the article show that the null-flux coil shape is a promising option for system performance optimization and can provide a theoretical basis for future improvements to the high-speed EDS maglev system. Full article

For marine development in harsh sea states, floating-body salvage equipment serves as critical support infrastructure. Aiming at the challenges of nonlinear dead-zone, model uncertainty, and actuator failures in the wave compensation systems of such equipment, this paper proposes an intelligent fault-tolerant control method based on neural networks. First, the dead-zone nonlinearity of the hydraulic system is compensated using an inverse model approach. Then, neural networks are employed to online learn unmodeled dynamics, while adaptive laws are designed to handle partial actuator failures and Lyapunov theory is used to prove the global stability of the closed-loop system, effectively enhancing the robustness and fault-tolerance of the wave compensation system under complex sea conditions. Unlike existing studies that rely on accurate system models, the proposed method integrates data-driven learning with model-based compensation. This integration enables adaptive handling of wave disturbances, model uncertainties, and actuator faults, thereby overcoming the strong model dependence and complex observer design inherent in traditional sliding-mode fault-tolerant control. Simulation and experiment results show that the method ensures high-precision dynamic tracking and compensation performance under various sea conditions. Full article

Strong crosswinds and train–tunnel aerodynamic interactions cause the aerodynamic loads acting on the train body to change more drastically when a high-speed maglev train enters a tunnel. This greatly raises the risk of safety incidents like derailment or overturning. This study employs the FLUENT 2023 R2 computational fluid dynamics simulation software with an overset grid method to numerically investigate the influence patterns of crosswinds on aerodynamic loads and relevant safety issues for a 600 km/h maglev train entering tunnels under various crosswind conditions. The findings show that (1) the marshaling location has a strong correlation with aerodynamic performance. When there is no crosswind, the head vehicle (HV) has the greatest chance of flipping, while the rear vehicle (RV) has the worst lift characteristics. All three vehicles experience significant sudden changes in lateral force coefficients prior to tunnel entry, indicating considerable derailment risks. (2) Aerodynamic loads on the HV show significantly greater sensitivity to crosswind velocity variations compared to the middle vehicle (MV) and RV, with the amplitude reduction in lateral forces in the HV showing approximately linear increase with wind speed. (3) A 50 km/h reduction in train speed decreases the amplitude of change in the lift coefficient and lateral force coefficient by approximately 4.8% and 8.9%, respectively, and the peak overturning moment in open air and tunnel by approximately 11.4% and 15.7%, respectively. These discoveries have both practical value for advancing high-speed maglev networks and theoretical significance for enhancing the safety and reliability of Chinese maglev systems. Full article

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article

This paper reviews the role of high-speed rail (HSR) and other fast rail technologies in decarbonising inter-urban transport. It first outlines the global deployment of HSR, with particular emphasis on Europe and China, and situates these networks within the wider geography of fast rail systems. The paper then compares HSR with competing modes such as air transport and passenger cars along key dimensions including door-to-door travel time, energy use and emissions. Building on a qualitative synthesis of the international literature, it discusses the environmental, economic and social impacts of HSR, highlighting conditions under which HSR can deliver substantial modal shift and life-cycle greenhouse gas savings, as well as situations where benefits are more limited or unevenly distributed. Finally, the review briefly considers emerging fast rail concepts such as Maglev and Hyperloop and argues that they should currently be treated as complementary, long-term options rather than immediate substitutes for conventional HSR. Full article

This paper proposes an evolutionary modeling method of scattering clusters based on Geometric-Based Stochastic Modeling (GBSM). In the single-bounce scenario of vacuum pipeline maglev train communication, the dynamic generation and extinction process and statistical behavior of multiple clusters at high speed are analyzed. The model abstracts the multipath component into a cluster structure. By iteratively updating the channel state and the birth and death cluster information after initialization, a dynamic model of the evolution process of scattering clusters in time-varying channels is constructed, which depicts the time evolution process of multipath clusters. Under the framework of GBSM, the correlation statistical characteristics of the scattering cluster birth and death process are further derived, and analytical integral form expression of the channel time autocorrelation function (ACF) is theoretically solved. The simulation results reveal the inherent law of channel time-varying characteristics under the joint action of high-speed train operation and closed pipe structure, and the results show that the proposed method can effectively capture the transient dynamic characteristics and long-term statistical trends of multipath clusters. The proposed model provides a practical basis for channel modeling in vacuum tube maglev wireless communication systems. Full article

Magnetic levitation (Maglev) technology has long garnered significant attention in the engineering community due to its inherent advantages, such as contactless operation, minimal friction losses, low noise, and high precision. Based on electromagnetic suspension (EMS) and electrodynamic principles, these systems are primarily developed for advanced transportation, while also inspiring emerging applications such as vibration isolation and flywheel energy storage. Despite progress, practical deployment faces critical challenges, including accurate modeling, robustness against nonlinear and uncertain dynamics, and control stability under complex conditions. Artificial intelligence (AI), particularly machine learning (ML) offers promising solutions. Studies show ML-based methods, i.e., improved particle swarm optimization (PSO) optimize proportional-integral-derivative (PID) to reduce controller output overshoot, deep reinforcement learning (DRL) reduces levitation gap fluctuation under complex conditions, ensemble learning achieves high fault diagnosis accuracy, and convolutional neural network-long short-term memory (CNN-LSTM) predictive maintenance cuts costs. This review summarizes recent AI-enabled advances in Maglev transportation system modeling, control, and optimization, highlighting representative algorithms, performance comparisons, technical challenges, and future directions toward intelligent, reliable, and energy-efficient transportation systems. Full article

The guest room and aisle of electric high-speed maglev train must be shielded from leakage magnetic flux produced by superconducting strong magnetic field. To reduce magnetic leakage, the superconducting magnetic levitation system structure is obtained by extended lagrangian optimization method. The optimized superconducting coil structure has the advantages of reducing magnetic leakage, improving magnetic field utilization, reducing the weight of the magnetic isolation plate and the weight of the maglev train, and enhancing the load-bearing capacity of the maglev train. Based on optimized superconducting coil parameters for high-speed maglev, the magnetic shielding effect at the aisle and the guest room, the magnetic flux density distribution at the magnetic shielding is calculated and analyzed through analytical calculation. The relevant conclusions indicate that the magnetic suspension structure has the advantages of reducing end coil leakage flux and the weight of the high-speed maglev train. Full article