High-speed Railway System Research Articles

A Fast Simulation Model of Pantograph–Stitched-Catenary Interaction in Long-Distance Travel

The increasing operation speed of high-speed trains allows the pantograph to continuously interact with the catenary over a long distance in a short time, and many new methods have been developed to efficiently calculate its dynamics. However, the existing methods only consider simple catenary systems, which limits their application in high-speed railway systems. In this work, a reduced pantograph–stitched-catenary interaction model is developed to simulate pantograph–stitched-catenary interactions during long-distance travel. Based on the existing reduced catenary model, the stitched catenary system is first considered, where the stitched wire is simplified into a part of the messenger wire supported by two spring-damping elements. The present model is validated by test results and the EN 50318:2018 standard, and it is subsequently used to study the dynamic performance of the pantograph–stitched-catenary system at an overdesigned speed in Sweden. The results show that the proposed model can be seven times faster than the traditional modal superposition method with the same accuracy in a stitched catenary system, and the existing catenary system cannot be operated at an overdesigned speed without increasing the contact wire tension. The present model gives an efficient solution to pantograph–stitched-catenary interaction problems.

Design and Seismic Performance Study of Multistage Controllable Isolation Bearing for High-Speed Railway Simply Supported Beam

The high-speed railway (HSR) system imposes stringent requirements for track smoothness. However, conventional seismic isolation bearings frequently fail to meet these demands. To address this challenge, a novel seismic isolation bearing was developed based on the principle of functional separation design. This innovative bearing effectively achieves the multistage control objectives, including amplitude limitation to ensure track smoothness during frequent earthquakes, energy dissipation to guarantee train running safety during design earthquakes, and structural integrity maintenance to prevent beam collapse during rare earthquakes. Firstly, an overview of the novel isolation bearing’s structural design and operational principle is provided. Subsequently, a corresponding mechanical model is formulated, with the parameters of the new bearing determined through finite element analysis. The study then compares the seismic performance of the general rubber bearing and the new bearing, using an HSR simply supported bridge as an engineering background. The dynamic response of the bridge under varying seismic waves, pier heights, and bridge spans is meticulously analyzed. The results indicate that the new bearing can achieve multistage control. Compared to general bearings, it reduces bridge displacement vibration by over 46.4% under frequent, design, and rare earthquakes. The bridge deformation under frequent earthquakes remains below 3 mm, thus meeting the track smoothness requirements for normal HSR operations. Additionally, the study reveals that higher pier heights increase the seismic response, peaking at 15 m. The vibration reduction provided by the new bearing varies but remains effective in most earthquake scenarios, with maximum reductions of 92.9% for displacement and 74.17% for bending moment. Furthermore, larger bridge spans also increase the seismic response, with the 24 m span bridge outperforming the 32 m span bridge. In conclusion, the novel seismic isolation bearing significantly enhances the seismic performance of HSR bridges, ensuring train running safety and operational reliability.

Performance evaluation of a racetrack high temperature superconductor winding in an HTS levitation platform

Abstract High temperature superconducting (HTS) is a key technology of next generation high-speed railway systems, which guarantees high current and highly efficient traction power supply. HTS devices can reduce system weight and thus improve loads of vehicles. On high-speed maglev trains, HTS windings wound by second-generation (2G) HTS tapes play an important role in suspension and traction. However, in complex ferromagnetic environments, the performances of windings are notably influenced by the varying magnetic fields. In this paper, a new maglev platform is designed and comprised of a winding of six racetrack HTS coils, an E shape iron core and a magnetic guide rail. The air core structure of the winding is modeled，tested and the electromagnetic characteristics are analyzed for three dimensional (3D) finite element analysis of the platform. An A formulation is used to calculate magnetic vectors A and infer magnetic flux densities and current densities for the structures. After the winding is installed on the iron core and the magnetic guide rail is suspended above the iron core, the air gap between the iron core and the rail is varied from 0 mm to 20 mm in models and experiments. The magnetic fields, critical currents and losses of the winding in cases of the air core and the iron core conditions are used for analysis. The comparison between the air core and the iron core conditions confirms the reliability of the proposed models. This study provides means for analysis of complex superconducting maglev systems.

A novel method for reducing the effect of doppler frequency shift in high-speed railway mobile communication using machine learning

Currently, high-speed railway systems are rapidly expanding worldwide, necessitating reliable and efficient mobile communication solutions. However, the Doppler frequency shift caused by the high speeds of trains presents significant challenges to communication systems, particularly those using OFDM in LTE. This paper presents a novel for Doppler frequency compensation in high-speed railway communication based on the results of estimating the train's velocity using machine learning algorithms. By leveraging advanced algorithm such as neural networks, our method dynamically predicts and compensates for Doppler shifts in real-time. In this proposed novel, the Doppler frequency value is calculated based on the actual train’s velocity and the scenario of a high-speed railway, then the Doppler frequency is used to compensate the carrier frequency offset (CFO) directly at the Access Point (AP) device on the train. We conduct simulations to evaluate the effectiveness of our proposed solution in a high-speed railway scenario. The results demonstrate a marked improvement in communication reliability and data integrity, highlighting the potential of machine learning to enhance the performance of mobile communication systems in high-speed railways. The results of the proposed model are evaluated based on the system’s bit error rate BER after the CFO compensation decreases and the ratio of average energy per bit (Eb) to noise power density (N0) (Eb/N0) increases. This shows that the proposed solution works effectively and reduces the system’s bit errors while improving the communication performance. This study opens new avenues for integrating intelligent systems in transportation networks, ensuring seamless connectivity, and improving passenger experience

Energy response analysis and seismic isolation strategy optimization of high-speed railway bridge-track system under earthquake action

The formulation and optimization of a seismic isolation strategy is crucial for improving the seismic performance of high-speed railway bridge-track systems (HSRBTS) under earthquake action. In line with this proposition, therefore, this paper studies the seismic isolation strategy optimization of HSRBTS based on an energy response analysis. More specifically, the energy dissipation distribution of common and isolation HSRBTS under different earthquake strength levels is analyzed. The results show that when the peak ground acceleration (PGA) is greater than 0.3g, the seismic input energy of isolation HSRBTS is smaller than common HSRBTS. With the increase of PGA, the total proportion of hysteretic energy dissipation increases while the proportion of damping energy dissipation decreases in common HSRBTS, with the opposite being observed in isolation HSRBTS. Bearings and piers are the main hysteretic energy dissipation components for HSRBTS. Thus, in view of the energy dissipation requirements and structural characteristics of HSRBTS, this paper proposes an energy-based optimization principle. Under the existing isolation HSRBTS, the seismic isolation strategy is optimized to achieve a uniform distribution in hysteretic energy dissipation, while the effectiveness of the optimized seismic isolation strategy is submitted to further verification.

The Analysis of Electric Phase Separation and Common Technical Issues of High-speed Railway System in China

The electric railway, as a vital economic transportation artery, offers high efficiency and effectiveness compared to other modes of transportation. The characteristics of efficient transport, comfortable ride and environmental friendliness have effectively driven regional economic construction and industrial economy along the route, which has significantly enhanced cultural and economic exchanges between different regions, becomes the preferred mode of transportation for medium-distance and long-distance travel, and plays an irreplaceable role in China’s current transportation system. Under the increasing demand for passenger flow, the high-speed and high-density tracking operation of trains has become the new normal of high-speed railway operation management. In the background of national science and technology innovation development strategy, how to further shorten the train tracking interval to improve transportation efficiency and further reduce the energy consumption of train operation to improve the energy utilization level of the railway industry is the focus of the current electric railway construction and operation management department. The electric phase separation is a special and intricate part of the traction power supply system in China’s electric railway, and it is an indispensable component. The article describes that some main techniques relating to electrical phase separation are utilized extensively in China’s high-speed railway, by studying the technologies of electric phase separation, a few new methods were given and shared, which will solve the issues of high energy consumption, increased breakdowns, large equipment loss, and the lower speed, to better serve economic and social development and people’s travel.

The spatially differentiated impact of high-speed railway on accessibility and socio-economic development of developing regions: A study in southwest China

The high-speed railway (HSR) plays a crucial role in bolstering regional connectivity and economic and social progress. Nevertheless, there is an ongoing debate around the impact of HSR on the economic and social development of the developing regions. On one hand, developing regions necessitate the implementation of HSR systems to facilitate their progress. On the other hand, they must also mitigate the potential drawbacks and adverse consequences associated. By employing accessibility analysis, urban potential analysis, and geographically-temporal weighted regression modeling, this study aims to comprehensively elucidate the spatially variable impact of HSR on developing regions such as Southwest China. The findings indicate that the implementation of HSR has yielded significant benefits in terms of accessibility, economic growth, and social development in developing regions. However, there is a growing disparity between cities that have HSR connections and those that do not. Additionally, smaller and medium-sized cities situated along the middle section of the HSR route experience relatively less pronounced impacts. This study posits that there is a necessity to enhance the expansion of the HSR network in developing regions, concurrently with the reinforcement of inter-city cooperation and the establishment of urban agglomerations, thereby promoting high-quality and balanced development of the developing regions.

Detection and Assessment of Seismic Response of High-Speed Railway Bridges Based on Smartphone Public Participation

The seismic response detection and operational safety assessment of high-speed railway (HSR) bridges play a crucial role in ensuring HSR systems’ operational safety and reliability. Smartphones have introduced intelligent inspection tools for structural health detection, becoming a new tool for intelligent structural inspection. Combining the public and smartphones is the key to public participation in structural health detection. This study utilizes smartphone-based structural seismic response inspection technology to investigate the framework of public participation in earthquake response inspection and assessment. This system comprises the Smart Bridge Brain (SBB), which integrates data from multiple sources and systems, an assigning mechanism for public participation inspection tasks, and smartphone-based HSR bridge structural seismic response inspection technology. At the same time, the Unreal Engine 5.0 software is used to create a mixed-reality virtual simulation experimental environment to validate the feasibility of this framework. The results indicate that the intelligent optimization of task allocation by the SBB successfully assigns detection tasks to each public participant. Public participants can promptly reach predefined damage structure detection targets and rapidly inspect bridge structural seismic response indicators using smartphones. In addition, this paper also conducts a comprehensive evaluation and analysis of the detection of the work efficiency index (WEI) within the system. Furthermore, optimization strategies for the efficient execution of detection tasks are proposed based on WEI variations influenced by different factors. The system framework is expected to enhance cluster-based HSR bridges’ intelligent disaster prevention and mitigation capabilities.

Transport capacity optimization for high-speed rail network considering flexible train composition and additional capacity pool

To balance the limited capacity and constantly changing demand in the high-speed rail network, a transport capacity optimization problem is investigated by adjusting train composition through uncoupling/coupling operations at the origin station, scheduling additional trains from the capacity pool, and optimizing the seat allocation among ODs. To make joint decisions, a two-stage stochastic programming model is formulated to minimize both operating cost and penalty cost for unsatisfied passengers, while incorporating various constraints such as flow conservation, loading capacity, flexible composition, etc. The two-stage model is linearized into a mixed-integer linear programming model, which can be effectively solved for small networks but is still intractable to solve for realistic networks. Therefore, a Lagrangian relaxation algorithm is proposed integrating parallel computing and sub-gradient method. Finally, two sets of case tests, including a toy network and a real-world case of a high-speed rail network in China, are implemented to show the performance of the proposed approach. The results illustrate that the parallel heuristic algorithm could generate a high-quality solution with a slight discrepancy from the optimal solution, while achieving a commendable computation time. Furthermore, the findings emphasize the advantages of applying flexible train composition and additional capacity pool in the high-speed railway system for cost-saving and supply–demand matching.

Explicit finite element simulations of dynamic low adhesion behavior between wheel and rail in the presence of high-frequency vibrations

PurposeDynamic low adhesion (DLA) has become an urgent problem for the high-speed wheel-rail system because of continuous decrease of adhesion redundancy in the past decades. This article aims to provide a simulation method to reveal the mechanism of DLA under high-frequency vibrations.Design/methodology/approachA transient wheel-rail rolling contact model is developed for a typical Chinese high-speed railway system using the explicit finite element (FE) method. Instantaneous adhesion exploitation levels are studied in the time domain, for which driving cases over corrugated rails are taken as an example. A speed up to 500 km/h is considered together with different traction coefficients and corrugation dimensions. DLA is expected when the instantaneous adhesion exploitation level reaches 1.0, that is adhesion saturates and full sliding contact occurs.FindingsThe instantaneous adhesion exploitation level can be very high in the presence of corrugation, even at low traction coefficients. DLA is found to occur as great vertical unloading takes place and causes a significant increase of creepage. An approach is further developed to determine the critical depth of corrugation over which DLA occurs.Originality/valueThis study employs the transient wheel-rail rolling contact model to predict the instantaneous adhesion exploitation level under high-frequency vibrations. The presented results reveal a mechanism of DLA being beneficial to guidelines for future railway practice.

One-dimensional analysis of pressure variations induced by trains passing each other in a tunnel

In this study, the asymptotic solutions of the pressure variations induced by two trains passing each other in a tunnel are theoretically investigated. The one-dimensional inviscid compressible airflow is analysed, and two methods to obtain numerically exact solutions and $M_{H}$ expansion formulas for approximate equations are presented, where $M_{H}$ is the Mach number of the high-speed train. The pressure coefficient, corresponding to the maximum value of the magnitude of the pressure, is expressed as $|c_{p}|_{max}=|c_{p,min}|=[({R}/({1-R}))$ $(1+\alpha )^{2}+({R(1-R)}/{(1-2R)^{2}})(1-\alpha )^{2}]+O[M_{H}]$ , where $c_{p,min}<0$ , $\alpha =U_{L}/U_{H}$ and $U_{L}$ and $U_{H}$ denote the speeds of the low- and high-speed trains, respectively, and $R$ is the cross-sectional area ratio of the train to the tunnel. The theoretical results indicate the dependence of the speeds of the two trains on the pressure distribution and that the maximum magnitude of the asymptotic pressure for a fixed value of $M_{H}$ is obtained for $\alpha =1$ and $\alpha =0$ when $R< R_{c}$ and $R>R_{c}$ , respectively, where $R_{c}$ denotes the critical blockage ratio. Because the airflow along the side of the low-speed train, induced by the low-speed train, is along the running direction of the high-speed train and reduces the relative velocity of the high-speed train as the two trains pass each other, $|c_{p}|_{max}$ for $\alpha =0$ is larger than $|c_{p}|_{max}$ for $\alpha =1$ when $R>R_{c}$ . It is theoretically demonstrated that, as conventional high-speed railway systems satisfy $R< R_{c}$ , a conservative pressure estimation can be established assuming $\alpha =1$ .

A reconstruction and recovery network-based channel estimation in high-speed railway wireless communications

The integration of high-speed railway communication systems with 5G technology is widely recognized as a significant development. Due to the considerable mobility of trains and the complex nature of the environment, the wireless channel exhibits non-stationary characteristics and fast time-varying characteristics, which presents significant hurdles in terms of channel estimation. In addition, the use of massive MIMO technology in the context of 5G networks also leads to an increase in the complexity of estimation. To address the aforementioned issues, this paper presents a novel approach for channel estimation in high mobility scenarios using a reconstruction and recovery network. In this method, the time-frequency response of the channel is considered as a two-dimensional image. The Fast Super-Resolution Convolution Neural Network (FSRCNN) is used to first reconstruct channel images. Next, the Denoising Convolution Neural Network (DnCNN) is applied to reduce the channel noise and improve the accuracy of channel estimation. Simulation results show that the accuracy of the channel estimation model surpasses that of the standard channel estimation method, while also exhibiting reduced algorithmic complexity.

Improvement of impact acoustic inspection for high-speed railway track slabs using time–frequency analysis and non-defective machine learning

Slab track is one of the most highly used ballastless track forms in Japan's high-speed railway system (Shinkansen) and is composed from track slabs (precast RC or PRC member), a filling layer (mixture of cement, asphalt emulsion and fine aggregates) and concrete bed. As they age, it has been reported that the supporting conditions of track slabs change due to damage in the filling layer that in turn affects running stability. It is necessary to evaluate supporting conditions of track slabs accurately during maintenance of slab tracks prior to signs of cracking. Some previous works show the usefulness of impact acoustics and non-defective machine learning using frequency response spectrums as feature values for evaluating supporting conditions of track slabs. In this study, we examine a method of non-defective learning to improve the evaluation of supporting conditions: time-frequency response characteristics of impact acoustics using time-frequency analysis and resulting spectrograms as feature values. As a result, we have improved accuracy rate (from 81.90 % to 86.72 %) and F-value (from 70.79 % to 78.75 %) more precisely than conventional method based on frequency response functions as feature values.

Simulating The Impact of High-Speed Train Line System Development on Urban Growth by Using Cellular Automata: Study Case Japan

A major question in the urban planning areas is to understand how urban areas expand. The development of transportation infrastructure such as high-speed railway system is likely to have considerable impacts on the urban growth especially to those area that have access to it. Understanding how the de-elopement will impact the urban growth will be a huge advantage in formulating the spatial plan and to tackle the potential problem. This research attempt to understand how the urban areas expand and analyse the expansion and its relationship with the development of high-speed railway systems by using Artificial Neural-Network Cellular Automata (ANN-CA) approach, utilizing the Modules for Land Use Change (MOLUSCE) plugin in QGIS. This approach has resulted in assisting us to understand the urban expansion pattern and analyse the important component that link the expansion with the infrastructure: Accessibility. By implementing the CA approach, this research also demonstrate that the ANN-CA approach is a promising approach for researcher to understand and analyse the spatial areas in the way it is intended: in spatial manner.

A seismic response prediction method based on a self-optimized Bayesian Bi-LSTM mixed network for high-speed railway track-bridge system

A seismic response prediction method based on a self-optimized Bayesian Bi-LSTM mixed network for high-speed railway track-bridge system

Research on capacity optimization of new energy hybrid energy storage system of high-speed railway

With the fast expansion of electrified railways, the need for energy is growing. Thus, improving railway coupling and interconnection, new energy, and energy storage is critical to support low-carbon and green railway development. Therefore, this paper proposes an optimal configuration method for the access capacity of wind power generation system (WPGS), photovoltaic power system (PVPS), and hybrid energy storage system (HESS) in the traction power supply system (TPSS) of high-speed railways. Firstly, the HESS’s mathematical model is developed, then the high-speed railway TPSS’s economic optimization operating model is built, and a HESS energy management approach is presented, taking the energy management strategy parameters and model capacity configuration parameters as the optimization variables. The mixed integer linear programming approach is used to resolve the actual example with the least daily operational cost of the high-speed rail system as the optimization goal, and the optimal capacity of WPGS, PVPS, and HESS is obtained. Finally, through comparison and analysis, it is verified that the economic benefit is the highest when the distributed complementary energy system covering WPGS, PVPS, and HESS is connected to the traction substation, and 10.47% of the daily expenditure is saved.

Calculation Method for the Transient Hot-Spot Temperature Rise of Oil-Immersed Bushing in High-Speed Railway

Traction oil-immersed bushing is the only channel for connecting the high-speed railway system with the external power grid, and its transient hot spot is one of the most important factors reflecting its operation status. Since the traction load of a high-speed railway has obvious impulse characteristics, the existing calculation method for bushing given in IEEE Std C57.19-100™-2012 is incompatible because it is designed for steady electrical loads. Thus, in this paper, the equivalent hot spot temperature rise of the bushing under varying traction loads is calculated by the traction parameters (i.e., operating time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_ot</i> , interval time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_it</i> , and per unit transient traction current <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_tc</i> ). Then, the difference value of transient hot spot temperature rise is modified by analyzing the change of transient hot spot temperature rise under varying traction loads. Finally, a calculation method is proposed for the transient hot spot temperature rise of the oil-immersed bushing in high-speed railway under different traction loads. A case study of a 110 kV oil-immersed bushing shows that the proposed method is speedy and accurate.

An adaptive controller for power quality control in high speed railway with electric locomotives with asynchronous traction motors

Introduction. Power quality in an electric railway system pertains to the dependability, consistency, and purity of the electrical power provided to different components and systems within the railway infrastructure. Assessing power quality offers considerable opportunities to improve the efficiency of railway systems. Problem. Managing the flow of active and reactive power effectively, decreasing harmonic currents, and addressing the negative sequence component are all critical parts of improving power quality for electrified rail systems. As a result, flexible AC transmission systems are the major means of minimizing or decreasing these difficulties. Purpose. This study describes a half-bridge reactive power railway power conditioner (HB-RPC) with a novel Ynev balancing transformer. HB-RPC is made up of four switching devices and two DC capacitors and the compensator’s stability is determined by the operating voltage of the DC-link. Any variations or imbalances in the DC voltage might cause the compensator to operate in an unstable manner. Novelty. Of a novel balanced transformer with HB-RPC in a high-speed railway system with two scenarios. Methods. The study utilized MATLAB/Simulink software for simulation purposes. The system integrates a fuzzy logic controller (FLC) and a PI controller to optimize DC voltage, ensuring its constancy and balance, with the objective of improving the overall stability of the system. Results. The simulation outcomes illustrate the efficacy of the control approach. Through a comparison of results between scenarios (two and four trains) with the PI-based-HB-RPC and the FLC-based-HB-RPC, the system exhibits enhanced stability for the proposed railway system when employing the FLC-based-HB-RPC, compared to a controller based on PI. Practical value. The proposed configuration elucidates its role in enhancing both the dynamic performance of the system and the power quality of the three-phase rail traction chain.

Influence of span-to-depth ratio on dynamic response of vehicle-turnout-bridge system in high-speed railway

For high-speed railways, the smoothness of the railway line significantly affects the operational speed of trains. When the train passes through the turnout on a long-span bridge, the wheel-rail impacts caused by the turnout structure irregularities, and the instability arising from the bridge's flexural deformation lead to a strong coupling effect in the vehicle-turnout-bridge system. This significantly affects both ride comfort and operational safety. For addressing this issue, the present study considered a long-span continuous rigid-frame bridge as an example and established a train-turnout-bridge coupled dynamic model of high-speed railway. Utilizing a self-developed dynamic simulation program, the study analysed the dynamic response characteristics when the train passes through the turnouts on the bridge. It also investigated the influence of different span-to-depth ratios of the bridge on the vehicle dynamic response when the train passes through the main line and branch line of turnouts and then proposed a span-to-depth ratio limit value for a long-span continuous rigid-frame bridge. The research findings suggest that the changes in the span-to-depth ratio have a relatively minor impact on the train’s operational performance but significantly affect the dynamic characteristics of the bridge structure. Based on the findings and a comprehensive assessment of safety indicators, it is advisable to establish a span-to-depth ratio limit of 1/4500 for a long-span continuous rigid-frame bridge.

A hybrid SNN-STLSTM method for human error assessment in the high-speed railway system

Over the years, many state-of-the-art technologies have reinforced safety in the high-speed railway driving process. However, the accident rate has not dropped significantly with the advanced technology onboard. The leading cause of this phenomenon is adverse human performance. The crucial aspects contributing to human performance are performance shaping factors (PSFs). The existing expert judgment techniques evaluate human error probabilities depending on the descriptions of PSFs’ influences on human performance. However, they cannot perform dynamic human error assessment with the real-time fluctuations of PSF interventions. To overcome this deficiency, we propose a deep learning-based method considering the instantaneous effect and time-dependent influence of PSF interventions on human performance and matching different neural network characteristics to human cognitive processes. The proposed SNN-STLSTM method combines the spiking neural network (SNN) auto-encoder and the sparse-temporal long short-term memory (STLSTM) network. The unsupervised SNN, a brain-like computational model, simulates the human brain’s response when PSF interventions occur instantaneously. Meanwhile, an auto-encoder architecture integrating the unsupervised SNN makes the representations learned by SNN more meaningful. In addition, for the irregularity of PSF interventions, the standard LSTM is extended to the architecture STLSTM to predict the long-term effects of PSFs on human performance. Finally, a case study of the Wuhan–Guangzhou (WH–GZ) high-speed railway (HSR) shows how Monte Carlo simulation based on Fault Tree Analysis is used to assess and collect human error data and then how PSF and human error data are used to train the whole framework. The comparative experiment and ablation study indicate that the proposed SNN-STLSTM method outperforms other artificial intelligence approaches. This study provides insightful findings that help to understand how and what aspects affect human performance in HSR operations. The proposed method can dynamically predict the human error probabilities of the drivers, which facilitates taking timely prevention measures and reduces the accident rate.