Rail System Research Articles

Modifying Injection Equipment Components for Their Adaptation to Work with Greener Hydrogen-Containing Fuels for Non-Road Vehicle Engines

This article presents the authors’ considerations regarding the possibilities of developing fuel equipment for modern compression ignition engines used in special and non-road vehicles. The paper discusses the process of fuel combustion and atomization in the chamber of a piston combustion engine. The paper then presents the concept of modifying the atomizer of a modern fuel injector for operation using hydrogen-containing fuels of plant origin. The authors present a review of tests performed using an engine dynamometer on a modern engine with a Common Rail system running on biofuel. The CI engine operated with standard and modified fuel injectors. During the tests, the external ecological characteristics of the engine were analyzed as a function of rotational speed; the values of injection doses at individual rotational speeds and their effects on the characteristics were read from the current parameters, and the pressure and temperature in the engine’s combustion chamber were measured. The research results show that implementing the changes proposed by the authors of this work is a good direction for the development of compression ignition engines.

Securing Your Eggs in Multiple Baskets — Assuring a Resilient and Secure Supply Chain

AbstractThe global supply chain is a complex system of systems made up of and relying on other complex systems of systems (SoS) to achieve its goals. To take a typical example, Enterprise A is supplied essential parts on a regular basis to manufacture its products. To place the order requires global financial systems, integrated email systems, the internet, multiple telecommunications systems, and supply software provided by large companies. To deliver the parts may require air and maritime transportation systems, the rail network, interstate highway systems, road haulage companies, state and local transportation systems and so forth. When any of these complex systems fail, the impact can be global, and the results catastrophic. Recent examples include the shortage of Personal Protective Equipment (PPE) during the COVID pandemic, computer chip shortages delaying the assembly and sales of cars, and, most recently, the baby formula shortage. These were due to disruptions in the supply chain caused by an overreliance on single sourced suppliers who failed to deliver, transportation disruptions, outsourcing of critical parts, supplies, medicines to distant countries, and/or an overreliance on “Just In Time” for inventory management. This is the case of placing too many eggs in too few baskets, and often just one basket. Counterfeit or substandard parts and products can enter the supply chain via graft, breaks in chain of custody, or carelessness. This has included critical mechanical parts on aircraft, chips containing spyware, and substandard or out of date medicines substituted for the real thing resulting in serious illness and death. This complex SoS needs to be examined, studied, and understood in the same way as a mission critical system; threats, vulnerabilities, and risks need to be identified and mitigated and assurance cases defined to ensure a solid and reliable supply chain. This paper will look at the supply chain of an example factory system to determine how some of these problems can be predicted, prevented, mitigated, and solved using the UAF, RAAML and assurance case techniques.

Application of the System‐Theoretic Process Analysis (STPA) technique to enabling systems in the rail industry

AbstractInvestigations into several recent railway safety incidents have concluded that failures of enabling systems contributed significantly to the root cause of those incidents, suggesting a tendency towards poor management of safety risk associated with subsystems outside the immediate System of Interest (SoI) boundary. This paper examines whether better application of systems thinking to the analysis of hazards, through use of the System‐Theoretic Process Analysis (STPA) technique, could have enabled safety risk to be better identified and managed in the circumstances surrounding a serious derailment that occurred in Victoria, Australia in February 2020.

Predictive-Cognitive Maintenance for Advanced Integrated railway Management

Railway systems play a vital role in modern transportation and Predictive-Cognitive Maintenance (PCM) has emerged as a transformative approach in the context of Advanced Integrated Railway Management for ensuring the safety, reliability, and efficiency of these systems. PCM leverages data analytics and machine learning to optimize railway system maintenance. This requires effective structural health monitoring (SHM) using low-cost sensor devices. This paper presents a prototype solar-powered wireless sensor node with a 3-axis MEMS accelerometer and energy-harvesting features for monitoring rail track vibrations. The node contains a microcontroller that runs embedded machine-learning models to preprocess the vibration data after train crossing. Abnormal vibrations, indicative of defects, were detected in real time using the TinyML inference at the edge. Instead of raw data, only the model results were wirelessly transmitted to a digital twin in the cloud. The digital twin aggregates data across the rail network for system-level assessment of the RUL and maintenance planning. This edge computing approach minimizes wireless transmission and cloud storage compared to raw sensor streaming. Embedded ML enables real-time damage detection, whereas the cloud digital twin enables system-level prognosis insights. The solar-powered platform enables long-term remote monitoring at low cost without wiring or battery changes. A full-scale physical model was used to validate the edge node prototypes against calculation models and wired accelerometers for impulse loads. The results demonstrate that these nodes can provide a sensor layer for cost-effective PCM in railway systems. In summary, this work proposes an edge computing and embedded ML approach for SHM that integrates cloud-based digital twins to enable the predictive-cognitive maintenance of railway infrastructure. Wireless nodes demonstrate potential for low-cost, convenient, and automated rail health monitoring.

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.

The Role of Digital Innovation in Modernizing Railway Networks: Case Studies and Lessons Learned

Digital innovation has become a cornerstone in the modernization of railway networks, offering transformative potential to address the complex challenges faced by this critical infrastructure. As global transportation demands evolve, rail systems are increasingly incorporating advanced technologies to enhance efficiency, safety, and passenger experience. This paper explores the pivotal role of digital innovation in modernizing railway networks, presenting detailed case studies and lessons learned from various global implementations. The first section of the paper examines several case studies highlighting successful digital transformation initiatives in railway systems. It includes an analysis of the deployment of Internet of Things (IoT) devices for real-time monitoring and predictive maintenance, advanced data analytics for optimizing scheduling and capacity management, and the integration of smart ticketing systems to improve customer convenience. By analyzing these case studies, the paper identifies key success factors and common challenges encountered during the implementation of digital technologies.

Robust dynamic train regulation integrated with stop-skipping strategy in urban rail networks: An outer approximation based solution method

In dense urban rail networks with high passenger demands, uncertain disturbances occur frequently, and the resulting train delays will likely spread over the whole network rapidly, hence degrading the service quality offered to passengers. To cope with the uncertainties of frequent disturbances in urban rail networks, this paper proposes a robust train regulation strategy based on the information gap decision theory, which allows the operators to adjust the conservativeness of adjustment schemes flexibly by varying system performances but without the need for prior knowledge of uncertain disturbances. Specifically, considering the coupling relationship between train dynamic flows and passenger dynamic flows, a mixed integer quadratically constrained programming (MIQCP) model is constructed for the robust train regulation problem to generate solutions with immunity against disturbance uncertainties, in which the envelope-bound model is used to characterizing the uncertain sets of disturbances. To meet the real-time requirements of train operation adjustment, a tailored outer approximation algorithm incorporating a two-phase heuristics method is devised to effectively solve the developed robust train regulation model, thereby quickly generating high-quality solutions. Moreover, the warm start technique and domain reduction technique are carefully developed to accelerate the solving procedure. Numerical experiments based on the Beijing metro network illustrate the robustness of the proposed train regulation strategies and the effectiveness of the designed solution approach.

A simplified method for estimating bridge frequency effects considering train mass

The dynamic response of a railway bridge depends on several parameters; the primary parameter is the fundamental natural frequency of vibration of the bridge itself. It is considered a critical parameter of the bridge as the driving or the forcing frequencies arising from moving trains may coincide with the fundamental frequency of the bridge and initiate a resonant response amplifying the bridge load effects. This condition may adversely affect the stresses experienced on bridge members and, consequently, the remaining fatigue life of the structure. Because the train adds additional time-varying mass to the bridge, this introduces a time-varying change in the bridge’s fundamental natural frequency of vibration. As a result, train critical speeds will have a certain range depending on the train configuration. This article presents a simplified method using a power-law relationship to predict the frequency characteristics of a bridge as a function of the train-to-bridge mass ratio. The method is presented in a generalized form, which enables the frequency characteristics to be determined for any given combination of trains and simply supported bridges of short to medium span typically found on the UK rail network. The method is then demonstrated in a case study of a single-span, simply supported plate girder bridge. By considering the BS-5400 train traffic types, the proposed method is used to calculate bridge frequency effects, dynamic amplification, and train critical speed bandwidth for each train type. The simplicity of the proposed method, as it does not require any complex computational modeling, makes it an ideal and effective tool for the practicing engineer to carry out a quick and economical assessment of a bridge for any given train configuration.

Positioning and tracking with ODE-LSTM algorithm for emerging smart rail systems

Positioning and tracking with ODE-LSTM algorithm for emerging smart rail systems

Precarious Lives of the Proletariat: Exploring the Human-Induced Catastrophe in the web series The Railway Men: The Untold Story of Bhopal 1984 (2023)

This study explores the concept of ‘slow violence,’ a term coined by Rob Nixon to describe the insidious, often unnoticed effects of power imbalances over time. It examines how ‘state vampirism’ and ‘industrial capitalism’ oppress marginalized populations, with a focus on India’s dependency on countries like the USA. The research juxtaposes exploitation and deprivation in human-induced disasters, such as the Bhopal Gas tragedy (1984) and Chornobyl (1986), through the lens of Marxist subalternism. It also analyzes the portrayal of these themes in the 2023 Netflix series “The Railway Men: The Untold Story of Bhopal 1984,” highlighting the plight of slum dwellers near Bhopal’s carbide factory. Ultimately, this paper questions the victimization process by capitalist multinationals, positioning Bhopal as a prime example of ‘corporate violence.’

FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN

Rail-fastening components are essential for ensuring the safety of urban rail systems by securing rails to sleepers. Traditional inspection methods rely heavily on manual labor and are inefficient. This paper introduces a novel approach to address these inefficiencies and the challenges faced by computer vision-based inspections, such as missed detections due to imbalanced samples and limitations in conventional image segmentation techniques. Our approach transitions the industry’s focus from qualitative to a more precise quantitative analysis of rail-fastening components. We propose Mask-FRCN, an advanced image segmentation network that incorporates three key technological enhancements: the fully refined convolutional network module (FRCN),which refines the segmentation boundaries for SFC-type fasteners; the Channel-WiseKnowledge Distillation (CWD) algorithm, which boosts the model’s inference efficiency; and the FCRM methodology, which enhances the extraction capabilities for features specific to SFC-type fasteners. Furthermore, we introduce a fastener system inspection and quantization method based on the Mask FRCN method (FIQ), a novel technique for quantifying the condition of components by using image features, template matching with random forests, and a clustering calculation method derived from segmentation results. Experimental results validate that our method significantly surpasses existing techniques in accuracy, thereby offering a more efficient solution for inspecting rail-fastening components. The enhanced Mask-FRCN achieves a segmentation accuracy of 96.01% and a reduced network size of 36.1 M. Additionally, the FIQ method improves fault detection accuracy for SFC-type fasteners to 95.13%, demonstrating the efficacy and efficiency of our innovative approach.

Analysis of Economic Development in Urban Agglomerations by High-Speed Rail Network: Taking Beijing-Tianjin-Hebei City Cluster as an Example

With the increasing impact of the opening of high-speed railway on the inner city, many indicators within the city have changed significantly, which plays an important role in the impact of the city's economy. Therefore, this paper explores how the high-speed railway network affects intra-city economic development based on the changes in three indicators of city size, per capita disposable income of residents and the structure of the three industries, as reflected by the population flow in the Beijing-Tianjin-Hebei city cluster. It is found that the opening of high-speed railway has led to a "siphon phenomenon" in the Beijing-Tianjin-Hebei urban agglomeration, which has both advantages and disadvantages for the development of large cities and small and medium-sized cities; it has a positive effect on the increase of disposable income of the residents; and it also has a positive effect on the development of tertiary industry.

Implementation efficiency of the Hyperloop vacuum train project in the Gulf countries

Recently, in the countries of the Cooperation Council for the Arab States of the Persian Gulf, much attention has been paid to improving railway transport using the latest technologies and achievements of science and technology. For economic, social and environmental reasons, the development of this type of transport is becoming one of the main tasks of the governments of these countries.A modern rail network will connect all GCC countries and provide another mobility option besides road, air and sea transport for both passengers and cargo in the region. The new national project is expected to make a significant contribution to the economy and prosperity of the countries in the region.The development of the transport system in this region, taking into account economic and climatic features, is justified by the state transport strategy to achieve global indicators not only of traditional safety, accessibility and quality of transportation, but also of environmental friendliness, multimodality and speed.As part of the long-term development of the transport system, among the priorities of the governments of Saudi Arabia, the United Arab Emirates and Qatar is the use of a vacuum maglev transport system to launch ultrahigh-speed train traffic by putting into operation the fastest train in the world with a speed of up to 1100 km/h, the project executor is an American company Hyperloop. The journey between Dubai and Riyadh will last only 48 minutes, the project becomes the main competitor to the highly developed air transport, and the train of the future will be the first means of transport between the Gulf countries.The relevance of the project lies in the integration of the economy and life of the people of the countries of the region, increasing the mobility of citizens and residents, creating an attractive platform for companies, manufacturers, resources and human capital, supporting joint investments between countries, as well as developing the tourism industry and environmental protection.

Feasibility Study of Traction Power Supply for Medium-Capacity Rail System Based on Rigid Overhead Conductor Rail System: Case Study on Pham Van Dong Route in Ho Chi Minh City, Vietnam

Feasibility Study of Traction Power Supply for Medium-Capacity Rail System Based on Rigid Overhead Conductor Rail System: Case Study on Pham Van Dong Route in Ho Chi Minh City, Vietnam

High-Speed Rail: A game changer for Spanish motorway transport?

Spain has deployed the world’s largest High-Speed Rail (HSR) network, in per capita terms, under the premise that this infrastructure provides a cleaner mode for reaching their environmental commitments. However, for observing environmental gains, there should be inter-modal substitution from polluter modes to the HSR. This paper analyzes the impact of new HSR openings on car traffic, using a Generalized and Staggered Difference-in-Difference approach. Unlike previous studies, we analyzes the entire Spanish HSR network. We matched the georeferenced data on car traffic with the origin–destination routes provided by Google Maps API to estimate the impact of the HSR on car traffic. We found that the HSR has not generated modal split, except for few of the HSR segments, in which we estimated a 6%–8% reduction in the annual average daily traffic. These results have public policy implications for the transport policy in the EU, and the effectiveness of decarbonization policies.

Design and Implementation of Electrochromic Smart Windows with Self-Driven Thermoelectric Power Generation.

Electrochromic smart windows can achieve controllable modulation of color and transmittance under an external electric field with active light and thermal control capabilities, which helps reduce energy consumption caused by building cooling and heating. However, electrochromic smart windows often rely on external power circuits, which greatly affects the independence and portability of smart windows. Based on this, an electrochromic smart window driven by temperature-difference power generation was designed and implemented. This smart window provides automatic and manual control of the reversible cycle of electrochromic glass from light blue to dark blue according to user requirements and changes in the surrounding environment, achieving adaptive adjustment of visual comfort and reducing energy consumption. The infrared radiation rejection (from 780 to 2500 nm) of the electrochromic smart window is as high as 77.3%, and its transmittance (from 380 to 780 nm) fluctuates between 39.2% and 56.4% with changes in working state. Furthermore, the temperature in the indoor simulation device with electrochromic glass as the window was 15 °C lower than that with ordinary glass as the window after heating with a 250 W Philips infrared lamp for ten minutes. After 2000 cycles of testing, the performance of the smart window was basically maintained at its initial values, and it has broad application prospects in buildings, vehicles, and high-speed rail systems.

Crop sequence boundaries using USDA National Agricultural Statistics Service historic cropland data layers

Gridded landcover datasets like the NASS Cropland Data Layer (CDL) provide a useful resource for analyses of cropland management. However, many farm operation decisions are made at the field level, not the pixel level. To capture relationships between land cover and field characteristics – size, contiguity, etc. – some method is needed to aggregate gridded data into crop fields. To provide a uniform and consistent approach for aggregation of gridded data at the field level over a series of years, this research project developed a set of Crop Sequence Boundaries (CSBs), which are polygons that delineate areas of homogeneous cropping sequences for the contiguous US. The CSBs are open-sourced algorithm-based, geospatial polygons derived using historic CDLs together with road and rail networks to capture areas with common cropping sequences. The CSB approach used geospatial functions in Google Earth Engine (GEE) and in the ArcGIS Pro application. These geospatial functions are run in parallel by sub-dividing the contiguous US into smaller regions based on road and rail boundaries to prevent overlaps or gaps in the data. As a new set of algorithmically delineated field polygons, the CSBs enhance applications requiring large-scale crop mapping with vector-based data.

An Advanced Hall Element Array-Based Device for High-Resolution Magnetic Field Mapping.

The precise mapping of magnetic fields emitted by various objects holds critical importance in the fabrication of industrial products. To meet this requirement, this study introduces an advanced magnetic detection device boasting high spatial resolution. The device's sensor, an array comprising 256 unpackaged gallium arsenide (GaAs) Hall elements arranged in a 16 × 16 matrix, spans an effective area of 19.2 mm × 19.2 mm. The design maintains a 1.2 mm separation between adjacent elements. For enhanced resolution, the probe scans the sample via a motorized rail system capable of executing specialized movement patterns. A support structure incorporated into the probe minimizes the measurement distance to below 0.5 mm, thereby amplifying the magnetic signal and mitigating errors from nonparallel probe-sample alignment. The accompanying interactive software utilizes cubic spline interpolation to transform magnetic readings into detailed two- and three-dimensional magnetic field distribution maps, signifying field strength and polarity through variations in color intensity and amplitude sign. The device's efficacy in accurately mapping surface magnetic field distributions of magnetic and magnetized materials was corroborated through tests on three distinct samples: a neodymium-iron-boron magnet, the circular magnetic array from a smartphone, and a magnetized 430 steel plate. These tests, focused on imaging quality and magnetic field characterization, underscore the device's proficiency in nondestructive magnetic field analysis.

Model-free adaptive full-order sliding mode control with time delay estimation of high-pressure common rail system

Model-free adaptive full-order sliding mode control with time delay estimation of high-pressure common rail system

Transient Analysis on the Crosswind Effect to the Aerodynamics of High-speed Train Travelled on the Bridge Between Two Tunnels at Jakarta -Bandung Track

The rapid evolution of global transportation technology is exemplified by Indonesia's innovative high-speed train initiative, linking Jakarta and Bandung in an impressive 45 minutes. Operating at 350 km/h, the HST CR400AF underscores the importance of aerodynamics in high-speed rail systems. This study delves into the significant impact of crosswind on key aerodynamic factors (drag, lift, rolling moment) within the tunnel-bridge-tunnel configuration. Leveraging Computational Fluid Dynamics (CFD) through ANSYS FLUENT, the analysis explores crosswind variations at 0 m/s, 10 m/s, and 25 m/s. Results reveal a proportional increase in aerodynamic load with higher crosswind intensities: 1.67 times for drag, 58.8 times for lift, and 29.8 times for rolling moment. Notable observations include pronounced aerodynamic load fluctuations during the "OUT" process, with the head section bearing the greatest load, followed by the tail and middle sections. These findings contribute valuable insights to the global discourse on enhancing safety and efficiency in high-speed rail systems