Freight Trains Research Articles

Substantiation of Parameters of the Network Model of the Air Distribution Due the Piston Effect in the Extra-Long Tunnels

The article analyzes the aerodynamic processes initiated by the piston effect in extra-long railway tunnels. The authors numerically modelled the movement of a freight train, 1 km long, in tunnels 3 and 6 km long. The analysis used the ANSYS Fluent environment. The modeling determined the air pressure at the front and back faces of the train, the draught loss in the tunnel clearance, as well as the air velocity in the tunnel clearance and in the tunnel. These data are used to develop a quasi-dynamic model of the piston effect for solving problems concerning air distribution with the application of network models. The air flow rates calculated in ANSYS Fluent and using the network models agree quite well. The time of the problem solving for a tunnel 3 km long using the network model is shorter by 5 orders of magnitude, which is of significant importance for both the study and multivariate design of ventilations systems for railway tunnels.

An energy harvesting shock absorber for powering on-board electrical equipment in freight trains

To realize smart detection and safe operation of freight trains, a continuous and stable energy source is required for electrical equipment on the train. It is a feasible scheme to harvest the vibration energy of train suspension to supply power for on-board electrical equipment. This paper presents an energy-harvesting shock absorber (EHSA) based on the slider-crank mechanism and ratchet-pawl mechanism, which provide a vibration reduction effect and renewable electricity. To determine the damping performance and the power generation performance of EHSA, a dynamic model is established based on MATLAB. According to the tests on the mechanical testing and sensing (MTS) bench, the maximum power generation mechanical efficiency of the EHSA is 67.75%, and the maximum output power of the EHSA is 1.65W. In addition, the charging tests on the MTS bench show that the proposed device is applicable to power on-board electrical equipment on freight trains.

Energy Saving Optimal Operation Strategy of Freight Trains under Complex Scenarios

Background: For the optimization of energy-saving driving of freight trains in complex operating environments, the use of reasonable train maneuvering methods can largely reduce the energy consumption of train traction. Recent patents on energy-efficient maneuvering strategies for complex scenarios of freight trains have been researched. Objective: Using the receding horizon algorithm and the improved NSGA-II algorithm to solve the target speed curve of freight trains to cope with the complex and changing operating environment, and to explore the recent patents of energy-saving maneuvering strategies for freight trains and methods. Methods: The recent patents of energy-efficient maneuvering strategies for freight trains in complex scenarios are investigated in this research. A multi-objective optimization model for freight train maneuvering with electrical phasing was developed with the objectives of reducing the traction energy consumption and running time of the train. A method for determining the optimal operating conditions of freight trains under complex line conditions is proposed. The offline optimization of the target speed curve under the electrical phasing constraints of freight trains and the online adjustment under the temporary speed restriction (TSR) are achieved by using the RH-INSGA-II (receding horizon-improved NSGA- II) algorithm. Results: Combined with an actual freight railroad line data as an example, simulation experiments were conducted and verified with HXD1 electric locomotive hauling 50 C80 wagons. Conclusion: The speed curve considering the split-phase constraint can effectively reduce the traction energy consumption. The electrical split-phase constraint affects the whole speed optimization process, not only the speed curve at the split-phase zone. Although the traction energy consumption is increased with the addition of the TSR on the line, the RH-INSGA-II algorithm dynamically changes the sequence of optimal train maneuvering conditions according to the planned train running time in order to avoid further amplification of the late train time.

Visual Fault Detection of Multiscale Key Components in Freight Trains

Fault detection for key components in the braking system of freight trains is critical for ensuring railway transportation safety. Despite the frequently employed methods based on deep learning, these fault detectors are extremely reliant on hardware resources and complex to implement. In addition, no train fault detectors consider the drop in accuracy induced by scale variation of fault parts. This paper proposes a lightweight anchor-free framework to solve the above problems. Specifically, to reduce the amount of computation and model size, we introduce a lightweight backbone and adopt an anchor-free method for localization and regression. To improve detection accuracy for multi-scale parts, we design a feature pyramid network to generate rectangular layers of different sizes to map parts with similar aspect ratios. Experiments on four fault datasets show that our framework achieves 98.44% accuracy while the model size is only 22.5 MB, outperforming state-of-the-art detectors.

Time Series Multi-Step Forecasting Based on Memory Network for the Prognostics and Health Management in Freight Train Braking System

In rail trains, the prognostics and health management system will make use of data from sensors deployed in key components. These data are typically non-stationary multi-variable time series with abrupt variations. The challenges of multivariate time series forecasting include how to identify the interactions between various variables, and how to assess the influence of historical data on present data. This paper presents the nature of the air brake system and its physical models. By analyzing the observed data obtained from the freight train air brake system, a Memory network-based Time series Multi-step fault Forecasting model (MTMF) is proposed. MTMF consists of a dual-encoder and a memory network module for feature extraction. The two encoders are used to extract features of short-term and long-term historical data respectively. The memory network is used to further mine the different weights of the long-term historical data. MTMF also designs a joint multi-step forecasting loss function, which is composed of shape, time and mean square error losses between the forecasting series and the real series. The improved loss function is no longer restricted to the differences in evaluation points. MTMF is evaluated on the real freight train braking system time series dataset. The results show that MTMF outperforms other forecasting methods on multivariate time series. The forecasting accuracy is increased by 5%-45%, demonstrating that the model is effective for non-stationary series and can better mine the dependence patterns for the train braking system.

<b>Proposal for Energy-saving Driving Method for Freight Trains Combining Constant-speed Operation and Coasting Operation</b>

<b>Proposal for Energy-saving Driving Method for Freight Trains Combining Constant-speed Operation and Coasting Operation</b>

Studying the load of composite brake pads under high-temperature impact from the rolling surface of wheels

The object of the research is the processes of thermal stress, perception and redistribution of loads by the brake composite pad during braking of the car in operation. In the current conditions, wedge-dual wear of composite brake pads is observed in the braking systems of freight cars, the feature of which is the deterioration of the braking efficiency of freight trains. With this type of wear, both an increase in the load on the brake pad and an "underuse" of the amount of pressure on it can occur. A comprehensive thermal calculation was carried out for composite brake pads with uniform and wedge-dual wear. The results of the calculation showed that the amount of pressure on an abnormally worn pad is 23.3 % less than that acting on a pad with nominal values. It has been proven that the change in the pressure force on the composite pad with different values of the wear parameters during braking leads to a change in the braking force that occurs between the wheel and the rail during braking. The calculation of the strength of the composite brake pad with wedge-dual wear was carried out. The obtained results will make it possible to develop measures to modernize the elements of the brake lever transmission of freight cars. The field of practical use of the obtained results is car-building enterprises. The conditions for the practical use of the results are the brake lever transmissions of carriages of cars with a gauge of 1520 mm. The conducted studies prove the negative impact of wedge-dual wear not only on braking efficiency, but also on the strength of brake pads. This makes it necessary to create measures aimed at its elimination, which will contribute to increasing the level of train traffic safety and significantly reducing the operational costs of maintaining freight cars

An approach to railway network sections modeling based on queuing networks

The paper is devoted to the study and long-term forecasting of railway network sections operating. We call a railway network section a set of several railway stations interacting with each other and lines between them. We propose a methodology for mathematical modeling of train traffic on the railway network based on the queuing theory. The resulting models are a set of mathematical descriptions of the incoming train traffic and the train running within the system. Several Markovian Arrival Process are used to describe train arrivals from different directions. Such a description makes it possible to take into account the parameters of separate train flows, which depend on the category of trains and their directions. A queuing network in which all nodes have a finite capacity simulates the movement of trains through the system. This mathematical apparatus allows us to consider the nonlinear structure of the railway network, the operation features of stations, the capacity of railway lines, and the influence of random factors. To apply the methodology, we have chosen two objects that differ in infrastructure and properties of train flows. The first is located in the east of Russia and focused on servicing freight trains. The second is located in Germany, close to Belgium and The Netherlands borders. It is characterized by the predominance of passenger traffic. We construct mathematical models and perform numerical simulations. Based on the results obtained, the maximum allowable load is estimated, and bottlenecks in the structure of transport systems are found. Besides, we give some recommendations on how to increase capacity in the long term.

Meta-Heuristic for inserting a robust train path in a non-cyclic timetable

ABSTRACT Many freight trains depart Swedish marshalling yards before or after their planned departure times. Today, a deviating departure time is allowed if no conflicting train path can be found a few stations ahead. This increases the risk that the train might be delayed to its destination and cause delays to other trains. We present a meta-heuristic that modifies a timetable by adding a train path (for our freight train) and, if necessary, adjusting surrounding train paths. The aim of the insertion of the additional train path and the adjustments of the existing ones is to obtain a large bottleneck robustness, that is, the largest possible minimal temporal distance to any other train in the timetable. We provide experimental results for a Swedish railway stretch with a non-cyclic timetable and heterogeneous traffic. We show that we quickly add a train path, while improving the robustness of the timetable.

Acceptability of autonomous trains with different grades of automation by potential users: A qualitative approach

While automation in public transport has gained new momentum in recent decades, the Autonomous Train (AT) is still in its infancy. To guide the deployment of the AT, it is necessary to study its acceptability by the public. The aim of this study was to investigate for the first time the factors that influence the acceptability of the AT. This qualitative study comprised interviews divided into 3 parts. The first part was devoted to free evocation of the AT, while the following ones asked participants to express themselves on the definition of the AT without a driver but with on-board staff (GoA3) and then without any staff on board (GoA4). Thirty participants were interviewed. The sample was balanced in age, gender, train usage, and living location. The semi-structured interviews were recorded, transcribed, and analysed according to the method of thematic content analysis. Overall, the results showed a lack of knowledge and an idealized representation of the AT. Participants expressed several concerns inherent in the deployment of ATs. Lastly, the intention to use was more favourable in GoA3 than in GoA4. In fact, the participants expressed rejection of GoA4, due to the absence of on-board staff and lack of trust in remote communication tools and train supervisors. This article is the first to explore the public acceptability of the AT by adopting an interview methodology to avoid priming participants’ responses. It thus adds to the field of research on the acceptability of autonomous vehicles the railway world which previously focused on driverless subway and freight trains only and now opens the idea of AT on a regular train service.

Statistical Analysis of Train Derailment Severity for Unit Trains Versus Manifest Trains

Using freight trains is a cost-effective and safe method for transporting numerous products over long distances. However, freight train derailments have significant consequences. Derailment severity, measured by the number of cars derailed per derailment, is an important risk factor. Based on prior literature and Federal Railroad Administration data, this research analyzes and estimates derailment severity of freight railroad transportation using a statistical method: the Truncated Geometric (TG) model. The methodology accounts for three train types: manifest train, loaded unit train, and empty unit train. Train length, speed, and gross tonnage per car are the input features, and the output variable is the number of cars derailed in a derailment incident. The TG model quantifies the influence of these factors contributing to derailment severity and estimates train derailment severity with a performance of the mean square error (MSE) of 68.91 and mean absolute error (MAE) of 6.05. When data outliers such as abnormally high or low severity are excluded, the MSE drops to 36.82 and the MAE drops to 4.22. Overall, the results indicate that the train derailment severity estimation performance based on the given factors is satisfactory. With all other factors being equal, a loaded unit train is likely to derail more cars than a manifest train and an empty unit train. When data outliers are excluded, there is no significant difference between manifest trains and empty unit trains as regards derailment severity, and both are less likely to derail than a loaded unit train.

Efficient visual fault detection for freight train braking system via heterogeneous self distillation in the wild

Efficient visual fault detection of freight trains is a critical part of ensuring the safe operation of railways under the restricted hardware environment. Although deep learning-based approaches have excelled in object detection, the efficiency of freight train fault detection is still insufficient to apply in real-world engineering. This paper proposes a heterogeneous self-distillation framework to ensure detection accuracy and speed while satisfying low resource requirements. The privileged information in the output feature knowledge can be transferred from the teacher to the student model through distillation to boost performance. We first adopt a lightweight backbone to extract features and generate a new heterogeneous knowledge neck. Such neck models positional information and long-range dependencies among channels through parallel encoding to optimize feature extraction capabilities. Then, we utilize the general distribution to obtain more credible and accurate bounding box estimates. Finally, we employ a novel loss function that makes the network easily concentrate on values near the label to improve learning efficiency. Experiments on four fault datasets reveal that our framework can achieve over 37 frames per second and maintain the highest accuracy of 98.88% in comparison with traditional distillation approaches. Moreover, compared to state-of-the-art methods, our framework demonstrates more competitive performance with lower memory usage and the smallest model size.

Lateral Variations Across the Southern San Andreas Fault Zone Revealed From Analysis of Traffic Signals at a Dense Seismic Array

AbstractWe image the shallow seismic structure across the Southern San Andreas Fault (SSAF) using signals from freight trains and trucks recorded by a dense nodal array, with a linear component perpendicular to SSAF and two 2D subarrays centered on the Banning Fault and Mission Creek Fault (MCF). Particle motion analysis in the frequency band 2–5 Hz shows that the examined traffic sources can be approximated as moving single‐ or multi‐point sources that primarily induce Rayleigh waves. Using several techniques, we resolve strong lateral variations of Rayleigh wave velocities and Q‐values across the SSAF, including 35% velocity reduction across MCF toward the northeast and strong attenuation around the two fault strands. We further resolve 10% mass density reduction and 45% shear modulus decrease across the MCF. These findings suggest that the MCF is currently the main strand of the SSAF in the area with important implications for seismic hazard assessments.

Logistics hub location for high-speed rail freight transport with road-rail intermodal transport network.

In this paper, we propose a new freight mode to describe how the designed HSR freight train serve for express delivery. We introduce the functions of the hubs and design the hybrid hub-and-spoke network of road-rail intermodal transportation from the perspective of planners, which features as single allocation rule and configures varying levels of hubs. The problem is accurately described by a mixed integer programming model with the objective to minimize total construction cost and total operation cost. We develop a hybrid heuristic algorithm based on a greedy strategy to obtain the levels of hubs, customer allocation and cargo routing. Taking the example of HSR freight network consisting of 50 cities in China, numerical experiments are conducted on the basis of forecasting data from the real-life express market to obtain the hub location schemes. The validity of the model and the performance of the algorithm are verified.

Curving Performance Analysis of a Freight Train Transporting 50-Meter-long Rail Using Multibody Dynamics Simulation

Long rails are normally used in highspeed railways to minimize the number of rail joints and the dynamic impact force that follows. However, transporting long rails using a freight train requires multiple wagons for each rail section, presenting potential safety and loading gauge issues, especially when going through curves. Thus, a safety assessment needs to be done prior to actual transport. Computational simulation can be used for preliminary assessment. Finite element analysis can be used to incorporate the flexibility of the rails into the analysis but requires significant manpower and computer power to perform. In this study, an alternative method to model rail flexibility using a multibody approach is presented. The rails are sectioned into multiple rigid bodies along their length and interconnected using rotational joints. The stiffness coefficient of the joints is defined as a function of the actual rail’s physical properties. This modelling technique results in a simplified multibody model that retains the original rail elastic properties. Simulations of the constructed rail model hauled using a freight train were done and the results were compared to on-track test measurements of the same configuration. The comparison generally showed good agreement, showing this modelling technique’s ability and accuracy to simulate the case.

Operational risk assessment of railway remote driving system

The level of automation in transportation systems is steadily on the rise, particularly in railways. However, this increase in automation implies a review of the previously established safety assessments, safety cases, and safety principles. Concretely, to ensure the overall safety of automated systems, it is crucial to provide conclusive assurance not only for their functional systems but also for any potential operational interactions that may arise unexpectedly. In this paper, we propose a risk assessment method to deal with the operational safety of the remote driving of freight trains. The established approach is based on the GAME principle (i.e. the French safety principle ‘Globally at least equivalent’), which tends to assure the non-regression of the railway safety level. Firstly, the approach aims to perform a risk gap analysis with respect to driver’s handbook procedures to identify the (operational) hazards and dangerous situations/scenarios generated by remote driving on the rail system. This analysis is completed by the identification of hazards due to the fact that the remote driving is not able to fully comply with the requirements of the driver’s handbook. Then, the methodology is used to identify the risk control measures and the safety requirements to meet safety targets (i.e. maintaining the safety level at least equivalent to that obtained so far with in-cab driver). The obtained results are presented and structured in a table inspired by the preliminary operational hazards analysis (P-OHA) process.

Reinforcement learning method for the multi-objective speed trajectory optimization of a freight train

With the rising urge to mitigate the green house effect, reducing the energy consumption of the freight train attracts much attention. Multiple constraints should be taken into account to solve the energy-efficient control problem, which can be reformulated as the multi-objective optimization. This paper proposes a Reinforcement Learning (RL) method for the multi-objective speed trajectory optimization with the goal of the energy-efficiency, punctuality and accurate parking simultaneously. Since the solution space for the optimization problem in this paper is large and discrete, a Gated Recurrent Unit (GRU)-based network is proposed to achieve the fast approximation of the optimal value function instead of the lookup Q-table. Meanwhile, a new architecture, including the embedding matrix, is used to model the control sequence that generates the speed trajectory. Besides, this paper constructs a Deep Q-Network (DQN) framework to train the GRU network without relying on the prior knowledge of the freight train model. Finally, the Intelligent Train Operation (ITO) algorithm is proposed and verified, using the data of Beijing–Guangzhou Railway Line and HXD1B electric locomotive. The case studies indicate that the reward function for the ITO algorithm converges rapidly and the energy consumption monotonically decreases with the trip time, which satisfies the multiple optimization objectives. In terms of saving the energy consumption, the ITO algorithm performs better than Fuzzy Predictive Control (FPC), Genetic Algorithm (GA) and the field test data. The computation time of different speed trajectories demonstrates that the ITO algorithm is applicable to generating the optimal speed trajectory off-line.

Ohio train derailment clean-up resulted in high levels of some gases

A freight train carrying industrial chemicals derailed near East Palestine, Ohio, in February 2023, and to avoid explosions, authorities conducted a controlled release and burned the cars’ contents. Residents were worried about their health and the environment, so researchers have been assessing the local air quality with stationary and mobile sampling methods. On July 12, ACS reports that in ACS’ Environmental Science & Technology Letters, they report that some gases, including acrolein, reached levels that could be hazardous.

Correlation between cargo properties and train overturning safety for a high-speed freight train under strong winds

The high-speed railway has become an important passenger transport mode. Relying on the abundant passenger transport experience and expansive railway network, the high-speed freight train (HSFT) is feasible to be developed and has great application potential. As a critical component of HSFT, the cargo plays an important role in the overturning safety of HSFT under strong wind conditions. Previous studies about the aerodynamic performance of the high-speed train usually regarded the loaded carbody as an individual rigid body. Limited attention was paid to the variability inside the vehicle. In order to improve the running safety of HSFT under strong wind conditions, the multibody dynamics simulation is adopted to demonstrate the effect of cargo property on the overturning risk of HSFT under the aerodynamic loads obtained through the computational fluid dynamics (CFD) simulations. It is demonstrated that the inner side crosswind is more dangerous than the outer side crosswind. The speed of HSFT should be limited according to the cargo quantity, and the maximum speed for a fully loaded HSFT can be 305 km/h when the crosswind velocity is 20 m/s. The larger cargo density should be encouraged because it is good for train overturning safety in severe working conditions.

ИССЛЕДОВАНИЕ КОЭФФИЦИЕНТА ТРЕНИЯ ПОЛИМЕРНЫХ МАТЕРИАЛОВ ДЛЯ 3D-ПЕЧАТИ ЭЛЕМЕНТОВ КОНСТРУКЦИИ ПОДВИЖНОГО СОСТАВА

Additive technologies are represented by various methods of 3D printing and are increasingly being used in various industries around the world. For example, printing with metal powders is used for the production of such structural elements as various arm supports in the aircraft industry or the manufacture of spacecraft hulls. The use of 3D printing with the use of polymer materials makes it possible to manufacture, for example, various gears for non-principle mechanisms. The use of such technology in pneumatics is also relevant. However, at the same time, the possibility of using these technologies for the manufacture of various pneumatic devices has not been widely studied up to now. In the course of studying this issue, a pneumatic gearbox was made. However, it was found out that one of the problems that does not allow to design and manufacture a normally functioning pneumatic device is an unknown value of friction factor of the materials of which its elements are made.
 The study objective is to find out the static friction factor of products made of polymer materials using 3D printing technologies. The necessity of this study is explained by the problems that arose as a result of testing a high-performance pneumatic gearbox manufactured using additive technologies applying various polymer materials. The main problem of this device is the density violation of fixed detachable joints of gearbox case parts and covers. Since the medium to be used is compressed air under a pressure of 0.9 MPa, any density violation can lead to the gearbox destruction or to air losses from the braking system of a freight train due to an increase in the number of leaks in the pipeline for additional power supply to the spare reserve.
 The paper presents the results of experimental studies on finding the friction factor of various types of materials used in 3D printing. The obtained friction factors are required for the design of seals of pneumatic devices according to generally accepted methods.