Heavy Haul Trains Research Articles

An optimal scheduling method for heavy haul trains with virtual coupling technology

ABSTRACT Heavy haul transportation enjoys the benefits of cost-efficiency by using long trains, but suffers from high cost in combination process and low efficiency in bottleneck areas. Virtual coupling technology (VCT) is helpful in resolving the dilemma. In this paper, we propose an optimal method for scheduling the route, timetable, and combination scheme of heavy haul trains with VCT in an integrated way. Specifically, six groups of constraints are first established, in which the sequence and train length problems incorporated by virtual coupling are properly handled. Then, a combined objective function comprised makespan and total flowtime indicators is proposed to optimize the throughput and turnover rate and further speeds up the optimization convergence. Finally, the optimization problem equipped with the proposed constraints and the combined objective function is transformed into an integer linear programming problem and solved by GUROBI. Simulation results demonstrate the effectiveness and advantages of the proposed method in leveraging VCT, and the benefits of virtual coupling over traditional physical coupling for heavy haul transportation.

Anti-saturation sliding mode tracking algorithm for heavy-haul trains when actuator saturation occurs

Anti-saturation sliding mode tracking algorithm for heavy-haul trains when actuator saturation occurs

Study on instrumental coupler for heavy haul train

As a crucial load-bearing component, coupler’s safety is pivotal for sustainable railway industry development and is extensively scrutinized due to its complex loading environment. However, current research predominantly concentrates on the longitudinal dynamic behavior of couplers, neglecting factors such as nodding and shaking during traversals along curves and ramps. This study proposes an innovative method aimed at identifying multiple loads on couplers, which includes longitudinal tension and compression, lateral shaking, and vertical nodding forces. A theoretical load identification method based on strain sum and difference on coupler shank faces under various loading scenarios is established by finite element analysis. To facilitate accurate measurement, Wheatstone bridges are employed for three-dimensional force measurement, facilitating strain calculation and temperature compensation. The validity of the proposed approach is confirmed through comprehensive validation, encompassing finite element simulation, laboratory experimentation, and vehicle tests. Results demonstrate the robustness of the method, with maximum load deviations of 2.32% longitudinally, 22.52% horizontally, and 19.86% vertically observed during laboratory tests. Results indicate the proposed method’s accuracy and efficiency in heavy haul train coupler load assessment.

The Wheel–Rail Contact Force for a Heavy-Load Train Can Be Measured Using a Collaborative Calibration Algorithm

The wheel–rail contact force is a crucial indicator for ensuring the secure operation of a heavy-load train. However, obtaining the real-time wheel–rail contact force of a heavy-load train is a challenging task as, due to safety considerations, it is not possible to install instrumented wheelsets on heavy-load trains. This work presents a novel approach to quantify the wheel–rail contact force of a heavy-load train, utilizing a cooperative calibration methodology. First, a ground measurement platform for the wheel–rail contact force of a heavy-load train is constructed on a selected rail section. The railway inspection car’s wheel–rail contact force measurement system is fine-tuned using a multilayer perceptron calibration approach, and the ground platform then uses the results to fine-tune the railway inspection car’s examined wheelset. Second, based on actual measured data on the wheel–rail contact force of a heavy-load train, and using the golden jackal optimization algorithm, the multilayer perceptron correction approach is employed to create a data relationship mapping model. This model correlates the corrected data on the wheel–rail contact force obtained from the railway inspection car with the wheel–rail contact force of a heavy-haul train with an axle load of 25 tons, and the precision of the mapping is guaranteed. Finally, by combining the wheel–rail contact force correction method for the railway inspection car and the contact force mapping model between the railway inspection car and the heavy-load train, collaborative calibration of the wheel–rail contact force of the heavy-load train is realized. The experimental results under two working conditions show that this method can realize high-precision, real-time measurement of the wheel–rail contact force of a heavy-load train. For the working condition of a straight-line section, the calibration error was within 1.593 kN, and the MAPE was 0.105%; for the working condition of a curved-line section, the calibration error was 2.344 kN, and the RMSE was 184.72 N.

Distributed Global Composite Learning Cooperative Control of Virtually Coupled Heavy Haul Train Formations

Distributed Global Composite Learning Cooperative Control of Virtually Coupled Heavy Haul Train Formations

Space–time collision avoidance and train dynamics constraints-driven optimal control for heterogeneous heavy-haul trains platoon operation considering air brake delay

The potential of heavy-haul train platoon operation technology in improving railway freight capacity is increasingly recognised, with extensive trial research currently underway. This study firstly utilising longitudinal train dynamics devised a space–time collaborative collision avoidance model for trainsets, accounting for air brake delay. Secondly, an air brake application strategy was proposed, focusing on collision avoidance and managing speeds on steep ramps. Subsequently, a constraint-driven optimal control scheme tailored for heavy-haul train platooning operations within an open railway environment was developed, which simulates train platooning amidst real-world dynamic traffic flow scenarios, offering a global perspective on railway network operations. Finally, we comprehensively evaluate the impact of line conditions on key platoon control parameters based on realistic railway operation scenarios. Findings show that constraint-driven optimal control, integrating train dynamics and space–time collaborative collision avoidance, effectively replicates the dynamic traits of train platoons. The consideration of air braking delays is crucial due to heterogeneous train formations and steep ramps, which can easily cause collisions. Moreover, on real-world railway lines, the average headway of heavy-haul trains weighing 10,000 tons and below can be compressed to 99.82 s when operating in platoons, significantly enhancing transportation capacity compared to existing semi-automatic blocking systems.

An experimental analysis of dynamic deformation and settlement resistance in a novel prestressed railway subgrade

ABSTRACT The dynamic deformation characteristics of track-subgrade systems under moving train loads provide valuable insights for vibration and serviceability assessment of the track-subgrade. This study developed three 1:5 scale track-subgrade models incorporating the novel prestressed subgrade structure and conducted two series of dynamic tests to examine how the heavy-haul train axle loads and prestress levels affected the track-subgrade dynamic deformation. The primary frequency of the subgrade dynamic displacement was observed closely aligned with the input loading frequency of 2 Hz, indicating the validity of the test setup. The track-subgrade dynamic displacement decreased with increasing distance from the loading source, of which the elastic deformation near the subgrade shoulder attenuated by approximately 79% compared to that at the sleeper end. The elastic deformation at the monitoring points approximately linearly increased with the axle load at rates ranging from 0.0018 to 0.0029 mm/t. Conversely, increasing prestress reduced the track-subgrade elastic deformation with average rates of 22.8 and 1.63 μm/100 kPa at the sleeper end and subgrade shoulder, respectively. Furthermore, application of prestress enhanced the track stiffness, with the equivalent track stiffness rising by 16.3% and 17.7% when the prestress increased from 0 to 50 kPa and from 50 to 100 kPa, respectively. The study also showed that the prestressed reinforcement structures could significantly mitigate the cumulative track-subgrade settlement over 60%, comparing the prestressed subgrade with 50 kPa prestress to the unreinforced subgrade, which highlighted the superior deformation resistance of prestressed subgrades under the train loads.

Fracture failure analysis of heavy-haul train couplers using convolutional neural network along with multilayer perceptron

Abstract Metal couplers are susceptible to unpredictable failure and fracture under long-term high-load conditions in heavy-haul railway transportation. The current mainstream manual inspection method has the disadvantages of high subjectivity and high a priori knowledge requirements, thus not meeting the rapid analysis requirements of production companies. Therefore, in this study, an automated failure analysis method is proposed for heavy-haul coupler fractures. First, a novel image segmentation method (PermuteNet) combining a visual multilayer perceptron and a convolutional neural network is designed to segment different failure patterns of fracture surfaces. The proposed method uses two newly proposed modules—permute attention module and context attention module—to improve the network’s ability to perceive weakly differentiated objects, thereby improving the recognition ability of the model for different failure patterns. In addition, a deep supervisory function is adopted to accelerate the convergence speed of the network. Finally, the proposed image segmentation method is deployed on a computer in conjunction with a developed client application to implement a single-click detection function for coupler fracture pattern analysis. Experiments are performed using the heavy-haul coupler fracture dataset established using on-site data; the proposed segmentation method achieves a mean intersection over union of 77.8%, which is considerably higher than that of other existing methods. By using the client software, the single-click detection function of the fracture area is realized. Thus, the proposed method provides a more convenient and accurate fracture identification solution for factory inspectors and has broad application prospects.

Research on abnormal wear mechanism of the coupler system in heavy-haul trains

Abnormal wear issues of the coupler system exist in heavy-haul trains and are rarely studied before. In this paper, we thoroughly report and investigate the abnormal wear phenomenon of the coupler using theoretical and experimental strategies. Firstly, the operational conditions of the trains with worn couplers are analyzed through field tests. Then, the causes of the coupler abnormal wear are theoretically analyzed according to structural characteristics. Next, A refined dynamic model of coupler considering the coupler components is developed and validated. The key influencing factors of abnormal wear are studied, including the wear depth of the component and the friction coefficient of the follower. Experiments and simulations prove that multiple braking of trains will lead to upward movements of the follower and then induce contact between the follower and the coupler yoke, further resulting in abnormal wear. Finally, countermeasures are proposed to mitigate the abnormal wear of the coupler system.

A heavy-haul train longitudinal-vertical coupled dynamics model and its dynamic behaviour under emergency braking

The heavy-haul train dynamic performance under emergency braking is complex and directly affects the reliability and running safety. To reveal the heavy-haul train dynamic behaviour in emergency braking process on the long downhill segments, a comprehensive heavy-haul train longitudinal-vertical coupled dynamics model (HTLVDM) that considers the air braking system and coupler draft gear system is established in this paper. HTLVDM considers the components of the locomotive and wagon, such as the wheelset, bogie frame, and car body. The coupler force model considers the nonlinear hysteresis characteristic of the coupler draft gear system. The braking delay time and pressure variation of the brake cylinders are taken into the air braking model. In addition, the validity of HTLVDM is verified by published data. Based on the real-world data of the Shuozhou-Huanghua Line and the established model, the HTLVDM's dynamic responses are analysed through numerical simulations. The results indicate that both the brake forces and coupler forces have a significant effect on the wheel-rail dynamic interactions and the dynamic response of the car body in the heavy-haul train.

Cyclic Air Braking Strategy for Heavy Haul Trains on Long Downhill Sections Based on Q-Learning Algorithm

Cyclic air braking is a key factor affecting the safe operation of trains on long downhill sections. However, a train’s cycle braking strategy is constrained by multiple factors such as driving environment, speed, and air-refilling time. A Q-learning algorithm-based cyclic braking strategy for a heavy haul train on long downhill sections is proposed to address this challenge. First, the operating environment of a heavy haul train on long downhill sections is designed, considering various constraint parameters, such as the characteristics of special operating routes, allowable operating speeds, and train tube air-refilling time. Second, the operating status and braking operation of a heavy haul train on long downhill sections are discretized in order to establish a Q-table based on state–action pairs. The training of algorithm performance is achieved by continuously updating Q-tables. Finally, taking the heavy haul train formation as the study object, actual line data from the Shuozhou–Huanghua Railway are used for experimental simulation, and different hyperparameters and entry speed conditions are considered. The results show that the safe and stable cyclic braking of a heavy haul train on long downhill sections is achieved. The effectiveness of the Q-learning control strategy is verified.

Dynamic identification of coupler force of heavy haul locomotive: An effective and long-term intelligent measurement method

Longitudinal impulse, as a common dynamic problem of heavy haul trains, could induce the lateral instability of the coupler and the separation of the locomotive and wagon connection and thus seriously hinder the train running safety. The coupler force undergoes complex changes during longitudinal impulse transmission and exhibits strong nonlinear characteristics, which poses significant challenges to the identification of the coupler force. To address these issues, this paper reports a novel quantitative method for the coupler force identification of heavy haul locomotives. Firstly, the moving average filtering method is used to preprocess the signal of longitudinal relative displacement between the coupler and carbody (LRDCC), and the approximate relative velocity (ARV) feature is also extracted from the preprocessed signal. Then, a Convolutional Neural Network (CNN) model is constructed to extract high-level features from LRDCC and ARV. The model with randomly initialized weight parameters does not require additional training and thus reduces the computational cost. Finally, an Extreme Learning Machine (ELM) is used as the regression layer to process the extracted high-level features and further improve the identification performance. Additionally, the dung beetle algorithm is used to obtain the optimal model parameters to improve the coupler force identification performance. Not only a dynamics simulation but a field test is carried out to show that the proposed method could accurately detect the coupler force of heavy haul locomotives. Furthermore, the proposed method shows promising prospects in replacing the traditional monitoring method under the actual long-term working conditions of trains.

Mechanism and deterioration pattern of sandstone surrounding rock voiding at bottom of heavy-haul railway tunnel

This study combines laboratory experiments and discrete element simulation methods to analyze the mechanism and deterioration patterns of sandstone surrounding rock voiding the bottom of a heavy-haul railway tunnel. It is based on previously acquired measurement data from optical fiber grating sensors installed in the Taihangshan Mountain Tunnel of the Wari Railway. By incorporating rock particle wastage rate results, a method for calculating the peak strength and elastic modulus attenuation of surrounding rock is proposed. Research indicates that the operation of heavy-haul trains leads to an instantaneous increase in the dynamic water pressure on the bottom rock ranging 144.4–390.0%, resulting in high-speed water flow eroding the rock. After 1–2 years of operation, the bottom water and soil pressures increase by 526.5% and 390.0%, respectively. Focusing on sandstone surrounding rock with high observability, laboratory experiments were conducted to monitor the degradation stages of infiltration, particle loss, and voiding of rock under the action of dynamic water flow. The impact of water flow on the “cone-shaped” bottom rock deformation was also clarified. The extent of rock deterioration and voiding was determined using miniature water and soil pressure sensors in conjunction with discrete element numerical simulations. The measured rock particle loss was used as a criterion. Finally, a fitting approach is derived to calculate the peak strength and elastic modulus attenuation of surrounding rock, gaining insight into and providing a reference for the maintenance and disposal measures for the bottom operation of heavy-haul railway tunnels.

A Time Headway Control Scheme for Virtually Coupled Heavy Haul Freight Trains

Abstract Virtual coupling of railway trains is an emerging technology that has the potential to significantly increase railway operational efficiency by reducing the train following distance from absolute braking distances to relative braking distances. Current research in this topic is mainly focused on passenger trains and uses distance-based headways. This paper studied virtual coupling for heavy haul freight trains and demonstrated that the distance headway scheme was challenging and sometimes impractical for heavy haul trains to achieve virtual coupling. A time-based headway scheme was then proposed to set the follower train to be a certain time behind the schedule of the leader train rather than a distance headway. The time-based headway required the follower train to reproduce the leader train's operational status at the same track location. This also allowed the follower train to copy any optimized train driving strategies from the leader train. Demonstrative simulations were carried out without the consideration of communication errors and train localization errors. The results show that a conventional distance headway simulation had maximum distance and speed errors of 716 m (36%, reference 2 km) and 24 km/h (66%, reference 36 km/h), respectively. A time-based headway simulation reduced the maximum distance and speed errors to 0.07 m (0%, reference 2 km) and 0.1 km/h (9%, reference 1.18 km/h), respectively.

Deep Q-Network Algorithm-Based Cyclic Air Braking Strategy for Heavy-Haul Trains

Cyclic air braking is a key element for ensuring safe train operation when running on a long and steep downhill railway section. In reality, the cyclic braking performance of a train is affected by its operating environment, speed and air-refilling time. Existing optimization algorithms have the problem of low learning efficiency. To solve this problem, an intelligent control method based on the deep Q-network (DQN) algorithm for heavy-haul trains running on long and steep downhill railway sections is proposed. Firstly, the environment of heavy-haul train operation is designed by considering the line characteristics, speed limits and constraints of the train pipe’s air-refilling time. Secondly, the control process of heavy-haul trains running on long and steep downhill sections is described as the reinforcement learning (RL) of a Markov decision process. By designing the critical elements of RL, a cyclic braking strategy for heavy-haul trains is established based on the reinforcement learning algorithm. Thirdly, the deep neural network and Q-learning are combined to design a neural network for approximating the action value function so that the algorithm can achieve the optimal action value function faster. Finally, simulation experiments are conducted on the actual track data pertaining to the Shuozhou–Huanghua line in China to compare the performance of the Q-learning algorithm against the DQN algorithm. Our findings revealed that the DQN-based intelligent control strategy decreased the air braking distance by 2.1% and enhanced the overall average speed by more than 7%. These experiments unequivocally demonstrate the efficacy and superiority of the DQN-based intelligent control strategy.

Investigation on Dynamic Stability of Cement-Stabilized Expansive Soil Subgrades Subjected to Repeated Heavy-Haul Train Loads

The dynamic characteristics of the filler are intricately linked to the stability of the subgrade. In this investigation, relying on Haoji (Haolebaoji-Ji’an, China) heavy-haul railway engineering, cyclic triaxial tests were executed to scrutinize the dynamic attributes exhibited by the 3%–5% cement-stabilized expansive soil (CSES) across a series of diverse cyclic stress, confining pressures, and frequencies. Concurrently, in situ vibration trials were undertaken to dissect the dynamic characteristics inherent to the CSES subgrade. The outcomes of cyclic triaxial tests indicate that the augmentation in both the dynamic shear strength and modulus of CSES by a factor of 2–3, coupled with an escalation of the critical dynamic stress threshold by five tosix times, is attributed to the heightened internal structural density within the CSES compared to virgin expansive soil. In identical settings, it is noteworthy that the mean critical dynamic stress threshold observed for CSES surpasses that of Group A filling by a factor of 1.5–1.7. Furthermore, the maximum critical dynamic stress exhibited by CSES achieves a 1.2-fold superiority over its lime-stabilized expansive soil (LSES). The outcomes gleaned from the in situ vibration tests elucidate that, when subjected to the passage of a high-velocity train traveling at 120 km/hr, bearing the load of 25–30 tons per axle, the subgrade surface exhibits dynamic stress ranging from 98.57 to 116.07 kPa. Meanwhile, the dynamic stress undergoes a notable escalation due to rainfall infiltration, intensifying by a factor of 1.02–1.28 times its original magnitude. The influence depth of dynamic stress extends 1.4–1.6 times beyond the designed subgrade bed thickness of 2.5 m. Notably, the critical dynamic stress of the filler surpasses the dynamic stress at the same position, underscoreing the capacity of 3%–5% CSES filling for heavy-haul railways to ensure long-term dynamic stability.

Multi-objective optimization of electro-pneumatic braking process with fuzzy logic control for heavy haul railway applications

ABSTRACT This paper presents a novel approach to multi-objective optimization in heavy haul trains’ electro-pneumatic braking systems using optimizable fuzzy control. Initial findings indicate that a straightforward replacement of pneumatically controlled valves with electro-pneumatic ones, without significant control schedule modifications, fails to substantially improve the dynamics behaviour of rolling-stock. Consequently, a more sophisticated strategy based on optimizable fuzzy logic control for the electro-pneumatic valves was developed. This approach utilizes multi-objective optimization to simultaneously minimize energy dissipation during braking and reduce force peaks in freight car draft gears. The optimal solution achieved demonstrates a 13.41% reduction in kinetic energy dissipation during downhill sections, along with a concurrent decrease in draft gear peak forces by 14.22% in traction and 3.16% in buff regimes compared to standard rolling-stock with pneumatically controlled valves. This optimized fuzzy braking controller outperforms standard procedures, highlighting its potential to enhance heavy haul train efficiency while adhering to safety standards.

Critical speed of ballasted railway tracks: Influence of ballast and subgrade degradation

Track substructure layers such as ballast and subgrade involve progressive degradation with time in relation to the loading history. Degradation of track components directly influences the dynamic track response including the displacements and stresses. Determining the influence of real-world substructure degradation problems such as particle breakage, ballast fouling and mud pumping on the critical speed of a given track is essential to design ballasted tracks for high-speed passenger and freight trains. In this paper, a coupled rheological-continuum model is developed to predict the track dynamic response for different train speeds and axle loads. In this novel approach, field dispersion curves obtained from MASW (Multichannel Analysis of Surface Waves) testing can be adopted along with the proposed analytical model to predict the critical speed of track as well as the corresponding dynamic stress state in the substructure layers. Analytical modelling shows that problems such as ballast fouling and mud pumping lead to reduction in the computed critical speed, while also amplifying the dynamic stress response at lower train speeds. The influence of substructure degradation is found to be more prominent for heavy-haul (freight) trains imparting higher axle loads, when compared to passenger trains. Practical insights highlighting the influence of track improvements such as placing a thicker ballast layer and the addition of a capping layer on the critical speed are presented in this study with the objective of rejuvenating future track design.

Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

This paper establishes a multi-objective optimization model for railway heavy-haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy-haul trains, introducing multi-objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon-inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi-objective transportation problems, the paper proposes a forward-learning pigeon-inspired optimization algorithm based on a surrogate-assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward-learning strategy to enhance learning from non-dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains.

On the Polygonal Wear Evolution of Heavy-Haul Locomotive Wheels due to Wheel/Rail Flexibility and Its Mitigation Measures

Wheel polygonal wear can immensely worsen wheel/rail interactions and vibration performances of the train and track, and ultimately, lead to the shortening of service life of railway components. At present, wheel/rail medium- or high-frequency frictional interactions are perceived as an essential reason of the high-order polygonal wear of railway wheels, which are potentially resulted by the flexible deformations of the train/track system or other external excitations. In this work, the effect of wheel/rail flexibility on polygonal wear evolution of heavy-haul locomotive wheels is explored with aid of the long-term wheel polygonal wear evolution simulations, in which different flexible modeling of the heavy-haul wheel/rail coupled system is implemented. Further, the mitigation measures for the polygonal wear of heavy-haul locomotive wheels are discussed. The results point out that the evolution of polygonal wear of heavy-haul locomotive wheels can be veritably simulated with consideration of the flexible effect of both wheelset and rails. Execution of mixed-line operation of heavy-haul trains and application of multi-cut wheel re-profiling can effectively reduce the development of wheel polygonal wear. This research can provide a deep-going understanding of polygonal wear evolution mechanism of heavy-haul locomotive wheels and its mitigation measures.