Scandinavian Journal of Urology and Nephrology

Inerters are widely used in structural vibration systems, but there is little research on their use in the elastic-suspension of motors in high-speed train bogies. The type of the motor suspension significantly influences its vibration characteristics. To fill this gap, this paper proposes the use of the frequency-variable characteristics of the inertia spring damping (ISD) structure. The motor elastic-suspension bogie dynamic model with the inerter was established, and the effect of the motor suspension parameters on the stability of the bogie system was studied. Finally, the inertia-suspension parameters were optimized by the Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) multi-objective optimization algorithm. The results showed that the ISD structure has frequency-varying characteristics, and the maximum equivalent stiffness and damping exist in the low frequency range (approximately 10 Hz). As the equivalent conicity increases, the optimal lateral frequency and damping ratio of the motor increase, which makes the inerter suspension bogie to meet this characteristic. The optimized motor suspension parameters showed that the linear critical speeds of the bogie with inerter suspension are improved, and the stability of the bogie is higher within the equivalent conicity of 0.1-0.4. This study can provide a reference for the stability design of the bogie structure in future.

The polygonization of railway vehicle wheelsets is a common type of fault that poses risks to the safety and stability of the vehicle. In permanent magnet direct drive locomotives, the rotations of the wheelsets are directly driven by the traction motor; thus, the abnormal vibration caused by the polygonal wheel can directly transmit to the traction system, resulting in strong electromechanical coupling vibrations. To investigate the effect of wheel polygonization on the dynamic response of the electromechanical coupling system in direct-drive locomotives, a locomotive-track coupled dynamic model is established in this paper. The unique structure of the direct drive traction system, the interaction of the wheel-rail system, as well as the electromechanical coupling path of the traction motor are comprehensively considered. Simulation results revealed that polygonal wheels can significantly amplify vibrations in the key components of the traction system; distinct frequencies related to polygons are observed in the electrical signals. Furthermore, the influence laws of wave depth and order of polygonal wear on the dynamic performance of the locomotive components are revealed. These research findings provide theoretical references for health monitoring and operation maintenance of wheel polygonization in permanent magnet direct drive locomotives.

Track Quality Index (TQI) is a crucial indicator for assessing the condition of rail and rapid transit tracks. With the rapid increase of railway mileage and the complexity of processing large volumes of track inspection data, traditional methods that rely on a single device to process track geometry inspection data and calculate TQI no longer meet the efficiency and accuracy requirements of modern railways. This paper aims to propose a parallel method for assessing TQI track maintenance. The method is based on a multi-node data platform, using parallel computing technology to distribute different tasks in TQI calculation process such as track inspection data mileage correction, invalid data identification, and standard deviation calculation to multiple nodes. This approach enables standardized and rapid processing of large-scale track geometry inspection data. Using track geometry inspection data from various lengths of high-speed railway lines in China to test the proposed method, the results show that the new method significantly improves computational efficiency compared to traditional methods while maintaining high accuracy. The application of this method will greatly enhance the efficiency and accuracy of railway maintenance, providing robust technical support for the management and maintenance of railway infrastructure.

Asymmetric terrains near tunnel entrances may affect stability of railway vehicles while entering tunnels. Numerical simulations with three-dimensional, unsteady, and compressible flow are conducted on aerodynamic characteristics of trains traveling through a tunnel with asymmetric mountains with different slope angles near its entrance, including the non-mountain (flat ground), 45°-mountain, and 90°-mountain. The aerodynamic loads (the side force, rolling moment, and yawing moment) and surface pressure of the vehicles, as well as the flow around them are analyzed. A comparison between the surface pressures of the vehicle 2 in the numerical simulation and full-scale tests showed a 2.2% difference in peak-to-peak values during tunnel entry, validating the simulation results. The aerodynamic loads of the trailing vehicle are more sensitive to the slope angle of the mountain near the tunnel entrance, compared with the leading vehicle. The aerodynamic loads increase with the increase in the slope angle. The effect of the slope angle on the rolling moment is the most remarkable, and the rolling moment of the trailing vehicle with the 90°-mountain is 256.6% larger than that without a mountain. A trend in the flow toward the side with the flat ground and the limited space around the side with the mountain cause and increase the instability of the trailing vehicle when entering the tunnel. These findings provide valuable insights for designing and optimizing tunnel entrances and enhancing the stability of trains navigating asymmetric terrains.

The lateral stiffness and damping of rail pads significantly influence train performance on curves, yet their effects are not well understood. This study investigates the lateral dynamics of three rail pad structures: groove (GRP), mesh-type (MTRP), and mesh-type with high damping (MTHDRP), and their impact on train curve performance. Results from lateral loading tests indicate that MTHDRP pads exhibit the highest lateral static stiffness, dynamic stiffness, and damping ratio, while GRP pads have the lowest, highlighting significant performance disparities. Finite element analysis reveals substantial deformations in GRP pads under high lateral loads, leading to increased stress levels. Strategies to increase the lateral stiffness across these pad types involve structural parameter adjustment and integration of harder materials, detailed further in the conclusions. Multibody dynamic simulations suggest that decreasing lateral stiffness while increasing damping not only increases train safety on curves but also reduces track system vibration. Under varying conditions of speed, curve radius, rail slope, and superelevation, MTHDRP consistently demonstrates favorable performance, underscoring the importance of lateral dynamics in the design, manufacturing, testing, and dynamic analysis of rail pads to optimize vehicle and track performance on curves.

This study proposes a technique to temporarily improve the friction force by jetting dry air with less than 30% humidity on the contact surface. The tangential contact force was first measured under the condition that a very small amount of water was on the contact surface and the velocity was 30 km/h to confirm that the proposed technique is effective even in rainy weather. The findings revealed that the adhesive force increased by reducing the amount of water on the contact surface. These experimental results indicate that the dry-air jetting technique effectively increases the friction coefficient, even under rainy conditions, because dry-air jetting can remove water droplets on the surface. Subsequently, an experiment was conducted on the tangential contact force using a twin-disc sliding-frictional rolling machine to verify our proposed technique under different dry conditions. The tangential contact force coefficient increased simultaneously with dry-air jetting and remained stable during dry-air jetting under dry and high-humidity environmental conditions. Furthermore, the findings clarified that the tangential contact force improvement effect of dry-air jetting depends on the combination of the contact patch and its surface properties. Finally, based on these experimental results, we compared the same rail vehicles with and without considering the braking distance as an example of a dry-air jetting system application, using a vehicle dynamics analysis constructed using the SIMPACK commercial software. Consequently, the validity of the proposed technique was confirmed for actual rail vehicles.

Rail grinding is widely employed in heavy-haul railways to mitigate abnormal rail wear. However, frequent grinding can result in even more significant material loss than regular wear. This paper presents a method for adjusting track layout parameters to alleviate the severe rail wear problem on curved lines. First, field experiments and simulation analysis have been used to analyze the impact of track parameters on wheel-rail contact and the feasibility of parameter adjustment. A numerical optimization model has been established based on Genetic Algorithm-Levenberg Marquardt- Backpropagation neural networks (GA-LM-BP neural network), with the track parameters as the independent variable and the goal of reducing wear as the objective. The chaotic microvariation adaptive genetic algorithm has been used to obtain an optimized solution set. Finally, the optimization effects are revealed by comparing the rail wear characteristics obtained from the original values and the optimal solution set.

Rail grinding is performed by infrastructure managers to control, reduce or prevent the growth of rail defects, such as rolling contact fatigue and corrugation. This is done using preventive methods (to attempt to prevent defects from forming) or corrective methods (to remove defects present in the rail). Trials were undertaken on preventive rail grinding machines used by Network Rail, with the aim of improving the finished quality of the rail whilst still achieving the metal removal and reprofiling required. An important aspect considered in the trials was the acoustic rail roughness and its relationship with grinding surface quality indices. The results demonstrated that, in the case of the operational machines used by Network Rail, the largest impact on the overall surface quality was the age and conditioning of the grinding stones. The trials also demonstrated the differences in Standard requirements for achieving good surface quality indices for grinding and good acoustic roughness levels. They further highlighted the importance of identifying rail corrugation prior to preventive grinding to reduce the likelihood of the grinding signature increasing roughness at corrugation wavelengths.

Multibody simulations of train dynamics commonly employ generic short tracks to validate and observe the behaviour of wagons in certain conditions. However, for proper wheel wear analysis, the entire railway track should be modelled, increasing computational cost and time to obtain worn profiles and wear parameters. The major novelty of this work is the description of a reproducible algorithm-based methodology to obtain a statistically representative shorter equivalent railway track for wear simulations. The proposed methodology includes combining a real measured track with multibody dynamic simulation. To verify the approach, a case study was conducted on a 505 km-long railway in Brazil. Additionally, two distinct vehicles were employed: a European wagon with a standard gauge and a Brazilian wagon with a meter gauge. The shorter equivalent tracks were compared in terms of worn area and wear depth to the initial track, and the generated tracks were cross-compared between vehicles to verify the robustness of the method. It is observed that wear simulations can be performed more efficiently and effectively, reducing the computational time by at least 94%, while still obtaining accurate results, given that the final equivalent tracks were 18.59 times shorter for the Brazilian model and 22.45 times shorter for the European model comparing to the real track, with the maximum deviation being 6.55% in flange depth and with a maximum root mean-square error (RMSE) of 871 nm.

Low adhesion between wheel and rail can be caused by autumn leaf fall, reacting and adhering with the steel, resulting in signals passed at danger, station overruns or in worst cases derailments and collisions. Various methods to remove this layer or increase friction have been developed including water jetting, abrasive cleaning, traction gels and chemical treatments, new interventions are trialled each year. However, representative low friction conditions are difficult to generate repeatedly and measure, as such the efficacy of these interventions remains difficult to establish. Twenty years ago British Rail Research stated “Paradoxically it has been the lack of low adhesion that has hindered the development of low adhesion remedies”. This still remains largely true today. In this paper a method is proposed to form a representative organic layer, by placing powdered leaf material on the railhead and rolling with a vehicle until it has blackened and bonded to the underlying steel. The layers were visually inspected using industry guidelines and their friction was measured using the new Rivelin Rail PRT300 tribometer. The method was found to be capable of reliably producing contamination levels from light to heavy as required for testing by varying the quantity of leaves added. Friction coefficients for medium and heavily contaminated rails were all below 0.05, these would typically be described as ultra-low adhesion. As an illustration of the method, a trial of a new (anonymised) railhead cleaning technique is included in this work as a case study. The layer formation and measurement methodology proposed could be adopted by the wider rail industry to provide a more data-driven approach to understanding friction enhancer and railhead cleaning technology performance, through optimising current treatments and assessing the performance of novel technology.

In this case study, we introduce a new approach by applying the most recent fatigue criterion, effective tensile stress, to a rail vehicle suspension spring for the first time. Without the need for curve fitting, this criterion can effectively predict fatigue life under positive and negative R ratios. We defined a general S-N function and failure rates based on published results from cylindrical dumbbell samples on 30 fatigue cases. The measured load-displacement curve validated the material model with an elastic constant ratio. Blocks of three different loading sets were applied to a rail vehicle suspension spring for 125k cycles. One crack initiation was observed at 82k cycles and propagated to 97 mm after the test. We successfully predicted crack initiation at 81k cycles (with a failure rate of 7%) and 92k cycles (with a failure rate of 10%) using the Palmgren–Miner law. This prediction agreed with the experiment’s observation, demonstrating the approach’s reliability. The general S-N function could be used for the design and failure analysis of rail vehicle suspensions, providing reassurance in the design process.

We propose and apply novel corrugation and corrugation-related noise metrics designed to help maintainers monitor corrugation actively (on rail) or passively (by proxy through noise) at locations in a transit property. The novelty of our approach consists of incorporating wavelength-variable corrugation filtering, and wavelength- and velocity-dependent noise weighting into existing rail corrugation and wayside noise metrics primarily for municipal rail maintenance. The modified metrics isolate corrugation and corrugation-related noise in a manner tailored to a property’s operational characteristics, such as train velocity. The proposed modified metrics are applied to a data set collected from a pre- and post-grind monitoring regimen at 17 measurement sites in a North American property to investigate their responses. This study demonstrates the benefit of adapting the filtering methodology used to produce summary corrugation statistics to suit a specific property’s corrugation needs. It also demonstrates the need to include target corrugation wavelengths and approximate train velocity to appropriately weight the noise spectrum in a sample. These developments supplement the available methods that property maintainers can adopt to monitor corrugation conditions at discrete locations throughout a system by providing tools/metrics that are specifically designed to correctly assess the appropriate wavelength of corrugation on the rail face, thereby allowing maintainers to then accurately monitor corrugation via noise by means of passive wayside measurements.

The precise and efficient identification of damage in point rails of turnouts represents a critical and challenging technological issue that requires immediate attention. Guided wave inspection, as a non-destructive testing method, holds significant potential for practical applications in the research field focused on the identification of damage in point rails of high-speed railway turnouts. This study integrates rigorous theoretical analysis, advanced numerical simulation techniques, and meticulous experimental investigations to comprehensively investigate the excitation, propagation, and reception processes of ultrasonic-guided waves within the point rail. By strategically applying excitation signals at optimized positions specifically tailored for point rail detection, employing a frequency of 30 kHz, appropriate excitation modes are carefully selected. Defect identification within the extended point rail is performed from a dual perspective, encompassing both cross-correlation analysis and time-frequency analysis. The obtained results substantiate that when employing the cross-correlation coefficient to detect cracks in the elongated point rail, the sensitivity of crack detection is significantly higher in the rail bottom region as opposed to the rail head region. Furthermore, through the analysis of variations in the cross-correlation coefficient and the application of wavelet transformations to the guided wave signals, it is not only feasible to ascertain the presence and location of damages within the rail base region of the extended point rail but also to evaluate the relative size of the cracks. The findings of this research contribute a robust theoretical foundation for the subsequent analysis and evaluation of damage detection in high-speed turnout point rails.

Railways are more energy efficient than any other mode of transportation, offering an opportunity to reduce energy consumption, CO2 emissions, and air pollution. However, to reduce the high maintenance costs and activities, the application of waste materials in the railway industry has received attention; they are used as ballast, sub-ballast, and sleeper constituent elements. Sleepers have shown promising potential for recycled material usage, and concrete and composite sleepers have received more attention. Therefore, railway sleepers employ a variety of materials such as waste rubber, silica fume, slag, fibres, fly ash, coal ash, recycled concrete aggregate, and recycled plastics. Considering the vast scope of the subject, a comprehensive technical study gap in producing sleepers with waste material applications is perceived. This paper is written to collect the investigations on waste materials application on the physical, mechanical, dynamical, and durability performance of railway sleepers. In this paper, concrete and composite railway sleepers have been considered. Then, waste impacts are gathered, compared, tabulated, and the mechanisms are discussed to evaluate the study focus and scope for future research. This study shows that silica fume, slag, and fly ash improved the mechanical properties of concrete sleepers, while crumb rubber negatively affected the mentioned properties and increased the concrete damping capability. Pozzolans are noted to have a positive role in durability characteristics. Fibres improved the mechanical and durability performance of composite sleepers. Rubber has also improved the durability and flexibility of composite sleepers but decreased strength.

The wheel flat detection in trains using Artificial Intelligence (AI) has emerged as a critical advancement in railway maintenance and safety practices. AI systems can effectively identify geometric deformation in wheel rotation patterns, indicative of potential wheel flat damage, resorting to wayside monitoring systems and machine learning algorithms. This study aims to propose an unsupervised learning algorithm to identify and localize railway wheel flats, which considers three stages: (i) wheel flat detection to distinguish a healthy wheel from a damaged one using outlier analysis, achieving 100 percent accuracy; (ii) localizing the damage to pinpoint the location of the defective wheel through the Hidden Markov Model (HMM); (iii) classification of wheel damage based on its severity using k-means clustering technique. The unsupervised learning algorithm is validated with artificial data attained from a virtual wayside monitoring system related to freight train passages with healthy wheels and defective wheels with single and multiple defects. The proposed methodology demonstrated efficiency and robustness for wheel flat detection, localization, and damage severity classification regardless of the number of defective wheels and their position.