Track Geometry Research Articles

Embedding Kalker's variational theory into railway vehicle system dynamics and its efficiency improvement

The wheel–rail contact plays a vital role in railway transportation, hence an accurate wheel–rail contact model is the key to the simulation of railway vehicle dynamics. Though Kalker's variational theory is the canon, fewer works try to embed it into railway vehicle dynamics, mainly due to the computational efficiency problem and the complex wheel–rail geometry processing. This paper proposes a wheel–rail geometry processing method to embed KVT into railway vehicle multi-body dynamics based on the trajectory coordinate formulation, which conveniently introduces the wheel–rail contact surface pair for tracking the potential contact areas. After this prior detection, coarse detection is performed to narrow the range of penetrated elements. Multiple contact patches can also be identified based on the connection topology of discrete elements of wheel–rail surfaces. Thus, Kalker's variational theory can be used on each separate patch for local contact evaluation. An initial prediction method for the contact status is embedded in the multi-body dynamics simulation to accelerate the solution of Kalker's variational problem, in which the tractions with contact divisions obtained from the neighbouring system states are selected as the iterative initial value. The importance of predicting the contact tractions and demarcation for the convergence of the iteration is revealed. The proposed method is fully demonstrated by the Manchester contact benchmark and simulations of a railway vehicle passing through a curved track. As an advantage of embedding Kalker's variational theory, the precise traction pattern is obtained simultaneously along with the vehicle dynamics simulation, and the influencing mechanism of the track geometry on the vehicle system is analysed. The improvement of solution efficiency is also verified in simulations.

Track and catenary reconstruction from aerial images for the realistic analysis of rail electrification systems

Currently, the modelling of the railway catenaries for straight tracks is well understood and demonstrated via an accepted number of applications and an international benchmark. However, in the presence of a general track geometry, including curved tracks, challenges still remain. The fundamental difficulty is how to address the catenary layout in the presence of curves for a long railway track in which a large number of catenary sections are defined. To support the catenary design, e.g. in new electrification projects, it is fundamental to identify the track geometry, which is not always accessible to the overhead line engineers. This work purposes a novel approach for track geometry reconstruction using aerial views. The reconstruction methodology is based on the general geometry rules for track design. The track geometry obtained is then used as the reference to define the layout of the catenary. The Network Rail Series 1 overhead equipment is used here and the 3D finite element model of the catenary is built on the curved track geometry. The results of this work demonstrate, not only the advances proposed to study more realistically the pantograph-catenary interaction, but also the need to consider general curved tracks in acceptance and interoperability studies.

Data-driven ballast layer degradation identification and maintenance decision based on track geometry irregularities

ABSTRACT Ballast layer defects are the primary cause for rapid track geometry degradation. Detecting these defects in real-time during track inspections is urgently needed to ensure safe train operations. To achieve this, an indicator, the track degradation rate (TDR) was proposed. This rate is calculated using track geometry inspection data to locate and predict railway-line sections with ballast layer defects. The TDR is determined by the monthly standard deviation of the rail longitudinal level, which is one aspect of track geometry. The Ballast Layer Health Classification (BLHC) is designed by assessing the two successive TDRs before and after track geometry maintenance actions. The BLHC is used to categorize the conditions of the ballast layer, including normal periodic deterioration, abrupt deterioration, effective maintenance, rising deterioration, and severe deterioration. Both the TDR and BLHC were validated through field assessments of ballast layer conditions, where the two indicators were found to be effective in revealing defects. The results indicate that the TDR is sensitive to ballast layer defects, while the BLHC can quickly identify the location of these defects. Consequently, the BLHC can provide real-time guidance for ballast layer maintenance.

Long-short term memory networks for modeling track geometry in laser metal deposition.

Modeling metal additive manufacturing processes is of great importance because it allows for the production of objects that are closer to the desired geometry and mechanical properties. Over-deposition often takes place during laser metal deposition, especially when the deposition head changes its direction and results in more material being melted onto the substrate. Modeling over-deposition is one of the necessary steps toward online process control, as a good model can be used in a closed-loop system to adjust the deposition parameters in real-time to reduce this phenomenon. In this study, we present a long-short memory neural network to model over-deposition. The model has been trained on simple geometries such as straight tracks, spiral and V-tracks made of Inconel 718. The model shows good generalization capabilities and can predict the height of more complex and previously unseen random tracks with limited performance loss. After the addition to the training dataset of a small amount of data coming from the random tracks, the performance of the model for such additional shapes improves significantly, making this approach feasible for more general applications as well.

The impact of short-wave effects on deterioration of track geometry

Short wavelength rail defects or short-wave effects of rail have a significant influence on the system behaviour of railways. The influence of additional dynamic loads due to short-wave effects on component wear and the general quality behaviour of track is shown by several research papers. However, the described effect cannot yet be quantified comprehensively. This paper attempts to fill this gap. With the help of extensive data analysis, we can show that short-wave effects lead to a track geometry that is up to 50% worse than on average. Furthermore, we show examples illustrating the influence of untreated welded joints on the development of isolated defects of the track geometry. A detailed in-depth analysis of sections with different boundary conditions finally shows that short-wave effects have a significant influence on track geometry if the amplitude of the effect exceeds 0.15 mm. This value can be recommended as a threshold value in Austria and as a reference value in other countries.

Optimizing maintenance of vertical curves in high-speed railways

The relationship between railway alignment and passenger comfort under high-speed driving conditions is even closer, requiring alignment smoothness to a greater extent. However, vertical curve dynamic detection alarm is a chronic, difficult-to-resolve issue in operational high-speed railways. Therefore, a novel method combining alignment parameters and track geometry from the perspective of smoothness is proposed to optimize the curve radius by controlling elevation differences and to calculate track adjustments by improving the long wavelength irregularity. To validate the performance, the proposed method is adopted to develop vertical curve maintenance schemes for Beijing-Hong Kong high-speed railway, and the daily average number of level II warnings of vertical acceleration decreased by 95% after applying the schemes. The results demonstrate that the proposed method effectively improves alignment smoothness in the dynamic detection alarm section. The method still needs to be subsequently extended to improve its adaptability to different irregularity bands and complex line conditions.

MODELS FOR FORECASTING RAILROAD TRACK GEOMETRY DEGRADATION USING MACHINE LEARNING METHODS

Railroad track maintenance has always been complex, both because of its responsibility in terms of ensuring the safety of train traffic, and because of the high labor intensity of work processes and continuous work planning. Diagnostics and monitoring of all elements of the railroad track is carried out to ensure the safety of train traffic. One of the main parameters affecting the safety and uninterrupted movement of trains is the condition of the track. Deviations and malfunctions in rail track geometry lead to both speed limitation and complete closure of the track for train traffic. Failure to correct faults in a timely manner can often lead to more serious consequences. The main parameters of track geometry have a significant impact on the smoothness of train movement and the risks of derailment of rolling stock. Therefore, monitoring and control of these parameters is a priority task to ensure the stable operation of railroads. To automate this process, data-driven fault detection and diagnosis models can be used. To solve the problem, we used modern methods of solving classification problems for tabular data collected by special track-measuring tools. Automated machine learning model generation systems served as the basis for solving the problem. These systems make it much easier to train and configure machine learning models, as well as to implement them in a production environment. The practical significance of this work is that the solution of the problem of predicting track geometry degradation can be considered as part of the decision-making system for track repair and maintenance.

Nano to macro-mechanical properties of laser directed energy deposited CoCrNi medium entropy alloy

The CoCrNi Medium Entropy Alloy is a popular subset of the Cantor alloy due to its superior cryogenic and elevated temperature properties. This study reports the successful deployment of a laser-based near-net shaping technique known as laser directed energy deposition (LDED) to fabricate the CoCrNi MEA. The effect of laser power and scan speed on track geometry is studied in detail. The bulk deposition is performed using a parametric combination yielding regular defect-free tracks with an aspect ratio greater than five and a maximum deposition rate. The EBSD analysis revealed a strong < 001 > cubic texture along the build direction, due to the thermal gradient in that direction. Across both the scan and build directions, the cellular and columnar substructure is observed. Solid solution strengthening and dislocation strengthening contribute 39 % and 42 % to yield strength. The nanoindentation analysis revealed hardness, reduced modulus, and elastic modulus of 6.32 ± 0.32 GPa, 222.08 ± 5.72 GPa, and 250.62 ± 8.25 GPa, respectively. The contact stiffness, reduced modulus, and hardness showed a positive linear relationship with strain rate. The strain rate sensitivity of MEA is in the range of 0.030–0.042, which is higher than that of conventional fcc metals. It is attributed to the higher lattice frictional stresses and different atomic-level structures of MEA than conventional fcc metals. Furthermore, chemical short-range ordering and a stronger peierls barrier resulted in a lower activation volume of the order of ∼100b3. The present study provides a detailed understanding of the effect of process parameters on melt-pool geometry, microstructure evolution, as well as the influence of strain rate on nanomechanical properties.

Investigation of the melt track geometry during selective laser melting of CuSn10

The CuSn10 alloy (bronze with 10% tin) has remarkable mechanical properties, including good elongation and medium hardness. Additive manufacturing of this powder compound is developing at a fast rate. In this study, optimization of the process parameters of the selective laser melting method was carried out to manufacture CuSn10 compounds. In addition, a numerical model for the simulation of the melt pool behavior was created by utilizing the Ansys 2021 R1 software, and a comparison was carried out between predicted numerical data and the obtained experimental results. The formation conditions of various melt traces were modeled, measured and validated for this aim. In the experimental stage, a constant laser power of 95 W was used, and the effect of the variation of the scanning speed was studied between 10 and 1500 mm/s. The results showed that the variation of the scanning speed is not enough, and optimization must be carried out by including other process parameters. This indicates that by adjusting the process parameters to have a 365 W power, the liquid phase can be achieved in the production process.

Effect of Annular Laser Metal Deposition (ALMD) Process Parameters on Track Geometry and Thermal History on Ti6Al4V Alloy Clad.

Annular laser metal deposition (ALMD) is a rising technology that fabricates near-net-shaped components. In this research, a single factor experiment with 18 groups was designed to study the influence of process parameters on the geometric characteristics (bead width, bead height, fusion depth, and fusion line) and thermal history of Ti6Al4V tracks. The results show that discontinuous and uneven tracks with pores or large-sized incomplete fusion defects were observed when the laser power was less than 800 W or the defocus distance was -5 mm. The laser power had a positive effect on the bead width and height, while the scanning speed had the opposite effect. The shape of the fusion line varied at different defocus distances, and the straight fusion line could be obtained with the appropriate process parameters. The scanning speed was the parameter that had the greatest effect on the molten pool lifetime and solidification time as well as the cooling rate. In addition, the microstructure and microhardness of the thin wall sample were also studied. Many clusters with various sizes in different zones were distributed within the crystal. The microhardness ranged from 330 HV to 370 HV.

Anthropogenic problems threatening major cities: Largest surface deformations observed in Hatay, Turkey based on SBAS-InSAR

The surface deformation caused by tectonic activities and anthropogenic factors poses a great threat to cities worldwide. The investigation and monitoring of these deformations are crucial in order to create risk analysis for the future. The problem in this case is to investigate the surface deformations and their negative effects caused by groundwater use and to identify possible landslide areas. In this study, the surface deformations in Hatay province were analyzed using SBAS-InSAR. The results from these analyses were evaluated by field observations. Sentinel-1 descending (183 datasets) and ascending (147 datasets) track geometries were selected to determine the surface deformation and its temporal evolution. Both east-west and vertical surface deformations were calculated, and the surface deformation profiles, surface 3D models and time series were created. These time series were associated with monthly precipitation data. The deformation area was interpreted with regard to available well-log data and geological setting of the study area. As a result of the study, a surface deformation resembling a bowl like structure was observed in the industrial zone located in the city center of Hatay-Güzelburç. The deformation rates are approximately 22.3 cm/year in the form of subsidence, 3.6 cm/year in the form of eastern movement and 10.1 cm/year in the form of western movement. The deformation of this bowl-like structure decelerated in the winter and accelerated in the summer due to excessive water use. The average monthly precipitation dataset supports these results. The stratigraphic data from water wells and the presence of limestone outside the eastern boundary of the deformation area show a thick clay layer in the eastern block of the bowl-shaped deformation structure. The difference between these two units, which causes a sharp anomaly at the eastern border of the deformation area, is interpreted as a probable normal fault. The second study area where surface deformations are observed is the landslide zone. The deformation was found to be 7.5 cm/year in a westward direction and 1.5 cm/year as subsidence.

Estimation of the trajectory and attitude of railway vehicles using inertial sensors with application to track geometry measurement

This paper describes a novel method for the estimation of the trajectory and orientation of a rigid body moving along a railway track. Compared to other recent developments in the literature, the presented approach has the significant advantage of using inertial sensors only, excluding global position and orientation sensors. The excluded sensors are compensated with an odometry system and previous knowledge of the design track geometry. The procedure is based on a kinematic model of the relative motion of the body with respect to the track, together with a Kalman filter algorithm. Two different approaches are used and compared for the estimation of the noise covariance matrices in the Kalman filter. One is based on the use of experimental results with a known output. The other one relies upon constrained maximum likelihood estimation. The calculated trajectory and orientation are applied in this research to the problem of track geometry measurement. A scale track is used for experimental validation, showing that results are sufficiently accurate for this application. The obtained results also reveal that the constrained maximum likelihood estimation performs similarly to the known-output method. This is very convenient because it allows a straightforward application of the algorithm in different scenarios.

Multi-Objective Optimization for Weld Track Geometry in Wire-Arc Directed Energy Deposition of ER308L Stainless Steel

1. JAFARI D., VANEKER T.H.J., GIBSON I., 2021, Wire and Arc Additive Manufacturing: Opportunities and Challenges to Control the Quality and Accuracy of Manufactured Parts, Materials & Design, 202, 109471, https://doi.org/10.1016/j.matd.... CrossRef Google Scholar

An innovative rheological approach for predicting the behaviour of critical zones in a railway track

The poor performance of critical zones along a railway line has long been a subject of concern for rail infrastructure managers. The rapid deterioration of track geometry in these zones is primarily ascribed to limited understanding of the underlying mechanism and scarcity of adequate tools to assess the severity of the potential issue. Therefore, a comprehensive evaluation of their behaviour is paramount to improve the design and ensure adequate service quality. With this objective, a novel methodology is introduced, which can predict the differential plastic deformations at the critical zones and assess the suitability of different countermeasures in improving the track performance. The proposed technique employs a three-dimensional geotechnical rheological track model that considers varied support conditions of the critical zone. The approach is successfully validated with published field data and predictions from finite element analysis. This methodology is then applied to a bridge-open track transition zone, where it is observed that an increase in axle load exacerbates the track geometry degradation problem. The results show that the performance of critical zones with weak subgrade can be improved by increasing the granular layer thickness. Interpretation of the predicted differential settlement for different countermeasures exemplifies the practical significance of the proposed methodology.

Numerical investigation on dynamic behaviour of critical zones in railway tracks under moving train loads

The critical zones along a railway line, such as bridge approaches, are areas of serious concern for infrastructure managers owing to their high susceptibility to accelerated track geometry degradation. To improve their performance, a comprehensive evaluation of their response under realistic train-induced loads is paramount. With this objective, the dynamic behaviour of a standard track-bridge transition under moving loads (involving wheel translation and rotation) is investigated through finite-element (FE) analyses. First, a three-dimensional (3D) FE model of a bridge approach is developed and validated against published field data. The validated model is employed to gain insights into the phenomena such as unsupported sleeper formation and principal stress rotation in track substructure elements. The adequacy of 3D cellular geoinclusions in improving the performance of critical zones is also assessed. The results reveal that train parameters (axle load, speed, wheel translation and rotation) significantly influence the stresses, displacements, and sleeper-ballast gap size. The presence of a weak subgrade near the bridge exacerbates the differential movement problem and increases the sleeper-ballast gap size. The findings from this study significantly contribute towards an improved understanding of the response of critical zones, especially the spatiotemporal stress variations, unsupported sleeper formation and influence of geoinclusion reinforcement.

Algorithms and complexity results for the single-cut routing problem in a rail yard

Rail yards are facilities that play a critical role in the freight rail transportation system. A number of essential rail yard functions require moving connected “cuts” of rail cars through the rail yard from one position to another. In a congested rail yard, it is therefore of interest to identify a shortest route for such a move. With this motivation, we contribute theory and algorithms for the Single-Cut Routing Problem (SCRP) in a rail yard. Two key features distinguish SCRP from a traditional shortest path problem: (i) the entity occupies space on the network; and (ii) track geometry further restricts route selection. To establish the difficulty of solving SCRP in general, we prove NP-completeness of a related problem that seeks to determine whether there is space in the rail yard network to position the entity in a given direction relative to a given anchor node. However, we then demonstrate this problem becomes polynomially solvable—and therefore, SCRP becomes polynomially solvable, too—for “Bounded Cycle Length” (BCL) yard networks. We formalize the resulting two-stage algorithm for BCL yard networks and validate our algorithm on a rail yard data set provided by the class I railroad CSX Transportation.

A Multi-objective approach for position alignment of track geometry measurements

This study aimed to develop a multi-objective approach for reducing the positional errors in geometry measurements of track as a linear asset. Accordingly, we evaluated and compared two alignment methods – recursive segment-wise peak alignment (RSPA) and modified correlation optimised warping (MCOW). Furthermore, a novel rule-based approach was introduced to avoid data loss while aligning the datasets of the measurements of linear assets. A case study was conducted to implement and assess the performance of these methods in reducing the positional errors in track geometry measurements. The results revealed that the rule-based method preserves all the single defects present in the datasets. Furthermore, RSPA outperforms MCOW when aligning peaks, whereas MCOW is more efficient when all the data points in the datasets have equal priority.

Range-Doppler Based Moving Target Image Trace Analysis Method in Circular SAR

The single channel Circular Synthetic Aperture Radar (CSAR) has the advantage of continuous surveillance of a fixed scene of interest, which can provide high frame rate image sequences to detect ground moving targets. Recent image-sequence-based CSAR moving target detection methods utilize the fact that the target signal moves fast in the image sequence. Knowledge of the target’s image trace(moving trace in the image sequence, which is equal to a target signature’s morphology in a full aperture CSAR image) can help design better detection methods. However, previous signature morphology studies are based on linear track geometry assumptions, which cannot handle CSAR’s nonlinear track. Hence, this paper proposes a new image trace method based on the range-Doppler principle. The proposed method can deduct the exact analytic function of an arbitrary moving target’s image trace in CSAR. The method assumes radar operates in side-looking mode and that the target moves on the ground plane. It combines the range-Doppler equations(i.e., iso-range and iso-Doppler relation) and Cartesian transformation between the ground and radar coordinate system to obtain the parametric functions of the image trace. Based on the method, three types of target motion (including linear and nonlinear motion) are analyzed. The proposed method is validated with both simulated and real data.

Extreme High Step Climbing: Nonlinear Analysis of a Highly-Deformable- Tracked Robot

The mobility on rough terrain is a crucial factor in designing rescue robots. Rubbles scattered across disaster site obstruct the robots, which may limit search areas and disturb rapid rescue. To improve mobility in such situations, we proposed a novel mobile mechanism, called mono-wheel track (MW-track), which is an elastic track driven by a single wheel without passive wheels. This mechanism demonstrated high mobility on step-shaped obstacles by using a self-adaptive track. However, the principle underlying this mechanism is still unclear. Herein, we provide a model of track deformation that estimates step-climbing capability, based on large-deformation analysis of the nonlinear beam. The model was evaluated by comparing 1) the curve geometry of the theoretical track with the actual leaf spring and 2) the theoretical value of the climbable height with the actual value measured by step-climbing tests using a mobile robot with the MW-track mechanism. Furthermore, several tests were conducted under various ground friction and track stiffness conditions to investigate their effect on climbable step height. The mechanism for target step height was determined through the model and tests. The model affords a mechanical procedure to determine design parameters for target obstacles.

A Literature Review on Train Motion Model Calibration

The dynamics of a moving train are usually described by means of a motion model based on Newton’s second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict train dynamics accurately if the mentioned train characteristics are carefully calibrated. The model constitutes the core element of a broad variety of railway applications, from timetabling tools to Driver Advisory Systems and Automatic Train Operation. Among the existing train motion model calibration techniques, those that use operational data are of particular interest, as they benefit from on- board recorded data, capturing the train dynamics during operation. In this literature review article we provide an overview of the train motion model calibration techniques that have been published in the scientific literature between January 2000 and December 2021 and either use operational data or can be minimally adapted to use it. To this end, we present a critical overview of the existing train motion model calibration approaches, distinguishing online calibration that analyzes data on- the-go and offline calibration that analyzes historical data batchwise. We propose a research agenda and highlight some potential goals to be tackled in the near future: from devising accurate online calibrators for eco-driving applications to quantitizing the physical sources of parameter variation. Last, we discuss practical recommendations for practitioners and scholars inferred from the current state of the art.