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The 10th in-train force in the time domain (Train 1) with plots progressively shifted upwards by 200 kN.
Source publication
This paper presents the results of the International Benchmarking of Longitudinal Train Dynamics Simulators which involved participation of nine simulators (TABLDSS, UM, CRE-LTS, TDEAS, PoliTo, TsDyn, CARS, BODYSIM and VOCO) from six countries. Longitudinal train dynamics results and computing time of four simulation cases are presented and compare...
Contexts in source publication
Context 1
... The in-train force of a selected wagon connection system in the time domain (e.g. Figure 4). ...
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... terms of the largest in-train forces, most simulators agreed that the magnitude decreases from the first wagon to the tail of the train as shown in Figure 3. Table 3 also shows that most simulators had good agreement in terms of the largest in-train forces. Most simu- lators, except BODYSIM and VOCO, reported the largest tensile and compressive in-train The time histories of the 10th in-train force simulated by different simulators are shown in Figure 4. To better present the results, the in-train forces were shifted in the figure. ...
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... example, UM was shifted up by 200 kN with respect to TABLDSS while CRE-LTS was then shifted up by another 200 kN with respect to UM and so on progressively for all nine simulator result traces. Figure 4 shows that BODYSIM and VOCO have generated unstable in-train forces. The in-train force simulated by VOCO has shown low frequency (lower than 0.2 Hz) vibrations with significant amplitudes (up to 150 kN). ...
Context 4
... VOCO had the shortest maximum compressive deflection with only 9.64 mm. This was also reflected in its in-train forces in Figure 4. VOCO had very small compressive forces. ...
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... the possible reasons for the unstable in-train forces generated by VOCO and BODYSIM as shown in Figure 4, the following analyses were provided: ...
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... has also made the plot of UM obviously thicker than most other simulators. The same phenomenon can also be observed from Figure 4 for the first simulation case. However, it has to be pointed out that the results of UM did not show signs of model instability; the exact reason for this effect is unknown to the organisers. ...
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... mean value of PoliTo was significantly larger (−1765 kN). Figure 14 shows the time history of the in-train forces of the 146th wagon connection system. It can be seen that: (1) similar to the third simulation case, PoliTo has also shown unstable draft gear modelling in this simulation case (see around the 300th second and the 800th second) while it had produced stable results for the first simulation case; and (2) BODYSIM has generated stable results for the fourth simulation case. ...
Citations
... For the sake of convenance, the Centre for Railway Engineering initiated the International Benchmarking of Longitudinal Train Dynamics Simulators, involving the main research teams in the world. Longitudinal train dynamics simulation results with four typical simulation cases were presented and compared in [17]. The results showed a basic agreement in the longitudinal dynamic forces. ...
Train-track coupled dynamics problems are particularly prominent in heavy-haul railways, due to the strong coupling effects of longitudinal , lateral, and vertical interactions between long heavy-haul trains and rail infrastructure. Poor train-track dynamic interaction performance could lead to a series of dynamics problems that significantly influence train operational safety and maintenance costs. This paper presents a comprehensive state-of-the-art review of train-track coupled dynamics theory and its applications in heavy-haul rail transportation. Several critical heavy-haul train-track coupled dynamics problems are discussed, including longitudinal coupler force induced safety issues, wheel-rail wear and rolling contact fatigue, and infrastructure degradation due to train-track interactions. The evolution of vehicle-track coupled dynamics theory and its extended development to heavy-haul train-track coupled dynamics is described briefly. A systematic review of the modelling methodology for key elements in heavy-haul train-track coupled dynamics is presented. Field test results focusing on the modelling validation are also briefly presented. The practical applications of heavy-haul train-track coupled dynamics theory are comprehensively discussed. Guidance is provided on further improving the heavy-haul train-track coupled dynamics and identifying future challenging research topics on better solving complicate engineering problems due to heavy-haul rail transportation.
... Only the in-train forces at the rear coupler of each vehicle were recorded and indexed correspondingly to the vehicle number, while the front forces were approximated from the preceding vehicle's rear forces but in the opposite direction. The simulations were performed using Universal Mechanics software [37] on a 64-bit Windows 10 Intel Core i7 2.5G system and verified against the international benchmark [38]. The Park solver [39] was used to solve these second-order differential equations. ...
... The coupling system comprises two AAR (Association of American Railroads) automatic couplers and draft gears. The draft gear model used is a 10 mm slack steel frictional draft gear, consistent with international benchmarks [38]. This model was chosen to ensure comparability with existing studies and to focus on controllable factors such as train configuration and coupler location. ...
Railway in-train forces are critical for ensuring safe and efficient train operations. However, real-time monitoring of these forces across multiple couplers in various trains remains challenging due to variations in train configurations and coupler locations. This paper proposes In-trainNet, a two-step data-driven framework that leverages an automatic train operation system to enhance in-train force monitoring. In the first step, a specially designed multi-task model is pre-trained to simultaneously estimate multiple in-train forces on multiple couplers for a specific train configuration. In the second step, a transfer learning scheme transfers and adapts the pre-trained model to different train configurations, significantly reducing the need for extensive training data and computational resources. Comparative experiments demonstrate the superior performance of the pre-trained model, which achieves higher accuracy and efficiency compared to single-task models. The integration of transfer learning further enhances the framework's adaptability, enabling robust and accurate monitoring across diverse train configurations. The proposed approach offers a promising solution for real-time, in-situ monitoring of railway in-train forces, with potential applications in both research and industrial applications.
... 19,20 Further, the study of Wu indicated that Universal Mechanism (UM) is suitable for modeling the rigid-flexible coupling problems. 21 However, the multi-layer track structures as a single element in the vehicle-track-bridge system without consideration of the interaction between these layers of track were simplified in previous studies, which could lead to the deviation of dynamic performance for track structure. ...
The dynamic performance of three slab ballastless tracks in the China Railway Track System (CRTS), which is supported by a simply supported box girder under various train loads, were comprehensively investigated with refined rigid-flexible coupled models. A refined rigid-flexible coupled model of vehicle-multi layer track-bridge system was developed to investigate the dynamic performance of three various CRTS slab tracks (i.e., CRTS I, II, III) under various conditions of train models, speeds and loads, respectively. The numerical analysis was conducted by using the combination of ANSYS and Universal Mechanism. It shows that the developed refined model has the capability of simulating the vehicle-slab track-bridge interaction with consideration multi-layer track structures, and the model predictions agree reasonably well with the experimentally measured wheel load reduction rate, lateral displacement and lateral acceleration of the bridge under different train speeds. The parameters of derailment system, vertical and lateral acceleration, the wheel-rail contact force, and Sperling index gradually become larger with the increase of the train speed, especially when the speed increases to 350 km/h. In addition, there is a significant increase in vertical wheel-rail contact force while a decrease in Sperling index when the train with full load capacity. Furthermore, the CRTS III slab track has demonstrated a better dynamic performance of track and bridge structures in comparison to CRTS I and CRTS II slab ballastless tracks.
... The safety of long trains is assessed by Railway Undertakings (RUs) with a statistical approach, by means of simulations of the longitudinal train dynamics (LTD) [1]. The latter are run with dedicated LTD codes [2,3] with the aim to calculate the ratio between the longitudinal compressive forces (LCFs) and the corresponding permissible LCFs (PLCFs), which can be determined experimentally, numerically [4] or by extrapolation rules [5]. However, the UIC 530-2 [6] and EN 15839 [7] rules defining the experimental methods to identify the PLCFs only consider the negotiation of a single S-curve in quasi-static running conditions. ...
Air brake operations can generate large values of compressive in-train forces, which can eventually be related to an increase of the derailment risk. Longitudinal train dynamics (LTD) simulations are commonly run to compute the in-train forces. However, typical LTD codes are not able to calculate the wheel-rail forces, which are needed for the evaluation of the safety indexes defined by the international standards. On the other hand, wheel-rail contact forces can be easily determined by means of multibody (MB) codes, but MB simulations of whole trains including many wagons are usually too expensive from a computational point of view. The present paper proposes to obtain metamodels of a single wagon built via kernel-based regressions, trained from the results of LTD and MB simulations of freight train air brake operations. Typical wagons running in Europe are considered as reference vehicles in the simulations. The derived metamodels are closed-form models that can be included in common LTD simulators for a fast evaluation of the safety indexes directly from the main outputs of LTD simulations of braking operations.
... The verification results are presented in Table 1 and Fig. 4. It should be noted that positive values represent tensile in-train forces, while negative values represent compressive in-train forces. The red curve in Fig. 4 is the simulation result from this work, while the black curve is from the benchmark [43]. The maximum speed, average speed, maximum in-train force, and mean maximum in-train force have magnitudes that agree well with the benchmark ( Table 1). ...
... The model performances were evaluated against the international benchmark [43], using four metrics: root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2 ), and training and inference time. MAE measures the average errors of approximated results, while RMSE measures the variance of errors between the actual and approximated values. ...
... Comparison of the simulated in-train force history of the 10th coupler with that from the international benchmark[43]. ...
Railway in-train forces are an essential element in assessing multiple aspects of rolling stocks. Conventional methods for obtaining the forces can be time-consuming and require significant investment in manpower and domain expertise, while only gathering the force data for specific service conditions one at a time. However, automatic train operation (ATO) systems can measure real-time information for trains and tracks by on-board and trackside devices, which could provide an opportunity for in-train forces monitoring. This paper presents a data-driven approach that uses ATO-measured data and a neural network model to monitor in-train forces under service conditions. To develop this approach, longitudinal train dynamics simulations (LTSs) for a freight train were conducted to establish the relationship between ATO measurements and in-train forces on specific couplers, which was embedded in a large amount of training data. After that, a specially developed self-attention-based causal convolutional neural network (SA-CNN) was employed to learn the underlying relationship and estimate the in-train force histories considering temporal dependencies. The comparative evaluation between the SA-CNN against four alternative neural network models revealed that the SA-CNN exhibits a slightly higher level of accuracy. Furthermore, the generalisation capability of the well-trained SA-CNN model was confirmed by numerical LTSs under four different service conditions. The results indicated that the data-driven approach has superior compatibility for arbitrarily combined inputs with significantly reduced computational time compared to LTSs. This approach holds the potential for achieving reliable in-situ monitoring of railway in-train forces, which is beneficial to both in-train force-related research and industrial applications.
... The MB model of the vehicle considers the rolling resistance via the definition of a specific force acting between the waggon frame and the track. Among the different expressions available in the literature [48], in the present paper the rolling resistance force F rr is calculated as a function of speed using the expression suggested in an international benchmarking activity of longitudinal train dynamics simulators [49,50]: ...
... Therefore, the necessity of simulation research on the impact law of longitudinal forces in extreme-long heavy-haul trains is self-evident. The key technologies and methods of the longitudinal dynamics simulation are discussed in references Chang et al. (2016Chang et al. ( , 2017, Wu et al. (2014Wu et al. ( , 2016, Wu et al. (2018) and Yang et al. (2010). ...
Purpose
The objective of this study is to investigate the impact of longitudinal forces on extreme-long heavy-haul trains, providing new insights and methods for their design and operation, thereby enhancing safety, operational efficiency and track system design.
Design/methodology/approach
A longitudinal dynamics simulation model of the super long heavy haul train was established and verified by the braking test data of 30,000 t heavy-haul combination train on the long and steep down grade of Daqing Line. The simulation model was used to analyze the influence of factors on the longitudinal force of super long heavy haul train.
Findings
Under normal conditions, the formation length of extreme-long heavy-haul combined train has a small effect on the maximum longitudinal coupler force under full service braking and emergency braking on the straight line. The slope difference of the long and steep down grade has a great impact on the maximum longitudinal coupler force of the extreme-long heavy-haul trains. Under the condition that the longitudinal force does not exceed the safety limit of 2,250 kN under full service braking at the speed of 60 km/h the maximum allowable slope difference of long and steep down grade for 40,000 t super long heavy-haul combined trains is 13‰, and that of 100,000 t is only 5‰.
Originality/value
The results will provide important theoretical basis and practical guidance for further improving the transportation efficiency and safety of extreme-long heavy-haul trains.
... TABLDSS is a combined simulation system of air brake system and longitudinal dynamics of trains. The simulation system received excellent score in the evaluation of International Benchmarking of Longitudinal Train Dynamics Simulators [20]. The simulation system performs a synchronous simulation of the air brake and the longitudinal train dynamics. ...
We establish a simulation model based on the theory of air flow to analyze the accelerated release effect of the quick release valve inside the air brake control valve. In addition, the combined simulation system of train air brake system and longitudinal train dynamics is used to analyze how the parameters of the quick release valve in the 120/120–1 brake control valve affect the propagation characteristics of the train brake pipe pressure wave, the release action range of the accelerated brake, and the longitudinal coupler force for a 20,000-ton heavy haul train on the section of the Datong–Qinhuangdao Railway. The results show that the quick release valve can effectively accelerate the rising speed of the train brake pipe pressure during the initial release, as the accelerated release effect is evident before the train brake pipe pressure reaches 582 kPa. The quick release valve can effectively accelerate the release of the rear cars, reducing the longitudinal coupler force impact due to time delay of the release process. The quick release valve can effectively reduce the tensile coupler force in the train by as much as 20% in certain cases.
... The authors' research group developed in past activities the in-house MATLAB LTDPoliTo code [6][7][8], which was validated on the simulation scenarios proposed in the context of the international benchmark of LTD simulators [9,10] (''the benchmark'' in the rest of the paper). However, the LTDPoliTo code in its original form does not consider the braking forces due to the air brake system, as the air brake forces were outside the scope of the benchmark and only dynamic braking forces provided by the locomotives were considered. ...
... Tensile forces are generated on the coupling connection systems because of the differences in the braked power of the E402B locomotive and of the Shimmns wagons. Moreover, the air brake force which eventually causes the deceleration of the vehicles is the product of the pressing force on the friction elements and the friction coefficient, see Eqs. (8) and (10). For the disc braked locomotive, the friction coefficient is approximately constant, while for the tread braked wagons equipped with cast-iron shoes, the friction coefficient is calculated according to the Karwatzki's expression, see Eq. (9), which predicts larger values of the friction coefficient at lower speed. ...
The present paper shows the development of a strategy for the calculation of the air brake forces of European freight trains. The model is built to upgrade the existing Politecnico di Torino longitudinal train dynamics (LTD) code LTDPoliTo, which was originally unable to account for air brake forces. The proposed model uses an empirical exponential function to calculate the air brake forces during the simulation, while the maximum normal force on the brake friction elements is calculated according to the indication of the vehicle braked weight percentage. Hence, the model does not require to simulate in detail the fluid dynamics in the brake pipe nor to precisely know the main parameters of the braking system mounted on each vehicle. The model parameters are tuned to minimize the difference between the braking distance computed by the LTDPoliTo code and the value prescribed by the UIC 544-1 leaflet in emergency braking operations. Simulations are run for different configurations of freight train compositions including a variable number of Shimmns wagons trailed by an E402B locomotive at the head of the train, as suggested in a reference literature paper. The results of the proposed method are in good agreement with the target braking distances calculated according to the international rules.
... Regarding the computational cost, Wu et al. (2018), associated with other 12 institutions from 6 countries, defined the train longitudinal dynamics simulation benchmark, based on the results of nine different simulators. Their work presents a comparison between the simulated train operation time and the wall time spent on simulating it. ...
... 1. In the longitudinal dynamics benchmark paper (Wu et al. 2018), four train configurations were simulated. Therefore, the average computing time among these results is used in the comparison. ...
... According to the results presented in Table 2, it can be observed that the proposed method presents a computational cost close to the majority of the analyzed methods. As highlighted in the literature review, most existing simulators have at least one significant simplification, such as Table 2. Computational cost comparison with the benchmark results (Wu et al. 2018 using look-up models, disregarding the effects of preload and/or applying some type of linear slope to simulate the draft gear load/unload transitions (Wu et al. 2018). Such simplifications can significantly decrease the processing time. ...
Train traction and brake dynamics simulations can be used to improve the safety and efficiency of heavy haul transportation. Virtual models can be used to analyze many operational conditions that are expensive or inviable to be measured due to costs and security risks. This paper presents a low computational cost simulation model that encompasses the longitudinal dynamics equations and the pneumatic brake system behavior. The model is used to evaluate the effects of the locomotive traction/dynamic-brake and of the pneumatic brake delays in the draft gear forces. A real-world scenario was analyzed, in which a standard train control procedure was applied for 168 ore freight cars propelled by two locomotives. For this analysis , the model was able to perform up to 8 simultaneous parallel simulations with a processing time of 607 s to conclude a route of 2884 s, which is very suitable for this kind of investigation.
