Flat shunting is a classical railway yard operation that aims to rearrange a given sequence of railcars into a desired sequence by using a shunting engine and a set of shunting tracks. The operation considered in this study is a one-end push–pull process, which is particularly relevant when an initial classification or drop-off process cannot directly form the target outbound order. Compared to train timetabling or routing problems, this microscopic operational task is difficult to handle both practically and theoretically, as the shunting tracks have a strict first-in-last-out (FILO) requirement (i.e., cars can be coupled or decoupled only from one end of a track). Focusing on the multi-stage flat shunting problem, we develop a mixed-integer linear programming (MILP) model to optimize the number of engine actions, as the mesoscopic cost, and the number of car movements, as the microscopic cost, during pull-back and assignment operations. The proposed model is solved using a standard optimization solver and a customized branch-and-bound algorithm. A sequencing state measure is proposed to quantitatively evaluate the deviation of a car distribution in a yard from the desired final car sequence, leading to an effective heuristic rule that significantly improves branching efficiency. The proposed approach saves more than 99
Under strong crosswind conditions, as a 600 km/h high-speed maglev train traverses an arc-shaped porous barrier, the train experiences abrupt and drastic changes in aerodynamic loads. To investigate this phenomenon, a three-dimensional transient computational fluid dynamics (CFD) model was developed, in which an overset grid technique combined with the improved delayed detached eddy simulation (IDDES) method was employed to simulate the coupled crosswind–train–bridge–barrier system. How barrier aperture and train speed influence aerodynamic force coefficients and flow field evolution is examined in this study. It is demonstrated that significantly greater abrupt changes amplitudes and peak change rates in aerodynamic coefficients during train entry and exit are caused by a solid barrier compared to a porous barrier, with the most intense dynamic response being shown by the tail car. Behind the solid barrier, lower mean values but higher fluctuations are exhibited by aerodynamic loads. The dynamic impact is intensified by increasing train speed, following a power-law growth, while both the mean and statistical dispersion of aerodynamic coefficients are elevated by enlarging the barrier aperture. A fundamental difference in wake flow is identified: Initial vortices are weakened and flow reorganization is promoted by the porous barrier, whereas larger-scale vortex shedding is induced by the solid barrier. Consequently, a greater pressure differential across the car body is produced under the solid barrier condition. An empirical basis for designing targeted measures to enhance the operational aerodynamic stability of high-speed maglev trains is provided by these findings.
Rolling noise is the dominant noise source on straight metro railways, predominantly generated by track vibrations at frequencies below 2000 Hz. To address this issue, a novel rail particle damper (RPD) based on particle damping was developed, offering a significant advance in mitigating railway noise. Unlike traditional approaches, the RPD design was systematically optimized through laboratory experiments and a coupled track–damper model incorporating modal analysis. The coupled track–damper model compared Euler and Timoshenko beam elements in vibration wave propagation and effectively predicted the effective range of the pinned–pinned mode vibrations. Field tests on an operational metro line confirmed the RPD’s efficacy, with reductions in decay rate and noise levels of 3.7 dBA in the near field and 2.4 dBA in the far field. These findings highlight the RPD’s innovative contribution to rail noise control, particularly through its ability to shorten the decay time of vibrational energy and suppress the propagating noise from the decay time-series data. This study establishes a foundation for further refining RPD designs to enhance railway propagating noise and vibration management.
This study presents a surrogate-based multi-objective nose shape optimization for a 400 km/h-class high-speed train, considering normal open-air, crosswind open-air, and tunnel-related scenarios. The KTX-Cheongryong nose shape is parameterized using Bézier curves and a section box approach to satisfy dimensional constraints specified in relevant technical regulations. The objective functions are the tail car drag coefficient under normal open-air conditions, the leeward rail rolling moment coefficient under a 40 m/s crosswind, and a micro-pressure wave-related proxy based on the circumferential integration of wall-normal velocity on an imaginary tunnel wall. A trackside pressure variation limit of 700 Pa at 250 km/h is imposed as a constraint. A dataset comprising 544 designs was generated using three-dimensional compressible steady Reynolds-averaged Navier–Stokes simulations and used to construct Gaussian process regression surrogate models. These models are coupled with the non-dominated sorting genetic algorithm II to obtain Pareto-optimal solutions, all of which dominate the KTX-Cheongryong across the three objectives. Extreme designs on the Pareto front, selected as boundary solutions for each objective, are compared with the corresponding single-objective optima to clarify objective trade-offs. The representative compromise design achieves an 8.9
As the requirements for railway operators to offer a safe and economically efficient transportation service have become extremely difficult to met due to the size and complexity of the rail network, vehicle-based track monitoring and fault detection have gained relevance as a way to abate maintenance-related costs via condition-based and predictive approaches. In this work, a cost-effective, permit-free vehicle-based track monitoring system implemented on a regular in-service vehicle is presented. The designed system makes use of inertial sensors mounted on the vehicle’s bogie and car body, as well as positioning technology based on global navigation satellite system (GNSS) to collect monitoring data for detection and spatial localization of track defects. The capabilities of the developed system are exemplified by means of a case study related to a track section with a temporary speed restriction (TSR)—a scenario where traditional acceleration-based detection often fails due to reduced vehicle speed. Here, it is demonstrated that the proposed approach, combining statistical and time-frequency analysis (e.g., wavelets), can effectively lead to the detection and localization of anomalies on the track using onboard inertial measurements, even under reduced vehicle speed.
