• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.119-125
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• 2016

This study examined the running dynamic characteristics of a hybrid type wheel-rail high speed train, in which the propulsion method of maglev is applied. A wheel-rail high speed train propelled by a superconducting linear synchronous motor (SC-LSM) is expected to be superior to a maglev train regarding economical and interoperable aspects, still having powerful thrust force as maglev. In this paper, regarding the two methods of applying the SC-LSM to an existing wheel-rail train, to investigate the influences of SC-LSM propulsion on the dynamic characteristics of wheel-rail high speed train, the dynamic model of train including interaction between the rotor and stator of SC-LSM is established. Through the simulation using the model, the influence of the interaction between the rotor and stator of SC-LSM on stability, ride comfort and the effect of guideway irregularity are investigated.

• Journal of the Korean Society for Railway
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• v.10 no.5
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• pp.589-595
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• 2007

Korea Railroad Research Institute has developed the driverless rubber-tired K-AGT (Korean-Automated Guideway Transit) system from 1999 to 2005 and has done its performance and reliability tests on the test line at Gyeongsan-city. Radio Frequency Communication-Based Train Control system of K-AGT, which employed Advanced Automated Train Control scheme, detects train position using the radio propagation delay between wayside and vehicle radio equipment. In this paper, we investigate whether the transmission power of radio system can be reduced to the permitted level announced by the Ministry of Information and Communication for license-free ISM(Industrial Scientific Medical) frequency bands. We first determine radio propagation model, using the measured data at test line, and perform simulation for radio coverage prediction. From the simulation results, we identify that the radio system operated with reduced power can provide good link quality in total test line.

• Tribology and Lubricants
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• v.37 no.4
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• pp.129-135
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• 2021

An air-bearing stage uses externally pressurized air as the lubricant between the stage and the rail. The supporting force generated by the supplied air makes the stage rise and move smoothly with extremely low friction. Mechanical contacts rarely happen, the bearing surfaces do not produce wear particles, and dust is not generated. It also has the advantage of having low energy loss and high precision. Because of its advantages, an air-bearing stage is used in several types of machines that require high precision. In this article, the effect of the pocket depth on the hammering phenomena of the air bearing is studied. An analysis program is developed to calculate the dynamic behavior of the stage by solving the Reynolds equation between the stage and the guideway and the equations of motion on the stage. The acceleration, constant movement, and deceleration are applied to the stage. The stage is modeled as a five-degree-of-freedom system. In the course of the dynamic behavior, the hammering phenomena occur under some special conditions. The deeper the pocket, the more unstable the behavior of the stage, and air hammering occurs when it exceeds a certain depth. In addition, the higher the supply pressure, the more unstable the behavior of the stage. However, hammering occurs even with a shallow pocket depth. Other conditions that affect the hammering phenomena are calculated and discussed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.11
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• pp.1309-1315
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• 2014

The switch for a maglev vehicle should be designed such that the vehicle safely changes its track without touching the guiderail. In particular, a medium-to-low-speed EMS -type maglev train relies heavily on a U-type electromagnet where it generates levitation force and guidance force simultaneously. Therefore, it is necessary to evaluate the safety of the vehicle whenever it passes the switch, as it lacks active control of the guidance force. Furthermore, when the vehicle passes a segmented switch, which is a group of curves made up of connected lines with a small radius of curvature, it may come into mechanical contact with the guiderail owing to the excessive lateral displacement of the electromagnet. The goal of this study is to analyze the influence of a segmented switch on the safety of major design-related variables for achieving improved running safety. We propose a three-dimensional multibody dynamics model composed of two cars with one body. Using the proposed model, we perform a simulation of the lateral air gap, which is one of the measurements of the running safety of the vehicle when it passes the switch. The analyzed design variables are the length between short span girder, the articulation angle, the length between two centers of a fixed girder at its ends, and the number of girders. On the basis of the effects of the considered design variables, we establish an optimized design of a switch with improved safety.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.643-651
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• 2020

A capsule train runs inside a sub-vacuum tube and can reach very high speed due to the low air resistance. A capsule train uses a superconducting electrodynamic suspension (SC-EDS) method for levitation, which allows for a large levitation gap and does not require gap control. However, SC-EDS has inherent characteristics such as the large gap variation and a small damping effect in the levitation force, which can degrade the running stability and ride comfort. To overcome this, a stability improvement device should be designed and applied based on dynamic analysis. In this study, a 1/10 small-scale testbed was developed to replicate the dynamic characteristics of a capsule train and investigate the performance of stability improvement devices. The testbed is composed of a 6-degree-of-freedom Stewart platform for the realization of bogie motion, a secondary suspension with a running stabilization device, and a carbody. Based on the dynamic similarity law proposed by Jaschinski, the small-scale testbed was manufactured, and a bogie motion algorithm was applied with the consideration of guideway irregularity and levitation stiffness. The experimental results from the testbed were compared with simulation results to investigate the performance of the testbed.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.