Prediction of a representative point for rail temperature measurement by considering longitudinal deformation

Abstract

Monitoring rail temperature is very important for determining the safe running speed of trains and to prevent buckling. In general, the maximum variation of the internal rail temperature can be >7 ℃ depending on the point of measurement. However, there is as yet no sufficient information about how to predict the measurement point to represent the thermal deformation due to temperature distribution. In this study, the authors report a new point, called the representative measurement point, at which the rail temperature can be measured. This point considers the average deformation of the rail through structural analysis by adopting experimental and actual rail temperature data. The authors designed and installed a measurement system similar to an actual rail environment. Using the system, various data were acquired (internal/surface rail temperature and weather data) for 10 months. On the basis of these data, an analysis was done to calculate the average deformation point through thermal analysis. Finally, the representative measurement point was proposed as the position at which the average deformation point converges regardless of weather or seasons. The authors believe that the method described herein is advantageous in that it could be used in a high-accuracy temperature-monitoring system and for predicting thermal deformation and buckling.

The Fundamental Approach of the Digital Twin Application in Railway Turnouts with Innovative Monitoring of Weather Conditions

“…The temperature distribution of CWR rails is not uniform, which significantly complicates the precise definition of measurement principles. The selection of a representative measuring point should take into account the average deformation associated with an uneven temperature distribution [ 19 , 20 ]. To perform the tests, an external DS18B20 temperature sensor was placed inside the crown of the rail type S49 (49E1).…”

A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

“…Rail temperatures are usually measured indirectly within a measurement station that simulates the environment of the railway installation [ 7 , 10 , 11 , 13 , 19 ]. As in previous work, we collected the rail temperature and weather data every 10 min at a measurement station from August of 2016 to May of 2017 [ 7 , 20 ]. The constructed measurement station consisted of a 500-mm long KS 50n rail, a data acquisition system (DAQ), a weather station, and K-type thermocouples.…”

“…Recently, we showed that the deformation of the KS 50N rail at 74 mm from the bottom of the rail represents the average deformation of the whole rail [ 20 ]. In this study, the rail temperature at the point of average deformation of the KS 50N rail was taken as the representative temperature of the whole rail.…”

Validation of a rail temperature model with experimental measurements

“…Notable models were developed by different authors. [6][7][8][9] Chapman et al 6 adapted from a model that predicts ice formation on road surfaces to model the rail surface temperature on the foot of the rail, which according to S. Hong et al 10 is not the most representative temperature to be used in buckling studies. Additionally, it uses an extremely simplified rail profile, with the cross-section divided into four regular regions: one wider rectangle for the base and four others stacked to represent the web and head of the rail.…”