Estimation of track parameters and wheel–rail combined roughness from rail vibration

نویسندگان

  • Qi Li
  • David J Thompson
چکیده

Rolling noise from railways is significantly affected by the wheel–rail combined roughness and the dynamic properties of the track. To facilitate vibration and noise predictions it is desirable to be able to determine these parameters accurately from field measurements. In this study an inverse method to determine these parameters is adopted and enhanced. Use is made of a track model based on a wavenumber finite element model of the free rail coupled to discrete supports, which allows for the pinned–pinned mode and cross–sectional deformation of the rail. The rail vibration induced by hammer impacts and the vibration during train passages are simulated using this model, and these results are then applied to illustrate the accuracy of the direct and indirect methods for the estimation of track decay rate. These methods are compared in a case study for a ballasted track for which hammer impact and train pass-by measurements have been obtained. Other track parameters can also be extracted from the measured data by using the advanced track model. Thereafter a more complete method is adopted to estimate the wheel–rail combined roughness from measured rail vibration under train passages. A comparison is conducted among the estimated roughness levels obtained from this full method, an existing simplified method and the direct measurement method. It is found that the simplified method overestimates the roughness around the pinned–pinned resonance frequency but gives a good estimation if the track decay 1 Department of Bridge Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China. 2 Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK. Corresponding author: Qi Li, Department of Bridge Engineering, Tongji University,1239 Siping Road, Shanghai 200092, China. Email: [email protected] rates of the loaded track are used.

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تاریخ انتشار 2017