Long time evolution of train dynamics with respect to track geometry

This paper aims at characterizing the long time evolution of the vehicle-track system. The knowledge of the evolution of such a system is of great concern for the railway industry, in order to maintain a high level of safety and comfort in the high speed trains. We propose a computational stochastic approach to predict the long time evolution of a given track portion. The approach is based on an adaptation of the global stochastic model of track irregularities previously identified with a large experimental data basis. The nonlinear stochastic dynamics of the train excited by track irregularities are carried out using a computational multibody dynamics model. Some indicators concerning the dynamic responses of the train are introduced in order to start off the maintenance or not of the given track portion.

[1]  Omar M. Knio,et al.  Spectral Methods for Uncertainty Quantification , 2010 .

[2]  Christian Soize,et al.  Stochastic Models of Uncertainties in Computational Mechanics , 2012 .

[3]  Mats Berg,et al.  Assessing track geometry quality based on wavelength spectra and track–vehicle dynamic interaction , 2008 .

[4]  Gustav Lönnbark Characterization of Track Irregularities With respect to vehicle response , 2012 .

[5]  Richard D. Deveaux,et al.  Applied Smoothing Techniques for Data Analysis , 1999, Technometrics.

[6]  Christian Soize,et al.  Track irregularities stochastic modeling , 2011 .

[7]  Sönke Kraft Parameter identification for a TGV model , 2012 .

[8]  D. W. Scott,et al.  Variable Kernel Density Estimation , 1992 .

[9]  C. Soize,et al.  A Posteriori Error and Optimal Reduced Basis for Stochastic Processes Defined by a Finite Set of Realizations , 2014, SIAM/ASA J. Uncertain. Quantification.

[10]  G. PERRIN,et al.  Identification of Polynomial Chaos Representations in High Dimension from a Set of Realizations , 2012, SIAM J. Sci. Comput..

[11]  Christian Soize,et al.  Karhunen-Loève expansion revisited for vector-valued random fields: Scaling, errors and optimal basis , 2013, J. Comput. Phys..

[12]  R. Ghanem,et al.  Stochastic Finite Elements: A Spectral Approach , 1990 .

[13]  Guillaume Perrin Random fields and associated statistical inverse problems for uncertainty quantification : application to railway track geometries for high-speed trains dynamical responses and risk assessment , 2013 .