MODELING THE TRACK GEOMETRY VARIABILITY

MODELING THE TRACK GEOMETRY VARIABILITY

Perrin, G.; Soize, C.; Duhamel, D.; Funfschilling, C.

Full Article:

At its building, the theoretical new railway line is supposed to be made of perfect straight lines and curves. This track geometry is however gradually damaged and regularly subjected to maintenance operations. The analysis of these track irregularities is a key issue as the dynamic behaviour of the trains is mainly induced by the track geometry. In this context, this work is devoted to the development of a stochastic modeling of the track geometry and its identification with experimental measurements. Based on a spatial and statistical decomposition, this model allows the spatial and statistical variability and dependency of the track geometry to be taken into account. Moreover, it allows the generation of realistic track geometries that are representative of a whole railway network. These tracks can be used in any deterministic railway dynamic software to characterize the dynamic behavior of the train.

At its building, the theoretical new railway line is supposed to be made of perfect straight lines and curves. This track geometry is however gradually damaged and regularly subjected to maintenance operations. The analysis of these track irregularities is a key issue as the dynamic behaviour of the trains is mainly induced by the track geometry. In this context, this work is devoted to the development of a stochastic modeling of the track geometry and its identification with experimental measurements. Based on a spatial and statistical decomposition, this model allows the spatial and statistical variability and dependency of the track geometry to be taken into account. Moreover, it allows the generation of realistic track geometries that are representative of a whole railway network. These tracks can be used in any deterministic railway dynamic software to characterize the dynamic behavior of the train.

Palavras-chave:

DOI: 10.5151/meceng-wccm2012-16655

Referências bibliográficas

• [1] O.P. Le Maître and O.M. Knio. Spectral Methods for Uncertainty Quantification. Springer, 2010.
• [2] G. Perrin, C. Soize, D. Duhamel, and C. Funfschilling. Track irregularities stochastic modeling. Probabilistic Engineering Mechanics, (submitted February 2012).
• [3] A. Nataf. Détermination des distributions de probabilité dont les marges sont données. Comptes Rendus de l’Académie des Sciences, 225:42–43, 1986.
• [4] M. Rosenblatt. Remarks on a multivariate transformation. Annals of Mathematical Statistics, 23:470–472, 1952.
• [5] N. Wiener. The homogeneous chaos. American Journal of Mathematics, 60:897–936, 1938.
• [6] R. Ghanem and P.D. Spanos. Polynomial chaos in stochastic finite elements. Journal of Applied Mechanics, Transactions of the ASME 57:197–202, 1990.
• [7] R. Ghanem and P. D. Spanos. Stochastic Finite Elements: A Spectral Approach, rev. ed. Dover Publications, New York, 2003.
• [8] C. Soize. Identification of high-dimension polynomial chaos expansions with random coefficients for non-gausian tensor-valued random fields using partial and limited experimental data. Computer Methods in Applied Mechanics and Engineering, 199:2150–2164, 2010.
• [9] G. Perrin, C. Soize, D. Duhamel, and C. Funfschilling. Identification of polynomial chaos representations in high dimension from a set of realisations. Society for Industrial and Applied Mathematics - Journal on Scientific Computing, (accepted May 2012).

Como citar:

Perrin, G.; Soize, C.; Duhamel, D.; Funfschilling, C.; "MODELING THE TRACK GEOMETRY VARIABILITY", p-105-115. In: In Proceedings of the 10th World Congress on Computational Mechanics [= Blucher Mechanical Engineering Proceedings, v. 1, n. 1]. São Paulo: Blucher, 2014.
ISSN 23580828, DOI 10.5151/meceng-wccm2012-16655