Track irregularities stochastic modeling

Abstract

High speed trains are currently meant to run faster and to carry heavier loads, while being less energy consuming and still respecting the security and comfort certification criteria. To face these challenges, a better understanding of the interaction between the dynamic train behavior and the track geometry is needed. As during its lifecycle, the train faces a great variability of track conditions, this dynamic behavior has indeed to be characterized on track portions sets that are representative of the whole railway network. This paper is thus 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. First, this paper describes a practical implementation of the proposed method and then applies this method to the modeling of a particular French high speed line, for which experimental data are available.

Probabilistic simulation for the certification of railway vehicles

“…When only bounds are known, the best distribution is a uniform distribution. For the vector valued parameters affected by uncertainty, random fields [16,17] have to be identified.…”

Stochastic Representations and Statistical Inverse Identification for Uncertainty Quantification in Computational Mechanics

“…All the details and the results given for this application are extracted form the work published in [28,46,47,59,66]. The objectives are the following.…”

“…The APSM and the associated generators presented and used hereinafter are those that have been published in [44,45,48,49,50,51,52,53,54,55,40,56,57,58]. Three illustrations are presented: the stochastic modeling of track irregularities for high-speed trains and its experimental identification [59], the stochastic continuum modeling of random interphases from atomistic simulations for a polymer nanocomposite [60], and the multiscale identification of the random elasticity field at mesoscale of a heterogeneous microstructure using multiscale experimental observations [61,62,63].…”

Statistical Inverse Problems for Non-Gaussian Non-Stationary Stochastic Processes Defined by a Set of Realizations

“…Among these methods, the polynomial chaos expansion (PCE) method [3] has given very promising results, even in cases when M ν ( [5,4,7]). This technique is based on a direct projection of C on a polynomial hilbertian basis, { ψ j (ξ), 1 ≤ j }, of all the second-order random vectors with values in R M , such that:…”