Probabilistic simulation for the certification of railway vehicles

The present dynamic certification process that is based on experiments has been essentially built on the basis of experience. The introduction of simulation techniques into this process would be of great interest. However, an accurate simulation of complex, nonlinear systems is a difficult task, in particular when rare events (for example, unstable behaviour) are considered. After analysing the system and the currently utilized procedure, this paper proposes a method to achieve, in some particular cases, a simulation-based certification. It focuses on the need for precise and representative excitations (running conditions) and on their variable nature. A probabilistic approach is therefore proposed and illustrated using an example. First, this paper presents a short description of the vehicle / track system and of the experimental procedure. The proposed simulation process is then described. The requirement to analyse a set of running conditions that is at least as large as the one tested experimentally is explained. In the third section, a sensitivity analysis to determine the most influential parameters of the system is reported. Finally, the proposed method is summarized and an application is presented.

[1]  Christian Soize,et al.  Construction of probability distributions in high dimension using the maximum entropy principle: Applications to stochastic processes, random fields and random matrices , 2008 .

[2]  Laura Mazzola,et al.  Virtual testing environment tools for railway vehicle certification , 2015 .

[3]  Oldrich Polach,et al.  Validation of simulation models in the context of railway vehicle acceptance , 2015 .

[4]  Manfred Zacher,et al.  Stationary and on-track tests using various vehicles , 2015 .

[5]  O. L. Maître,et al.  Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics , 2010 .

[6]  Peter Lumb.  Statistics and Probability in Civil Engineering , 1973 .

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

[8]  Saltelli Andrea,et al.  Sensitivity Analysis for Nonlinear Mathematical Models. Numerical ExperienceSensitivity Analysis for Nonlinear Mathematical Models. Numerical Experience , 1995 .

[9]  Oldrich Polach,et al.  A new approach to define criteria for rail vehicle model validation , 2014 .

[10]  C. Funfschilling,et al.  Effect of parameter uncertainty on the numerical estimate of a railway vehicle critical speed , 2011 .

[11]  James O. Berger,et al.  Uncertainty analysis and other inference tools for complex computer codes , 1998 .

[12]  Andreas Haigermoser,et al.  Describing and assessing track geometry quality , 2014 .

[13]  Sönke Kraft,et al.  Propagation of variability in railway dynamic simulations: application to virtual homologation , 2012 .

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

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

[16]  Stefano Bruni,et al.  Effect of Suspension Parameter Uncertainty on the Dynamic Behaviour of Railway Vehicles , 2011 .

[17]  E. Jaynes.  Information Theory and Statistical Mechanics , 1957 .

[18]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[19]  O Polach,et al.  On non-linear methods of bogie stability assessment using computer simulations , 2006 .