Improved calibration of simulation models in railway dynamics: application of a parameter identification process to the multi-body model of a TGV train

Sönke Kraft 1 Guillaume Puel 2 Denis Aubry 2 Christine Fünfschilling 1
1 SNCF - Direction de l'Innovation et de la Recherche
2 MSSMat - Laboratoire de mécanique des sols, structures et matériaux
Abstract : This paper aims at estimating the vehicle suspension parameters of a TGV (Train à Grande Vitesse) train from measurement data. A better knowledge of these parameters is required for virtual certification or condition monitoring applications. The estimation of the parameter values is performed by minimising a misfit function describing the distance between the measured and the simulated vehicle response. Due to the unsteady excitation from the real track irregularities and nonlinear effects in the vehicle behaviour, the misfit function is defined in the time domain using a least squares estimation. Then an optimisation algorithm is applied in order to find the best parameter values within the defined constraints. The complexity of the solution surface with many local minima requires the use of global optimisation methods. The results show that the model can be improved by this approach providing a response of the simulation model closer to the measurements.
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Submitted on : Saturday, December 21, 2013 - 12:31:01 PM
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Sönke Kraft, Guillaume Puel, Denis Aubry, Christine Fünfschilling. Improved calibration of simulation models in railway dynamics: application of a parameter identification process to the multi-body model of a TGV train. Vehicle System Dynamics, Taylor & Francis, 2013, 51 (12), pp.1938-1960. ⟨10.1080/00423114.2013.847467⟩. ⟨hal-00921785⟩

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