Inverse identification of mechanical parameters of high-speed train suspensions submitted to random track irregularities

David Lebel 1, 2 Christian Soize 1 Christine Funfschilling 2 Guillaume Perrin 3
1 MSME - Laboratoire de Modélisation et Simulation Multi Echelle
2 SNCF : Innovation & Recherche
3 DAM/DIF - DAM Île-de-France
Abstract : The objective of the work presented here is the inverse identification of parameters describing the mechanical characteristics of high-speed train suspensions for maintenance purposes. This identification is achieved by comparing simulation results to on-track accelerometric measurements. It requires the introduction of an output predictive error as a noise added to simulation outputs to take into account measure and model uncertainties, the introduction of an objective function defined on the parameter domain, and the optimization of this objective function, performed by an EGO (Efficient Global Optimization) algorithm thanks to the construction of a Kriging metamodel of the objective function.
Keywords : Statistical inverse problem experimental identification high-speed train suspensions random track irregularities kriging metamodel.
Document type :
Conference papers
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download
https://hal-upec-upem.archives-ouvertes.fr/hal-01575047
Contributor : Christian Soize <>
Submitted on : Thursday, August 17, 2017 - 3:58:11 PM
Last modification on : Thursday, July 18, 2019 - 4:36:07 PM

File

conference-2017-ICOSSAR-Vienna...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01575047, version 1

Collections

UPEC-UPEM | MSME_MECA | MSME | INSMI | CEA | DAM

Citation

David Lebel, Christian Soize, Christine Funfschilling, Guillaume Perrin. Inverse identification of mechanical parameters of high-speed train suspensions submitted to random track irregularities. 12th International Conference on Structural Safety & Reliability, ICOSSAR 2017, Aug 2017, Vienna, Austria. pp.1-9. ⟨hal-01575047⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

1121

Files downloads

107

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD