Robust Model Calibration Using Determinist and Stochastic Performance Metrics

  • P. LépineEmail author
  • S. Cogan
  • E. Foltête
  • M.-O. Parent
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


The aeronautics industry has benefited from the use of numerical models to supplement or replace the costly design-build-test paradigm. These models are often calibrated using experimental data to obtain optimal fidelity-to-data but compensating effects between calibration parameters can complicate the model selection process due to the non-uniqueness of the solution. One way to reduce this ambiguity is to include a robustness requirement to the selection criteria. In this study, the info-gap decision theory is used to represent the lack of knowledge resulting from compensating effects and a robustness analysis is performed to investigate the impact of uncertainty on both deterministic and stochastic fidelity metrics. The proposed methodology is illustrated on an academic example representing the dynamic response of a composite turbine blade.


Uncertainty Model calibration Info-gap approach Performance metric Robust solution 


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Copyright information

© The Society for Experimental Mechanics, Inc. 2016

Authors and Affiliations

  • P. Lépine
    • 1
    • 2
    Email author
  • S. Cogan
    • 1
  • E. Foltête
    • 1
  • M.-O. Parent
    • 2
  1. 1.Institut FEMTO-STUniversité de Bourgogne-Franche-ComtéBesançonFrance
  2. 2.SNECMA-SAFRANRond-point René Ravaud, RéauMoissy-CramayelFrance

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