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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)

Abstract

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.

Keywords

Uncertainty Model calibration Info-gap approach Performance metric Robust solution 

References

  1. 1.
    SNECMA and HERAKLES: Aube de Turbomachine en Matériau Composite et Procédé pour sa Fabrication - Brevet. WO 2011/080443 A1 (2011)Google Scholar
  2. 2.
    Berman, A.: Multiple acceptable solutions in structural model improvement. AIAA J. 33 (5), 924–927 (1995)CrossRefzbMATHGoogle Scholar
  3. 3.
    Atamturktur, S., Liu, Z., Cogan, S., Juang, H.: Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization-based approach. Struct. Multidiscip. Optim. 51, 659–671 (2014)CrossRefGoogle Scholar
  4. 4.
    Govers, Y., Link, M.: Stochastic model updating - Covariance matrix adjustment from uncertain experimental modal data. Mech. Syst. Signal Process. 24 (3), 696–706 (2010)CrossRefGoogle Scholar
  5. 5.
    Smith, A.F.M., Roberts, G.O.: Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. J. R. Stat. Soc. 55 (1), 3–23 (1993)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Chib, S., Greenberg, E.: Understanding the Metropolis-Hastings algorithm. Am. Stat. 49 (4), 327–335 (1995)Google Scholar
  7. 7.
    Aherne, F.J., Thacker, N.A., Rockett, P.I.: The Bhattacharyya metric as an absolute similarity measure for frequency coded data. Kybernetika 34 (4), 363–368 (1998)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Ben-Haim, Y.: Info-Gap Decision Theory: Decisions Under Severe Uncertainty. Academic, New York (2006). [ISBN: 978-0-12-373552-2]Google Scholar
  9. 9.
    Hall, J.W., Lempert, R.J., Keller, K., Hackbarth, A., Mijere, C., McInerney, D.J.: Robust climate policies under uncertainty: a comparison of robust decision making and info-gap methods. Risk Anal. 32 (10), 1657–1672 (2012)CrossRefGoogle Scholar
  10. 10.
    Ben-Haim, Y., Zacksenhouse, M., Keren, C., Dacso, C.C.: Do we know how to set decision thresholds for diabetes? Med. Hypotheses 73 (2), 189–193 (2009)CrossRefGoogle Scholar
  11. 11.
    Kuczkowiak, A.: Modèle hybride incertain pour le calcul de réponse en fonctionnement d’un alternateur. PhD thesis, Université de Franche-Comté (2014)Google Scholar
  12. 12.
    Naouar, N., Vidal-Sallé, E., Schneider, J., Maire, E., Boisse, P.: Meso-scale FE analyses of textile composite reinforcement deformation based on X-ray computed tomography. Compos. Struct. 116, 165–176 (2014)CrossRefGoogle Scholar
  13. 13.
    Couégnat, G.: Approche multiéchelle du comportement mécanique de matériaux composites à renfort tissé. PhD thesis, Université Sciences et Technologies-Bordeaux I (2008)Google Scholar
  14. 14.
    Dupin, C.: Etude du comportement mécanique des matériaux composites à matrice céramique de faible épaisseur. PhD thesis, Bordeaux 1 (2013)Google Scholar
  15. 15.
    Naouar, N., Vidal-Salle, E., Schneider, J., Maire, E., Boisse, P.: 3d composite reinforcement meso F.E. analyses based on X-ray computed tomography. Compos. Struct. 132, 1094–1104 (2015)Google Scholar

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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