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Variable-fidelity modeling of structural analysis of assemblies

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  • Nicolas Courrier
  • Pierre-Alain Boucard
  • Bruno Soulier

Abstract

This paper deals with the advantages of using variable-fidelity metamodeling strategies in order to develop a valid metamodel more rapidly than by using traditional methods. In our mechanical assembly design, we use the term “variable-fidelity” in reference to the convergence (or accuracy) level of the iterative solver being used. Variable-fidelity metamodeling is a way to improve the prediction of the output of a complex system by incorporating rapidly available auxiliary lower-fidelity data. This work uses two fidelity levels, but more levels can be added. The LATIN iterative algorithm is used along with a “multiparametric” strategy to calculate the various data and their different fidelity levels by means of an error indicator. Three main categories of variable-fidelity strategies are currently available. We tested at least one method from each of these categories, which comes to a total of five methods for calculating a valid metamodel using low- and high-fidelity data. Here, our objective is to compare the performances of these five methods in solving three mechanical examples. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Nicolas Courrier & Pierre-Alain Boucard & Bruno Soulier, 2016. "Variable-fidelity modeling of structural analysis of assemblies," Journal of Global Optimization, Springer, vol. 64(3), pages 577-613, March.
    • Handle: RePEc:spr:jglopt:v:64:y:2016:i:3:p:577-613
    DOI: 10.1007/s10898-015-0345-9
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    References listed on IDEAS

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    1. Pruliere, E. & Chinesta, F. & Ammar, A., 2010. "On the deterministic solution of multidimensional parametric models using the Proper Generalized Decomposition," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 791-810.
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    Cited by:

    1. Mickaël Binois & David Ginsbourger & Olivier Roustant, 2020. "On the choice of the low-dimensional domain for global optimization via random embeddings," Journal of Global Optimization, Springer, vol. 76(1), pages 69-90, January.

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