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Multi-fidelity Metamodels Nourished by Reduced Order Models

  • S. Nachar
  • P.-A. BoucardEmail author
  • D. Néron
  • U. Nackenhorst
  • A. Fau
Chapter
  • 41 Downloads
Part of the Lecture Notes in Applied and Computational Mechanics book series (LNACM, volume 93)

Abstract

Engineering simulation provides better designed products by allowing many options to be quickly explored and tested. In that context, the computational time is a strong issue because using high-fidelity direct resolution solvers is not always suitable. Metamodels are commonly considered to explore design options without computing every possible combination of parameters, but if the behavior is nonlinear, a large amount of data is required to build this metamodel. A possibility is to use further data sources to generate a multi-fidelity surrogate model by using model reduction. Model reduction techniques constitute one of the tools to bypass the limited calculation budget by seeking a solution to a problem on a reduced-order basis (ROB). The purpose of this study is an online method for generating a multi-fidelity metamodel nourished by calculating the quantity of interest from the basis generated on-the-fly with the LATIN-PGD framework for elasto-viscoplastic problems. Low-fidelity (LF) fields are obtained by stopping the solver before convergence, and high-fidelity (HF) information is obtained with converged solutions. In addition, the solver ability to reuse information from previously calculated PGD basis is exploited.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • S. Nachar
    • 1
  • P.-A. Boucard
    • 1
    Email author
  • D. Néron
    • 1
  • U. Nackenhorst
    • 2
  • A. Fau
    • 2
  1. 1.Université Paris-Saclay, ENS Paris-Saclay, CNRS, LMT - Laboratoire de Mécanique et TechnologieCachan CedexFrance
  2. 2.LUH - Leibniz Universität Hannover [Hannover] 44961Gottfried Wilhelm Leibniz Universität HannoverHannoverGermany

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