Multi-fidelity Metamodels Nourished by Reduced Order Models

Stéphane Nachar (1) , Pierre-Alain Boucard (1) , David Néron (1) , Udo Nackenhorst (2) , Amélie Fau (2)

1 LMT - Laboratoire de Mécanique et Technologie
2 Leibniz Universität Hannover=Leibniz University Hannover

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.

Domaines: Mécanique des structures [physics.class-ph]

Stéphane Nachar, Pierre-Alain Boucard, David Néron, Udo Nackenhorst, Amélie Fau. Multi-fidelity Metamodels Nourished by Reduced Order Models. Peter Wriggers; Olivier Allix; Christian Weißenfels. Virtual Design and Validation, 93, Springer International Publishing, 2020, Lecture Notes in Applied and Computational Mechanics, 978-3-030-38155-4. ⟨10.1007/978-3-030-38156-1⟩. ⟨hal-02460014⟩