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Optimisation de structures viscoplastiques par couplage entre métamodèle multi-fidélité et modèles réduits

Abstract : Engineering simulation provides the best design products by allowing many design options to be quickly explored and tested, but fast-time-to-results requirement remains a critical factor to meet aggressive time-to-market requirements. In this context, using high-fidelity direct resolution solver is not suitable for (virtual) charts generation for engineering design and optimization.Metamodels are commonly considered to explore design options without computing every possibility, but if the behavior is nonlinear, a large amount of data is still required. 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 the present work 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 fields are obtained by stopping the solver before convergence, and high-fidelity information is obtained with converged solution. In addition, the solver ability to reuse information from previously calculated PGD basis is exploited.This manuscript presents the contributions to multi-fidelity metamodels and the LATIN-PGD method with the implementation of a multi-parametric strategy. This coupling strategy was tested on three test cases for calculation time savings of more than 37x.
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Submitted on : Tuesday, October 22, 2019 - 10:04:09 AM
Last modification on : Tuesday, October 6, 2020 - 8:24:06 AM


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  • HAL Id : tel-02324894, version 1


Stéphane Nachar. Optimisation de structures viscoplastiques par couplage entre métamodèle multi-fidélité et modèles réduits. Mécanique des solides [physics.class-ph]. Université Paris-Saclay, 2019. Français. ⟨NNT : 2019SACLN051⟩. ⟨tel-02324894⟩



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