Details
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Applied and Computational Mechanics |
| Place of Publication | Cham |
| Pages | 61-79 |
| Number of pages | 19 |
| ISBN (electronic) | 978-3-030-38156-1 |
| Publication status | Published - 4 Mar 2020 |
Publication series
| Name | Lecture Notes in Applied and Computational Mechanics |
|---|---|
| Volume | 93 |
| ISSN (Print) | 1613-7736 |
| ISSN (electronic) | 1860-0816 |
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.
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computational Theory and Mathematics
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Lecture Notes in Applied and Computational Mechanics. Cham, 2020. p. 61-79 (Lecture Notes in Applied and Computational Mechanics; Vol. 93).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Multi-fidelity Metamodels Nourished by Reduced Order Models
AU - Nachar, S.
AU - Boucard, P. A.
AU - Néron, D.
AU - Nackenhorst, U.
AU - Fau, A.
PY - 2020/3/4
Y1 - 2020/3/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85081589024&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-38156-1_4
DO - 10.1007/978-3-030-38156-1_4
M3 - Contribution to book/anthology
AN - SCOPUS:85081589024
SN - 978-3-030-38155-4
T3 - Lecture Notes in Applied and Computational Mechanics
SP - 61
EP - 79
BT - Lecture Notes in Applied and Computational Mechanics
CY - Cham
ER -