Details
| Original language | English |
|---|---|
| Pages (from-to) | 537-554 |
| Number of pages | 18 |
| Journal | Computational mechanics |
| Volume | 57 |
| Issue number | 4 |
| Early online date | 2 Jan 2016 |
| Publication status | Published - Apr 2016 |
Abstract
Model Order Reduction (MOR) methods are employed in many fields of Engineering in order to reduce the processing time of complex computational simulations. A usual approach to achieve this is the application of Galerkin projection to generate representative subspaces (reduced spaces). However, when strong nonlinearities in a dynamical system are present and this technique is employed several times along the simulation, it can be very inefficient. This work proposes a new adaptive strategy, which ensures low computational cost and small error to deal with this problem. This work also presents a new method to select snapshots named Proper Snapshot Selection (PSS). The objective of the PSS is to obtain a good balance between accuracy and computational cost by improving the adaptive strategy through a better snapshot selection in real time (online analysis). With this method, it is possible a substantial reduction of the subspace, keeping the quality of the model without the use of the Proper Orthogonal Decomposition (POD).
Keywords
- Adaptive strategy, Galerkin projection, Model order reduction, Nonlinear dynamic analysis, PSS, Ritz vector
ASJC Scopus subject areas
- Engineering(all)
- Computational Mechanics
- Engineering(all)
- Ocean Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computational Theory and Mathematics
- Mathematics(all)
- Computational Mathematics
- Mathematics(all)
- Applied Mathematics
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Computational mechanics, Vol. 57, No. 4, 04.2016, p. 537-554.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - An adaptive model order reduction by proper snapshot selection for nonlinear dynamical problems
AU - Nigro, P. S.B.
AU - Anndif, M.
AU - Teixeira, Y.
AU - Pimenta, P. M.
AU - Wriggers, P.
N1 - Funding Information: The fourth author acknowledges the support by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) under the grant 303091/2013-4 as well as expresses his gratitude to the Alexander von Humboldt Foundation for the Georg Forster Research Award that made possible his stay at the University of Duisburg-Essen and the Leibniz University of Hannover. Funding Information: The first author acknowledges the support by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) under the grant 201656/2012-4 that made possible his stay at the Leibniz University of Hannover.
PY - 2016/4
Y1 - 2016/4
N2 - Model Order Reduction (MOR) methods are employed in many fields of Engineering in order to reduce the processing time of complex computational simulations. A usual approach to achieve this is the application of Galerkin projection to generate representative subspaces (reduced spaces). However, when strong nonlinearities in a dynamical system are present and this technique is employed several times along the simulation, it can be very inefficient. This work proposes a new adaptive strategy, which ensures low computational cost and small error to deal with this problem. This work also presents a new method to select snapshots named Proper Snapshot Selection (PSS). The objective of the PSS is to obtain a good balance between accuracy and computational cost by improving the adaptive strategy through a better snapshot selection in real time (online analysis). With this method, it is possible a substantial reduction of the subspace, keeping the quality of the model without the use of the Proper Orthogonal Decomposition (POD).
AB - Model Order Reduction (MOR) methods are employed in many fields of Engineering in order to reduce the processing time of complex computational simulations. A usual approach to achieve this is the application of Galerkin projection to generate representative subspaces (reduced spaces). However, when strong nonlinearities in a dynamical system are present and this technique is employed several times along the simulation, it can be very inefficient. This work proposes a new adaptive strategy, which ensures low computational cost and small error to deal with this problem. This work also presents a new method to select snapshots named Proper Snapshot Selection (PSS). The objective of the PSS is to obtain a good balance between accuracy and computational cost by improving the adaptive strategy through a better snapshot selection in real time (online analysis). With this method, it is possible a substantial reduction of the subspace, keeping the quality of the model without the use of the Proper Orthogonal Decomposition (POD).
KW - Adaptive strategy
KW - Galerkin projection
KW - Model order reduction
KW - Nonlinear dynamic analysis
KW - PSS
KW - Ritz vector
UR - http://www.scopus.com/inward/record.url?scp=84961119235&partnerID=8YFLogxK
U2 - 10.1007/s00466-015-1238-y
DO - 10.1007/s00466-015-1238-y
M3 - Article
AN - SCOPUS:84961119235
VL - 57
SP - 537
EP - 554
JO - Computational mechanics
JF - Computational mechanics
SN - 0178-7675
IS - 4
ER -