An adaptive model order reduction by proper snapshot selection for nonlinear dynamical problems
- verfasst von
- P. S.B. Nigro, M. Anndif, Y. Teixeira, P. M. Pimenta, P. Wriggers
- 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).
- Organisationseinheit(en)
-
Institut für Kontinuumsmechanik
- Externe Organisation(en)
-
Universidade de Sao Paulo
- Typ
- Artikel
- Journal
- Computational mechanics
- Band
- 57
- Seiten
- 537-554
- Anzahl der Seiten
- 18
- ISSN
- 0178-7675
- Publikationsdatum
- 04.2016
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Numerische Mechanik, Meerestechnik, Maschinenbau, Theoretische Informatik und Mathematik, Computational Mathematics, Angewandte Mathematik
- Elektronische Version(en)
-
https://doi.org/10.1007/s00466-015-1238-y (Zugang:
Geschlossen)
