Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-10303
Authors: Grunert, Dennis
Fehr, Jörg
Haasdonk, Bernard
Title: Well-scaled, a-posteriori error estimation for model order reduction of large second-order mechanical systems
Issue Date: 2019
metadata.ubs.publikation.typ: Preprint
metadata.ubs.publikation.seiten: 38
URI: http://elib.uni-stuttgart.de/handle/11682/10320
http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-103207
http://dx.doi.org/10.18419/opus-10303
metadata.ubs.bemerkung.extern: Submitted to the International Journal for Numerical Methods in Engineering.
Abstract: Model Order Reduction is used to vastly speed up simulations but it also introduces an error to the simulation results, which needs to be controlled. The performance of the general to use, a-posteriori error estimator of Ruiner et al. for second-order systems is analyzed and a bottleneck is found in the offline stage making it unusable for larger models. We use the spectral theorem, power series expansions, monotonicity properties, and self-tailored algorithms to speed up the offline stage largely by one polynomial order both in terms of computation time as well as storage complexity. All properties are proven rigorously. This eliminates the aforementioned bottleneck. Hence, the error estimator of Ruiner et al. can finally be used for large, linear, second-order mechanical systems reduced by any model reduction method based on Petrov-Galerkin reduction. The examples show speedups of up to 28.000 and the ability to compute much larger systems with a fixed amount of memory.
Appears in Collections:07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

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