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Missing point estimation in models described by proper orthogonal decomposition
- Source :
- IEEE Transactions on Automatic Control. Nov, 2008, Vol. 53 Issue 10, p2237, 15 p.
- Publication Year :
- 2008
-
Abstract
- This paper presents a new method of missing point estimation (MPE) to derive efficient reduced-order models for large-scale parameter-varying systems. Such systems often result from the discretization of nonlinear partial differential equations. A projection-based model reduction framework is used where projection spaces are inferred from proper orthogonal decompositions of data-dependent correlation operators. The key contribution of the MPE method is to perform online computations efficiently by computing Galerkin projections over a restricted subset of the spatial domain. Quantitative criteria for optimally selecting such a spatial subset are proposed and the resulting optimization problem is solved using an efficient heuristic method. The effectiveness of the MPE method is demonstrated by applying it to a nonlinear computational fluid dynamic model of an industrial glass furnace. For this example, the Galerkin projection can be computed using only 25% of the spatial grid points without compromising the accuracy of the reduced model. Index Terms--Model reduction, parameter-varying systems, proper orthogonal decomposition, time-varying systems.
Details
- Language :
- English
- ISSN :
- 00189286
- Volume :
- 53
- Issue :
- 10
- Database :
- Gale General OneFile
- Journal :
- IEEE Transactions on Automatic Control
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.189653278