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• W1984267420 abstract "Large scale finite element models are routinely used in design and optimization for complex engineering systems. However, the high model order prevents efficient exploration of the design space. Many model reduction methods have been proposed in the literature on approximating the high dimensional model with a lower order model. These methods typically replace a fine scale model with a coarser scale model in schemes such as coarse graining, macro-modeling, domain decomposition and homogenization. This paper takes a systems perspective by stating the model reduction objective in terms of the approximation of the mapping between specified input and output variables. Methods from linear systems theory, including balance truncation and optimal Hankel norm approximation, are reviewed and compared with the standard modal truncation. For high order systems, computational load, numerical stability, and memory storage become key considerations. We discuss several computationally more attractive iterative schemes that generate the approximate gramian matrices needed in the model reduction procedure. A numerical example is also included to illustrate the model reduction algorithms discussed in the paper. We envision that these systems oriented model reduction methods complementing the existing methods to produce low order models suitable for design, optimization, and control." @default.
• W1984267420 created "2016-06-24" @default.
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• W1984267420 date "2005-01-01" @default.
• W1984267420 modified "2023-10-16" @default.
• W1984267420 title "Order Reduction for Large-Scale Finite Element Models: A Systems Perspective" @default.
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• W1984267420 doi "https://doi.org/10.1615/intjmultcompeng.v3.i3.60" @default.
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