Nonlinear model reduction based on smooth orthogonal decomposition

被引：0

作者：

Chelidze, David ^{[1
]}

Chelidze, Gregory ^{[2
]}

机构：

[1] Univ Rhode Isl, Nonlinear Dynam Lab, Mech Engn & Appl Mech, Kingston, RI 02881 USA

[2] Georgian Tech Univ, Transportat & Machine Building Dep, Machine Design Div, Tbilisi 0177, Georgia

来源：

NINTH IASTED INTERNATIONAL CONFERENCE ON CONTROL AND APPLICATIONS | 2007年

基金：

美国国家科学基金会;

关键词：

smooth orthogonal decomposition; nonlinear model reduction;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Large scale nonlinear model reduction based on smooth orthogonal decomposition (SOD) is presented. SOD is a multivariate time series analysis tool that provides optimal, low-dimensional representation of time series that are as smooth in time as possible. SOD is used to identify linear subspaces containing linear and nonlinear normal modes and span by smooth orthogonal modes (SOMs). Large finite element model (FEM) of a vibrating cantilever beam in a two-well potential is used to illustrate the model reduction. The SOMs of the simulated unforced, undamped FEM are used for model reduction. The performance of damped, forced FEM is then compared with three and five SOM based reduced-order models for various forcing parameters and close agreement is observed even for three SOM based :reduced order model.

引用

页码：325 / +

页数：2

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