NONLINEAR MODEL REDUCTION USING GROUP PROPER ORTHOGONAL DECOMPOSITION

被引：0
|
作者：
Dickinson, Benjamin T. [1 ]
Singler, John R. [2 ]
机构：
[1] Oregon State Univ, Sch Mech Ind & Mfg Engn, Corvallis, OR 97331 USA
[2] Missouri Univ Sci & Technol, Dept Math & Stat, Rolla, MO 65409 USA
来源：
INTERNATIONAL JOURNAL OF NUMERICAL ANALYSIS AND MODELING | 2010年 / 7卷 / 02期
关键词：
model reduction; proper orthogonal decomposition; group finite element; nonlinear; FINITE-ELEMENT METHODS; MANUFACTURED SOLUTIONS; COHERENT STRUCTURES; GALERKIN PROCEDURES; FEEDBACK-CONTROL; HEAT-EQUATION; ORDER; COEFFICIENTS; TURBULENCE; DYNAMICS;
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We propose a new method to reduce the cost of computing nonlinear terms in projection based reduced order models with global basis functions. We develop this method by extending ideas from the group finite element (GFE) method to proper orthogonal decomposition (POD) and call it the group POD method. Here, a scalar two-dimensional Burgers' equation is used as a model problem for the group POD method. Numerical results show that group POD models of Burgers' equation are as accurate and are computationally more efficient than standard POD models of Burgers' equation.
引用
收藏
页码：356 / 372
页数：17
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