Parameterized model order reduction for linear DAE systems via ε-embedding technique

被引：2
|
作者：
Jiang, Yao-Lin [1 ]
Xu, Kang-Li [1 ]
Chen, Chun-Yue [1 ]
机构：
[1] Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Shaanxi, Peoples R China
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2019年 / 356卷 / 05期
关键词：
ALGEBRAIC EQUATION SYSTEMS; DECOMPOSITION;
D O I：
10.1016/j.jfranklin.2018.08.032
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we present a new method in the reduction of large-scale linear differential-algebraic equation (DAE) systems. The approach is to first change the DAE system into a parametric ordinary differential equation (ODE) system via the epsilon-embedding technique. Next, based on parametric moment matching, we give the parameterized model order reduction (MOR) method to reduce this parametric system, and a new Arnoldi parameterized method is proposed to construct the column-orthonormal matrix. From the reduced-order parametric system, we get the reduced-order DAE system, which can preserve the structure of the original DAE system. Besides, the parametric moment matching for the reduced-order parametric systems is analyzed. Finally, the effectiveness of our method is successfully illustrated via two numerical examples. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2901 / 2918
页数：18
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