Structure-preserved MOR method for coupled systems via orthogonal polynomials and Arnoldi algorithm

This study focuses on the topic of model order reduction (MOR) for coupled systems with inhomogeneous initial conditions and presents an MOR method by general orthogonal polynomials with Arnoldi algorithm. The main procedure is to use a series of expansion coefficients vectors in the space spanned b...

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Bibliographic Details
Published in:IET circuits, devices & systems Vol. 13; no. 6; pp. 879 - 887
Main Authors: Qi, Zhen-Zhong, Jiang, Yao-Lin, Xiao, Zhi-Hua
Format: Journal Article
Language:English
Published: Stevenage The Institution of Engineering and Technology 01.09.2019
John Wiley & Sons, Inc
Subjects:
Algorithms
Approximation
coupled structure
coupled systems
expansion coefficients vectors
general orthogonal polynomials
inhomogeneous initial conditions
Initial conditions
Linear equations
Methods
model order reduction
Model reduction
multiorder Arnoldi algorithm
Ordinary differential equations
Partial differential equations
Polynomials
recursive formula
reduced order systems
Research Article
structure-preserved MOR method
general orthogonal polynomials
recursive formula
polynomials
model order reduction
inhomogeneous initial conditions
multiorder Arnoldi algorithm
reduced order systems
expansion coefficients vectors
coupled structure
structure-preserved MOR method
coupled systems
ISSN:1751-858X, 1751-8598, 1751-8598
Online Access:Get full text
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Summary:This study focuses on the topic of model order reduction (MOR) for coupled systems with inhomogeneous initial conditions and presents an MOR method by general orthogonal polynomials with Arnoldi algorithm. The main procedure is to use a series of expansion coefficients vectors in the space spanned by orthogonal polynomials that satisfy a recursive formula to generate a projection based on the multiorder Arnoldi algorithm. The resulting model not only match desired number of expansion coefficients but also has the same coupled structure as the original system. Moreover, the stability is preserved as well. The error bound between the outputs is well-discussed. Finally, numerical results show that the authors’ method can deal well with those systems with inhomogeneous initial conditions in the views of accuracy and computational cost.
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ISSN:1751-858X
1751-8598
1751-8598
DOI:10.1049/iet-cds.2018.5076