Data-driven model order reduction with proper symplectic decomposition for flexible multibody system

Flexible multibody system plays an important role for the simulation of mechanism system. Due to the requirement of precision or high complexity of the model, the number of the finite elements of flexible multibody system will increase rapidly, which will lead to the decrease in the computational ef...

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Vydané v:Nonlinear dynamics Ročník 107; číslo 1; s. 173 - 203
Hlavní autori: Peng, Haijun, Song, Ningning, Kan, Ziyun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.01.2022
Springer Nature B.V
Predmet:
Automotive Engineering
Classical Mechanics
Computational efficiency
Computing costs
Computing time
Control
Decomposition
Dynamical Systems
Engineering
Interpolation
Mathematical models
Mechanical Engineering
Model reduction
Multibody systems
Original Paper
Parameters
Proper Orthogonal Decomposition
Reduced order models
Vibration
Symplectic model order reduction
Flexible multibody system
Proper symplectic decomposition
Parameter interpolation
Data-driven model
ISSN:0924-090X, 1573-269X
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Shrnutí:Flexible multibody system plays an important role for the simulation of mechanism system. Due to the requirement of precision or high complexity of the model, the number of the finite elements of flexible multibody system will increase rapidly, which will lead to the decrease in the computational efficiency. In order to save the computational cost for simulating flexible multibody system, a novel model order reduction strategy based on the idea of data-driven model is proposed. In addition, the proposed method which is called symplectic model order reduction is in light of proper symplectic decomposition and symplectic Galerkin projection. At first, the snapshot matrix is obtained by an empirical data ensemble of the full-order model, and the transfer symplectic matrix of high dimension to low dimension is obtained by reduced-order bases using the method of cotangent lift. Then, the discrete governing equations of reduced-order model (ROM) are derived by symplectic discretization. Furthermore, a systematic study of model order reduction in system level and component level is provided in the paper. In addition, for adaption of ROM to parameter variation, a parameter interpolation method is offered to obtain the ROM. Eventually, several examples are used to verify the effectiveness of the proposed method, and the results show that the proposed method has better numerical accuracy and higher computational efficiency with respect to classic proper orthogonal decomposition-based ROM.
Bibliografia:ObjectType-Article-1
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-021-06990-3