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Reduced Order Models in Analysis of Stochastically Parametered Linear Dynamical Systems
Hridya P. Lal, Jainendra K. Dubey, ,
Published in Elsevier Ltd
Volume: 144
Pages: 1325 - 1331
This study focusses on the development of reduced order models, which minimize the computational costs without compromising on the accuracy in the numerical analysis of stochastically parametered linear dynamical systems. A scheme based on polynomial chaos expansion (PCE) and system equivalent reduction expansion process (SEREP) has been developed that enable formulation of reduced order models. Further measures for enhancing the computational efficiency include using sparse grids in conjunction with code parallelization. Interfacing algorithms have been developed that enable finite element (FE) modeling of complex systems using commercial FE softwares and the developed codes. © 2016 The Authors.
About the journal
JournalData powered by TypesetProcedia Engineering
PublisherData powered by TypesetElsevier Ltd
Open AccessYes
Concepts (12)
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    Computational efficiency
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    Dynamical systems
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    Finite element method
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    Computational costs
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    Linear dynamical systems
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    Polynomial chaos expansion (pce)
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    Reduced order modelling
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    Reduced order models
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    Linear control systems