Dual Craig-Bampton component mode synthesis method for model order reduction of nonclassically damped linear systems

Gruber, Fabian; Rixen, Daniel J.

doi:10.1016/j.ymssp.2018.04.019

Cited by 28 publications

(8 citation statements)

References 30 publications

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“…The displacement, velocity, and acceleration of the global structure are then obtained from { x p } = Φ m p { q m } , { x · p } = Φ m p { q · m } , and { x ·· p } = Φ m p { q ·· m } . Gruber and Rixen (2018) extended this substructuring method to nonclassically damped linear systems in the state-space representation.…”

Section: Forward Substructuring Methodsmentioning

confidence: 99%

A review on dynamic substructuring methods for model updating and damage detection of large-scale structures

Weng

Zhu

Xia

et al. 2019

Advances in Structural Engineering

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Substructuring methods possess many merits in model updating and damage identification of large-scale structures. With substructuring methods, a global structure is divided into a number of independent substructures. Only the substructures are repeatedly analyzed and the re-analysis of the global structure is thereby avoided. This article reviews widely used dynamic substructuring methods for model updating and damage identification of large-scale structures. These methods can be categorized into forward and inverse substructuring approaches. The former is a conventional process that assembles the vibration properties of each substructure to obtain the vibration properties of the global structure. The latter, on the contrary, disassembles the vibration properties of the global structure into those of the substructures. In each category, both frequency and time domain methods have been developed and will be reviewed.

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Section: Forward Substructuring Methodsmentioning

confidence: 99%

A review on dynamic substructuring methods for model updating and damage detection of large-scale structures

Weng

Zhu

Xia

et al. 2019

Advances in Structural Engineering

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“…An advancement of POD was also proposed based on the use of separated representations, the so-called proper generalized decomposition (PGD) [17]. More recently, the component-mode synthesis (CMS) approach [18,19] has been used for non classically damped linear systems [20] as well as in HS as a method to reduce the order of the comprising NS and PS [21]. Furthermore, a quadratic manifold [22] and a non-intrusive [23] approach were proposed for MOR of nonlinear structural systems.…”

Section: Introductionmentioning

confidence: 99%

Model order reduction for real-time hybrid simulation: Comparing polynomial chaos expansion and neural network methods

Tsokanas

Simpson²,

Pastorino³

et al. 2022

Mechanism and Machine Theory

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Section: Introductionmentioning

confidence: 99%

Model Order Reduction for Real-Time Hybrid Simulation: Comparing Polynomial Chaos Expansion and Neural Network methods

Tsokanas¹,

Simpson²,

Pastorino³

et al. 2021

Preprint

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Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario by combining numerical and physical substructures. To ensure high fidelity of the simulation results, it is often necessary to conduct hybrid simulation in real-time. One of the challenges arising in real-time hybrid simulation originates from high-dimensional nonlinear numerical substructures and, in particular, from the computational cost linked to the computation of their dynamic responses with sufficient accuracy. It is often the case that the simulation time-step must be decreased to capture the dynamic behavior of numerical substructures, thus resulting in longer computation. When such computation takes longer than the actual simulation time, time delays are introduced and the simulation timescale becomes distorted. In such a case, the only viable solution for doing hybrid simulation in real-time is to reduce the order of such complex numerical substructures.In this study, a model order reduction framework is proposed for real-time hybrid simulation, based on polynomial chaos expansion and feedforward neural networks. A parametric case study encompassing a virtual hybrid model is used to validate the framework. Selected numerical substructures are substituted with their respective reduced-order models. To determine the robustness of the framework, parameter sets are defined to cover the design space of interest. A comparison between the full- and reduced-order hybrid model response is delivered. The attained results demonstrate the performance of the proposed framework.

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Dual Craig-Bampton component mode synthesis method for model order reduction of nonclassically damped linear systems

Cited by 28 publications

References 30 publications

A review on dynamic substructuring methods for model updating and damage detection of large-scale structures

A review on dynamic substructuring methods for model updating and damage detection of large-scale structures

Model order reduction for real-time hybrid simulation: Comparing polynomial chaos expansion and neural network methods

Model Order Reduction for Real-Time Hybrid Simulation: Comparing Polynomial Chaos Expansion and Neural Network methods

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