Model order reduction for real-time hybrid simulation: Comparing polynomial chaos expansion and neural network methods
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Date
2022-12
Publication Type
Journal Article
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yes
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Abstract
Hybrid simulation is used to investigate the dynamic response of a system by combining numerical and physical substructures. To ensure high fidelity results, it is necessary to conduct hybrid simulation in real-time. One challenge in real-time hybrid simulation originates from high-dimensional nonlinear numerical substructures and, in particular, from the computational cost linked to the accurate computation of their dynamic responses. When the computation takes longer than the actual simulation time, time delays are introduced distorting the simulation timescale. In such cases, the only viable solution for performing 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 is used to validate the framework. Selected numerical substructures are substituted with their respective reduced-order models. To determine the framework’s robustness, parameter sets are defined covering the design space of interest. Comparisons between the full- and reduced-order hybrid model response are delivered. The attained results demonstrate the performance of the proposed framework.
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published
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Journal / series
Volume
178
Pages / Article No.
105072
Publisher
Elsevier
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Subject
Real-time hybrid simulation; Model order reduction; Polynomial chaos expansion; Feedforward neural networks; Dynamic response
Organisational unit
03930 - Stojadinovic, Bozidar / Stojadinovic, Bozidar
03890 - Chatzi, Eleni / Chatzi, Eleni
Notes
Funding
764547 - Dynamic virtualisation: modelling performance of engineering structures (EC)