scholarly journals Towards real time assessment of earthfill dams via Model Order Reduction

2022 ◽  
Vol 199 ◽  
pp. 103666
Author(s):  
Christina Nasika ◽  
Pedro Díez ◽  
Pierre Gerard ◽  
Thierry J. Massart ◽  
Sergio Zlotnik
Keyword(s):  
Real Time ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Model Order ◽  
Earthfill Dams ◽  
Time Assessment

Model order reduction for real-time data assimilation through Extended Kalman Filters

2017 ◽  
Vol 326 ◽  
pp. 679-693 ◽  
Author(s):  
David González ◽  
Alberto Badías ◽  
Icíar Alfaro ◽  
Francisco Chinesta ◽  
Elías Cueto
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Kalman Filters ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Time Data ◽  
Model Order ◽  
Extended Kalman Filters ◽  
Real Time Data

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

2021 ◽  
Author(s):  
Nikolaos Tsokanas ◽  
Thomas Simpson ◽  
Roland Pastorino ◽  
Eleni Chatzi ◽  
Bozidar Stojadinovic
Keyword(s):  
Real Time ◽  
Hybrid Model ◽  
Model Order Reduction ◽  
Polynomial Chaos ◽  
Hybrid Simulation ◽  
Order Reduction ◽  
Chaos Expansion ◽  
Model Order ◽  
Reduced Order ◽  
Simulation Time

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.

1803 Real Time Large Deformation Simulation using Model Order Reduction

2013 ◽  
Vol 2013.26 (0) ◽  
pp. _1803-1_-_1803-3_
Author(s):  
Katsuyuki SUZUKI ◽  
Masaru OKAYASU ◽  
Ryuta OKADA
Keyword(s):  
Real Time ◽  
Large Deformation ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Model Order ◽  
Deformation Simulation

scholarly journals An error estimator for real-time simulators based on model order reduction

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Icíar Alfaro ◽  
David González ◽  
Sergio Zlotnik ◽  
Pedro Díez ◽  
Elías Cueto ◽  
...  
Keyword(s):  
Real Time ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Error Estimator ◽  
Model Order
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