Uncertainty quantification of pure and mixed mode interlaminar fracture of fibre-reinforced composites via a stochastic reduced order model

2021 ◽  
pp. 114683
Author(s):  
S. Pouresmaeeli ◽  
B.G. Falzon
Keyword(s):  
Uncertainty Quantification ◽  
Mixed Mode ◽  
Reduced Order Model ◽  
Reinforced Composites ◽  
Order Model ◽  
Reduced Order ◽  
Interlaminar Fracture ◽  
Fibre Reinforced Composites

Uncertainty Quantification in Mathematics-Embedded Ontologies Using Stochastic Reduced Order Model

2017 ◽  
Vol 29 (4) ◽  
pp. 912-920 ◽  
Author(s):  
Xin Li ◽  
Jose-Fernan Martinez ◽  
Martina Eckert ◽  
Gregorio Rubio
Keyword(s):  
Uncertainty Quantification ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order

Model Order Reduction of Transmission Line Model

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Transmission Line ◽  
Large Scale ◽  
Original System ◽  
Reduced Order Model ◽  
Benchmark Problems ◽  
Transmission Line Model ◽  
Order Model ◽  
Line Model ◽  
Reduced Order ◽  
Numerical Effort

Transmission Line model are an important role in the electrical power supply. Modeling of such system remains a challenge for simulations are necessary for designing and controlling modern power systems.In order to analyze the numerical approach for a benchmark collection Comprehensive of some needful real-world examples, which can be utilized to evaluate and compare mathematical approaches for model reduction. The approach is based on retaining the dominant modes of the system and truncation comparatively the less significant once.as the reduced order model has been derived from retaining the dominate modes of the large-scale stable system, the reduction preserves the stability. The strong demerit of the many MOR methods is that, the steady state values of the reduced order model does not match with the higher order systems. This drawback has been try to eliminated through the Different MOR method using sssMOR tools. This makes it possible for a new assessment of the error system Offered that the Observability Gramian of the original system has as soon as been thought about, an H∞ and H2 error bound can be calculated with minimal numerical effort for any minimized model attributable to The reduced order model (ROM) of a large-scale dynamical system is essential to effortlessness the study of the system utilizing approximation Algorithms. The response evaluation is considered in terms of response constraints and graphical assessments. the application of Approximation methods is offered for arising ROM of the large-scale LTI systems which consist of benchmark problems. The time response of approximated system, assessed by the proposed method, is also shown which is excellent matching of the response of original system when compared to the response of other existing approaches .

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