Hybrid Finite-Volume Reduced-Order Model Method for Nonlinear Aeroelastic Modeling

2014 ◽  
Vol 51 (6) ◽  
pp. 1805-1812 ◽  
Author(s):  
Andrew G. B. Mowat ◽  
Arnaud G. Malan ◽  
Louw H. van Zyl ◽  
Josua P. Meyer
Keyword(s):  
Finite Volume ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Model Method

scholarly journals POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder

2017 ◽  
Vol 8 (1) ◽  
pp. 210-236 ◽  
Author(s):  
Giovanni Stabile ◽  
Saddam Hijazi ◽  
Andrea Mola ◽  
Stefano Lorenzi ◽  
Gianluigi Rozza
Keyword(s):  
Circular Cylinder ◽  
Finite Volume ◽  
Vortex Shedding ◽  
Dimensional Space ◽  
Reduced Order Model ◽  
Galerkin Projection ◽  
Governing Equations ◽  
Reduced Basis ◽  
Order Model ◽  
Reduced Order

Abstract Vortex shedding around circular cylinders is a well known and studied phenomenon that appears in many engineering fields. A Reduced Order Model (ROM) of the incompressible ow around a circular cylinder is presented in this work. The ROM is built performing a Galerkin projection of the governing equations onto a lower dimensional space. The reduced basis space is generated using a Proper Orthogonal Decomposition (POD) approach. In particular the focus is into (i) the correct reproduction of the pres- sure field, that in case of the vortex shedding phenomenon, is of primary importance for the calculation of the drag and lift coefficients; (ii) the projection of the Governing equations (momentum equation and Poisson equation for pressure) performed onto different reduced basis space for velocity and pressure, respectively; (iii) all the relevant modifications necessary to adapt standard finite element POD-Galerkin methods to a finite volume framework. The accuracy of the reduced order model is assessed against full order results.

Reduction of a Finite Volume Model for Control of the Solidification Front in a Remelting Process

2014 ◽  
Author(s):  
Luis Felipe Lopez ◽  
Joseph J. Beaman
Keyword(s):  
Finite Volume ◽  
Solidification Front ◽  
Reduced Order Model ◽  
Distributed Parameter ◽  
Order Model ◽  
Reduced Order ◽  
Finite Volume Model ◽  
Volume Model ◽  
Estimation And Control ◽  
And Control

Remelting is used in the production of superalloy ingots. In these processes, stabilization of the solidification front is crucial in the prevention of segregation defects. However, models that account for solidification dynamics often are distributed-parameter multi-physics models that are not used in process control due to their complexity. This paper outlines model reduction for a remelting process based on a multi-physics finite volume model. A reduced-order model is constructed from a state-space realization where only transport phenomena are included. Balancing-free square-root singular perturbation approximation is used to construct a minimal reduced system, and then modal residualization is performed to remove modes that lie outside of the bandwidth of the actuators. The obtained reduced-order model was used to design an LQG controller. Simulation results verify that using the proposed reduced-order model for estimation and control can result in more accurate solidification control, when compared to a simplified model that accounts only for thermal processes.

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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