Reduced order model analysis method via proper orthogonal decomposition for transient heat conduction

Model Reduction via Proper Orthogonal Decomposition

Improvement of Reduced Order Modeling based on Proper Orthogonal Decomposition

This report focuses on improving the stability as well as the approximation properties of Reduced Order Models (ROM) based on Proper Orthogonal Decomposition (POD). The ROM is obtained by seeking a solution belonging to the POD subspace and that at the same time minimizes the Navier-Stokes residuals. We propose a modified ROM that directly incorporates the pressure term in the model. The ROM is...

Reduced Order Controllers for Spatially Distributed Systems via Proper Orthogonal Decomposition

A method for reducing controllers for systems described by partial di erential equations (PDEs) is presented. This approach di ers from an often used method of reducing the model and then designing the controller. The controller reduction is accomplished by projection of a large scale nite element approximation of the PDE controller onto low order bases that are computed using the proper orthog...

A reduced-order model for a bubbling fluidized bed based on proper orthogonal decomposition

This paper presents the development of a reduced-order model (ROM) for dynamics of fluidized beds, based on the proper orthogonal decomposition (POD) method. Several implementations of this ROM were developed for a two-dimensional bubbling fluidized bed using numerical results from a full computational fluid dynamics (CFD) simulation of the bed. The solutions of the ROM were used to investigate...

Proper Orthogonal Decomposition Model Order Reduction of nonlinear IC models

Future simulation for nanoelectronics requires that circuit equations can be coupled to electromagnetics, to semiconductor equations, and to heat transfer. The consequence is that one has to deal with large systems. Model Order Reduction (MOR) is a means to speed up simulation of large systems. Existing MOR techniques mostly apply to linear problems and even then they have to be generalized to ...